Technical Report Appendix L – Climate Change Studies in TE2100

Thames Estuary 2100

Managing ﬂ ood risk through London and the Thames estuary

423677_TE21_Append_L_AW.indd 1 24/3/09 10:54:53 We are the Environment Agency. It’s our job to look after your environment and make it a better place – now, and for future generations. Your environment is the air you breathe, the water you drink and the ground you walk on. Working with business, Government and society as a whole, we are making your environment cleaner and healthier. The Environment Agency: using science to create a better place.

Published by:

Thames Estuary 2100 Environment Agency Thames Barrier, Eastmoor Street Charlton, London SE7 8LX Tel: 08708 506 506 Email: [email protected] www.environment-agency.gov.uk

423677_TE21_Append_L_AW.indd 2 24/3/09 10:54:53 Appendix L to Technical Report Climate Change Studies in TE2100

1 Purpose Appendix L to the TE2100 Plan Technical Report sets out the approach taken within Thames Estuary 2100 to adapt to the effects of climate change. It gives the background to the way the project has approached this issue as the science has developed. The supporting research is described in seven supporting annexes to this Appendix as follows:

• Annex 1 TE2100 Met Office Hadley Centre Summary of Climate Change Projections Phase 2 EP17 Study • Annex 2 TE2100 Met Office Hadley Centre Projections of 21st Century Extreme Sea Levels Phase 2 EP17 Study • Annex 3 TE2100 Met Office Hadley Centre Area-wide river flow modelling: Climate change impacts on flood frequency Phase 2 EP17 Study • Annex 4 Thames Estuary 2100 Downscaling Future Skew Surge Statistics at Sheerness, Kent Phase 3 Studies – Synthesis Report • Annex 5 Definition of Climate Change Allowances for Use in TE2100 Phase 3ii HR Wallingford • Annex 6 Joint Probability and Interaction Technical note EP7.3 Part C Climate change allowances to use in the TE2100 programme HR Wallingford 2005 • Annex 7 Climate Change Benefits realisation summary – summarises links with other projects

2 Summary TE2100 is the first major project in the UK to have put climate change adaptation at its core. It has essentially approached this through two main routes. Firstly it has developed a methodology to test different flood risk measures against differing climate change scenarios, which has led to the concept and development of flexible, adaptable options. Secondly it has directly carried out research to try to better define the uncertainties surrounding future climate change projections working with the Met Office Hadley Centre (MOHC) and other key organisations. Throughout the project links have been developed and maintained with key climate change initiatives and developments such as the IPPC 4th Assessment Report, the Stern Review and UKCIP. Links and exchanges were also made with authorities faced with managing other worldwide mega-cities vulnerable to rising sea levels such as from New York and China. These are detailed in annex 7 to this appendix. Doc: TE2100 Plan Appendix L – Climate Change 1 Author: Tim Reeder Date & Version: 27 Apr 09 v6

The TE2100 project decided at an early stage to ensure that the uncertainties inherent in climate change were incorporated into the decision making process. Through our work with ESPACE (European Spatial Planning Adapting to Climate Change EC Project) and further developments and iterations within the project we have developed techniques for scenario neutral analysis. This involves identification of critical thresholds in the flood risk management system and is further described in section 3. The TE2100 plan describes options set out against the DEFRA scenario. These options have been devised through an iterative approach. The options have been developed and tested as described in this section. This illustrates how the approach has now resulted in a set of options that are resilient and adaptable to the uncertainty surrounding climate change.

In phase 1 studies were carried out to scope the effect of climate change on the Thames Estuary Flood Risk management system and its future development. This identified the uncertainties that were inherent in future prediction of climate variables critical to driving changes in flood risk. At that time the most critical issue in terms of its uncertainty and importance was any changes in storm surge size or frequency related to future climate change. The UKCIP02 scenarios predicted a significant increase in storm surges. However results from other Global Circulation Models (GCMs) predicted different results with a possible decrease in storm surge size (see section 4).

Therefore in phase 2 the project commissioned research with the Met Office and others described further in section 4 to better understand and define the uncertainty surrounding climate change. This work was progressed in conjunction with the research for the next set of UK Climate Change scenarios (UKCP09). This research was scheduled to finish towards the end of the TE2100 project timeline. Therefore in the interim a set of scenarios were developed. These were used in the Early Conceptual Option and subsequent High Level Option development in phase 2 and during the first part of the phase 3 option development. The project has now used the outputs from this Met Office research to reassess which future scenario is the most probable to enable the options presented here to be developed.

This research highlights that whilst we have constrained the uncertainty surrounding the effects of climate change on storm surge, the uncertainty surrounding sea level rise and river flows has to some extent increased. However, our research shows that flood risk in the Thames Estuary will increase significantly through the century. Our plan is needed now because the risks are already changing. Regardless of the success or otherwise of global efforts to reduce greenhouse gas emissions, further climate change is inevitable. To continue to manage flood risk from the tidal Thames will require substantial investment over the next 50 years and it will be essential that adaptability and flexibility are key parts of the plan, as described in Chapter 10 of the TE2100 Plan Technical Report.

Doc: TE2100 Plan Appendix L – Climate Change 2 Author: Tim Reeder Date & Version: 27 Apr 09 v6

We have developed our plan on the basis of our evaluation of the most probable projection of change while ensuring we do not reduce our options if the future evolves differently. It is crucial then that the plan is regularly reviewed and that we monitor how the climate and flood risk changes. This will require us to measure local changes to key climate variables, rainfall patterns over the Thames catchment, and to global measures such as rates of ice sheet loss at the poles. This will be part of the implementation of the TE2100 Flood Risk management Plan.

3 Decision Making and Climate Change

TE2100 has devised an approach to the development of the strategy centred on trying to deal with the uncertainties in projections of future climate and development along the Thames. It has developed a method of testing different flood management options or packages of measures relevant to each reach of the Estuary, which are then progressively iterated and tested against a decision testing framework. This framework has tested the suitability of the options against differing futures driven by a range of socio-economic and climate change scenarios.

Using this method it has been possible to detect thresholds, which have informed the development of options. For example modifying the existing barrier and defences will only cope with a certain level of sea level rise and increase in storm surge. The approach is based largely on the Risk, Uncertainty and Decision Making Technical Report produced by the Environment Agency for UKCIPi and other tools and assessment criteria based on existing and developing guidance. TE2100 has also worked with partners in Holland, Germany and Belgium in the ESPACE Project (European Spatial Planning Adapting to Climate Events) to develop and refine transnational methods. This included the development of a scenario neutral approach which has been one of the key successes of TE2100ii.

As a development from this work and using the threshold or scenario neutral approach, TE2100 produced its High level Options in 2007, which have been the subject of extensive online stakeholder engagement. These were a set of adaptation response options (HLO1, 2, 3a, 3b, and 4). Each option consists of a pathway or route through the century that can be adapted to the rate of change that we experience. They are described in Figure 1 which essentially shows how the four differing options perform against the range of new TE2100 climate change scenarios.

i Willows et al. 2003. Climate Adaptation: Risk Uncertainty and Decision-making. UKCIP Technical Report. UKCIP Oxford. ii Reeder Donovan Wicks CIWEM Annual Conference 2005 Doc: TE2100 Plan Appendix L – Climate Change 3 Author: Tim Reeder Date & Version: 27 Apr 09 v6

Figure 1: High Level Options. The dark blue path shows a possible future adaptation route (or pathway) in the event of extreme change. The vertical dashed lines show the new TE2100 scenarios.

It can be seen from Figure1 that not only are the options flexible, but it is possible to move from one adaptation option to another depending on the actual rate of change that occurs in reality. This illustrates the benefits of looking at higher scenarios in terms of a robust analysis.

Following on from the High Level Options our plan sets out the latest refined options. These have been now designed assuming the DEFRA scenario as laid out in DEFRA PAG guidance 2006iii (see section 2). Nevertheless the options are all adaptable if we experience greater rates of climate change. The exact way in which they can be adapted is set out in Chapter 10 of the TE2100 Plan Technical Report.

The effectiveness of the final plan will depend on a continuing process of periodic review – every 5 years or so. Critical to this will be the need for ongoing review of the progress of climate change and revised future projections. The Environment Agency will be working with the Met Office Hadley Centre and others to ensure that this monitoring is in place.

In summary TE2100 has developed a methodology which has led to a Plan that is based on the most likely envelope of change, but is flexible and adaptable to an uncertain future.

iii Flood and Coastal Defence Appraisal Guidance FCDPAG3 Economic Appraisal Supplementary Note to Operating Authorities – Climate Change Impacts October 2006

Doc: TE2100 Plan Appendix L – Climate Change 4 Author: Tim Reeder Date & Version: 27 Apr 09 v6

4 TE2100 and climate change science

There is growing evidence of changes to our climate that will affect flood risk. Several studies report increasing winter precipitation, larger multi-day rainfall totals, and higher contributions from intense daily events since the 1960s. There is a high degree of confidence in models projecting increased winter rainfall across the UK. Similar increases to the marine climate have also been recorded. Global mean sea level have risen by 2mm/yr during the last century with an increase to 3mm/yr during the 1990s and beyond, which could be the result of the human impact on climate change. Projections for future sea levels globally indicate that the current trend will increase. However, these changes become highly uncertain when extrapolated to the local scales. The interaction of the climate with river catchments is highly complex and dependent on the pattern of rainfall as well as seasonal or annual totals.

In the early stages of the TE2100 project we anticipated that we would be recommending a major investment in the Thames Barrier and associated gates and defences at some point in the century so it was absolutely key to reduce or better quantify the uncertainty in the future degree of climate change and hence flood risk if at all possible.

To reduce or better quantify the uncertainties surrounding the effects of climate change we have commissioned research to address this. We have also built relationships with other research programmes to keep abreast of developing science.

The effect of climate change on world ocean, sea and river levels is a key driver of the TE2100 plan. In London and the Thames Estuary climate change is likely to have an effect on:

• average sea and tide levels • the frequency and severity of North Sea storm surges • fluvial flows coming down the Thames and its tributaries.

In addition to the approach set out above in section 3, it is essential to try to better understand the uncertainty and probability of future climate change effects. Therefore the project has been working directly with the Met Office Hadley Centre (MOHC), the Proudman Oceanographic Laboratory (POL) and the Centre for Ecology and Hydrology (CEH) to drive research on this issue. At the start of the project the UKCIP02 scenarios projected an increase at the mouth of the Thames of up to 1.3m in storm surge by the 2080s whilst other modelling studies projected a decrease (see Figure 2). This range of uncertainty could lead to a very large variation in the level of future flood risk management planning and a large associated difference in costs. The new research was commissioned specifically to look at the uncertainties

Doc: TE2100 Plan Appendix L – Climate Change 5 Author: Tim Reeder Date & Version: 27 Apr 09 v6 surrounding storm surge, relative sea level rise and river flows in a consistent manner. This work was commissioned in conjunction with the MOHC research going ahead for the UKCP09 scenarios. It has the added benefit of giving an output on mean sea level and extreme sea level relevant to the whole of the UK. The research has been a major contributor to the UKCP09 marine projections.

Figure 2: Projected 21st century changes in 50 year storm surge height due to changes in storminess alone forecast by three different climate models (clockwise from top left: HadCM2/HadRM2, ECHAM4 and HadAM3H/HadRM3H), illustrating the large uncertainty which surrounded extreme sea level change in the Thames Estuary prior to the TE2100 project. (After Lowe and Gregory 2005).

Doc: TE2100 Plan Appendix L – Climate Change 6 Author: Tim Reeder Date & Version: 27 Apr 09 v6

4.1 Interim scenarios

The results of the research described above were scheduled to arrive late in the development of the TE2100 project. Therefore the project devised a set of four scenarios to use in the interim to develop the options (see annex 6). The first three – DEFRA, Medium High, and High Plus were based on DEFRA PAG3 guidance and the UKCIP02 scenarios. However, following scientific research on ice cap melt presented at the Avoiding Dangerous Climate Change Conference (http://www.stabilisation2005.com/index.html) in 2005, a H++ scenario was devised to identify a worst-case estimate. This included worst case estimates for each element of extreme water level change.

4.2 Latest findings

The latest findings from the research by MOHC / POL / CEH are contained in the TE2100 Met Office Hadley Centre reports (these are included as annexes 1 - 3 to this appendix). The general approach of this work was to use climate models to simulate the necessary driving data for models of storm surges and river flows. Global climate models were downscaled to the regional scale needed in the study and consistent scenarios were used for the surge and river flow work. Projections were made of the recent past and extended to 2100, assuming future emissions follow the SRES A1B scenario. Results from Met Office Hadley Centre climate models were supplemented with those from other climate models used in the most recent IPCC assessment and, where appropriate, observations were used. In addition independent statistical downscaling techniques were carried out by Loughborough University which arrived at similar conclusions on storm surge (report attached as annex 4).

The key findings are :-

• Sea level rise in the Thames over the next century due to thermal expansion of the oceans, melting glaciers and polar ice is likely to be between 20cm and 90cm.

• There is still much uncertainty over the contribution of polar ice melt to sea level rise. At the extreme it may further raise sea levels up to 2m (including thermal expansion) - although this is thought highly unlikely.

• Although still uncertain, climate change is less likely to increase storm surge height and frequency in the North Sea than previously thought.

• Future peak freshwater flows for the Thames are also uncertain. At Kingston they could increase by around 40% by 2080.

Doc: TE2100 Plan Appendix L – Climate Change 7 Author: Tim Reeder Date & Version: 27 Apr 09 v6

4.3 The main implications for the project are as follows

4.3.1 Sea level rise The maximum likely contribution of sea level rise is broadly the same as the guidance given by Defra in 2006 (0.94 metres by 2100). This means our previous assumptions on sea level rise based upon Defra guidance are reasonable.

4.3.2 Polar ice melt The potential for sea level rise to be further increased by polar ice melt was factored in to our original worst case High plus plus scenario of 4.2 metres maximum water levels. This had taken an extremely precautionary approach for worst case ice cap melt. The increase of 2 metres (including thermal expansion) from this study can now be taken as the contribution of sea level rise in a revised worst case scenario, using more recent, albeit still highly uncertain ice sheet dynamics science.

4.3.3 Storm surge We also factored an increase in maximum water levels due to increased heights of storm surge into our previous worst case planning scenarios. The results of these studies suggest however that it is unlikely that we will see an increase in maximum water levels through this source.

4.3.4 Worst case scenario Our TE2100 worst case scenario for increases in maximum water levels in the Thames this century is now revised down to 2.7 metres including surge. Although our research suggests that storm surges are less likely to contribute to any increase in maximum water levels, we have still included a precautionary extreme value of 0.7 metres. (note the report actually refers to 0.95 metres but 0.7 was used as a change in 5 yr return that is a more reliable estimate).

This worst case scenario is highly unlikely to occur as is it is derived from a combination of extreme values for thermal expansion, polar ice melt, and storm surge. Further research would be needed to try and understand the probability of all these extremes occurring, but for now it allows us to understand the maximum levels that could plausibly happen and therefore set the outer envelope for planning for the future.

4.3.5 Estuary responses to managing extreme water levels Under our previous worst case scenario, which was accepted as very unlikely, the only option available to manage maximum water levels by the end of the century was an estuary barrage. With our revised worst case scenario (again very unlikely) we should be able to manage water levels without resorting to a tide excluding barrage this century.

4.3.6 Freshwater flows Our previous planning was based on a 20% increase in river flows over the century based on UKCIP02. As a result, we will be testing our flood

Doc: TE2100 Plan Appendix L – Climate Change 8 Author: Tim Reeder Date & Version: 27 Apr 09 v6 management options against this potential increase to 40%. The contribution of freshwater flow to flood risk is low apart from in the western reaches of the estuary, where there is less tidal influence. It is important to note that even using state of the art modelling, these figures have high uncertainty bands attached to them.

4.4 Detailed recommendations resulting from the latest science

The outcome of this research has been successful in enabling the project to have better understood the uncertainty surrounding the effects of climate change. The key implications of the research for the use in the current formation and appraisal of the draft plan are contained in TE2100 Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii by HR Wallingford (Annex 4 to this Appendix L).

The key recommendations which have largely been followed in the development of the plan are as follows :-

4.4.1 It is recommended that the allowance for future sea level rise continues with the precautionary Defra (2006) allowance for accelerated sea level rise of up to 1m after 100 years. This recommendation is made on the basis that the recent modelling undertaken by the Hadley Centre gives a range of potential sea level rise scenarios of up to 88cm by 2095.

4.4.2 88cm represents the maximum from the scenarios modelled in the EP17 studies. It is therefore recommended that sensitivity testing to lower rates of sea level rise (lower than 1m by 2100) be incorporated in the assessment of options.

4.4.3 The EP17 studies have found no detectable trend of increasing surge magnitudes from the modelling exercises they undertook.

4.4.4 The EP17 studies managed to reproduce an event similar in scale to that of 1953, after a reworking of an ensemble model run adjusting the timing of meterological event relative to tides, and after substituting the modelled tide with an improved one. No larger events than that of 1953 were generated. It is therefore recommended to continue to base extreme event storm tide hydrographs on the event of February 1953. Section 6 above outlines the method still used to provide boundary conditions for events higher than have occurred on record.

4.4.5 It is recommended that the sensitivity of the results to different assumptions on the shape of the storm tide profiles is tested using relevant models. The hydraulic modelling of peak levels upstream is extremely sensitive to the assumptions made in the derivation of a hydrograph shape. If the rising limb of the storm tide hydrograph becomes steeper than physically feasible (as a result of the simple assumptions being used to construct it) then the peak levels upstream can be significantly affected.

Doc: TE2100 Plan Appendix L – Climate Change 9 Author: Tim Reeder Date & Version: 27 Apr 09 v6

4.4.6 The EP17 studies have identified a potential increase in peak flows of 40% in 100 years. This differs from previous advice on future changes to fluvial inputs. For the reasons discussed in Section 4.2, it is recommended at the present time to continue with the assumption of a 20% increase in peak flows by 2050/2100, but to also test the sensitivity of options to 40% increases.

4.4.7 Based upon the two datasets analysed, it was concluded that no change in the dependence of fluvial flows at Kingston and peak sea levels at Southend was predicted due to future climate change.

5. Conclusion

The TE2100 project has put climate change and adaptation at its core during its development. In summary the use of threshold analysis and the production of flexible adaptation options as well the development of better climate change predictions has been critical to the production of a plan that can manage flood risk in the Estuary for the next 100 years. This has been enhanced by cooperation and working with many wider climate change initiatives. The project’s work has been extensively quoted as leading the field in terms of how a major long term planning project should approach this new challenge of an uncertain future climateiv . Having set out a robust and resilient Plan for Consultation, the eventual success of the plan will depend on periodic review and effective monitoring of climate change as it is experienced and is forecast.

iv Nicholls, R.J. et al. July 2006 OECD Working Party on Global and Structural Policies Metrics for Assessing the Economic Benefits of Climate Change Policies : Sea Level Rise

Doc: TE2100 Plan Appendix L – Climate Change 10 Author: Tim Reeder Date & Version: 27 Apr 09 v6

References

i Willows et al. 2003. Climate Adaptation: Risk Uncertainty and Decision- making. UKCIP Technical Report. UKCIP Oxford. ii Reeder Donovan Wicks CIWEM Annual Conference 2005 iii Flood and Coastal Defence Appraisal Guidance FCDPAG3 Economic Appraisal Supplementary Note to Operating Authorities – Climate Change Impacts October 2006 iv Nicholls, R.J. et al. July 2006 OECD Working Party on Global and Structural Policies Metrics for Assessing the Economic Benefits of Climate Change Policies : Sea Level Rise

Annex 1 TE2100 Met Office Hadley Centre Summary of Climate Change Projections Phase 2 EP17 Study Annex 2 TE2100 Met Office Hadley Centre Projections of 21st Century Extreme Sea Levels Phase 2 EP17 Study Annex 3 TE2100 Met Office Hadley Centre Area-wide river flow modelling: Climate change impacts on flood frequency Phase 2 EP17 Study Annex 4 Thames Estuary 2100 Downscaling Future Skew Surge Statistics at Sheerness, Kent Phase 3 Studies – Synthesis Report Annex 5 Definition of Climate Change Allowances for Use in TE2100 Phase 3ii HR Wallingford Annex 6 Joint Probability and Interaction Technical note EP7.3 Part C Climate change allowances to use in the TE2100 programme HR Wallingford 2005 Annex 7 Climate Change Benefits realisation summary – summarises links with other projects

Doc: TE2100 Plan Appendix L – Climate Change 11 Author: Tim Reeder Date & Version: 27 Apr 09 v6

Thames Estuary 2100 Summary of Climate Change Projections

Phase 2 EP17 Study

EA Study Lead: Tim Reeder

Consultants: Met Office Hadley Centre

Status: Final Draft Date: 17 Nov. 08 Annex 1 of 7 Appendix L to TE2100 Plan

Climate Change Projections for TE2100

Summary prepared by Dr. Jason A. Lowe

Direct contact: email: [email protected] Tel: 0870 900 0100

This report was prepared in good faith. Neither the Met Office, nor its employees, contractors or subcontractors, make any warranty, express or implied, or assumes any legal liability or responsibility for its accuracy, completeness, or any party’s use of its contents.

The views and opinions contained in the report do not necessarily state or reflect those of the Met office.

Page 2 of 4

This summary document lists the final products supplied by the Met Office, its collaborators and subcontractors to the Environment Agency in order to inform decision making in the TE2100 project. Authors are Met Office staff unless otherwise stated. This results package consists of five items:

1. This summary document.

2. The report “Met Office Hadley Centre Projections of 21st Century Extreme Sea Levels for TE2100” prepared by Tom Howard, Jason Lowe, Anne Pardaens, Jeff Ridley and Kevin Horsburgh (Proudman Oceanographic Laboratory).

3. The report “Area-wide river flow modelling for the Thames Estuary 2100 project: Climate change impacts on flood frequency” prepared by A.L. Kay (Centre for Ecology and Hydrology), V.A. Bell (Centre for Ecology and Hydrology) and J.A. Lowe (Met Office).

4. The report “Area-wide river flow modelling for the Thames Estuary 2100 project: Model formulation and assessment” prepared by V.A. Bell, R.J. Moore, S.J. Cole and H.N. Davies (all Centre for Ecology and Hydrology).

5. High frequency data time series of simulated extreme water levels in the southern North Sea near the mouth of the Thames and river flow data within the Thames basin. These were prepared to the specification of the Environment Agency and supplied previously.

The general approach of this project was to use climate models to simulate the necessary driving data for models of storm surges and river flows. Global climate models were downscaled to the regional scale needed in this study and consistent scenarios were used for the surge and river flow work. Projections were made of the recent past and extended to 2100, assuming future emissions follow the SRES A1B scenario. Results from Met Office Hadley Centre climate models were supplemented with those from other climate models used in the most recent IPCC assessment and, where appropriate, observations were used.

The headline results of this project are: • The new simulation of present day surge levels has more skill than an earlier system used by the Hadley Centre. The results show that using short time slices for storm surge climate experiments (the previous standard technique) can produce misleading conclusions. This provided justification for simulating longer periods in this current work.

• 21st century changes in the storminess-driven component of extreme water levels are found to be small for the SRES A1B scenario. Regional relative time-mean sea level rise between present day and the end of the 21st century is estimated to range from 19cm to 88cm when thermal expansion and ice melt are included, regional deviations from the global mean are accounted for and vertical land movement is added. This time-mean sea level rise range also includes emissions scenario uncertainty.

Page 3 of 4

• An illustrative H++ (high rate of sea level rise) scenario was constructed using the simulated storminess changes in the most extreme IPCC model and expert judgement informed by published paleo climate observations. Such large rates of sea level rise are currently considered extremely unlikely to occur in the 21st century.

• Potential future changes in fluvial flood risk were studied using the climate modelling system to drive a distributed hydrological model, the Grid-to-Grid (G2G) model.

• Across the Thames Basin, changes in flood frequency between Current and Future periods have been analysed, and the results for each ensemble member are presented at a range of return periods. There is considerable variation in the results. Areas underlain by chalk generally show lower percentage changes than other regions. However almost all changes are increases, generally averaging around 5-10% in chalk areas and around 30-50% elsewhere, for peak flows with up to a 20-year return period.

Our overall conclusion is that current climate science can provide some useful information on the range of future sea level rise and peak river flow. However, even using the current state-of- the-art techniques the uncertainty bounds are found to be large. For this reason we suggest that continued monitoring of the ice sheets, sea level and river flow should be a priority. We also conclude that improving ice sheet models should take a high priority in the efforts to advance climate modelling over the next few years. Additionally, as the climate change signal becomes larger in the future there will be the additional possibility of using more observational constraints to refine the model-derived uncertainty bounds on future extreme sea levels and peak river flow projections.

Page 4 of 4

Thames Estuary 2100 Met Office Hadley Centre Projections of 21st Century Extreme Sea Levels

Phase 2 EP17 Study

EA Study Lead: Tim Reeder

Consultants: Met Office Hadley Centre & Proudman Oceanographic Laboratory

Status: Final Draft Date: 12 Nov. 08 Annex 2 of 7 Appendix L to TE2100 Plan

Met Office Hadley Centre Projections of 21st Century Extreme Sea Levels for TE2100

1 Introduction...... 5 2 Ensemble projections...... 6 3 H++ ...... 32 4 Conclusions...... 43 5 Acknowledgements...... 44 6 References...... 45 7 Appendix 1: Surge model sensitivity tests...... 50 8 Appendix 2: Technical note on the surge model runs...... 53

Prepared by Tom Howard, Jason Lowe, Anne Pardaens, Jeff Ridley and Kevin Horsburgh (Proudman Oceanographic Laboratory).

Authorised for issue by: Jason Lowe

Date: 06/06/2008 Revised: 27/10/2008

Direct contact: email: [email protected] Tel 0870 900 0100

The views and opinions contained in the report do not necessarily state or reflect those of the Met Office.

Page 2 of 54

Executive summary The overall aim of this Met Office Hadley Centre contribution to the TE2100 project is to provide the latest information on potential climate driven changes in water levels in the Southern North Sea near the Thames Estuary. The report describes the system used by the Met Office Hadley Centre (who worked with the Proudman Oceanographic Laboratory) in order to simulate extreme water level events. It also describes the results of applying this system.

Our results are split into two packages, an ensemble projection and an H++ scenario. The ensemble projection represents our best estimate of the likely range in future extreme water levels based on the current generation of climate models at optimum spatial resolution and current scientific understanding. The H++ model scenario describes a range of extreme sea- level rise for vulnerability analysis. The top end of this range is based on the most extreme models and upper uncertainty bounds from observed past changes. It is considered very unlikely1 to occur during the 21st century; however, it cannot yet be ruled out completely.

Summary of the ensemble projections: • The simulations of present day2 extreme water levels in this project have more skill3 than those from an earlier system used by the Hadley Centre.

• The results show that using short time slices (typically two slices of 30 years each at either end of the 21st century, the previous standard technique) can lead to misleading conclusions, providing justification for simulating longer periods (one continuous 149-year simulation) in this current work.

• 21st century changes in the storminess-driven component of extreme water levels are found to be small for the SRES A1B scenario. This implies that future changes in extreme water levels will predominantly be driven by changes in regional time-mean sea level.

• Regional relative time-mean sea level rise between present day and the end of the 21st century is estimated to range from 19 cm to 88cm when thermal expansion and ice melt is included, regional deviations from the global mean are accounted for and vertical land movement is added. This range also includes emissions scenario uncertainty.

Summary of the H++ scenario: • Some global climate models (one in particular) project changes in European storminess that are considerably larger than the MOHC ensemble of models. A crude scaling approach suggests this could produce a 21st century increase in the level of a 50-year return period skew surge of around 95cm.

1 It is not possible to quantify this low probability; "very unlikely" in this context does not refer to the IPCC definition. 2 In this report “present day” refers to the representative period 1980-1999 3 The new simulations validate better against observations than the old simulation did.

Page 3 of 54

• A top-end time-mean sea level rise component is based on sea level variations from proxy observations of the last interglacial period and modelling of the spatial pattern of sea level change from melting ice sheets. This component is estimated to be 2m.

Our overall conclusion is that current climate science can provide some useful information on the range of future sea level rise. However, even using the current state-of-the-art techniques the uncertainty bounds are found to be large, with the upper bound being especially uncertain. For this reason we suggest continued monitoring of the ice sheets and sea level should be a priority. We also conclude that improving ice sheet models should take a high priority in the efforts to advance climate modelling over the next few years. Additionally, as the climate change signal becomes larger in the future there will be the additional possibility of using more observational constraints to refine the model-derived uncertainty bounds on future sea level rise projections.

Page 4 of 54

1 Introduction The overall purpose of TE2100 is to develop a tidal flood risk management plan for the Thames estuary through to the end of the century. The Met Office Hadley Centre has been commissioned to investigate the potential future climate-driven changes in extreme water levels in the Southern North Sea near the Thames Estuary. This study is justified by observations showing a recent increase in sea levels around the United Kingdom (Jenkins et al., 2007), and by previous simulations projecting uncertain changes in extreme sea levels (Hulme et al., 2002; Lowe and Gregory, 2005; Woth et al., 2006).

Climate science cannot yet provide a single precise prediction of what the climate will be like at the end of the 21st century. This is due to uncertainty in emissions, uncertainty in understanding and climate models, and because of natural variability. What it can usefully provide is information on likely ranges of 21st century climate change. It can also provide some useful information on low-probability but physically plausible high-end projections of change. Therefore, our results package has two components:

Ensemble projections This component contains information on the likely range of future extreme water levels. The results were obtained from the spread of the latest and most credible computer model projections available. Ensemble projection information might be useful when planning adaptive responses for the most likely future changes currently expected during the coming decades.

H++ This contains a new high-end water level scenario based on proxy observations. This type of scenario is considered unlikely to occur during the next century as it would represent an increase in mass loss from ice sheets of more than two orders of magnitude compared to recent observed increases in the ice sheet contribution to sea level rise. However, the possibility of these large increases occurring cannot yet be excluded based on current models or observations. The top end of the H++ scenario depends on expert interpretation of limited high-end model results and observations from past climate change events. This type of information might be taken into account when considering whether it is reasonable or not to rule out any consideration of a particular type of adaptive response. Like the ensemble projections information package, the H++ information package has two components: changes in extreme events associated with changes in atmospheric storminess and changes in local relative time-mean sea level.

Page 5 of 54

2 Ensemble projections. In this section we describe the modelling system used to make projections of future surges, the statistical methods used and the results of applying these methods. We also present range estimates of local time-mean sea level rise based on IPCC analysis.

2.1 Climate Modelling System Climate models are currently the most credible tools for making century-scale projections of future climate (Houghton, 2004). A range of different climate models exist, from the simplest energy balance models to the most sophisticated full ocean-atmosphere general circulation model (see, for example, McGuffie and Henderson-Sellers, 2004). The most complex models divide the world into a series of grid boxes and simulate the behaviour of the atmosphere and oceans on this grid by solving the equations which describe their motion and thermodynamics.

The Met Office Hadley Centre global climate model, HadCM3 (Gordon et al., 2000 Pope et al., 2000) is a general circulation model which has previously been shown to have considerable skill at simulating the global climate (e.g. Stott et al. 2000). The atmosphere has 19 levels in the vertical and a horizontal resolution of 2.5°x3.75°. The ocean has 20 vertical levels and a horizontal resolution of 1.25°x1.25°. In this project the global climate model is used to provide atmospheric boundary conditions for a regional atmospheric climate model, HadRM3, which is set up to simulate climate over Europe in more detail. In the TE2100 project the highest resolution (25km) version of the regional climate model is used. There has been some debate in the literature (IPCC, 2007) as to whether this type of modelling system can reliably simulate blocking anticyclone events, which are known to be a significant component of the summer climate of our region, but this is not a problem in this work since very few extreme surge events occur during the summer.

In order to investigate uncertainty in the climate change projections a perturbed parameter ensemble approach is used. Many important physical processes cannot be explicitly resolved by climate models, typically because they occur on a scale smaller than the model grid. An example is the formation of cloud within a model grid box. Such processes must be parameterized, that is, described in terms of their expected impact on the scale of the model grid box. In the perturbed parameter ensemble approach (Collins et al. 2006; Murphy et al. 2007), instead of taking a single best estimate for key atmospheric parameters the uncertainty in the parameters is treated. This is achieved by running a number of slightly different (but plausible) versions of the climate model, each with different parameter settings. The settings are chosen by expert judgement and then applied in a fast-to-run climate model with a simple ocean (Williams et.al., 2001) This model version allows the climate sensitivity to be derived for a given set of parameter perturbations and also credibly simulates the change in many atmospheric variables for a climate stabilised at twice pre-industrial levels of atmospheric carbon dioxide. A subset of these model versions are then chosen to span the uncertainty in future projections (Figure 1) and full transient coupled (ocean-atmosphere) global climate simulations are

Page 6 of 54

produced with these settings. Ultimately, there is a compromise between the enormous number of possible combinations of parameter values and the available computer resources. Seventeen versions of the full global model are used, one unperturbed member (standard HadCM3, also often referred to as “the standard model” or “the standard member”) and sixteen perturbed versions (or “ensemble members”).

Figure 1. Frequency plot of climate sensitivity from the fast-to-run simplified ocean models (red). Black lines show the climate sensitivity of the 17 AOGCMs used in TE2100: Solid black:11 models used to generate final result; Broken black lines: 6 additional members (see section 2).

The global climate models are “spun up” to approximately stable states, representing pre industrial climate. Flux corrections are applied as part of the experimental design to minimise model drift and improve the simulation of regional features, such as the European storm track. The advantage of this is that it reduces bias arising from the limitations of model resolution. A possible disadvantage is that it may constrain the future changes, since the future fluxes may be different. Historic greenhouse gas and aerosol particle forcings are then applied between 1860

Page 7 of 54

and present day, followed by projected values to 2100. The future period uses the SRES A1B scenario (Nakicenovic et al., 2000).

Each of the 17 global climate models is then used to drive a corresponding version of the regional model, which has equivalent parameter perturbations. These changes are equivalent rather than always identical to the global model parameter changes because some parameter schemes are scale-dependent and this must be accounted for. In turn, the high resolution hourly 10m-wind components and surface pressure from the regional climate model are used to drive a model of storm surges.

The climate modelling system can also provide estimates of the thermal-expansion component of time-mean sea-level rise. However, because the Met Office Hadley Centre ensemble (hereafter MOHC ensemble) is based around one model structure in which only atmospheric parameters are perturbed, the time-mean sea-level rise is instead estimated from the “CMIP3” ensemble of models in the IPCC Fourth Assessment study (hereafter IPCC AR4 ensemble), which have a range of different atmosphere/ocean components. Although we do not have regional data for these models, this approach can be used for time-mean sea-level rise as, unlike the surge model drivers, time-mean sea level change typically occurs on large spatial scales and so does not require the final downscaling step. The IPCC AR4 ensemble is also used to place into context the storm climate results from the MOHC ensemble.

The IPCC AR4 ensemble represents an "ensemble of opportunity" in that it collates model results from a range of pre-existing models. These are models that have been developed by different modelling groups and are generally fairly dissimilar in structure (e.g. in terms of model resolution and parameterizations of physical processes which take place at the sub grid scale). The range of projections derived from these models represents some measure of uncertainty. This is complementary to the approach used in the MOHC ensemble of climate models, which were designed to understand uncertainties in model projections.

2.2 Surge Modelling System The winds and surface pressure from the MOHC regional climate model are used to drive a 12km resolution barotropic storm surge model. The same model (POL CS3) is used operationally to provide coastal flood warning in the UK as part of the Storm Tide Forecasting Service (STFS). The model produces a numerical solution of the discretized nonlinear shallow water equations with friction, and is described in detail by Flather (2000). Validation of the operational model is performed monthly by comparison with observed sea level data from the UK national tide gauge network (see http://www.pol.ac.uk/ntslf/surgemonthlyplots), and an annual summary of STFS performance is published (e.g. Wortley et al., 2007). The operational model has been shown to perform particularly well during extreme storm surges in the southern North Sea (Horsburgh et al. 2008), forecasting surge in the Thames estuary to within 10cm when driven by re-analysed meteorology. The tide-surge model covers the entire northwest European continental shelf as shown in Figure 2. Tidal input at the model open boundaries

Page 8 of 54

consists of the largest 26 constituents. Modelled surges are derived by subtracting a tidal model run from one forced by both tide and atmospheric forcing from the regional climate model. There is no observational evidence for regional trends in either storm surge frequency or magnitude over recent decades. In a global study, Woodworth and Blackman (2004) found that trends in extreme high water levels were dominated by changes to mean sea level. There have been many previous attempts to use coupled climate models to estimate a future storm surge climate in the North Sea (e.g. Langenberg et al. 1999; Lowe, Gregory and Flather, 2001 [henceforth LGF]; Hulme et al. 2002; Woth et al. 2005). Typically these studies use coarser regional climate models than here, and although some results (e.g. Hulme et al. 2002) suggest upward temporal trends in extreme surges along the east coast of Britain, the higher return levels contain unacceptable uncertainty (Lowe and Gregory 2005) and lack credible verification. This work attempts to provide a robust quantification of that uncertainty.

