Assessment of Forest Influence on Weather Patterns at a Local Scale European Commission (DG Environment) July 2012 Michael Sanderson and Edward Pope

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Task A3: Assessment of forest influence on weather patterns at a local scale European Commission (DG Environment) July 2012 Michael Sanderson and Edward Pope

Contents

Executive Summary ...... 2

Task A3_D1: Sensitivity of weather patterns to forest areas at a local scale ...... 3

1. Introduction ...... 3

2. Met Office Nested Modelling Suite ...... 3

3. Selection of Regions ...... 5

4. Results ...... 8 4.1 Summer period (5th – 13th August) ...... 9 4.2 Effect of forest size ...... 13 4.3 Spring period (10th – 18th April) ...... 15

5. Summary ...... 18

Task A3_D2: Literature review of past studies on local and micro-weather conditions ...... 21

Task A3_D3: Implications of sensitivity tests and relevant literature for forest protective functions ...... 24

References ...... 26

Appendix A ...... 30

Appendix B ...... 33

Executive Summary

• The influence of forests on weather at five locations in Europe has been studied using a series of nested models based on the UK Met Office weather forecast model. Simulations were made for short periods in both spring and summer. • In each location, an area of approximately 50,000 km2 was deforested and afforested. • Temperatures above the locations were consistently warmer following afforestation than when the area was deforested. • Over the length of the simulations, rainfall was, on average, higher over the afforested regions than the deforested regions during the summer period. However, the changes were small and not statistically significant. • The wind speeds were reduced and the height over which surface moisture was mixed in the surface of the atmosphere was larger as the forest cover was increased, owing to the increased turbulence above forested areas. • Low cloud amounts above the forests became larger as the forest cover was increased, but the changes were small. • No clear effect on surface humidity at each location or the surrounding area was found. • These results suggest that the effects of changes in forest cover on weather are confined to the regions where the changes have occurred; however, effects on weather downwind of the forested areas cannot be ruled out. Greater numbers of simulations would be needed to examine downwind changes.

Task A3_D1: Sensitivity of weather patterns to forest areas at a local scale

1. Introduction

In this report, results from the modelling studies of the influences of forests on local weather will be described. Forest influences in five areas of Europe which have different climates were examined for two periods during spring and summer. The aim was to sample the influence of forests at various locations on a range of weather patterns, in order to better determine links between forest cover and weather.

A variety of models have been or could be used to simulate the effects of forests and changes in forest cover on weather (described in the report for Task B1). Here, the Met Office Nested Modelling Suite has been used, which is described in section 2.

2. Met Office Nested Modelling Suite

A brief description of the nested modelling system is given here; further technical details are given in Appendix A. The principle behind nested modelling is that weather and climate information which are consistent with the large-scale atmospheric circulation can be downscaled over a limited area but at high resolution. A series of nested models are used which have progressively increasing resolutions, and so can represent local topographic and other effects more realistically. Meteorological data from the global model are used to drive the first nested model, and then data from the first nested model are used to drive the next nested model, and so on until the final nested model is reached. A similar series of nested models, in which the highest resolution was 1 km, has been used to study a heavy rainfall event over the South Island of New Zealand (Webster et al., 2008). The nested modelling approach is designed to be used for short durations to examine particular weather events in detail; it is not meant to be used for simulations lasting for one or more year. The high computational cost and data storage requirements also limits the length of the simulations.

In the present study, a global model and two nested models were used, which have resolutions of 60 km, 12 km and 4km respectively. An example of the three model domains, each of which has its own grid, is illustrated in Figure 1. The outermost grid shows the global configuration of the MetUM, the model used operationally by the Met

Office to produce numerical weather forecasts across the entire globe; only the portion which covers Europe is shown here. The domains of the12 km and 4 km grids are shown by the solid and dashed lines respectively on Figure 1. Meteorological data produced by the global model were used to initialise and drive (via boundary conditions, details of which are given in Appendix A) the 12 km model, which in turn produced boundary conditions to drive the 4 km model.

Figure 1. Example of the three domains used for the simulations. The map shows part of the global model domain. The solid line and dashed lines show the location of the 12 km and 4 km model domains respectively. These particular domains were used for the simulation examining deforestation and afforestation in southern Sweden. The positions of the 12 km and 4 km domains were different for each location studied.

An important difference between these three models is the treatment of convection. In the global and 12 km models, the spatial resolutions are not high enough to explicitly capture the physics of convection; instead, this phenomenon is represented using a column-based parameterisation scheme. However, in the 4 km model, this scheme is not required as the resolution is high enough to represent convection and its effects directly. Furthermore, to ensure that the simulations resemble the observed meteorological conditions as closely as possible, they were constrained using measured sea surface temperatures which were updated at daily intervals. Further details of the models used in the present study are provided in Appendix A.

As described above, the general principle of the nested suite is that the global model drives a 12 km model, which, in turn, drives a 4 km model. However, in the present study

this approach was extended in order to understand the effects of varying forest cover in a given region of interest. In particular, for each region to be studied, the 12 km model was used to drive three simultaneous simulations, each with a resolution of 4 km, which were identical except for the forest cover. The control simulation used the current forest cover, and the other two adopted increased and decreased forest cover respectively. These changes in forest cover were only implemented in the 4 km models. Using this approach, all three 4 km simulations are driven by the same 12 km model which provides the best way for making a scientific comparison between the results produced by the different 4 km simulations. The approach also provides a significant improvement in computational efficiency which is an important consideration given the intensive nature of the simulations.

3. Selection of Regions

In order to assess the sensitivity of weather patterns to forest areas at local and regional scales, five regions across Europe were selected which represent a broad range of biogeographical regions and climate zones while sampling the influence of the Atlantic and Mediterranean/Black Sea basins. The selection also took into account of the need to investigate regions whose weather patterns are potentially interconnected. For example, due to the prominence of westerly winds across Europe, changes in forest cover could be felt ‘downstream’, i.e., eastwards of the modified forest. For these reasons, areas within Spain, Italy, Germany, Austria and Sweden were chosen as appropriate regions for investigation and their locations are shown in Figure 2. For Spain, a larger area was also deforested and afforested, so that any impact of the size of the area could be assessed; this larger area is discussed below.

To study the influence of local afforestation and deforestation on weather patterns in the vicinity, the vegetation cover was modified within an oval-shaped area (as illustrated in Figure 2), by either replacing grasslands by trees (afforestation), or replacing trees by grass (deforestation). The oval shape was preferred over a rectangular (or any other geometrical) shape to avoid possible spurious results which might occur at or near any corners. A smooth transition between land cover types was used at the boundary of the oval area in order to avoid any effects which might occur from an abrupt change in land cover type. The characteristics of these five areas are summarised in Table 1, and the larger area of Spain which was studied is described further in section 4.2. The similar

longitudes for selected regions in Italy, Austria, Germany and Sweden means that it is also possible to investigate the influence of latitude in determining the behaviour and response of forest change.

Sweden

Germany

Austria

Italy

Spain

Figure 2. Overview of areas where forest cover was modified.

Name of Coordinates of centre Modified Forested Fractions Region Latitude Longitude Area / 1000 km2 Standard Afforested Sweden 56.8°N 14.6°E 43 0.50 0.65 Germany 51.4°N 13.2°E 66 0.08 0.40 Austria 47.3°N 12.5°E 50 0.43 0.55 Italy 43.1°N 12.7°E 39 0.15 0.49 Spain 40.2°N -1.2°E 47 0.13 0.37 Spain (L) 39.6°N 0.2°E 95 0.12 0.47 Table 1. Summary of the six regions studied. The coordinates shown are the centre of the oval areas shown in Figure 2, and the larger area of Spain (marked as “L”) is illustrated in Figure 4. The oval areas were either afforested or deforested by converting grasslands to trees and vice- versa. The final two columns show the average forested fraction over the areas of study in the standard land surface map and after afforestation; the forest fractions are zero in the deforestation experiments.

The forest fractions vary considerably in both the standard and afforestation scenarios between regions. In the standard forest cover, the largest forest fractions are found in Sweden and Austria and the smallest in Germany. For all regions, the afforestation

scenario increases forest cover by factors between 1.5 and 5.0 relative to the standard cover. Further details regarding each of the five regions are given in Appendix B. For all regions, the period 5th – 13th August 2002 was chosen to study the effects of forests on local weather. A severe flooding event in central Europe occurred between 6th and 13th August 2002 (RMS, 2003) which was mostly caused by a storm following the well-known “Vb” track (Figure 3). This event occurred in two phases. The first period of heavy rain took place on August 6th and 7th and was caused by a weak area of low pressure. This rain fell on the south-western areas of the Czech Republic and north- eastern Austria. Although some localised flooding occurred, much of the excess water was contained by existing flood defences. However, the remaining capacity of the defences was insufficient to prevent the subsequent flooding which happened a few days later. An extratropical cyclone classified as Type Vb (RMS, 2003) produced very heavy rainfall over a similar area between 11th and 13th August. These storms travel over south-west France1 or north-east Spain, over the north Mediterranean Sea and Italy, before moving in a more northerly direction to central Europe.

Figure 3. European Storm tracks (“Zyklonenbahnen”) as originally classified by van Bebber (1891). The Vb storm often begins over the eastern Atlantic and travels along the Va track, where it can branch into other directions. On Vb track, the storm crosses the northern Mediterranean where it can absorb moisture and then heads in a northerly direction to cross central Europe.

1 For this reason, a future investigation of forest cover change in France could be of interest

Two regions, north-east Spain and central Italy, were chosen to see if changes in forest cover could alter the characteristics of the August 2002 Vb storm. Some of the other regions (Austria, Germany) also lie on or near the typical Vb pathways. Southern Sweden is generally not affected by Vb storms, but has a different climate to the other locations. An earlier time period (10th – 18th April) was selected to investigate any differences in the influence of forests on weather between spring and summer, and ensured that the impact of forests across a broad range of weather patterns could be assessed.

Ideally we would like to investigate the impact of increasing the forested/deforested area gradually to determine whether the influence of the forest cover on weather patterns is continuous, or whether there are tipping points at which the impacts become notably more significant and easily quantifiable. However, given the time constraints of the project and the complexity of the models - especially interpreting the outcomes - the model experiments have focused almost entirely on investigating the impact of modifying forest cover within a well-defined area. Only for Spain has the effect of different forest/deforested areas been investigated, as shown in Figure 4. The large area is about twice the size of the smaller area, and was chosen to test the impact on humidity and moisture over the western Mediterranean Sea. The results of all the model simulations are described in the following section.

Figure 4. Illustration of the two areas which were deforested and afforested in Spain.

4. Results

The description and interpretation of the model results focus on the key meteorological variables identified in Task A2 which were shown to be influenced by forests. These are rainfall, surface temperature, surface moisture fluxes, wind speeds and humidity. When

analysing the results, it is important to note that, owing to the way interactions between the atmosphere and the land surface are represented, the model “surface” in forested locations is actually the top of the canopy. Therefore, the values of the meteorological variables analysed here are those immediately above the canopy. Consequently, the model does not simulate temperatures or other weather information either in or below the canopy of the trees. This is perhaps significant given that forests can insulate surface temperatures from extremes in the diurnal cycle, and means that there is likely to be a non-negligible quantity of cool air that is not accounted for by the model.

As indicated in the previous sections, the models were run for 8-9 days. Occasionally, technical problems meant that simulations of only 5 days could be completed. In all cases, results from the first day were not included in the analyses as the modelled weather will be adjusting to the initial conditions and land surface during this ‘spin-up’ period. For each region studied, the meteorological variables are shown as differences between the afforested or deforested scenarios and the control run. Additionally, the differences between the afforested and deforested scenarios are shown which highlight the role of the forests on weather. First, results for the summer period (5th – 13th August) are discussed, followed by those for spring (10th – 18th April). The general findings are introduced first, followed by a more detailed description for each region.

4.1 Summer period (5th – 13th August)

The effects of changes in forest cover on rainfall, surface temperature, surface moisture flux and wind speeds are summarised in Figure 5. The data illustrate changes in each of the four variables relative to the deforestation scenario as a function of mean fractional forest cover over the region studied (Figure 2). The data shown in Figure 5 demonstrate clearly that increasing the forest cover results in higher mean surface temperatures and lower wind speeds in all regions. The effect of forest cover changes on rainfall is less clear. For Sweden and the two areas in Spain, the rainfall increases slightly as the forest cover is increased. Over Italy, less rainfall is simulated in the control scenario than the deforestation scenario, but there is an increase in rainfall in the afforestation scenario. The rainfall over Austria and Germany is larger in both the control and afforestation scenarios than the deforestation scenario, but has decreased between the control and afforestation scenario. Overall, these results suggest that rainfall over the afforested area is larger than when it is deforested, but there is also considerable variability of rainfall which partly obscures the effects of the forest cover changes. Longer

simulations, and simulations in different years, would allow a better picture of the impact of the forest cover changes to be found.

Figure 5. Change in rainfall, temperature, surface moisture flux and wind speed as a function of mean fractional forest cover over the areas studied (Figure 2) for summer. The data shown are changes relative to the deforestation scenario, which by definition has a fractional forest cover of zero. The solid circle and open triangles indicate the fractional forest cover in the control and afforestation scenarios respectively.

For the regions studied here, the surface moisture flux generally appears to decrease slightly as the forest cover is increased, suggesting that evaporation from the surface is more important than transpiration by forests. This result implies the soil moisture levels in these simulations are high. Teuling et al. (2010) showed that evaporation from grasslands can be higher than forested areas under such conditions, but the moisture flux would be higher from forested areas if the soils were drier. The exception is Austria, which exhibits a very small increase in the moisture flux. An explanation for this difference is not obvious, but could be because the soil moisture in Austria was lower

than the other regions. Therefore, in order to determine a more coherent picture of the influence of forests on surface moisture fluxes, it would be necessary to consider a much larger sample of regions in a broader region of biogeographical zones.

The mean changes in rainfall, surface temperature, moisture flux and 10 m wind speed between the afforestation and deforestation scenarios during the summer period (August) are summarised in Table 2. The values shown are averages over the areas which were afforested and deforested (see Figure 2). Positive values indicate the variable has a mean value which is larger in the afforestation scenario than the deforestation scenario. The changes in surface temperature indicate that temperatures are warmer above the forests, and the wind speeds are always lower in the afforestation scenario than the deforestation scenario, by 13-17%. Rainfall is always (on average) larger in the afforestation scenario than the deforestation scenario, but the differences are very small, and rainfall in the afforestation scenario was greater than in the deforestation scenario only 44 – 58% of the time. The rainfall changes were less than 10%, except over Germany and Sweden. Changes in the surface moisture flux are also small. None of the differences are significant at the 5% confidence level.

Rainfall Temperature Moisture Flux 10 m Wind Speed / mm hr-1 / °C / mm hr-1 / m s-1 Austria 0.06 0.16 0.001 -0.34 Germany 0.06 0.13 -0.002 -0.38 Italy 0.05 0.22 -0.003 -0.51 Spain 0.005 0.23 -0.001 -0.48 Spain (Large) 0.005 0.26 -0.006 -0.57 Sweden 0.02 0.53 -0.017 -0.50 Table 2. Mean differences between the afforestation and deforestation scenarios for four meteorological variables for the August (summer) simulations. The differences are average values calculated over the areas where forest cover was altered. A positive value indicates the variable has a mean value which is larger in the afforestation scenario than the deforestation scenario.

Changes in surface humidity, turbulent mixing height, very low and low cloud amounts above the areas under study (see Figure 2) were also examined. The results are shown in Figure 6. There was no clear overall effect of forest cover change on humidity, although the humidity increases above the forests at the inland locations (Austria, Germany and Spain), but decreases over the areas adjacent to coastlines (Italy, Spain (L) and Sweden). These changes appear to be at least partially independent of the

changes in the surface moisture flux (see Figure 5), suggesting that enhanced mixing of moisture evaporated from the surface could be the reason for the increased moisture levels. As expected, the turbulent mixing height increases as the forest cover is increased. This variable is a measure of the depth of the atmosphere over which mixing from the surface occurs. Forests induce turbulence in the air above owing to their high aerodynamic roughness, so a rougher surface will generally have a higher mixing height. The very low cloud amount appears to decrease slightly with increasing forest cover, but the changes are very small. The low cloud amount increases in four out of six locations with increasing forest cover, but the effect at the remaining two locations is unclear.

-1 Figure 6. Change in humidity (Q, kg(H2O) kg(air) ), turbulent mixing height, very low and low cloud amounts as a function of mean fractional forest cover over the areas studied (Figure 2) for summer. The data shown are changes relative to the deforestation scenario, which by definition has a fractional forest cover of zero. The solid circle and open triangles indicate the fractional forest cover in the control and afforestation scenarios respectively.

4.2 Effect of forest size

Two different areas of Spain were studied, the smaller was located inland and the larger along the Mediterranean coast (Figure 4), and the area of the larger region was twice the size of the smaller one.

Figure 7. Hourly time series of differences in rainfall, temperature, surface moisture flux and wind speed for the small area of Spain (Figure 4, left-hand panel). The three lines in each panel show temperature differences between the afforestation and control scenarios (green), deforestation and control scenarios (red) and afforestation and deforestation scenarios (blue).

Figure 8. Hourly time series of differences in rainfall, temperature, surface moisture flux and wind speed for the large area of Spain (Figure 4, right-hand panel). The three lines in each panel show temperature differences between the afforestation and control scenarios (green), deforestation and control scenarios (red) and afforestation and deforestation scenarios (blue).

Time series of rainfall, surface temperature, surface moisture flux and 10 m wind speeds for summer are shown in Figure 7 and Figure 8 for the small and large areas of Spain respectively. These results indicate that changing the forest cover in each of these two

regions has similar effects on rainfall, surface temperature, surface moisture flux and wind speeds. However, the diurnal cycles of all four variables are more regular when the large area of eastern Spain is afforested or deforested (Figure 8) compared with the small area (Figure 7). This could be because the effects of the large-scale atmospheric circulation have been damped down by a greater amount when the large area is deforested or afforested, or simply because the climate variables have been averaged over a larger area. For the large area (Figure 8) the temperature and surface moisture fluxes are strongly correlated. Focusing on the differences between the afforested and deforested simulations (blue lines), during the morning the moisture flux from afforested area is higher than the deforested area, and the temperatures in the former are cooler than the latter. Later in the day, the moisture flux in the deforestation scenario rises above that of the afforested scenario, reversing the sign of the temperature difference. This effect is less clear for the smaller area studied (Figure 7).

4.3 Spring period (10th – 18th April)

The effects of changes in forest cover on rainfall, surface temperature, surface moisture flux and wind speeds during spring are summarised in Figure 9. The data illustrate changes in each of the four variables relative to the deforestation scenario as a function of mean fractional forest cover over the region studied. For Spain, only the small area (Figure 4, left-hand panel) was considered.

These results indicate that increasing the forest cover results in lower wind speeds and higher surface temperatures. Similar results were obtained for the summer period (Figure 5), but the temperature increases for spring are smaller, as there is less incoming solar energy during spring than summer. For temperature, the exception is Italy, where, initially, the surface temperature is lower in the control scenario (which uses the standard vegetation cover) than either the deforestation or afforestation scenarios. However, this may be an artefact of the period studied. The temperature time series for Italy is shown in Figure 10. It can be seen that there are periods, mostly after the 15th April, when temperatures in the afforestation scenario are warmer than those in the control and deforestation scenarios. It is not clear why the temperature differences between the 11th and 12th, and the 14th and 15th of April are reversed in order. The surface moisture flux data do not show any shift in behaviour during these periods compared with the other times (data not shown).

Figure 9. Change in rainfall, temperature, surface moisture flux and wind speed as a function of mean fractional forest cover over the areas studied (Figure 2) for spring. The data shown are changes relative to the deforestation scenario, which by definition has a fractional forest cover of zero. The solid circle and open triangles indicate the fractional forest cover in the control and afforestation scenarios respectively.

Figure 10. Time series of hourly temperature differences for Italy during spring. The three lines in each panel show temperature differences between the afforestation and control scenarios (green), deforestation and control scenarios (red) and afforestation and deforestation scenarios (blue).

-1 Figure 11. Change in humidity (Q, kg(H2O) kg(air) ), turbulent mixing height, very low and low cloud amounts as a function of mean fractional forest cover over the areas studied (Figure 2) for spring. The data shown are changes relative to the deforestation scenario, which by definition has a fractional forest cover of zero. The solid circle and open triangles indicate the fractional forest cover in the control and afforestation scenarios respectively.

There is no consistent effect of changing forest cover on rainfall in any of the regions studied. For Spain and Italy, rainfall is smallest in the deforestation scenario, and larger amounts are simulated in the control and afforestation scenarios. Over southern Sweden, very little change with forest cover is simulated, suggesting that the rain is produced by weather fronts which would not be strongly affected by changes in forest cover. For Austria, the least rainfall is simulated in the control simulation, and for Germany, a sharp reduction in rainfall is modelled with increasing forest cover. This behaviour is different to that simulated during the summer period, when there is a general trend of increasing rainfall with increasing forest cover in all regions studied.

For the regions studied here, the surface moisture flux is simulated to decrease as the forest cover is increased during the summer period, except for Austria, where a small increase is simulated. This result implies that soil moisture levels are high in these simulations, except in Austria. Rerunning the simulations for August 2003 might produce different results, as the soils would be much drier.

The simulated changes in humidity, turbulent mixing height, very low and low cloud amounts as a function of forest cover during spring are shown in Figure 11. Overall, there is an increase in the turbulent mixing height, but the changes are smaller than those simulated during the summer period (Figure 6). There is no consistent effect on humidity or cloud amounts.

5. Summary The effect of changes in forest cover on weather for five different regions of Europe has been investigated using a series of nested models. Three simulations were completed for each region – a control simulation which used the present-day vegetation cover, and two further simulations to study the effects of afforestation and deforestation. These simulations were run for two time periods: 5th – 13th August 2002 (summer) and 10th – 17th April 2002 (spring).

Near surface temperatures (just above the forest canopy) were warmer with increasing forest cover in all five locations and both time periods, and the enhancement of surface temperatures was greater in summer than spring. These results were expected; forests have low albedos, absorb much of the incoming solar radiation and increase the temperature of the air above the canopy via the transfer of sensible heat. Grasses (used to replace forests in these simulations) have higher albedos, and so absorb less radiation. Consequently, daily mean temperatures just above the afforested areas are usually warmer than those over deforested areas. In some regions (Sweden, Spain (both large and small areas) and Germany), temperatures in the deforested area are similar to or warmer than the afforested areas during the morning (for a few hours centred on 6 a.m.). The grassland used to replace forests warms more quickly than the forests during the morning, and so briefly has higher surface temperatures than the forest, but afterwards the forests are always the warmest.

Wind speeds became lower and the turbulent mixing height became larger as forest cover was increased in all regions and both time periods. The high aerodynamic roughness of forests (compared with grasslands) reduces the surface wind speeds and increases turbulence in the air stream. This increased turbulence could be the reason for the enhanced humidity seen over the inland locations during summer (Austria, Germany and Spain), although advection of moister air may also play a role. The simulated changes in humidity do not appear to be strongly controlled by the surface moisture flux. Cloud cover changes were small in all time periods and locations.

