Utilising Palaeoclimate Data in Water Resource Planning: Recent Work and Proposed ARC Linkage Project

Utilising palaeoclimate data in water resource planning: recent work and proposed ARC Linkage Project

Ben Henley1, Joëlle Gergis1, David Karoly1

School of Earth Sciences, University of Melbourne, Australia Overview

1. Recent work in using multi-proxy palaeoclimatology understanding hydrological variability: regional rainfall and streamflow reconstructions in South-eastern Australia, extension of instrumental data using historical records (Gergis, Gallant, Ashcroft, Karoly)

2. Overview of Proposed ARC Linkage Project 2015: “Megadrought likelihood and its water resource impacts in Australia” (incorporating palaeoclimate and climate model data into water supply planning)

3. Understanding decadal variability: tracking and reconstructing the Interdecadal Pacific Oscillation (Henley, Gergis, Karoly)

Motivating Factors

1. Millennium drought

2. Growing population

3. Short observed records

4. Highly variable and persistent climate

5. Vulnerability to decadal-multidecadal “megadroughts” April–November rainfall deciles since 1996 (BoM, State of the Climate 2014). 6. Uncertainty due to climate change

7. Public expense and energy intensity of water supply system augmentation Record low inﬂows to Melbourne catchments

• During 1997-2011, Melbourne catchments received inflows 30% below long term averages, even including the heavy rain in 2010 and 2011 • How much is natural decadal variability and how much is due to human- influenced increases in temperature? How unusual is this low inflow?

Source: Melbourne Water What can palaeoclimate science provide?

Tree growth rings Biological and geological indicators capture natural climate variability on seasonal–centennial timescales e.g. tree rings, corals, ice cores, lake sediments, cave records

High resolution (i.e. seasonal–annually resolved) palaeoclimate records extend the instrumental climate record centuries into the past • Provides estimates of pre- Ice laminations instrumental natural climate variations to assess recent climate extremes

• Palaeoclimate reconstructions are a tool for comparison with climate models:

• Assess the role of ‘natural forcing’ e.g. solar, volcanic, internal ocean– atmospheric processes (ENSO, SAM, IOD) and anthropogenic GHG forcing

Coral banding SE Australian rainfall reconstrucon, 1783–1988

r=0.57+/-0.08 33% of annual variaons captured • 12 proxies used for May–April SEA rainfall reconstrucon back to 1783

r=0.85+/-0.15 72% of decadal variaons captured Very high skill in decadal band, but also good inter-annual skill

Gergis et al. (2012), Climac Change River Murray streamﬂow reconstrucon, 1783-1988

r=0.49 24% of annual variaons captured

Water Resources Research

r=0.72 52% of decadal variaons captured Gallant and Gergis (2011),

- Nine proxies used for August–July streamﬂow reconstrucon (none from within the MDB) - Losing reconstrucon skill at the catchment level, but the results are promising - Reconstrucon can only capture rainfall component of streamﬂow so is limited

Recent River Murray streamﬂow deﬁcits in a longer- term context

• Where does the River Murray streamﬂow deﬁcits sit in a longer-term context?

River Murray streamflow Using RECONSTRUCTED OBSERVATIONS from the 10,000- member reconstrucon ensemble it was esmated that there is only a 2.3% chance that the 1998– 2009 streamﬂow deﬁcit has been exceeded since 1783 (length of the reconstrucon)

Also used SIMULATED stascal modeling of River Murray streamflow (100,000 year synthec simulaons based on parameter derived from our 10,000 palaeostreamflow reconstrucons) to esmate that the 1998–2009 streamflow deficit has an Average Recurrence Interval of 1 in 1500 years

Gallant and Gergis (2011), Water Resources Research Extended Instrumental Record

Ashcroft, L., Gergis, J., & Karoly, D. J. (2014). A historical climate dataset for southeastern Australia , 1788 – 1859. Geoscience Data Journal, 1, 158–178. Proposed ARC Linkage Project 2015

Megadrought likelihood and its water resource impacts in Australia: Incorporating palaeoclimate data and future projections into water planning

Ben Henley, David Karoly, Murray Peel, Joëlle Gergis, Ailie Gallant, Rory Nathan, K.S. Tan, Geoff Steendam, Bertrand Timbal Previous projects