In the TE2100 project the surge model is run with meteorological forcing from each member of the MOHC regional climate model ensemble, producing the combined response to winds, surface pressure gradients and tides. This captures the tide-surge interaction where the principal effect of the surge on the tide is to alter the times of high and low water and the effect of the tide on the surge is the modulation of surge production. Since wind stress is most effective at generating surge in shallow water, peak residuals are consistently obtained 3-5 hours prior to the predicted high water (Horsburgh and Wilson 2007). A more significant and practical measure is the skew surge, which is the difference between the elevation of the predicted astronomical high tide and the nearest experienced high water (e.g. de Vries et al. 1995). The surge model parameters (e.g. frictional coefficients) are not perturbed because previous operational use has shown that the uncertainty in future surge height is very likely to be dominated by uncertainty in driving winds and pressure rather than surge model parameters. A technical note on the surge model runs is in appendix 2.

It is important to emphasize here that the surge models do not include time-mean sea level change directly. Time-mean sea level change is considered in sections 2 and 3. However, LGF found that to a first-order approximation, time-mean sea-level rise and changes in surge can be added linearly around the United Kingdom. This is investigated further in appendix 1.

Page 9 of 54

Figure 2. Regional Climate Model Ensemble domain (outer square) and approximate CS3 domain (shaded).

In order to extract information near to the Thames mouth from the CS3 surge model we need to choose the relevant model grid box from CS3. The grid box centred at (0.58° east, 51.5° north) is the obvious choice. To confirm that results are not strongly sensitive to this choice, however, we first demonstrate the strong spatial correlation between modelled extremes at this point and its neighbours. Figure 3 shows the spatial distribution of the Pearson product-moment correlation between a 30-year time series of annual maximum residuals at the Thames mouth grid box and the corresponding time series for neighbouring points. (The Sheerness tide gauge falls within the next grid box to the east [see right-hand panel of Figure 3] so it is the “Sheerness” grid box data that is used for validation against the Sheerness tide gauge data in section 2.4.)

Page 10 of 54

Figure 3. Left-hand panel: Spatial correlation of extreme residuals around the CS3 grid box at the Thames mouth. Right-hand panel: Nominal “Thames mouth” and “Sheerness” grid boxes.

2.3 Surge Model Statistical Analysis Methods For the purpose of the following statistical analysis we use the skew surge, which is the difference between the predicted high tide and the nearest experienced high tide as described previously. This is the best way to inspect the effects of the meteorological forcing in some isolation from the deterministic tide on which it is superimposed, whilst still maintaining the important physical effects of tide-surge interaction in the model runs. Earlier work (e.g. LGF, Hulme et al., 2002, Flather et al. 1998) used residuals (i.e. the time series obtained by subtracting the tidal run from the fully forced run). An indication of the improved statistical usefulness of skew surge over non-tidal residual is given in Figure 4, which shows a scatter plot of 693 modelled skew surges against tidal elevation at the Thames grid box for a one year run of the standard model, and a corresponding plot for the 611 largest residuals from the same year (subject to the requirement of 12 hours separation between any two). There is an insignificant correlation between skew and tide (P value > 90%) but a significant negative correlation between residual and tide (P value < 1%). This is associated with non-linear tide/surge interaction (e.g. Horsburgh and Wilson, 2007), but a simple physical interpretation is that residuals are greater close to low water. In general, therefore, we adopt the skew surge in this work. In our validation section, however, we also use non-tidal residual for consistency and comparison with the validation methods of earlier work.

Page 11 of 54

Figure 4. Correlation between modelled tidal elevation and two indicators of storm surge (skew surge: left panel, and non-tidal residual: right panel) for the Thames grid box.

We use a parametric extreme value model to fit the distribution of simulated extreme skew surge heights. The approach follows closely that advocated by Coles (2001), in particular his example 3.5.3 and chapter 6. The approach is well-established and supported in the literature (e.g. Zhang et.al., 2004; Butler et.al., 2007). Since we are interested in possible future changes in the skew surge height we compare a fit with time-varying parameters to a stationary fit (in which parameters are invariant over time). For each of the 17 model ensemble members a 149- year sequence of the annual maximum skew surges at Sheerness can be fitted by a generalised extreme value distribution, described by the cumulative distribution function:

−1 ξ ⎪⎧ ⎡ ⎛ z − μ ⎞⎤ ⎪⎫ G()z = exp⎨− ⎢1+ξ⎜ ⎟⎥ ⎬ ⎩⎪ ⎣ ⎝ σ ⎠⎦ ⎭⎪ where μ , σ and ξ are the location, scale and shape parameters respectively and z is the skew surge. The parameter values are found using the maximum likelihood.

We can make better use of the available data by fitting the r largest skew surges per year. Providing that the value of r is not so large as to introduce bias by moving out of the extreme tail of the distribution, this approach reduces the uncertainty in the fitted parameters and return levels compared to the single annual maximum approach. Tawn (1992) suggests the choice of r = 5 . Our own case study suggests that parameter estimates are stable at least as far as r = 5 . Thus results here are based on r = 5 unless otherwise stated. In an effort to ensure independence of the 5 largest values per year we insist on a separation of at least 60 hours between any two maxima. (We suggest that 60 hours is ample but, as an aside, if we did have dependence in these data, it would only call into question the statistical significance of any trends that were found. As discussed below, we do not find any statistically significant

Page 12 of 54

increasing trends and thus the issue of independence is not a sensitive one here.) The expression for the joint probability density function used in the r largest approach is given by, for example, Butler et al. (2007). As pointed out therein, the use of the annual r largest is a natural approach when we anticipate the possibility of trends because it relies on a relative definition of what constitutes an extreme value so that we don't, for example, face the issue of whether or how to vary the threshold in the peaks-over-threshold approach.

The method we use allows for linear temporal trends in the location and scale parameters. Following Zhang et al (2004) we do not consider change in the shape parameter. The difference between the maximised log-likelihoods of the two models (with and without trends being allowed) can be shown to behave as a chi squared distribution and thus the statistical significance of trends can be established while accounting for long period (multi-decadal) variability. This approach is an advance on the time-slice analysis used in previous work (e.g. Lowe and Gregory, 2005; Woth et al., 2006), which typically used simulations of approximately 30 years.

2.4 Validation of climate/surge simulator

2.4.1 Near-present day extremes Validation of the climate/surge modelling system was performed using climate data simulated for the near present day to produce a surge climatology that can be compared with observations from tide gauges. Table 1 shows a comparison of the inferred 50-year return level from the fitted parametric model of the unperturbed (standard) member of the MOHC ensemble with the observed values reported in LGF. For consistency with LGF, this validation was performed with non-tidal residuals rather than skew surge. With the exception of only one location, the new system represents an improvement over earlier work.

As expected, some differences do occur between model and observations with models typically underestimating surge extremes compared to observations. This is largely due to the resolution, both spatial and temporal, of the atmospheric forcing and the local bathymetric resolution of the surge model. Modelled elevations represent an average value over a grid box of area 12 km x 12 km, and this will generally differ from a corresponding tide gauge value at a specific location due to local effects (e.g. local winds and wave setup). Despite these limitations, at the Thames model grid point (compared here with the Southend tide gauge) considerable skill in simulated extreme surges is demonstrated.

Page 13 of 54

Table 1. 50-year return levels (RL) of residuals for 15 UK ports. ‘+’ indicates a location at which the new results show an improvement on the LGF results. The TE2100 data are taken from the first 30 years of the standard model run. The observed results were originally presented by Flather et al. (1998). Port Obs TE2100 50yr RL LGF 50yr RL Improvement 50yr RL (m) ratio (m) ratio (m) Wick 1.11 1.02 0.92 0.91 0.82 + Aberdeen 1.25 1.05 0.84 0.82 0.65 + North Shields 1.66 1.12 0.67 0.96 0.58 + Whitby 1.98 1.19 0.60 1.09 0.55 + Immingham 2.14 1.60 0.75 1.52 0.71 + Lowestoft 2.36 1.89 0.80 1.85 0.78 + Felixstowe 2.50 2.01 0.80 2.05 0.82 Southend 2.91 2.82 0.97 2.36 0.82 + Dover 1.77 1.60 0.91 1.44 0.81 + Newlyn 1.02 0.70 0.69 0.65 0.64 + Ilfracombe 1.49 1.20 0.80 0.88 0.59 + Milford Haven 1.44 1.05 0.73 0.85 0.59 + Holyhead 1.51 1.18 0.78 1.03 0.68 + Heysham 3.16 2.32 0.73 1.60 0.50 + Millport 1.72 1.70 0.99 1.34 0.78 +

Further validation specific to our area of interest was performed using skew surge data calculated from observed sea level records for Sheerness, which are available from the British Oceanographic Data Centre (BODC). 5, 50 and 500 year return levels derived from these data (Figure 5) were compared with two surge simulations, one driven by the standard MOHC ensemble member and one driven by the ERA40 reanalysis product. ERA40 is produced by the European Centre for Medium-Range Weather Forecasting and contains a reanalysis of the global atmosphere and surface conditions for the 45 years from September 1957 to August 2002 (http://www.ecmwf.int/research/era/do/get/era-40). The ERA40 winds and pressure were downscaled to 25km resolution over Europe using the same regional climate model as deployed in the climate simulations.

The values of skew surge return levels shown in Figure 5 are based on GEV fits to 149 years, 35 years and 40 years of annual maxima for the climate model, observations and ERA40-forced model respectively. A significant (at the 5 % level) difference between observations and model can be seen in the 5-year return level. This is associated with a difference of about 17 cm in the GEV location parameter, so a simple correction to the model skew surge to account for the bias due to resolution limitations might be to add 17 cm to the model results. However, such a

Page 14 of 54

correction has not been applied here because most of the uncertainty in the return level at longer return periods (which are of greater concern to planners) arises from the uncertainty in the shape parameter. Since the shape parameter of the fit to the model data is larger than that of the fit to the observations, this compensates, at long return periods, for the difference in the location parameter. Furthermore, such a correction would have no influence on the detection of time-trends in the model extremes.

Figure 5. Sheerness skew surge return levels (bars) and 95% confidence intervals (lines). The ERA40 500 year confidence interval exceeds the bounds of the plot, the upper quantile being at about 3.5 m. Obs: observed results, Mod: climate model driven results and ERA: downscaled ERA40 driven result.

2.4.2 Simulation of an event comparable at Southend to the 1953 event

The skew surge at Southend during the 1953 coastal flooding event has been estimated from archived paper records to be 2.1 m above a predicted tide of 2.47 m AOD (Horsburgh et al., 2008) about 48 hours after the full moon. The largest skew surge in 2873 years of the MOHC ensemble runs was 1.78 metres and this happened to arrive on a modelled tide of 1.69 m. It is

Page 15 of 54

reasonable to ask whether, with different timings of storm movement relative to the spring-neap cycle, the model runs could simulate an extreme sea level comparable to the observed 1953 event. We tested this by deliberately adjusting the phase relationship between the meteorological forcing and the tide. In this experiment the residuals were re-derived in the usual way (by subtracting a tidal run) in order to capture tide-surge interaction. Initial phase adjustments around the modelled tide of 1.69 m produced a skew surge of 2 m (see Table 2). By subsequently adjusting the phase relationship of the same storm around a modelled spring tide of 2.35 m we produced a maximum skew surge of 1.82 m, corresponding to a total water level of 4.17 m AOD.

Table 2. Results of sensitivity tests in which the surge/tide phase relationship was adjusted. Source Skew surge (m) Max Elevation Max Elevation (model tide) (m (corrected tide) AOD) (m AOD) Model: apparent As found 1.78 3.47 largest forcing found in MOHC ensemble runs Phase chosen to 2.00 3.69 (diagnosed by size maximise skew of skew) (on 1.69 m tide) Phase chosen to 1.82 4.17 4.61 maximise skew (on 2.35 m tide) Observations of the 1953 event 2.10 4.57 (from Horsburgh et al., 2008)

Numerical models of continental shelf scale typically underestimate the amplitude of tidal elevation when compared to tide gauge data. This is primarily due to limitations in their ability to simulate the higher harmonics (e.g. M4, M6) which are generated by non-linear processes in very shallow water. Accuracies of the most important semi-diurnal constituents are typically of the order 10 cm (Kwong et al., 1997), although even M2 can be in error by as much as 30 cm in regions of high tidal range. For POL CS3, the shelfwide RMS error is 20 cm (Williams and Horsburgh, 2006) which is typical of other European 12 km resolution models. For these reasons, in operational predictions of coastal flooding the tidal component is predicted using harmonic methods (i.e. tide tables) which are generally more accurate than any modelled tide. Surges from numerical models are then added to these to produce the predicted total sea level.

The results of either making (“Corrected tide”) or not making (“Model tide”) this replacement in our results are shown in Table 2 and in the case of the highest total water level the result is illustrated in Figure 6 . It is worth emphasizing here that this correction is applied only for this case study of surge/tide phase relationship, and not to any other results in this report.

Page 16 of 54

Furthermore, even if it were applied elsewhere it would have no effect on the value of the skew surge, no effect on trend detection and no effect on the surge residual.

When the 2.35 m modelled tide is replaced with its equivalent from tide tables, then a total level of 4.61 m is obtained. This is comparable with the maximum level of 4.57 m during the 1953 surge. Finally we remark that it is possible that there is in the MOHC ensemble runs a more severe meteorological forcing event which happened to arrive at a less-favourable (in terms of producing a large skew surge) phase and thus was not diagnosed from the skew surge data. We have found one equally extreme event simply by searching for the largest residual. These case studies indicate that the modelling system is able to produce events of comparable magnitude to the 1953 coastal flooding event.

Figure 6. Comparison of water levels (elev) for an extreme modelled event and estimated 1953 maximum at Southend. Black curves: Modelled total water level; Green curves: modelled tide; Red curve: Modelled residual. Straight black line: estimated 1953 maximum total water level; Straight green line: 1953 predicted astronomic tidal maximum.

Page 17 of 54

2.5 Diagnosis of Temporal Trends in Surge Extremes Here we describe our approach for identifying trends in the extreme skew surge climate simulated by the climate/surge modelling system. The approach is an improvement over the time-slice methods which have typically been used previously.

2.5.1 Quantifying the problem with the time-slice method Previous work (e.g. Hulme et al., 2002) attempted to identify trends by fitting stationary extreme value distributions to 30-year time slices at either end of the (typically 150 year) period of interest. It then looked at the difference between these samples (i.e. subtract the 1960-1990 value from the 2070-2100 value). This makes establishing the statistical significance of any changes difficult because it does not sample the long-period variability known to affect European storminess (Jenkins et al. 2007, from which our Figure 7 is taken). A re-sampling bootstrap approach can provide an estimate of short-period variability in short time slices but longer period variability (longer than a few decades) is more difficult to estimate.

Figure 7. The total number of severe storms per decade over the UK and Ireland during the half year period October to March, from the 1920s to 2000. Error bars show ± one standard deviation. Taken directly from Jenkins et al. (2007).

A more direct illustration of the effects of multi-decadal variability can be demonstrated by looking at the range arising from stationary fits to different 30-year periods of skew surge results (Figure 8). The green crosses show the 50-year return period level derived from a 30 year sample time-centred on each cross. It can be seen that the variability associated with this signal is comparable to the size of the century-scale change which has been reported in

Page 18 of 54

previous work (of the order of 50cm) using the differences between two 30-year time slices. Clearly, including the long period multi decadal variability is critically important to establishing the significance of climate trends. Since we are no longer constrained to using 30-year time slices in the current work this provides a clear demonstration of how the new methods, which make use of increases in available computer time, improve on earlier techniques.

Figure 8. Annual maximum total elevation from 143 years of CS3 simulation (black dots). Derived 50-year return level according to a GEV fit to two 30 year time slices at the beginning and end of the run, with 95% confidence interval (large green dots). Derived 50-year return level according to a GEV fit to all 143 years, with 95% confidence interval (large red dots). 50-year return period according to a 30-year sample time-centred on each cross (green crosses).

2.5.2 Climate driven changes in extremes The key output from this part of the project is to establish projections of climate driven changes in extreme sea levels. Figure 9 shows, for each of the 17 MOHC ensemble members, the annual maximum skew surges and 50-year return levels with time-trend from the parametric model fitted to the 5 largest skew surges each year. The MOHC ensemble members can readily be separated into two quite distinct populations: high (shown in red) and low (shown in green). The panel labelled "x" is the unperturbed (standard) run, which belongs to the high population. By comparison with Figure 5 we can deduce that the high population (eleven members) has the best agreement with observations while the low population (six members) does not verify well

Page 19 of 54

by this measure. The six low-population members have in common a perturbation to a diffusion parameter (which is included in the climate model for numerical stability). This particular perturbation results in a significant smoothing out of the regional climate model data fields, in particular the synoptic storms, and renders them less valid when compared to observed storms. Although some other aspects of climate are well simulated by these six members, they are excluded from further analysis here because of the importance of the realistic simulation of the strength of synoptic storms for our purpose. A similar approach is taken in the river flow analysis , which forms a further component of the TE2100 science scenario(Kay et. al., 2008), because precipitation is also affected in this 6-member population.

Before considering the statistical significance of the trends, we note that the absolute physical significance is not very large: i.e. the maximum fitted trend represents an increase of just 7 cm in 100 years, which can be compared with observed global mean sea-level rise during the 20th century of around 17cm (IPCC, 2007). Even without considering the fitted lines, a visual inspection of the data points in Figure 9 gives no overall impression of increasing trends. Furthermore, in more than half of the high-population (red) MOHC ensemble members, the maximum skew surge occurs less than half way through the run.

These first impressions are confirmed by our more rigorous statistical test. The only MOHC ensemble member for which the trend in skew surges has been found to be statistically significant at the 5% level using the approach described in section 2 using the 5 largest skews per year is the member labelled "i". In this case the trend is one of decreasing extreme water levels. Moreover, the trend is only significant at the 5% level if this member is considered as an isolated experiment. If it is regarded as one of 11 independent realisations the result ceases to be significant at the 5% level.

To evaluate a single trend representative of the eleven ensemble members we regard each one as equally likely and consider the distribution of the eleven trends. The mean and the error-in- the-mean of this distribution are -0.35 mm/year and 0.17 mm/year respectively, giving a p value of just less than 5% for a two-tailed test. In other words, we have a borderline statistically significant (at the 5% level), physically small, negative trend.

Page 20 of 54

Figure 9. Annual maximum skew surges and 50-year return level with time-trend from the parametric model fitted to the 5 largest skew tides each year. Dots show annual maxima. Colours are explained in the text.

This result differs from that in UKCIP02 (Hulme et al. 2002), which found a significant increase in surge heights in the Thames region. The difference is likely to be partly due to the avoidance of time slices in the new methodology, but may also be influenced by the different experimental design (e.g. resolution of both models, focus on skew surge).

Page 21 of 54

2.6 Relative time-mean sea level rise In addition to changes in storm surge height, future extreme sea levels in the southern North Sea will be affected by changes in the local relative time-mean sea level. In turn, local time- mean sea level variations will results from changes in the ocean density, changes in ocean circulation and the addition of extra freshwater to the ocean from melting land ice. Vertical land movement must also be included.

2.6.1 Choosing the source of global mean sea level rise Global sea level changes in the UKCIP02 report (Hulme et al. 2002) comprised projections made by an un-flux-adjusted version of the Met Office Hadley centre’s HadCM3 model (Gordon et al. 2000, Pope et al. 2000) and a range derived from the IPCC 3rd assessment report (IPCC, 2001). Estimates of relative UK land movement were also included.

More recently, new scenarios have been produced using several different methods. Rahmstorf (2007) produced an estimate of sea level rise by first deriving a transfer function between observed 20th century surface temperature rise and observed sea level changes. He then took climate model estimates of future temperature and applied his transfer function to estimate 21st century sea level. This gave a maximum rise during the 21st century of 1.4m, which is significantly in excess of the Third Assessment report values. As noted by Rahmstorf (2007), however, his assumption of a linear transfer function may lead to an over or under estimate of sea level. For the thermosteric sea level response to global warming, which arises from changes in ocean heat uptake, Rahmstorf’s methodology significantly overestimates this component over the 21st century, relative to a direct projection from the ocean temperature change in his particular coupled climate model (an "intermediate complexity" model). This is presumably due to the transfer function not taking into account changes in the pattern and magnitude of future heat flux into the ocean and changes in ocean circulation. It should also be noted that the increase in global-mean surface temperature over the 21st century in this model projection is 5°C, a factor of approximately 6 greater than the increase over the 20th century for which the empirical relationship was derived. Finally, several authors (e.g. Holgate et al., 2007) have highlighted concerns over the statistical methods used in this study. Thus, after investigation of this method we do not believe it is a suitable way to proceed.

In 2007 the IPCC published an updated estimate of global mean sea level rise (IPCC, 2007) using a range of complex and simple climate models and ice melt simulations. Unfortunately, this report is not easy to interpret. A range for future sea level rise between present day and the 2090s of 18 to 59cm is presented, incorporating both emissions scenario and modelling uncertainty. Thermal expansion is projected to contribute 70-75% of the central estimate of sea level rise. The remainder is allocated to the melting of glaciers, ice caps and a net contribution from the Greenland and Antarctic ice sheets. This range should not be compared directly with the earlier Third Assessment Report estimates because the new work represents a different uncertainty range (5th to 95th percentile as opposed to 2.5th to 97.5th percentile in the Third Assessment Report) and uncertainties were combined using different methodologies and assumptions (e.g., greater independence of uncertainty from the different contributions to mean sea level was assumed in the Fourth Assessment Report).

Page 22 of 54

The new IPCC report also presents an additional term, based on a temperature scaling of recent observations of increased ice discharge from the Greenland and Antarctic ice sheets, which could add up to an additional 20cm of sea level rise. Further, the report notes that even larger values of sea level rise cannot yet be ruled out. This is because the observational record of increased ice discharge is short and computer models are not yet able to robustly simulate the ocean-ice climate system (which includes simulation of local ocean warming, floating ice shelves, ice sheets and rapidly-moving ice streams). So, for our H++ scenario, we take a pragmatic approach, using the 18 to 59cm range for the ensemble predictions and deriving a low probability high impact case based on expert judgement and paleo climate observations. The range of IPCC AR4 global mean sea level rise estimates for the three emissions scenarios considered here as part of the ensemble projection package is shown in Table 3. The H++ estimates can be found in section 3. For ease of reference, cumulative carbon emissions for each scenario are shown in Table 4; these can be compared with estimated average emissions from fossil fuels and cement production for the 1990s of around 6.4 GtC/year (see, for example, Houghton, 2004). Recently Raupach et.al have noted that the emissions growth rate since 2000 was greater than that of the A1FI scenario. However this may not continue in the future, for example if the world were to follow the UK's recent commitment to 80% GHG emissions reductions by 2050, this would take emissions below the B1 scenario. Table 3. Global mean sea level rise estimates from present day to the 2090s for the SRES B1, A1B and A1FI scenarios. B1 A1B A1FI 5th percentile (m) 0.18 0.21 0.26 Mid range (m) 0.28 0.35 0.43 95th percentile (m) 0.38 0.48 0.59

Table 4. Cumulative CO2 emissions 1990-2100 (Gt C) for each of the three scenarios, taken from Nakicenovic et. al. (2000) B1 A1B A1FI 983 1499 2189

2.6.2 Regional deviations from the global mean Projected increases in sea level are not geographically uniform and changes in the distribution of sea level with respect to the global mean adds another component of uncertainty to regional sea level projections. Because the ensemble projections of 21st century sea level rise (Table 3) are dominated by the thermal expansion component, the uncertainty in deviations from the global mean are likely to depend primarily on an interplay between regional ocean density changes and dynamics (Lowe and Gregory, 2005). Atmospheric surface pressure changes influence regional sea level through the "inverted barometer" effect, but this is a relatively small influence given the projected pressure changes. The uncertainty in local deviations of European sea level rise from the global mean value could be estimated directly from the MOHC ensemble

Page 23 of 54

or by obtaining the climate model data used in the IPCC 4th assessment and performing further analysis.

The IPCC 4th assessment analysed regional patterns of sea level change for 16 models forced by the SRES A1B scenario (for which the largest number of model projections were available). Using the IPCC results for the uncertainty in local deviations from the global mean sea level rise has the advantage that the models used in the IPCC study have a range of different atmosphere and ocean components, whereas the MOHC ensemble only had the atmospheric parameters adjusted. However, while the MOHC ensemble was specifically designed to sample uncertainty, albeit only considering versions of a particular base model, the models used in the IPCC analysis were not. Thus a necessary step is to evaluate the pattern of spatial variability in both ensembles and then, taking a precautionary approach, select the ensemble with the largest range around the European region. The spread of local sea level rise near the United Kingdom relative to the global mean is found to be much larger in the IPCC AR4 ensemble than in the MOHC ensemble (Figure 10). For this reason, we choose to base our estimate of the uncertainty in mean sea level around the UK on the IPCC AR4 ensemble. Interestingly, the two methods do agree on the amount of variation in some regions. This might be chance but it could indicate that uncertainty in the regions of agreement is dominated by uncertainty in atmospheric parameters, while uncertainty in other regions is dominated by uncertainty in ocean parameters.

Proceeding with the IPCC AR4 ensemble, Figure 11 shows sea level changes around the UK (not including melt of glaciers, ice caps and ice sheets) given by 11 suitable IPCC models. For the majority of these models the change in sea level around the UK is similar to their global mean change. There are, however, exceptions to this with, for example, one model giving an increase of nearly twice the global mean and one model giving an increase of half the global mean. Table 5 details the ratio of the sea level rise around the UK to the global mean value.

Table 5. The projected 21st century local (around the United Kingdom) and global sea level rise from 11 climate models for the SRES A1B simulation. Ice melt is excluded. UK region Global mean Ratio of UK change (cm) change (cm) to global mean 24.9 27.1 0.919 21.6 20.1 1.077 17.5 21.7 0.806 35.6 33.1 1.075 17.7 16.4 1.081 9.6 18.1 0.534 48.3 24.7 1.958 17.5 19.8 0.884 33.7 22.4 1.504 12.4 12.7 0.973 21.9 19.5 1.127

Page 24 of 54

Figure 10. Inter-model differences (represented by twice the standard deviation) in changes in sea level distribution over the 21st century relative to the global mean (cm). Upper panel: IPCC AR4 ensemble. Lower panel: MOHC ensemble. The upper panel represents the subset of the AR4 models used for Figure 11, but the complete IPCC AR4 ensemble gives a very similar result.

Page 25 of 54

Figure 11. Sea level rise projections (cm) around the UK for the end of the 21st century under the SRES A1B scenario. The data is from a range of AOGCMs which submitted data to the CMIP3 project (originally collated and processed by Jonathan Gregory as part of the IPCC AR4). The data were regridded from the grids on which they were provided to the grid used by the HadCM3 model (1.25 x 1.25 degrees) and a common UK land region imposed.

Page 26 of 54

For other emissions scenarios (SRES B1 and A1FI) local deviations from the global mean are scaled up or down using the ratio of the global mean thermal expansion of the target scenario to that in the A1B scenario as calculated from the 11 suitable models. The efficacy of scaling the spatial pattern of thermal expansion from one scenario to another using the global mean value was demonstrated by Nicholls et al. (2008). The correlation across model variants between local deviations of the thermal expansion term and the global mean thermal expansion was assumed to remain constant across emissions scenario.

2.6.3 Combining time mean sea level rise components The final stages of dealing with the time mean sea level component is to combine the global mean thermal expansion, local deviations of this term from the global mean, and the ice melt terms into a total. Then vertical land movement (including uncertainty) is added on. In our scenarios the observed vertical land movement estimate for Sheerness is taken as 1.09+/- 0.64mm/yr from Bingley et al. (2007), in preference to the dataset used in UKCIP02 (Hulme et al. 2002). The UKCIP02 approach to vertical land movement has recently received some criticism (Gehrels, 2006). Table 6 lists the combined sea level rise amounts for the three scenarios of interest at the mouth of the Thames between present day and the 2090s.

Table 6. Thames Region relative time mean sea level change (metres) from present day to 2095 under 3 different scenarios with 90% confidence intervals. A1Fi A1B B1 Mean 0.56 0.47 0.40 min 0.24 0.21 0.19 max 0.88 0.73 0.61

2.6.4 Using observational sea level rise constraints There is now a body of research (e.g. Williams et. al. 2006) that attempts to constrain model projections of climate according to an assessment of how well the same models reproduce aspects of present-day climate. Such methods were applied to surface temperature projections for the IPCC 4th assessment. However, similar methods were not used for sea level projections as the constraints for sea level (e.g. a shorter observational record) are less robust. Further work by the scientific community is required to establish the suitability of particular constraints for sea level rise. We do not apply such constraints here. This means that the distributions presented are model frequency distributions, rather than formal observationally constrained probability distributions.

We have, however, evaluated the annual mean sea level climatology in the control simulations of the IPCC AR4 models. This allows us to make a broad check on whether the models give a reasonable representation of present-day sea level pattern. We do not make a detailed regional assessment of the models’ representation of sea level patterns as there are notable differences between observationally based mean dynamic topography (MDT) datasets, much of which is

Page 27 of 54

due to uncertainty in the underlying geoid (e.g. Bingham and Haines 2006). In addition, MDTs tend to represent a fairly short observational period (less than decadal) rather than a long-term mean. Despite these limitations the ensemble mean of the IPCC AR4 models’ sea surface height patterns was found to show broadly good agreement (the same large-scale features, which are of generally similar magnitude, are present in both model and observations) with the observed pattern of sea surface height (e.g. Rio and Hernandez, 2004), giving us reasonable confidence in the models’ representation of physical processes that currently determine regional sea surface height distribution.

2.7 Combined extreme water level return period curves Here we combine a present-day baseline extreme skew surge distribution with estimates of relative time-mean sea level change to produce simulated extreme water return level curves (measured relative to present day tides) for the 2090s under the three scenarios of interest.

For our present-day baseline we have a choice of using tide-gauge observations, our climate- model driven simulation, or our ERA40-driven simulation (c/f Figure 5). Noting that the BODC observational data available for this site excludes both 1953 and 1978, and taking a precautionary approach, we choose the ERA40-driven option since this gives the most extreme (and the most uncertain) distribution of the three (see again Figure 5).

Both the magnitude and the uncertainty of the modelled skew surge trend (section 2.5.2) are small enough to be neglected here.

The result of this combination is represented in the following figures, which show a quantity which we term the future skew surge. This can be thought of as the difference between the maximum observed water level for an event in the 2090s and a corresponding present day astronomical high tide.

The small asymmetry in the confidence interval around the estimate (which can most easily be seen at the large return periods) is associated with the surge extremes. In contrast the uncertainties in the mean-sea-level change are assumed normal (and thus symmetrical).

In view of the arguments put forward in section 2.5.1 in favour of the use of long time samples in identifying trends in surge extremes, it is reasonable to ask whether the uncertainty in the baseline should be inflated to compensate for the relatively short time span of the ERA40-driven simulation. However our tests (described in Appendix 3) suggest that such inflation would not be appropriate.

Page 28 of 54

Thames Future Skew Surge 2090s RL curve for A1B with 90% CI Return Level (metres) 01234

2 5 10 20 50 100 200 500

Return Period (years) Figure 12. 2090s future skew surge return level curve for the region of interest under the A1B scenario. Broken lines show the 90% confidence interval.

Page 29 of 54

Return Level (metres) 01234

2 5 10 20 50 100 200 500

Return Period (years) Figure 13. 2090s future skew surge return level curve for the region of interest under the B1 scenario. Broken lines show the 90% confidence interval.

Page 30 of 54

Return Level (metres) 01234

2 5 10 20 50 100 200 500

Return Period (years) Figure 14. 2090s future skew surge return level curve for the region of interest under the A1FI scenario. Broken lines show the 90% confidence interval.

Page 31 of 54

3 H++ The IPCC Fourth Assessment report highlights that substantial changes in sea level are possible from some ice sheet processes for which there is currently little scientific understanding. There is also the possibility that the changes in storminess simulated in the models presented in the IPCC analysis might produce surge increases beyond the range of the MOHC ensemble presented in section 2. To address these concerns we present a scientifically plausible high end sea level rise scenario. While we do not attempt to derive a probability for this scenario it should be viewed as being very unlikely4 to occur during the 21st century. It is presented to provide justification for not ruling out high level options for adaptation until the science is more certain. It also provides strong justification for continuing to monitor sea level and land level movements, and the large ice sheets.

3.1 H++ surge component The skew surge component comes from comparing 21st century changes in simulated large- scale indicators of storminess and then selecting the IPCC model with the largest change in storm intensity over the United Kingdom region. A simple downscaling methodology is used to estimate the increase in surge height.

3.1.1 Changes in atmospheric storminess Storm surges are driven by atmospheric storminess. To understand our simulated 21st century storm surges, and to be able to comment on their robustness, it is useful to compare characteristics of the storms in the different MOHC global climate model ensemble members used to drive the surge model. These are further placed in context by showing similar diagnostics for the climate models used in the IPCC 4th assessment (Figure 15). The storm indicators are based on the Blackmon band pass filtered daily atmospheric surface pressure (Blackmon, 1976), a well-recognised indicator of storm track strength and location. To obtain a tractable indication of latitude and strength of the track of storms arriving at the British Isles we consider a section along approx 4° west. A quadratic relationship between latitude and strength along this section is fitted to the gridded model results and the maximum of this quadratic is taken to give the location and strength of the track.

4 It is not possible to quantify this low probability; "very unlikely" in this context does not refer to the IPCC definition.

Page 32 of 54

Figure 15(a) Global climate model 21st century change in the latitudinal track of winter storms against the change in intensity of storms for the MOHC ensemble (red diamonds: high population; green triangles: low population) and the IPCC 4th assessment models (blue squares)

Page 33 of 54

Figure 15. (b): Present day strength and latitude, using the same symbols as Figure 15(a), with crosshairs showing the ERA40 strength and latitude.

By this measure, the MOHC ensemble typically shows a slight weakening of the winter storm track accompanied by a southerly movement. The IPCC AR4 ensemble has a mix of strengthening and weakening storm tracks, with some members showing northward movement and some a southerly movement. The amount of southerly displacement is less than that seen in more than half the MOHC ensemble. A comparison of each model’s present day simulation with ERA40 reanalysis data suggests that many of the IPCC AR4 ensemble members have a greater present day bias than the MOHC ensemble, although this is not true for all of the models. Indeed, the IPCC model with the largest increase in storm track intensity (the MIUB- ECHO-G model, labelled Q in Figure 15) has a particularly small present day bias in storm intensity. Thus, while there is evidence to reject or down-weight some of the IPCC AR4 ensemble this can not be applied to the model with the largest change in storm intensity. These results suggest it is necessary to investigate the effect that this model’s projected changes in 21st century storminess could have on extreme sea levels.

Page 34 of 54

3.1.2 Simulation of regional results from MIUB-ECHO-G MIUB-ECHO-G is a global climate model and we do not have available the necessary high temporal and spatial resolution wind and pressure fields needed to drive the surge model. As far as we are aware this global model has not been downscaled using a consistent regional climate model to a scale suitable for driving the surge model, nor can its outputs be fed into a Hadley Centre regional climate model. An alternative strategy is to attempt to scale the MOHC results in a way which appears consistent with MIUB-ECHO-G. One approach is to scale the mean sea- level pressure gradient field from the MOHC regional climate model used to drive the surge model by the index of storm track strength (the change in which forms the abscissa of Figure 15).

Figure 16. Profiles of Blackmon band pass filtered pressure deviation at about 4° west for scaled and unscaled MOHC global model variant aexso and for present day and future MIUB-ECHO-G. ERA 40 profile is also shown. Symbols show the maximum of a fitted parabola for each profile.

Page 35 of 54

The pink line in Figure 16 shows the profile of Blackmon band-pass filtered pressure deviation at 4 degrees west for the MOHC ensemble member named aexso, which we have chosen to scale to look like MIUB-ECHO-G. The scaling is chosen so that the fitted maximum of the scaled profile (purple symbol in Figure 16) matches the fitted maximum of the MIUB-ECHO-G profile (blue symbol in Figure 16). The small difference in latitude of the maxima is ignored. The scale factor is thus found to be 1.34

Since the Blackmon band-pass filtered pressure deviation is essentially a linear function of the pressure we interpret the scaling as a linear multiplication of the pressure gradients in the regional climate model (named afixo) variant that is associated with the global model aexso. However, investigation of the relationship between model pressure and 10 metre windspeed suggests that the gradient wind over the sea is considerably better correlated with the 10 m windspeed than is the geostrophic wind so whilst we have scaled the pressure gradients by the fixed scale factor (1.34), we have scaled the 10 m windspeed by the corresponding gradient wind (i.e. a smaller factor in regions of strong cyclostrophic curvature). This does not mean that we regard the gradient wind as applying at the 10 m level. Instead, we calculate the ratio between the gradient wind in the un-scaled case and that in the scaled case and use this ratio to scale up the 10 m wind. In this way we make some allowance for attenuation by the atmospheric boundary layer. We do not allow scaling to change the 10 m wind direction. The impact of this scaling on the return level curve is shown in Figure 17.