During summer, total rainfall over afforested areas is greater than rainfall over deforested areas in all regions, but the differences are mostly small (of the order of a few percent), and were not significant. For spring, the effect of changes forest cover on rainfall varied between regions, with enhancements for Spain and Italy, little change over Sweden and a decrease over Germany.

The surface moisture flux exhibits considerable diurnal variation. In some areas (Spain) it appears to moderate the rise in surface temperature during the afternoon. The effects of forest cover changes on specific humidity (mass of water vapour per unit mass of air) above the areas under study, and the surroundings, were also investigated. No clear change was seen at any time or location. Low cloud amounts seem to increase slightly with increasing forest cover, presumably owing to increased turbulence and moisture levels above the forest canopy, but the effect on very low cloud amounts is less clear.

No evidence was found (neither from the model simulations described under Task A3_D1 nor the literature reviews in Tasks A2 and A3_D2) to suggest that the changes in forest cover modified the severity of the Vb storm which caused the severe flooding in central Europe during 2002. Even if an effect had been found, it would be necessary to repeat the simulation with other models to support or refute this result.

The results of this study should be regarded as an initial investigation. In order to fully evaluate the effects of forests on weather locally and more distant locations, a number of additional simulations could be performed which are outside the scope of the present study. Here, only two 9 day periods in one year have been used to assess the effects of changes in forest cover on local weather. A better picture of the overall effect of changes in forest cover could be realised by using longer simulations (e.g., 1 – 3 months) and different years, so a wider range of weather events and synoptic conditions

are sampled. Extra locations could also be used, such as north-west France, the UK and others in south-east Europe. An additional nested model with a resolution of 1 km could be used, as this model would simulate convective clouds and rainfall more realistically. Previous studies by da Silva and Avissar (2006) and Webster et al. (2008) have shown that a 1 km model reproduced observed rainfall from convective events more accurately than a 4 km model, and that the 4 km model tends to underestimate observed rainfall amounts. However, the computational cost would increase by an order of magnitude if a 1 km nested model was also used. The forest cover within the regions studied could also be increased to 100%. Although this would be unrealistic, simulations using this maximum possible cover could provide additional useful results.

The COPS project (Convective and Orographically-induced Precipitation Study) took place in southern Germany and eastern France from June to August 2007 (Wulfmeyer et al., 2009). A wide range of instruments, such as lidar, rainfall radar, cloud radar and surface-based observations, were used to collect high quality and high resolution data which could be used for initialisation and validation of weather prediction models. The data collected by the COPS project could be used to validate results from the nested modelling suite. However, it would not be possible to confirm the simulated effects of afforestation and deforestation by the nested suite.

Task A3_D2: Literature review of past studies on local and micro-weather conditions

In this section, published studies on the small-scale impacts of forests on local and micro-weather conditions are summarised and discussed. The studies include modelling and observations, together with more qualitative analyses. The effects of urban areas on local weather are also described.

The COPS project (Convective and Orographically-induced Precipitation Study) took place in southern Germany and eastern France from June to August 2007 (Wulfmeyer et al., 2009). A wide range of instruments, such as lidar, rainfall radar, cloud radar and surface-based observations were used to collect high quality and high resolution data which could be used for initialisation and validation of weather prediction models. The results from this study showed that evapotranspiration of moisture by forests was a very important component of convective cloud formation and rainfall.

The urban heat island (UHI) of cities is a well known phenomenon that has been observed since the 19th century. Bohnenstengel et al. (2011) have used a series of nested models to simulate the urban heat island of London. The model used is very similar to the nested suite describe in section 2 of Task A3_D1 above, in that several models were multiply nested, and the highest resolution model (1 km) was centred over London. A more sophisticated scheme to simulate energy exchange between the surface and the atmosphere, and the properties of the urban environment was also used. This study focused on the urban heat island of London, and the time studied (6th – 9th May 2008) was characterised by a high pressure system over the UK and light easterly winds with very little cloud; rainfall from convective systems did not occur. The study by Bohnenstengel et al. (2011) showed that increasing the fraction of grass within London reduced the magnitude of the UHI, although a minimum cover of 10-20% was required before any noticeable effect was seen. This modelling system could be used to simulate the effects of increased tree cover in the city of London, and any impacts of the urban heat island on the formation of convective systems and rainfall.

A study by Météo-France and other consortia (Météo-France, 2009) examined the role of forests in mitigating the urban heat island of Paris in 2030. Simulations using a mesoscale model showed that extending the forest cover around the city by 40%, along with other measures such as highly reflective paints, suggested that the UHI would be

3°C cooler in suburban areas and 2°C cooler in Paris itself. The forests would also be exploited for timber and fuel.

It has been proposed that the UHI could alter mesoscale circulations and the formation of convective clouds and rainfall. Investigations of this possible effect in southern parts of the USA since the late 1960s found evidence of a slight increase in warm-season rainfall over the urban centre, but much larger enhancements downwind of the urban area (Shepherd et al. (2002) and references therein). Shepherd et al. (2002) have analysed three years worth of satellite-derived rainfall recorded around several cities in the southern part of the USA, and found significant enhancement of rainfall downwind of the urban centre, by 15 – 51%. On average, a slight increase in rainfall over the urban centres was seen, but this result was not conclusive

Schlünzen et al. (2010) have analysed long-term records of temperature and rainfall located in and around the city of Hamburg, Germany. They found that rainfall at locations 30 – 40 km downwind of the city was enhanced by 5 – 20% compared with upwind areas. Similar results were found around the city of St Louis, USA. Thielen et al. (2000) simulated urban impacts on convective rainfall for Paris using a 2D mesoscale model, and found two maxima in rainfall downwind of the city. The maximum closest to the city was attributed to the UHI itself, whereas the second maximum was thought to be caused by the enhanced roughness of the urban area. However, a later modelling study by Rozoff et al. (2003) using a more realistic representation of the urban area and energy balance found that the distinct heat fluxes of the urban centre were the cause of enhanced storm activity downwind of the urban centre. Model simulations and analysis of observations by Mölders (1998) showed that an enhancement of rainfall occurs downwind of the German cities Leipzig and Dresden. Other studies have considered the impact of aerosols produced in urban areas and found that the aerosols caused the rainfall to decrease (Givati and Rosenfeld, 2004; Bigg, 2008). It should be noted that rainfall in and around only a few cities in Europe have been studied, and only a small number of events have been analysed. It is still not clear whether this effect of urban areas on rainfall is common, and for which cities in Europe it is important for.

Trusilova et al. (2008) used a high resolution (10 km) regional climate model to simulate the effects of urbanisation in Europe on surface temperature and rainfall. They found that the diurnal temperature range in European cities was reduced by 1.2°C compared to rural areas. Winter rainfall in cities was enhanced by ~8% but summer rainfall was

~19% smaller. The urban areas had important effects on weather in the surrounding areas too.

Donnison (2012) has reviewed the available literature on the effects of trees planted as shelterbelts on crop yields and livestock protection in Europe. Overall, the available studies suggest that shelterbelts constructed with deciduous trees of the correct size and porosity can enhance crop yields by reducing wind speeds within fields (and so protecting crops from damage), increase temperatures and reduce evapotranspiration losses. The trees can also partly reduce flooding because the porosity of the soil is increased by penetration of the tree roots. The use of deciduous trees reduces the effects of shading on crop yields as there are periods when the trees have no leaves. Shelterbelts may have the largest effects where winds directions are stable and water availability limits plant growth.

Task A3_D3: Implications of sensitivity tests and relevant literature for forest protective functions

The modelling results presented under Task A3_D1 suggest that the main impacts of forests on weather are to enhance local temperatures above the canopy and reduce wind speeds. Overall, experiments where certain areas were deforested and afforested indicated that rainfall during the summer (here, 6th – 13th August) was higher over the forested areas, but the increases were small (< 10%, mostly just 3-4%) and were not statistically significant. During spring, similar effects were seen, but the effect on rainfall was less clear, as both increases and decreases were simulated. The COPS project (Wulfmejer et al., 2009) studied summer convective rainfall over parts of south-west Germany and eastern France, and found that evapotranspiration of moisture by forests was very important for the formation of convective clouds and rainfall.

The modelling study by Météo-France (2009) has shown that increasing forest cover in the suburbs of Paris, together with other measures, could reduce the temperatures in the city by 2-3°C.

The review by Donnison (2012) indicates that trees planted to form shelterbelts can improve crop yields in Europe, by reducing crop damage caused by winds and reducing evaoptranspiration rates and so improving the water use efficiency of crops. To maximise the benefits, the type of trees used, the density and orientation of the shelterbelt must be planned carefully. Shelterbelts are of greatest benefit during droughts or other extreme events.

The effects of forests on soil conservation and stability cannot be simulated directly with the nested suite (Task A3_D1). However, the literature review presented in Task A2_D3 has shown that forests are important for soil conservation and stability. On sloping areas, the roots of trees mechanically anchor soils and prevent their slippage. Donnison (2012) noted that the removal of shelterbelts in parts of France had resulted in erosion of soils, and that regulations now exist for the replanting of these shelterbelts. Studies by Polemio and Petrucci (2010) and Wasowski et al. (2010) on landslides in Italy found that landslip events were correlated with heavy rainfall and the number of wet days. Despite the drying trend in the areas studied, landslips have increased in frequency owing to larger areas of sloping land being ploughed for crops. Models to simulate the effects of

weather and climate on the stability and failure of slopes, and the role of vegetation on the stability of slopes (for example, Glendinning et al., 2009) could be driven using data produced by the nested modelling suite to test the effect of forest cover on soil stability.

Forests can also limit the effects of heavy rainfall, and are important for the maintenance of clean water supplies. The tree roots increase the soil porosity, allowing water to infiltrate the soil more easily and reducing surface runoff. Forest soils also act to filter out suspended matter and produce clean water. High resolution river flow models are available, for example, TRIP (Oki and Sud, 1998; Oki et al., 1999) and G2G (Bell et al., 2007). Meteorological and surface runoff data from the nested modelling simulations presented here could be used to drive these models and examine any changes in river flows and subterranean water movement following afforestation and deforestation of the study areas.

The forest understory is an important component which reduces soil erosion. Raindrops collect together on leaves before falling to the ground, and can cause splash damage. The understory helps to prevent this happening. The health of the forest is important for maintaining stable soils, because landslides occur more frequently in forests with unhealthy trees and open spaces.

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Appendix A

In this appendix, full technical details of the nested modelling suite and other information regarding the models are given.

Nested Model Simulations

The numerical simulations of the influences of forest areas on weather patterns at a local scale were performed using a series of nested models. These models are structurally identical but are run at increasing horizontal resolutions (here, 60 km, 12 km and 4 km) but the areas modelled becomes progressively smaller. A similar series nested models, in which the highest resolution was 1 km, has been used to study a severe weather event over the South Island of New Zealand (Webster et al., 2008).

In the present study, the outermost model uses the global configuration of the MetUM, the model used operationally by the Met Office to produce numerical weather forecasts across the entire globe. The horizontal resolution of the global model is approximately 60 km over Europe, with 70 levels in the vertical between the surface and an altitude of 65 km. The two nested models have horizontal resolutions of approximately 12 km and 4 km, but the vertical resolutions are the same as the global model. Both the nested models use a rotated grid centred at the same point, which minimises differences in the horizontal directions over the domain. Lateral boundary conditions for the 12 km model were created from the global configuration of the model; similarly, boundary conditions for the 4 km model were derived from the 12 km model. These boundary conditions were linearly interpolated in time between consecutive fields and updated every hour for the 12 and 4 km models.

A number of improvements have been made to the 4 km model. First, the large-scale precipitation scheme was modified to treat rain prognostically, so that the horizontal and vertical advection of falling rain is included. In the coarser resolution models, advection of rain is not important, and the rain is diagnosed using the meteorological conditions simulated at each time step. Second, a fully three-dimensional potential temperature advection scheme was employed which gives an improved gravity-wave response. However, in order to run stably, the time step of the 4 km model had to be reduced to 30 s. A further change was to include an additional shear-dependent horizontal viscosity somewhat akin to the Smagorinsky-Lilly turbulence scheme to prevent the build-up of

unphysical grid-scale noise (Webster et al., 2008). Another important difference between the global and 12 km, and the 4 km models is the treatment of convection. In the global and 12 km models, convection is represented using a column-based parameterisation scheme. In the 4 km model, this scheme is not used as the resolution is high enough to represent convection and its effects directly.

The simulations were initialised using data obtained from European Centre for Medium- Range Weather Forecasts (ECMWF) global analysis fields, and all simulations were initialised at 0 hours on the first day. In order to constrain the simulations, observed sea surface temperatures from the OSTIA analysis (Stark et al., 2007) for the periods of interests were used by all three models, and were updated at daily intervals. OSTIA uses satellite data together with in-situ observations to determine the sea surface temperature. The analysis is produced daily at a resolution of 1/20° (approx. 5 km) and is then aggregated to the grid sizes used by each of the three models.

The changes to the land surface, to study the effects of afforestation and deforestation on local weather, were only implemented in the 4 km model. In order to reduce computational load, three versions of the 4 km model were initialised and integrated from the 12 km model. These three versions had the standard land cover, an afforested land cover and a deforested land cover within a specified region. The land cover changes are described in more detail in the following section.

Land Surface

Land surface processes (for example, evaporation, transpiration, heat exchange and runoff) are simulated by the JULES land surface scheme (Best et al., 2011). The land surface within each model grid box may divided into one or more of nine possible land cover types, including trees, grasses, shrubs, urban, inland water, soil and ice. The JULES model simulates surface temperature, humidity, evaporation and many other processes separately for each land surface type.

The sum of the land type fractions within any given grid box must be equal to 1. Regions which are predominantly urban, inland water or ice are unlikely to be targets for forestry. Therefore, when afforesting a region, all grasses were converted to trees. Conversely, when deforesting a region, all of the trees were converted into grasses.

An oval shaped area was defined for each region, and the land surface within the oval area was deforested and afforested. The physical area within which forest cover was modified varied somewhat according to its location. In general, the semi-major axis subtended 1˚, while the semi-minor axis subtended 0.5˚. The area is comparatively small, but, perhaps, represents the size of a region that could plausibly be reforested without displacing large numbers of people or animals. To broaden the investigation, we also modified the forest cover in a much larger region along the Spanish east coast. This region is of potential significance for determining weather patterns in Spain, as well as severe flooding events in central Europe, as highlighted by Millán (2008). In this case, the semi-major axis subtended 3.5˚, while the semi-minor axis subtended 0.8˚. In general the length of 1˚ of latitude is taken to be approximately 111 km. However, since the Earth is only approximately spherical, the length associated with 1˚ of longitude varies significantly with latitude. The formula for calculating the distance of 1˚ of longitude as a function on latitude is given by:

π a cosφ ∆1 = lon 180(1− e 2 sin 2 φ)1/ 2 where a is the radius of the Earth, φ is the latitude, and e is the eccentricity of the Earth, which has a value of ≈ 0.0067 according the World Geodetic System (1984).

Appendix B

Further details of the five regions selected for afforestion and deforestation experiments are given here, including an explanation of why they were chosen.

Sweden

Sweden has the largest forest area in the EU, with approximately 76% of its area covered by trees. The low albedo of the forest probably acts to warm Europe's northern latitudes, while evapotranspiration provides moisture which will be deposited as rain or snow. The prevailing circulation (as indicated by wind speeds and directions at a pressure of 850 hPa) moves air eastwards into Finland and the Baltic region - also highly forested areas - suggesting that Sweden's forests may primarily influence countries in the north-east of Europe. However, in the presence of northerly or north-easterly winds, Sweden's forests could influence weather in central Europe, perhaps reaching as far west as France. It would therefore be interesting to investigate the impact of deforestation in Sweden on weather patterns in central Europe and the Baltic region.

Germany

Land use changes in eastern Germany could affect weather in eastern Europe, particularly Poland, and perhaps the Baltic region. In contrast to Austria – its neighbour to the south - the part of Germany that is north of the continental divide should experience a higher fraction of precipitation contributed by the Atlantic Ocean, than from the Mediterranean and Black Seas. Studying the response of two regions in such close proximity, but separated by the continental divide, should permit a comparison of the relative influences of the Atlantic and Mediterranean basins. Furthermore, Germany lies downstream of Austria for Vb storms moving northward. Consequently, it would be of interest to investigate the impact of both deforestation and afforestation in this region to assess whether land cover change influences the precipitation patterns in the event of a Vb storm.

Austria

The region including Austria, Slovakia and Hungary is a comparatively densely forested region in central Europe, which probably falls under the hydrological influence of the Mediterranean and Black Sea basins. The region also experiences significant quantities of summer precipitation as a consequence of evaporation from the continental land surface. Forests in this region certainly lie in the path of Vb storm tracks, and so could potentially influence the evolution of such events. For example, it would be interesting to find out if increased forest cover contributes additional moisture to the storms, thereby intensifying the flooding, or whether higher surface temperatures modify the convective condensation level in such a way that it alters the spatial distribution of the precipitation.

Aside from the consideration of Vb storm tracks, changes in forest cover in this central location could plausibly influence weather patterns in the Balkans and eastern Europe, for example Romania and Bulgaria, which have also experienced floods in recent years, notably in 2005.

Italy

Central and northern Italy lies on or near the typical path of a Vb storm as it traverses the Mediterranean and begins moving northwards. In addition, the east coast of Italy affords relatively sparse forest cover, in comparison to the west coast of the country. This is probably partly a result of being in the rain shadow of the Apennines for westerly storms. As a result, the region is likely to experience warm, drying winds due to the Föhn effect, meaning that local precipitation rates could potentially be enhanced by increasing forest cover. Furthermore, given that the east coast of Italy is on the leeward side of the Apennines for a Vb storm, increasing the forest coverage could modify the properties of the storm. For example, higher evapotranspiration rates could contribute additional moisture to the storm. Alternatively, the decrease in albedo could lead to higher surface temperatures, which would heat the air. In principle, these effects could exacerbate flooding in central Europe, or perhaps modify the distribution of the precipitation such that the severity of the flood is reduced. Verification of both hypotheses would require investigation.

Spain

According to the Köppen scale, Europe comprises approximately 10-11 climate zones. Spain accommodates eight of the eleven European climate zones and has both large total forest area and covering fraction. In addition, prevailing circulation patterns (identified from the 850 hPa wind) move air eastwards across Spain into the rest of Europe, making Spain an ideal candidate for investigating the ‘downstream' influence of forests. There are several regions of Spain that would provide interesting studies on the impact of afforestation and deforestation. For example, the north may be a good candidate region for investigating the impact of deforestation on the weather of Atlantic Europe, particularly France. In contrast, modifying forest cover in south east Spain may be a good candidate region for investigating the influence of forest cover on weather in the western Mediterranean and beyond. Given the extensive work by Millán (2008), which suggests possible links between deforestation on the mountain slopes near Valencia and the loss of summer storms, we have chosen to study changes in forest cover at approximately that locations, in the provinces of Valencia and Castellón. In addition, we have also studied the impact for changing the forest cover along the entire east coast of Spain, ranging from Gerona in the north east, to Almería in the south, and reaching as far in land as Teruel, Cuenca and Albacete.

Met Office Tel: 0870 900 0100 FitzRoy Road, Exeter Fax: 0870 900 5050 Devon EX1 3PB [email protected] United Kingdom www.metoffice.gov.uk Task A4: Assessment of forest influence on weather patterns at an EU scale European Commission (DG Environment) July 2012 Monia Santini, Paola Mercogliano, Myriam Montesarchio, Edward Pope and Michael Sanderson

Executive Summary ...... 4

Task A4 ...... 5

Task A4_D1: Key recurrent weather phenomena in the EU ...... 5

Introduction ...... 5

1. Storms ...... 7 1.1. Northern Europe ...... 7 1.2. Central and Eastern Europe ...... 8 1.3. Mediterranean Europe and Southern Europe ...... 10

2. Heat waves ...... 11 2.1. Northern Europe ...... 12 2.2. Central and Eastern Europe ...... 13 2.3. Mediterranean and Southern Europe ...... 13

3. Cold waves ...... 14

Task A4_D2: Sensitivity of climate patterns to forest cover from analyses of high- resolution model runs...... 16

1. Introduction ...... 16

2. Simulated domains ...... 17

3. Experimental setup ...... 18

4. Results and discussion ...... 20 4.1. Temperature and Precipitation ...... 20 4.1.1. Southern Italy: comparison between past and future period...... 20 4.1.2. Romania: comparison between past and future period...... 25 4.1.3. Temperature and precipitation analysis under different land cover ...... 29 4.2. Hydrological balance ...... 32 4.3. Extremes ...... 38 4.3.1. Temperature ...... 38 4.3.2. Precipitation ...... 44 4.3.3. Maximal wind speed at 10 metres ...... 46

Task A4_D3: Assessment for EU-wide results using the nested UM runs ...... 49

1. Introduction ...... 49

2. Results ...... 50 2.1. Weather simulations ...... 50 2.2. Climate ...... 52

3. Limits and gaps ...... 53

Task A4_D4: Weather Interactions between the Mediterranean, Atlantic and Black Sea basins ...... 55

1. Introduction ...... 55

2. Atmospheric Transport of Water Vapour ...... 57

3. North Atlantic Oscillation (NAO) ...... 61

4. Inflow and Outflow from the Mediterranean Sea ...... 62

5. Sea Level Rise in the Mediterranean and Black Sea ...... 64

References ...... 67

Annex A4_D2.1 - The regional model COSMO-CLM ...... 79

Annex A4_D2.2 – Supplementary figures ...... 82

Executive Summary

• The weather-related phenomena of most concern across the EU are identified as storms, floods, heat waves (including droughts) and cold waves. Storms and floods occur in large parts of Europe, while cold waves and extreme winters primarily affect northern, western and eastern Europe; the Mediterranean has principally suffered from warm/dry extremes and coastal flooding. • The COSMO-CLM regional climate model has been used to investigate the impact of afforestation and deforestation on the climate of two regions which are representative of the Mediterranean and Black sea basins: southern Italy and Romania. In Italy, afforestation caused a decrease of mean temperature, with deforestation increasing temperatures; these results are in contrast to those produced by the Met Office nested suite, presented in Task A3. The same trends were observed for Romania in the summer months, while winter showed the opposite result. • The mean annual precipitation showed a (slight) increase for higher forest cover in both Italy and Romania, with the changes being concentrated in the respective wet seasons for both areas, i.e. autumn-winter in southern Italy and summer in Romania. • Looking at climate extremes, afforestation appears, on average, to reduce the frequency and severity of high temperature events, while also reducing precipitation extremes (day frequency and percentile values) and wind speed (mean and variance). • The output from the COSMO-CLM simulations has been compared with the results from Met Office nested suite simulations, in order to highlight similarities and differences, and to describe knowledge gaps that need to be addressed in forthcoming studies. • The interactions between the Atlantic Ocean, and the Mediterranean and Black Seas are summarized, focusing particularly on their seasonal contribution to precipitation across the EU. The key long-term influences on this interaction are found to be the North Atlantic Oscillation, and water and heat exchanges across the Gibraltar and Dardanelles Straits.