Climatic Change (2012) 111:923–944 DOI 10.1007/s10584-011-0263-x icsinPpr|Dsuso ae icsinPpr|Dsuso ae | Paper Discussion | Paper Discussion | Paper Discussion | Paper Discussion On the long-term contextOpen Access of the 1997–2009 ‘Big Dry’ Hydrol. Earth Syst. Sci. Discuss., 11, 4579–4638, 2014 Hydrology and www.hydrol-earth-syst-sci-discuss.net/11/4579/2014/ in South-EasternEarth System Australia: insights from a 206-year doi:10.5194/hessd-11-4579-2014 HESSD Sciences © Author(s) 2014. CC Attribution 3.0 License. multi-proxy rainfallDiscussions reconstruction11, 4579–4638, 2014

This discussion paper is/has been under review for the journal Hydrology and Earth System Part 2: Estimation Joëlle Gergis & Ailie Jane Eyre Gallant & Karl Braganza & David John Karoly & Sciences (HESS). Please refer to the corresponding ﬁnal paper in HESS if available. and uncertainty ofWATER RESOURCES RESEARCH, VOL. 47, W00G04, doi:10.1029/2010WR009832, 2011 Kathryn Allen & Louise Cullen & Rosanne Dannual’Arrigo runo& Ian◆ and Goodwin & Pauline Grierson & Uncertainty in runo◆ basedShayne McGregor on Global reservoir yield Climate Model precipitation and M.An C. experimental Peel et al. streamflow reconstruction for the River Murray, Australia, 1783–1988 temperature data – Part 2: Estimation and AilieTitle J. Page E. Gallant1 and Joëlle Gergis1 uncertainty of annual runoReceived:◆ 16and December reservoir 2010 /Accepted: 8 July 2011Abstract /PublishedReceived 30Introduction July online: 2010;4 revised November 21 January 2011 2011; accepted 17 February 2011; published 26 April 2011. Springer Science+Business Media B.V. 2011 # [1] We present an experimental reconstruction of River Murray streamflow to assess Conclusions References yield present‐day variations in the context of the past two centuries. Nine annually resolved TablespaleoclimateFigures proxy records from the Australasian region are used to develop a 1 2 1 3 M. C. Peel , R. Srikanthan , T. A. McMahon ,Abstract and D. J. KarolyThis study presents the first multi-proxyreconstruction reconstruction of streamflow of rainfallfrom 1783 variability to 1988. An ensemble of reconstructions is presented, providing probabilistic estimates of River Murray flows for each year back in 1 from the mid-latitude region of south-eastJern AustraliaI (SEA). A skilful rainfall Department of Infrastructure Engineering, University of Melbourne, 3010 Victoria, Australia time. The best‐estimate reconstruction captures approximately 23% (50%) of annual 2Water Division, Bureau of Meteorology, Melbourne,reconstruction 3001 Victoria, Australia for the 1783–1988 period was possible using twelve annually-resolved 3 (decadal)J naturalizedI streamflow variability. High and low streamflow phases and their School of Earth Sciences and ARC Centre of Excellencepalaeoclimate for Climate records System Science, from the Australasian region.association An innovative with decadal Monte climate Carlo variability calibration in the Pacific are discussed. Reconstructed University of Melbourne, 3010 Victoria, Australia Back Close and verification technique is introduced to provideRiver the Murray robust streamflow uncertainty shows estimates considerable needed variation since 1783. We estimate that there Received: 28 March 2014 – Accepted: 22 April 2014 – Published: 5 May 2014 isFull a Screen 2.3% / chance Esc that the 1998–2008 record low decadal streamflow deficit has been for reliable climate reconstructions. Our ensembleexceeded median since reconstruction European settlement. captures Stochastic 33% of simulations of the decadal variations in Correspondence to: M. C. Peel ([email protected])inter-annual and 72% of decadal variations inRiver instrumental Murray streamflow SEA rainfall are computed observations. using Wethe paleostreamflow reconstruction to Printer-friendly Version Published by Copernicus Publications on behalf of theinvestigate European Geosciencesthe stability Union. of regional SEA rainfallestimate with large-scale model parameters. circulation From associated these simulations, with we estimate that the 1998–2008 El Niño–Southern Oscillation (ENSO) and theInteractivestreamflow Inter-decadal Discussion deficit Pacific has an Oscillation approximate (IPO)1 in 1500 over year return period. As climate models are assessed relative to short instrumental records, future projections of decadal‐scale variations 4579 in Murray‐Darling Basin (MDB) streamflow may be inadequately represented. Given the immense socioeconomic importance of Australia’s “food bowl,” future paleoclimate Electronic supplementary material The online versionand of modeling this article efforts (doi:10.1007/s10584-011-0263-x) should be directed at understanding variability at this scale. This contains supplementary material, which is available to authorizedwould greatly users. enhance our capacity to estimate regional sensitivity of the MDB’s hydroclimate to further anthropogenic influences. J. Gergis (*) : A. J. E. Gallant : D. J. Karoly School of Earth Sciences, University of Melbourne, Melbourne,Citation: VICGallant, 3010, A. J. Australia E., and J. Gergis (2011), An experimental streamflow reconstruction for the River Murray, Australia, e-mail: [email protected] 1783–1988, Water Resour. Res., 47, W00G04, doi:10.1029/2010WR009832.