Page 36 of 54

Figure 17. Skew surge return level curve for MOHC ensemble member aexso with pressure scaled by factor 1.34 and 10 m winds scaled by the corresponding gradient wind (blue line).The other lines show the curves for the eleven MOHC ensemble members (black line: standard model; red lines: other ten members).

The UK Gale Index is another well-recognised measure of storminess, and provides an alternative way of comparing models. Whereas the Blackmon band-pass filtered pressure deviation gives a spatially-varying measure of temporal variations in pressure at a location, the UK Gale Index (Hulme and Jones, 1991) gives a temporally-varying measure of spatial variations in pressure at a particular time.

To amplify a MOHC ensemble member to match MIUB-ECHO-G in terms of the Gale index (GI) we amplify the MSLP gradients such that the GI extremes of the amplified distribution look like those of MIUB-ECHO-G. So, we need to choose a MOHC ensemble member and an amplification factor. To do this we first take 18 annual maximum gale indices from MIUB-ECHO-

Page 37 of 54

G at the end of the 21st century and fit to these a Gumbel distribution using the method of moments. For the Gumbel distribution the 50 year return level is given by:

R50 = μ + 3.9σ where μ is the location parameter and σ is the scale parameter. The point (,)R50 σ for these 18 years of MIUB-ECHO-G is shown by the white cross in Figure 18. This can be thought of as our target. Also shown on the plot are lines, one for each MOHC ensemble member, joining points for that MOHC ensemble member amplified by factors 1.0 through to 1.34. Our criteria for choosing a MOHC ensemble member are that we wish to amplify as little as possible and that we wish to be close to the white cross. So we choose the member labelled “q” on the plot (full name, afixq), and the amplification factor turns out to be about 1.06. This is much smaller than our 1.34 factor in the storm-track scaling described above. The resulting return level curve is shown by the yellow line in Figure 19.

Figure 18. Illustrating choice of MOHC ensemble member to scale by Gale Index.

Page 38 of 54

Figure 19 Skew surge return level curves for member afixq scaled by 1.06 (yellow line). Other lines as in previous return level plot.

The pressure scaling factor of 1.34 (blue line in the return level plots) results in an increase of about 0.95 m in the 50 year return level (compared to the pooled MOHC ensemble estimate). There are reasons to think that this is an over-estimate. In particular the scaling based on the gale index (yellow line in the return level plots) and a further scaling using the empirical orthogonal functions (see, for example, Jolliffe, 1990) of the daily mean sea level pressure (not shown) both put this increase at less than 0.2 m. Furthermore, a map of correlation between negative anomalies in daily mean sea level pressure and extreme positive skew events at the Thames indicates that the correlation is stronger in the region of the Baltic than anywhere along 4° west. The ratio of the future to present day Blackmon BPF storm track intensity over the Baltic region is about 1.2, considerably smaller than the maximum value (1.34) along 4° west. We chose the 4 degrees west band at the start of our work on the H++ surge component. It

Page 39 of 54

seems physically reasonable as a proxy for the west coast of the UK (i.e. “as storms arrive at the UK”). Had we considered the E.O.F.s first we might not have arrived at such a large result. However we have been unable to demonstrate conclusively than any one of these approaches is better than the others so at present we are unable to completely discount the largest scaling and the corresponding increase of about 0.95 m in the 50 year return level by the end of the 21st century, which we therefore take to be the top of the H++ range of surge. We do also note that the storm analysis was based on short time slices, and so might suffer from some of the issues discussed in section 2. The storm surge component of the bottom of the H++ range is taken to be zero.

3.2 H++ time mean regional sea level component In the field of sea-level rise projection, some of the models available to us, especially those describing large ice sheets, are not yet well-developed. Except on the broadest scale, we can claim little demonstrable skill in simulating the past contribution of ice sheets to sea-level change. Foremost among the reasons for this lack of skill is a lack of testing data (Vaughan and Arthern, 2007). There is a general lack of well-mapped and well-dated histories of how ice sheets were driven to change in the past that might be used to calibrate and test ice-sheet models. Until such histories can be determined from the geological record and are used to build confidence in a new generation of ice-sheet models that can capture all of the significant processes that lead to ice-sheet change, there will continue to be uncertainty in the prediction of the ice-sheets’ contribution to future sea level rise.

Observations do show that the discharge of fresh water from Greenland has doubled in the last ten years as the northern glaciers have begun to accelerate in the manner which had previously been observed only in the south (Rignot & Kanagaratnam, 2006).However, this period is too short to establish if this change is part of a long term trend or a manifestation of decadal variability. The West Antarctic ice sheet is grounded below sea-level and the discharge rate of the fringing glaciers is sensitive to increases in regional ocean temperature. Many glaciers have seen increased speeds as their floating ice tongues (ice shelves) have thinned and, in some cases, broken up entirely (Rignot, 2006).

Using a simple scaling of the estimated recent contribution to sea level changes from accelerated ice flow with global mean surface temperature (this scaling is used as an illustrative possibility), the IPCC Fourth assessment estimated that this might give up to 17cm (for the A1FI emissions scenario) additional global mean sea level rise during the 21st century. However, whilst they did not rule out such larger increases, they noted that rapid ice sheet changes, such as the collapse of the West Antarctic Ice Sheet are not considered likely to occur in the 21st century, and we support this view here. Adding the 17cm scaled discharge contribution to our maximum previous estimate for the UK (Table 6, A1FI 95th percentile) gives us 105cm of sea level increase, which we take to be the bottom of the H++ range.

Recent assessments of the last interglacial (LIG) climate and its sea level, have pointed mostly to the likelihood that melting of the Greenland ice sheet contributed 2 or more metres of sea- level equivalent at that time (Cuffey & Marshall, 2000). This hypothesis is supported by the

Page 40 of 54

substantial orbitally driven excess of Northern Hemisphere summer insolation 130,000 years ago relative to the present day, with no corresponding Antarctic excess (Berger, 1978). Arctic temperatures may have been 3-5°C higher than present. When corrected for local temperature and salinity changes, the oxygen isotopic composition of marine carbonate, such as that precipitated by foraminifera, provides a record of global ice volume. Using a hydrological model, Rohling et al. (2008) constructed a sea level record with a temporal resolution of ~300 years using the oxygen isotopic data from two Red Sea cores. Aligning the Rohling et al. (2008) data against two different age models yields rates of sea level rise of 1.6 ± 0.8 and 0.9 ± 1.1 m per century for about 6 centuries in the LIG.

Thus, for the top end of our H++ scenario we adopt the upper value from the Rohling et al. (2008) work giving a global average sea level rise between 2000 and 2100 of approximately 2.4m. While we cannot rule out this amount of sea level rise, we note that there is no evidence to suggest it will occur during the next 100 years and it would require a massive increase in the current observed contribution of ice sheets to sea level rise. For example, even if the tide water glaciers, the fastest flowing glaciers around Greenland, were to increase their discharge of ice to the ocean by an order of magnitude, they would still only raise sea level 10cm by 2100. The fastest flowing glaciers around Antarctica are currently an order of magnitude slower than those in Greenland. In the case of Greenland direct melting and surface runoff for a 3°C global temperature rise could contribute a further 12cm to sea level rise.

In the time-mean sea level rise component of the ensemble projections (section 2) the global mean rise was dominated by thermal expansion and the ice sheets played a much smaller role. Therefore, it was reasonable to assume that spatial deviations from the global mean change would be dominated by local changes in ocean density and circulation. In the H++ scenario mean sea level changes are likely to be dominated by sizeable changes in land ice so the major contribution to regional variations will be different too. Isostatic rebound of the solid earth, following ice sheet mass changes, has a time scale on the order of 1000 years and a rather short scale length from the ice covered region. Thus, although a complete loss of the Greenland ice sheet would result in a local rebound of the underlying solid earth by ~500m, the component of this influencing the United Kingdom would be negligible. A much bigger effect on the century time-scale from large changes in ice sheet mass results from gravitational changes. Water is drawn to proximity of the ice sheets through the gravitational attraction of their mass – the gravitational geoid. The melting and loss of mass in the ice sheets results in a rapid reduction of the adjacent sea level as the water is distributed elsewhere. The change in the pattern of sea level associated with a change in ice mass can be determined through a spherical harmonic expansion describing the revised geoid (Tamisiea et al., 2001). Using the spatial patterns derived by these authors for a known change in Greenland and Antarctic ice sheet loss allows us to adjust the top of the H++ range to a value more suitable for our study region. This gives a local total time-mean sea level rise component of 1.8m. Adding in the upper estimate of the observed vertical land movement term would give approximately 2m of local relative rise by 2100. This is the top of our H++ relative mean sea-level rise range.

Recently Pfeffer et al. (2008) provided an alternative estimate of constraints on 21st century SLR. They consider the degree of acceleration of outlet glaciers and ice streams on Greenland

Page 41 of 54

and Antarctica that would lead to large increases in SLR. After considering maximum observed glacial movement rates they concluded SLR in excess of 2m was "physically untenable". When our slightly larger thermal expansion estimate is combined with Pfeffer et al.'s (2008) ice melt and GIA is allowed for, a worst case risk rise is again estimated at approximately 2m. This alternative evidence for 2m as a sensible maximum value in sensitivity testing adds extra confidence in our estimates for the top of the H++ relative mean sea-level rise range.

Climate models can credibly replicate the enhanced polar warming described by e.g. Masson- Delmotte et. al. (2006). A small number of models can also simulate enhanced sea ice loss in recent years. However, we emphasize that the reasoning behind the top end of H++ is not dependent on the models’ ability to simulate these factors so getting them precisely right is not critical to H++.

3.3 H++ Combined surge and time mean regional sea level. Taking the maximum increase in the 50-year return period skew surge events (scaled from the IPCC models with the largest change in storm intensity over our region of interest) and the observationally derived time-mean sea level rise gives a total H++ sea level rise of 2.95m by 2100. The components of this are summarised in Table 7.

Once again, we highlight that the top end of this H++ scenario is based on upper uncertainty ranges from proxy observations and the climate model with the most extreme change in storm intensity. The majority of climate models project a smaller change and there is no evidence that the proxy based rapid time-mean sea level rise from the past will occur in the next century. Therefore, this scenario is an illustration of an unlikely future storyline, but one that cannot be ruled out completely. Adapting immediately to this amount of sea level change would very likely be over- adaptation. However, there is some merit in a precautionary approach of evaluating the adaptation options required for H++ and continuing with precision monitoring of sea level, land movement and ice sheets so as to identify any changes that might indicate that H++ is becoming more or less likely. One aspect of this would be describing an optimal monitoring network and another would be estimating, for each monitoring method, when we would expect the signal to be separable from the natural variability, to some level of statistical significance.

Table 7 Components of H++ increase in extreme sea level at the Thames Estuary for 2100. Global mean 2.4 m Local GIA correction -0.6 m Vertical land movement 0.2 m Surge extremes (50 yr RL) 0.95 m Total 2.95 m

Page 42 of 54

4 Conclusions This report describes future projected changes in extreme water levels in the Southern North Sea near the Thames Estuary. A comparison of the features of this work and the earlier work of Lowe, Gregory and Flather (LGF) is presented in the following table.

Feature LGF TE2100 Comments Global Climate HadCM2 HadCM3 Many scientific advances: Model see Pope et.al 2000 Experimental Single realisation Ensemble Can now quantify design uncertainty in surge trends RCM resolution 50km 25km Increased resolution Surge model 35km 12km Increased resolution resolution Validation against Better at 1 UK Better at 14 UK mainland Improved validation surge mainland gauge gauges observations Trend detection 2x30 year time slices 1x149 year continuous We believe the continuous method is better for the reasons described in section 2.5.1 Mean Sea Level Single value taken Distribution based on Can now quantify change from IPCC second ensemble of models from uncertainty in mean sea assessment report IPCC fourth assessment level change. report Regional mean Not considered Based on ensemble of New feature sea level models from IPCC fourth variations assessment report H++ No Yes Treatment of possible increased ice sheet melt .

Our results are split into two information packages, an ensemble projection and an H++ scenario. The ensemble projection represents our best estimate of the likely range in future extreme water levels based on the current generation of climate models and current scientific understanding.

The key conclusions from the ensemble projections are: • The results show that using short time slices (the previous standard technique) can lead to misleading conclusions, providing justification for simulating longer periods in this current work.

• 21st century changes in the storminess driven component of extreme water levels (specifically the skew surge) are found to be small for the SRES A1B scenario. This result is contrary to that in UKCIP02 (Hulme et al., 2002). It implies that future changes in

Page 43 of 54

extreme water levels will predominantly be driven by changes in regional time-mean sea level, as is the case for recent decades (Woodworth and Blackman, 2004). It also means that the skew surge return period curves apply to emissions scenarios that have less climate change than SRES A1B, such as the B1 scenario.

• Regional relative time-mean sea level rise (metres) between present day and the end of the 21st century has been estimated as shown below. This includes the contribution from thermal expansion and ice melt, regional deviations from the global mean and vertical land movement.

A1Fi A1B B1 Mean (m) 0.56 0.47 0.40 Min (m) 0.24 0.21 0.19 Max (m) 0.88 0.73 0.61

The H++ scenario is based on the most extreme models and upper uncertainty bounds from paleo climate observations of past sea level changes and is considered very unlikely to occur during the 21st century. However, it can not yet be ruled out completely at this time. The key conclusions from the H++ scenario are: • Some global climate models project changes in European storminess considerably larger than the MOHC ensemble of models. Using a crude scaling approach suggests this could produce a 21st century increase in 50-year return period skew surges of around 95cm, although alternative scaling methods suggest a more modest increase.

• A time-mean sea level rise component has also been constructed for H++. This component is estimated to be approximately 2m.

5 Acknowledgements We wish to acknowledge the contributions made to this work by: • Jonathan Gregory (Met Office) • Tim Reeder and Rob Wilby (EA) • Jonathan Tawn and Emma Eastoe (Lancaster University) • Graham Siggers and Ben Gouldby (HR Wallingford) • Glen Harris, the QUMP team and Peter Good (Met Office) • Jonathan Tinker (Met Office)

We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. Sea level data from tide gauges were supplied by the British Oceanographic Data Centre (BODC).

Page 44 of 54

6 References Berger, A.L. (1978) Long-Term Variation of Caloric Insolation Resulting from the Earth's Orbital Elements Quat. Res. 9, 139-167

Bingham, R.J. and Haines, K. (2006), Mean dynamic topography: intercomparisons and errors, Phil. Trans. R. Soc. A, 364,903-916

Bingley, R.M., Teferle, F.N., Orliac, E.J., Dodson, A.H., Williams, S.D.P., Blackman, D.L., Baker, T.F., Reidman, M., Haynes, M., Aldiss, D.T., Burke, H.C and Chacksfield, B.C. (2007) Absolute fixing of tide gauge bench marks and land levels. Joint Defra/EA Flood and Coastal Erosion Risk Management R&D Programme R&D Technical Report FD2319/TR.

Blackmon ML (1976) A climatological spectral study of the 500mb geopotential height of the northern hemisphere. J Atmos Sci 33: 1607-1623

Butler, A., Heffernan, J.E., Tawn, J.A., Flather, R.A. and Horsburgh, K.J. (2007) Extreme value analysis of decadal variations in storm surge elevations. Journal of Marine Systems 67 pp189- 200

Coles, S. (2001) An Introduction to Statistical Modeling of Extreme Values. 2001. Springer. ISBN 1-85233-459-2.

Collins, M., Booth, B., Harris, G., Murphy, J., Sexton, D., Webb, M. (2006). Towards quantifying uncertainty in transient climate change. Clim. Dyn. v27 pp127-147.

Cuffey, K. and Marshall, S., (2000) Substantial contribution to sea-level rise during the last interglacial from the Greenland ice sheet. Nature 404, 591-594 de Vries, H., M. Breton, T. Demulder, J. Ozer, R. Proctor, K. Ruddick, J.C. Salomon, and A. Voorrips (1995), A comparison of 2D storm-surge models applied to three shallow European seas. Environmental Software, 10(1), 23-42.

Flather, R. A. (2000) Existing operational oceanography, Coastal Engineering, 41, 13-40.

Flather, R. A., J. A. Smith, J. D. Richards, C. Bell and D. L. Blackman (1998). Direct estimates of extreme storm surge elevations from a 40-year numerical model simulations and from observations. The Global Atmosphere and Ocean System, 6: 165-176

Gehrels, R (2006) Sea level rise and coastal subsidence in southwest England. Report and Transactions of the Devonshire Association for the Advancement of Science 138: 25-42.

Page 45 of 54

Gordon, C., C. Cooper, C.A. Senior, H. Banks, J.M. Gregory, T.C. Johns, J.F.B. Mitchell, and R.A. Wood, (2000). The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Climate Dyn., 16, 147- 168.

Holgate, S., Jevrejeva, S., Woodworth P. and Brewer, S. (2007)_Comment on "A Semi- Empirical Approach to Projecting Future Sea-Level Rise" Science 317, 1866 (2007)

Horsburgh, K. J., Williams, J. A., Flowerdew, J., Mylne, K., Wortley, S. (2008) The worst North Sea storm surge for 50 years: performance of the forecasting system and implications for decision makers. In Proceedings of the 43rd Defra Flood and Coastal Management Conference 2008, Defra Flood Management Division, London.

Horsburgh, KJ, and Wilson, C (2007) Tide-surge interaction and its role in the distribution of surge residuals in the North Sea. Journal of Geophysical Research 112(c8)

Houghton, J.T. (2004) Global warming – the complete briefing. 3rd Edition. Cambridge University Press.

Hulme, M., Jenkins, G.J., Lu, X., Turnpenny, J.R., Mitchell, T.D., Jones, R.G., Lowe, J., Murphy, J.M., Hassell, D., Boorman, P., McDonald, R. and Hill, S. (2002). Climate Change Scenarios for the United Kingdom: The UKCIP02 Scientific Report. Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK. 120pp.

Hulme, M. and Jones, P. D., (1991): Temperatures and windiness over the United Kingdom during the winters of 1988/89 and 1989/90 compared to previous years. Weather 46, 126-136

IPCC, (2001): Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J.T.,Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 881pp.

IPCC, (2007): Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.

Jenkins, G.J., Perry, M.C., and Prior, M.J.O, (2007). The climate of the United Kingdom and recent trends. Met Office Hadley Centre, Exeter, UK.

Jolliffe, I.T. Principal Component Analysis: A Beginner's Guide - I. Introduction and application, Weather, 45:375-383, 1990

Page 46 of 54

Kay, A.L, Bell, V.A. and Lowe ,J.A. (2008) Area-wide river flow modelling for the Thames Estuary 2100 project: Climate change impacts on flood frequency

Kwong, S.C.M., Davies, A. M. and Flather, R.A. (1997) A three-dimensional model of the principal tides on the European shelf. Progress in Oceanography, 39 (3), 205-262

Lowe, J.A. and Gregory, J.M., (2005). The effects of climate change on storm surges around the United Kingdom. Philosophical Transactions of the Royal Society London, 363, 1,313-1,328. doi:10.1098/rsta.2005.1570.

Lowe, J.A., Gregory, J.M., Flather, R.A. (2001). Changes in the occurrence of storm surges around the United Kingdom under a future climate scenario using a dynamic storm surge model driven by the Hadley Centre climate models. Climate Dynamics 18: 179-188.

Masson-Delmotte, V; Kageyama, M; Braconnot, P; Charbit, S; Krinner, G; Ritz, C; Guilyardi, E; Jouzel, J; Abe-Ouchi, A; Crucifix, M; Gladstone, R; Hewitt, C; Kitoh, A; LeGrande, A; Marti, O; Merkel, U; Motoi, T; Ohgaito, R; Otto-Bliesner, B; Peltier, W; Ross, I; Valdes, P; Vettoretti, G; Weber, S; Wolk, F; YU, Y, (2006). Past and future polar amplification of climate change: climate model intercomparisons and ice-core constraints. Climate Dynamics, Volume 26, Number 5, pp. 513-529

McGuffie, K and Henderson-Sellers, A. (2004) A Climate Modelling Primer (Wiley, New York)

Murphy, J.M., Booth, B.B.B., Collins, M., Harris, G., Sexton, D. and Webb, M. (2007). "A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles". Philosophical Transactions of the Royal Society A 365: 1993-202.

Nakicenovic N. et al. (2000) Emissions Scenarios 2000 – Special Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge.

Nicholls, R.J, Carter, T.R., Warrick, R.A., Lowe, J.A., Lu, X., O'Neill, B.C., Hanson, S.E. and Long, A. J., 2008: Guidelines on Constructing Sea-Level Scenarios for Impact and Vulnerability Assessment of Coastal Areas. Supporting Material, Intergovernmental Panel on Climate Change Task Group on Data and Scenario Support for Impact and Climate Analysis (TGICA).

Pope VD, Gallani ML, Rowntree PR, Stratton RA, (2000), The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3, Climate Dynamics vol. 16 no. 2- 3, p. 123-146.

Pugh, DT, (1987). "Tides, Surges and Mean Sea-Level", John Wiley & Sons. 467 pages.

Rahmstorf, S. (2007), A semi-empirical approach to projecting future sea-level rise, Science, 315,368-370.

Page 47 of 54

Raupach, M.R., Marland, G, Ciais, P, Le Quere, C, Canadell, J.G., Klepper, G, Field, C.B. (2007) Global and regional drivers of accelerating CO2 emissions, PNAS.

Rignot, E.and Kanagaratnam, P., 2006, Changes in the velocity structure of the Greenland ice sheet, SCIENCE, 986-990.

Rignot, E., 2006, Changes in ice dynamics and mass balance of the Antarctic icesheet, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A- MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 364, 1637-1655.

Rio, M.-H. and Hernandez, F. (2004), A mean dynamic topography computed over the world ocean from altimetry, in situ measurements, and a geoid model, J. Geophys. Res., 109, C12032, doi:10.1029/2003JC002226

Rohling, E.J., Grant, K., Hemleben, C., Siddall, M., Hoogakker, B.A.A., Bolshaw, M. and Kucera, M. (2008) High rates of sea-level rise during the last interglacial period. Nature Geoscience, 1, 38-42. (doi:10.1038/ngeo.2007.28)

Stott, P. A., Tett, S. F. B., Jones, G. S., Allen, M. R., Mitchell, J. F.B. Jenkins, G. J., (2000), External control of twentieth century temperature by natural and anthropogenic causes. Science, 290, 2133-2137.

Tamisiea, M.E., Mitrovica, J.X., Milne, G.A., Davis, J.L. (2001), Global geoid and sea level changes due to present-day ice mass fluctuations, J. Geophys. Res., 106, 30849-30863.

Tawn, JA, (1992). Estimating probabilities of extreme sea-levels. Appl. Statist. 41 No. 1 pp 77- 93

Vaughan, D. G., and R. J. Arthern (2007), Why is it so hard to predict the future of ice sheets? Science, 315, 1503-1504.

Williams, J.A. and Horsburgh,K.J. (2006) Evaluation of the tides produced by the operational storm surge model. POL Internal Document Number 176, 141pp. Proudman Oceanographic Laboratory.

Williams KD, Ringer MA, Senior CA, Webb MJ, McAvaney BJ, Andronova N, Bony S, Dufresne JL, Emori S, Gudgel R, Knutson T, Li B, Lo K, Musat I, Wegner J, Slingo A, Mitchell JFB (2006) Evaluation of a component of the cloud response to climate change in an intercomparison of climate models. Clim Dyn 26:145 -165

Williams KD, Senior, CA and Mitchell, JFB (2001) Transient climate change in the Hadley Centre models: The role of physical processes Journal of Climate, 14(12), 2659-2674

Woodworth, P. L., and D. L. Blackman (2004) Evidence for systematic changes in extreme high waters since the mid-1970s, Journal of Climate, 17, 1190-1197.

Page 48 of 54

Wortley S, Crompton E, Orrell R, Smith D, Horsburgh K and Williams J. (2007) Storm Tide Forecasting Service Operational Report to the Environment Agency for the period 1st June 2006 to 31st May 2007. Met Office. United Kingdom.

Woth, K, Weisse, R, von Storch, H, (2006). Climate change and North Sea storm surge extremes: an ensemble study of storm surge extremes expected in a changed climate projected by four different regional climate models. Ocean dynamics 56 (1): 3-15.

Zhang, X, Zwiers, F and Li, G, 2004. Monte Carlo Experiments on the Detection of Trends in Extreme Values. Journal of Climate. 17, 1945-1952.

Page 49 of 54

7 Appendix 1: Surge model sensitivity tests

7.1 Effect on surge of time-mean sea level change To investigate the effect of a change in the time-mean sea level on a large surge event we compared two surge model runs: one forced by an extreme meteorological event from the MOHC standard ensemble member and another with the same forcing but a uniform 3 metre increase in bathymetric depth. The results in terms of the sea surface elevation time series at the Thames grid box of the surge model are shown in Figure 20. In each case we also produced a tide-only simulation (i.e. without meteorological forcing) so that we could compare the residual. It can be seen that the primary effect is on the timing; in both meteorologically-forced and tide-only mode the signal arrives about 40 minutes earlier with the increased bathymetry. This change is physically reasonable: both tides and surges are shallow water waves with celerity given by √(gh), so they would travel faster in deeper water.

Figure 20. Modelled effect of 3m increase in mean sea level on a large modelled surge. Solid lines: simulation with 3m increase in bathymetry. Broken lines: no increase in bathymetry. Black: total elevation. Green: tide only. Red: surge residual.

Page 50 of 54

The effect on the total sea surface elevation is less than 5 cm. The skew surge is reduced by an amount less than 25 cm, but in either case the change may be due to the faster tide producing a change in the tide/surge phase relationship (see below) and so should not be interpreted as a systematic change brought about by the change in the water depth. Smaller changes in bathymetry (not shown) produce correspondingly smaller changes in phase and elevation.

7.2 Surge/tide phase relationship The effect of adjusting the surge/tide phase relationship is discussed in section 2 and illustrated in Figure 6 and Table 2. The importance of non-linear interactions is further demonstrated in Figure 21, where we can see that the same meteorological forcing which produces a residual of nearly 4 metres if it arrives at an early stage of the rising tide (dark blue lines) produces only a 2 metre residual if it arrives at high tide (red lines). To avoid cluttering the plot the tide-only elevations values have been rescaled as follows: plotted value = tide divided by 4 minus 1.5 metres.

Figure 21. Thames grid box residual for various meteorological-forcing/tide phase relationships. Lag is the number of hours by which meteorological forcing is lagged relative to operational run, but the plot is time-shifted so that meteorological-forcing time remains constant while tide phase changes. The tide phase is illustrated by the not-to- scale phase indicator at the bottom of the plot.

Page 51 of 54

7.3 Effect of changes in latitude of storm track We experimented with changing the latitude of the most extreme meteorological forcing event as diagnosed by skew surges in the ensemble run. We took the meteorological forcing of this event and moved it by various latitude angles to the north and south. Since the event itself includes considerable latitudinal movement, it is not possible to separate the effect of changing the latitude from that of changing the phase relationship in this instance, but we observed that no latitudinal movement produced a significantly larger total water level at the Thames grid box than that seen in the phase-change experiments. A more complete investigation could be made by varying the latitude of a whole 150-year run, but the computational effort to do this is beyond the scope of this work.

7.4 Effect of changing storm intensity Our main results are shown in Figure 17 and Figure 19. Here we describe some further analysis of these results and present a simple case study.

In the much-simplified case of a steady wind blowing along a non-rotating channel of uniform depth, we anticipate a power-law relationship between the wind speed and the steady-state surface elevation at the downwind end of the channel, assuming a well-mixed vertical profile and the usual power-law relationship between the drag and the wind speed, i.e. that the drag increases slightly more rapidly than the square of the wind speed (Pugh, 1987, p198) Employing the further simplification of a linear relationship between the wind speed and the atmospheric horizontal pressure gradient (as in the geostrophic wind relationship), we might expect a power-law relationship between the atmospheric horizontal pressure gradient and the elevation. The apparent anomaly in assuming that rotational effects are not significant on the sea scale but are dominant on the atmospheric scale can perhaps be justified by the short length and time scales over which an intense surge at the Thames mouth can develop, particularly in the presence of a strong easterly. If we assume that such a relationship applies then analysis of Figure 17 suggests a power-law index of about 2.03. As a test we can apply this index as a predictor of the scaling shown by the yellow line in Figure 19; the agreement (not shown) is very good. The value 2.03 is too precise for this approximate index, but quoting such a value helps to remind us that it is “near 2, but not exactly 2”.

Case Study. We took the simulation described by Figure 6 and row 4 of Table 2 and changed the meteorological forcing, using both the gradient wind approximation and the geostrophic approximation to derive the wind speed from the pressure field, allowing for the boundary layer attenuation as described previously in section 3. We used pressure gradient scaling factors between zero and 1.5 Results are shown in Figure 22.

Page 52 of 54

Figure 22. Effect on skew surge of changing the pressure gradient scaling factor for a single extreme event. Solid line: 10 m wind scaled by gradient wind. Broken line: 10 m wind scaled by geostrophic wind.

8 Appendix 2: Technical note on the surge model runs. To streamline the model architecture, the surge model is run in 12 month chunks. These time chunks do not correspond to calendar years. Instead, the model year begins in June and ends in May. This simplifies the counting of independent surge events because it is very unlikely that any of the annual 5 largest events will occur around the end of May/beginning of June, because typically the most intense storms do not occur in summer. The climate model is run in 360-day mode, with 360 days in each simulated year and each climate-model month consisting of 30 climate-model days. The surge model is spun up through May and runs from June through to the end of the next May. Climate/surge model dates are synchronised for the first 30 days of the spin-up (May), but not through the rest of the year due to the difference in the number of days in climate-model and real months. One result of this is that tidal data for 5 days is discarded each model year (6 for a leap year) and so tides and elevations are not contiguous between one model year and the next. Surge model output from the spin-up month is always discarded. 9 Appendix 3: Uncertainty in return levels derived from 43 years of data. To investigate whether the uncertainty in return levels inferred from the ERA40 RCM simulation should be inflated to compensate for inference from only 43 almost5 consecutive years we use

5 Our available ERA40-driven data runs from June 1958 through May 2002 but not including June 1991 to May 1992.

Page 53 of 54

our model data, which is in chunks of 149 years for each ensemble member. The test is as follows.

Method First we take 3 non-overlapping samples of 43-consecutive years (5 largest skew surges from each year) from each of the eleven ensemble members. For each sample we infer the uncertainty in the N-year return level, so that we have 33 values, each one a confidence-interval size. We take the mean of these to give a representative value of confidence-interval size for consecutive years (call this C).

Then we do the same again, but constructing each sample from 43 randomly-chosen years (all 43 years from the same ensemble member, with replacement) to give 33 values of confidence- interval size for random years. Again we take the mean of these to give a representative value of confidence-interval size for random years (call this R).

We repeat the procedure above many times to give a distribution for R, against which C can be compared.

Our null hypothesis is that the confidence-interval size for consecutive years (C) is not significantly less than confidence-interval size for random years (R).

Results For return periods (N) of both 50 and 500 years, C lies comfortably inside the distribution of R (in both cases it is actually above the median, and below the 85th percentile), supporting the null hypothesis.

Discussion We also followed the same procedure for the minimum instead of the mean. Had the minimum of the uncertainties in the consecutive samples been significantly small compared to the distribution of minima from the random samples, this might have suggested a time-window of small variability in the continuous data. However, again the figure for the consecutive data was found to be above the median for the random data.

As a further check we also completed a simpler test where the inferred uncertainty in the N- year return level was replaced by the standard deviation of the 43 annual maxima (lower ranks were discarded in this simpler test). Again C lay comfortably within the distribution of R, suggesting that the variability in consecutive years is not demonstrably smaller than that for randomly-chosen years.

Conclusion It is not appropriate to inflate the uncertainty in the return levels inferred from the 43-year ERA40-driven simulation, according to data from 149-year model simulations.

Page 54 of 54

Thames Estuary 2100

Area-wide river flow modelling: Climate change impacts on flood frequency

Phase 2 EP17 Study

EA Study Lead: Tim Reeder

Consultants: Centre for Ecology and Hydrology & Met Office Hadley Centre

Status: Final Draft Date: 17 Nov. 08 Annex 3 of 7 Appendix L to TE2100 Plan

Area-wide river flow modelling for the Thames Estuary 2100 project

Climate change impacts on flood frequency

A.L. Kay, V.A. Bell and J.A. Lowe

TE2100 Project

May 2008

iii

Contractor This document was produced by:

CEH Wallingford Maclean Building Crowmarsh Gifford Wallingford, Oxon OX10 8BB Tel: +44 (0) 1491 838800 Fax: +44 (0) 1491 692424

Further copies of this report are available from: CEH Wallingford, Maclean Building, Crowmarsh Gifford, Wallingford, Oxon, OX10 8BB, UK Tel: 01491-838800 Fax: 01491-692424 e-mail: [email protected]

Contents

Executive Summary...... viii Introduction ...... 1 Background ...... 1 The Grid-to-Grid model ...... 2 Flood frequency...... 3 Regional Climate Model ensemble ...... 4 Regional Climate Model data ...... 6 Precipitation...... 6 Potential evaporation...... 7 Comparison between time-slices and with observations ...... 9 Use of the RCM ensemble ...... 12 Climate change impacts on flood frequency ...... 14 Results from individual ensemble members ...... 14 Results averaged over ensemble subsets ...... 16 Results compared to an RCM-based estimate of current natural variability...... 20 Conclusions ...... 24 Summary ...... 24 Discussion ...... 25 Acknowledgements...... 27 References ...... 28 Appendices ...... 31 Appendix A Results from eleven individual ensemble members ...... 31

List of figures

Figure 1 Map showing the RCM grid superimposed over Great Britain, the catchment of the Thames to Kingston (39001), and the locations of the two grid boxes for which data have been analysed: one over the Thames Basin (NT) and one over County Durham (CD)...... 6 Figure 2 Ratio of each 1km SAAR to the mean SAAR of its 25km RCM grid box, mapped over Great Britain...... 7 Figure 3 RCM-derived estimates of monthly potential evaporation at Amersham (Southeast England) over a one year period ending March 1962...... 9 Figure 4 Cumulative rainfall and PE over each time-slice, and the difference between the two time-slices, for each ensemble member (solid coloured lines), for an RCM grid box located to the north of the Thames basin (labelled NT in Figure 1). The dashed black line on the left-hand rainfall and PE plots shows comparable observational data for the 1961-1990 period. Key: afgcx - thick black, afixa - thick red, afixq - thick green, afixb - black, afixc - red, afixd - green, afixf - blue, afixh - yellow, afixi - brown, afixj - grey, afixk - violet, afixl - cyan, afixm - magenta, afixn - orange, afixo - indigo, afixp - maroon, afixr - turquoise...... 10 Figure 5 As Figure 4 but for an RCM grid box located over County Durham (labelled CD in Figure 1)...... 10 Figure 6 Monthly mean rainfall and PE over each time-slice, and the difference between the two time-slices, for each ensemble member (solid coloured lines), for the same Thames RCM grid box as in Figure 4. The dashed black line on the left-hand rainfall and PE plots shows comparable observational data for the 1961-1990 period. Key as for Figure 4. . 11 Figure 7 Flood frequency curves for six catchments in the Thames Basin, derived from simulations using the Current time-slice of each RCM ensemble member as input to the G2G (solid lines, except for the six-member subset with low rainfall, which are represented by thin dotted lines, with colours as for Figure 4). Also shown for each catchment is the median observed flood frequency and its 95% bounds (respectively thick dashed and dotted black lines)...... 12 Figure 8 Map showing the main Thames (flowing from left to right) and eighteen of its principal tributaries...... 14 Figure 9 Percentage change in peak flow (1970s to 2080s) at the 2-year return period, for 11 ensemble members, for the main Thames (bottom graph) and eighteen of its principal tributaries (left and right of other graphs). Each river ‘flows’ from left to right, with the right-most plotted point for each tributary being that at which it joins the main Thames (whose outlet is plotted at the far right, at position zero). The tributaries are ordered by the point at which they join the main Thames (see Figure 8). Colour key as for Figure 4...... 15 Figure 10 As Figure 9 but for the percentage change in peak flow (1970s to 2080s) at the 10- year return period. Colour key as for Figure 4...... 16 Figure 11 Mean impact on flood frequency (at six return periods) from the eleven-member ensemble, mapped over the Thames Basin...... 17 Figure 12 Percentage change in flood peak at the 10-year return period versus baseflow index (BFI; which is higher in areas of greater permeability), for each river point over the Thames Basin...... 18 Figure 13 Mean impact on flood frequency (at six return periods) mapped over the Thames Basin for four subsets of the eleven-member ensemble, determined according to parameter perturbations given in Table 1: a) dependence of stomatal conductance on CO2 ‘on’; b) dependence of stomatal conductance on CO2 ‘off’; c) 4,3 R_layers; d) 2,1 R_layers...... 19 Figure 14 As Figure 9 (percentage change in peak flow at the 2-year return period), but including an RCM-based estimate of current natural variability (upper and lower 50, 75 and 95% - black dashed lines). Colour key as for Figure 4...... 21 Figure 15 As Figure 10 (percentage change in peak flow at the 10-year return period), but including an RCM-based estimate of current natural variability (upper and lower 50, 75 and 95% - black dashed lines). Colour key as for Figure 4...... 22 Figure 16 Number of ensemble members (out of eleven) giving percentage changes in flood frequency outside the upper 95% natural variability bound (at six return periods), mapped over the Thames Basin...... 23

Figure 17 Change in flood frequency at 6 return periods (2, 5, 10, 20, 50 and 100 years; solid coloured lines) over a 150 year period. The flood frequency is derived from a 30-year moving window of annual maximum peak flows (black crosses) for the main Thames outlet (Figure 8), and is plotted at the centre year of each 30-year period. The vertical black dotted lines indicate the position of the standard time-slices often used in climate modelling (1970s, 2020s, 2050s and 2080s), and illustrate how conclusions on impacts could differ, if these time-slices were to vary slightly...... 26

List of tables

Table 1 The climate model ensemble members and selected model parameter perturbations (afgcx is the standard, unperturbed ensemble member)...... 5 Table 2 Modelled percentage changes in flood peaks at different return periods, for the Thames at Kingston (1970s to 2080s)...... 24

vii

Executive Summary

The overall purpose of the Thames Estuary 2100 project is to develop a Flood Risk Management Plan for the Thames Estuary (including London) over the next 100 years; a period long enough that climate change must form an important consideration. Here, the changing fluvial flood risk induced by storm rainfall is the focus of concern. Since the 1960s, daily precipitation in the UK has tended to be more intense in winter and less intense in summer. The impact of changes in rainfall on river flows will depend on both the nature of the rainfall and the physical characteristics of the catchment draining to the river. For fast- responding catchments, such as those in impermeable or high relief areas, the characteristics of the specific rainfall event are critical. Such catchments tend not to have the deep soils and permeable geology that lead to the long-term hydrological “memory” of larger lowland catchments. Catchments across the Thames Basin are typical of these lowland catchments where the longer-term balance between rainfall and evaporation is particularly important.