Task A4

The objectives for this task are:

• A4_D1 - identify key recurrent weather-related phenomena of concern across the EU and provide specific analyses and assessment of the results with respect to these phenomena. • A4_D2 - Assess the sensitivity of weather/climate patterns to forest areas at EU scales by running the CMCC very-high resolution GCM over pan-European and Regional Climate Model (RCM) over sub-continental domains. • A4_D3 – Integrate outputs from Task A4_D2 with results from the EU-wide component of the nested UM model runs performed for Task A3. • A4_D4 - Prove particular assessment on the weather interactions between the Mediterranean, Atlantic and Black Sea Basins.

Task A4_D1: Key recurrent weather phenomena in the EU

Introduction

It is clear that Europe’s climate and weather patterns are changing. In particular, Klein- Tank et al. (2002) showed that mean temperatures have increased by ~0.5˚C per decade since the 1970s. Over the same period, precipitation has generally increased in the north of Europe, but decreased in the south, with the number of wet winter days showing a similar trend. However, it is not only the mean climate that is changing, but also that the frequency and severity of extremes and extreme weather events. For example, several studies (e.g. Klein-Tank and Können, 2003; Alexander et al., 2006) have shown that the number of warm1 days and nights and warm spell days2 is increasing across Europe, while climate model simulations project an increase in the frequency and severity of heat waves (Clark et al., 2010; Fischer and Schär, 2010). Alexander et al. (2006) and Zolina et al. (2010) have also found an increase in the number of heavy precipitation days3 across much of Europe (excluding the Iberian

1 These are days and nights having temperatures above the 90th percentile for the calendar-day temperature distribution calculated from the 1961-1990 reference period. 2 Cold (warm) spells are periods of at least six consecutive days with daily mean temperatures below (above) the lower (upper) 10th percentile of the temperature distribution for each calendar day in the 1961-1990 reference period. 3 The number of days with heavy precipitation above 10 mm per day.

Peninsula), along with an increase in the duration of wet spells (consecutive days with significant rain) and an increase in the frequency of extreme rainfall events. There is a higher risk of longer dry spells, and arid and semi-arid areas are expanding. In northern Europe, on the other hand, the precipitation totals are generally increasing, particularly in winter. Given these results, it is no surprise that Europe is projected to be a ‘hotspot’ which will experience further significant changes in its climate (e.g. Giorgi, 2006). This result is significant because an increase in the severity and/or number of extreme weather events will have a negative impact on the natural and urban environments, as described in detail below. The weather-related phenomena of concern can be attributed to general groups of extreme event: storms, heat waves and cold waves.

• Storms are associated with high winds, flooding, lightning strikes and hail. These events can cause damage to urban environments, public infrastructure, property, crops, trees and other vegetation, and may also lead to landslides and soil erosion. • A heat wave is defined by the International Disaster Database (http://www.emdat.be/4) to be ‘a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region’. They can lead to increased heat stress (especially in urban areas), increased energy consumption for air conditioning, damage to infrastructure (e.g. melting roads, thermal expansion of rails causing buckling), drought5, reduced crop yields, low river flows adversely affecting natural ecosystems, restrictions on water usage, damage to natural vegetation potentially leading to desertification and soil loss if followed by heavy rainfall events. • A cold wave is defined by the EMDAT database to be ‘a prolonged period of excessively cold weather’ and includes ‘the sudden invasion of very cold air over a large area’. Along with frost a cold wave can cause damage to agriculture, infrastructure and property, and can also lead to drought later in the year if it significantly reduces winter precipitation totals. Additional impacts include increased energy consumption for heating and increased mortality.

4 EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be, Université Catholique de Louvain, Brussels (Belgium). 5 The EMDAT database defines a drought to be a ‘Long lasting event; triggered by lack of precipitation. A drought is an extended period of time characterised by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine’. However, as shown in Task A2_D2/D3, there are multiple definitions of drought.

The EMDAT database (http://www.emdat.be/) provides information on extreme events since 1900. From this data, and using the definitions provided by the database, it is possible to show that storms have been the recurrent weather phenomenon of concern across Europe, with a clear prevalence in western Europe (W). Table 1 also shows that cold waves are more common in east (E), north (N), and west (W) Europe than heat waves and droughts, while in Mediterranean Europe (S) “warm/dry” extremes appear to be more frequent.

Extreme type E N S W Total Drought 13 3 16 5 37 Heat Wave 17 2 27 13 59 Cold wave 58 13 20 29 120 Storm 74 84 64 195 417 Table 1: Summary of climatological extremes in Europe according to the EMDAT database.

Below, we describe more in detail some examples of the recurrent weather-related phenomena that fall into the groups defined above: storms, heat waves (including meteorological droughts) and cold waves.

1. Storms

1.1. Northern Europe

Extra-tropical cyclones are an important feature of weather and climate at mid-latitudes. Their passage is associated with strong winds, precipitation and temperature changes (e.g. Chang et al., 2002; Ulbrich et al., 2009). The north Atlantic is an active area for the production of extra-tropical cyclones (Chang et al., 2002; Raible et al., 2008; Ulbrich et al., 2009). Sickmöller et al. (2000) identified 3 main clusters of Atlantic cyclone tracks which affect Europe: i) those which move north-eastwards; ii) those which move eastwards, i.e. zonally; and iii) those which are stationary. Within this framework, north- west Europe is particularly vulnerable to damage caused by strong winds and intense precipitation associated with these weather systems. Research by Raible et al. (2008) and Ulbrich et al. (2009) has shown that the magnitude of the linear trend in cyclone numbers and even the sign of the trend might depend on the method used to detect and track cyclones. A project currently underway (IMILAST6) aims to compare many different storm tracking algorithms, understand the differences and suggest ways of improving them.

6 http://www.proclim.ch/imilast/index.html

In general, cyclones are less numerous and weaker during the summer months (e.g. Raible et al., 2008; Ulbrich et al., 2009), but the numbers vary significantly on decadal and longer timescales (Sickmöller et al., 2000; Gulev et al., 2001; Matulla et al., 2007; Wang et al., 2009; Donat et al., 2011a). Partly because of this variability and partly because of difficulties in robustly identifying cyclones in weather forecast and climate model data, longer-term trends in storminess are difficult to determine. For example, Donat et al. (2011a) have described a significant increase in extreme wind speeds found over Scandinavia, the British Isles, the North Sea and central Europe, whereas trends in the remaining regions are mostly not significant (Ulbrich and Christoph, 1999). A small region around the Adriatic Sea exhibits negative trends, along with the south of the Iberian Peninsula. If correct, these results suggest that there is likely to be an increased chance of winter storms causing damage in northern Europe, and a slight decrease in regions of southern Europe. In contrast, Gulev et al. (2001), Matulla et al. (2007) and Wang et al. (2009) found an increase in storminess between the 1960s and its peak in the early 1990s, followed by a subsequent flattening or decrease of the trend.

Climate models employing a range of emissions scenarios (e.g. Ulbrich and Christoph, 1999; Ulbrich et al., 2009; Donat et al., 2011b) predict that storminess may increase in the future. However, there does not appear to be a clear consensus on either the spatial pattern, or the magnitude of the expected changes. For example, some models produce enhanced storminess over north-west Europe, while others predict enhanced storminess extending into northern regions of central and eastern Europe. If this is the case, the combination of increased storminess with increased precipitation (e.g. Klein-Tank et al., 2002), increased duration of wet spells (e.g. Zolina et al., 2010) and increased frequency of extreme precipitation events (e.g. Alexander et al., 2006) all suggest that northern Europe may experience floods more frequently in the future.

1.2. Central and Eastern Europe

As indicated by the discussion above (Chang et al., 2002; Donat et al., 2011a), cyclones affect the weather and climate of central and eastern Europe. Bielec-Bąkowska and Piotrowicz (2012) investigated the frequency of cyclone activity in Poland and found that the storms primarily originate from the north Atlantic, with a smaller contribution provided by the western Mediterranean basin, e.g. the near the Gulfs of Lyon and Genoa. As with previous studies, Bielec-Bąkowska and Piotrowicz (2012) found a peak in cyclone

activity during the winter months with significant interannual variability, but no clear trend in the annual number of cyclones since 1900.

Periods of high cyclone activity will transport heat and moisture to the centre of Europe, leading to milder, wetter conditions (Sickmöller et al., 2000). In the summer, these conditions can lead to floods (e.g. summer 2002), but this is not necessarily the case during the winter. Winter flooding events in central and eastern Europe are more often caused by melting snow building up behind ice jams (Mudelsee et al., 2003). Frozen soil has a low capacity for absorbing surface water, meaning that surface runoff from melting snow will not be absorbed by soils and can easily cause floods. In contrast, during mild winters the soil is frozen for a shorter period of time, meaning that runoff can be more readily captured by the soil. Therefore, if climate change leads to a greater occurrence of cold winters, there could be an increase in winter floods. However, Mudelsee et al. (2003) have found a decrease in winter flooding events, probably as a result of regional warming and a reduced occurrence of freezing conditions.

During the summer months, floods in central and eastern Europe are most commonly associated with storm events classed as Vb, which have been described in detail in Tasks A2_D2 and A3. These storms begin over the eastern Atlantic Ocean, and travel over the Iberian Peninsular and the north-western Mediterranean before moving north- eastwards into central Europe, where extreme precipitation can lead to severe floods (e.g. Kreienkamp et al., 2010). Studies suggest that the moisture which contributes to the floods originates from across Europe with significant contributions from the western Mediterranean (e.g. Ulbrich et al., 2003), the European land area, as well as the Aegean and Black Seas (e.g. James et al., 2004). As described below, the Gulf of Genoa and the Aegean Sea are known to be centres of high cyclogenesis in the Mediterranean region. With higher sea surface temperatures, it seems possible that these regions could contribute more moisture to Vb storms, thereby exacerbating summer floods in central and eastern Europe.

Despite the apparent high frequency of Vb flooding events (e.g. 1997, 2002, 2005, 2009 and 2010), it is important to reiterate that there is, as yet, no consensus on whether the frequency and intensity of Vb events is changing. This is partly because the inter-annual variability of Vb storm numbers is very large. For example, Fricke and Kaminski (2002) suggest that Vb patterns have become more frequent and intense over the last 150 years. In contrast, Mudelsee et al. (2004) were unable to find a significant increase,

although they used monthly mean data which can make it difficult to identify Vb events. Similarly, Hofstätter and Chimani (2012) did not detect any trend in Vb numbers.

As described in Task C2, precipitation in central and eastern Europe is often higher during the summer than the winter, suggesting that it is convective in origin. Studies show that evapotranspiration from the European land area provides the largest contribution to this moisture (Sodemann and Zubler, 2010). These convective events can be extreme, leading to severe flash flooding events - for this reason the Convective and Orographic Precipitation (COPS) study (Wulfmejer et al., 2011) was initiated in order to study and better predict episodes of convective precipitation in south-west Germany.

1.3. Mediterranean Europe and Southern Europe

Several studies (e.g. Bartholy et al., 2009) indicate that cyclogenesis in southern Europe is prominent along most of the Mediterranean Sea’s European coast, with local maxima occurring along the east coast of Spain (near Valencia), the Gulfs of Lyon and Genoa (Buzzi et al., 2003), the east coast of Italy, and the Aegean Sea. Cyclogenesis has also been shown to be significant over Turkey and the north-west part of the Black Sea (Trigo et al., 2002; Spanos, 2006). Notably, these studies show that the particularly active areas around the Gulf of Genoa, Aegean Sea and the Black Sea produce cyclones all year round.

The rate of cyclogenesis varies throughout the year, with some studies (Trigo et al., 2002) suggesting that winter cyclones are essentially subsynoptic lows, triggered by the major North Atlantic synoptic systems being affected by local orography and/or low-level baroclinicity over the northern Mediterranean coast. In spring and summer, thermally induced low pressure regions become progressively more important. However, there appears to be no agreement on the intra-annual variation of the number of cyclones that form during the year. For example, Kouroutzoglou et al. (2011) found a decrease in the summer months, while others (Trigo et al., 2002; Bartholy et al., 2009) found an increase. The discrepancy probably occurs as a result of differences in the automated algorithms used for detecting cyclones (Raible et al., 2008).

Regions such as the east coast of Spain produce many more cyclones in the summer than in the winter (Bartholy et al., 2009). Despite this, summer precipitation totals are lower than in the winter, mainly because summer cyclones tend to be shorter-lived and

bring less moisture over the land. Notably, the strong winds and intense precipitation associated with explosive cyclones are primarily a cold-period phenomenon. Conte et al. (2002) and Kouroutzoglou et al. (2011) independently showed that the monthly distribution of explosive cyclogenesis is at a minimum from June to September, and peaks in December and January. However, Diodato and Bellochi (2010) note that the most hazardous months in the central Mediterranean are September-November, which have exhibited a shift towards more intense rainfalls in recent times. This fact is also reflected in the number of torrential rain events affecting the east coast of Spain. Both studies agree that the frequency of explosive cyclones has generally decreased during this time, although there are large inter-annual variations. However, it is unclear if the extremity of these events has changed over time.

2. Heat waves

As described in the Introduction, temperature measurements across Europe indicate an increase in the number of warm days and nights and the number of warm spell days (Trenberth et al., 2007). In addition, models employing a range of climate scenarios predict an increase in the severity and duration of heat waves in the second half of the 21st century (Beniston 2004; Meehl and Tebaldi 2004; Schär et al., 2004; Clark et al., 2010). Kuglitsch et al. (2009) note that this is significant because ‘heat waves have discernible impacts including a rise in mortality and morbidity (Knowlton et al., 2009), increased strain on infrastructure (power generation, water supply, transportation) (Smoyer-Tomic, et al., 2003) and consequent impacts on society’.

Heat waves are exacerbated by low levels of soil moisture meaning that cooling provided by the evaporation of moisture from the land surface is significantly reduced. For example, heat waves in 1976, 1994, 2003 and 2005 were all preceded by a pronounced spring precipitation deficit (Fischer et al., 2007). Indeed, there is evidence to suggest that heat waves in Europe often follow a deficit of winter and springtime precipitation in southern Europe and the Mediterranean (Vautard et al., 2007, see also Kürbis et al., 2009), with the drought and heat spreading northwards during the summer. The rainfall amounts for spring (February to May) and the average temperature anomalies during the following summer (June, July and August) are shown in Figure 1 for the years 1976, 1994, 2003 and 2005. It can be seen that the rainfall deficit during spring (red and yellow colours) has lead to very high summer temperatures. It is also worth noting the very large spatial extent of the 2003 heat wave which affected all of

Europe. Clearly, the extended hot and dry periods that characterise heat waves may also worsen droughts by accelerating the evaporation of moisture, especially from a deforested land surface (Teuling et al., 2010).

Figure 1: Top row: Observed summer mean temperature anomalies in June, July and August (JJA) in (a) 1976, (b) 1994, (c) 2003, and (d) 2005 with respect to the climatological mean over 1970 – 2000 (NASA GISTEMP analysis). Bottom row: Observed precipitation (GPCC) percentages of normals averaged over the months February – May (FMAM) in (e) 1976, (f) 1994, (g) 2003, and (h) 2005. (Reproduced from Fischer et al., 2007).

2.1. Northern Europe

Fischer et al. (2007) described 4 major heat waves to affect parts of Europe over the past 40 years – 1976, 1994, 2003 and 2005. Northern Europe was affected to varying degrees in each of these events (see Figure 1). For example, the heat wave of 1976 primarily affected northern France and southern England, but also reached southern Scandinavia. Warmer than average temperatures in the summer of 1994 affected much of central and southern Europe as well as southern Scandinavia. Average temperatures in the summer of 2003 were much higher than average for most of Europe, and this summer is currently the hottest on record. In 2005, summer temperatures were anomalously high primarily over the Iberian Peninsula and northern Scandinavia. For each of these years, mean summer temperatures were 1-3 ˚C higher than the

climatological average for the period. This result suggests that northern Europe is not immune to heat waves; however, they are generally less severe than those in southern Europe due to the lower night time temperatures at northern latitudes (e.g. Meehl and Tibaldi 2004).

2.2. Central and Eastern Europe

Of the heat waves described above, central and eastern Europe were most significantly affected by higher than average temperatures in 1994 and 2003. Interestingly, the 1994 event followed a period of below average rainfall from February to May in eastern Europe around the Black Sea, while the 2003 heat wave followed a period of below average precipitation across southern, central and eastern Europe (e.g. Vautard et al., 2007; Fischer et al., 2007). Southerly winds can then advect anomalously warm and dry air northwards, bringing heat waves to the temperate regions of Europe. Using regional model simulations of the 1994 heat wave in Europe, Vautard et al. (2007) found that soil moisture deficits induce warmer conditions by up to 5 – 6°C over the initially drier regions in the south, and by up to 2°C in regions further to the north, over France, Switzerland and Southern Germany. If this link between heat waves and precipitation deficit in the Mediterranean and southern Europe is robust, then central and eastern parts of Europe are likely to experience more frequent and intense heat waves as Mediterranean precipitation continues to decrease (Klein-Tank et al., 2002; Bartholy et al., 2009). Findings presented by Kyselý (2010) also suggest that the probability of central Europe experiencing a very long heat wave has increased by an order of magnitude over the past 25 years. It is also worthy of note that the 2010 heat wave affected many parts of eastern Europe.

The 2006 heat wave was centred further to the north of Europe, primarily affecting the UK, Netherlands, Belgium, Germany, Poland, France and Switzerland (Rebetez et al., 2007). However, it is not clear whether this event was also associated with a winter/spring precipitation deficit.

2.3. Mediterranean and Southern Europe

It is well known that the Mediterranean is becoming drier and warmer (e.g. Klein-Tank et al., 2002). In addition, Conte et al. (2002) and Kuglitsch et al. (2009) have shown that the frequency and intensity of heat waves in the Mediterranean region have increased.

This trend seems likely to continue since the Mediterranean is expected to be one of the prominent and vulnerable climate change hot spots (Giorgi, 2006; Diffenbaugh et al., 2007) that will experience a large number of extremely hot temperature events, an increase in summer heat wave frequency and duration, and increasing summer temperature variability (e.g. Schär et al., 2004).

As summarised by Conte et al. (2002), the Mediterranean experiences two main types of heat wave. These hot spells can be either sudden and very intense, but of short duration (3-5 days), or more gradual and less intense, but of long duration (i.e. 10 days or more). Short-duration heat waves can be defined as a sudden and disruptive increase of air temperature, reaching 7 -15˚C above the normal monthly mean computed for the period 1951-1980. In contrast, long-lasting heat waves give rise to a gradual air temperature increase, with temperatures that are about 5˚C higher. The impacts of heat waves are important because they may contribute to drought, desertification and forest fires, and may negatively influence the health of the population.

3. Cold waves

There are far fewer studies on changes in cold waves in Europe than studies on heat waves, and most studies have looked at temperatures recorded in specific areas. Radinović and Ćurić (2012) defined heat wave and cold wave thresholds using the mean and standard deviations of daily maximum and minimum temperatures. They studied changes in heat waves and cold waves at three locations, of which two were located in Serbia. They found that the number of cold waves and the length of the cold waves had decreased between 1991 and 2008. Miȩtus and Filipiak (2004) examined temperature data recorded at four locations in the Gulf of Gdansk, Poland, between 1951 and 1998. They also found that the number and length of cold waves had decreased over this period. Hulme et al. (2002) examined changes in the number and length of cold waves in the UK using daily minimum temperatures from the central England temperature record (Parker and Horton, 2005). They found that cold waves had become less frequent during the twentieth century, particularly during March and November. Dias et al. (2006) analysed the effect of daily temperature extremes on mortality on people aged between 45 and 64 in Madrid, and found that cold periods had the largest effect on females. They also noted that diseases such as influenza were also important causes of mortality during winter. Kysleý (2007) examined the effect of persistence of

weather patterns on extreme temperatures in Prague, and found the effect was more important for heat waves than cold waves.

Task A4_D2: Sensitivity of climate patterns to forest cover from analyses of high-resolution model runs.

1. Introduction

As highlighted by the previous tasks of this project the influence of forests on weather and climate is complex. The key processes involved are related to albedo, evapotranspiration, aerodynamic effects and interaction with CO2. In particular, forests have a lower albedo than other types of vegetation and some other land cover types. As a result, they tend to absorb more incoming solar energy, with a consequent warming of the land surface. At the same time, forests have a higher evapotranspiration rate than other forms of vegetation, which can cool the land surface and enhance the formation of clouds. These clouds reflect a more of the incoming solar energy, further cooling the climate. In addition, forests also cause a decrease in wind speed due to the aerodynamic roughness and swaying of the trees.

In the case of temperate forests, published findings do not provide a clear consensus on the overall impact of this biome (see Task A1). In general, studies suggest that annual mean temperatures are controlled by the effect of the low albedo during winter, which warms the local climate, and evapotranspiration during summer, which cools the local climate. However, local and seasonal variations in soil moisture can also affect the importance of evaporative cooling. Furthermore, carbon emissions from deforestation could be approximately balanced by the higher albedo of the crops and grass which would replace the forests (Betts et al., 2008), so that the net climatic effects of temperate deforestation would be negligible. Alternatively, reduced evapotranspiration as a consequence of the loss of the trees could amplify the warming (Bonan, 2008), although reduced canopy cover can increase soil evaporation (Pitman et al., 2009). Relatively few studies (such as Anav et al., 2010) have investigated the effect of changes in forest cover over temperate regions using a high resolution climate model, concentrating on the analysis of precipitation and extremes of temperature. In addition, the results are strictly dependent on: i) the model parameterization and implementation/simplification of physical processes; ii) the scheme chosen to reproduce afforestation and deforestation in the simulation model; and iii) the characteristics of the local microclimate.

The specific aim of this task was to refine and improve the evaluation of climate sensitivity under changing land cover/use (more specifically forests). This has been achieved by running climate simulations over two regions (described in the next section)

at very high spatial resolution (approximately 3.8 km) using the regional climate model COSMO-CLM (see Annex A4_D2.1). This model was driven by the global climate model CMCC-MED (Scoccimarro et al., 2011) which has a resolution of 85 km using the A1B emission scenario as a CO2 driver. These simulations cover the period 2015-2045 and consist of a control run assuming the current land cover (using the GLC2000 dataset: http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php) and two land-use change simulations where the land cover has been modified with the aim of simulating afforestation and deforestation processes. A further simulation was performed for the period 1971-2000 on the same two domains. The intention being to better distinguish the global effects of climate change from those induced by land cover changes, with respect to the period 2015-2045.

2. Simulated domains

The choice of the two domains, south Italy and Romania (shown in Figure 1) was driven by the aim to investigate two different climatic areas - the former representative of the Mediterranean region, and the latter indicative of the Black Sea basin - and characterized by opposite land cover trends over the last century.

Figure 1: Black rectangles indicate the domains analyzed: the south of Italy (on the left) and Romania (on the right).