K. Braganza Australian Bureau of Meteorology, GPO Box 1289, Melbourne,1. Introduction VIC 3001, Australia provides groundwater recharge, and the remaining 4% becomes runoff feeding streamflow (ABS, online report, [2] The Murray‐Darling River system and its tributaries 2010). Intensive irrigation systems draw water from the form a vast catchment covering around 1 ×106 km2 in K. Allen River Murray to support approximately 40% of Australia’s southeast Australia (Australian Bureau of Statistics, Water School of Biological Sciences, Monash University, Melbourne, VIC 3800, Australia total agricultural production and nearly 70% of all irrigated and the Murray‐Darling Basin—A statistical profile, 2000– crops and pastures [Hennessy et al., 2007]. 01 to 2005–06, 2010, http://www.abs.gov.au/AUSSTATS/ L. Cullen : P. Grierson [4] The streamflow losses in the river system are being [email protected]/mf/4610.0.55.007, hereafter referred to as ABS, exacerbated by a prolonged drought in the region, which School of Plant Biology, University of Western Australia,online Perth, report, WA 2010). 6907, TheAustralia River Murray, which forms the was in its 13th year during 2010. Determining the extent to southern portion of the basin, runs for almost 2500 km which the prolonged drought in the region is caused by R. D’Arrigo across the heavily populated states of New South Wales, natural decadal‐scale variability and/or anthropogenic cli- Tree-ring Laboratory, Lamont Doherty Earth Observatory,Victoria, Palisades, and SouthNY 10964, Australia. USA As such, it is a complex mate change is now a major research priority. According to economic and natural resource of immense importance to the Intergovernmental Panel on Climate Change’s Fourth the livelihood of a diverse group of stakeholders. I. Goodwin Assessment Report, annual streamflow in the MDB is likely [3] The River Murray’s highly variable streamflow in part Department of Environmental and Geography, Macquarie University, Sydney, NSW 2109, Australia to fall 10%–25% by 2050 and 16%–48% by 2100 [Hennessy reflects the region’s erratic natural rainfall variability and et al., 2007]. At present, the decadal scale streamflow deficit flow regulation engineered to support urban water supplies, S. McGregor is approximately 50% below the 1892–2008 average on the agriculture, and hydroelectricity generation [Murphy and basis of streamflow data from the Murray‐Darling Basin Climate Change Research Centre, University of New SouthTimbal Wales,, 2008]. Sydney, Although NSW the Murray2052, Australia‐Darling Basin (MDB) Authority (MDBA) detailed in section 2.1.2. This figure receives an average annual rainfall equivalent of 530,618 already exceeds the “worst‐case scenario” estimates of the GL, 94% of this total evaporates or transpires, a further 2% 2100 climate change projections for the region. The short length of instrumental records (around 100 years) limits 1School of Earth Sciences, University of Melbourne, Melbourne, our comparisons of decadal‐scale natural variability with Australia. climate models, making our understanding of its causes and distinguishing it from anthropogenic climate change diffi- Copyright 2011 by the American Geophysical Union. cult. Consequently, there is need to investigate the nature 0043‐1397/11/2010WR009832

W00G04 1 of 15 Major challenges and key questions

1. What is the future likelihood of severe droughts like the millennium drought? (underlying risk + climate change)

2. How do we extend the observed record and take into account palaeoclimate data uncertainty?

April–November rainfall deciles since 1996 (BoM, State of the Climate 2014). 3. How can we incorporate the reliable information from the latest climate change projections?