Against this background this report uses a distributed hydrological model, the Grid-to-Grid (G2G) model, to assess future changes in river flows in the Thames Basin. Use of the grid- based methodology in conjunction with an ensemble of high-resolution climate model output has made it possible here, for the first time, to estimate the spatial effects of climate change on peak river flows across the Thames. The G2G hydrological model has been used with a perturbed parameter Regional Climate Model (RCM) ensemble to analyse changes in flood frequency across the Thames basin, across two 30-year time-slices—Current (Oct 1960 - Sep 1990) and Future (Oct 2069 - Sep 2099)—under the A1B emissions scenario.

A comparison of observed flood frequency with derived flood frequencies for the Current period suggested that flood frequency is assessed well by the G2G modelling framework using a subset of RCM ensemble members for some catchments, but over-estimates flood frequency in other catchments. However, other factors need to be borne in mind when making this comparison, such as G2G model error, flow gauge error, and the consequences of artificial influences like abstraction (not included in the G2G) on observed flow records.

Across the Thames Basin, changes in flood frequency between Current and Future periods have been analysed, and the results for each ensemble member are presented at a range of return periods. There is considerable variation in the results, by ensemble member, by return period and by location, with areas underlain by chalk generally showing lower percentage changes than other regions. The range of results is quite large. However almost all changes are increases, generally averaging around 5-10% in chalk areas and around 30-50% elsewhere for peak flows with up to a 20-year return period. It is important to note that the possible future climates and river flow estimates encompassed by the RCM ensemble have not been weighted according to quality or likelihood. However, 6 of the 17 original ensemble members have been excluded as they led to unrealistic simulations of the strength of storms and consequently low estimates of current rainfall over the Thames region. In view of the large uncertainty range, it is recommended that the mean and range of percentage change in river flows across the 11 RCM ensemble members are used for decision making.

A comparison of the modelled changes in flood frequency with an RCM-based estimate of current natural variability showed that, whilst for some rivers (or parts of rivers) there are few changes outside of the range of current natural variability, for other rivers there are more changes outside of the range. The latter locations could be considered as sites where further monitoring/modelling may provide early warning of statistically significant changes in observed flows, due to climate change.

The large estimated increase in future peak flows is discussed in a wider historical context. Trend analyses of observed flow records throughout the 20th century have so far detected no apparent long-term trend in UK flood magnitude. Over recent years (the last thirty to forty), apparent increases in winter precipitation appear to have led to an upward trend in peak flows in many UK rivers: however no such recent trend is evident for the lower Thames.

viii

Ongoing improvements to both the hydrological and climate models used in the study should lead to greater confidence in estimates of future changes in river flows across the Thames. Uncertainty in the hydrological model estimates can arise from a range of sources including emissions scenario, model structure (for both the climate and hydrological models), and parameterisation (again for both the climate and hydrological models). For catchments considered to be particularly susceptible to increases in future flood risk, additional analysis using a catchment rainfall-runoff model (such as the Probability Distributed Model) adjusted for local conditions is recommended. However several studies have suggested that the greatest uncertainty comes from sources related to the modelling of the future climate, particularly the choice of driving GCM, rather than from emissions or hydrological modelling. The RCM perturbed-parameter ensemble applied in this study represents the first attempt at deriving fine-scale information consistent with a range of large-scale regional changes which result from this global modelling uncertainty, and demonstrates how these large-scale uncertainties translate into uncertainty in future flood risk. More work is required to determine how representative these results are of the implications of the full range of climate modelling uncertainty.

Introduction

Background The overall purpose of the Thames Estuary 2100 project is to develop a Flood Risk Management Plan for the Thames Estuary (including London) over the next 100 years, a period long enough that climate change must form an important consideration. Here, the changing fluvial flood risk induced by storm rainfall is the focus of concern. Since the 1960s, daily precipitation in the UK has tended to be more intense in winter and less intense in summer (Osborn and Hulme 2002; Maraun et al. 2008). The impact of changes in rainfall on river flows will depend on both the nature of the rainfall and the physical characteristics of the catchment draining to the river. For fast-responding catchments, such as those in impermeable or high relief areas, the characteristics of the specific rainfall event are critical. Such catchments tend not to have the deep soils and permeable geology that lead to the long-term hydrological “memory” of larger lowland catchments. Catchments across the Thames Basin are typical of these lowland catchments, where the longer-term balance between rainfall and evaporation is particularly important.

Previous research for the UK Ministry of Agriculture, Fisheries and Food (MAFF) on the climate change impacts on peak flows for the Thames at Kingston and the Severn at Haw Bridge (Reynard et al. 1999) indicated changes of less than 20% for the 2050s under a previous (IS92a) emissions scenario. This led to the development of MAFF’s Flood and Coastal Defence Project Appraisal Guidance (FCDPAG; MAFF 2001), which stated “In view of the current uncertainty… the sensitivity analysis of river flood alleviation schemes should take account of potential increases of up to 20% in peak flows over the next 50 years.” The latest guidelines (Defra 2006) indicate that for peak flows there is no change in the guide figure of 20% for the period 2025 to 2115. This is a reflection of the research results of project W5-032 (Reynard et al. 2004) which did not provide sufficient scientific evidence to alter the original guidelines, even for the 2080s time period.

Most research into the effects of climate change on UK river flows, including that which led to the above FCDPAG Guidance for flood defences, has used catchment hydrological models to provide estimates of changes in flow at single locations or a small set of locations (e.g. Kay et al. 2008, New et al. 2007, Fowler and Kilsby 2007, Wilby and Harris 2006, Wilby et al. 2006, Nawaz and Adeloye 2006, Cameron 2006, Kay et al. 2006, Arnell 2003, Reynard et al. 2001). Generally, the hydrological model is calibrated to catchment conditions, using model parameters to adjust the modelled catchment response to rainfall, in order to reproduce the spatial heterogeneity of soil/geology/topography. The model parameters can also be adjusted to take into account artificial influences on flows, such as abstraction. The work presented here uses a single model (Grid-to-Grid, or G2G) and set of parameters for the whole of the Thames Basin, using digital datasets to provide the spatial information needed to simulate spatial differences in the response of the catchment to rainfall. Model output consists of a (1km) grid of river flow estimates across the region of application. Bell et al. (2008) assessed the G2G model performance at selected locations against observations and found that it provided reasonably good flow estimates for catchments all across the Thames Basin, providing a spatially variable estimate of catchment response to rainfall, while maintaining a water balance across the whole region. It is important to note, however, that the model does not currently simulate anthropogenic effects on river flows such as abstractions and effluent returns, leading to poorer model performance in some areas. Use of the grid-based methodology in conjunction with high resolution climate model output has made it possible here, for the first time, to estimate the spatial effects of climate change on peak river flows across the Thames Basin. It is important to note that emphasis has been placed on a spatially variable estimate of river flows across a large region. For a more detailed impact study at a particular location, a catchment model calibrated to the area of interest would provide additional guidance, taking into account local influences such as abstraction, effluent returns and problems estimating the sub-surface catchment area in groundwater-dominated regions.

The aim of the work presented here is to model fluvial flow in the Thames Basin in the present and future using the CEH G2G model encompassing flow-routing and runoff-production

schemes. This section provides a brief description of the G2G hydrological model, together with a description of methods and data used to couple the new model with an ensemble of regional climate model outputs to estimate future changes in river flows across the Thames Basin.

The Grid-to-Grid model The Grid-to-Grid (or G2G) model used here consists of a distributed rainfall-runoff model formulated to employ terrain, soil and land-cover datasets. The model formulation presented in Bell et al. (2008) contains enhancements to the prototype G2G formulation previously trialled in the Upper Thames catchments (Moore et al. 2006, 2007). This in turn was a development of the elevation-dependent formulation also presented in these papers and in Bell et al. (2007a,b). The new formulation includes the introduction of probability distributed soil-moisture and runoff within a grid-cell, and the option of nonlinear routing within rivers. Digital datasets are used to configure and parameterise a grid-based model of runoff- production, lateral flow generation and flow-routing across a landscape. The gridded flow- directions which define the lateral pathways of water are one of the most important components of the model as they determine the water-balance contributing to flow at every location. A new scheme to derive 1km-resolution flow directions from hydrologically-corrected 50m river networks has been used here, following the recommendations of Davies and Bell (2008). They found the scheme of Paz et al. (2006) performed reasonably accurately across the whole of mainland Britain, giving the smallest errors in derived catchment area (generally less than 5% for the Thames catchments) when compared to other methods.

The nature and limited availability of certain soil properties has necessitated the use of various approximations in applying the prototype model formulation. Many aspects of sub- surface hydrology are ill-defined, leading to the need to adjust or “calibrate” some model parameters to gain best agreement between observed and modelled river flows. As the G2G model is designed for area-wide use, care has been taken not to calibrate the model to individual catchments for which flow observations are available. Instead, flow measurements for catchments with a predominant soil-type have been used to determine whether the hydraulic properties associated with the soil-type provide realistic estimates of the relative volumes of surface and sub-surface runoff. Manual adjustment of soil hydraulic properties (usually effective soil depths) is applied recursively to different catchments and sub- catchments until a good estimate of downstream surface- and base-flow volumes across a range of soil-types is achieved. Parameters governing the temporal development of flow peaks are set at a regional level (in this case the whole of the Thames Basin) and are determined by manual calibration to observed flows at a number of locations. In the future, improvements to the nature and availability of spatial datasets for soil, geology and land-cover properties will be expected to strengthen the model’s underpinning by physical properties, reducing reliance upon calibration of model parameters.

The overall performance of the model compared to observations of river flow for 34 catchments across the Thames Basin was presented by Bell et al. (2008). A summary of this model assessment study is provided here by way of background. The main model input data derive from 15 minute rainfall observations from a dense network of 103 raingauges. These data have been interpolated using a multiquadric surface fitting technique (Cole and Moore, 2008) to derive the gridded rainfall estimates across the Thames Basin required by the G2G model. Gridded potential evaporation estimates are provided by MORECS (Met Office Rainfall and Evaporation Calculation System), a monthly climatological dataset for 201, 40 km x 40 km squares across the UK (Thompson et al. 1982; Hough et al. 1997). Model performance has been assessed with respect to flow observations across a range of catchments, while taking note of the accuracy of the flow gauge used to obtain the observed estimate of river flow. The G2G does not explicitly model artificial controls on flow, such as effluent returns and abstractions for public water supply, and so, in affected catchments, the G2G will appear to under- or over-estimate flows respectively. Similarly, processes such as flood-plain storage and attenuation which influence the occurrence of flood inundation are not currently included. Flood-plain storage has the effect of reducing the intensity of extreme river flows, and such flows would in practice be effectively reduced if current levels of available storage are maintained. The effect of urban development on runoff-production has been

introduced through reducing soil storage in these areas. Generally, adjustment of soil parameters has been required for soils overlying permeable geology, such as the Chalk, where the baseflow component of river flow depends on the volume of water stored in both the soil and the aquifer. With a lack of data on groundwater hydraulic properties, storage in these areas has been augmented by increasing the effective soil depth and assuming that the soil hydraulic properties apply at all depths. In time, greater availability of soil and geological data should help remove the need for this approximation.

Model performance has been assessed with reference to quality-controlled river levels/flows provided by the Environment Agency at a 15 minute resolution. Five years of observations (January 1997 to December 2001) were available for model calibration and assessment, although periods of missing data have reduced the record-length in places. The data record was divided into two separate periods for model calibration and validation. Model performance for the calibration and validation periods is summarised in terms of the R2 goodness-of-fit statistic for the study catchments. A value of 1 indicates a perfect model whilst a value of 0 indicates that the model is only as good as using the mean flow value as the simulated flow for all times. Note that R2 can be negative if the flow simulations are worse than that provided by the mean flow (assumed unknown when simulating). The R2 statistic provides a relative measure of model simulation accuracy, permitting some comparison across different catchments.

For most types of catchment, the model produces a realistic response to rainfall in catchments where (a) the response to rainfall is predominantly natural, (b) the gauge is considered to be reasonably accurate and (c) there are no significant data problems (flow and rainfall). In these areas, the R2 ranges from 0.160 to 0.84 (median 0.781) for the calibration period and from -2.58 to 0.837 (median 0.710) for the validation period. The poor lower range in each case is for the Mimram at Panshanger Park, for which observed flow volumes are thought to be reduced by abstractions in the headwaters. If this catchment is excluded, the overall results are improved, with the R2 goodness-of-fit ranging from 0.661 to 0.84 (median 0.782) for the calibration period and from 0.583 to 0.837 (median 0.740) for the validation period.

For catchments where conditions are less favourable for naturalised river flow modelling, there is significant variation in the results. These can often be attributed to a range of causes such as anthropogenic influences and an inaccurate flow gauge. A full set of observed and simulated flow hydrographs is presented in Appendices B and C of Bell et al. (2008). Overall, these hydrographs indicate that the distributed model is able to broadly reproduce a wide range of hydrological behaviour in catchments which have very different responses to rainfall.

Against this favourable G2G model performance reported by Bell et al. (2008), the G2G model is used here with some confidence to investigate the climate change impacts on flood frequency across the Thames Basin. Historical observation-based estimates of rainfall and potential evaporation used as G2G model input by Bell et al. (2008) are here replaced by hourly regional climate model (RCM) estimates from 17 ensemble runs over 30-year current and future time periods. Flow scenarios arising from the use of the RCM estimates with the G2G are developed for the main River Thames and twelve major tributaries entering the tidal Thames. The RCM provides dynamic downscaling from a global climate model (GCM) in producing the rainfall and PE required as input to the G2G hydrological model. Final output from this study consists of estimates of spatial changes in return period of flows across the Thames Basin.

Flood frequency In order to be able to estimate flood frequency for each river point modelled by the G2G, annual maximum (AM) flows are stored for each point (by UK water-year, 1 October to 30 September, rather than calendar year). The AM are then ordered and their Gringorten plotting positions determined (Gringorten 1963). A generalised logistic distribution (recommended for UK catchments; Robson and Reed 1999) is then fitted to the AM at each point, using L- moments. This method assumes stationarity over the data period, and the fitted curve should

not be used for extrapolation much beyond the length of the data period (so results presented later for 50- and 100-year return periods should be treated with caution).

Regional Climate Model ensemble The Met Office Hadley Centre global climate model, HadCM3 (Gordon et al. 2000; Pope et al. 2000), has previously been shown to have considerable skill at simulating the global climate. Furthermore, when combined with the regional atmospheric climate model, HadRM3, the modelling system is able to reproduce many of the observed features of the United Kingdom and European climate. In this work the gridded rainfall and potential evaporation estimates from a system consisting of the HadCM3 global model downscaled to 25km using the HadRM3 regional model are used to as input to the G2G hydrological model. When realistic rainfall and PE inputs are used, the G2G has been shown to produce reliable estimates of natural river flows (Bell et al. 2008).

For the TE2100 project a perturbed parameter approach is used to address uncertainty in climate projections. Many important physical processes cannot be explicitly resolved by climate models, typically because they occur on a scale smaller than the model grid. An example is the formation of cloud within a model grid box. Such processes must be parameterized, that is, described in terms of their expected impact on the scale of the model grid box. In the perturbed parameter ensemble approach (Collins et al. 2006; Murphy et al. 2007), instead of taking a single best estimate for key parameters, the uncertainty in the parameters is treated. This is achieved by running a number of slightly different (but plausible) versions of the climate model, each with different parameter settings. The settings are chosen by expert judgement and then applied in a fast-to-run climate model with a simple ocean. A subset of these model versions is then chosen to span the uncertainty in future projections and full transient coupled (ocean-atmosphere) global climate simulations are produced. Ultimately, there is a compromise between the enormous number of possible combinations of parameter values and the available computer resources. Seventeen versions of the full global model are used, one unperturbed member (standard HadCM3) and sixteen perturbed versions.

Each version of the full global climate model is downscaled to 25km resolution using a version of the regional climate model, which includes the land-surface model MOSES (Cox et al. 1999), with equivalent parameter changes. These changes are equivalent, rather than always identical to the global model parameter changes, because some parameter schemes are scale-dependent and this must be accounted for. Some aspects of the selected climate model parameter perturbations relevant to the hydrological modelling are summarised in Table 1, along with the short names for each version; afgcx represents the standard, unperturbed ensemble member. The dependence of stomatal conductance on atmospheric carbon dioxide levels has been included in nine of the ensembles. These effects may be important for climate impact and modelling studies as increased carbon dioxide is thought to lead to leaf stomatal closure and so a reduction in plant evapotranspiration (Gedney et al. 2006).

For each ensemble member, data were available for two time-slices. The first (Current) time- slice runs from 1 October 1960 to 30 September 1990 and the second (Future) time-slice runs from 1 October 2069 to 30 September 2099 and is available for the A1B SRES emissions scenario (IPCC 2000). Note that both time-slices have been chosen so as to cover exactly 30 water-years, although it should be noted that the length of an RCM year is only 360 days, comprising of twelve 30-day months. The G2G model was run for each time-slice including a 9-month run-in period before each (i.e. from 1 January 1960 and 1 January 2069 for Current and Future respectively). The changes in flood frequency were then assessed by comparing the results for the Current and Future time-slices, assuming approximate stationarity within each time-slice. In addition to the time-slice data, data for three of RCM ensemble members were available for the whole simulation period: 1 January 1950 to 30 November 2099. These are identified as afgcx, afixa and afixq.

Table 1 The climate model ensemble members and selected model parameter perturbations (afgcx is the standard, unperturbed ensemble member).

R_layers Dependence of Presence of Atmospheric Ensemble (number of soil stomatal surface-canopy model advection member levels accessed conductance energy scheme: identifier for E ): on CO in exchange in diffusion exponent T 2 forest, grass MOSES MOSES afgcx 2 4,3 on 0 afixa 2 2,1 off 1 afixb 1 2,1 on 1 afixc 2 2,1 off 0 afixd 1 2,1 on 0 afixf 1 2,1 off 0 afixh 2 4,3 off 0 afixi 2 4,3 off 0 afixj 2 2,1 off 0 afixk 2 3,2 on 0 afixl 2 4,3 on 1 afixm 2 4,3 on 0 afixn 1 4,3 on 1 afixo 2 4,3 on 0 afixp 1 4,3 off 0 afixq 2 4,3 on 1 afixr 1 2,1 off 0

Regional Climate Model data This section describes how the precipitation and potential evaporation estimates, required as input by the G2G on a 1km grid aligned with GB national grid references, are derived from RCM data on its ~25km (0.22˚) rotated lat-long grid (shown in Figure 1). Also presented is a comparison of the ensemble RCM precipitation and potential evaporation between time-slices and with observational series, for two grid boxes over England (Figure 1), and a description of how the RCM ensemble will subsequently be applied.

Thames at Kingston

Precipitation Precipitation is a direct output from the RCM (stash code 05216: total precipitation rate (kg/m2/s), available at an hourly time-step), and thus all that is required is to downscale the data to the 1km grid of the G2G. As rainfall is highly spatially variable, the Standard Average Annual Rainfall (SAAR) 1km dataset for the UK for the period 1961-1990 has been used to downscale from the 25km RCM scale to the 1km G2G scale. This dataset means that an average SAAR value can be calculated for a given area (e.g. an RCM grid box), by taking the mean of the SAAR values from all the 1km points contained within the given area. Thus for each 1km point a SAAR weighting can be produced, by dividing the SAAR value for that point by the average SAAR for the RCM grid box containing the point (Figure 2). For each time- step, at each 1km point, the rainfall for the RCM grid square containing that point is then multiplied by the SAAR weighting, to produce the rainfall input for the 1km point. The same SAAR weighting is used for both the Current and Future time-slices of the RCM data (with an

assumption that there will be no major changes in the direction of travel of weather fronts). Note that although the RCM precipitation is available at an hourly time-step, it is applied within the G2G at a 15-minute time-step by dividing each hourly value by four.

Figure 2 Ratio of each 1km SAAR to the mean SAAR of its 25km RCM grid box, mapped over Great Britain.

Potential evaporation The G2G requires potential evaporation (PE) for a short grass land-cover, but the only PE available from the RCM is that from open water (stash code 03312: potential evaporation rate (kg/m2/s), available at a daily time-step), which is obviously much higher. However, one can be converted to the other using additional variables available from the RCM, as described below. Note that although the PE estimates derived from the RCM data are available at a daily time-step, they are used within the G2G at a 15-minute time-step by spreading each daily total equally throughout the day.

Derivation of MORECS-type grass PE from open water PE

Several procedures have been devised to provide estimates of PE from climate data (Shuttleworth 1993), but the most physically-based and well-established of these is the Penman-Monteith equation (Monteith 1965).

-2 -1 -2 The Penman (1948) equation for potential evaporation over open water, λE0 (Jm s or Wm ), neglects both ground heat conduction and heat-storage effects. This equation for open water can be adapted to represent the potential evaporation from a vegetated surface (or

“evapotranspiration”) by replacing the assumed atmospheric resistance to vapour transfer with a combination of atmospheric and canopy resistance to vapour transfer (Monteith 1965). This modification is given by the Penman-Monteith equation which expresses the latent heat -2 loss from a surface with a vegetative canopy, λET (Wm ), in terms of the bulk surface -1 -1 (canopy) resistance rc (sm ) and the bulk aerodynamic resistance ra (sm ). Penman-Monteith estimates of PE from a vegetated surface have been evaluated in many environments and are a widely-used estimate of PE from land-surfaces.

If the Penman equation for PE over open water, λE0, is expressed as

C λE = , (1) 0 Δ + γ then the Penman-Monteith equation for PE from a vegetated surface, λET, can be written as

C ⎛ r (Δ + γ ) /γ ⎞ λE = = λE ⎜ a ⎟ . (2) T r 0 ⎜ r (Δ + γ ) /γ + r ⎟ Δ + γ (1+ c ) ⎝ a c ⎠ ra where numerator C = ΔRn+ρcp(es−e)/ra is common to both expressions and λ = latent heat of –1 -2 -1 vaporisation (2465000 Jkg ), E0 = rate of water loss (kgm s ), Δ = rate of change of -1 saturated vapour pressure with temperature (mb°C ), γ = psychrometric constant, Rn = net -2 -3 radiation (Wm ), ρ = near surface air density (kgm ), cp = specific heat of air at constant -1 pressure (1005 Jkg ), es = saturation vapour pressure at screen temperature (mb) and e = screen vapour pressure (mb).

Thus PE from a vegetated surface, λET, can be expressed as PE from open water, λE0, multiplied by a factor, and to convert from the latter to the former requires

a) a value for λE0, b) a value for the numerator term in equation (2), ra(Δ+γ)/γ, and c) a value for the canopy resistance, rc.

The canopy resistance, rc, can either be chosen by the user (e.g. in accordance with MORECS values for short grass which vary between 80 sm-1 in the winter and 40 sm-1 in May), or can take the value output from MOSES, which is consistent with MOSES vegetation and changes in accordance with climatic conditions. Note that the RCM does not currently output the value for canopy resistance, rc, directly. This may be calculated from two further -1 MOSES variables, canopy conductance, gc (ms ), and soil moisture availability, fsmc (dimensionless), as rc = fsmc/gc. For the Thames application, PE for a vegetated surface has been estimated using a value for canopy resistance that changes in accordance with climatic conditions as above, and this requires a total of four MOSES/RCM variables (each at a daily time-step).

Estimates of PE have been examined in greater detail at a location in Southern Britain (Amersham) for a one year period ending March 1962. Figure 3 compares two time-series of monthly estimates of potential evaporation from 25km RCM (HadRM3P) output with MORECS estimates from the same period. It is apparent that PE from open water can be up to twice as high as PE from vegetated surfaces, particularly during summer. The MORECS PE estimates based on meteorological observations are shown alongside the λET estimates calculated using climate model output. They provide a visual comparison of magnitude only and they should not be expected to be coincident. The graphs indicate that PE from vegetation estimated using RCM variables is comparable to MORECS estimates, although MORECS PE estimates are higher in the winter. It is important to remember that identification of “true” PE from vegetation is problematic, as the MORECS values of rc for short grass, used here, are inappropriate for regions with other land-cover to different degrees.

150

ET 130 PET (veg) E0 MORECSPEE (open-wat 110 T

) Morecs PET E T 90 (mm) 70

50 PET (mm/month 30

-10 t r ril y e ly er er e a u un J b b Ap M J to March em emb bruary August v Januarye p Oc F Se No December Tim e (m onths)

Figure 3 RCM-derived estimates of monthly potential evaporation at Amersham (Southeast England) over a one year period ending March 1962.

Notes: a) PE from vegetated surfaces calculated in this way can become negative during winter periods when condensation (dew) occurs. b) Vegetation canopy (bulk stomatal) response is influenced by light, temperature, CO2 levels and loss of water from stomatal guard cells. Light and low CO2 levels lead to stomatal opening, while darkness and high CO2 levels lead to stomatal closure. Using a value of rc consistent with projected climate change effects on carbon dioxide levels in the atmosphere may be important for climate impact and modelling studies (Gedney et al. 2006). c) The formulation for PE from vegetated surfaces neglects the feedbacks incurred through the assumption that evaporation in MOSES occurs at the actual rate rather than the potential rate. For example, this means that the model surface and air temperatures take values consistent with actual rather than potential evaporation rates.

Comparison between time-slices and with observations It is useful to compare the ensemble RCM precipitation and PE both between the Current and Future time-slices for each ensemble member, to assess how the G2G model inputs are changing between the two time-slices, and with observational series, to aid an assessment of the performance of each member individually and of the ensemble as a whole. However, it should be noted that any comparison of RCM data with observational data needs to be interpreted with care, as the Current run of each ensemble member is not meant to reproduce exactly what happened in that period, but is simply one possible representation of what could have happened in that period, given the existence of stochasticity and natural variability. In addition, differences in resolution between RCM grids and observational grids could cause discrepancies, particularly for rainfall and particularly in regions with high topographical variability.

Figure 4 shows, for one RCM grid box located in the north of the Thames basin, the cumulative rainfall and PE over each time-slice, and the difference between the two time- slices, for each ensemble-member. The rainfall plots clearly show a split of the ensemble into two subsets, one (consisting of six ensemble members: afixb, afixd, afixf, afixn, afixp and afixr) with much lower cumulative rainfall than the other (consisting of the remaining eleven members, including afgcx). It should be noted however that there is not such a split into two subsets for grid boxes further north in Britain, as shown in Figure 5 for a grid box over County Durham (the location of each grid box is shown in Figure 1).

Figure 4 Cumulative rainfall and PE over each time-slice, and the difference between the two time-slices, for each ensemble member (solid coloured lines), for an RCM grid box located to the north of the Thames basin (labelled NT in Figure 1). The dashed black line on the left-hand rainfall and PE plots shows comparable observational data for the 1961-1990 period. Key: afgcx - thick black, afixa - thick red, afixq - thick green, afixb - black, afixc - red, afixd - green, afixf - blue, afixh - yellow, afixi - brown, afixj - grey, afixk - violet, afixl - cyan, afixm - magenta, afixn - orange, afixo - indigo, afixp - maroon, afixr - turquoise.

Figure 5 As Figure 4 but for an RCM grid box located over County Durham (labelled CD in Figure 1).

Figure 4 also shows, for the 1961-1990 time-slice, a comparison with observed rainfall and PE (dashed black lines). The observed rainfall has been taken from a corresponding box within the Met Office’s dataset of 5km grid-interpolated daily rainfall. The ‘observed’ PE has been taken from a corresponding box within the Met Office’s MORECS dataset of 40km gridded monthly mean PE (Thompson et al. 1982; Hough et al. 1997). To allow for the 360- day years of the RCM, the observed values have been multiplied by the factor 360/365 before plotting.

The comparison with observed rainfall in Figure 4 shows that it is the upper subset, with eleven ensemble members, which has the more realistic cumulative rainfall for this Thames grid box than the lower subset, with six members. Figure 6 shows that the cumulative rainfall for these six members is low, compared to observations and to that of the other eleven members, because of excessively low monthly mean rainfall in the summer and autumn. The comparison with MORECS PE in Figure 4 shows that two of these same six members (afixb and afixn) also have very high cumulative PE relative to observations and to the rest of the ensemble members, due to excessively high PE in summer and autumn (Figure 6).

Figure 6 Monthly mean rainfall and PE over each time-slice, and the difference between the two time-slices, for each ensemble member (solid coloured lines), for the same Thames RCM grid box as in Figure 4. The dashed black line on the left-hand rainfall and PE plots shows comparable observational data for the 1961-1990 period. Key as for Figure 4.

The low cumulative rainfall for 1961-1990 apparent in six of the ensemble members over the Thames appears to arise from the configuration of the advection scheme, as it is these six members which have an advection diffusion exponent of 1 rather than 2 (Table 1), resulting in unrealistic simulations of the strength of storms and low estimates of current rainfall over the Thames region. Causes of the low cumulative PE for 1961-1990 for afixb and afixn are less clear, but may be related to the use of an advection diffusion exponent of 1 coupled with the presence of the surface-vegetation canopy energy exchange in the MOSES land-surface scheme.

Use of the RCM ensemble Figure 7 shows flood frequency curves for a number of catchments in the Thames Basin, derived from simulations using the Current time-slice of each RCM ensemble member as input to the G2G. These plots demonstrate that the six-member subset with low rainfall almost always results in lower flood peaks than the others.

Cherwell at Enslow Mill Lambourn at Shaw

Wey at Tilford Thames at Kingston

Cobbins Brook at Sewardstone Rd Darent at Hawley

Figure 7 Flood frequency curves for six catchments in the Thames Basin, derived from simulations using the Current time-slice of each RCM ensemble member as input to the G2G (solid lines, except for the six-member subset with low rainfall, which are represented by thin dotted lines, with colours as for Figure 4). Also shown for each catchment is the median observed flood frequency and its 95% bounds (respectively thick dashed and dotted black lines).

Also shown for each catchment in Figure 7 is the median observed flood frequency (and its 95% bounds), derived by resampling any 30 observed (non-missing) AM daily mean flows available for the catchment between 1961 and 2005. The comparison of observed flood frequency with RCM-derived flood frequencies (which have also been estimated using daily mean flows in this case) suggests that the flood frequency is modelled well by (some versions

of) the RCM in some catchments, but that the RCM over-estimates flood frequency in other catchments. However, other factors need to be borne in mind when making this comparison, such as G2G model error, flow gauge error and the consequences of artificial influences like abstraction (not included in the G2G) on observed flow records. This is discussed in the G2G modelling report for this project (Bell et al. 2008). It should also be noted that, as for the comparison of RCM and observed rainfall and PE, the comparison of flows simulated with RCM data to observed flows needs to be interpreted with care, as the Current run of each ensemble member is not meant to reproduce exactly what happened in that period, but is simply one possible representation of what could have happened in that period, given the existence of stochasticity and natural variability.

The existence of the six-member subset of the RCM ensemble with excessively low summer and autumn rainfall (two of which also have excessively high summer and autumn PE) is thus problematic in terms of flow modelling, as it leads to an incorrect catchment water balance and, even given that only changes between Current and Future time-slices will be considered, there is the potential for the results to be misleading. Thus it has been decided to exclude this six-member subset from further analysis and work exclusively with the remaining eleven ensemble members.

Climate change impacts on flood frequency

Results from individual ensemble members There is considerable variation in the results for the percentage change in flood frequency, for different locations within the Thames Basin, between ensemble members and for different return periods. Maps for all eleven ensemble members, for six return periods (2, 5, 10, 20, 50 and 100 years), are given in Appendix A.

In order to be able to better compare ensemble members, results have been extracted for each one, for each point down the main Thames and for eighteen of its principal tributaries (Figure 8). These have been plotted together in Figure 9 (for the 2-year return period) and Figure 10 (for the 10-year return period). As well as showing the results for all eleven ensemble members together, these graphs illustrate how the percentage change in peak flow varies as one moves down the rivers, and at the points where tributaries join the main Thames. In particular, this highlights the dependence of the impact on soil type, as rivers that start on chalk before flowing into clay regions have a sudden change in impact as they cross from chalk to clay (e.g. half way down the Pang, three-quarters of the way down the Kennet and one-third of the way down the Colne).

Figure 8 Map showing the main Thames (flowing from left to right) and eighteen of its principal tributaries.

Figure 9 Percentage change in peak flow (1970s to 2080s) at the 2-year return period, for 11 ensemble members, for the main Thames (bottom graph) and eighteen of its principal tributaries (left and right of other graphs). Each river ‘flows’ from left to right, with the right-most plotted point for each tributary being that at which it joins the main Thames (whose outlet is plotted at the far right, at position zero). The tributaries are ordered by the point at which they join the main Thames (see Figure 8). Colour key as for Figure 4.

Figure 10 As Figure 9 but for the percentage change in peak flow (1970s to 2080s) at the 10-year return period. Colour key as for Figure 4.

Results averaged over ensemble subsets Figure 11 shows the mean impact over the eleven-member ensemble, at six return periods. Here it has been assumed that no ensemble member (of the remaining eleven) is more or less likely than any other, and so a non-weighted mean has been calculated (at each return period) from the percentage changes in flood frequency for each of the eleven ensemble members (i.e. averages of the eleven sets of maps in Appendix A).

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2H H2E2IH IH2E2PH PH2E2QH QH2E2RH RH2E2SH SH2E2TH TH2E2VH 72hnge2in2flood2peks VH2E2IHH @IWUHs2to2PHVHsA b2IHH t2given2return2periods

Figure 11 Mean impact on flood frequency (at six return periods) from the eleven- member ensemble, mapped over the Thames Basin.

The maps show significant variation in results for different regions of the Thames basin, again highlighting the dependence of the impact on soil type, as regions overlying chalk have much lower percentage changes than other regions. Figure 12 shows the percentage change at the 10-year return period plotted against baseflow index (BFI, which is an indication of the permeability of the ground, with a higher index value meaning higher permeability). This shows that areas with lower BFI generally have a higher percentage change, although areas with higher BFI do not necessarily have a lower percentage change.

20 % change in flood peak at 10−yr return period

0 0 0.2 0.4 0.6 0.8 1 BFI

Figure 12 Percentage change in flood peak at the 10-year return period versus baseflow index (BFI; which is higher in areas of greater permeability), for each river point over the Thames Basin.

As the results vary considerably according to the RCM ensemble member used, and therefore its associated parameter perturbations, (unweighted) averages have also been calculated over different subsets of the eleven-member ensemble, determined according to some of the parameter perturbations given in Table 1. These subsets are:

a) dependence of stomatal conductance on CO2 ‘on’ (afgcx, afixk, afixl, afixm, afixo, afixq); b) dependence of stomatal conductance on CO2 ‘off’ (afixa, afixc, afixh, afixi, afixj); c) Number of soil levels (‘R_layers’) accessed for evapotranspiration in forest and grass: 4,3 (afgcx, afixh, afixi, afixl, afixm, afixo, afixq); d) Number of soil levels (‘R_layers’) accessed for evapotranspiration in forest and grass: 2,1 (afixa, afixc, afixj).

The pair of subsets a) and b) are complementary, as are the pair c) and d) (although the latter pair only includes ten of the eleven ensemble members altogether, as one member, afixk, has 3,2 R_layers). Results are shown in Figure 13, where comparing complementary pairs illustrates how much difference is made by these particular parameter perturbations. That is, the average impact for subset a) is greater than that for subset b). Likewise, the average impact for subset c) is greater than that for subset d). However, it should be noted that detailed conclusions on the effect of specific parameter perturbations on flood frequency impacts is difficult, as multiple parameters are varied between ensemble members.