The impact on climate of afforestation and deforestation has been analyzed for the two domains for the period 2015-2045. For the south of Italy, the computational grid was composed of 80 points in longitude and 80 in latitude. The domain in Romania was composed of a grid of 80 points in longitude and 95 in latitude. For each domain, three different simulations have been performed:

1. a simulation assuming the actual land cover (named BASE), used as control run; 2. a simulation assuming land cover changes under afforestation (named AFF); 3. a simulation assuming land cover changes under deforestation (named DEF).

For the same areas, the historical period 1971-2000 was simulated following the IPCC 20C3M protocol for the 20th century, in order to analyze the differences between the past and the future, under the actual land cover, attributable to climate changes.

3. Experimental setup

To simulate afforestation (AFF) and deforestation (DEF) processes, the parameters describing the roughness length, root depth, minimum and maximum leaf area index, minimum and maximum plant cover and ground fraction covered by deciduous (D) and evergreen (E) forest have been adjusted. In particular, the parameters modified in AFF and DEF experiment are:

• FOR_D = ground fraction covered by deciduous forest; • FOR_E = ground fraction covered by evergreen forest; • PLCOV_MN = plant cover data set for time of rest; • PLCOV_MX = plant cover data set for vegetation time; • LAI_MN = leaf area index data set for time of rest; • LAI_MX = leaf area index data set for vegetation time; • ROOTDP = root depth;

• Z0 = roughness length.

Values of above parameters for D and E are reported in Table 1 (classes 2 and 4, respectively).

Table 1: Characteristic parameters for plants according to GLC2000 classification.

To go a step beyond existing studies that did not consider “realistic” criteria in allocating or removing forests, trees were added and removed near locations where tree cover already exists, i.e. to account for biophysical and climatic constraints on forest growth, or the fact that forest removal usually occurs at the boundary of existing forests. More precisely, for grid points within which forest cover was greater than zero but less than 1 that location was completely afforested or deforested. For afforestation, the proportion of deciduous and evergreen trees was not altered. At locations where the forest fraction was equal to 0 or 1, no changes in land cover were made.

In Italy, the forest area of Campania was increased and decreased by 105% and 85%, respectively. For Romania it was increased and decreased by 55% and 86%, respectively, as shown in Table 2.

location/type forest area (million hectares) Experiments BASE AFF DEF AFF DEF Italy_D 0.97 1.81 0.17 86% 83% Italy_E 0.14 0.47 0 235% 100% Italy_D+E 1.11 2.28 0.17 105% 85% Romania_D 1.82 2.98 0.28 64% 85% Romania_E 0.65 0.86 0.08 32% 88% Romania_D+E 2.47 3.84 0.36 55% 86%

Table 2: Differences (million ha and %) in the forest area under AFF and DEF for the two domains of interest.

The characteristic parameters of co-existing deciduous/evergreen forest were weighted- averaged (based on their percentage) in the AFF simulation, while those of sparse herbaceous vegetation or grasses (class 14 in Table 1) were used in the DEF simulations, resetting to zero the values when negative. Note that in calculating Z0 (the roughness length) the soil surface contribution was also considered.

4. Results and discussion

The following results have been calculated over the entire domains of interest, including both modified and unmodified grid points. While this can reduce the magnitude of the effect due to forest cover change in the experiment, it shows the mean value of the changes across the whole domain. The following periods have been taken into account: 1972-2000, and 2016-2045. The first year of all the simulations has been excluded from the analysis as this spin-up period is highly affected by the initial conditions.

4.1. Temperature and Precipitation

4.1.1. Southern Italy: comparison between past and future period.

Figure 2 shows the simulated annual average temperatures at a reference height of 2 meters, for the entire domain of southern Italy. The values for the reference scenario (actual land cover) for the past period (1972-2000) and future (2016-2045) are displayed using a black line; the values for the AFF scenario are shown by the green line, while the red line denotes the DEF scenario. The same colours are adopted for the related linear regression curves.

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Figure 2: Trends of mean temperature at 2-metres (top) and precipitation (bottom) for the southern Italy domain.

Several remarks can be made: First, the annual mean temperature increased at a rate of +2.28°C per 100 years for the past period (1972-2000), becoming even more pronounced in the future for the unchanged emission scenario (+2.56°C per 100 years). Secondly, the inter-annual variability of annual mean temperatures is very large, and any

individual year can have a mean temperature which is very different to the long-term mean shown by the trend lines.

Looking at the future trend, much smaller variations (± 0.01°C per 100 years) can be recognized; the three lines appear almost parallel, meaning that changes in the land cover do not significantly affect the trend of increasing temperatures over time. However, the DEF scenario appears warmer, while the AFF simulation produces cooler temperatures. Notably, the effect of deforestation seems to be stronger than for afforestation; this result can be linked to a more important land cover parameter modification for this scenario.

For precipitation (bottom panel of Figure 2) the trend line is almost horizontal during the past period (1972 – 2000), and has a value of just +40 mm per 100 years, which is small compared with the total rainfall (about 740 mm per year). However, for the future period (2016-2045), there is a noticeable downward trend of -329 mm per 100 years, with negligible differences between the three land cover scenarios. For both temperature and rainfall, the anomaly induced by climate change results dominates when compared to the variations induced by land cover variation.

Figure 3 shows that a more significant difference between the scenarios is present for the minimum temperature, but that the difference is very small for maximum temperature. As in the previous case, there is considerable interannual variation in both minimum and maximum temperatures.

Looking at the trend lines for temperature and precipitation, it is worth noting that, over this area, climate change increases the rate of change of temperature, while for precipitation climate change leads to a decreasing trend which is not recognizable at the present. To analyze the effects due to the climate change, the seasonal average values and extremes over the two periods 2016-2045 and 1972-2000 for the baseline land cover configuration have been calculated

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Figure 3: Trends of minimum (top) and maximum (bottom) temperature at 2-metres for the southern Italy domain.

The summer and winter seasonal average values show a general increase of the 2- metre temperature of 1-2°C (Figure 4). In the summer period, in particular, this increase occurs across almost the entire domain. Moreover, the number of days per year with a maximum temperature greater than 35°C increases from a range of 20-25 days up to a range of 35-40 days on the eastern coast (see Figure A2 A in Annex A4_D2.2). The

extremes of temperature, the 10th and 90th percentiles of minimum and maximum temperature (see Figure A2 B and Figure A2 C in Annex A4_D2.2), also show the same trend.

Figure 4: Difference of 2-meter temperature between 2016-2045 and 1972-2000 in winter (top) and summer (bottom) for southern Italy.

For precipitation, the 50th and 90th percentiles of monthly values (Figure 5) show a general decrease, except for some areas. The largest decreases occur at locations with a higher elevation.

Figure 5: Difference of 50th (top) and 90th (bottom) percentile of monthly precipitation between 2016-2045 and 1972-2000 for southern Italy.

4.1.2. Romania: comparison between past and future period.

The trends in 2-metre temperature and precipitation for the entire domain of Romania are shown in Figure 6 and Figure 7. Data for the past period (1972-1999) and the future period (2016-2045), with actual land cover (black line), DEF scenario (red line) and AFF scenario (green line), have been analyzed.

As was the case for southern Italy, there is a signal of rising temperature and decreasing precipitation. Of the variables considered here, the minimum temperature at 2-metres seems to be most affected by changes in the forest cover, i.e. it increases in the case of deforestation (DEF) and decreases in the case of afforestation (AFF). Furthermore, the variations of the future trend due to climate change are more significant than the variations due to the land cover change.

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Figure 6: Trends of mean temperature at 2-metres (top) and precipitation (bottom) for Romania.

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Figure 7: Trends of minimum (top) and maximum (bottom) temperature at 2-metres for Romania.

As for southern Italy, the summer and winter average values show a general increase of the 2-meter temperature between 1-2°C (Figure 8), especially in the summer months. Moreover, the number of days per year with maximum temperature greater than 25°C increases from about 90 up to 110 days at locations at low altitude (see Figure A2 D in Annex A4_D2.2). The extremes of temperature, the 10th and 90th percentiles of minimum and maximum temperature (see Figure A2 E and Figure A2 F in Annex A4_D2.2) show the same trend.

Figure 8: Difference of 2-meter temperature between 2016-2045 and 1972-1999 in winter (top) and summer (bottom) for Romania. Black line indicates national boundaries here and in the successive maps for Romania.

Moreover, the 50th and 90th percentiles of monthly values of the precipitation (Figure 9) show a general decrease in most areas.

Figure 9: Difference of 50th (top) and 90th (bottom) percentile of monthly precipitation between 2016-2045 and 1972-1999 for Romania.

4.1.3. Temperature and precipitation analysis under different land cover

A comparison of the control run with the two land cover change experiments for southern Italy shows a decrease of mean temperature in the case of afforestation (AFF), both for summer and winter seasons, and an increase in the case of deforestation (DEF), (see Figure 10). However, for Romania, the behaviour is different (Figure 11). In winter, a decrease occurs in the DEF case for grid points which are characterized by high altitude and by a local modification of the land cover. In contrast, for the AFF simulation, there is a very slight increase in the north-west part of the domain. In the summer, the opposite trend is shown. The mean seasonal differences, however, are stronger in the summer period.

Figure 10: Differences of mean temperature between AFF (left) / DEF (right) and control run for winter (upper pictures) and summer (bottom pictures) in southern Italy

Figure 11: Differences of mean temperature between AFF (left) / DEF (right) and control run for winter (upper pictures) and summer (bottom pictures) in Romania.

Regarding the mean annual precipitation (Figure 12), the results show a slight increase in the AFF scenario compared to the control for both Italy and Romania, with the exception of some points characterized by a high altitude and local modification of the land cover, which exhibit a strong precipitation decrease. The opposite trend is found for the DEF scenario relative to the control run. However, in southern Italy, land cover change causes a stronger difference in absolute values than in Romania. In Italy, the precipitation differences can reach 200 mm/year, whereas in Romania their amount does not exceed 50 mm/year. Analyzing these differences, according to season, shows

that they occur in the wet seasons for both areas, i.e. autumn and especially winter in southern Italy, and summer in Romania (see Figure A2 G and Figure A2 H in Annex A4_D2.2).

Figure 12: Differences of annual cumulative precipitation (mm/year) between AFF (left) / DEF (right) and control run for southern Italy (upper pictures) and Romania (bottom pictures).

4.2. Hydrological balance

In order to evaluate the effect of potential AFF/DEF scenarios, it is worth investigating the surface water balance for the two spatial domains over the periods of interest, and for each land cover scenario. As before, the monthly values are averaged over the entire domain. The water balance can be represented by the relation:

P − (E + T ) = WS + GR + SR where P = precipitation, E = evaporation, T = transpiration ,WS = water storage, GR = ground (i.e. subsurface) runoff and SR = surface runoff). In the analysis below, only the sum of E and T is shown and is referred to as evapotranspiration.

The sum of the components WS and GR represents the water content (liquid or ice) within the soil; the former represents the storage available for evaporation processes and influencing infiltration processes, while the latter can be considered to be the quantity of water that is no longer available.

First, the budgets for the southern Italy domain are considered (Figure 13). Precipitation is always positive because it adds water to the surface, whereas runoff and evapotranspiration are negative as they involve a removal of moisture. The water storage term is positive when precipitation is greater than the losses of moisture due to runoff and evapotranspiration, and is negative when precipitation is less than the sum of the loss terms.

100 precipitation subsoil runoff surface runoff water storage evapotranspiration 80 60

h 40 t n

o 20 m /

m 0 m -20 -40 -60 -80 -100 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec date (mmm) Figure 13: Hydrological cycle of control run for southern Italy.

The trend of precipitation and evaporation reflects the climatic features of the region, i.e. the season October-March is cold and wet (having high rainfall and low evaporation values), while the season April-September is hot and dry (having low rainfall and high evaporation values). The evaporation peak occurs in the first part of the dry season (May-June), during which the weather forces governing the evaporative processes are significant and the soil shows high water content provided by precipitation in the previous months. In the second part of the dry season, the weather forcing remains relevant; however the soil is no longer replenished by precipitation events.

As a consequence of these trends, the water content within the soil increases during the wet season (WS > 0) and decreases during the dry season (WS < 0). During the winter season, decreasing gradients can be attributed to the reduced ability of the soil to store water as the soil moisture increases. These changes induce an increase in subsoil runoff and surface runoff gradients. The surface runoff dynamics, however, are strictly linked to the rainfall intensity and land cover features.

The comparison between the data for the period 2016-2045 and the period 1972-2000 (Figure 14) for the same land cover (BASE) is intended to highlight those variations in the hydrological balance which are attributable to climatic changes and not to modified land cover. The clearest difference is the general reduction in precipitation values, with two exceptions in March and August. This reduction causes a decrease in all values related to both storage and surface/subsoil runoff components. Therefore, the net result is an estimated reduction of evapotranspiration values in the future.

25 precipitation subsoil runoff surface runoff water storage evapotranspiration 20 15

1h 0 t n

o 5 m /

m 0 m -5 -10 -15 -20 -25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 14: Differences in hydrological cycle betweendate ( mthemm period) 2016-2045 and the period 1972- 2000 for southern Italy.

In Figure 15, the effects of afforestation are analysed by showing the differences in the components of the hydrologic balance between the control and AFF scenarios.

8 rec a on su so runo sur ace runo wa er s ora e eva o rans ra on 7 p ipit ti b il ff f ff t t g p t pi ti 6 5 4

3h t

2n o

1m /

-1m -2 -3 -4 -5 -6 -7 -8 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec d ate (mmm) Figure 15: Differences in hydrological cycle between AFF experiment and control run for southern Italy. Positive values indicate that the particular component is larger in the AFF experiment than the control.

All of the differences shown in Figure 15 are small and fall within the range ± 2 mm / month. As can be seen, the evapotranspiration values are consistently higher in the AFF scenario than the control run owing to the effect of increased vegetation cover during the wet season and during the first part of the dry season while. In the second part of the dry season, the vegetation cover tends to prevent the soil from fully drying. As a result, for the AFF scenario, an average water content reduction is simulated during the entire year (except for the period June-July).

The increased vegetation cover also reduces the magnitude of the surface runoff during the entire year. Part of this water infiltrating into the soil becomes available for evaporation processes. For this reason, these values also increase during the wet season.

Figure 16 demonstrates the variation in the same hydrological parameters induced by the DEF scenario. In this case, the changes with respect to the control scenario are larger than those for the AFF scenario, having values within the range ± 7 mm / month. The variations in precipitation are very small while the changes in evapotranspiration are more significant. Across the year, deforestation induces a general reduction of evaporative fluxes, with larger differences occurring during the dry season. For the DEF scenario, the water storage values are visibly higher in the summer. During the winter, the increase in the surface runoff processes, probably induced by complete wetting of the surface layers, triggers a growth in subsoil runoff values.

8 reci itation subsoil runoff surface runoff water stora e eva otrans iration 7 p p g p p 6 5 4

3h t

2n o

1m /

-1m -2 -3 -4 -5 -6 -7 -8 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec d ate (mmm) Figure 16: Differences in hydrological cycle between DEF experiment and control run for southern Italy. Positive values indicate that the particular component is larger in the DEF experiment than the control.

Figure 17 shows the monthly values of precipitation, evapotranspiration, runoff and water storage for the Romania region. The data are averaged over the entire area and the entire period of analysis 2016-2045, adopting the current land cover.

100 precipitation subsoil runoff surface runoff water storage evapotranspiration 80 60

h 40 t n

o 20 m /

m 0 m -20 -40 -60 -80 -100 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec date (mmm) Figure 17: Hydrological cycle of control run for Romania.

In this case, the summer season is characterized as humid (since the precipitation peak occurs in June) and hot. The high temperatures and rainfall support high evapotranspiration rates, and the runoff values are comparatively small. During the winter, rainfall takes slightly lower values, and the water storage terms increase.

Figure 18 shows the difference in the water budget fluxes between 1972-1999 and the period 2016-2045 under the same land cover. The first point of note is the reduction of precipitation between May and September. However, this must be subjected to further investigation because it appears to be due to a shift in the average value rather than a decreasing trend. The difference appears to be more significant in the summer, while it

takes much smaller values during the winter. The change in precipitation directly induces a reduction of evaporation of similar magnitude during the summer.

25 precipitation subsoil runoff surface runoff water storage evapotranspiration 20 15

1h 0 t n

o 5 m /

m 0 m -5 -10 -15 -20 -25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec d ate (mmm) Figure 18: Differences in hydrological cycle between the period 2016-2045 and the period 1972- 1999 for Romania.

In May, the rainfall decrease is converted mainly to a reduction in soil water content. It seems likely that this could be due to the effects of the previous wetting conditions of the soil, supporting the evaporation atmospheric demand. Other changes in the hydrological components have much smaller values and are related to the reduction of precipitation. The hydrological behaviour follows the rainfall trend simulated by the model; indeed, for the investigated area, no clear agreement exists among different numerical climate simulations, so that these results are model dependent.

The comparison between the trends in the future for unchanged land cover conditions and assuming an AFF scenario is shown in Figure 19. The differences are very small (± 2 mm / month); this can essentially be attributed to the area investigated A large part of this area of Romania is already covered by vegetation (mainly crop and deciduous forest). For this reason the extra afforestation-induced variations in parameters lead to very minor variations in the water budget. However, it does induce an increase in evapotranspiration during most of the year, while during the summer season evapotranspiration is reduced slightly by the simultaneous reduction in rainfall. In addition, because of the increased water retention by the soil surface due to larger vegetation cover, the surface runoff in AFF scenario displays slightly lower values.

8 7 precipitation subsoil runoff surface runoff water storage evapotranspiration 6 5 4

3h t

2n o

1m /

-1m -2 -3 -4 -5 -6 -7 -8 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec d ate (mmm) Figure 19: Differences in hydrological cycle between AFF experiment and control run for Romania.

Finally, the differences in the water budget terms in the future for unchanged land cover conditions and assuming a deforestation (DEF) scenario are shown in Figure 20.

8 reci itation subsoil runoff surface runoff water stora e eva otrans iration 7 p p g p p 6 5 4

3h t

2n o

1m /

-1m -2 -3 -4 -5 -6 -7 -8 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec d ate (mmm) Figure 20: Differences in hydrological cycle between DEF experiment and control run for Romania.

The evaporation values are generally reduced (especially in the summer, where it is important to remember that the simultaneous presence of high rainfall and high temperatures allow the soil to support the evaporative demand). At the same time, this leads to an increase, over the same period, of the water content in the soil. As seen for southern Italy domain, the vegetation cover reduction produces an increase in the values of surface runoff.

4.3. Extremes

In this section, several extreme values of temperature are discussed. They have been obtained with Meteolab (http://www.meteo.unican.es/en/software/meteolab), an open- source Matlab toolbox for statistical (data mining) analysis in meteorology, which allows basic meteorology and climate analysis computations to be performed in an easy form on observations, numerical weather and climate models (gridded fields). This toolbox also computes several indicators in different time scales (monthly, seasonally or yearly) from daily data.

4.3.1. Temperature

The change in the number of cold nights per year (defined as a day with a minimum temperature below 0°C) is shown in Figure 21. Similar behaviour is simulated for both the southern Italy and Romania domains in the AFF scenario, but not in the DEF case.

Figure 21: Differences of number of days with minimum temperature below 0°C between AFF (left) / DEF (right) and control run for southern Italy (top row) and Romania (bottom row).

On average, the number of cold nights increases in the AFF case and decreases in the DEF scenario. Overall, temperatures seem to be lower in the AFF scenario than the

control run, with the reverse occurring in the DEF scenario. However, the changes are greater in Romania. Moreover, in the Romanian DEF case, adjacent zones tend to react in the opposite way, with both positive and negative differences seen.

The change in the number of warm nights (defined as a day with a minimum temperature higher than 20°C) has also been calculated, and is shown in Figure 22. In this case, there are fewer warm days in the AFF experiment in both domains compared with the control run. The differences are larger for southern Italy in which several locations near the western coast have up to 10 fewer warm days per year than the experiment with the present-day land cover. In Romania, a large part of southern Carpathians experiences an increase of 6 days in the DEF case.

Figure 22: Differences of days with minimum temperature higher than 20°C between AFF (left) / DEF (right) and control run for southern Italy (upper pictures) and Romania (bottom pictures).

In addition, the number of warm days (with maximum temperatures above 35°C for southern Italy (Figure 23) and 25°C for Romania (Figure 24)) has been calculated.

These different thresholds were chosen owing to the different climate conditions in the two domains.

In both regions, there is a decrease in the number of warm days in the AFF scenario and an increase in the DEF scenario. The increase in forest cover appears to bring lower temperature extremes, while a decrease in forest cover shows the opposite trend. This effect occurs in the grid points with a low altitude. Moreover, in several points in Romania, the results show differences of up to 10 days for the DEF experiment. This result is strictly linked to the grid points subjected to land cover modification and it also depends on the specific methodology used for the AFF and DEF scenarios.

Figure 23: Differences of days per year with maximum temperatures higher than 35°C between AFF (left) / DEF (right) and control run for southern Italy.

Figure 24: Differences of days per year with maximum temperatures higher than 25°C between AFF (left) / DEF (right) and control run for Romania.

Changes in the number of cold days (defined as a day with maximum temperature lower than 0°C) are shown in Figure 25. The changes occur at locations with a high altitude and a local modification of the land cover. There is a decrease in the number of cold days in the AFF case, and an increase in the DEF case. For Romania, there is a

decrease of up to 8-9 days and, unlike southern Italy, these differences are evident across a wider area, over almost all of the Carpathians.

Figure 25: Differences of days with maximum temperature lower than 0°C between AFF (left) / DEF (right) and control run for southern Italy (upper pictures) and Romania (bottom pictures).

The differences in the 10th and 90th percentiles of daily minimum and maximum temperatures were also analysed for both domains. These percentiles are commonly used as thresholds to define cold and warm days and nights. The differences in these percentiles may be used as an illustration of how much hotter or colder extreme temperatures could be in the future period compared with the control. Results for southern Italy are shown in Figure 26 and Figure 27. Afforestation results in a decrease in daily minimum temperature (Figure 26), while the opposite trend is seen after deforestation in most parts of the domain. For the 10th percentile of the daily maximum temperature (Figure 27), an increase is simulated in the north-west part of the domain in the same area where there is a decrease of the number of days with maximum temperature lower than 0°C (Figure 25).

Figure 26: Differences of 10th (upper pictures) and 90th (bottom pictures) percentile of daily minimum temperature between AFF (left) / DEF (right) and control run for southern Italy.

Figure 27: Differences of 10th (upper pictures) and 90th (bottom pictures) percentile of maximum daily temperature between AFF (left) / DEF (right) and control run for southern Italy.

Figure 28: Differences of 10th (upper pictures) and 90th (bottom pictures) percentile of minimum daily temperature between AFF (left) / DEF (right) and control run for Romania.

Figure 29: Differences of 10th (upper pictures) and 90th (bottom pictures) percentile of maximum daily temperature between afforestation (left) / deforestation (right) and control run for Romania.

In the Romania domain, there are no significant differences in the AFF case, probably due to small changes in the forest land cover in this scenario (Figure 28 and Figure 29). In the DEF case, there is a decrease of the 10th percentile and an increase of the 90th percentile of both daily minimum and daily maximum temperatures. However, it is evident from the figures that, depending on the percentile value under examination, different areas are affected by the changes in land cover.