4. Incorporate this knowledge into a practical context in the case study regions: 1. Melbourne Water catchments, 2. regional Victorian catchments Palaeoclimate database

° ° 90 E 180 E Aus2k Palaeoclimate Annual resolution Decadal resolution (or lower) database (>240 records) ° 0

• High resolution: (tree ° ring, corals, ice cores, 15 S regular updating with ° 30 S new records)

° • Low resolution: (lake, 45 S cave, marine): age ° models recently updated 60 S

° • Nested ensemble PCR 75 S ° 90 S allows for extension of reconstructions Palaeoclimate + Observed

Palaeo-reconstruction Instrumental

400

350

300

250

200 Inflow (GL/yr) 150 Future?

100

0 1500 1525 1550 1575 1600 1625 1650 1675 1700 1725 1750 1775 1800 1825 1850 1875 1900 1925 1950 1975 2000 2025 Simulation Year

Early instrumental Pros and Cons of Data Sources

Data type Pros Cons

Palaeoclimate Long, our only record of Higher uncertainty, pre-instrumental sparse coverage, variability complex to analyse

Instrumental High quality, accurate, Short, limited decadal precise, good coverage variability Climate model Dynamical processes, High degree of scatter, projections Our only crystal ball model specific biases into the future! ARC Linkage Project Description

Subproject 1. Assessing the underlying likelihood of megadrought occurrence in Australia

• Develop new/updated palaeo-hydrologic reconstructions

• Quantify the likelihood of megadroughts in past centuries

• Evaluate the nature of decadal to multidecadal droughts by comparing key statistics of hydrologic variability and persistence in the pre-instrumental and observed periods from existing and new palaeoclimate reconstructions in the study regions. ARC Linkage Project Description

Subproject 2. Incorporating palaeo-hydrological variability and climate change projections into stochastic hydrological simulations.

• Incorporate the underlying decadal to multidecadal hydrologic variability (palaeo) into stochastic hydrological simulations • Assess the skill of CMIP5 simulations of decadal-scale hydrologic variability using a suite of skill metrics tailored for key hydrological variables, (build on work from Peel et al and VicCI) • Constrain climate projections: weight model projections • Develop a suite of stochastic hydrological simulations for the period 2015-2100, for RCP’s 2.6, 4.5, 8.5) in case study regions • Assess the feasibility of conditional decadal projections for 2015-2030 using simulations constrained with hydrological skill metrics and conditioned on climate mode state (e.g. Henley et al. 2013) ARC Linkage Project Description

Subproject 3. Water supply drought risk evaluation

• Evaluate the water supply drought risk / Yield in the study regions by incorporating the stochastic simulations from subproject 2 into water supply system models

• Simplified system model for research outputs

• Stochastic simulations available to partners for in-house risk evaluation Overview of Methodology

1. Updated Palaeo 5. CMIP5 GCMs reconstructions (Nrep) historical & past1k

Merge Assess

2. Palaeo + Observed: 6. Hydrological skill Nrep sequences evaluation PhD 1 & PhD 2 & Postdoc Calibrate Constrain Postdoc

3. Stochastic 7. Weighted Hydrologic Hydrological Model Projections RCPs

Quantify Evaluate

4. Uncertainty in 8. Future scenarios of parameter distributions climate change impacts

Simulate Apply change factors

Postdoc, 9. Stochastic Replicates Evaluate 10. Water Supply PhD1 & PhD 2 of Inflow, Rainfall, PET Drought Risk Analysis Tracking and reconstructing the IPO

A Tripole Index for the Interdecadal Pacific Oscillation

Correlation between SST and IPOUKMO (unﬁltered), composite of 10 realisations of HadISST2.1. Black boxes indicate TPI regions: region 1: 25°N–45°N, 140°E–145°W; region 2: 10°S–10°N, 170°E–90°W; region 3: 50°S–15°S, 150°E–160°W.