PEyer SEyer PEyer SEyer

IHEyer PHEyer IHEyer PHEyer

SHEyer IHHEyer SHEyer IHHEyer

`2H `2H H2E2IH H2E2IH IH2E2PH IH2E2PH PH2E2QH PH2E2QH QH2E2RH QH2E2RH RH2E2SH RH2E2SH SH2E2TH SH2E2TH TH2E2VH 72hnge2in2flood2peks TH2E2VH 72hnge2in2flood2peks VH2E2IHH @IWUHs2to2PHVHsA VH2E2IHH @IWUHs2to2PHVHsA b2IHH t2given2return2periods b2IHH t2given2return2periods a) stomatal conductance dependence ‘on’ b) stomatal conductance dependence ‘off’

PEyer SEyer PEyer SEyer

IHEyer PHEyer IHEyer PHEyer

SHEyer IHHEyer SHEyer IHHEyer

Figure 13 Mean impact on flood frequency (at six return periods) mapped over the Thames Basin for four subsets of the eleven-member ensemble, determined according to parameter perturbations given in Table 1: a) dependence of stomatal conductance on CO2 ‘on’; b) dependence of stomatal conductance on CO2 ‘off’; c) 4,3 R_layers; d) 2,1 R_layers.

Results compared to an RCM-based estimate of current natural variability An RCM ensemble for the Current time-slice can be used to gain an RCM-based estimate of current natural variability, by pooling data from the ensemble. However in this case, with a perturbed parameter ensemble rather than an initial condition ensemble, the estimation has to be done with care as the ensemble members used need to be from the same population. That is, for flood modelling they should not include any members with parameter perturbations that give very different results in terms of the distributions of various precipitation (or PE) characteristics.

An analysis carried out by the Met Office Hadley Centre suggested that the six-member subset consisting of afgcx, afixh, afixl, afixm, afixo and afixq could be pooled, as these have essentially the same settings for a number of key parameters, the perturbation of which leads to significant changes in certain precipitation characteristics. For the purpose of flow modelling, where PE can also make an important contribution, the subset has been reduced to five members by excluding afixh, as this has a different setting for the dependence of stomatal conductance on CO2 in comparison to the other five (‘off’ versus ‘on’; Table 1), which has a strong effect on PE.

An RCM-based estimate of current natural variability has thus been obtained for each river point by:

a) Pooling the AM data for the current time-slice from the five-member subset consisting of afgcx, afixl, afixm, afixo and afixq; b) Resampling (with replacement, 200002 times) sets of 30 AM (from the 5 x 30 AM for each river point) and fitting a flood frequency curve to each set; c) Calculating the differences (at specific return periods) between 100001 pairs of fitted flood frequency curves; d) Calculating the upper and lower 50, 75 and 95% bounds from the set of differences.

The latter bounds are shown on the graphs in Figure 14 and Figure 15, for the 2- and 10-year return period respectively. The modelled changes in flood frequency between the Current and Future time-slices can then be seen in the context of the natural variability that might be expected without the presence of climate change. It can be seen that some rivers, or parts of rivers, show few changes outside the range of current natural climate variability, particularly at the lower return period, (e.g. the Kennet and the Lee), whereas others show many changes outside the range of natural variability (e.g. the Cherwell and the Wey). This alters according to the return period being considered, as well as location, as is shown on the maps in Figure 16, where darker colours indicate areas where more of the modelled changes in peak flows are outside the range of the RCM-based estimate of natural variability.

Locations which consistently show modelled changes outside the range of natural variability are candidates for so-called ‘early-bird’ sites, where further monitoring could be expected to show evidence of statistically significant changes in observed flows before many other sites (Wilby 2006). However, just because a location shows few, if any, modelled changes outside of the range of natural variability does not mean that it is safe from flooding in the future, as current flood defences may not accommodate the full range of natural variability. Also, it should be remembered that an RCM-based estimate of current natural variability has been used here, which may not fully represent real variability. Natural variability could also alter under climate change.

Figure 14 As Figure 9 (percentage change in peak flow at the 2-year return period), but including an RCM-based estimate of current natural variability (upper and lower 50, 75 and 95% - black dashed lines). Colour key as for Figure 4.

Figure 15 As Figure 10 (percentage change in peak flow at the 10-year return period), but including an RCM-based estimate of current natural variability (upper and lower 50, 75 and 95% - black dashed lines). Colour key as for Figure 4.

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

H2E2I P Q R S T U 52gws2where272hnge2in2flood2peks2@IWUHs2to2PHVHsA2 V2E2W exeeds2WS72nturl2vriility2@gw2estimteAD IH2E2II t2given2return2periods

Figure 16 Number of ensemble members (out of eleven) giving percentage changes in flood frequency outside the upper 95% natural variability bound (at six return periods), mapped over the Thames Basin.

Conclusions

Summary The G2G hydrological model has been used with a perturbed parameter RCM ensemble to simulate flood frequency across the Thames basin, for two 30-year time-slices: Current (October 1960 to September 1990) and Future (October 2069 to September 2099, under the A1B emissions scenario). The initial 17-member ensemble was reduced to 11, after an analysis of the precipitation from a grid box over the Thames Basin suggested that a particular parameter setting used for 6 members resulted in precipitation which was too low compared to the other ensemble members and to observations. A comparison of observed flood frequency with RCM-derived flood frequencies for the Current period suggested that flood frequency is modelled well by the remaining RCM ensemble members in some catchments, but that the RCM over-estimates flood frequency in other catchments. However, other factors need to be borne in mind when making this comparison, such as G2G model error, flow gauge error, the consequences of artificial influences like abstraction (not included in the G2G) on observed flow records, and the fact that the RCM Current runs are not reproductions of the 1961-1990 period but represent possible 1961-1990 periods.

The changes in flood frequency between Current and Future were then analysed, and the results for each ensemble member were presented in Appendix A, at six return periods (2, 5, 10, 20, 50 and 100 years, the latter two of which should be treated with caution as they require some extrapolation of the fitted flood frequency curve). There was considerable variation in the results, by ensemble member, by return period and by location, with areas underlain by chalk generally showing lower percentage changes than other regions. Almost all changes are increases, generally averaging around 5-10% in chalk areas and around 30- 50% elsewhere, for peak flows with up to a 20-year return period. However, for RCM ensemble member afixa, many changes are negative (decreases in peak flows), probably due to large increases in PE (Figure 4 and Figure 6), whereas for other ensemble members (e.g. afgcx and afixl) changes are much higher than average, with increases of over 80% in some areas. Table 2 summarises the modelled changes in flood peaks at different return periods specifically for the Thames at Kingston. The values used for certain RCM perturbed parameters also seemed to have specific effects on the impact of climate change on flood frequency. For instance, the average impact over the subset where stomatal conductance dependence is ‘on’ is greater than that over the complementary subset where stomatal conductance dependence is ‘off’.

Table 2 Modelled percentage changes in flood peaks at different return periods, for the Thames at Kingston (1970s to 2080s)

RCM Return period (years) ensemble member 2 5 10 20 50 100 afgcx 24.6 37.3 46.5 56.3 70.4 82.3 afixa 8.0 3.4 -3.4 -11.1 -22.0 -30.1 afixc 31.5 34.1 35.9 37.6 40.0 41.8 afixh 23.0 22.2 22.5 23.2 24.6 26.0 afixi 33.2 33.6 30.7 26.5 19.5 13.5 afixj 14.8 15.4 16.3 17.4 19.1 20.7 afixk 24.3 33.5 40.8 48.9 60.9 71.2 afixl 40.4 53.5 61.1 68.2 77.1 83.6 afixm 14.0 27.4 39.0 52.1 72.9 91.6 afixo 12.4 15.2 24.2 38.1 65.3 94.3 afixq 45.3 43.3 43.2 43.7 45.1 46.6 mean 24.7 29.0 32.4 36.4 43.0 49.2

A comparison of the modelled changes in flood frequency with an RCM-based estimate of current natural variability showed that, whilst some rivers (or parts of rivers) show few changes outside of the range of current natural variability, others show many changes outside of this range. The latter locations could be considered as sites where further monitoring may provide early warning of statistically significant changes in observed flows, due to climate change. However, no site should be considered immune from the impacts of climate change as current flood defences may not accommodate the full range of natural variability. Also, it should be remembered that an RCM-based estimate of current natural variability has been used here, which may not fully represent real variability. Natural variability could also alter under climate change.

Discussion Towards the downstream end of the fluvial Thames (for example near Kingston/Teddington), the (average) estimated change in modelled flood peaks for a 20-year return period is 36% (range -11% to +68%), rising to 43% (range -22% to +77%) for a 50-year return period. These estimated changes seem large compared to the latest FCDPAG guidance (Defra 2006), which indicates that sensitivity analyses of river flood alleviation schemes should take account of potential increases of up to 20% for the period 2025 to 2115. This latter guidance was a precautionary response to the research findings of projects FD0424-C (Reynard et al. 1999) and W5-032 (Reynard et al. 2004). The Thames modelling work presented here, using the latest climate model data from the Met Office Hadley Centre, provides the first evidence of changes in peak flows that exceed 20% by the 2080s. Changes in peak flows of around 40% for the 2080s need not be regarded as being “at odds” with the Defra guidelines, but contributing to the evidence base leading up to a refinement of the current sensitivity range.

The large estimated increase in future peak flows should ideally be considered in a wider historical context than a modelling study over two 30-year time-slices might provide. Over the past thirty to forty years there has been some evidence of a positive trend in high river flow indicators (Hannaford and Marsh 2008), generally thought to be linked to changes in winter precipitation arising from changes in atmospheric circulation patterns. However no such recent trend is evident for the lower Thames, and trend analyses of flow records throughout the 20th century (Black 1996; Robson 2002; Hannaford and Marsh 2008) have so far detected no apparent long-term trend in UK flood magnitude. Historically, snowmelt and frozen ground have been major contributing factors in a significant proportion of major floods on the Thames (Griffiths 1983), but compelling evidence for continuing temperature increases strongly suggest that snow-melt driven flood events will continue to decline in both frequency and magnitude.

Historically, the lower Thames has proved resilient to climate variability. There is no long-term trend in annual maximum flows over the 126 year series for the Thames (Marsh 2004) despite increases in temperature and a major change in the seasonal partitioning of rainfall (in the 19th century summer rainfall was on average greater than for the winter). River management in recent times (for example channel straightening, bed reprofiling, and improvements to the efficiency of weirs) has led to greater channel storage and conveyance. This has resulted in fewer floods in the lower Thames. At Kingston, for example, an increase of around 30% in the channel capacity over the last 70 years means that flows that would have caused significant flooding in the 1930s can now be accommodated within banks.

The modelling of current and future river flows undertaken here has relied on observations and climate model output from 1960 onwards (a period over which increases in high river flows has been detected in some areas). The hydrological modelling also excludes the influence of snowmelt on high river flows, although in the years since 1960, significant snowfall has occurred on relatively few occasions (as in the winter of 1962/3 and 1981/2). It is important to note that the 1961-1990 period is considered to be notably ‘flood poor’ (Hannaford and Marsh 2008); historically flood frequencies have varied substantially, a phenomenon normally assumed to be a natural consequence of the UK’s inherent climatic variability. The relatively low frequency of major flood events on the Thames during the 1961- 1990 period implies that the changes in return periods predicted for the 2080s should be treated with some caution, although this would be more likely to affect estimates of

percentage changes from a baseline of observed flows (rather than a modelled baseline using RCM data, as applied here). The graphs in Figure 17 show flood frequency for a 30-year moving window, constructed from a 150-year time-series of annual maximum flows (for the main Thames outlet shown in Figure 8), for three RCM ensemble members (afgcx, afixa and afixq). The vertical black dotted lines on each graph indicate the position of the standard time- slices often used in climate modelling (1970s, 2020s, 2050s and 2080s), and illustrate how

a) afgcx

b) afixa

c) afixq

Figure 17 Change in flood frequency at 6 return periods (2, 5, 10, 20, 50 and 100 years; solid coloured lines) over a 150 year period. The flood frequency is derived from a 30- year moving window of annual maximum peak flows (black crosses) for the main Thames outlet (Figure 8), and is plotted at the centre year of each 30-year period. The vertical black dotted lines indicate the position of the standard time-slices often used in climate modelling (1970s, 2020s, 2050s and 2080s), and illustrate how conclusions on impacts could differ, if these time-slices were to vary slightly.

conclusions on impacts could differ, if these time-slices were to vary slightly. For instance, in Figure 17a, the increase in the 100-year return period flood between the 1970s and 2080s is approximately 50%, but if the baseline (Current) period were centred only 5 years later, the increase would be only about 25%. Hence a high flow peak in a particular thirty-year time- slice could skew its derived flood frequency such that comparisons between two time-slices can show an increase or decrease depending on which precise periods are used.

Further development of the G2G hydrological model is ongoing, for instance through the inclusion of a snowmelt component and by improved representation of groundwater dominated areas. Improvements to the nature and availability of spatial datasets for soil, geology and land-cover properties will be expected to strengthen the G2G’s underpinning by physical properties, reducing reliance upon calibration of model parameters. There is currently no allowance in the G2G for the effect of out-of-bank flows on flood attenuation, which would typically lead to a reduction in high flood peaks during flood events. The introduction of a discharge-dependent wave speed and spatially variable estimates of bankfull capacity would therefore be beneficial. The inclusion of changing land-use patterns, particularly increased urbanisation, and changing patterns of abstraction, could be considered. Such model enhancements are likely to lead to improved confidence in estimates of river flows and, in turn, improved assessment of climate change impacts on flood frequency.

Uncertainty in the model estimates of the hydrological impact of climate change can arise from a range of sources including emissions scenario, model structure (for both the climate and hydrological models) and parameterisation (again for both the climate and hydrological models). For catchments considered to be particularly susceptible to increases in future flood risk, additional analysis using a catchment model (such as the PDM) adjusted for local conditions is recommended. However, several studies have suggested that the greatest uncertainty comes from sources related to the modelling of the future climate, particularly the choice of driving GCM, rather than from emissions or hydrological modelling (Kay et al. 2008, Prudhomme and Davies 2008, Wilby and Harris 2006). The RCM perturbed-parameter ensemble applied in this study represents the first attempt at deriving fine-scale information consistent with a range of large-scale regional changes which result from this global modelling uncertainty. It demonstrates how these large-scale uncertainties translate into uncertainty in future flood risk. More work is required to determine how representative these results are of the implications of the full range of climate modelling uncertainty.

Acknowledgements This work was produced for the Environment Agency under subcontract to Met Office Hadley Centre. Thanks to Nick Reynard and Terry Marsh for helpful discussions.

References

Arnell, N.W. (2003). Relative effects of multi-decadal climatic variability and changes in the mean and variability of climate due to global warming: future streamflows in Britain. Journal of Hydrology, 270, 195-213.

Bell, V.A., Kay, A.L., Jones, R.G. and Moore, R.J. (2007a). Development of a high resolution grid-based river flow model for use with regional climate model output. Hydrology and Earth System Sciences, 11(1), 532-549.

Bell, V.A., Kay, A.L., Jones, R.G. and Moore, R.J. (2007b). Use of a grid-based hydrological model and regional climate model outputs to assess changing flood risk. International Journal of Climatology, 27, 1657-1671.

Bell V.A., Moore R. J., Cole S. J. and Davies H. N. (2008). Area-wide river flow modelling for the Thames Estuary 2100 project: Model formulation and assessment. Progress report to the Met Office (Hadley Centre), CEH Wallingford, 62pp.

Black A. R. (1996). Major flooding and increased flood frequency in Scotland since 1988. Phys. Chem. Earth, 20, 463-468.

Cameron, D. (2006). An application of the UKCIP02 scenarios to flood estimation by continuous simulation for a gauged catchment in the northeast of Scotland, UK (with uncertainty). Journal of Hydrology, 328, 212-226.

Cole S.J. and Moore R.J. (2008). Hydrological modelling using raingauge- and radar-based estimates of areal rainfall. Journal of Hydrology, in press.

Collins, M., Booth, B.B.B., Harris, G.R., Murphy, J.M., Sexton, D.M.H. and Webb, M.J. (2006). Towards quantifying uncertainty in transient climate change. Climate Dynamics, 27, 127-147.

Cox, P.M., Betts, R.A., Bunton C.B., Essery, R.M., Rowntree, P.R. and Smith, J. (1999). The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Climate Dynamics, 15, 183-203.

Davies, H.N. and Bell, V.A. (2008). Assessment of methods for extracting low resolution river networks from high resolution digital data. Hydrol. Sci. J., in press.

Fowler, H.J. and Kilsby, C.G. (2007). Using regional climate model data to simulate historical and future river flows in northwest England. Climatic Change, 80, 337-367.

Gedney, N., Cox, P.M., Betts, R.A., Boucher, O., Huntingford, C. and Stott, P.A. (2006). Detection of a direct carbon dioxide effect in continental river runoff records. Nature, 439, 835-838.

Gordon, C., Cooper, C., Senior, C.A., Banks, H., Gregory, J.M., Johns, T.C., Mitchell, J.F.B. and Wood, R.A. (2000). The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Climate Dynamics, 16, 147-168.

Griffiths P.P. (1983). A chronology of Thames floods. Thames Water (Engineering Directorate) Water Resources Report. 54 pp.

Gringorten L.J. (1963). A plotting rule for extreme probability paper. Journal of Geophysical Research, 68(3), 813–814.

Hannaford J. and Marsh T. (2008). High-flow and flood trends in a network of undisturbed catchments in the UK. Int. J. Clim., 28, 1325-1338.

Hough, M., Palmer, S., Weir, A., Lee, M. and Barrie, I. (1997). The Meteorological Office Rainfall and Evaporation Calculation System: MORECS version 2.0 (1995). An update to Hydrological Memorandum 45, The Met. Office, Bracknell.

IPCC (2000). Special report on emissions scenarios (SRES): A special report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.

Kay, A.L., Davies, H.N., Bell, V.A. and Jones, R.G. (2008). Comparison of uncertainty sources for climate change impacts: flood frequency in England. Climatic Change,. doi: 10.1007/s10584-008-9471-4.

Kay, A.L., Jones, R.G. and Reynard, N.S. (2006). RCM rainfall for UK flood frequency estimation. II. Climate change results. Journal of Hydrology, 318, 163-172.

MAFF (2001). Flood and Coastal Defence Project Appraisal Guidance: Overview. Ministry of Agriculture, Fisheries and Food, May 2001. 42pp.

Marsh T.J. (2004). The January 2003 flood on the Thames. Weather, 59(3), 59-62.

Maraun, D., Osborn, T.J. and Gillett, N.P. (2008). United Kingdom daily precipitation intensity: improved early data, error estimates and an update from 2000 to 2006. International Journal of Climatology, 28(6), 833-842, doi:10.1002/joc.1672.

Monteith, J.L. (1965). Evaporation and environment. Symp. Soc. Exptl. Biol., 19, 205-234.

Moore, R. J., Bell, V. A., Cole, S. J. and Jones, D.A. (2007). Rainfall-runoff and other modelling for ungauged/low-benefit locations. Science Report SC030227/SR1, Research Contractor: CEH Wallingford, Environment Agency, Bristol, UK, 249pp.

Moore, R.J., Cole, S.J., Bell, V.A. and Jones, D.A. (2006). Issues in flood forecasting: ungauged basins, extreme floods and uncertainty. In: I. Tchiguirinskaia, K. N. N. Thein & P. Hubert (eds.), Frontiers in Flood Research, 8th Kovacs Colloquium, UNESCO, Paris, June/July 2006, IAHS Publ. 305, 103-122.

Murphy, J.M., Booth, B.B.B., Collins, M., Harris, G.R., Sexton, D.M.H. and Webb, M.J. (2007). A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles. Philosophical Transactions of the Royal Society A, 365, 1993-2028.

Osborn T.J. and Hulme M. (2002). Evidence for trends in heavy rainfall events over the United Kingdom. Philosophical Transactions of the Royal Society A, 360, 1313-1325 (doi:10.1098/rsta.2002.1002).

Nawaz, N.R. and Adeloye, A.J. (2006). Monte Carlo assessment of sampling uncertainty of climate change impacts on water resources yield in Yorkshire, England. Climatic Change, 78, 257-292.

New, M., Lopez, A., Dessai, S. and Wilby, R. (2007). Challenges in using probabilistic climate change information for impact assessments: an example from the water sector. Phil. Trans. R. Soc. A, 365, 2117-2131.

Paz, A.R., Collischonn, W. and Lopes da Silveira, A. L. (2006). Improvements in large-scale drainage networks derived from digital elevation models. Water Resour. Res., 42(8), W08502, doi:10.1029/2005WR004544.

Penman, H.L. (1948). Natural evaporation from open water, bare soil and grass, Proc. Royal Soc. London, Ser. A, 193, 120-148.

Pope V.D., Gallani M.L., Rowntree P.R. and Stratton R.A. (2000). The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3. Climate Dynamics, 16(2-3), 123-146.

Prudhomme C and Davies HN (2008) Assessing uncertainties in climate change impact analyses on river flow regimes in the UK. Part 2: future climate. Climatic Change, doi: 10.1007/s10584-008-9461-6.

Reynard, N.S., Prudhomme, C. and Crooks, S.M. (1999). Climate change impacts for fluvial flood defence. Report to Ministry of Agriculture, Fisheries and Food, FD0424-C, Institute of Hydrology, Wallingford, March 1999, 29pp.

Reynard, N.S., Prudhomme, C. and Crooks, S.M. (2001). The flood characteristics of large UK rivers: potential effect of changing climate and land use. Climatic Change, 48, 343-359.

Reynard, N.S., Crooks, S.M. and Kay, A.L. (2004). Impact of climate change on flood flows in river catchments. Report to Department for Environment, Food and Rural Affairs and the Environment Agency, Technical Report W5-032/TR, CEH Wallingford, March 2004, 99pp.

Robson A.J. and Reed D.W. (1999). Statistical procedures for flood frequency estimation. Vol. 3, Flood Estimation Handbook. Institute of Hydrology, Wallingford, UK, 338pp.

Robson A.J. (2002). Evidence for trends in UK flooding. Phil. Trans. Roy. Soc. 360, 1327- 1343.

Shuttleworth, W.J. (1993). Evaporation. In: Handbook of Hydrology, D. Maidment (Ed.), McGraw-Hill, New York.

Thompson, N., Barrie, I.A. and Ayles, M. (1982). The Meteorological Office Rainfall and Evaporation Calculation System: MORECS (July 1981). Hydrological Memorandum No. 45, Met Office, Bracknell.

Wilby R. L. (2006). When and where might climate change be detectable in UK river flows? Geophys. Res. Lett., 33, L19407, doi:10.1029/2006GL027552.

Wilby, R.L. and Harris, I. (2006). A framework for assessing uncertainties in climate change impacts: Low-flow scenarios for the River Thames, UK. Water Resources Research, 42, W02419, doi: 10.1029/2005WR004065.

Wilby, R.L., Whitehead, P.G., Wade, A.J., Butterfield, D., Davis, R.J. and Watts, G. (2006). Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River Kennet, UK. Journal of Hydrology, 330, 204-220.

Appendices

Appendix A Results from eleven individual ensemble members afgcx

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

aﬁxa

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

aﬁxc

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

aﬁxh

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

aﬁxi

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

aﬁxj

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

aﬁxk

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

aﬁxl

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

aﬁxm

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

aﬁxo

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

aﬁxq

PEyer SEyer

IHEyer PHEyer

SHEyer IHHEyer

`2ETH ETHE2EQH EQH2E2EIS EIS2E2H H2E2IS IS2E2QH QH2E2TH TH2E2IHH 72hnge2in2flood2peks IHH2E2PHH @IWUHs2to2PHVHsA t2given2return2periods b2PHH

Thames Estuary 2100 Downscaling Future Skew Surge Statistics at Sheerness, Kent

Phase 3 Studies – Synthesis Report

EA Study Lead:

Consultants: Rob Wilby, Independent Climate Change Science Advisor

Status: Final Draft Date: 5 December 2008 Annex 4 of 7

Appendix L to TE2100 Plan

We are the Environment Agency. It's our job to look after

your environment and make it a better place - for you, and

for future generations.

Your environment is the air you breathe, the water you drink and the ground you walk on. Working with business,

Government and society as a whole, we are making your

environment cleaner and healthier.

The Environment Agency. Out there, making your

environment a better place.

Produced by:

Environment Agency TE2100 Project Thames Barrier

Eastmoor Street Charlton London SE7 7PN Tel: 0208 3054160 Email: [email protected] www.environment-agency.gov.uk

2 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent

Executive Summary

This report investigates potential changes in storminess- driven extreme water levels in the Thames Estuary. Daily weather patterns over Eastern England were statistically related (“downscaled”) to the skew surge observed at Sheerness in Kent. This surge index is derived from the difference between the highest measured water level in a tidal cycle and the predicted astronomical tide. The largest such surge observed between 1961 and 2000 was 2.02m on 21 February 1993.

Next, the skew surge was downscaled from the 21st century weather patterns projected by four General Circulation Models (GCMs) under medium-high greenhouse gas emissions. Consistent with a Met Office study, the skew surge shows no trend over the full period 1961-2100 for any projection. Furthermore, estimated 50- and 100-year surges for the 2080s cannot be confidently separated from those estimated for the 1961-1990 baseline. Modest changes in surge level were found in a few experiments but these were attributed to the sampling of multi-decadal variability.

These results suggest that the skew surge will continue to exhibit large variability between decades, and any changes projected for the 21st century will be small compared with the sea level rise component of extreme water levels. However, the study findings are based on a very small sample of climate model experiments and a single emissions scenario. Further work is also needed to assess the sensitivity and realism of modelled weather patterns (that cause surges) to changes in greenhouse gas concentrations.

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 3 Contents

Contents 5 1 Introduction 6 2 Data sources and surge indices 7 2.1 Tidal data 7 2.2 Re-analysis data 8 2.3 Climate model output 8 3 Historical storminess 10 3.1 Introduction 10 3.2 Seasonal and annual variations 10 3.3 Tidal cycle 10 3.4 Dependence on atmospheric circulation 11 4 Statistical downscaling 12 4.1 Introduction 12 4.2 The Statistical DownScaling Model (SDSM) 13 4.3 Model calibration and testing 13 5 Skew surge scenarios 16 5.1 Introduction 16 5.2 Return level curves for the GCM ensemble 16 5.3 Discussion of changes in return levels 18 6 Conclusions 19 7 Acknowledgements 20 Supporting materials 21 Appendix 1 Bibliography of SDSM applications 24 Appendix 2 Enhancements to SDSM in TE2100 26

4 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 1 Introduction

The overarching objective of TE2100 is to develop a tidal flood risk management plan for the Thames estuary through to the end of the 21st century. In a separate investigation, the Met Office Hadley Centre was commissioned to investigate potential climate-driven changes in extreme water levels in the southern North Sea near the Thames Estuary (Howard et al., 2008). Their ensemble projections had two components: time-mean sea level rise combined with changes in storminess.

This study provides an independent assessment of only changes in the storminess component. The analysis is based on statistically derived changes in storm surge at Sheerness in Kent. These scenarios were constructed by statistically downscaling from daily weather patterns predicted by four General Circulation Models (GCMs) under a medium-high emissions scenario. This report provides a synthesis of the findings previously described in three Project Records (dated 3 August 2007; 23 August 2007; 30 April 2008).

The report begins with a brief overview of the main data sources (i.e., tidal levels and climate model output) used to evaluate changes in the storminess driven component of 21st century extreme water levels at Sheerness. Next, the general principles of statistical downscaling are described, followed by examples of downscaling model skill and limitations. Output from four GCMs is then used to downscale skew surge statistics for different return periods. The final sections offer interpretations of the findings and set out the main conclusions arising from the research.

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 5 2 Data sources and surge indices

2.1 Tidal data

Hourly residuals (calculated from observed sea level minus predicted astronomical tide) were obtained for the tide gauge at Sheerness (51º 26’ 44.3’’N, 0º 44’ 36.1’’W). The data begin in January 1965 but records are incomplete for much of the late 1960s and are not available for the years 1976 to 1979 inclusive. A quality flag accompanies each record with codes for improbable, null or interpolated values. A zero tolerance approach was followed concerning acceptance of data for analysis and model calibration, so any 24-hour period containing such a flag was rejected, leaving 73% coverage for the period 1961-2000.

Five daily surge indices were derived from the hourly data:

[1] SKEW – the “skew surge” computed from the difference between the highest measured water level in a tidal cycle (irrespective of timing) and the expected high water (see Figure 1);

[2] SMAX – the daily maximum surge estimated from hourly residuals (as widely employed in earlier downscaling analyses);

[3] TMAX – the surge taken from the hourly residual coinciding with the highest tide (to investigate possible associations between tidal cycle and surge);

[4] DMAX – the surge taken from the hourly residual coinciding with maximum depth;

[5] MAXDEPTH – measured daily maximum water depth from hourly data (i.e., net of the tidal cycle plus storm surge component).

Tide Height Residual

9 3.5 8 SKEW surge 3 7 2.5 6 5 2 4 1.5 3 Height (m) Height 1 2 (m) Residual 1 0.5 0 0 00 02 04 06 08 10 12 14 16 18 20 22

Figure 1 The event on 21 February 1993 is an example of when the maximum hourly surge residual preceding the maximum tide. This event produced a skew surge of 2.02m – the largest in the available record between 1961 and 2000.

6 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 2.2 Re-analysis data

Information on observed large-scale atmospheric circulation patterns were derived from the ERA- 40 and NCEP re-analysis. All data were re-gridded to conform to the grid-box system of Met Office Hadley Centre’s coupled ocean–atmosphere model HadCM3 (Figure 2). All predictors (with the exception of the geostrophic wind direction, see below) were normalised with respect to their 1961 to 1990 averages. However, daily predictors were also derived for the period 1961–2000.

The UKSDSM archive contains daily predictor variables for selected grid boxes covering the British Isles (Figure 2). The domain is represented by nine grid boxes each measuring 2.5º latitude by 3.75º longitude. Of the nine cells, six are land, and three are ocean. To obtain more realistic estimates of forcing over land areas that are represented by ocean grid boxes, data from the two nearest land cells were averaged. For example, predictor variables for Southwest England (SW) are the average of data from the Wales (WA) and Southern England (SE) grid boxes.

Figure 2 Location and nomenclature of the grid boxes in the UKSDSM archive

2.3 Climate model output

Future climate scenarios were obtained from predictor variables for the same grid-boxes supplied by four GCMs (CGCM2, CSIRO, ECHAM4, and HadCM3) under two emission scenarios (the Medium-High [A2] and Medium-Low [B2] Emissions of the Intergovenmental Panel on Climate Change Special Report on Emission Scenarios [IPCC SRES]) for the period 1961-2100 (Table 1). As with the re-analysis data, all GCM predictors were normalised with respect to their respective 1961-1990 climatologies, and re-gridded to conform to the Hadley Centre model grid (Figure 2).

Table 2 lists the daily predictor variables held in the UKSDSM data archive. The predictor suite contain variables describing atmospheric circulation, thickness, stability and moisture content. In practise, the choice of predictor variables is often constrained by data availability from GCM archives. The predictors in Table 1, therefore, represent a compromise between maximum overlap between ERA40, NCEP and GCM archives, as well as the range of choice for downscaling.

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 7 Model Source Climate References sensitivity

CGCM2 Canadian Center for 3.59 Flato and Boer (2001) Climate Modelling and Analysis

CSIRO Commonwealth Scientific 3.50 Gordon and O’Farrell Mk2 and Industrial Research (1997) Organisation in Australia

ECHAM4 Max-Planck-Institut for 3.11 Stendel et al. (2000) Meteorology and Deutches Klimarechenzentrum in Hamburg

HadCM3 UK Meteorological Office’s 3.38 Gordon et al. (2000) Hadley Centre

Table 1 Sources of the four GCMs used in the analysis

Predictor Description Predictor Description TEMP Mean temperature at 2m THETA Surface airflow direction MSLP Mean sea level pressure ZSUR Near surface vorticity H850 850 hPa geopotential height Z850 Vorticity at 850 hPa H500 500 hPa geopotential height Z500 Vorticity at 500 hPa USUR Near surface westerly wind DSUR Near surface divergence U850 Westerly wind at 850 hPa D850 Divergence at 850 hPa U500 Westerly wind at 500 hPa D500 Divergence at 500 hPa VSUR Near surface southerly wind QSUR Near surface specific humidity V850 Southerly wind at 850 hPa Q850 Specific humidity at 850 hPa V500 Southerly wind at 500 hPa Q500 Specific humidity at 500 hPa FSUR Near surface wind strength RSUR Near surface relative humidity F850 Wind strength at 850 hPa R850 Relative humidity at 850 hPa F500 Wind strength at 500 hPa R500 Relative humidity at 500 hPa

Table 2 Daily variables held in the UKSDSM archive. Italics indicate secondary (airflow) indices derived from pressure fields (surface, 500 and 850 hPa)

8 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 3 Historical storminess

3.1 Introduction

Prior to calibrating the statistical downscaling model, a few diagnostic tests were undertaken using selected surge statistics. The intention was to identify any long-term trends or seasonality in residuals, and to examine dependencies on airflow direction, or tidal cycle.

3.2 Seasonal and annual variations

The left panel of Figure 3 shows the month of occurrence of the top 1% most extreme SMAX values at Sheerness. In line with expectations, it is evident that over half (54%) of these maxima occurred during winter months (December to February).

The right panel of Figure 3 shows that the annual maximum series of SMAX increased by an average +3.6 mm/yr over the period 1965-2000 (or +0.3 mm/yr if the 1993 outlier is excluded). The timing of the surge has occurred on average +2.6 days/decade later. The different symbols denote surge timing: open circles (occurred between September and November), black circles (between December and February), and crosses (between March and May). Once again, it is evident that more than half (59%) of the annual maxima occurred in winter.

3.5 3.5

3 3

2.5 2.5 2 2 SMAX (m) SMAX 1.5

1.5 SMAX (m) Annual 1

1 0.5 123456789101112 1961 1966 1971 1976 1981 1986 1991 1996 2001

Figure 3 Variation of SMAX at Sheerness by month and year

3.3 Tidal cycle

As has been previously reported the surge residuals show strong dependency on the phase of the tidal cycle. Amongst the largest SMAX, 62% occured on the rising tide, 11% at (or within one hour of) a high tide, 8% on the falling tide, and 19% at (or within one hour of) a low tide (Figure 4, left panel).

The MAXDEPTH index was also found to depend on the tidal cycle. Only two of the largest 189 heights were recorded more than one hour either side of the high tide (these occurred on the 21 February 1993 and 6 March 1968) (see Figure 4, right panel).

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 9

SMAX PHASE MAX DEPTH PHASE

3.5 1 9 1

3 8.5 0.5 0.5 8 2.5 0 7.5 0 2

SMAX (m) SMAX 7 -0.5 -0.5 1.5 (m) MAXDEPTH 6.5

1 -1 6 -1 -24 -21 -18 -15 -12 -9 -6 -3 0 3 6 9 12 15 18 21 24 -24-21-18-15-12-9-6-30 3 6 9 1215182124 Hours between SMAX and highest tide Hours between MAXDEPTH and highest tide

Figure 4 Timing of the daily SMAX (>1.0 m) and MAXDEPTH in relation to tidal cycle

3.4 Dependence on atmospheric circulation

The first four surge indices were investigated with respect to NCEP re-analysis predictor variables for the EE grid box (Figure 2), including the direction of airflow. This choice was based on the outcome of a sensitivity analysis of predictability of surge indices as a function of re-analysis product and grid box established.

Figure 5 shows the average direction of surface airflow (THETA) on days when SMAX >1.0m. THETA is expressed as the number of degrees from North. In general, the largest surges are associated with airflows from the N-NW sector. Only one event coincided with airflows from due east (on 26 December 1985).

3.5

2.5

2 Surge (m) Surge 1.5

1 0 45 90 135 180 225 270 315 360 THETA

Figure 5 Dependence of SMAX on airflow direction (THETA, degrees from north)

Table 3 shows the correlation between surge indices and the NCEP re-analysis predictor variables chosen for statistical downscaling. As expected, the surge index SMAX with strongest meteorological forcing and least astronomical component had greatest predictability.

Predictors SKEW SMAX TMAX DMAX

USUR 0.15 0.19 0.13 0.16

VSUR -0.30 -0.42 -0.29 -0.30

DSUR -0.33 -0.47 -0.32 -0.34

U500 0.28 0.29 0.27 0.28

F850 0.28 0.37 0.26 0.30

Table 3 Correlation coefficients between selected predictors and surge indices 4 Statistical downscaling

4.1 Introduction

It is recognised that GCMs are restricted in their usefulness for local impact studies by their coarse spatial resolution (typically of the order 50,000 km2) and inability to resolve important sub-grid scale features such as clouds and topography.

As a consequence, two sets of techniques have emerged as a means of deriving local-scale surface weather from regional-scale atmospheric predictor variables. Firstly, statistical downscaling is analogous to the “model output statistics” (MOS) and “perfect prog” approaches used for short- range numerical weather prediction. Secondly, Regional Climate Models (RCMs) simulate sub- GCM grid scale climate features dynamically using time-varying atmospheric conditions supplied by a GCM bounding a specified domain.