4.3.2. Precipitation

In this section, results concerning precipitation extremes are shown. As a general rule, the fractional changes in precipitation are smaller than temperature and appear to be higher in southern Italy. The 90th percentile map (Figure 30) shows a decrease of the monthly precipitation in the afforestation experiment and an increase in the deforestation experiment. The differences occur mostly at high altitude locations where a local modification of land cover has been carried out. In southern Italy, there are differences of 40 mm / month in some highly localized areas, whereas in Romania the differences are smaller, 10-15 mm / month with a wider spatial distribution.

Figure 30: Differences of 90th percentile of monthly precipitation between afforestation (left) / deforestation (right) and control run for southern Italy (upper pictures) and Romania (bottom pictures).

Figure 31: Differences of number of days per year with total precipitation higher than 20 mm between afforestation (left) / deforestation (right) and control run for southern Italy (upper pictures) and Romania (bottom pictures).

Figure 32: Differences of total precipitation in 5 days (maximum yearly value) between afforestation (left) / deforestation (right) and control run for southern Italy (upper pictures) and Romania (bottom pictures).

The differences in the number of days per year with daily precipitation higher than 20 mm is shown in Figure 31, and the change in total precipitation amount in 5 days (annual maximum value) is shown in Figure 32. The latter highlights how forest land cover change in southern Italy may cause greater differences in absolute values in very well defined regions, than is the case for Romania.

4.3.3. Maximal wind speed at 10 metres

Finally, the monthly mean value of maximum wind speed at 10 metres above the land surface has been analyzed, first in terms of the 10th and 90th percentiles, and subsequently plotted as a probability density function (pdf) and cumulative distribution function (CDF). Figure 33 and Figure 34 show a decrease of the wind speed in the afforestation run and an increase in the deforestation scenario. The values of these changes are similar in both the areas examined, with a maximum difference of 1.5 m/s for the 10th percentile and 2 m/s for the 90th percentile.

Figure 33: Differences of 10th percentile of maximal wind speed at 10 metres height between afforestation (left) / deforestation (right) and control run for southern Italy (upper pictures) and Romania (bottom pictures).

Figure 34: Differences of 90th percentile of maximal wind speed at 10 metres height between afforestation (left) / deforestation (right) and control run for southern Italy (upper pictures) and Romania (bottom pictures).

The cumulative distribution function (CDF) of daily maximum wind speeds at 10 m above the forest canopy is shown in Figure 35 and Figure 36 for Italy and Romania respectively.

Figure 35: Cumulative distribution function of daily maximum wind speed at 10 metres height in case of afforestation (blue line), control run (red line) and deforestation (green line) for southern Italy.

Figure 36: Cumulative distribution function of daily maximum wind speed at 10 metres height in the case of afforestation (blue line), control run (red line) and deforestation (green line) for Romania.

These results show that wind speeds increase in the order AFF, BASE and DEF. The effect of the forest is greater at larger wind speeds in both domains.

Task A4_D3: Assessment for EU-wide results using the nested UM runs

1. Introduction

In this task, the results from the weather (Task A3) and climate (Task A4) simulations are summarised and integrated in order to highlight similarities and differences in both approach and results so that limitations and advantages of the two studies can be discussed.

As described in Task A3, a severe flooding event in central Europe occurred between 6th and 13th August 2002; this event was mostly caused by a storm following the well- known “Vb” track. The period 5th – 13th August 2002 was thus chosen to study the effects of forests on local weather. For comparison, an earlier time period (10th – 18th April) was then selected to investigate any differences in the influence of forests on weather between spring and summer of the same year, and to ensure that the impact of forests across a broad range of weather patterns could be assessed.

Areas within Spain, Italy, Germany, Austria and Sweden were chosen to provide a representative sample of forests across Europe’s biogeographical zones. These regions were also selected to assess the influence of forests in the Atlantic and Mediterranean / Black Sea basins. More specifically, the regions in Spain and Italy were chosen to determine whether moderate changes in forest cover could alter the characteristics of the August 2002 Vb storm. In the case of Spain, a two forest areas were studied, with the larger area being approximately twice the size of the smaller area. The regions in central Europe (Austria, Germany) also lie on or near the typical Vb pathways and could, in principle, influence the properties of the storms. In contrast, southern Sweden is generally not affected by Vb storms, but is representative of a somewhat different climate.

The nested modelling approach uses a global model and two nested models, which have spatial resolutions of 60 km, 12 km and 4km respectively. In general, the meteorological data produced by the global model were used to initialise and drive the boundary conditions of the 12 km model, which is then used to drive the 4 km model. However, in this case, the 12 km model was used to drive three simultaneous 4 km resolution simulations, which were identical except for the forest cover. The control

simulation used the current (observed) forest cover, while the others adopted increased (AFF) and decreased (DEF) forest cover respectively.

As described in Task A4_D2, the regional model COSMO-CLM at 0.035° resolution (~3.8 km), is forced by boundary conditions dictated by the global CMCC model at 0.75° resolution (~80 km), and under the A1B emission scenario, was used to investigate climatic effects of temperate afforestation and deforestation for two different domains located in southern Italy and Romania for the period 2015-2045.

The historical period 1971-2000 was also simulated following the IPCC 20C3M protocol for the 20th century, in order to analyze the differences between the past and the future, under the actual land cover, attributable to climate changes.

2. Results

2.1. Weather simulations

Results for the summer period (summarised in Table 3) indicate that temperatures are higher at locations where forest cover is increased. In addition, the wind speeds are always lower in afforestation scenario than the in deforestation scenario, by 13-17%. However, the effect of forest cover change on rainfall is less clear. Rainfall is, on average, larger in the afforestation scenario than the deforestation scenario, but the differences are generally very small, being less than 10%, except over Germany 22% and Sweden, Furthermore, rainfall in the afforestation simulation was greater than in the deforestation scenario only 44 – 58% of the time. There is also considerable variability of rainfall, which partly obscures the effects of changes in forest cover.

Changes in the surface moisture flux are also small. Notably, for the regions studied here, the surface moisture flux generally appears to decrease slightly as forest cover is increased, suggesting that evaporation from the surface is more important than transpiration by forests. The exception is Austria, which exhibits a very small increase in the moisture flux. An explanation for this difference is not obvious, but could be because Austria’s soil moisture was comparatively low and that trees roots have access to moisture reserves that are not available to vegetation types with shallower roots.

However, none of the differences in surface moisture fluxes is significant at the 5% confidence level.

Rainfall Temperature Moisture Flux 10 m Wind Speed / mm hr-1 / °C / mm hr-1 / m s-1 Austria 0.06 0.16 0.001 -0.34 Germany 0.06 0.13 -0.002 -0.38 Italy 0.05 0.22 -0.003 -0.51 Spain 0.005 0.23 -0.001 -0.48 Spain (Large) 0.005 0.26 -0.006 -0.57 Sweden 0.02 0.53 -0.017 -0.50 Table 3: Mean differences between the afforestation and deforestation scenarios for four meteorological variables for the August (summer) simulations. The differences are average values calculated over the areas where forest cover was altered. A positive value indicates the variable has a mean value which is larger in the afforestation scenario than the deforestation scenario.

As described previously, two different areas of Spain were studied. The smaller was located near the Valencia region, while the larger area covered the majority of Spain’s east coast. Results indicate that forest cover change in each of these two regions has similar effects on rainfall, surface temperature, surface moisture flux and wind speeds. However, the diurnal cycles of all four variables are more regular when the large area of eastern Spain is afforested or deforested compared with the small area. This result could be a consequence of increased forest cover leading to more stable weather patterns, or may be because the quantities are averaged over a larger area, meaning that local variations in weather are averaged out. For the large area, it is more evident that the temperature and surface moisture fluxes are strongly correlated, suggesting that temperature is largely controlling the moisture flux.

The effects of forest cover change on rainfall, surface temperature, surface moisture flux and wind speeds during spring indicate that increased the forest cover results in lower wind speeds and higher surface temperatures in all cases, with the exception of Italy. However, the temperature increases for spring are noticeably smaller than were found in the summer. This is primarily because there is less incoming solar energy during the spring period. In addition, there appears to be no consistent effect of changing forest cover on rainfall for any of the regions studied. This suggests that any precipitation changes which did occur in the simulations may be a consequence of normal variability (or “noise”), rather than an informative signal. For example, in Spain and Italy, rainfall was smallest in the deforestation scenario. Over southern Sweden, very little change in

precipitation total is simulated, suggesting that the rain is produced by weather fronts which would not be strongly affected by changes in forest cover. For Austria, the least rainfall is simulated in the control simulation, while a sharp (and probably unrealistic) reduction in rainfall is modelled with increasing forest cover in Germany. No clear effect on surface humidity at each location or the surrounding area was found.

2.2. Climate

Results aggregated for the domains in southern Italy and Romania between the past and future climate periods are very similar:

 the warming trend appears to be stronger in the future, when quantified in terms of average and extremes;  For rainfall in the past, the trend line has a relatively shallow slope with a value of +40 mm /100 years). In contrast, a noticeable downward trend (-329 mm / 100 years) appears for the period 2016-2045 in Italy.  For Romania a slight increasing trend, but with lower absolute value than in the past, can be detected. It is noteworthy that modelling results concerning rainfall are highly uncertain in eastern Europe. In both cases, therefore, the anomaly induced by climate change dominates over the modifications induced by land cover change.

Looking at the future trends obtained by land cover change, the effect of deforestation seems to result in greater climate change than the afforestion scenario: this result can be linked to a more significant land cover parameter modification for this scenario. In particular, spatially explicit results for the two domains show some differences:

 For Italy, a decrease of mean temperature occurs in the case of the afforestation both for summer and winter seasons, and an increase occurs in the deforestation scenario;  For Romania in winter, higher elevations with modified land cover experience decrease of mean temperature in the deforestation case, whereas afforestation causes a very slight temperature increase in the upper-west part of the domain; in the summer period, the opposite trend is shown;

 Regarding the mean annual precipitation - in both domains, but more evident in Italy, a general slight increase is registered in the afforestation experiment; the opposite trend is shown for deforestation;  Changes are concentrated in the respective wet seasons for both areas, i.e. autumn-winter in southern Italy and summer in Romania.

Concerning the hydrological balance: - The net result of precipitation reduction is an estimated decrease of evapotranspiration (ET) values in the future. This more evident in summer for Romania; - In both domains, the strong variation in the land cover parameters induced by the DEF scenario causes a much higher magnitude of the variations (ET reduction);

In terms of extremes, the simulations indicate that afforestation appears, in general, to reduce the frequency of extreme high temperatures. The impact of afforestation on absolute values of extremes also confirms this trend in Italy. The impact of afforestation is less evident in Romania, where deforestation increases both high minimum and maximum temperature extremes, but decreases the low temperature extremes. Afforestation also appears to reduce precipitation and wind speed extremes.

3. Limits and gaps

A comparison of the model findings indicates that the afforestation results in higher temperatures in the weather simulations, but lower temperatures in the climate simulations. This is likely to be a consequence of differences in the way in which interactions between the atmosphere and the land surface are represented in the nested modelling approach and COSMO-CLM. Notably, in the nested suite, the model “surface” in forested locations is actually the top of the canopy. Therefore, the temperatures are those immediately (at 1.5 m) above the canopy. Consequently, the model does not simulate temperatures or other weather information either within or below the forest canopy. This is significant given that forests can insulate surface temperatures from extremes in the diurnal cycle, and means that there is likely to be a non-negligible mass of cool air that is not accounted for by the model. In contrast, the COSMO-CLM climate simulations provide the temperature at 2 metres above the ground surface, which can be within the forest canopy.

Results for precipitation and water balance components are less significant, but show a general wetting under afforestation. Less clear results for precipitation in both approaches can be related to a series of factors, including the following:

- High spatial variability of soil properties which have a key role in determining the hydrological balance; - Higher (than for temperature) inter-annual and intra-annual variability of precipitation patterns, which are drivers of hydrological balance and, therefore, water recycling via evapotranspiration and soil moisture. - Spatial clustering of forest cover changes (upstream/downstream) with respect to hydrological, topography-driven, processes.

Both approaches agree that afforestation contributes to a reduction in mean and extreme wind speeds.

The general principle behind the dynamical downscaling approaches applied by the Met Office and CMCC is that the large-scale atmospheric circulation can be downscaled over a limited area to a high spatial resolution. It is important to note here that in both approaches land cover was only modified within the high-resolution “nested” domain and not in the lower resolution model which produced the boundary conditions. Ideally it could be preferable to investigate the impact of increasing or decreasing the forest area gradually, both spatially and temporally. Using this approach, it would be possible to determine whether the influence of the forest cover on weather (and climate) patterns is continuous, or whether there are thresholds at which the impacts become more significant.

The computational expense of high-resolution simulations places limits on the duration and number of simulations that can reasonably be used for studies of this sort. However, from the results presented in Tasks A3 and A4, it is clear that an ensemble of simulations would provide a better picture of the impact of changes in forest cover on weather and climate. Ideally this ensemble would include simulations across a greater range of biogeographical regions for different periods of interest and emission forcings, and study the effect of using slightly different boundary conditions.

Task A4_D4: Weather Interactions between the Mediterranean, Atlantic and Black Sea basins

The objective of this task is to provide an assessment of the weather interactions between the Mediterranean, Atlantic and the Black Sea basins.

1. Introduction

The European land area can be divided into two main drainage basins: the Atlantic and the Mediterranean, including the Black Sea. The continental water divide is illustrated in Figure 37, and follows the high ground across Europe. As described by Millán (2008), all waters to the right of the divide drain into the Mediterranean, including the Black sea; to the left they drain into the Atlantic including Baltic, North, and Norwegian seas (Figure 38).

Figure 37: European Relief. The thick black line marks the approximate boundary between regions principally influenced by the Atlantic and Mediterranean basins (reproduced from Millán, 2008).

In addition, even if meteorological conditions are such that there is a flow of air across the divide (i.e. from one basin to another), the majority of the moisture will remain on the wind side of the divide. Moist air encountering mountain slopes will be forced upwards, causing it to expand and cool. As a result, a fraction of the water vapour in the air will condense, leading to enhanced rainfall on the wind side of the mountains. Consequently, an air mass which passes over the mountains can become much drier and warmer,

leading to Föhn-type winds. Therefore, if there are prevailing wind directions across mountains, one side of the range will tend to be consistently wetter than the other side.

Figure 38: Map of European river catchment (EEA, 2008). http://www.eea.europa.eu/data-and- maps/data/european-river-catchments-1/

Under different meteorological conditions air flows from the different basins may converge at the land surface near the continental divide. This convergence is known to lead to intense precipitation. However, it is unclear how much water vapour is transported across the continental divide and exactly where this occurs. Nevertheless, it is clear that, at least in some cases, there must be significant transport of water across the divide, for example the central European flood on 2002 (e.g. Ulbrich et al., 2003; Stohl and James 2004; James et al., 2004) in which a Vb storm travelled from the Atlantic Ocean, across the Mediterranean Sea and into central Europe. In order to determine how much moisture crosses the continental divide, it is necessary to estimate the frequency with which certain types of weather pattern occur and the period for which they typically persist.

2. Atmospheric Transport of Water Vapour

While the continental divide represents the approximate boundary between air flows driven by the different basins, this provides only one way of quantifying the spatial influence of the basins. However, even for this measure, the extent of the spatial influence of each basin varies across a range of timescales. For example, Sodemann and Zubler (2010) estimated the relative contribution of moisture from the different basins to Alpine precipitation for the period from January 1995 to August 2002 using ECMWF (ERA-40) reanalysis data. To do this they used ECMWF atmospheric circulation data to identify the air masses responsible for the precipitation and tracked them backwards in time to determine the geographic origin of the moisture. Sodemann and Zubler (2010) found that the annual average contributions of different moisture sources to Alpine precipitation were: the North Atlantic contributed 39.6%, Mediterranean 23.3%, North Sea and Baltic 16.6% and the European land surface 20.8%. However, they noted strong seasonal variability in these contributions (Figure 39). North Atlantic moisture dominates precipitation during the colder months (November to April), while in summer (June, July, and August) the largest single component is moisture evaporated from the European land surface. The largest contribution from the Mediterranean was found in October (Figure 39). As a result, the meteorological influence of a given basin can be of greater or lesser spatial extent depending on the prevailing weather patterns at that time.

These findings are consistent with the general patterns of objectively classified large- scale weather patterns across Europe, referred to as Grosswetterlagen (Gerstengabe et al., 1999; James, 2007). More specifically, by analysing ERA-40 data, James (2007) found that the frequency of westerly flows across Europe was at a maximum in December and January, with a second peak in August, but were at a minimum during May (Figure 40). This result suggests that the influence of the Atlantic basin generally expands during the winter while conditions favouring westerly and northerly winds persist. In contrast, during the summer, the influence of the European continent becomes more significant, thereby effectively increasing the geographical range controlled by the Mediterranean / Black Sea basin.

Figure 39: Combined mean seasonality of all Mediterranean (MW + ME), all North Atlantic (NW + NE + SW + SE), oceanic European (CE + A) and land sources (% of total accounted precipitation). Reproduced from Sodemann and Zubler (2010).

Figure 40: Mean seasonal cycle of large-scale circulation types over Europe, derived from Objective-GWL frequencies derived from ECMWF ERA40 data. The curves show the percentage of the time occupied by westerly (green), easterly (lilac), northerly (blue), southerly (red) and cyclonic (dark gold) circulation types, respectively. Reproduced from James (2007).

Some studies (e.g. Rank and Papesh, 2005) have used geographical variations in the ratios of isotopes of oxygen (i.e. O16, O18) and hydrogen (i.e. H, deuterium (D) and tritium (T)) to estimate the relative contribution to precipitation of moisture originating from the Atlantic and Mediterranean basins and the European land area. However, the results

can be difficult to interpret and are often inconclusive. Nevertheless, it is generally argued that lower tritium content of Alpine precipitation signals an increased contribution from the Mediterranean.

As described in the Introduction for this task, flooding events associated with Vb storms are known to result from the transport of moisture across the continental divide (Ulbrich et al., 2003; James et al., 2004). Since the Vb phenomenon is described in detail in Task A2_D2/D3, we only provide a briefly background here, to avoid repetition. The typical path taken by Vb storms is south east from the Atlantic across northern Spain or south west France, before turning east and travelling across the Italy and moving northwards across the Alps. Therefore, given the location of the August 2002 floods, moisture must have been transported from the Mediterranean and Black Sea to areas of Europe north of the Alps. Consequently, a Vb storm represents an interesting and complex example of the interaction between the Atlantic and Mediterranean basins. One reason this phenomenon is so complex is that the August 2002 Vb storm was a combination of an atmospheric pressure disturbance originally generated in the Pacific, which subsequently travelled across the Atlantic, and cyclogenesis in the Mediterranean. As a result, the severe floods associated with Vb storms are best described as an interaction between particular atmospheric disturbances and cyclones formed in the Mediterranean (especially the gulf of Genoa) resulting in the transfer of moisture across the continental divide.

More generally, the meteorological conditions which lead to Vb storms can occur frequently at any time of year, however they will not necessarily persist for many days (James, 2007). If the meteorological conditions change rapidly, the Vb pattern will not persist for very long. As a result, the interaction between the Atlantic and Mediterranean basins will be minimal because little moisture is likely to traverse the continental divide. Instead, the interaction will be greatest if the Vb conditions persist for several days, forcing greater quantities of moisture to cross the Alps, leading to floods in central Europe.

Using a similar approach to that employed by Sodemann and Zubler (2010), James et al. (2004) studied the origin of water vapour that contributed to the central European flood of August 2002. They used ECMWF ERA-40 data to identify the air masses which provided the moisture responsible for the flood and used a procedure to track these air masses back in time to find the origin of the moisture (shown in Figure 41). Their analysis of the backward air trajectories showed that the Ligurian Sea (south of the gulf

of Genoa) and the Aegean Sea provided the moisture which was important during the initial stages of the flood, i.e. 7th-8th of August 2002. However, strong evaporation from the eastern European land surface and the Black Sea became dominant later on. Indeed, it is clear from Figure 41 that the Mediterranean and Black Seas show enhanced evaporation rates during period 7th-12th of August.

Figure 41: Eight day backward trajectories starting over Zinnwald-Georgenfeld, Saxony at (a) 06:00 UTC and (b) 21:00 UTC on 12th August 2002. The trajectory colours indicate the change in specific humidity just before arrival at Zinnwald. Positive values indicate precipitating airmasses. Reproduced from James et al. (2004).

Somewhat confusingly, the interpretation of the atmospheric circulation data by James et al. (2004) is different to that of Ulbrich et al. (2003), who particularly highlighted the importance of moisture advected from the western Mediterranean in the flooding (Figure 42). This origin is also consistent with the hypothesis of Millán (2008) in which accumulated atmospheric moisture along the eastern Mediterranean coast owing to a local reduction in summer storms can be incorporated into a Vb storm passing through the region. The difference in interpretation is important because both studies used the same data, and similar approaches - indeed, the backward trajectories produced by Ulbrich et al. (2003) and James et al. (2004) appear to be very similar for August 12th. However, James et al. (2004) appear to have calculated a larger number of trajectories and also described their approach in much more detail. The differences between these two studies would seem to suggest that it is difficult to be more precise about the source of the moisture, other than it mostly originates from the Mediterranean and Black Seas.

Figure 42: Back trajectories computed from the ECMWF operational analysis. Trajectories start at 1200 GMT on 12 August 2022, near the station of Zinnwald at pressure levels 400, 500, 600 and 700 mbar, and are computed for a time span of 5 days. Vertical displacement of the back trajectories is shown by the changing colours. Reproduced from Ulbrich et al. (2003).

3. North Atlantic Oscillation (NAO)

Aside from the physical interaction between air masses from opposite sides of the continental divide, a given drainage basin can also exert a more indirect, long-range influence, often described as a teleconnection. Indeed, it is well known that the North Atlantic Oscillation (NAO) – the oscillation in the surface pressure difference between Iceland and the Azores – influences weather patterns across Europe. For example, using the CRU dataset, Trigo et al. (2004) demonstrated that for winter months with a high NAO index (> 1.0), northern Europe (i.e. U.K., Scandinavia and the Benelux countries) experiences higher than average rainfall, whereas southern Europe experiences lower than average rainfall, stretching from Western Iberia to the Black Sea. The situation is reversed for winter months with a low NAO index (< -1.0), i.e. southern Europe experiences increased rainfall, while northern Europe experiences cold, dry conditions.

4. Inflow and Outflow from the Mediterranean Sea

The relationship between the NAO and precipitation anomalies in the Mediterranean basin is of wider geographical significance, as described below. Indeed, the water and heat budget in the entire Mediterranean basin is also controlled by the water exchange with the Atlantic Ocean through the Strait of Gibraltar (approximately 14 km wide at its narrowest section and of about 300 m depth). In the Mediterranean, evaporation exceeds the sum of precipitation and river runoff. As an example, Mariotti et al. (2002), investigating the hydrological cycle of the Mediterranean and found that the region has a freshwater deficit of ~500 mm yr-1 due to an excess of evaporation over precipitation and runoff. Thus, the Mediterranean Sea is often called an “evaporation basin”. Theoretically, if the Strait of Gibraltar became closed, the sea level in the Mediterranean would decrease at a rate of about 0.5-1.0 m yr-1 (Laubier, 2005).