Henley, B. J., Gergis, J., Karoly, D. J., Power, S. B., Kennedy, J., & Folland, C. K. (2015). A Tripole Index for the Interdecadal Pacific Oscillation. Climate Dynamics, in review. Tracking and reconstructing the IPO

Published indices for the IPO and PDO 3

1 Advantages:

(a) 0 u Simplicity: box based index allows for -1 direct comparison between Standardised units -2 observational and climate model data IPO index from Parker et al. (2007) PDO index, filtered (Mantua et al., 1997) -3 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 u Native units of °C

IPO u UKMO Explains similar proportion of variance 3 as PC-based index 2 u Consistent with indices of other modes 1 of climate variability (Nino3.4, NAO) (b) 0

-1 u TPI is a robust and stable Standardized units -2 representation of the IPO HadISST1 HadISST2.1 range ERSST -3 u TPI data will be available on NOAA 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 climate indices page, and will be used TPI 1 in the calibration of a new 0.8 palaeoclimate reconstruction of IPO 0.6 0.4 0.2 (c) 0 -0.2 -0.4 -0.6

Temperature Anomaly (°C) -0.8 HadISST1 HadISST2.1 median HadISST2.1 range ERSST HadSST3 median HadSST3 5-95% limits -1 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Land: Annual correlation between May-Apr rainfall and May-Apr IPO (unfiltered) Ocean: Loading pattern of EOF2 of LP filtered global HadISST2 (Composite of 10 rsns) Open Circles: Proxy locations; Rectangles: Tripole Index (TPI) box locations

(Henley et al., 2015) IPO reconstruction: Pacific coral proxies

IPO Reconstruction with 24 proxy records, 19 nests 3

−1

−2

−3 Reconstruction Reconstruction (LP Filtered) ReconstructionIPO IPO Reconstruction with 24 proxy records IPO (LP Filtered) 19 nests, mean RE: 0.42, mean CE: 0.29, rsq instr= 0.61 −4 1.5 1780 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000

0.5

−0.5

−1

−1.5 Reconstructed IPO index (unfiltered, monthly)

−2 −5 0 5 Instrumental IPO index (unfiltered, monthly) References

Ashcroft, L., Gergis, J., & Karoly, D. J. (2014). A historical climate dataset for southeastern Australia , 1788 – 1859. Geoscience Data Journal, 1, 158–178. doi:10.1002/gdj3.19 Ashcroft, L., Karoly, D. J., & Gergis, J. (2013). Southeastern Australian climate variability 1860-2009: a multivariate analysis. International Journal of Climatology, n/a–n/a. doi:10.1002/joc.3812 Gallant, A., Gergis, J., & Karoly, D. (2012). Estimating hydroclimatic variations in Melbourne’s water supply catchments using palaeoclimate proxy data , 1788- - ‐ 1987. Technical report prepared for Melbourne Water. Gallant, A. J. E., & Gergis, J. (2011). An experimental streamflow reconstruction for the River Murray, Australia, 1783-1988. Water Resources Research, 47, W00G04. doi:10.1029/2010WR009832 Gergis, J., Gallant, A. J. E., Braganza, K., Karoly, D. J., Allen, K., Cullen, L., … McGregor, S. (2012). On the long-term context of the 1997-2009 “Big Dry” in South-Eastern Australia: insights from a 206-year multi-proxy rainfall reconstruction. Climatic Change, 111, 923–944. doi:10.1007/s10584-011-0263-x Henley, B. J., Gergis, J., Karoly, D. J., Power, S. B., Kennedy, J., & Folland, C. K. (2015). A Tripole Index for the Interdecadal Pacific Oscillation. Climate Dynamics, in review. Henley, B. J., Thyer, M. A., & Kuczera, G. (2013). Climate driver informed short-term drought risk evaluation. Water Resour. Res., 49. doi:10.1002/wrcr.20222 Henley, B. J., Thyer, M. A., Kuczera, G., & Franks, S. W. (2011). Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data. Water Resources Research, 47. doi:10.1029/2010WR010034 McMahon, T. A., Peel, M. C., & Karoly, D. J. (2015). Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation. Hydrology and Earth System Sciences, 19, 361–377. doi:10.5194/ hess-19-361-2015 Peel, M. C., Srikanthan, R., McMahon, T. A., & Karoly, D. J. (2014). Uncertainty in runoff based on Global Climate Model precipitation and temperature data – Part 2: Estimation and uncertainty of annual runoff and reservoir yield. Hydrology and Earth System Sciences Discussions (Vol. 11, pp. 4579–4638). doi:10.5194/hessd-11-4579-2014