Climate Model Grid Scale GCM

Downscaling

RCM

Aggregation Precipitation SDS Topography Vegetation Soils

Land

Figure 6 A schematic illustrating the general approach to downscaling

Statistical downscaling involves deriving physically-sensible empirical relationships between the local variable(s) of interest (such as daily precipitation) and large-scale atmospheric predictors (such as sea level pressure, vorticity, humidity, etc) supplied by GCMs (Figure 6). In this case, the predictor variables are selected from the UKSDSM suite (Table 2) and related to the chosen surge statistic(s).

The two main assumptions of statistical downscaling are that: (1) large-scale forcing by the atmospheric circulation indices provides the majority of the explanatory power (i.e., local forcing is insignificant); and (2) the empirical functions remain valid under changed climate conditions.

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 11 4.2 The Statistical DownScaling Model (SDSM)

The Statistical DownScaling Model (SDSM) is a MS Windows based decision support tool for regional and local scale climate change impact assessments. Full technical details, including model validation and usage are described by Wilby et al. (2001). The tool is freely available by registration at: https://co-public.lboro.ac.uk/cocwd/SDSM/. The provenance of SDSM as a robust tool has been established elsewhere through numerous applications in the UK and beyond (see Appendix 1).

SDSM is best categorised as a hybrid of the stochastic weather generator and regression based downscaling methods (Wilby and Wigley, 1997). The stochastic element is used to inflate the variance of downscaled output to better agree with the observed daily data, and to generate ensembles of climate time series that differ in their individual time evolution, inter-annual means and variance. This enables confidence estimates to be set around model projections that partly account for the variance that is unexplained by the model. For convenience, 20 ensemble members are generated in each experiment.

The functionality of SDSM was enhanced as part of the TE2100 project (Appendix 2). The main developments were: • Expanded extreme event diagnostics, including annual series of the Nth largest events, quantile-quantile plots; • Frequency estimation for extremes, including well-known distributions such as Generalised Extreme Values (GEV), Gumbel, and stretched exponential; • Visualization and diagnostic tools for daily data, including probability plots and percentiles; • Robust tests of downscaling model stationarity, including Chow, lag-1 autocorrelation, and Durban-Watson; • Incorporation of auto-correlation process for time-series modelling.

4.3 Model calibration and testing

SDSM was calibrated using daily NCEP predictor variables for the EE grid-box, and log- transformed surge indices (SMAX and SKEW). The former was analysed for comparability with previous studies (e.g., Woth et al., 2005; Woth, 2005); the latter for consistency with the Met Office Hadley Centre study.

SDSM may be calibrated using an entire daily time-series (unconditional model) or by truncating the same record at a specified threshold (conditional model). Conditioning has the advantage of fitting the model to only the (extreme) events of greatest interest. The calibration can be further stratified by either month or season. Figure 7 shows that the conditional model better reproduces observed probabilities of SMAX exceeding 1.0 m.

0.01 Observed SDSM-Unconditional SDSM-Conditional 0.008

0.006

0.004

0.002

0 11.522.53 Max 24hr surge (m)

Figure 7 Probability distributions of observed and downscaled SMAX surge index

12 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent Through a composite analysis of SMAX predictor variables for different conditioning thresholds, it was determined that the optimum truncation level should be set between 0.2 and 0.3 m (Figure 8). Between these limits the U500 and F850 predictors show the most marked increases, yet ~31% and ~19% of the surge data are still retained for calibration.

1.5 All Zero 0.1 0.2 0.3 0.4 0.5 0.6 1.0

0.5

0.0

-0.5

Sub-sample mean -1.0

-1.5 MSLP USUR VSUR DSUR U500 F850 Predictor

Figure 8 Mean predictor values for SMAX exceeding specified thresholds. Note that MSLP was included for information only and was not used in the downscaling.

Surge modelling should also replicate the magnitudes and frequencies of much rarer events. Following the recommendations of van den Brink (2005) the GEV distribution was applied. Experimentation with GEV fits to observed and statistically downscaled annual maximum series of SMAX and SKEW indices suggest that SDSM conditioning thresholds of respectively 0.2 and 0.3 m are optimal (Figure 9). At these values the observed statistics lie very close to the ensemble median of the downscaled surges and well within the 95% confidence interval of each ensemble. In fact, the 50 year winter surge (SMAX) of 2.68 m is predicted to within 0.01 m by SDSM.

Sheerness annual (GEV) Sheerness annual (GEV)

2.2 4.5

2 4

1.8 3.5

1.6 3

1.4 Observed 2.5 SDSM 97.5 %ile 1.2 2.5 %ile 2

1 1.5

0.8 1

0.6 0.5 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Return period (years) Return period (years) Figure 9 GEV distributions of observed and downscaled annual SMAX (left panel) and SKEW (right panel) with conditioning at 0.2 and 0.3 m respectively based on 1961-2000

The effect of the SDSM conditioning was further examined using annual series of the 99th percentile of residuals (SMAX) in winter (as employed by Woth et al., 1995; Debernard and Røed, 2008). Unlike dynamical downscaling, the SDSM series are not expected to exactly match, because of the stochastic component of the model. Nonetheless, given the simplicity of the statistical downscaling, a credible simulation is achieved. The preferred variant of SDSM (T>0.2) also replicates the weak trend towards higher levels since the 1970s (Figure 10).

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 13 2.5 2.5

2 2

1.5 1.5 Observed SDSM (T>0.3)

1 1 SDSM (T>0.2) SDSM (T>0.5)

0.5 0.5

Figure 10 Observed and downscaled annual series of the 99th percentile surge (SMAX) using three conditioning thresholds (T)

On even closer inspection, the same model variant reproduces some (but by no means all) of the water level behaviour during the stormiest period of the calibration data (Figure 11). During the winter of 1992/93 the largest residual (SMAX) of ~3.0 m was recorded. Although SDSM predicted the preceding sequence of daily maximum surges, the highest level on 21 February 1993 was underestimated (for more detail see also Figure 1).

3 SDSM-M1 Observed 2.5

1.5

Surge height (m) height Surge 0.5

0 01/12/92 31/12/92 30/01/93 01/03/93

Figure 11 Observed and downscaled daily surge (SMAX) during the winter 1992/93. The SDSM results are taken from ensemble member M1.

From this experiment it may be surmised that SDSM is capable of reproducing the overall extreme water level distribution (as in Figure 9) but not the precise time series attributes of individual events (as in Figure 11). Overall, the deterministic part of the downscaling from atmospheric circulation patterns explains ~30% of the observed variance in extreme surges, thus leaving ~70% that must be generated stochastically. This should be kept in mind when interpreting the surge scenarios in the following section.

14 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 5 Skew surge scenarios

5.1 Introduction

For the purpose of the scenario analysis and consistency with the Met Office Hadley Centre study, the SKEW surge is preferred. As noted by Howard et al. (2008) the SKEW surge has the advantage of retaining the meteorological forcing in some isolation of the tide on which it is superimposed. Conversely, the residual surge (SMAX) shows a significant negative correlation with the tide due to non-linear tide/surge interactions (for more detail see: Horsburgh and Wilson, 2007). A bias towards the rising limb of the tide is clearly evident in the Sheerness record (Figure 4).

Again, for comparability with the Met Office analysis, return level curves are produced for the downscaled SKEW surge at Sheerness. Downscaling was performed using predictor variables for EE taken from the transient (1961-2100) experiments of the four GCMs listed in Table 1 under the SRES A2 emissions scenario. Return levels were estimated by fitting the 3-parameter GEV distribution to the annual maximum SKEW surges of each member (N= 20) in the ensemble, for each GCM. This enables calculation of the ensemble median and 95% confidence range for two time-slices (1961-1990 and 2071-2100) as well as for the complete period (1961-2100).

5.2 Return level curves for the GCM ensemble

Estimated return level curves are potentially subject to several uncertainties arising from: climate model physics and parameterisation; the method of statistical downscaling; multi-decadal climate variability; the GEV-parameter estimation itself; and errors in the observations. For future return levels, emissions scenario uncertainty must also be considered.

With these points in mind, it is evident that the return curves fitted to the downscaled SKEW surge over-estimate the magnitude of higher frequency extremes, and generally underestimate lower frequency events when compared to observations (Figure 12). The one exception is CSIRO which tends to overestimate the surge level at all return periods.

2.5 CGCM2 CSIRO ECHAM4 HadCM3 OBSERVED

1.5

SKEW (m) SKEW 1

0.5

0 110100 Return period (years)

Figure 12 Fitted return level curves for the downscaled SKEW surge at Sheerness derived from the ensemble median of each GCM for the time-slice 1961-1990

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 15 Comparable return level curves were derived for downscaled SKEW surge scenarios for the 2080s (the central curve shown in each panel of Figure 13). The difference between the respective points on these curves and those for the baseline period (Figure 12) are generally less than the combined GCM-downscaling model biases, and certainly fall within the 95% confidence range of the baseline (the dashed lines in Figure 13). In other words, the ensemble median for the 2080s cannot be confidently separated from the baseline ensemble, so the series may be considered stationary.

CGCM2 CSIRO

2.5 2.5

2 2

1.5 1.5

SKEW (m) SKEW 1 SKEW (m) SKEW 1

0.5 0.5

0 0 110100 110100 Return period (years) Return period (years)

ECHAM4 HadCM3

2.5 2.5

2 2

1.5 1.5

SKEW (m) SKEW 1 SKEW (m) SKEW 1

0.5 0.5

0 0 1 10 100 110100 Return period (years) Return period (years)

Figure 13 As in Fig.12 but for the 2080s, with 95% confidence range for the baseline

This assertion is supported by a visual inspection of the behaviour of selected ensemble members of downscaled SKEW surge for the period 1961-2100 (Figure 14). The time series suggests no overall trend for any of the GCMs (Figure 14) – a result that is consistent with Howard et al. (2000).

Figure 14 Annual maximum SKEW surges downscaled from the four GCMs: CGCM2 (top left), CSIRO (top right), ECHAM4 (bottom left) and HadCM3 (bottom right). In each case ensemble member M1 has been plotted.

16 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent Time-slice changes in the SMAX surge between 1961-1990 and the 2080s can yield increases of ~1.0 m for individual members (downscaled from CSIRO) but these changes are typically within the uncertainty range of the baseline ensemble, suggesting that multi-decadal variability is the cause.

5.3 Discussion of changes in return levels

Tables 4 and 5 provide GEV estimates based on downscaled output from each GCM at the 50 and 100 year return levels respectively. Changes are calculated using the conventional time-slice methodology (i.e., by comparing the 1961-1990 and 2080s estimates), as well as under the assumption that the data are stationary (i.e., for the entire period of each model run).

The largest increase in median 50 year return level produced by the time-slice method is ~0.14 m. This was downscaled from CGCM2 and is less than the combined GCM-downscaling bias. The other three models show no change or reductions in return levels. Note that only CSIRO yields a 95% ensemble range for the 50 year level that includes a SKEW surge as large as the event on 21 February 1993 (Figure 1). But no such modelled event falls within the 21st century. However, three GCMs did yield confidence intervals with a surge >2.0 m at the 100 year return level (Table 5).

GEV estimate CGCM2 CSIRO ECHAM4 HadCM3 1961-2100 1.35 1.56 1.44 1.37 (1.01-1.58) (1.36-1.86) (1.25-1.71) (1.11-1.58) 1961-1990 1.32 1.63 1.37 1.45 (1.07-1.68) (1.16-2.45) (1.14-1.78) (1.20-1.76) 2080s 1.45 1.42 1.38 1.36 (0.90-1.91) (1.18-1.82) (1.19-1.69) (1.19-1.59) Change +0.14 -0.20 +0.01 -0.10 Observations 1.47 1.47 1.47 1.47 Bias -0.16 +0.16 -0.10 -0.02

Table 4 Ensemble median estimates of the 50-year SKEW surge (m) at Sheerness downscaled from four GCMs under SRES A2 emissions for 1961-2100, 1961-1990 and 2071- 2100 with accompanying 95% confidence ranges (in grey). Combined GCM-SDSM biases were estimated by comparing downscaled SKEW with observations for the base period

GEV estimate CGCM2 CSIRO ECHAM4 HadCM3 1961-2100 1.47 1.74 1.53 1.47 (1.10-1.76) (1.45-2.23) (1.33-1.81) (1.20-1.76) 1961-1990 1.41 1.87 1.47 1.57 (1.09-1.91) (1.24-3.15) (1.16-2.04) (1.28-1.95) 2080s 1.63 1.57 1.46 1.45 (0.96-2.35) (1.27-2.20) (1.21-1.87) (1.23-1.78) Change +0.22 -0.30 -0.01 -0.11 Observations 1.61 1.61 1.61 1.61 Bias -0.20 +0.26 -0.14 -0.04

Table 5 As in Table 4 except for the 100-year SKEW surge (m)

The downscaled multi-model GEV estimates for the entire period (1961-2100) bracket those reported for the pooled Met Office Hadley Centre ensemble (Figure 12 in Howard et al., 2008). This is an encouraging result given that entirely different modelling schemes have been employed. However, only one GCM (CSIRO) yields 50 and 100 year return levels that exceed values estimated from observations.

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 17 6 Conclusions

In line with the Met Office study, 21st century changes in the downscaled SKEW surge component of extreme water levels was found to be small for the SRES A2 emissions scenario. In fact, changes in the ensemble median SKEW surge for the 2080s could not be confidently separated from the uncertainty range of the baseline ensemble. So the series may be considered stationary over the full period 1961-2100. Apparent changes in surge level for individual ensemble members may be attributed to multi-decadal variability (which is large).

There are several interpretations of this outcome: 1. The small sample of GCMs employed in the downscaling does not fully reflect the true climate model uncertainty, including the possibility of much larger increases in storminess. For example, the Met Office study showed, by scaling output from one outlier GCM (MIUB- ECHO-G), an increase of ~0.95 m in the 50 year return level skew surge by the end of the 21st century. However, the possibility that this is simply an artefact of multi-decadal variability between the two time-slices cannot be discounted (as noted above). 2. The small changes in SKEW surge at Sheerness must be considered within the broader context of patterns of changing surge risk around the North Sea. For example, Woth et al. (2005) found that the largest increases in the 99.5th percentile SMAX surge suggested by four Regional Climate Models (RCMs) occurs in the German Bight. Even here, the largest projected increases under SRES A2 emissions are 20-30 cm by the 2080s. Projected changes along the UK coast were not statistically different from zero. 3. The statistical downscaling captures only those changes in surge that are forced by large- scale atmospheric circulation. Even then the predictor variables account for just ~30% of the observed variance in SKEW surge (and ~40% for the more meteorologically dependent SMAX surge). If the atmospheric circulation of the host GCMs over the domain of interest is insensitive to greenhouse gas forcing, then the deterministic component of the surge response will be modest. 4. Possible interactions between surge-bathymetry are not included. However, the Met Office study suggests that the primary effect of rising sea levels is to bring forward the timing of the peak; the effect on total sea surface elevation was found to be less than 0.05 m. On the basis of the above evidence it is concluded that the skew surge will continue to exhibit large variability between decades but changes projected for the 21st century will be small compared with the sea level rise component of extreme water levels. However, this is not grounds for complacency. For example, the joint threat posed by tidal surge and fluvial flooding could be explored using the same statistical downscaling framework. Likewise, continued monitoring is essential for gathering data on long-term atmosphere-surge forcing at local scales.

18 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 7 Acknowledgements

The author wishes to thank the following individuals who have contributed to this work:

• Christian Dawson (Loughborough University) • Tim Reeder, Bill Donovan and Mike Steel (Environment Agency) • Jason Lowe (Met Office) • Kevin Horsburgh (Proudman Oceanographic Laboratory)

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 19 Supporting materials

Alexander, L.V., Tett, S.F.B. and Johnsson, T. 2005. Recent observed changes in severe storms over the UK and Iceland. Geophysical Research Letters, 32, L13704. Bijil, W. 1997. Impact of a wind climate change on the surge in the southern North Sea. Climate Research, 8, 45-59. Butler, A., Heffernan, J.E., Tawn, J.A., Flather, R.A. and Horsburgh, K.J. 2007. Extreme value analysis of decadal variations in storm surge elevations. Journal of Marine Systems, 67, 189-200. Carretero, J.C., Gomez, M., Lozano, I. et al. 1998. Changing waves and storms in the northeast Atlantic? Bulletin of the American Meteorological Society, 79, 741-760. Coles, S. and Tawn, J. 2005. Bayesian modelling of extreme surges on the UK east coast. Philosophical Transactions of the Royal Society A – Mathematical, Physical and Engineering Sciences, 363, 1387-1406. Dawson, R.J., Hall, J.W., Bates, P.D. and Nicholls, R.J. 2005. Quantified analysis of the probability of flooding in the Thames estuary under imaginable worst-case sea level rise scenarios. International Journal of Water Resources Development, 21, 577-591. Debernard, J., Saetra, O. and Roed, L.P. 2002. Future wind, wave and storm surge climate in the northern North Atlantic. Climate Research, 23, 39-49. Debernard, J.B. and Røed, L.P. 2008. Future wind, wave and storm surge climate in the Northern Seas: a revisit. Tellus, 60A, 427-438. Defra, 2005. Dependence between extreme sea surge, river flow and precipitation: a study in south and west Britain. Report FD2308/TR3, Department for Environment, Food and Rural Affairs, London, pp62 Flather, R.A. and Smith, J.A. 1998. First estimates of changes in extreme storm surge elevation due to doubling CO2. The Global Atmosphere & Ocean System, 6, 193-208. Flato, G.M. and Boer, G.J. 2001. Warming asymmetry in climate change simulations. Geophysical Research Letters, 28, 195-198. Gaslikova, L. and Weisse, R. 2006. Estimating near-shore wave statistics from regional hindcasts using downscaling techniques. Ocean Dynamics, 56, 26-35. Gonnert, G. 1999. The analysis of storm surge climate change along the German coast during the 20th century. Quaternary International, 56, 115-121. Gordon, C., Cooper, C., Senior, C.A., Banks, H.T., Gregory, J.M., Johns, T.C., Mitchell, J.F.B. and Wood, R.A. 2000. The simulation of SST, sea ice extents and ocean transport in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics, 16, 147-168. Gordon, H.B. and O’Farrell, S.P. 1997. Transient climate change in the CSIRO coupled model with dynamic sea ice. Monthly Weather Review, 125, 875-907.Heyen, H., Zorita, E. and von Storch, H. 1996. Statistical downscaling of monthly mean North Atlantic air-pressure to sea level anomalies in the Baltic Sea. Tellus Series A – Dynamic Meteorology and Oceanography, 48, 312-323. Holt, T. 1999. A classification of ambient climatic conditions during extreme surge events of western Europe. International Journal of Climatology, 19, 725-744. Horsburg, K.J. and Wilson, C. 2007. Tide-surge interaction and its role in the distribution of surge residuals in the North Sea. Journal of Geophysical Research, 112, C08003. Howard, T., Lowe, J., Pardaens, A., Ridley, J. and Horsburgh, K. 2008. Met Office Hadley Centre projections of 21st century extreme sea levels for TE2100. Met Office, Exeter, 52pp. Langenberg, H., Pfizenmayer, A., von Storch, H. and Sundermann, J. 1999. Storm-related sea level variations along the North Sea coast: natural variability and anthropogenic change. Continental Shelf Research, 19, 821-842. Lavery, S. and Donovan, B. 2005. Flood risk management in the Thames Estuary looking ahead 100 years. Philosophical Transactions of the Royal Society A – Mathematical, Physical and Engineering Sciences, 363, 1455-1474. Lionello, P., Nizzero, A. and Elvini, E. 2003. A procedure for estimating wind waves and storm- surge climate scenarios in a regional basin: the Adriatic Sea case. Climate Research, 23, 217- 231. Lowe, J.A. and Gregory, J.M. The effects of climate change on storm surges around the United Kingdom. Philosophical Transactions of the Royal Society A – Mathematical, Physical and Engineering Sciences, 363, 1313-1328.

20 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent Lowe, J.A., Gregory, J.M. and Flather, R.A. 2001. Changes in the occurrence of storm surges in the UK under a future climate scenario using a dynamic storm surge model driven by the Hadley Centre climate models. Climate Dynamics, 18, 188-1997. McRobie, A., Spencer, T. and Gerritsen, H. 2005. The big flood: North Sea storm surge. Philosophical Transactions of the Royal Society A – Mathematical, Physical and Engineering Sciences, 363, 1263-1270. Méndez, F.J., Menéndez, M., Luceño, A. and Losada, I.J. 2007. Analyzing monthly extreme sea levels with a time-dependent GEV model. Journal of Atmospheric and Oceanic Technology, 24, 894-911. Méndez, F.J., Menéndez, M., Luceño, A. and Losada, I.J. 2006. Estimation of the long-term variability of extreme significant wave height using a time-dependent Peak Over Threshold (POT) model. Journal of Geophysical Research, 111, C07024. Pfizenmayer, A. and von Storch, H. 2001. Anthropogenic climate change shown by local wave conditions in the North Sea. Climate Research, 19, 15-23. Pirazzoli, P.A., Costa, S., Dornbusch, U. and Tomasin, A. 2006. Recent evolution of surge-related events and assessment of coastal flooding risk on the eastern coasts of the English Channel. Ocean Dynamics, 56, 498-512. Ponte, R.M. 1994. Understanding the relation between wind-driven and pressure-driven sea-level variability. Journal of Geophysical Research – Oceans, 99, 8033-8039. Pryor, S.C., Schoof, J.T. and Barthelmie, R.J. 2005. Climate change impacts on wind speeds and wind energy density in northern Europe: empirical downscaling of multiple AOGCMs. Climate Research, 29, 183-198. Rockel, B. and Woth, K. 2007. Extremes of near-surface wind speed over Europe and their future changes as estimated from an ensemble of RCM simulations. Climatic Change, 81, 267-280. Senior, C.A., Jones, R.G., Lowe, J.A., Durman, C.F. and Hudson, D. 2002. Predictions of extreme precipitation and sea-level rise under climate change. Philosophical Transactions of the Royal Society A – Mathematical, Physical and Engineering Sciences, 360, 1301-1311. Stendel., M, Schmidt, T., Roeckner, E. and Cubasch, U. 2000. The climate of the 21st century: transient simulations with a coupled atmosphere-ocean general circulation model. Danmarks Klimacenter Rep # 00-6. Svensson, C. and Jones, D.A. 2002. Dependence between extreme sea surge, river flow and precipitation in eastern England. International Journal of Climatology, 22, 1149-1168. Tsimplis, M.N., Shaw, A.G.P., Flather, R.A. and Woolf, D.K. 2006. The influence of the North Atlantic Oscillation on the sea-level around the northern European coasts reconsidered: thermosteric effects. Philosophical Transactions of the Royal Society A – Mathematical, Physical and Engineering Sciences, 364, 845-856. Tsimplis, M.N., Woolf, D.K., Osborn, T.J., Wakelin, S., Wolf, J., Flather, R., Shaw, A.G.P., Woodworth, P., Challenor, P., Blackman, D., Pert, F., Yan, Z. and Jevrejeva, S. 2005. Towards a vulnerability assessment of the UK and northern European coasts: the role of regional climate variability. Philosophical Transactions of the Royal Society A – Mathematical, Physical and Engineering Sciences, 363, 1329-1358. Van den Brink, H.W., Konnen, G.P. and Opsteegh, J.D. 2003. The reliability of extreme surge levels estimated from observational records of order hundred years. Journal of Coastal Research, 19, 376-388. Van den Brink, H.W., Konnen, G.P. and Opsteegh, J.D. 2005. Uncertainties in extreme surge level estimates from observational records. Philosophical Transactions of the Royal Society A – Mathematical, Physical and Engineering Sciences, 363, 1377-1386. Von Storch, H. and Reichardt, H. 1997. A scenario of storm surge statistics for the German Bight at the expected time of doubled atmospheric carbon dioxide concentrations. Journal of Climate, 10, 2653-2662. WASA Group, 1998. Changing waves and storms in the Northeast Atlantic. Bulletin of the American Meteorological Society, 79, 741-760. Weisse, R. and Günther, H. 2007. Wave climate and long-term changes for the Southern North Sea obtained from a high-resolution hindcast 1958-2002. Ocean Dynamics, 57, 161-172. Wilby, R.L. and Dawson, C.W. 2007. Using SDSM 4.1 – A decision support tool for the assessment of regional climate change impacts. User Manual prepared on behalf of the Environment Agency, Thames Estuary 2100 Project, pp93. Wilby, R.L. and Wigley, T.M.L. 1997. Downscaling general circulation model output: a review of methods and limitations. Progress in Physical Geography, 21, 530-548.

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 21 Wilby, R.L., Dawson, C.W. and Barrow, E.M. 2002. SDSM - a decision support tool for the assessment of regional climate change impacts. Environmental and Modelling Software, 17, 145- 157. Wilby, R.L., Beven, K.J. and Reynard, N.S. 2008. Climate change and fluvial flood risk in the UK: More of the same? Hydrological Processes, 22, 2511-2523. Wolf, J. and Flather, R. 2005. Modelling waves and surges during the 1953 storm. Philosophical Transactions of the Royal Society A – Mathematical, Physical and Engineering Sciences, 363, 1359-1375. Woodworth, P.L., Flather, R.A., Williams, J.A., Wakelin, S.L. and Jevrejeva, S. 2007. The dependence of UK extreme sea levels and storm surges on the North Atlantic Oscillation. Continental Shelf Research, 27, 935-946. Woth, K. 2005. North Sea storm surge statistics based on projections in a warmer climate: How important are the driving GCM and the chosen emission scenario? Geophysical Research Letters, 32, L22708, doi:10.1029/2005GL023762. Woth, K., Weisse, R. and von Storch, H. 2005. Climate change and North Sea storm surge extremes: an ensemble study of storm surge extremes expected in a change climate projected by four different regional climate models. Ocean Dynamics, 56, 3-15.

22 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent Appendix 1

Bibliography of SDSM applications

Abraham, L.Z. 2006. Climate change impact on Lake Ziway watershed water availability, Ethiopia. Unpublished MSc Thesis, University of Applied Sciences, Cologne, 123pp. Aspen Global Change Institute (AGCI), 2006. Climate Change and Aspen: An Assessment of Impacts and Potential Responses. Appendix B, p107-111. Aspen Global Change Institute, Colorado, pp147. Bootsma, A., Gameda, S. and McKenney, D.W. 2005. Impacts of potential climate change on selected agroclimatic indices in Atlantic Canada. Canadian Journal of Soil Science, 85, 329- 343. Coulibaly, P. 2004. Downscaling daily extreme temperatures with genetic programming. Geophysical Research Letters, 31, L16203. Crawford, T. 2007. Future climate change: modelling the implications of shifts in rainfall characteristics for runoff in Northern Ireland. Unpublished Ph.D. thesis, Queen's University Belfast, Northern Ireland. Crawford, T., Betts, N.L. and Favis-Mortlock, D.T. 2007. GCM grid box choice and predictor selection associated with statistical downscaling of daily precipitation over Northern Ireland. Climate Research, 34, 145-160. Diaz-Nieto, J. and Wilby, R.L. 2005. A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom. Climatic Change, 69, 245-268. Dibike, Y.B. and Coulibay, P. 2007. Validation of hydrological models for climate scenario simulation: the case of Saguenay watershed in Quebec. Hydrological Processes, 21, 3123- 3135. Fealy, R. 2006. An assessment of the relationship between glacier mass balance and synoptic climate in Norway: Likely future implications of climate change. Unpublished PhD Thesis, University of Maynooth, Ireland. Gachon, P. and Dibike, Y. 2007. Temperature change signals in northern Canada: convergence of statistical downscaling results using two driving GCMs. International Journal of Climatology, 27, 1623-1641. Goodess, C., Osborn, T. and Hulme, M. 2003. The identification and evaluation of suitable scenario development methods for the estimation of future probabilities of extreme weather events. Tyndall Centre for Climate Change Research, Technical Report 4. http://www.tyndall.ac.uk/research/theme3/final_reports/it1_16.pdf Goodess, C.M., Anagnostopoulo, C., Bardossy, A., Frei, C., Harpham, C., Haylock, M.R., Hundecha, Y., Maheras, P., Ribalaygua, J., Schmidli, J., Schmith, T., Tolika, K., Tomozeiu, R. and Wilby, R.L. 2006. An intercomparison of statistical downscaling methods for Europe and European regions - assessing their performance with respect to extreme temperature and precipitation events. Climatic Change, in press. Guangul, S.G. 2003. Modelling the effect of climate and land-use changes on hydrological processes: An integrated GIS and distributed modelling approach. Published PhD Thesis, Vrije Universiteit, Brussels, Belgium. Harpham, C. and Wilby, R.L. 2005. Multi-site downscaling of heavy daily precipitation occurrence and amounts. Journal of Hydrology, 312, 235-255. Haylock, M.R., Cawley, G.C., Harpham, C., Wilby, R.L. and Goodess, C.M. 2006. Downscaling heavy precipitation over the UK: a comparison of dynamical and statistical methods and their future scenarios. International Journal of Climatology, 26, 1397-1415. Hessami, M., Gachon, P., Ouarda, T.B.M.J. and St-Hilaire, A. 2008. Automated regression-based statistical downscaling tool. Environmental Modelling and Software, 23, 813-834. Hewitson, B.C. and Wilby, R.L. 2008. A climate scenarios portal for the MENA region. Phase I Report on behalf of the World Bank, Washington, 53pp. Khan, M.S., Coulibaly, P. and Dibike, Y. 2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 319, 357-382.

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 23 Khan, M.S., Coulibaly, P. and Dibike, Y. 2006. Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors. Hydrological Process, 20, 3085- 3104. Lines, G.S. and Pancura, M. 2005. Building climate change scenarios of temperature and precipitation in Atlantic Canada using the Statistical DownScaling Model (SDSM). Meteorological Service of Canada, Atlantic Region. Science Report series 2005-9, Dartmouth, Canada, 41pp. Liu, X., Coulibaly, P. and Evora, N. 2008. Comparison of data-driven methods for downscaling ensemble weather forecasts. Hydrology and Earth System Sciences, 12, 615-624. London Climate Change Partnership (LCCP), 2002. A climate change impacts in London evaluation study. Final Technical Report, Entec UK Ltd. Lu, X. 2006. Guidance on the Development of Climate Scenarios within the Framework of National Communications from Parties not Included in Annex I (NAI) to the United Nations Framework Convention on Climate Change (UNFCCC). National Communications Support Programme (NCSP), UNDP-UNEP-GEF. MacDonald, O. 2004. Coupling glacier mass balance and meltwater yields in the European Alps with future climate change: downscaling from integrations of the HadCM model. Unpublished PhD Thesis, University of Salford, UK. Reynard, N., Crooks, S., Wilby, R.L. and Kay, A. 2004. Climate change and flood frequency in the UK. Proceedings of the 39th Defra Flood and Coastal Management Conference, University of York, UK. Scibek, J. and Allen, D.M. 2006. Modeled impacts of predicted climate change on recharge and groundwater levels. Water Resources Research, 42, W11405. Wetterhall, F., Bárdossy, A., Chen, D., Halldin, S., and Xu, C. 2007. Daily precipitation-downscaling techniques in three Chinese regions. Water Resources Research, 42, W11423, doi:10.1029/2005WR004573. Wetterhall, F., Halldin, S. and Xu, C.Y. 2007. Seasonality properties of four statistical-downscaling methods in central Sweden. Theoretical and Applied Climatology, 87, 123-137. Whitehead, P.G., Futter, M. and Wilby, R.L. 2006. Impacts of climate change on hydrology, nitrogen and carbon in upland and lowland streams: Assessment of adaptation strategies to meet Water Framework Directive objectives. Proceedings of the British Hydrological Society Conference, Durham, UK. Whitehead, P.G., Wilby, R.L., Butterfield, D., and Wade, A.J. 2006. Impacts of climate change on nitrogen in a lowland chalk stream: An appraisal of adaptation strategies. Science of the Total Environment, 365, 260-273. Wilby, R.L. 2003. Past and projected trends in London’s urban heat island. Weather, 58, 251-260. Wilby, R.L. 2005. Constructing wet season precipitation scenarios for a site in the Anti Atlas Mountains, Morocco. Proceedings of the Conference on Optimising Land and Water Resources in Arid Environments, Agadir, Morocco. Wilby, R.L. 2008. Constructing climate change scenarios of urban heat island intensity and air quality. Environment and Planning B: Planning and Design, 35, 902-919. Wilby, R.L. 2008. Dealing with uncertainties of future climate: The special challenge of semi-arid regions. Water Tribune: Climate Change and Water Extremes, Expo Zaragoza, Spain. Wilby, R.L. 2008. Climate change scenarios for the Republic of Yemen. Report on behalf of the World Bank, 27pp. Wilby, R.L. and Direction de la Météorologie National. 2007. Climate change scenarios for Morocco. Technical Report prepared on behalf of the World Bank, Washington, 23pp. Wilby, R.L., Dawson, C.W. and Barrow, E.M. 2002. SDSM - a decision support tool for the assessment of regional climate change impacts. Environmental and Modelling Software, 17, 145-157. Wilby, R.L., Tomlinson, O.J. and Dawson, C.W. 2003. Multi-site simulation of precipitation by conditional resampling. Climate Research, 23, 183-194. Wilby, R.L., Whitehead, P.G., Wade, A.J., Butterfield, D., Davis, R. and Watts, G. 2006. Integrated modelling of climate change impacts on the water resources and quality in a lowland catchment: River Kennet, UK. Journal of Hydrology, 330, 204-220. Wilby, R.L.and Harris, I. 2006. A framework for assessing uncertainties in climate change impacts: low flow scenarios for the River Thames, UK. Water Resources Research, 42, W02419, doi:10.1029/2005WR004065.

24 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent Appendix 2

Enhancements to SDSM in TE2100 SDSM 4.2 includes a number of enhancements to Version 3.1 that were sponsored by the Environment Agency of England and Wales, under the TE2100 project . Frequency analysis for extremes • Allows the User to fit distributions to observed and downscaled data (as either a whole data set or by isolating particular seasons or months): GEVs, stretched exponential, Empirical and Gumbel distributions. Results can be viewed in either tabular form or as line charts. • User can also plot PDFs of observed and modelled data and Quantile-Quantile plots (settings allow all charts to be changed). • The user can save these analysed results to a text file and a threshold can be applied. • A line plot can be made – allowing the user to compare observed data with ensembles (either as means, all ensembles or individual ensembles).

Step-wise regression • Examines all possible combinations of predictors. Analyses models using either AIC or BIC criteria which user can select in advanced settings.

Optimisation Algorithm • In addition to the dual simplex algorithm of SDSM 3.1, an ordinary least squares algorithm has been implemented. This is much quicker and efficient. It can be selected in Advanced Settings.

Screen Variables • The User can now apply an autoregression component alongside other predictors.

Calibrate Model • An autoregressive term can now be included in the model. • Residual analysis has been added so that following calibration SDSM allows the user to plot residuals of the model either as a scatter diagram or a histogram (both of which can be amended through additional settings). • The Chow test has been added so the user can also now assess the calibrated model for stationarity.

Weather Generator • Additional information is captured within the *.PAR file (i.e., SDSM version, auto regression and process)

Scenario Generator • Additional information is provided on the model before generation begins.

Summary Statistics (replaces Analyse Data screen) • A raft of new statistics have been added; Extreme Range, Minimum Range, Maximum N-day Total, Mean Wet-Day Persistence, Mean Dry-Day Persistence, Correlation for Spell Lengths, Median Wet-Spell Length, Median Dry-Spell Length

Time Series Analysis • Includes a raft of additional STARDEX indices for analysis: Mean dry spell, Mean wet spell, Median dry spell, Median wet spell, SD dry spell, SD wet spell, Spell length correlation, Dry day persistence, Wet day persistence, Maximum dry spell, Maximum wet spell, Nth largest value, Largest n day total, Percentage of precipitation above annual percentile, Percentage of all precipitation from events greater than long-term percentile, Number of events greater than long term percentile (the User can enter their own thresholds and percentile values).

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 25

Miscellaneous improvements

• Default file directory established in settings to ensure that every screen searches in the same directory for files each time. • Improved interface so that it is now easier to move between stages of the process, with bigger screens, and improved colour schemes. • Soft reset when error occurs so that User settings are not reset if a problem occurs. • Splash screen changed (can now be removed). • Advanced Settings enables fixed or stochastic threshold for conditional processes. • Error trapping and efficiency improved throughout. • Help files and User manual updated accordingly.

26 Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent

Would you like to find out more about us, or about your environment?

Then call us on 08708 506 506 (Mon-Fri 8-6) email [email protected] or visit our website www.environment-agency.gov.uk incident hotline 0800 80 70 60 (24hrs) floodline 0845 988 1188

Environment first: This publication is printed on paper made from 100 per cent previously used waste. By-products from making the pulp and paper are used for composting and fertiliser, for making cement and for generating energy.