High evaporation causes an increase in salinity, leading in turn a decrease in water temperature. Consequently, the drivers responsible for the exchange of water at the Strait of Gibraltar are the evaporation in the Mediterranean and salinity gradient. Generally speaking, the exchange between the Mediterranean Sea and the Atlantic Ocean occurs as the more saline and denser Mediterranean deep waters go out to the Atlantic Ocean, while the lighter Atlantic surface waters enter, with a positive net inflow towards the Mediterranean Sea (Bryden and Kynder, 1991; Bozec et al., 2011) as illustrated in Figure 43. The turnover period for water entering through the Strait of Gibraltar is estimated to be between 80 and 200 years (Hopkins, 1999).

Such exchange is highly variable, with strong fluctuations at semidiurnal frequency, sub- inertial fluctuations in the range of few days to few weeks linked to meteorological forcing, and also seasonal and interannual variations. Estimates of water and salinity exchange at the Straits of Gibraltar can be derived from the works of Bryden and Kinder (1991) and Bryden et al. (1994).

The Mediterranean basin is an important contributor to the heat and salt content of the North Atlantic. Furthermore, it is known that a component of the Mediterranean outflow travels at mid-ocean depths (~1000 m) northwards along the coasts of Portugal and Spain and eventually to the west of Ireland (e.g. Lozier and Stewart, 2008; see Figure 44). As a result, the Mediterranean can affect the characteristics of the North Atlantic thermohaline circulation. In turn, this can affect the NAO, meaning that it would be

possible for changing weather patterns in the Mediterranean to exert a broader influence across Europe.

Figure 43: Schematic of the exchange of water between the Atlantic Ocean and the Mediterranean Sea at the Strait of Gibraltar. The letters T, S and ρ in blue and red refer to temperature, salinity and density of Atlantic and Mediterranean Sea water respectively, and Tr represents transported quantities. The subscripts atl and gib refer to Atlantic water and Mediterranean Sea water at Gibraltar. The letters E, P, R and Q in green refer to evaporation, precipitation, runoff and heat respectively. Figure reproduced from Bozec et al. (2011).

On this subject, it is relevant to note that Mariotti et al. (2002) found that, between 1948 and 1998, the Mediterranean winter water deficit increased by about 24% and by 9% annually, and that this is a result of long-term positive anomalies of the NAO since the 1970s. These results suggest that, in response to the changes in the freshwater flux, significant variations in the characteristics of Mediterranean waters and the Gibraltar flux may also have occurred during this period, mostly driven by the influence of the NAO. This result suggests that past and future global climate changes which can affect storm tracks (Arpe and Roeckner, 1999) and changes in the land surface conditions (Reale and Shukla, 2000) may be linked to significant changes of the hydrological cycle in the Mediterranean region (Bethoux and Gentili 1999). These changes in turn may potentially impact the Atlantic thermohaline circulation by changing the characteristics of the water flux at the Gibraltar Strait (Reid, 1979; Hecht et al., 1997; Johnson, 1997). Indeed, Calmanti et al. (2006) and Millán (2008) have suggested that the Mediterranean outflow could act as a fluidic switch which can modify the end path of the Gulf Stream in the North Atlantic. In principle, this could affect Atlantic depression tracks during the late summer by keeping them more northerly (more +ve NAO). The result would be summer floods in the UK, and increased drought in southern Europe. If the hypothesis of Millán

(2008) is correct, the same system could revert to another state in which the storm tracks are pushed further south bringing intense rainfall to the Atlantic coasts of Portugal, Spain and France, and dry cold winters to the UK (-ve NAO) before reverting to the previous state. However, it is unclear how long it might take for this behaviour to be activated, but it could be from 10 to 20+ years.

Figure 44: Path taken by outflow from the Mediterranean Sea, together with the circulations associated with the subtropical gyre and sub-polar gyre (solid lines). The dotted and dashed lines represent possible further paths taken by the Mediterranean outflow. Figure reproduced from Lozier and Stewart (2008).

It is clear that there is significant interaction between the Atlantic and Mediterranean basins both in terms of the convergence of air masses from different sides of the continental divide, and the correlation between the NAO and precipitation excesses and deficits in different regions of Europe. The latter is of particular importance since the transfer of moisture across the continental divide is influenced by long term trends in weather patterns. In addition, it seems highly likely that the outflow of saline water from the Mediterranean into the Atlantic does influence the north Atlantic circulation, but more work must be done to better understand this link and the possible impacts, which may be significant for Europe.

5. Sea Level Rise in the Mediterranean and Black Sea

As an example, the impact of sea level rise is noteworthy (Ramieri et al., 2011). In the cascading Mediterranean and Black seas changes in salinity appear to be very differently from the global mean: while Mediterranean Sea level is not changing or even

decreasing (especially in the Eastern Mediterranean) the sea level in the Black Sea is rising faster than the global mean. The increased salinity in the Mediterranean Sea for reasons above described may lead to a partial drop in sea level in the Mediterranean because the related increase of water density would lead to a decrease in volume. This process represents the halosteric component of the sea level variability (Cazenave and Nerem, 2004). Further rise in global mean sea level will cause the corresponding regional sea level to harmonize with the global trend. However, the rate of induced changes in the Mediterranean sea level is not fully understood at the moment and deserves further investigation (Vellinga et al., 2010), depending very much on the poorly understood behaviour around the Strait of Gibraltar.

The Black Sea is a nearly enclosed basin connected to the Mediterranean Sea by the narrow Dardanelles Strait (7 km wide and 55 m average depth). For the Dardanelles Strait estimates of water and salinity exchange can be derived, for example, from Stashchuka and Hutter (2001) and Besiktepe (2003). In contrast to the Mediterranean Sea it is an estuarine basin with low salinity, because its catchment area is about five times larger than the sea, resulting in a very high influx of freshwater (300 km3 year−1) (Stanev 2005; Kosarev 2008). The total freshwater flux is much higher than evaporation and the inflow of much saltier water from the Mediterranean Sea. The Black Sea, even though directly connected to the Mediterranean Sea, showed an increasing sea level trend since the beginning of 20th century (Stanev and Peneva, 2002), which is in contrast to the observations for the Mediterranean. This specific trend is due to internal (smaller scale) physical processes not related to the global ocean behaviour.

Reproducing a physically reasonable exchange for both Straits (Gibraltar and Dardanelles) with numerical models is not straightforward. Sannino et al. (2004, 2007, 2009) reproduced most of the aspects of the exchange for Gibraltar including the hydraulic criticality. According to their results, the minimum requirements necessary for a model to properly simulate the small scale nature of the exchange of water are: a horizontal resolution of about 0.5 km and a vertical resolution of about 10 m, the inclusion of explicit tidal forcing, and physical parameterizations taking into account diapycnal mixing (i.e. mixing across gradients of salinity) and entrainment.

However, at present, the explicit representation of such small-scale processes in Mediterranean numerical models designed for climate studies, running over several decades, is beyond currently available computer resources. For example, in the ocean models coupled to an atmospheric model in the CIRCE project (Gualdi et al., 2012), the

Strait of Gibraltar is represented by only few grid points and vertical levels, and entrainment of water and mixing processes are represented using relatively simple parameterizations. The different and approximated representations of the Strait are one of the causes of the different water exchange flows produced by the models in the CIRCE project. The gaps and limits in the ability of models to simulate exchange of water discussed for Gibraltar also apply to the Dardanelles (Gualdi et al., 2012).

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Annex A4_D2.1 - The regional model COSMO-CLM

Global climate models (GCMs) are used to study the current climate and to obtain projections on the future climate using different anthropogenic emission scenarios (Solomon et al., 2007). The most important advantage of using GCM outputs is the guaranteed physical consistency between variables (Hulme et al., 1990; Giorgi and Mearns, 1991; Robock et al., 1993). However, higher spatial resolution is needed for regional climate studies (Cohen, 1990) , and for studies on adaptation strategies. As detailed in task B1, dynamical downscaling of GCM scenarios to higher resolution scales can be provided with regional models (RCMs) that utilize GCM simulations as initial and boundary conditions (see Figure A1.1).

Figure A1.1: Representation of dynamical downscaling.

The use of RCMs is advantageous because they are able to simulate the details of the surface climate, and more realistically capture the structure and evolution of synoptic events, representing the input for impact models (IPCC, 1997). These advantages are noted in several studies, for example the simulations performed in the PRUDENCE project (Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects, 2002-2005) (Christensen and Christensen, 2007). However, the use of RCMs introduces additional uncertainty in climate simulations; indeed the performance of an RCM is critically affected by the quality of the driving data provided by the GCM.

At CMCC, the regional climate model COSMO - CLM (Rockel and Geyer, 2008) is currently used to perform climate simulations: it is the climate version of the COSMO - LM model (Steppeler et al., 2003), which is the operational non-hydrostatic, mesoscale weather forecast model initially developed by the German Weather Service and then by the European Consortium COSMO.

The model has subsequently been updated by the CLM-Community, in order to develop a version for climate applications (COSMO - CLM). This model can be used with a spatial resolution of between 1 and 50 km, even if the non-hydrostatic formulation of the dynamical equations in COSMO-LM made it eligible especially for use at a horizontal grid resolution of less than 20 km (Böhm et al., 2006).

These values of resolution are usually close to those requested by impact modellers; in fact, these resolutions allow a better description of the orography than global models, where there is an over- and underestimation of valley and mountain heights, leading to errors in precipitation estimates. Moreover, the non-hydrostatic formulation allows a good description of convective phenomena, which are generated by the vertical movement of air and moisture. Convection can redistribute significant amounts of moisture and heat on small temporal and spatial scales. Furthermore, convection can cause severe precipitation events such as thunderstorm or clusters of thunderstorms.

Another advantage related to the usage of COSMO-CLM, when compared to other RCMs, is that the continuous development of COSMO-LM allows improvements in the code that are also adopted in the climate version, ensuring that the central code is continuously updated.

The mathematical formulation of COSMO-CLM is built around the Navier-Stokes equations for a compressible flow. The atmosphere is treated as a multi-component fluid (made up of dry air, water vapour, liquid and solid water) for which the perfect gas equation holds, and subject to gravity and to the Coriolis force. The model includes several parameterizations in order to take account of several phenomena that take place on unresolved spatial scales, but which exert a significant influence on the meteorological interest scales; for example, interaction with the orography.

In COSMO-CLM, the adopted soil model is TERRA_ML, in which the hydraulic and thermal processes within the soil are estimated in order to obtain the surface temperature and the surface vapour pressure constituting the bottom boundary conditions for the atmospheric part of the model. In the model, the soil is discretized into a finite number of layers; the presence of snow cover or of water ponding is estimated by assuming the presence of two surface tanks; for each soil layer, instead, two budgets are defined: mass (for water in solid and liquid phase) and energy.

The presence of vegetation is parameterized by the variables:

 plant cover, representing the fractional area covered by plants;  leaf area index (LAI), defined as the ratio of total leaf area to the underlying soil- surface area (Hillel, 1980);  root depth;  surface roughness length, characterizing the aerodynamic properties of the surface and regulating mass, momentum and energy exchange between soil and atmosphere; increasing this value, are improved turbulent exchanges between surface and bottom layer of the atmosphere;  albedo e.g. the percentage of short-wave radiation (solar radiation) reflected from the surface because of colour or structure of the soil surface.

In order to simulate the annual variation trend, for the first three parameters a simple sinusoidal variation law between maximum (corresponding to the growing season) and minimum (corresponding to the rest period) is assumed. Using the approaches implemented in the soil model TERRA-ML, it is possible to investigate the influence of vegetation on the components of the hydraulic soil surface budget. For a more detailed description of COSMO-CLM and its soil model refer to the documentation here http://www.clm-community.eu/index.php?menuid=123#documentation.

Table A1.1 summarizes the main features of the configuration of the regional climate model COSMO – CLM utilized for the simulations.

Horizontal resolution 0.035° (~3.8 km) Number of vertical levels in the 40 atmosphere Number of soil levels 5 Soil scheme TERRA_ML Time step 15 s Melting processes yes Convection scheme TIEDTKE (Tiedtke, 1989) Frequency of radiation computation 1 hour Time integration Runge-Kutta (2nd order) (Wicker and Skamarock, 1998)) Frequency update boundary condition 6 hours

Table A1.1: Main features of the COSMO-CLM configuration.

Annex A4_D2.2 – Supplementary figures

Figure A2 A: Difference of days per year with maximum temperature higher than 35°C between 2016-2045 and 1972-2000 for southern Italy.

Figure A2 B: Difference of 10th (upper panel) and 90th (bottom panel) percentile of minimum temperature between 2016-2045 and 1972-2000 for southern Italy.

Figure A2 C: Difference of 10th (upper panel) and 90th (lower panel) percentile of maximum temperature between 2016-2045 and 1972-2000 for southern Italy.

Figure A2 D: Difference of days per year with maximum temperature higher than 25°C between 2016-2045 and 1972-1999 for Romania.

Figure A2 E: Difference of 10th (top) and 90th (bottom) percentile of minimum temperature between 2016-2045 and 1972-1999 for Romania.

Figure A2 F: Difference of 10th (top) and 90th (bottom) percentile of maximum temperature between 2016-2045 and 1972-1999 for Romania.

Figure A2 G: Difference of seasonal cumulative precipitation between afforestation (left) / deforestation (right) and control run for autumn (upper) and winter (bottom) in southern Italy.

Figure A2 H: Difference of summer cumulative precipitation between afforestation (left) / deforestation (right) and control run for Romania.

Met Office Tel: 0870 900 0100 FitzRoy Road, Exeter Fax: 0870 900 5050 Devon EX1 3PB [email protected] United Kingdom www.metoffice.gov.uk

Task B1: Methodologies/models to quantify forest influences on weather European Commission (DG Environment) July 2012 Author Edward Pope, Michael Sanderson and Monia Santini

Contents

Executive Summary ...... 2

Introduction ...... 3

TASK B1_D1: Compare and contrast different methodologies and modelling techniques to quantify influences of forests on weather across the EU ...... 4 1. Cloud Resolving Models (CRMs) ...... 4 2. Numerical Weather Prediction (NWP) models ...... 6 3. Climate models ...... 8 4. Comparison of different modelling techniques ...... 11

TASK B1_D2: Recommended methodologies and modelling techniques ...... 12

References ...... 14

TASKS B1_D3 and B1_D4: Produce system diagrams showing linkages and sensitivities ...... 17 Introduction ...... 17 Linkage tables and figures ...... 18 Table 2: Land degradation (see Figure 4) ...... 18 Table 3: Drought (see Figure 5) ...... 18 Table 4: Storm damage (see Figure 6) ...... 18 Table 5: Heat waves (see Figure 7) ...... 19 Table 6: Flooding (see Figure 8) ...... 19

Executive Summary

• Task B1_D1 provides an evaluation of a range of numerical methods that could be used for investigating the influence of forests on weather in the EU. The techniques summarised in this section include cloud resolving models, numerical weather prediction models and climate models.

• The most suitable method for investigating links between forests and weather depends on the spatial and temporal scales of interest. Cloud resolving models are of most benefit for high spatial resolution studies (e.g. <1 km), while climate models are most applicable for studying large spatial domains (e.g. >100 km) and long time periods (e.g. >decades).

• Task B1_D2 provides a summary of the interactions between forests and a broad range of weather-related impacts. These have been described using tables and linkage diagrams which apply the DPSIR framework.

• The presented tables and figures summarise the pathways by which forests may affect, and be affected by, land degradation due to erosion, drought, storm damage, extreme high temperatures and flooding.

Introduction

The aim of this task is to propose and substantiate the most adequate methodologies and models to quantify the influences of forests described in the previous Tasks (e.g. A2, A3 and A4). Therefore, in Task B1_D1, we have reviewed the potential methodologies and modelling techniques which could be used to study the influences of forests across a wide range of temporal and spatial scales. For Task B1_D2, the most suitable methodologies are proposed and justified. Finally, for Tasks B1_D3 and B1_D4, tables and figures are given which address how forests affect, and are affected by, a range of weather impacts – land degradation due to erosion, drought, storm damage, extreme high temperatures and flooding. The linkages and sensitivities between forests and weather-related impacts are described within the DPSIR framework which connects indicators of a particular impact to the drivers, pressures, and possible responses which could mitigate the impacts. The diagrams illustrate pathways by which forests interact with their broader environment, and the direction of the influences.

When reading this report, it is important to remember that the interaction between the land surface and the atmosphere is extremely complex and, therefore, difficult to model accurately. Owing to computational constraints and limitations in our understanding, models necessarily employ approximations which implement the physics in slightly different ways. This means that different models will be suited to different spatial and temporal scales as a result of the assumptions made in their implementation. Consequently, when comparing simulation outputs, different magnitudes of vegetation- atmosphere feedbacks for the same region can be a result of a different experimental set-up (i.e. model domain, resolution, time period) as well as arising from different representations of the land surface and its interactions with the atmosphere (Gálos, 2009).

In addition, it is important to note that vegetation feedbacks have a weaker influence on atmospheric circulation and weather patterns than large-scale anthropogenic greenhouse-gas forcing (Betts, 2007; Göttel et al., 2008). Despite this, the interaction between the land surface and the atmosphere remains an extremely important set of processes in determining local climate and weather patterns. Indeed, it is clear that there are regions (e.g. Spain, central and northern Europe) where weather and climate change are significantly affected and altered by land-atmosphere feedbacks and, hence, by land

use / land cover changes (Gálos, 2009). Moreover, results of both observational and modelling studies presented in Tasks A1 to A4 show that climatic feedbacks, especially of temperate forests, differ strongly between regions, as they can cool or warm the regional climate. A possible reason for this phenomenon could be the large spatial variability of the climatic and soil conditions as well as the physical characteristics of vegetation, such that either the albedo-effect or the evaporative cooling dominates, or a situation where both are important.

TASK B1_D1: Compare and contrast different methodologies and modelling techniques to quantify influences of forests on weather across the EU

For this task, we have presented descriptions of a range of numerical models which are used within the scientific community to simulate weather across a wide range of different spatial and temporal scales, and to study relevant atmospheric phenomena such as convection. All of these models could be used to study the effects of forests on current weather, and how changes in forest cover could affect the weather patterns. In this section, these models are briefly described, starting with very high resolution Cloud Resolving Models, before considering lower resolution Numerical Weather Prediction Models and Climate Models. For completeness, we have produced a table in Task B1_D2 that compares the relative merits of the different approaches. However, it is important to note that there is no single model which could answer all possible questions on how forests influence weather locally as well as on larger scales. Consequently, a range of models would need be used in order to answer the scientific questions of interest.

1. Cloud Resolving Models (CRMs)

CRMs, weather forecast models and climate models all represent the same physics and solve the appropriate equations in order to simulate the evolution of the atmosphere. However, the methods used to solve the equations differ. Indeed, some techniques, while perfectly adequate for resolutions used by climate models (10s of km or more), are less appropriate for CRMs. This is because CRMs are very high resolution models which aim to resolve the scales which dominate the dynamics of clouds. As such, they use

much more sophisticated representations of cloud microphysics and turbulence than are found in weather and climate models. For example, Gray (2003) describes the use of a CRM to study convective rain showers over part of the UK using simulations of 1 day or shorter. This model used a horizontal resolution of 500 m, and a vertical resolution of 250 m, and represented the atmosphere between the surface and an altitude of 7 km. CRMs have been used extensively for the development and evaluation of convective parameterisations for weather and climate models.

In an earlier study, Avissar and Liu (1996) used a high resolution version of the Regional Atmospheric Modeling System (RAMS) model to simulate the effects of different land surface wetness patterns on convective clouds and rainfall. In this study, the RAMS model was used with horizontal resolutions of 250 m and 500 m over a domain of 30 km × 30 km, and an altitude of 9 km. They found that, when the land surface is homogeneous (i.e. all wet or all dry), the clouds were distributed randomly. However, when the land surface has different patterns of wet and dry regions (in 10 km blocks), which warm up at different rates during the day, the model produced small scale circulation patterns which enhance the amount of water vapour that is condensed and precipitated as rainfall. These results suggest that enhanced convection could occur over a patchwork of forests and fields, and further experiments with high resolution models using explicit land cover types would provide further useful information on how forests and land use patterns affect local weather conditions.

Owing to their complexity, CRM differential equations are usually integrated for short time periods (e.g., a few days), although more recently simulations lasting up to 30 days with these models have been made (Hohenegger et al., 2008; Schlemmer et al., 2011). Models of this sort could be used to produce detailed studies investigating the impact of changes in forest cover on the formation of convective clouds, rainfall and the associated mesoscale circulation systems.

However, the future of CRMs is uncertain because advances in computing power mean that very high resolution weather prediction models (with resolutions of approximately 1 km) are now routinely used to provide accurate forecasts over, for example, the UK. The very high spatial resolution of these weather models means that details of small scale processes can be captured much more accurately than before. As a result, a separate CRM may no longer be required. At the UK Met Office, a very high resolution

weather prediction model (UKV, 1.5 km) is currently being assessed to see if it could replace the CRM described by Gray (2003).

2. Numerical Weather Prediction (NWP) models

NWP models are used to forecast weather over periods from a few hours to several days ahead. A variety of models are used with differing resolutions. For example, at the UK Met Office, a global weather forecast model with a resolution of approximately 25 km at mid-latitudes is run to provide a forecast for 5 days ahead; results from this simulation are used to drive a higher resolution model (12 km) whose domain covers the North Atlantic and Europe (the NAE model). Finally, the NAE model is used to drive a very high resolution model (1.5 km, called the UKV) whose domain covers the UK only. The domains of these three models are illustrated in Figure 1. These models also represent a much greater depth of the atmosphere than is captured by CRMs, typically up to altitudes of 40 – 70 km above the land surface.

Figure 1. Illustration of the three models used by the UK Met Office to provide weather forecasts for the United Kingdom. The global model is run initially; data from this model are then used to drive the NAE model, whose domain is shown in red. The NAE model is then used to drive the very high resolution (1.5 km) UKV model whose domain is highlighted in pale brown.

Although computationally expensive, this series of models could be used to study the effects of changes in forest cover in the high resolution model. These NWP models could also be used to study the effects of forest cover changes on known weather events, such as the formation of convective clouds and subsequent rainfall. A related approach is to use a series of nested models. In this method, several versions of the same model with increasing resolutions are used to produce very high resolution simulations over a limited area. This approach is different to the NWP weather forecast models, as the nested models can be located anywhere on the Earth’s surface. This nested modelling approach was used by Webster et al. (2008) to study an intense rainfall event over the South Island of New Zealand. In that study, five versions of the Met Office weather forecast model (based on the global model described above) were used with progressively increasing resolutions as illustrated in Figure 2. The simulations begin with the global version of the model (here, with a resolution of 60 km), and the remaining four limited-area models were multiply nested within the global model at horizontal resolutions of 12 km, 4 km, 2 km and 1 km.