Environment Agency Downscaling Future Skew Surge Statistics at Sheerness, Kent 27

Thames Estuary 2100 Summary of Climate Change Projections

Phase 3(ii) Topic 3.3 Definition of Climate Change Allowances for use in TE2100 Studies

Wind effects modelled

No winds in model

Consultants: Met Office Hadley Centre

EA Study Lead: HR Wallingford Ltd.

Status: Final Draft Date: Dec. 2008 Annex 5 of 7 Appendix L to TE2100 Plan Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

Document Information

Project Thames Estuary 2100 Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in Report title TE2100 Phase 3ii Client Environment Agency Client Representative Tim Reeder Project No. DTM6070 Report No. EX 5859 Project Manager Graham Siggers Project Director Peter Hawkes

Document History

Date Release Prepared Approved Authorised Notes 1/09/08 1.0 gbs pjh Draft 08/12/08 2.0 gbs pjh mpd Final

Produced by:

HR Wallingford on behalf of the Environment Agency HR Wallingford Report Number EX 5859

Environment Agency TE2100 Project Thames Barrier Eastmoor Street Charlton London SE7 8LX Tel: 0208 3054160 Email: [email protected] www.environment-agency.gov.uk

EX 5859 ii R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

Summary

Thames Estuary 2100 Phase 3(ii)

Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

Report EX 5859 December 2008

This report reviews the Phase 3i boundary conditions currently being used for option testing and appraisal within TE2100, in the light of the recently completed EP17 climate change studies. It is recommended that no change is made to the definition of these boundary conditions, but that sensitivity testing be undertaken, in particular to the assumptions on the shape of the extreme tide modelled (because modelled upstream peak levels are sensitive to the assumed profile of storm surge), to the possibility of lower sea level rise scenarios (than 1m in 100 years as is the precautionary allowance recommended by Defra), and to the possibility of greater increases in peak fluvial flows of as much as 30-50%. It is further concluded that, on the basis of analysing two datasets, no change to dependence between Kingston fluvial flows and Southend sea levels was detectable from the EP17 climate modelling.

EX 5859 iii R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

EX 5859 iv R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

Contents

Title page i Document Information ii Summary iii Contents v

1. Introduction...... 1 1.1 Background...... 1 1.2 Objectives ...... 1 1.3 New Sources of Information...... 1

2. Previous Extreme values and climate change allowances used IN TE2100 Phase 3i...... 2

3. Summary of recent results from EP17 studies ...... 4

4. Review of Climate Change Allowances for TE2100 Phase 3(ii)...... 5 4.1 Review of climate change allowances Relating to Tidal Flooding used in TE2100 decision-making...... 5 4.2 Review of climate change allowances Relating to Fluvial Flows used in TE2100 decision-making...... 6 4.2.1 Recommendation...... 7

5. Future Changes to dependence between Peak River Flows at Kingston/Teddington and Extreme Sea levels at Southend...... 9 5.1 Methodology for dependence analysis ...... 9 5.2 Results of dependence analysis...... 10 5.3 Discussion...... 10

6. Review of time-series storm-tide sea level for model boundary conditions ...... 11 6.1 Introduction...... 11 6.2 The largest extreme water level from the Hadley Centre model ensembles...... 11 6.3 Propagating the largest skew surge event up into the Thames Estuary ...... 11 6.4 Thames Estuary 2D model results ...... 12 6.5 Discussion of results...... 12 6.6 Recommendations on storm tide hydrograph shapes for hydraulic modelling for Thames Estuary 2100 Phase 3ii ...... 12

7. Uncertainty in river flow estimates ...... 13

8. Conclusions...... 13

9. References ...... 15

Tables Table 1 TE2100 Phase 3i Extreme sea levels and predicted changes to those levels under different climate change scenarios...... 3 Table 2 TE2100 Phase 3i Extreme flows at Kingston and predicted changes to these flows under different climate change scenarios...... 3 Table 3 TE2100 Phase 3i Changes to rainfall, wind and wave characteristics under two different climate change scenarios...... 4 Table 4 Predicted percentage increases to extreme fluvial flows ...... 6 Table 5 Modelled peak water levels in the Thames Estuary...... 12

EX 5859 v R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

Contents continued

Figures Figure 1 A sampled year illustrating how the neap-neap maximum water levels were chosen for the dependence analysis Figure 2 AFGCX: Water level vs river flow for the neap-neap water levels extracted for the dependence analysis (1961 – 1990) Figure 3 AFGCX: Water level vs river flow for the neap-neap water levels extracted for the dependence analysis (2070-2099) Figure 4 AFIXA: Water level vs river flow for the neap-neap water levels extracted for the dependence analysis (1961 – 1990) Figure 5 AFIXA: Water level vs river flow for the neap-neap water levels extracted for the dependence analysis (2070 – 2099) Figure 6 Dependence factor: Water level vs river flow Figure 7 Domain of TELEMAC2D extended model of the Thames Estuary (showing linkage to CS3 model) Figure 8 Distribution through time of wind conditions as used as input into the TELEMAC2D Thames Estuary model Figure 9 Time-series modelled tide/surge event in the Thames Estuary

EX 5859 vi R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

1. Introduction

1.1 BACKGROUND The Terms of Reference for this study are based upon a proposal originally prepared by HR Wallingford in response to the Topic 3.3 Consultants Brief (Version 1) developed by TE2100 (23 April 2008), and further expanded upon in terms of the expected division of labour between HR Wallingford and the Hadley Centre in a telephone conference on Monday 12th May. Subsequently, TE2100 requested a revised proposal (Version 2, submitted 2 June 2008) for Phase 1 activities only, plus the Phase 2 study of future changes to dependence between modelled river flows at Kingston and sea levels at Southend/Sheerness.

1.2 OBJECTIVES This report provides an update to interim report (TE2100, 2007), and assesses the requirement for updating those recommendations on boundary conditions for use in hydraulic modelling of flood risk management options on the Thames Estuary.

There are three components of work in this report:

• Provide a review of previous climate change allowances used in TE2100 decision- making (Section 4).

• Undertake a limited analysis of the joint dependence and any change in dependence between Teddington flows and Southend extreme sea level (this considers two joint datasets from the recent ensemble modelling undertaken by Hadley/CEH). (Section 5).

• Provide recommendations on design marine boundary conditions (surge and tide hydrograph) along with an appropriate methodology of scaling storm hydrographs to different return period extremes and climatic futures for use in TE2100 options development and appraisal studies (Section 6).

1.3 NEW SOURCES OF INFORMATION Since (TE2100, 2007) was published, the main new sources of information available represent outputs from the Thames Estuary 2100 EP17 studies (TE2100, 2008a-c). Additionally, a draft report on the treatment of uncertainty in (amongst other things) extreme sea levels at Southend/Sheerness and extreme fluvial flows at Kingston/Teddington was supplied by Professor Jim Hall of Newcastle University.

The new information is listed below:

• The summary report “Climate change projections for TE2100”. Draft Summary prepared by Dr Jason Lowe (Met Office), June 2008.

• The draft report “Met Office Hadley Centre Projections of 21st Century Extreme Sea Levels for TE2100” prepared by Tom Howard, Jason Lowe, Anne Pardaens, Jeff Ridley and Kevin Horsburgh (Proudman Oceanographic Laboratory), June 2008.

EX 5859 1 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

• The draft report “Area-wide river flow modelling for the Thames Estuary 2100 project: Climate change impacts on flood frequency” prepared by A.L. Kay (Centre for Ecology and Hydrology), V.A. Bell (Centre for Ecology and Hydrology) and J.A. Lowe (Met Office), June 2008.

• High frequency data time series of simulated extreme water levels in the southern North Sea near the mouth of the Thames and river flow data within the Thames basin. June 2008.

• Topic 2.2 – Decision Support Framework Refinement. Task 2 – Handling Uncertainty. Technical Working Paper 1: Collecting Evidence on Sources of Uncertainty. Draft report prepared by Prof. Jim Hall (University of Newcastle), August 2008.

2. Previous Extreme values and climate change allowances used IN TE2100 Phase 3i

The following five interim recommendations were made in (TE2100, 2007). Extracts of the report text are given in italics below:

1. Climate change scenarios and marginal extremes (extreme sea level and fluvial flows) developed in Phase 2 (TE2100, 2006) should continue to be used until such time that the EP17 studies deliver the definitive TE2100 present and future boundary conditions next year. These values are presented in Tables 1 to 3 below. 2. The studies reviewed here indicate it is not unreasonable to continue to develop and refine options based upon the assumption of a Defra (2006a) precautionary allowance for sea level rise, with an ongoing commitment to monitoring local and global (through IPCC) changes to mean sea level. 3. The EP17 studies will define the magnitude of extreme sea levels present (deterministic) and future (probabilistic, including central estimate and lower probability climate change scenarios). The studies will also provide the design storm tide-surge hydrographs to support the development of TE2100’s final plan. 4. TE2100 should manage the delivery date of EP17 in accordance with the phased requirements for the development of its plan. 5. A view should be taken by TE2100 on the possibility of increasing tide range associated with future mean sea level rise. This may have particular implications for morphology modelling. If inconclusive, then some sensitivity testing may be required in Phase 3 (to test the sensitivity of model results to the assumption of no changes to tide ranges when there are, or vice-versa).

EX 5859 2 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

Table 1 TE2100 Phase 3i Extreme sea levels and predicted changes to those levels under different climate change scenarios

Extreme sea level at Southend (mODN) Return Period 2005 level Medium High High+ High++ Defra (2006) 2050 21002050 21002050 2100 2050 2100 mean level 0.10 0.28 0.55 0.74 1.70 1.38 3.30 0.41 1.04 2 3.75 3.97 4.31 4.43 5.45 5.07 7.05 4.06 4.69 5 3.94 4.22 4.64 4.67 5.77 5.31 7.37 4.25 4.88 10 4.09 4.41 4.89 4.86 6.02 5.50 7.62 4.40 5.03 20 4.22 4.58 5.13 5.03 6.25 5.67 7.85 4.53 5.16 50 4.39 4.81 5.44 5.26 6.56 5.90 8.16 4.70 5.33 100 4.57 5.03 5.73 5.48 6.84 6.12 8.44 4.88 5.51 200 4.70 5.21 5.96 5.65 7.07 6.29 8.67 5.01 5.64 500 4.87 5.43 6.27 5.87 7.37 6.51 8.97 5.18 5.81 1000 5.03 5.63 6.54 6.07 7.63 6.71 9.23 5.34 5.97 2000 5.17 5.82 6.79 6.25 7.87 6.89 9.47 5.48 6.11 5000 5.37 6.07 7.13 6.50 8.20 7.14 9.80 5.68 6.31 10000 5.51 6.26 7.37 6.68 8.44 7.32 10.04 5.82 6.45

Table 2 TE2100 Phase 3i Extreme flows at Kingston and predicted changes to these flows under different climate change scenarios Increases to extreme 8% 19% 16% 40% 40% 100% 20% 20% river flows:

Kingston river flows (m3/s)

Return Period 2005 flow Medium High High+ High++ Defra (2003) 2050 2100 2050 2100 2050 2100 2050 2100

2 305 329 363 354 427 427 610 366 366 5 400 432 476 464 560 560 800 480 480 10 466 503 555 541 652 652 932 559 559 20 534 577 635 619 748 748 1,068 641 641 50 693 748 825 804 970 970 1,386 832 832 100 777 839 925 901 1,088 1,088 1,554 932 932 200 869 939 1,034 1,008 1,217 1,217 1,738 1,043 1,043 500 1,007 1,088 1,198 1,168 1,410 1,410 2,014 1,208 1,208 1000 1,126 1,216 1,340 1,306 1,576 1,576 2,252 1,351 1,351 2000 1,257 1,358 1,496 1,458 1,760 1,760 2,514 1,508 1,508 5000 1,453 1,569 1,729 1,685 2,034 2,034 2,906 1,744 1,744 10000 1,622 1,752 1,930 1,882 2,271 2,271 3,244 1,946 1,946

EX 5859 3 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

Table 3 TE2100 Phase 3i Changes to rainfall, wind and wave characteristics under two different climate change scenarios

Variable Medium High Defra 06 2050 2100 2056 2100

Extreme rainfall 8% 19% 20% 20% Extreme wave height 1% 3% 10%* 10% Wave period 1% 2% No figures provided Wind speed 1% 3% 10%* 10% *If using 2050, then the wind speed and wave height allowances are only 5%

3. Summary of recent results from EP17 studies

According to the EP17 Summary Report (TE2100 (2008a), extracts of report text in italics below), the headline results of this project are:

• The new simulation of present day surge levels has more skill than an earlier system used by the Hadley Centre. The results show that using short time slices for storm surge climate experiments (the previous standard technique) can produce misleading conclusions. This provided justification for simulating longer periods in this current work.

• 21st century changes in the storminess-driven component of extreme water levels are found to be small for the SRES A1B scenario. Regional relative time-mean sea level rise between present day and the end of the 21st century is estimated to range from 19.4cm to 87.8cm when thermal expansion and ice melt are included, regional deviations from the global mean are accounted for and vertical land movement is added. This time-mean sea level rise range also includes emissions scenario uncertainty.

• Potential future changes in fluvial flood risk were studied using the climate modelling system to drive a distributed hydrological model, the Grid-to-Grid (G2G) model.

EX 5859 4 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

The overall conclusion was that … current climate science can provide some useful information on the range of future sea level rise and peak river flow. However, even using the current state-of-the-art techniques the uncertainty bounds are found to be large.

4. Review of Climate Change Allowances for TE2100 Phase 3(ii)

4.1 REVIEW OF CLIMATE CHANGE ALLOWANCES RELATING TO TIDAL FLOODING USED IN TE2100 DECISION-MAKING Considering each of the Phase 3i conclusions (listed in Section 2) in turn:

1. Firstly it appears that updates to the emissions scenarios may become available from the EP17 work. These, however, will only become available with the publication of UKCIP ’08 in November 2008. They are not available at present. 2. Having reviewed the outputs of the EP17 studies, it appears not unreasonable to continue to develop and refine options based upon the assumption of a Defra (2006) precautionary allowance for sea level rise, with an ongoing commitment to monitoring local and global (through IPCC) changes to mean sea level. This recommends a precautionary allowance for accelerated sea level rise of up to 1m by 2100. 3. From the EP17 studies, the largest single “skew surge” (the difference between modelled peak water level and the nearest predicted astronomical high water) simulated at Sheerness from an ensemble of 11 (out of a total of 17) model simulations, each of 150 years duration, was 1.78m. This event was predicted to occur during neap tides, resulting in a non-extreme peak water level. Hadley therefore took the QUMP atmospheric forcing that gave this largest skew surge and moved the forcing a few days until it arrived about 48 hours after full moon on a low spring tide. When this was done and the model rerun, an event peaking at 4.2m OD was generated at Sheerness. Finally (and after completion of all modelling), when the CS3 predicted tide was substituted with the Sheerness harmonic tide (as is done for operational use) the event peaked at 4.6-4.7m, very similar to the 1953 storm event (the largest on record). a. Since no events were simulated that are larger than have as yet been recorded (the 1953 event), this report recommends that the present-day extreme values for sea level as derived by JBA (2003) and as listed in TE2100 (2006a) continue to be used. b. Because there were no modelled events greater than the recorded storm surge of 1953, the EP17 studies were not able to provide design storm tide-surge hydrographs to support the development of TE2100’s final plan. c. The EP17 studies found no significant trend in increased storminess (in terms of storm surges), so the simple addition of future projected increases in mean sea level to present-day estimated extreme sea levels can continue for (hydraulic) modelling the incidence of storms in the future. 4. No increases to tide range were predicted through the EP17 modelling. This remains an area where there are conflicting views in published material. As the authors’ view is that such a signal has not yet been detected in the data, it is recommended that the hydraulic modelling assumes there is no predicted future

EX 5859 5 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

increase in tide range at Southend. It may be advisable, however, to undertake some limited sensitivity testing where this assumption is considered important.

For interest, it is noted that hydraulic modelling of a present day spring tide plus a component of sea level rise at Southend leads to a prediction of a slight increase in tide range in the Estuary upstream of Westminster.

4.2 REVIEW OF CLIMATE CHANGE ALLOWANCES RELATING TO FLUVIAL FLOWS USED IN TE2100 DECISION-MAKING In (TE2100, 2006a), an allowance for a 20% increase to peak fluvial flows at Kingston/Teddington was made for extreme river events (and rainfall) occurring in 2050 or 2100.

The recent studies completed by CEH provide the following predicted increases to different return period fluvial flow events for the Thames at Kingston. Within the document (TE2100, 2008b) there is some overlap in the use of the terms “flood frequency” and “peak flows”. It has subsequently been clarified by CEH that the percentage changes provided in Table 4 below relate to peak flows. This Table illustrates the numbers leading to the study’s conclusion of a 30-50% increase in peak fluvial flows.

Table 4 Predicted percentage increases to extreme fluvial flows Return Period Climate Scenario 2yr 5yr 10yr 20yr 50yr 100yr Afgcx 25 37 46 56 70 82 Afixa 8 3 -3 -11 -22 -30 Afixc 32 34 36 38 40 42 Afixh 23 22 22 23 25 26 Afixi 33 34 31 27 20 14 Afixj 15 15 16 17 19 21 Afixk 24 34 41 49 61 71 Afixl 40 53 61 68 77 83 Afixm 14 27 39 52 73 91 Afixo 12 15 24 38 65 94 Afixq 45 43 43 44 45 46 Average 25 29 32 36 43 49

A discussion of this finding is provided by CEH (TE2100, 2008b) and is repeated (in italics) below:

Across the Thames Basin, changes in flood frequency between Current and Future periods have been analysed, and the results for each ensemble member are presented at a range of return periods. There is considerable variation in the results, by ensemble member, by return period and by location, with areas underlain by chalk generally showing lower percentage changes than other regions. The range of results is quite large. However almost all changes are increases, generally averaging around 5-10% in chalk areas and around 30-50% elsewhere, for peak flows with up to a 20-year return period. It is important to note that the possible future climates and river flow estimates encompassed by the RCM ensemble has not been weighted according to quality or likelihood. However, 6 of the 17 original ensemble members have been excluded as they

EX 5859 6 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

led to unrealistically low estimates of current rainfall over the Thames region. In view of the large uncertainty range, it is recommended that the mean and range of percentage change in river flows across the 11 RCM ensemble members are used for decision making.

Based upon these new findings, a decision is required as to whether TE2100 should update the current allowances for a 20% increase in fluvial flows at this time, to an increased percentage increase of 30-50%.

There is considerable variability in Table 4, as stated in TE2100 (2008a). In almost all scenarios the magnitude of the percentage increase or decrease in peak fluvial flows increases with return period (in many cases approximately in proportion to the log of the return period). Without looking at the corresponding rainfall, it is not easily understood what might cause this.

Nonetheless, a 31% increase in peak fluvial flows represents the average of all changes up to and including the 20 year return period.

4.2.1 Recommendation Notwithstanding the findings from the recent EP17 studies, it is recommended that TE2100 continues to test options and undertake project appraisal on the basis of an assumed 20% increase in peak fluvial flows in 2050 and 2100 (previous advice).

This recommendation is made for the following reasons:

1. The results presented in (TE2100, 2008b) represent an initial draft output from EP17 just released.

EX 5859 7 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

2. To our knowledge, the results represent the first suggestion from any work that the predicted increases to peak flows are of the order 30-50% by 2100. 3. It is CEH’s recommendation that some caution be used in the treatment of the numbers and the comparison with previous guidance. At this time, sufficient confidence to influence changes to Policy is not present. 4. No out-of-bank flows are represented in the modelling. Flood storage in the upper and middle Thames basin will have a major impact on flood flows. 5. The reason for the predicted increase in peak flows of 30-50% is not fully understood, given the maximum increase in annual precipitation appears to be about 5 percent, and given the 20 percent increase in winter precipitation expected in the forthcoming UKCIP08 Scientific Report (50th percentile A1B). 6. A review of these modelling conclusions undertaken by CEH (TE2100, 2008b).

Point 6 is expanded upon below (quoted TE2100, 2008b extracts of discussion in report is shown in italics):

This is a very large estimated increase in future peak flows, and must be considered in a wider historical context than a modelling study over two thirty-year time-slices might provide.

Over the past thirty to forty years there has been some evidence of a positive trend in high river flow indicators (Hannaford and Marsh 2008), generally thought to be linked to changes in winter precipitation arising from changes in atmospheric circulation patterns. However no such recent trend is evident for the lower Thames, and trend analyses of flow records throughout the 20th century (Black 1996; Robson 2002; Hannaford and Marsh 2008) have so far detected no apparent long-term trend in UK flood magnitude. Historically, snowmelt and frozen ground have been major contributing factors in a significant proportion of major floods on the Thames (Griffiths 1983), but compelling evidence for continuing temperature increases strongly suggest that snow-melt driven flood events will continue to decline in both frequency and magnitude.

…The modelling of current and future river flows undertaken here has relied on observations and climate model output from 1960 onwards (a period over which increases in high river flows has been detected in some areas). The hydrological modelling also excludes the influence of snowmelt on high river flows, although in the years since 1960, significant snowfall has occurred on relatively few occasions (as in the winter of 1962/3 and 1981/2). It is important to note that the 1961-1990 period is considered to be notably ‘flood poor’ (Hannaford and Marsh 2008); historically flood frequencies have varied substantially, phenomenon normally assumed to be a natural consequence of the UK’s inherent climatic variability. The relatively low frequency of major flood events on the Thames during the 1961-1990 period implies that the changes in return periods predicted for 2069-2099 should be treated with some caution, although this would be more likely to affect estimates of percentage changes from a baseline of observed flows (rather than a modelled baseline using RCM data, as used

EX 5859 8 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

here). The graphs… show flood frequency for a 30-year moving window, constructed from a 150-year time-series of annual maximum flows, for three RCM ensemble members (afgcx, afixa and afixq). They show how a high flow peak in a particular thirty-year time-slice could skew its derived flood frequency such that comparisons between two time-slices can show an increase or decrease depending on which precise period is used as a baseline.

This discussion essentially outlines that such a signal has not yet been detected in the Thames. However, it does not go so far as to say the conclusions are not to be upheld. Based upon a precautionary approach to change and the very early nature of the recent results from EP17, it is at present considered to continue with the previous established figures (20% increase in peak flows by 2050 and 2100).

However, it is also strongly advised that the performance and appraisal of options be understood in terms of their sensitivity to a possible greater increase in peak fluvial flows of 30-50%.

5. Future Changes to dependence between Peak River Flows at Kingston/Teddington and Extreme Sea levels at Southend

5.1 METHODOLOGY FOR DEPENDENCE ANALYSIS This Section assesses changes to the dependence between high sea levels and high river flows from the EP17 modelling results. The dependence might change, for example, due to a change in storm tracking or storm propagation speed. Preliminary discussion between HR Wallingford, TE2100 and the Hadley Centre and analysis suggested that there would most likely be no change in dependence. Therefore, the analysis approach adopted was designed to be based on statistical analysis of data sets likely to show the greatest, if any, change (rather than undertaking analysis of all the model datasets). If this analysis showed no change, then this would be considered sufficient to draw a conclusion of “No change”. If a significant change was observed, then further analysis would be done in an attempt to identify the reasons for that change (“No change” was not an absolutely foregone conclusion, as Defra FD2308 (Defra, 2005) had previously demonstrated and explained a significant change in dependence between flood risk variables elsewhere in the UK).

The Met Office provided hourly sea levels at Southend, over the period 1960-2099 for seventeen future climate change scenarios. CEH provided corresponding hourly river flows at Kingston for three of these scenarios. HR Wallingford chose to use the unperturbed (central) model run (AFGCX) and, based on CEH comments about which other scenarios might show significantly different behaviour, model run AFIXA rather than model run AFIXQ. For each scenario, two thirty-year spells of data were used: 1961-1990 (“present-day”) and 2070-2099 (future). Conveniently, as it might affect the dependence analysis, it is noted that there is no mean sea level change signal in the data series supplied by the Met Office.

The same data preparation and analysis methods were used as had previously been applied to measured river flow and sea level data in TE2100 EP7.

EX 5859 9 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

The sea level data were divided into neap-to-spring-to-neap cycles, each of approximately 14 days. Within each cycle, the highest sea level was noted (see example selections for 1961 in Figure 1) together with the corresponding river flow, averaged over the same calendar day as the noted sea level. (This would, of course, tend to miss the highest river flows of all, but this did not matter for the way the records would be used.) This provided approximately 750 records for each of four data series, representing present-day and future conditions for each of AFGCX and AFIXA.

5.2 RESULTS OF DEPENDENCE ANALYSIS The four sets of records are plotted in Figures 2-5. A strong positive dependence between high sea levels and high river flows would show itself in Figures 2-5 in a tendency for the points to lie on a line between the bottom left and top right of the diagrams. Visual inspection of Figures 2-5 does not reveal this behaviour. Indeed, if anything the points tend towards the top left to bottom right of diagrams, suggesting a slight negative dependence.

The results of statistical analyses of these data sets are shown in Figure 6, together with corresponding results from analysis of measured data, reproduced from TE2100 EP7. The vertical axis of Figure 6 is labelled dependence factor, actually a correlation coefficient, for which values in the range +0.1 to +1.0 would indicate positive dependence and values in the range -0.1 to -1.0 would indicate negative dependence (and -0.1 to +0.1 approximate independence). The horizontal axis is labelled probability of non-exceedence (range zero to one) for which higher values indicate greater emphasis on high values of sea level and river flow.

Figure 6 shows very little difference in dependence between the four data sets derived from climate model runs, and a sufficiently large negative correlation to indicate a genuine effect rather than just random occurrence of storms in unrelated data series. This was considered sufficient to conclude “No change” in dependence due to future climate change.

Figure 6 also reproduces the small positive dependence previously derived from measured data, and this remains the actual level of dependence recommended for use in TE2100 (TE2100, 2006d).

5.3 DISCUSSION It would be possible to investigate further the discrepancy between the negative dependence between high sea levels and high river flows found from the climate model data, and the positive dependence derived from measured data. A likely reason is the time lag between surges arriving at Southend and rainfall building to high river flow at Kingston, a time lag which might be slightly different between measurements and climate model. This further investigation would not affect the main results of this project, but might help to allay any suggestion that the climate model runs do not reproduce present-day dependence well enough for use in this analysis.

EX 5859 10 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

6. Review of time-series storm-tide sea level for model boundary conditions

6.1 INTRODUCTION This Section describes the use of the validated extended TELEMAC2D model of the Thames Estuary to propagate the largest modelled event arising from recent model investigations by the Hadley Centre. The Hadley Centre modelling work is described in (TE2100, 2008c) but in summary comprises an ensemble of global coupled climate models (HadCM3) driving an ensemble of regional atmospheric climate models (HadRM3), which, in turn, drive an ensemble of the Proudman Oceanographic Laboratory (POL) surge model (CS3).

6.2 THE LARGEST EXTREME WATER LEVEL FROM THE HADLEY CENTRE MODEL ENSEMBLES The largest single “skew surge” (the difference between modelled peak water level and the nearest predicted astronomical high water) simulated at Sheerness from 11 (out of a total of 17) model simulations, each of 150 years duration, was 1.78m. This event was predicted to occur during neap tides, resulting in a non-extreme peak water level.

Hadley therefore took the QUMP atmospheric forcing that gave this largest skew surge and moved the forcing a few days until it arrived about 48 hours after full moon on a low spring tide. When this was done and the model rerun, an event peaking at 4.2m OD was generated at Sheerness.

Finally (and after completion of all modelling), when the CS3 predicted tide was substituted with the Sheerness harmonic tide (as is done for operational use) the event peaked at 4.6-4.7m, very similar to the 1953 storm event (the largest on record).

6.3 PROPAGATING THE LARGEST SKEW SURGE EVENT UP INTO THE THAMES ESTUARY The TELEMAC2D model of the Thames Estuary (Figure 7) was extended in 2006 to link to the CS3 model, and was subject to extensive validation tests as reported in (TE2100, 2006a). Hadley Centre provided required inputs to the extended TELEMAC2D model, in terms of time-varying water levels at 14 CS3 nodes, and a single time-varying wind speed, for the largest modelled event described above (peaking at 4.2m at Southend in the CS3 model, before any substitution of the tidal component).

Two model runs were set off, with and without the time-varying wind conditions which were assumed to blow over the whole 2D model domain. The wind conditions are shown in Figure 8, and are seen to peak during the storm event at approximately 20 m/s (severe gale) from a northerly direction.

The objective was to use a higher resolution Estuary model to investigate the propagation of the largest event up into the Thames Estuary to confirm peak levels predicted at Southend and further up into the Estuary.

EX 5859 11 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

6.4 THAMES ESTUARY 2D MODEL RESULTS Figure 9 shows time-series water levels (m OD) predicted by the model at Southend, Tower Pier, and Richmond. The dotted lines represent the model run including wind conditions, and the solid lines represent the model run with no winds. Table 5 below summarises the model results in terms of predicted peak water levels at Southend and more locations up the Estuary.

6.5 DISCUSSION OF RESULTS It is seen that switching off the wind conditions results in the same predicted peak water level at Sheerness / Southend (4.2m OD) as achieved in the CS3 model. However, including the wind conditions in the simulation (albeit with the assumption that the wind speed at any instant is the same over the whole TELEMAC-2D model domain) adds 0.3m to the model predicted peak level (4.5m OD).

The model runs predict peak levels for this event of 4.2m OD (no winds) and 4.5m (with time-varying, spatially constant winds) at Southend / Sheerness. If the tidal component of these model results is then substituted with an improved harmonic tide (as is done for operational use), then it is understood that the model predicted peak level at Southend / Sheerness would be approximately 4.7m OD (no winds) and 5.0m OD (with time-varying, spatially constant winds).

Table 5 Modelled peak water levels in the Thames Estuary No wind Wind Difference Richmond 5.47 5.43 -0.05 Westminster 4.94 5.10 0.16 Tower 5.01 5.25 0.24 Erith 4.85 5.16 0.31 Tilbury 4.59 4.92 0.33 Coryton 4.40 4.70 0.29 Southend 4.22 4.52 0.30 Margate 3.50 3.80 0.30

6.6 RECOMMENDATIONS ON STORM TIDE HYDROGRAPH SHAPES FOR HYDRAULIC MODELLING FOR THAMES ESTUARY 2100 PHASE 3II Because the studies undertaken by POL/Hadley have not led to the modelling of any events greater than on record, no new information on design storm hydrographs is available. Therefore it is recommended that, as advised in (TE2100, 2006b), TE2100 continues to base storm tide hydrographs on the event of 1953.

Specifically, the boundary conditions at Southend are defined (for events with higher peak levels than occurred in 1953) as follows:

1. Take the 1953 event and disaggregate into predicted tide and surge residual. 2. Peak levels are defined according to JBA (2003) levels (TE2100, 2006a). 3. Linearly scale the surge residual to generate larger peak levels, as required. 4. Add the 1953 tide to the scaled surge residual to create a 1953-based hydrograph associated with the required peak level.

EX 5859 12 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

5. In the absence of outputs from the continental shelf (CS3) model corresponding to events greater than that of 1953, it is recommended the hydraulic model boundary conditions continue to be represented in the manner described above. There is significant uncertainty in upstream peak levels as a function of storm tide shape and it is recommended that the sensitivity of modelled results be tested against different storm tide hydrograph shapes1. 6. In addition to the sensitivity to storm-tide shape, the hydraulic modellers should not ignore the potentially significant effect of time-varying winds on extreme levels in the Thames Estuary (TE2100, 2006d).

As yet, the continental shelf models as used for this project have not generated a storm- tide event larger than that which occurred in 1953. While such an event would not be common, it is not however believed that any physical reason has yet been demonstrated that precludes such an event from ever occurring.

7. Uncertainty in river flow estimates

Professor Jim Hall of Newcastle University was commissioned to investigate, and attempt to quantify, uncertainty throughout the whole process of developing a flood risk management plan. The results are published in (TE2100, 2008d). A review of uncertainty in extreme flows recommends a reduction of present-day 100 year extreme flows at Kingston (by 15%), together with an increase in future 100 year peak flows (by 40% by 2100). Coincidentally, the report therefore recommends a similar 100 year peak flow in 2100 as TE2100(2006a).

It is accepted that there may be different methods for extrapolating to predict the magnitude of extreme events, more extreme than may have occurred. The uncertainty does not just lie in the extrapolation technique but also the few important (largest peak flows on record) data values themselves. The magnitude of the largest fluvial flow on record was referred to as 800 cumecs in (Inglis et al, 1955), then 1,085 cumecs in other later references, before being the subject of review in several different recent papers reconfirming as 800 cumecs.

The reader is referred to (TE2100, 2006b) for a transparent presentation of the determination of the extreme 100 year flow. Given the assumptions behind that method, and the fact that it is based closely upon the “observed data” themselves, it is not felt that there is sufficient evidence to recommend altering the assumed present-day 100 year flow that has been used to date on all hydraulic modelling.

8. Conclusions

1. It is recommended that the allowance for future sea level rise continues with the precautionary Defra (2006) allowance for accelerated sea level rise of up to 1m after 100 years. This recommendation is made on the basis that the recent

1 In (TE2100, 2006b), it was recommended that the sensitivity of modelled upstream peak levels be tested by assuming a storm tide hydrograph similar to the event of February 1938. It was also recommended that a few different storm-tide shapes be generated by looking at the full century of tidal records. However to date these have not been made available.

EX 5859 13 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

modelling undertaken by the Hadley Centre gives a range of potential sea level rise scenarios of up to 88cm by 2095. 2. 88cm represents the maximum predicted sea level rise from the scenarios modelled in the EP17 studies. The range of predicted increases in mean sea level is 19-88cm. It is therefore strongly recommended that sensitivity testing to lower rates of sea level rise (e.g. sensitivity to future sea level rise of between 22cm and 88cm over the next 100 years) be incorporated in the assessment of options. 3. The EP17 studies have found no detectable trend of increasing surge magnitudes from the modelling exercises they undertook. 4. The EP17 studies managed to reproduce an event similar in scale to that of 1953, after a reworking of an ensemble model run adjusting the timing of meterological event relative to tides, and after substituting the modelled tide with an improved one. No larger events than that of 1953 were generated. It is therefore recommended to continue to base extreme event storm tide hydrographs on the event of February 1953. Section 6 above outlines the method still used to provide boundary conditions for events higher than have occurred on record. 5. As per the advice given in (TE2100, 2006b), and even more importantly now that no (larger than recorded) storm tide profiles have been generated through the EP17 modelling, it is recommended that the sensitivity of the results to different assumptions on the shape of the storm tide profiles is tested using the models. The hydraulic modelling of peak levels upstream is extremely sensitive to the assumptions made in the derivation of a hydrograph shape. If the rising limb of the storm tide hydrograph becomes steeper than physically feasible (as a result of the simple assumptions being used to construct it) then the peak levels upstream can be significantly affected. 6. The EP17 studies have identified a potential increase in peak flows of 40% in 100 years. This differs from previous advice on future changes to fluvial inputs. For the reasons discussed in Section 4.2, it is recommended at the present time to continue with the assumption of a 20% increase in peak flows by 2050/2100, but to also test the sensitivity of options to 40% increases. 7. Based upon the two datasets analysed, it was concluded that no change in the dependence of fluvial flows at Kingston and peak sea levels at Southend was predicted due to future climate change.

EX 5859 14 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

9. References

Defra, 2005. Joint Probability: Dependence Mapping and Best Practice: Technical Report on dependence mapping R&D Technical Report FD2308/TR1, March 2005 (specifically Sections 4.5 and Appendix 5).

TE2100, 2006. Water Levels and Flows in the Thames Estuary. HR Wallingford Report EX5260.

TE2100, 2006a. Estuary Processes Consolidation Report, HR Wallingford EX5337.

TE2100, 2006b. Tidal/fluvial interaction on the tidal Thames. HR Wallingford Report EX5288.

TE2100, 2006c. An investigation into the interaction of tide and surge at Sheerness with interim recommendations on seaward boundary conditions for modelling flood risk management options). HR Wallingford Report EX5414. November 2006.

TE2100, 2006d. EP7: Joint Probability and Interaction. HR Wallingford Report EX5267.

TE2100, 2007 Phase 3 Studies. Interim boundary conditions for modelling present-day and future extreme events in the Thames Estuary. HR Wallingford Report EX5573.

TE2100, 2008. Projected changes in extreme sea levels in the Southern North Sea. Howard, T and Lowe, J. Hadley Centre Interim Report to the Environment Agency.

TE2100, 2008a. Climate change projections for TE2100. Draft Summary prepared by Dr Jason Lowe (Met Office Hadley Centre), June 2008.

TE2100, 2008b. Area-wide river flow modelling for the Thames Estuary 2100 project: Climate change impacts on flood frequency. Draft report prepared by A.L. Kay (Centre for Ecology and Hydrology), V.A. Bell (Centre for Ecology and Hydrology) and J.A. Lowe (Met Office), June 2008.

TE2100, 2008c. Met Office Hadley Centre Projections of 21st Century Extreme Sea Levels for TE2100. Draft report prepared by Tom Howard, Jason Lowe, Anne Pardaens, Jeff Ridley and Kevin Horsburgh (Proudman Oceanographic Laboratory), June 2008.