NZLAM-1 NZLAM-2

NZLAM-4

NZLAM-12 Figure 2. Illustration of the nested modelling approach. Four model domains with horizontal resolutions of 12 km, 4km, 2 km and 1 km are shown. The 12 km model (outer domain) is driven using meteorological fields generated by a global model simulation, which uses a horizontal resolution of 60 km. This figure reproduced from Webster et al. (2008).

Initially, the global weather forecast model was integrated for 24 hours and the results are used to provide lateral boundary conditions at the edge of the 12 km model domain 7

(Figure 2). The 12 km model then generated boundary conditions for the 4 km model, and so on down to the 1 km model. Using this approach, actual events (such as heavy rainfall or storms) can be studied at very high resolution. The forest cover in the smallest domain could be changed to investigate whether (and how) the event of interest would be altered. This methodology was used for Task A3 to study the impacts of changes in forest cover in five different regions of Europe, focusing on a storm which caused extensive flooding in central Europe during August 2002.

Other centres also use a series of models to provide weather forecasts which could also be used to study the effects of land cover changes on local weather. The European Centre for Medium-Range Weather Forecasts (ECMWF) uses a global model at differing resolutions (16 km to 62 km) to provide forecasts out to 10 to 32 days ahead.

3. Climate models

Climate models are treated separately here, although many are almost identical to numerical weather prediction models, so the distinction is to some extent artificial. Climate models are based on the same physics and equations as numerical weather prediction models. For example the UK Met Office used the Unified Model for both weather forecasting and climate simulations. Another weather forecast model adapted for climate evaluation is the COSMO model (Steppeler et al., 2003), which is used for weather forecasting in Germany, and whose climate version (COSMO-CLM; Rockel et al., 2008) is continuously under development by a consortium of EU university and research centres1.

Like NWP models, climate models exist in both global and regional forms. One of the main differences between climate and NWP models is resolution. Climate models are used to simulate past and future climates over very long periods (e.g., 30 - 100 years or more), rather than produce forecasts a few days ahead. Consequently, they are almost always used at lower spatial resolutions than NWP models. For example, current global climate models have horizontal resolutions of approximately 100 km, compared with resolutions of roughly 20 km used by global NWP models. Owing to limits on computing power and data storage, it is generally not possible to use models with similarly high

1 (CLM community), including CIRA as CMCC partner 8

resolutions to simulate climate over long time periods. An exception is the study by Kendon et al. (2012) described later in this section.

Owing to the long timescales of climate simulations (years) compared with weather forecasts (days to months), global climate models also simulate oceanic circulation, and represent chemical processes in the atmosphere and interactions with the biosphere.

Climate models also simulate the effects of changing levels of carbon dioxide (CO2), methane (CH4) and other greenhouse gases on global climate, and the response of regional climates to the global forcing. They also include changing aerosol levels in the atmosphere and scenarios describing land use changes. Climate models can also be coupled to vegetation models so that the response of vegetation to a changing climate, and subsequent feedbacks, can be simulated. For these reasons, the current generation of global climate models are more properly called Global Environment Models (or GEMs).

Owing to their low horizontal resolutions, GEMs cannot resolve small-scale atmospheric circulations and land surface processes that are affected by orography and land cover, but these models do simulate the average global and regional climate well (IPCC, 2007). GEMs have been used to study the effects of large-scale changes in forest cover (e.g., full deforestation or afforestation of the globe or selected continents) on climate and any subsequent impacts on remote locations (described in reports for Tasks A1 and C1_D1)

To produce more detailed climate simulations for a selected region, Regional Climate (or Circulation) Models (RCMs) can be used. Such “limited-area” models have to be nested within a global model (as shown in Figure 3), as they require boundary conditions which are generated from a global model simulation. The technique is like zooming on the area of interest, which has finer horizontal resolution within the global model (McGregor 1997). As well as GEMs, regional climate models can also be used for long-term simulations. Indeed the regional model generates the same climate as the global model, but at higher resolution (typically 10 – 50 km), and so can represent the effects of mountains, land cover and fine scale physical and dynamical processes in more detail (Christensen and Christensen, 2007). More recently, an interactive vegetation scheme (TRIFFID; Cox, 2001) and an improved land surface scheme (MOSES 2; Essery et al., 2001) have been coupled to the Met Office’s regional climate model HadRM3 to examine the effects of forest cover change on rainfall and river flows in the Amazon basin and the wider South American continent.

The dynamical downscaling of GEMs via RCMs is usually carried out in one direction, which means large-scale meteorological fields from GEM simulations provide initial and time-dependent lateral boundary conditions for the higher resolution RCM simulations, but there is no feedback from the RCM to the larger scales. Recently, two-way nesting methods have been developed and applied (e.g. Lorenz and Jacob, 2005). Here, the circulations produced by the nested regional model feed back to the global model.

Figure 3. Illustration of the regional modelling approach, where boundary conditions are dictated by a global model (PRUDENCE special issue in Climatic Change, 2007, Vol.81, Suppl. 1).

Although RCMs have higher resolutions than global models, many important processes (for example, the formation of convective clouds) cannot be described in their full complexity as they occur on small spatial scales (of the order of a few km). Physical processes that occur on spatial scales smaller than the model’s resolution are necessarily represented using simplifications called parameterisations.

One key process that is commonly parameterised is convection (Hohenegger et al., 2008). Such schemes are designed to describe the average effects of convection in a lower resolution models within each model grid box. Even if the convection scheme worked perfectly, it would not and is not designed to represent individual storms and local rainfall events (Kendon et al., 2012). Additionally, convective parameterisation schemes have generally been designed to represent convection in tropical regions where the underlying assumptions are most valid (Hohenegger et al., 2008). These schemes are therefore less appropriate when considering convective rainfall events over Europe. Many convection schemes also simulate an onset of convection and rainfall too 10

early during the day, typically by 3 – 7 hours. This problem is especially noticeable during the summer over Europe, when synoptic-scale forcing is weak and the influence of the chosen convection scheme is large (Schlemmer et al., 2011).

A recent study by Hohenegger et al. (2008) investigated feedbacks between soil moisture and rainfall using a regional model at two different resolutions: a low resolution version (25 km) which used a convective parameterisation scheme, and a higher resolution (2.2 km) which could resolve convection directly. This study provides useful indications on how modelled responses to land cover changes might also vary with resolution and the treatment of convection. The two configurations of the model exhibited different signs and magnitude of the soil moisture – rainfall feedback. In the low resolution version, rainfall increased as the soil moisture increased. In contrast, the high resolution model exhibited a negative feedback, so that more rainfall was simulated from drier soils. Hohenegger et al. (2008) also noted that the use of a different convective parameterisation scheme in the low resolution model gave a similar result to the high resolution model.

A version of the UKV model (resolution 1.5 km) which only covers southern England and Wales has been used as a high resolution regional climate model to simulate climate over a 20 year period, and the results were compared with those from a regional climate model (see section 3) which had a resolution of 12 km (Kendon et al., 2012). The UKV model has a sufficiently high resolution to represent the effects of convection directly, whereas the 12 km model is reliant on a convective parameterisation scheme. The UKV model was shown to give a much better representation of rainfall duration, intensity and spatial extent compared to the 12 km model (Kendon et al., 2012).

4. Comparison of different modelling techniques

A wide range of different models have been described briefly. One of the most important effects of forests on weather is the transfer of moisture from the surface to the lower atmosphere and the formation of convective clouds and rainfall. Only cloud resolving models and very high resolution weather forecast and climate models (e.g., RAMS (Avissar and Liu, 1996), UKV (Kendon et al., 2012), the nested modelling approach (Webster et al., 2008) and the COSMO-CLM (Schlemmer et al., 2011)) can resolve convective processes directly. The use of numerical weather prediction models (e.g.,

the nested modelling suite described by Webster et al. (2008) means specific weather events can be studied, and the effects of forest cover changes on those events can also be examined. Additionally, the high computational and data storage costs associated with very high resolution climate simulations mean very few such simulations can be made.

However, these models can only simulate weather and climate over small areas. If the effects of forests and forest cover changes on weather and climate over larger areas are to be simulated, a regional or global climate model would be required. Overall, to answer most scientific questions regarding the effect of forests on weather and climate, a range of models with varying resolutions, domains and complexities are likely to be needed.

TASK B1_D2: Recommended methodologies and modelling techniques

It is clear from the descriptions presented in Task B1_D1 that different model types are suited to answering different scientific questions. For example, the study of short duration forest impacts at high resolution can be best achieved by using CRMs (e.g. RAMS) or NWP models (e.g. UKV, COSMO-CLM, or the nested suite) which can resolve convection directly. However, owing to the high computational cost of such models, they can only be used for simulations over small areas and relatively short periods (30 days), an exception being the study by Kendon et al. (2012).

To obtain a full understanding of the impact of forests on local and regional scales, and from short to long periods of interest, it is necessary to employ a range of modelling techniques including high resolution models for short periods of interest (e.g. days - months), and regional or global climate models for longer periods of interest (years - decades). Even then, there would be some uncertainty when comparing simulation output from models which implement the important physical processes differently.

In Table 1, we present a comparison of the model types according to whether they satisfy the following criteria: i) Can the model resolve convection? ii) Can the model be used to study the impact of forests on local weather? iii) Can the model be used to study the impact of forests on regional weather?

iv) Can the model be used to study the impact of forests on climate?

The answer to each question is given as ‘yes’ or ‘no’ depending on whether the model type has the capability of satisfying the given criterion. However, it is important to remember that Table 1 only represents a general guide to the uses of each model type. Within each model type (e.g. CRM, NWP, etc.), individual models will implement physical processes slightly differently and can be run at different spatial resolutions. Consequently, it is always important to understand the characteristics of a model before using it to simulate a particular process.

Model type Resolves Can be used to Can be used to Can be used for to convection investigate investigate investigate impact weather/forest weather/forest of forests on interactions on interactions on climate local scales regional scales CRM (e.g. Yes Yes No No RAMS)

NWP (e.g. Yes Yes Yes No nested suite)

RCM (e.g. No Yes Yes Yes HadRM3)

GCM(e.g. No No Yes Yes HadGEM3)

Table 1: Comparison of different model types described in Task B1_D1.

In the future, given the likely increase in computing power, it could be possible to run high resolution models over larger areas and for longer periods of time. Dynamic vegetation models (e.g. TRIFFID) could be used in these climate simulations to investigate changing vegetation patterns over periods of several decades or even longer time periods. This is important because the age of the forest can have a strong impact on its effect on local and regional weather and climate.

References

Avissar, R., and Y. Liu, 1996. Three-dimensional numerical study of shallow convective clouds and precipitation induced by land surface forcing, J. Geophys. Res., 101, 7499– 7518.

Betts, R., 2007. Implications of land ecosystem-atmosphere interactions for strategies for climate change adaptation and mitigation. Tellus, 59B, 602–615 doi: 10.1111/j.1600- 0889.2007.00284.x

Christensen, J. H. and O. B. Christensen, 2007. A summary of the PRUDENCE model projections of changes in European climate by the end of this century, Clim. Change, 81 (Suppl. 1), 7–30.

Cox, P., 2001. Description of the "TRIFFID" Dynamic Global Vegetation Model. Hadley Centre technical note 24.

Doms, G. and Forstner, J., 2004. Development of a kilometer scale NWP-System: LMK, COSMO Newsletter No.4, 159-167.

Essery, E., Best, M, Cox, P., 2001. MOSES 2.2 Technical Documentation. Hadley Centre technical note 30.

Gálos, B., 2009. Analysis of forest-climate interactions, applying the regional climate model REMO. PhD Thesis dissertation. University of West Hungary, Faculty of Forestry.

Göttel H., J. Alexander, E. Keup-Thiel, D. Rechid, S. Hagemann, T. Blome, A. Wolf and D. Jacob, 2008. Influence of changed vegetation fields on regional climate simulations in the Barents Sea Region. Clim. Change (BALANCE Special Issues) 87, 35-50.

Gray, M.E.B., 2003. The use of a cloud resolving model in the development and evaluation of a probabilistic forecasting algorithm for convective gusts. Meteorol. Appl. 10, 239–252.

Hohenegger, C., P. Brockhaus and C. Schär, 2008. Towards climate simulations at cloud-resolving scales, Meteorol. Z., 17, 383–394, doi:10.1127/0941-2948/2008/0303.

IPCC, 2007. http://www.ipcc.ch/publications_and_data/publications_and_data_reports.shtml

Kendon, E.J., N.M. Roberts, C.A. Senior and M.J. Roberts, 2012. Realism of rainfall in a very high resolution regional climate model, J. Climate, doi:10.1175/JCLI-D-11-00562.1

Lorenz, Ph. and D. Jacob 2005. Influence of regional scale information on the global circulation: A two-way nesting climate simulation, Geophys. Res. Lett., 32, L18706, doi: 10.1029/2005GL023351.

McGregor, J.L. 1997. Regional climate modelling. Meteorol. Atmos. Phys. 63, 105-117, doi: 10.1016/j.jcp.2006.10.024.

Rockel B., Will A., and Hense A. 2008. The regional Climate Model COSMO-CLM (CCLM), Meteorol. Z., 17, 347-348.

Schlemmer, L., C. Hohenegger, J. Schmidli, C. S. Bretherton and C. Schär, 2011. An idealized cloud-resolving framework for the study of midlatitude diurnal convection over land, J. Atmos. Sci., 68, 1041–1057. doi:10.1175/2010JAS3640.1

Scoccimarro E., S. Gualdi, A. Bellucci, A. Sanna, P.G. Fogli, E. Manzini, M. Vichi, P. Oddo, and A. Navarra, 2011. Effects of Tropical Cyclones on Ocean Heat Transport in a High Resolution Coupled General Circulation Model. J. Climate, 24, 4368-4384.

Shaffrey, L.C. and Co-authors, 2009. U.K. HiGEM: The New U.K. High-Resolution Global Environment Model—Model Description and Basic Evaluation. J. Climate, 22, 1861–1896. doi:10.1175/2008JCLI2508.1

Steppeler J., Doms G., Schättler U., Bitzer H.W., Gassmann A., Damrath U., Gregoric G. 2003. Meso-gamma scale forecasts using the nonhydrostatic model LM, Meteorol. Atmos. Phys., 82, 75-96.

Webster, S., M. Uddstrom, H. Oliver and S. Vosper, 2008. A high-resolution modelling case study of a severe weather event over New Zealand. Atmos. Sci. Lett., 9, 119–128, doi:10.1002/asl.172.

TASKS B1_D3 and B1_D4: Produce system diagrams showing linkages and sensitivities

Introduction

The aim of this task is to identify linkages and sensitivities between forests and weather at different scales across the EU. To achieve this, we have created tables and diagrams which summarise connections between forests and the weather-related phenomena that were presented in detail in Task A2_D2/D3. The information used for this task is drawn from literature (Task A2) and results from our simulations studies (Tasks A3 and A4). This information has then been aggregated to the assess how forests can affect, and be affected by, the following impacts: i) land degradation due to erosion; ii) drought; iii) storm damage; iv) extreme high temperatures; v) flooding. These impacts have been chosen because they are representative of the broad range of weather-related phenomena that can affect forests, and which can be influenced by forests themselves. As such, it provides an informative reference for summarising the linkages between forests and weather-related impacts, which can be applied in a general way across Europe. The tables and diagrams are also accompanied by a brief summarising paragraph.

In producing the tables and diagrams, we have employed a general form of the DPSIR framework2 which uses driving forces, pressures, states, impacts and responses to describe the possible interactions between society and the environment. However, for the purposes of this task, we have replaced ‘states’ with the ‘indicators’ of the impact of interest. Thus, the impacts are listed together with indicators of the impact, the drivers of the impact, the pressures resulting in the impact, and possible responses to mitigate the impact. In all cases, the role of forests is highlighted in order emphasise the direct and indirect linkages between forests and weather-related events. It is also important to note that the lists in the tables are intended to be representative and not exhaustive.

The tables are followed by diagrams illustrating the causal links and effects. The arrows indicate the direction of influence. Consequently, some diagrams are rather complex, illustrating the wide range of interactions at work.

2 http://www.eea.europa.eu/publications/92-9167-059-6-sum/page002.html 17

Linkage tables and figures

Table 2: Land degradation (see Figure 4)

Land degradation due to erosion can be driven by poor land management practices such as deforestation, but may also occur as a result of changes in the local and regional climate, such as increased rain and erosion, or droughts followed by heavy rain. As can be seen from Table 2, there are a large number of indicators of erosion. Poor land management techniques can arise because of pressures such as limited land availability, a lack of awareness of better methods, or the expense of raw materials. To reduce these pressures, and address the drivers, it would be advisable to provide clear guidelines and to encourage land owners to monitor their land for early signs of erosion which can be fixed before they escalate. Figure 4 illustrates that the drivers lead to the changes in the indicators. This can exacerbate the pressures, leading to responses, but also lead to direct responses.

Table 3: Drought (see Figure 5)

Drought could be a particular problem for Europe in the future. Indeed, if the links between Mediterranean precipitation deficit and droughts in the rest of Europe are robust, the continued drying of the Mediterranean region could exert a much wider geographical influence. Table 3 describes the drivers of drought as climate change, land surface change, and poor water management techniques. The impact of these drivers can be quantified through the listed drought indices described. The pressures resulting in these drivers are primarily due to an increasing population demanding more fresh water. In order to address the pressures and drivers, governments can initiate plans for better water management, as well as better farming and industrial practises which use water more efficiently. More generally, governments can also do more to address the causes of anthropogenic climate change, thereby reducing the impact of the drivers. The flow of Figure 5 is similar to that of Figure 4: the drivers are partly influenced by pressures, and lead to the changes in the indicators, prompting a response.

Table 4: Storm damage (see Figure 6)

Storm damage can affect urban areas and infrastructure as well as vegetation and crops. Natural and anthropogenic changes in the climate system can lead to changes in large scale atmospheric circulation patterns, while changes to the land-surface (e.g. 18

urbanisation or deforestation) can affect the likelihood of flooding due to surface runoff, and the moisture content of storms on more regional and local scales. The indicators of increased storminess are likely to include the more frequent occurrence of strong winds, heavy rainfall events, increased cyclogenesis and increased cost incurred by winds and floods. The main pressure that has to be considered is the growing demand for land due to the growing population, meaning that areas previously considered unsuitable for farming and habitation are now occupied. Therefore, the best response is to prepare for future extreme conditions by using high resolution climate models to make projections for changes in storminess, and storm damage. In addition, increasing forest cover will help to reduce the impact of damaging gusts in urban areas, while reducing average wind speeds, and slowing flow rates of surface runoff.

Table 5: Heat waves (see Figure 7)

As described in task A2_D2/D3, heat waves are predicted to become more intense and more frequent as mean temperatures and climate variability rise. These changes are driven by shifting atmospheric circulation patterns caused by climate change (natural and man-made), and also by poor land-use techniques which modify the surface radiation balance. In addition, increasing urban population size is likely to exacerbate urban heat island effects. To reduce the impact of extreme temperatures, possible response include assessing predictions from climate studies, including the impact of planting forests locally, and increasing urban tree cover.

Table 6: Flooding (see Figure 8)

Changes in atmospheric circulation patterns and land-use may also lead to an increased likelihood and severity of flooding events. This is also partly because of the urbanisation of floodplains and the removal of floodplain forests which act to slow surface runoff flow rates. Of course, these drivers occur due to the pressure of an increasing population and demand for housing in urban areas. As a response to this, governments and administrative organisations should prepare by evaluating how flood risk is likely to change in the future and adapting to this risk by increasing vegetation cover in urban areas to reduce surface runoff. Figure 8 outlines the general linkages between the drivers, indicators, pressures and responses.

Table 2: Land degradation due to erosion

Impact Indicator Drivers Pressures Response Earth system Other Land 1) Bare soil 1) Increased 1) Poor forest 1) Limited 1) Provide degradation rainfall rates. health due to land guidelines for due to 2) Thin topsoil bad forestry availability for good forestry erosion techniques, farming, practises, e.g. 3) Exposed 2) Increased e.g. tree recreation Reduced tree roots frequency of removal and Impact heavy rainfall leading to habitation. Logging (RIL) 4) Silted dams episodes. gaps; techniques; replacement appropriate 5) Muddy 3) Changes in with 2) Demand land-use runoff water natural inappropriate for forest techniques for vegetation due species; over- products (e.g. farmers. 6) Cracks in to climate grazing; wood for soil surface change. removal of building across a slope forest litter. materials, 4) Increased fuel, etc.) 7) Increased damage to 2) Monitor number of forest caused land for signs landslides by diseases of erosion and (e.g. per unit and pests fix small time, per unit (possibly due problems area) to climate before they change). escalate.

Table 3: Drought

Impact Indicator Drivers Pressures Response Earth system Other Drought Drought 1) Natural 1) Over-use of 1) Increasing 1) Proper indices, climate variability water water demand management measuring: or changing resources by in homes, and 1) weather patterns, homes, farming and conservation meteorological including: farming and industry. of water drought decreased industry. resources. 2) agricultural average rainfall, 2) Increasing drought increased 2) Removal of demand on 3) hydrological frequency of dry forests and land for 2) Use of drought. days, increased vegetation farming and appropriate average which are building. farming temperatures. crucial for practises. regulating the 2) Changes in local natural hydrological 3) Prior vegetation cover cycle. analysis of due to climate the impact of change. 3) Planting planting or inappropriate removing a tree species in forest. dry areas, thereby 3) reducing dry Addressing season river causes of flows. man-made climate change.

Table 4: Storm damage

Impact Indicator Drivers Pressures Response Earth system Other Storm 1) Increased 1) Changes in 1) Possibility of 1) Demand 1) Use damage frequency of large-scale deforestation for land for projections strong winds. atmospheric leading to farming, from climate circulation higher recreation models to 2) Increased patterns and moisture and predict frequency of moisture content of habitation. changes in heavy rainfall content due to storms atmospheric episodes. natural climate (including Vb), circulation variability, or higher winds (including 3) Increased man-made (due to lower modified frequency of Vb climate surface drag) forest cover). storm tracks. change. and reduced flood 2) Increase 4) Increased protection. urban forest cyclogenesis in cover to Mediterranean 2) reduce impact and Black Sea Inappropriate of local wind (e.g. gulfs of land-use (e.g. gusts, Lyon and Genoa, building on capturing the Adriatic and flood plains). rainfall and Aegean Seas). reducing runoff. 5) Annual average cost of 3) Develop damage impact attributed to models to storms (e.g. predict cost of floods and high damages. winds).

Table 5: Extreme high temperatures

Impact Indicator Drivers Pressures Response Earth system Other Extreme high 1) Increased 1) Changes in 1) Poor land- 1) High 1) Use climate temperatures average large-scale use practises demand for models to temperatures. atmospheric that dry out resources, produce and patterns due to soils. and land assess 2) Heat wave natural climate especially in predictions of indices. variability or 2) Changes in cities. extreme man-made forest cover temperatures, 3) Heat stress climate that modify including the indices. change, e.g. surface drag influence of higher and forests on global temperatures, turbulence, temperatures reduced and access to and heat waves, precipitation, deep soil given projected reduced soil moisture which demand for land moisture. are known to and resources. influence the intensity and duration of 2) Planting heat waves. urban and peri- urban forests to provide shade 3) Urban heat and evaporative island effects. cooling.