TE2100, 2008d. Topic 2.2 – Decision Support Framework Refinement. Task 2 – Handling Uncertainty. Technical Working Paper 1: Collecting Evidence on Sources of Uncertainty. Draft report prepared by Prof. Jim Hall (University of Newcastle), August 2008.

EX 5859 15 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

EX 5859 16 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

Figures

EX 5859 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

Time series water levels 0 neap-neap maximum

Water level (m) level Water -1

-2

-3

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 /1 1 1 1 2 3 3 4 4 5 5 5 6 6 7 7 8 8 9 9 0 0 1 1 2 2 /0 /0 /0 /0 /0 /0 /0 /0 /0 /0 /0 /0 /0 /0 /0 /0 /0 /0 /0 /1 /1 /1 /1 /1 /1 1 6 1 5 2 7 1 6 1 6 1 5 0 5 0 4 9 3 8 3 8 2 7 2 7 0 1 3 1 0 1 0 1 0 1 3 1 3 1 3 1 2 1 2 1 2 1 2 1 2

Figure 1 A sampled year illustrating how the neap-neap maximum water levels were chosen for the dependence analysis

AFGCX: Water Level vs River Flow (1961 - 1990) 600

500

400

300

200 Daily average river Flow (m^3/sec.)

100

0 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 Neap-neap maximum w ater Level (m)

Figure 2 AFGCX: Water level vs river flow for the neap-neap water levels extracted for the dependence analysis (1961 – 1990)

EX 5859 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

AFGCX: Water Level vs River Flow (2070 - 2099) 600

500

400

300

200 Daily average river Flow (m^3/sec.)

100

0 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 Neap-neap maximum w ater Level (m)

Figure 3 AFGCX: Water level vs river flow for the neap-neap water levels extracted for the dependence analysis (2070-2099)

AFIXA: Water Level vs River Flow (1961 - 1990) 600

500

400

300

200 Daily average river Flow(m^3/sec.)

100

0 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 Neap-neap maximum w ater Level (m)

Figure 4 AFIXA: Water level vs river flow for the neap-neap water levels extracted for the dependence analysis (1961 – 1990)

EX 5859 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

AFIXA: Water Level vs River Flow (2070 - 2099) 600

500

400

300

200 Dailyaverage river Flow (m^3/sec.)

100

0 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 Neap-neap maximum w ater Level (m)

Figure 5 AFIXA: Water level vs river flow for the neap-neap water levels extracted for the dependence analysis (2070 – 2099)

0.4 AFGCX 1961 - 1999 0.3 AFGCX 2070 - 2099 AFIXA 1961 - 1999 0.2 AFIXA 2070 - 2099 Measured Jan1994-Aug2003 0.1

-0.1 Dependence factor -0.2

-0.3

-0.4 0.0 0.2 0.4 0.6 0.8 1.0 Probability of non-exceedance

Figure 6 Dependence factor: Water level vs river flow

EX 5859 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

275000 4 5 1 2 3

9 10 6 7 8

250000 14 15 11 12 13

19 20 16 17 18

225000 24 21 22 23

29 26 27 28

TE2100 Outer Thames Estuary flow model 34 200000 31 32 33

39 36 37 38

44 175000 41 42 43 CS3 model grid centres 49 46 47 48

525000 550000 575000 600000 625000 650000 675000 700000

Figure 7 Domain of TELEMAC2D extended model of the Thames Estuary (showing linkage to CS3 model)

Figure 8 Distribution through time of wind conditions as used as input into the TELEMAC2D Thames Estuary model

EX 5859 R. 2.0 Thames Estuary 2100: Phase 3(ii) Topic 3.3 – Definition of Climate Change Allowances for Use in TE2100 Phase 3ii

Wind effects modelled

No winds in model

Figure 9 Time-series modelled tide/surge event in the Thames Estuary

EX 5859 R. 2.0

Thames Estuary 2100 Summary of Climate Change Projections

Joint Probability and Interaction Technical note EP7.3 Part C Climate change allowances to use in the TE2100 programme

EA Study Lead: Tim Reeder

Consultants: HR Wallingford Ltd.

Status: Final Draft Date: Sep. 05 Annex 6 of 7 Appendix L to TE2100 Plan

Joint Probability and Interaction Technical note EP7.3 Part C

Climate change allowances to use in the TE2100 programme

Annex 6 of 7 Appendix L to TE2100 Plan

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

SUMMARY EP7.3C

Several projects within the TE2100 programme consider the potential impacts of future climate change. This part of the project summarises the climate change scenarios and the associated changes in sea level, river flow, wind, waves and rainfall, so that they can be used in a consistent way across the other projects.

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

CONTENTS EP7.3C

SUMMARY EP7.3C 81

EP7.3.7 Introduction to climate change allowances to use in the TE2100 programme – EP7.3C 7

EP7.3.8 The scenarios 7

EP7.3.9 The variables 8

EP7.3.10 Summary of source information relevant to TE2100 8 EP7.3.10.1 Information from Defra (2003) 8 EP7.3.10.2 Information from UKCIP (2002) 9 EP7.3.10.3 Development of the High Plus and High Plus Plus scenarios 10

EP7.3.11 Summary of future climate change values for use in Early Conceptual Options testing 10

EP7.3.11 References EP7.3C 12

Tables

Table 7.3.10 Summary of climate change allowances to 2100 16 Table 7.3.11 Detail of climate change allowances, to 2050 and 2100, for use elsewhere in Early Conceptual Options testing (all figures quoted as increases relative to present-day, 2005, values) 17 Table 7.3.12 Extreme sea levels (m OD Southend) and river flows (m3/s Kingston), 2005, 2050 and 2100, for use in Early Conceptual Options testing 18

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

EP7.3.7 INTRODUCTION TO CLIMATE CHANGE ALLOWANCES TO USE IN THE TE2100 PROGRAMME – EP7.3C

Several projects within the TE2100 programme consider the potential impacts of future climate change. This part of EP7.3 summarises the climate change scenarios and the associated changes in wind, rainfall, sea level etc, so that they can be used in a consistent way across the other projects.

After initial consultation with other projects during EP7.1 and EP7.2, a first version of this note was circulated for discussion in May 2005. Following discussion, consideration of additional future climate scenarios and, in particular, meetings on 21 July and 11 August 2005, the note was updated to its current form.

TE2100 is currently working with the Hadley Centre on obtaining more probabilistic scenarios for climate change. These are planned to be available to feed into the project towards the end of Phase 2, to inform further development of the High Level Options.

EP7.3.8 THE SCENARIOS

UKCIP (2002) provides the most detailed future climate change projections for the UK, focusing on four emissions scenarios, broadly representing the range of conditions which may occur in the future. These are not intended as predictions, since there is no attempt to assign a probability of occurrence to any of these scenarios. The four scenarios are designated UKCIP02 Low, Medium Low, Medium High and High Emissions.

Defra (2003) provides simple numerical adjustments for various commonly used parameters used in flood and coastal engineering calculations, so that all such studies can be assessed on a common basis. These adjustments are neither predictions nor projections, but are usually referred to as appropriate precautionary allowances. This provides a fifth climate change ‘scenario’ for use in TE2100.

Two further worst case scenarios for extreme sea level were developed by the Environment Agency, in discussion with the TE2100 programme team. These High Plus and High Plus Plus scenarios are loosely based on physically possible changes, intended to represent plausible, if unlikely, future developments. The High Plus Plus scenario was developed based on current science on ice sheet melt and rapid climate change. It is intended to represent a highly unlikely but not totally implausible worst case.

The scenarios are summarised in this note primarily in terms of the changes that would occur over a 100-year period from present-day (nominally 2005) and 2100. For some TE2100 projects, an assessment of the situation half-way through the 100-year period (nominally 2050) is necessary. The changes associated with this half-way position are described in terms of simple proportions of the total change projected for 2100.

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

EP7.3.9 THE VARIABLES

The variables of interest within TE2100, which may be subject to future climate change, are, in approximate order of importance:

• extreme sea level at high water (relative to land level, and including surge); • extreme river flow; • extreme rainfall (possibly for more than one duration); • extreme wave conditions (deep water wave height and period); • extreme wind speed (possibly for more than one duration and direction); • dependence between any relevant pairings of the above; • mean sea level (relative to land level).

All of the variables are covered in Defra (2003). River flow and wave conditions are not covered in UKCIP (2002) as these are not represented in regional and global climate modelling. Change in dependence is not covered in UKCIP (2002) or Defra (2003) and so is not mentioned further here.

The High Plus and High Plus Plus scenarios were developed and agreed in discussion with the Environment Agency, within the TE2100 programme, and are not based directly upon climate model results or published guidelines.

WP7.3.10 SUMMARY OF SOURCE INFORMATION RELEVANT TO TE2100

EP7.3.10.1 Information from Defra (2003)

The climate change allowances described in Defra (2003) are variously given in terms of sea level rise per year, change over a fifty year period or change by the 2080s, but all are given explicitly in a form suitable for use in flood risk calculations.

The mean sea level rise allowance is given as 6 mm/year, so 0.60 m over 100-years to 2100, and 0.30 m by the half-way (2050) stage.

The rainfall and river flow sensitivity tests are given as a 20% increase over 50-years, so this allowance is applied both at the half-way stage and at 2100.

The wind speed and wave height sensitivity tests are given as 10% increases by the 2080s, so these are applicable to 2100 for use in TE2100. This allowance is more in the nature of a sensitivity test than a prediction, and so is probably best applied in full at the half-way (2050) stage as well.

These changes are summarised in Column 2 of Table 7.3.10

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

EP7.3.10.2 Information from UKCIP (2002)

General approach UKCIP (2002) refers to the time represented by its main future climate projections as “the 2080s”, but takes this time as being 110 years on from its present climate (nominally 1961-1990). After discussion with the Environment Agency TE2100 team, it was agreed to apply the UKCIP02 changes projected for “the 2080s” to present-day (2005) conditions and to treat the results as representative of 100-years of climate change to 2100, as required for use in TE2100.

UKCIP does not assume that future climate changes will occur at a linear rate over the coming century, but that for most scenarios the rate of change will gradually increase over the period. UKCIP (2002) suggests that intermediate changes are estimated from change in mean temperature (pattern scaling) than on time (linear interpolation with year). Based on the rate of change in mean temperature given in UKCIP02, and on the general expectation that change will accelerate, it was agreed to take the half-way (2050) changes as 40% of the overall changes.

Information relevant to the Thames was extracted from UKCIP (2002) and is summarised in Table 7.3.10. Some interpretation was needed and this is noted in the remainder of this Section.

Mean and extreme sea level The lowering of land level in London (isostatic effect) was taken as 1.5 mm/year (from Table 12 of UKCIP, 2002) throughout the period and UKCIP scenarios of interest, i.e. 0.15 m by 2100. (This “lowering” is being further investigated as part of another TE2100 project (DC10). The findings of that study may suggest a different rate of lowering, but this would not affect the UKCIP-based scenarios.)

The rise in absolute mean sea level (ice melt and heat expansion effects) by 2100 was taken as the central estimate for the 2080s (Table 11 of UKCIP, 2002) for each UKCIP scenario, i.e. 0.23, 0.26, 0.30 and 0.36 m for the Low, Medium Low, Medium High and High Scenarios, respectively.

An additional rise in extreme sea level with 50 year return period was estimated from results given in UKCIP (2002), reflecting a change in storm tracking and surge propagation behaviour projected for the 2080s. Differences between Tables 11 and 12 of UKCIP (2002) and Figure 73 of UKCIP (2002) suggested an additional allowance of 0.60 m in London, for increased surge, on top of mean sea level increase.

Although there was little difference between UKCIP02 scenarios, it was agreed to apply the additional 0.60 m for the 50 year return period sea level under the Medium High scenario by 2100. Corresponding values for the other scenarios were to be estimated in proportion to the mean sea level rise values. Corresponding values for other return periods were to be estimated based on a straight line relationship between log return period (in years) and the ‘storminess allowance’ (i.e. difference between mean and extreme sea level rise).

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

Extreme rainfall and river flow Change in extreme rainfall (and, in the absence of specific projections, river flow) by 2100 was estimated for each of the four UKCIP02 scenarios from Figure 55 of UKCIP (2002). Although Figure 55 relates specifically to the 2 year return period, the same percentage changes were also assumed for higher return periods.

Extreme wind speed and wave height Change in extreme wind speed (and, in the absence of specific projections, wave height) by 2100 was estimated for each of the four UKCIP02 scenarios from Figure 76 of UKCIP (2002). Although Figure 76 relates specifically to daily averaged wind speed and the 2 year return period, the same percentage changes were also assumed for higher return periods.

EP7.3.10.3 Development of the High Plus and High Plus Plus scenarios

The High Plus scenario was based on a general increase in high tide levels (loosely referred to here as mean sea level rise) of 1.6 m, nominally representing 1.0 m for global mean sea level rise, plus 0.4 m for change in tidal propagation affecting high tide levels and 0.2 m for the isostatic effect. The High Plus Plus scenario was based on twice this amount of ‘mean sea level rise’, i.e. 3.2 m increase in high tide levels, derived from estimates of worst case major ice sheet melt.

For both cases, an additional ‘storminess’ component was added to obtain the equivalent estimation of extreme sea level rise, based around an assumed additional increase in extreme sea level of 1.0 m for the 1000 year return period. Corresponding values for other return periods (e.g. 0.57 m for 50 year) were estimated based on a straight line relationship between log return period (in years) and the ‘storminess allowance’ (i.e. difference between mean and extreme sea level rise).

Increases in river flow of 40% and 100% were taken for the High Plus and High Plus Plus scenarios, respectively.

As for the scenarios based on UKCIP02, it was agreed to take the half-way (2050) changes as 40% of the overall changes.

EP7.3.11 SUMMARY OF FUTURE CLIMATE CHANGE VALUES FOR USE IN EARLY CONCEPTUAL OPTIONS TESTING

Table 7.3.10 summarises the changes in sea level, river flow, rainfall, waves and wind speed associated with each of the seven future climate change scenarios considered.

Four scenarios were chosen for use in Early Conceptual Options testing, namely Defra (2003), UKCIP02 Medium High, High Plus and High Plus Plus. For these four scenarios, the list of changes for use in subsequent calculations is given in more detail in Table 7.3.11. The corresponding actual sea levels at Southend and river flows at Kingston are given in Table 7.3.12, taking as present-day figures the preferred values given in Section EP7.3.

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

These data have been used for the Early Conceptual Options (ECO) as reported in HR Wallingford 2005, Interim Consolidation Report.

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

EP7.3.12 REFERENCES EP7.3C

Defra (2003). Supplementary note on climate change considerations for flood and coastal management. Available from: http://www.defra.gov.uk/environ/fcd/pubs/pagn/Climatechangeupdate.pdf.

United Kingdom Climate Impacts Programme (2002). Climate change scenarios for the United Kingdom: The UKCIP02 scientific report. Available from: http://www.ukcip.org.uk/scenarios.

HR Wallingford 2005, Thames Estuary 2100, Estuary Processes, Technical Note EP8, Interim Consolidation Report, November 2005

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

Tables

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

Table 7.3.10 Summary of climate change allowances to 2100

Variable Name of climate change scenario Defra (2003) UKCIP02 UKCIP02 UKCIP02 UKCIP02 High Plus High Plus Low Medium Medium High Plus Low High

Extreme1 sea Increase3 by Increase2 Increase2 Increase2 Increase2 Increase2 Increase2 level 0.60m by 0.89m by 0.96m by 1.05m by 1.19m by 2.17m by 3.77m Extreme river Increase4 by No information, but assume an increase in extreme river Increase2 Increase2 flow 20% flow corresponding to the increase in extreme rainfall by 40% by 100% Extreme Increase4 by Increase2 Increase2 Increase2 Increase2 N/A5 N/A5 rainfall 20% by 13% by 16% by 19% by 22% Extreme Increase4 No information, but assume an increase in extreme wave N/A5 N/A5 wave height by height corresponding to the increase in extreme wind speed conditions 10% and period by 5% Extreme wind Increase4 by Increase2 Increase2 Increase2 Increase2 N/A5 N/A5 speed 10% by 1% by 2% by 3% by 4% Mean sea Increase3 by Increase2 Increase2 Increase2 Increase2 Increase2 Increase2 level 0.60m by 0.38m by 0.41m by 0.45m by 0.51m by 1.60m by 3.20m 1. The example given here is for a 50 year return period; use log return period relationship for other return periods. 2. Assume 40% of this by the half-way stage (2050). 3. Assume 50% of this by the half-way stage (2050). 4. Assume 100% of this by the half-way stage (2050). 5. At present, these are not known and not needed, but can be agreed later if the need arises.

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

Table 7.3.11 Detail of climate change allowances, to 2050 and 2100, for use elsewhere in Early Conceptual Options testing (all figures quoted as increases relative to present-day, 2005, values)

Variable Name of climate change scenario Defra (2003) UKCIP02 Medium High High Plus High Plus Plus 2050 2100 2050 2100 2050 2100 2050 2100

Mean sea level at Southend (m) 0.30 0.60 0.18 0.45 0.64 1.60 1.28 3.20 2-year still water level at Southend (m) 0.30 0.60 0.22 0.56 0.68 1.70 1.32 3.30 5-year still water level at Southend (m) 0.30 0.60 0.28 0.70 0.73 1.83 1.37 3.43 10-year still water level at Southend (m) 0.30 0.60 0.32 0.80 0.77 1.93 1.41 3.53 20-year still water level at Southend (m) 0.30 0.60 0.36 0.91 0.81 2.03 1.45 3.63 50-year still water level at Southend (m) 0.30 0.60 0.42 1.05 0.87 2.17 1.51 3.77 100-year still water level at Southend (m) 0.30 0.60 0.46 1.16 0.91 2.27 1.55 3.87 200-year still water level at Southend (m) 0.30 0.60 0.51 1.26 0.95 2.37 1.59 3.97 500-year still water level at Southend (m) 0.30 0.60 0.56 1.40 1.00 2.50 1.64 4.10 1000-year still water level at Southend (m) 0.30 0.60 0.60 1.51 1.04 2.60 1.68 4.20 2000-year still water level at Southend (m) 0.30 0.60 0.65 1.62 1.08 2.70 1.72 4.30 5000-year still water level at Southend (m) 0.30 0.60 0.70 1.76 1.13 2.83 1.77 4.43 10000-year still water level at Southend (m) 0.30 0.60 0.75 1.86 1.17 2.93 1.81 4.53

Extreme river flow 20% 20% 8% 19% 16% 40% 40% 100% Extreme rainfall 20% 20% 8% 19% Extreme wave height 10% 10% 1% 3% Wave period 5% 5% 1% 2% Wind speed 10% 10% 1% 3%

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

Table 7.3.12 Extreme sea levels (m OD Southend) and river flows (m3/s Kingston), 2005, 2050 and 2100, for use in Early Conceptual Options testing

Variable 2005 Name of climate change scenario values Defra (2003) UKCIP02 Medium High High Plus High Plus Plus 2050 2100 2050 2100 2050 2100 2050 2100

Mean sea level 0.10 0.40 0.70 0.28 0.55 0.74 1.70 1.38 3.30 2-year still water level 3.75 4.05 4.35 3.97 4.31 4.43 5.45 5.07 7.05 5-year still water level 3.94 4.24 4.54 4.22 4.64 4.67 5.77 5.31 7.37 10-year still water level 4.09 4.39 4.69 4.41 4.89 4.86 6.02 5.60 7.62 20-year still water level 4.22 4.52 4.82 4.58 5.13 5.03 6.25 5.67 7.85 50-year still water level 4.39 4.69 4.99 4.81 5.44 5.26 6.56 5.90 8.16 100-year still water level 4.57 4.87 5.17 5.03 5.73 5.48 6.84 6.12 8.44 200-year still water level 4.70 5.00 5.30 5.21 5.96 5.65 7.07 6.29 8.67 500-year still water level 4.87 5.17 5.47 5.43 6.27 5.87 7.37 6.51 8.97 1000-year still water level 5.03 5.33 5.63 5.63 6.54 6.07 7.63 6.71 9.23 2000-year still water level 5.17 5.47 5.77 5.82 6.79 6.25 7.87 6.89 9.47 5000-year still water level 5.37 5.67 5.97 6.07 7.13 6.50 8.20 7.14 9.80 10000-year still water level 5.51 5.81 6.11 6.26 7.37 6.68 8.44 7.32 10.04

2-year river flow 305 366 366 329 363 354 427 427 610 5-year river flow 400 480 480 432 476 464 560 560 800 10-year river flow 466 559 559 503 555 541 652 652 632 20-year river flow 534 641 641 577 635 619 748 748 1068 50-year river flow 693 832 832 748 825 804 970 970 1386 100-year river flow 777 932 932 839 925 901 1088 1088 1554 200-year river flow 869 1043 1043 939 1034 1008 1217 1217 1738 500-year river flow 1007 1208 1208 1088 1198 1168 1410 1410 2014 1000-year river flow 1126 1351 1351 1216 1340 1306 1577 1577 2252 2000-year river flow 1257 1508 1508 1358 1496 1458 1760 1760 2514 5000-year river flow 1453 1744 1744 1569 1729 1685 2034 2034 2906 10000-year river flow 1562 1874 1874 1687 1859 1812 2187 2187 3124

TE2100 – FINAL REPORT EP7.3C – JOINT PROBABILITY AND INTERACTION

Thames Estuary 2100

Annex 7 Wider Climate Change Collaboration

EA Study Lead: Tim Reeder

Consultants: Environment Agency

Status: Final Draft Date: March. 09 Annex 7 of 7 Appendix L to TE2100 Plan

Annex 7 Wider Climate Change collaboration

The TE2100 project has put climate change at its centre since it started in 2002. There are three main drivers behind the TE2100 project. These are :- • Increasing development in or near the tidal floodplain. • An ageing flood defence infrastructure • Increasing flood risk as a result of the effects of climate change.

As a major infrastructure project the approach in TE2100 has been to cooperate with and enhance various existing scientific and technical programmes. By doing this the project has benefited from some cost effective methods of procuring work and has had a number of synergistic benefits of working with developing science and practice.

The climate change work can be divided into two broad categories. The first has been looking at how to make decisions given the uncertainties that climate change presents. The second has been contributing to better understanding of science linked with climate change.

Decision Making and Climate Change

It was recognised at the start of the project that it needed to develop robust methods to devise a long term flood risk management plan that would be resilient robust given the uncertainties that climate change presents. The following is a list of the methods by which this has been achieved showing where benefits have been derived and learning disseminated.

1 ESPACE Project The project initiated a key input to the ESPACE project at the start in 2003. The ESPACE (European Spatial Planning Adapting to Climate Events) project was an Interreg 3b EC initiative led by Hants County Council (http://www.espace- project.org/). It had several themes including one centred around decision tool development. TE2100 led on the development of a strategic decision testing tool or framework that could test varying measures against different future scenarios. This was developed alongside tools in Holland and Bavaria gaining transnational input and refinement. The decision framework developed has been central to the approach of TE2100 in testing progressive iterations of options and refining them as the project has continued. The techniques centre around the use if the UKCIP EA Decision making framework and the development of scenario neutral modelling techniques using ISIS Doc: TE2100 Plan Appendix L3 – Climate Change Annex 7 Author: Tim Reeder Date & Version: 20 Mar 09 v4

TUFLOW modelling. This was initially used for the first iteration of options in the High Level Economic Appraisal. It has been further developed and used in the early conceptual options and the Phase 3i and Phase ii Appraisals for TE2100. The approach has been invaluable to the project and has led to the development of flexible decision pathways as options. In the extension to ESPACE (Sep 07 – May08) the decision pathways approach developed by TE2100 has been developed to see how best to use in the spatial planning system. This work has been informed by VROM, the Dutch Spatial Planning Ministry and has also informed the DEFRA national risk assessment under the Climate Change Bill. As part of ESPACE TE2100 has also further developed the Flood Ranger approach to stakeholder engagement which has proved very useful for showing the uncertainties and difficulties that climate change presents. We have taken the product developed by the Foresight Project – see below and enabled it to be opened up so that any catchment can be used. Flood Ranger World – the latest gaming version has been well received at a recent EA internal Science Conference.

Incoming Benefits Learning from European partners particularly the Dutch and Bavarian partners. The benefits of flood storage and the need to engage with stakeholders were prominent Financial income from the EC.

Outgoing benefits The techniques on decision testing and flexible decision pathways have broken new ground on adaptation and have been taken up by European partners an others – see international liaison.

Direct Costs £130k Gross excluding time

Financial Benefits £150k income including time. Learning from 5 million euro project.

2 Foresight project The project was proactive in engaging with the OST Foresight Flood Defence Project. This was running at the start of the TE2100 project. Several workshops were attended by TE2100 members. The Foresight project developed a national assessment at a broad scale to assess the long term effects of socio economic and climate change on the costs and benefits of flood risk management in the UK..

Doc: TE2100 Plan Appendix L3 – Climate Change Annex 7 Author: Tim Reeder Date & Version: 20 Mar 09 v4

TE2100 derived a significant amount of learning from the project and helped test the techniques centred around understanding drivers pathways and related analysis. TE2100 held a Foresight Workshop in 2004. The Foresight project also developed the Flood Ranger Software to help demonstrate long term climate change effects and decision making to stakeholders. TE2100 was instrumental in driving and steering this work.

Incoming Benefits Learning from National Foresight experts. The benefits of understanding future risk through conceptual models and the use of socio economic scenarios were prominent. The significance of the differing drivers of flood risk were reviewed by national experts at a local scale Use of Foresight experts at low cost.

Outgoing benefits The techniques developed in Foresight of testing the effectiveness of responses against uncertain futures were given a “real” test in a more focussed project such as TE2100.

Direct Costs £20k Gross excluding time

Financial Benefits Learning from £750k project.

3 Atlantis Project The Atlantis Project was a European funded project looking at extreme climate change scenarios and how societies might respond to them (http://www.uni- hamburg.de/Wiss/FB/15/Sustainability/atlantis.htm). It was led by a group of European scientists and focused on three case studies namely the Thames Estuary, The Rhone Delta and Holland. The project’s timing was fortunate from TE2100’s perspective. It enabled some broad estimations of flood risk and its relationship to climate change to be made at an early point in the project. It also introduced the project to looking at large improbable rates and scale of climate change that may otherwise not have occurred at this early and crucial part of the project. The project posed a scenario in 2030 of there being a significant risk of 5 metres sea level rise in the next 50 years or so. A workshop was held at TE2100 to see how best this would assessed and worked out from a socio political viewpoint. A further workshop involving role play from key TE2100 stakeholders was very illuminating in terms of how decisions would made. This thinking led indirectly into the development of our flexible options that can cope with extreme change. Doc: TE2100 Plan Appendix L3 – Climate Change Annex 7 Author: Tim Reeder Date & Version: 20 Mar 09 v4

Incoming Benefits Learning from a transnational project that was breaking new ground in terms of strategic thinking. The start of developing a process and options that could cope with extreme change. Use of international experts at no cost.

Outgoing benefits The thinking developed in the TE2100 workshop was critical to the success of the project and was invaluable in providing a high profile case study with an influential set of stakeholders. The work provided part of the input by Tim Reeder to the Avoiding Dangerous Climate Change Conference at Exeter at which TE2100 learned a lot and exchange its own learning (see below)

Direct Costs £5k time input

Financial Benefits Learning from transnational project.

4 Stern Review TE2100 were seen as the most advanced major project in the UK looking at embedding climate change in decision making for major investments. The project outputs in 2005 such as the High Level Economic appraisal, the Early Conceptual Options work, and the ESPACE pilot work were used to inform Stern’s analysis. Tim Reeder was interviewed by live TV on the launch of the Stern review providing a visual link from Stern to TE2100. The work with Stern raised the profile and reputation of TE2100 and helped the Stern analysis.

Incoming Benefits The opportunity to feed in project thinking to a major economic study of climate change that in turn was very influential in raising the issue of climate change. Links through to key Treasury staff.

Outgoing benefits

Doc: TE2100 Plan Appendix L3 – Climate Change Annex 7 Author: Tim Reeder Date & Version: 20 Mar 09 v4

The Stern review benefited from our work and experience that was driving ahead in the field of uncertainty and decision making given the effects of climate change.

Direct Costs £5k time commitment time

Financial Benefits Input to major national and international economic study of the effects of climate change that will help underpin the development and recommendations in the final TE2100 plan.

5 IPCC The team writing the 4th Assessment Report of the International Panel on Climate Change requested that TE2100 provide a case study on how to adapt to climate change. This was provided in early 2005. The latest developments in TE2100 could not be included in the assessment due to the long time lags in the writing process. Nevertheless TE2100 provided key input into the process. It was seen as one of few projects demonstrating proactive planning for the effects of climate change.

Incoming Benefits Ability to inform and influence international assessment of climate change. (The IPCC team were awarded the Nobel Peace Prize). Contacts with many international experts.

Outgoing benefits The project informed the IPCC of its approach to decision making and developing science (see below).

Direct Costs £5k time

Financial Benefits Not quantified

Doc: TE2100 Plan Appendix L3 – Climate Change Annex 7 Author: Tim Reeder Date & Version: 20 Mar 09 v4

6 Local Regional Partnerships TE2100 has built strong relationships to key climate change partnerships and networks since its inception. This has been achieved through ESPACE (see above) but also mainly through the London Climate Change Partnership, which was set up in 2001 at about the same time as TE2100 was getting going. This has enabled TE2100 to cement a strong relationship on the issue with key stakeholders including GOL and the GLA. The GLA have as result been given several presentations on the project during its progress. Presentations on TE2100 have been given at key Partnership events with co speakers including Sir David King and others. We have also shared our findings and approach with other sectors such as the finance sector, who are interested in our scenario neutral approach. The Project Scientist has a combined role with climate change issues for Thames Region and therefore close links have been maintained with groups looking at developing guidance on climate change particularly the Three Regions Group sub group of the London Climate Change Partnership, Climate South East and the East of England Climate Change Partnership, which have produced guidance on development and retrofitting to tackle climate change adaptation including flood risk.

Incoming Benefits Close links with key stakeholders such as GOL, GLA, ABI and others on climate change and regional issues. Guidance produced at little direct cost relevant to the project – see http://www.london.gov.uk/trccg/

Outgoing benefits The project has been seen as leading the way on the use of threshold analysis to tackle the uncertainties of climate change.

Direct Costs £5k time (We have contributed over £50k in funds and about 5k per annum in staff time from the Region to cover all issues e.g. water resources etc)

Financial Benefits Several relevant reports such as the Developers Checklist Retrofitting guide which cost about £40k each to produce

Doc: TE2100 Plan Appendix L3 – Climate Change Annex 7 Author: Tim Reeder Date & Version: 20 Mar 09 v4

7 International Liaison

As the project has progressed it has generated considerable interest in the field of climate change because of the high visibility of London and the Thames Barrier. This has resulted in numerous liaisons. The more notable ones are described here. In 2005 during the UK G8 and EU Presidency the Project Scientist gave a talk on TE2100 for foreign correspondents and delegates. This formed a high profile part of the visit of the G8 countries. In 2006 post Katrina the US House of Representatives Flood Officers requested a visit to the project. Presentations were given on the approach to climate change in TE2100; this had an important influence over the visiting audience. In 2007 the Dutch Reijkswaterstadt visited to discuss the technical approach to climate change adaptation and option development taken by TE2100. This was influential in the Dutch tipping points analysis carried out subsequently. In the same year the Project Scientist collaborated with key staff in New York to provide a joint paper comparing flood risk and climate change in New York and London. This was presented at the international CIWEM conference in London in October 2007. In 2008 delegations were entertained from the Japanese national flood risk department and from New Zealand and South Africa. The British Council requested that their programme for G8 plus 5 Youth Champions could be shown the issues around TE2100. They were given an educational trip down the river by the project team. Sir John Harman welcomed them to the day. This visit contributed to a joint communiqués from Youth Champions from the G8 plus 5 to the Hokkaido G8 meeting.

Incoming Benefits Building links with international colleagues to learn from other approaches. Reputation building nationally and internationally.

Outgoing benefits The project has been seen as leading the way on the use of threshold analysis to tackle the uncertainties of climate change. This has been praised particularly in the USA and Holland

Direct Costs £20k time and expenses estimate to cover all visits

Financial Benefits

Doc: TE2100 Plan Appendix L3 – Climate Change Annex 7 Author: Tim Reeder Date & Version: 20 Mar 09 v4

The work with other government departments has reinforced our messages and the need for the project to continue.

The Science of Climate Change

To reduce or better quantify the uncertainties surrounding the effects of climate change TE2100 has commissioned research to tackle this. It has also built relationships with other research programmes to keep abreast of developing science. The following are the key initiatives that have been undertaken.

1 Direct Climate Change research Following a scoping project in Phase 1 TE2100 commissioned a substantial research programme with the Met Office Hadley Centre (MOHC) and Proudman Oceanographic Laboratory (POL) in 2005. This was aimed at reducing the uncertainties surrounding the effects of climate change particularly on the effects on storm surge, which at the time were not well understood. At the same time MOHC were requested to keep the project up to date with emerging science. The work was only possible by building on the dynamic downscaling work that MOHC were commissioned to provide for DEFRA to produce the UKCP09 new probabilistic climate change scenarios. This meant that the outputs would arrive late in the development of TE2100. However by taking the approach to option development described above this was not critical. The results have now been produced and will feed into TE2100 as well as the marine report for UKCP09. They have considerably improved our understanding of the effects of climate change. TE2100 will be providing the key case study for the marine report in UKCP09 (see annex 1-3).

Incoming Benefits Building links with key scientists at the sharp end of climate change science. Gaining a better understanding of the likely effects of climate change on sea level, storm surge and river flows. This could lead to substantial savings in future investment by reducing uncertainty on storm surge in particular. Reputation building nationally and internationally. Cooperation with a national research programme gaining the synergies of a several million pound effort.

Outgoing benefits Contributing the bulk of the scientific research for the marine report for UKCP09. Informing other projects and the EA generally on developing science. Informing international visitors and interested parties of our approach.

Doc: TE2100 Plan Appendix L3 – Climate Change Annex 7 Author: Tim Reeder Date & Version: 20 Mar 09 v4

Direct Costs £340k MOHC POL contract cost. £20 k per annum staff cost.

Financial Benefits The reduction in uncertainty could involve opting for lower cost options and putting off expensive options – up to £14 billion depending on choices made.

2 Indirect research As the project has progressed it has kept in touch with Agencies engaged in climate change research. The Project Scientist presented a paper to the Avoiding Dangerous Climate Change Conference in Exeter in 2005. At this conference emerging information about accelerated ice cap melt was presented. This alerted the project to the possibility of sea level rise scenarios considerably worse than those reported in the 3rd IPCC Assessment report, which were reflected in the UKCIP02 scenarios. As a result in collaboration with MOHC a High plus plus scenario was developed representing a very unlikely but worst case scenario (see annex 6). This has been the subject of considerable interest from elsewhere and ensured that the project developed its options within a large enough envelope of possible change. Links were made with the British Antarctic Survey to get an up to date understanding of the latest observations. BAS visited the project and a useful interchange of information took place. In 2007 Jim Hansen of NASA produced a paper outlining a worst case ice cap figure of 5 metres sea level rise in the 21st century. This was higher than the high plus plus scenario. As a result of links with MOHC and BAS and others, the project as part of its phase 3i studies was able to credibly discount this finding (see annex 2). TE2100 participated in a UNEP study in 2006 looking at response to long term sea level rise. This learnt from our approach on looking at worst cases. We continue with MOHC keep an eye open to the developing science and this will be reflected in the UKCP09 marine report.

Incoming Benefits

Building links with key scientists at the sharp end of climate change science. Gaining a first hand account from field scientists as to developing observations and predictions. Developing an approach to unlikely but not implausible worst case scenarios. Reputation building nationally and internationally. Cooperation with national research programmes gaining an early warning of developments in science.

Doc: TE2100 Plan Appendix L3 – Climate Change Annex 7 Author: Tim Reeder Date & Version: 20 Mar 09 v4

Outgoing benefits The development of worst case scenarios that have been disseminated to other organisations and projects cf New York, Japan, UNEP, etc The formation of links with field science such as BAS, which can link their science to a real world close to home issue.

Direct Costs £15k staff time estimate.

Financial Benefits The decision to plan within a worst case envelope has helped sell the project to concerned key stakeholders such as the Mayor and others. This has supported the eventual plan which has cost £16 million.

For further information, please contact Tim Reeder, TE2100 Project Scientist

Email: [email protected]

Doc: TE2100 Plan Appendix L3 – Climate Change Annex 7 Author: Tim Reeder Date & Version: 20 Mar 09 v4

423677_TE21_Append_L_AW.indd 3 24/3/09 10:54:54 Have your say For comments on this plan for consultation and to find out more about how we are planning for a changing estuary: Thames Estuary 2100 [email protected] or 08708 506 506

Would you like to find out more about us, or about your environment? Then call us on 08708 506 506 (Mon–Fri 8–6) email [email protected] or visit our website www.environment-agency.gov.uk

incident hotline 0800 80 70 60 floodline 0845 988 1188

423677_TE21_Append_L_AW.indd 4 24/3/09 10:54:54