Table 6: Flooding

Impact Indicator Drivers Pressures Response Earth system Other Flooding 1) Increased 1) Changes in 1) Increased 1) High 1) Use climate numbers of large-scale surface runoff demand for models to flooding atmospheric owing to land, and produce and events. patterns due to urbanisation forest assess natural climate and removal of resources. projections of 2) Greater variability or urban/peri- extreme rainfall number of man-made urban tree 2) Population events leading heavy rainfall climate cover. growth, and to flooding, events. change, e.g. growth of including the larger numbers urban areas influence of 3) Increased of low pressure 2) Changes in forests on local monthly and systems, land-use and 3) Increase in weather seasonal increased forest cover concreted patterns. rainfall totals frequency that modify soil surfaces in and/or severity moisture levels urban areas of convective and local 2) Planting storms. rainfall floodplain patterns. forests to partly mitigate high runoff resulting 3) Changes to from episodes local flood of heavy rain defences that may worsen 3) Building impacts in storm drains to other areas. capture runoff

Figure 4: Linkage diagram for land degradation following deforestation, indicating possible responses

DRIVERS

Earth system Other

RESPONSES Changes in Poor forest health due to bad Increased natural Increased damage to forest forestry techniques, e.g. tree Increased frequency of vegetation caused by diseases and pests removal leading to gaps; rainfall rates. heavy rainfall due to (possibly due to climate replacement with inappropriate Provide guidelines for good Monitor land for signs of erosion episodes. climate change). species; over-grazing; removal forestry practises, e.g. Reduced and fix small problems before they change. of forest litter. Impact Logging (RIL) techniques; escalate. appropriate land-use techniques for farmers.

INDICATORS PRESSURES

Number of Cracks in soil Limited land availability for Demand for forest products (e.g. Exposed tree Muddy runoff landslides Bare soil Thin topsoil Silted Dams surface farming, recreation and wood for building materials, fuel, roots water per unit time, across a slope habitation. etc.) per unit area.

Figure 5: Linkage diagram for effects of drought and the role of forests in moderating the impacts

Figure 6: Linkage diagram showing the effects of storms on the land surface, and the role that can be played by forests

INDICATORS

Increased Increased Increased cyclogenesis Annual average cost of Increased frequency of frequency of in Mediterranean (e.g. damage attributed to frequency of heavy rainfall Vb storm gulfs of Lyon and storms (e.g. floods and strong winds. episodes. tracks. Genoa). high winds).

D Changes in large-scale De-forestation leading to R atmospheric circulation higher moisture content of Inappropriate patterns and moisture storms (including Vb), land-use (e.g. I content due to natural higher winds (due to lower building on V climate variability, or man- surface drag) and reduced flood plains). made climate change. flood protection. E R Earth system Other S

Use projections from climate Increase urban forests cover Develop impact models models to predict changes in to reduce impact of local to predict cost of atmospheric circulation Demand for land for wind gusts, capturing rainfall damages. (including modified forest farming, recreation and and reducing runoff. cover). habitation. RESPONSES PRESSURES

Figure 7: Linkage diagram showing the effects of rising temperatures and heat waves, and the possible role of forests in mitigation

INDICATORS

Heat stress Increased indices. Heat wave average indices. temperatures.

D Changes in large-scale Poor land- Urban heat Changes in forest cover that atmospheric patterns due to use practises island R modify surface drag and natural climate variability or that dry out effects. PRESSURES access to deep soil moisture I man-made climate change, soils. which are known to influence High demand for resources, and e.g. higher temperatures, V the intensity and duration of land especially in cities. reduced precipitation, heatwaves. E reduced soil moisture. R S Earth system Other

Use climate models to produce and assess predictions of extreme temperatures, including the Planting urban and peri-urban influence of forests on global temperatures and forests to provide shade and heat waves, given projected demand for land and evaporative cooling. resources.

RESPONSES

Figure 8: Linkage diagram showing drivers of flooding and the role of forests in mitigating floods

RESPONSES

Planting floodplain High demand forests and urban Use climate models to for land and trees. Increasing produce and assess INDICATORS forest storm drain capacity projections of extreme Population resources. rainfall events leading to Greater Increased growth flooding including the number of numbers of influence of forests on local heavy rainfall flooding PRESSURES weather patterns events. events.

Changes in large-scale atmospheric patterns due to Changes in Increased natural climate variability or land use that Changes to runoff due to man-made climate change, modifies local local flood urbanisation e.g. larger numbers of low rainfall defences that and tree loss pressure systems, increased patterns. could worsen frequency and/or severity of impacts in convective storms. other areas.

Earth system Other DRIVERS

Met Office Tel: 0870 900 0100 FitzRoy Road, Exeter Fax: 0870 900 5050 Devon EX1 3PB [email protected] United Kingdom www.metoffice.gov.uk Task B2: Information Gaps and Future Scientific Questions European Commission (DG Environment) July 2012 Edward Pope, Michael Sanderson and Monia Santini

Contents

Executive Summary ...... 2

TASK B2_D1: Tabular matrix summarising key information from previous ...... 4

TASK B2_D2: Knowledge gaps and future areas for scientific investigation ...... 7 1. Land surface scheme treatment of vegetation ...... 7 2. Impact and production of aerosols by forests ...... 11 3. General note on feedbacks ...... 12 4. Limitations of the UM nested suite ...... 13 5. Lack of geographically explicit forest data ...... 15 6. Lack of meteorological data at forest locations ...... 15

Summary ...... 18

References ...... 20

Executive Summary

1. The key information from previous tasks has been summarised in a tabular matrix (Task B2_D1), highlighting the local and regional impact of forests on temperature, precipitation, wind, water quality and quantity and soils.

The remaining points refer to Task B2_D2.

2. The accuracy of land surface schemes could be improved by including more plant functional types such as broad leaf evergreen, broad leaf deciduous, needle leaf evergreen, needle leaf deciduous trees and by accounting for vegetation growth such that forest roughness and root depth change over time. Better methods for initialising soil moisture would also help to improve weather predictions.

3. Biogenic Volatile Organic Compounds (BVOCs) emitted by boreal forests may significantly increase cloud condensation nuclei concentrations such that these forests have an overall cooling impact on the global climate. However, while this process is potentially important for climate projections, many of the interactions with other aerosols and cloud water droplets and the associated feedbacks are not well understood and require considerable further study. This also applies more generally to a wider range of feedbacks, especially those involving ecosystems.

4. The nested UM suite is an extremely effective way of simulating multiple forest cover regimes. However, given that the grids are coupled one-way, changes in weather patterns occurring in the highest resolution model (here, 4 km) cannot influence weather patterns in the lower resolution models. In addition, the results show clear numerical instabilities which are representative of realistic weather events.

5. Many of the most widely used vegetation cover data sets are derived using numerical models which make assumptions about historical population size and non-forested areas, or use ecosystem models and estimates of past climate. Therefore, the HILDA data set, which uses historical records and maps,

represents a significant - though incomplete - improvement, and provides a historical record of forest cover change since 1900.

6. One of the most significant obstacles in determining direct links between weather and forests in the Europe is the absence of long time-series of meteorological measurements at locations where forest cover has changed. There are a small number of studies that have recorded temperatures in forested and comparable open locations, but other data, such as rainfall and humidity, were not recorded.

Scale of Temperature Precipitation Wind Water (QQ) Soils Influence (inc. floods)

TASK B2_D1: Tabular matrix summarising key information from previous

For this task we have summarised the impact of forests on local and regional weather- related phenomena, as described in the previous tasks. In particular, the table below provides a description of the influence exerted by forests on temperature, precipitation (including floods), wind, water quality and quantity (QQ), and soils. For clarity, the impacts on local and regional weather patterns have been considered separately. Owing to the large amount of data compiled in the previous tasks, it is not possible to include every detail in the present table. Therefore, the information presented here is representative of the impacts, rather than an exhaustive list.

Local Forest canopy Increased evapo- Forests reduce High ET rates Healthy, well- provides insulation transpiration (ET) average wind (particularly managed from extremes of due to presence of velocities due to from young forests diurnal and seasonal forests leads to increased surface forests) can reduce cycles; provides increased rainfall in drag. reduce dry erosion rates. essential shade for the local region, season river riparian zones, and providing Can provide flows, Can reduce protects delicate potentially protection and especially if frequency of ecosystems. important benefits shelter for crops, non-native shallow (<1 in semi-arid areas improving species are m deep) Temperatures above of Europe. farming efficiency planted, even landslides, forest canopy can by reducing if but not on show amplified Flood plain forests damage to crops precipitation steep slopes. diurnal cycle: can reduce severity and decreasing is locally increased ET from 6- of floods by water increased. Cannot 11am leads to cooler reducing flow rates consumption. reduce mornings. Reduced as a result of Can protect frequency of ET from 12-6 pm increased hydraulic and improve deep (>3 m) leads to warmer roughness. water quality landslides. afternoons, by capturing especially in Tree cover can atmospheric Increased summer. reduce surface pollutants springtime runoff rates in and reducing ET can dry Provide shade in urban sediment in out soils urban environments; environments, runoff, but leading to capture precipitation, thereby reducing can also cooler enhance evaporative flood risk and acidify water springs, but cooling, reduce damage. supplies if warmer energy usage. pollutant summers. concentration is high.

Scale of Temperature Precipitation Wind Water (QQ) Soils Influence (inc. floods) Large Afforestation at high Forests cannot Large scale Regions Large-scale scale and latitudes causes prevent severe afforestation downstream forests regional warming due to floods caused by programs have of forests minimise influence lower albedo rainfall events contributed to a likely to be erosion rates, (compared to open occurring on large reduction in more humid maintaining ground/snow) and spatial and average wind than they healthy soils. low ET rates. temporal scales. speeds across would Influence in southern northern otherwise be, Greater Europe is unclear – Despite compelling hemisphere. e.g. large moisture probably leads to arguments, no Unclear whether forests in reserves of cooler springs, but statistically distribution of Sweden deeper soils warmer summers significant evidence wind speeds (e.g. probably can play an (when compared to exists linking ‘gustiness’) has enhance important open ground), but changes in forest also changed. precipitation role in large regional cover and severity in northern governing differences in of storms, e.g. Europe – regional behaviour exist. deforestation in increase climates - eastern Spain and availability of evaporative Models show that floods in central fresh water, cooling from forest root depth, Europe caused by and maintain moist soils surface roughness Vb storms. quality by during and soil moisture filtering out summer content strongly Possible link atmospheric months helps influence the between and water- to prevent intensity and deforestation borne extreme duration of heat around western pollutants. temperatures waves. Mediterranean . basin and increased cyclogenesis e.g. in gulfs of Lyon and Genoa.

TASK B2_D2: Knowledge gaps and future areas for scientific investigation

The objective of this task is to identify major knowledge gaps and uncertainties in the literature and simulations described in the previous tasks. The secondary aim is to describe scientific areas and potential investigations that would help to better understand the link between forests and weather in the European Union. The main knowledge gaps and areas for potential investigation identified and discussed here are listed below:

1. Limitations in the land surface scheme - particularly the parameterisation of vegetation root depth, drag, and the initialisation of soil moisture. 2. The impact of the production and emission of forest aerosols, particularly at northern latitudes. 3. General note on feedbacks 4. Limitations of the UM nested suite approach and a comment on other simulations 5. Lack of geographically explicit forest data 6. Lack of meteorological data at forest locations.

Many of these points are discussed in detail in the previous reports, e.g. A1_D1 and A2_D1. Therefore, we have opted to summarise those findings, rather than repeat them in full, and primarily focus on describing the limitations associated with models.

1. Land surface scheme treatment of vegetation

The representation of the land surface in numerical models provides the bottom boundary condition to the atmosphere and is a major component of the overall earth- system which affects the transport of heat, moisture, momentum, carbon and trace gases (Ashton, 2012). As such, it exerts a particular influence on the near surface evolution of the atmosphere, making it an important element of accurate forecasts and climate predictions. However, the land surface is extremely complex and very difficult to simulate exactly. Therefore, any computational scheme which parameterises land surface processes must necessarily rely on certain assumptions and approximations, which can strongly influence weather predictions and climate projections (e.g. Cox et al. 1998). Consequently, in order to determine the limitations of the approach, it is necessary to understand the general implementation of the land surface scheme and both the explicit and implicit approximations. For example, it is well known that

phenomenological parameters such as ‘forest roughness’ and ‘root depth’ are two of the most important variables in determining the duration and intensity of heat waves in climate models (e.g. Clark, et al. 2010). These parameters are particularly significant because they control the turbulent transfer of heat away from the land surface, and access to soil moisture. Observational studies of severe heat waves in Europe (e.g., summer 2003) have shown that a deficit of winter rainfall often precedes these events (Beniston and Diaz, 2004).

The Met Office weather and climate models represent land surface processes through the Met Office Surface Exchange Scheme (MOSES) version 2 (Essery et al., 2003), which forms part of the Joint UK Land Environment Simulator (JULES1; Best et al., 2011). In this land surface scheme, the observed land use within each model grid box is divided into nine different types, of which five are vegetation. Within this implementation, the observed vegetation in each grid-box is not represented by species, but is re- classified into broad categories by determining the Plant Functional Type (PFT) which most closely matches the plant physiology. This is necessary because different vegetation types behave in characteristically different ways, from their respiration and evapotranspiration rates to their albedo.

Other land use types considered in JULES are urban, inland water, bare soil and land ice. Separate surface temperatures, sensible and latent heat fluxes, snow depths, and canopy moisture loads are calculated for each surface type. However, soil moisture - a crucial variable influencing both climate and vegetation - is only calculated as a grid-box average value.

In general, the parameterisations used to approximate the behaviour of vegetation provide a robust and computationally efficient tool with which to investigate the interaction between vegetation and weather. Nevertheless, there remain some limitations which inhibit our current understanding of how forests may influence weather patterns. Most notably, while it is possible to simulate the growth and competition between different PFTs using the models like TRIFFID (Cox, 2001), LPJ (Sitch et al., 2003) and ORCHIDEE (Krinner et al., 2005), these models are not currently used in Numerical Weather Prediction (NWP) model. As a result, the plant heights, and associated parameters, remain fixed over time. Within the context of the model, this is potentially significant because plant height strongly influences the interaction between

1 JULES is currently being incorporated into the JULES IMPACTS MODEL (JIM).

the land surface and the atmosphere. However, it is important to note here that the assumption of static vegetation remains valid on timescales of days to weeks, such as those presented in report A3_D1. This is not necessarily the case for simulations running over months and years, in which it is probably necessary to account for vegetation growth and the associated changing heights, root depths, evapotranspiration rates, leaf areas and drag coefficients.

As described above, MOSES treats trees differently according to whether they are classified as broad leaf or needle leaf. For example, needle leaf trees are assumed to have much shallower roots than broad leaf trees. While this is a reasonable assumption, the root depth is also taken to be a constant, regardless of the soil type or local climate. This assumption this is important because root depth can determine the quantity of soil moisture available to a tree, thereby strongly influencing the evapotranspiration rate which affects latent heat fluxes and hence surface temperature and precipitation. Similarly, the albedo associated with each tree type is taken to be a fixed number. As with root depth, albedo should change with the tree species and height, but will also be affected by the leaf area and distribution. As a final example, the forest roughness parameter – otherwise known as aerodynamic drag - associated with a given tree type is assumed to vary as a function of the vegetation height only2. Since the vegetation maintains a constant height, this parameter also remains fixed. Very recently, attempts have been made to include the swaying of trees on the drag on winds, but this effect is not currently included in JULES.

In order to develop more complete studies of the influence of vegetation on weather patterns, it will certainly be necessary to incorporate vegetation growth, and a greater number of PFTs. Indeed, some advances in better modelling the interaction between crops and weather are being made, with the latest version of JULES including an additional 12 PFTs. However, for the purposes of future studies investigating relationships between forests and weather, it would be of interest to see a broader variety of tree types available, for example needle leaf evergreen, needle leaf deciduous, broad leaf evergreen and broad leaf deciduous (e.g. Bonan et al. 2002). In addition, the simulation of forest fires and subsequent feedbacks with the atmosphere and surface hydrology could be important for climate projections (Golding and Betts, 2007). Clearly,

2 This approximation was introduced because drag is a complex phenomenon to model and is computationally extremely expensive to simulate explicitly.

projections of such processes would be highly uncertain and would have to be interpreted with care.

Soil moisture is also an important parameter in determining climate and weather patterns. In particular, the physical properties of the soil influence the heat and water exchange between the land surface and atmosphere through subtle changes in surface temperature, humidity and precipitation. The partitioning of net radiation into sensible, latent and ground heat fluxes is controlled by the soil physical properties and soil moisture. Indeed, soil moisture on its own has been shown to have a large influence on the local weather over land in summer. Fischer et al. (2007) showed that summer weather is hugely dependent on the accumulations of soil moisture during the previous winter and spring. A reduction in soil moisture in spring may lead to warmer, drier summers. However, soil moisture is extremely variable over space, making measurements difficult. This variability can partly be attributed to variability in hydrological inputs such as rainfall and snow melt. However, most variability is attributable to variations in soil physical properties, vegetation and topography.

The soil moisture in the nested UM suite used in Task A3 is initialised using ERA-Interim reanalysis data provided the European Centre for Medium Range Weather Forecasting (ECMWF). The ERA-Interim data are produced using a global circulation model (GCM) that is constrained by surface and satellite-based observations to produce a coherent and close representation of actual meteorological conditions. The horizontal resolution of the model is 0.75˚ x 0.75˚ which corresponds to ~80 km at European latitudes. These data are somewhat coarser than MetUM, the Met Office global weather forecast model (the version used in this project has a resolution of approximately 60 km at European latitudes), the ERA-Interim data must be interpolated to the MetUM resolution. As a result, the initial soil moisture will not accurately represent the natural variations which occur on small scales which can potentially lead to inaccuracies in projections if the initial soil moisture is either too high or too low. For example, errors in soil moisture levels could lead to excess precipitation or warmer than expected temperatures. Furthermore, since the ECMWF and Met Office global models are not identical, there will be some “spin up” time at the start of the simulation whereby the soil moisture adjusts from its initial levels to a value consistent with the modelled climate. However, it is important to note that the equilibrium value may still differ considerably from satellite- based soil moisture data sets, e.g. ASCAT (Bartalis et al 2007). Soil hydrology and thermodynamics are extremely complex and difficult to model accurately (e.g. Cox et al,

1998). Global soil moisture data sets only provide an estimate of surface soil moisture and do not provide any information about the deeper levels. Therefore, future studies would benefit from higher resolution initial conditions for soil moisture, and better observations. These improvements would be particularly important for improving the accuracy of shorter timescale simulations such as those described in Task A3.

2. Impact and production of aerosols by forests

Aside from carbon uptake, forests exert another complex indirect effect on weather and climate via the production and emission of hydrocarbons. For example, forest fires release the stored carbon into the atmosphere as carbon dioxide, methane, and other trace gases, together with smoke particles that can have a significant impact on climate. However, there are also less well-known chemical effects of forests on local and global climate. Notably, forests release a range of reactive hydrocarbons which can alter the lifetime of methane and partly control concentrations of ozone (e.g. Pacifico et al., 2009), both of which are important greenhouse gases. These reactive species can also produce aerosols, which have a number of different effects on climate. Model simulations of aerosols produced by boreal forests which are relatively far from major anthropogenic sources of pollution suggest that these aerosols could be important in controlling the regional aerosol budget.

As described in Task A1_D1, vegetation produces biogenic volatile organic compounds (BVOCs), with the most important emitted by boreal forests being monoterpenes. BVOCs can be oxidised in the atmosphere to form secondary organic aerosols (SOAs). These aerosols can act as effective cloud condensation nuclei (CCN) by enabling the condensation of water vapour (Quaas et al., 2004). SOAs cool the climate in two ways, directly by reflecting incoming solar radiation, and indirectly by encouraging cloud formation and increasing cloud lifetimes. Indeed, Spracklen et al. (2008) and O’Donnell et al. (2011) have argued that the reason many studies indicate boreal forests warm northern latitudes is because most climate models ignore the impact of SOAs produced by the BVOCs emitted by forests. These authors found that boreal forests could double regional cloud condensation nuclei concentrations, such that these forests have an overall cooling impact on global climate. In particular, Spracklen et al. (2008) identified a potentially important feedback: increased temperature drives increased BVOC emissions which can drive faster particle growth rates, greater CCN concentrations and increased aerosol radiative cooling. If correct, their argument suggests that the cooling impact of

boreal forests would be minimal during cool climatic periods, since the emission of monoterpenes is small during cold conditions. However, this process and the extent of its influence are poorly understood not least because the strength of emission depends on tree species and varies according to temperature and light levels. Nevertheless, this is clearly an important consideration that could significantly alter current predictions of the impact of changes in forest cover at high latitudes, and therefore would have implications for policy makers. For this reason, more work needs to be done to understand how the emission rates of BVOCs change as a function of their environment and tree species, in order to better assess their overall impact on global climate. Such studies will require field and laboratory studies measurements to better estimate BVOC emissions and the subsequent chemical reactions.

3. General note on feedbacks

The different mechanisms by which forests interact with the atmosphere and modify weather and climate are still not fully understood. As described above, feedbacks exist between these processes and the atmosphere which are not well understood. Many of these feedbacks occur at small spatial scales which cannot be resolved by global climate models, and can only be simulated by higher resolution models for limited areas.

The importance of the ecosystem feedbacks like those on the hydrological cycle suggests that there is space for management options that could potentially reduce the likelihood of extended droughts, or the impact on crops (e.g. Donnison, 2012). Several factors influencing water use by tree plantations can be controlled by management and there is scope to design and manage forest plantations for increased water use efficiency. Plantation design (edges, firebreaks, streamlines, use of mixed species) has the potential to modify atmospheric coupling of forest plantations with impact on their water use. Furthermore, proper management of natural and planted forests has a relevant impact on the water available downstream for agricultural and civil uses (Vanclay et al, 2009). Urban and peri-urban forests help to reduce average temperatures (discussed further in Task C2). Other studies suggest there are likely to be benefits such as a reduction in extreme temperatures and wind gusts (e.g. Lion et al., 2009; Bohnenstengel et al., 2011), though this can depend strongly on how much the forest cover is increased (McCarthy et al., 2011).

4. Limitations of the UM nested suite

The modelling studies described in Task A3 used a nested approach (see Webster et al. (2008) for more details) whereby the Met Office Unified Model was executed at three different horizontal resolutions for a multi-day period over the regions of interest (Spain, Italy, Germany, Austria and Sweden). There are several advantages of using this approach. In particular, the technique ensures that local changes in forest cover can be studied in detail, but without the vast computational expense of simulating the entire globe at such high resolution. In addition, the nested grid arrangement ensures that meteorological variables vary smoothly from low to high resolution, thereby improving computational stability. A general description of the set up is as follows: the coarsest resolution simulation is a re-run of the operational 60-km resolution global forecast model. The 12 km resolution grid is then nested within the global grid, and centred on the region of interest. To improve computational efficiency, the 12 km run is used to drive 3 simultaneous nested 4 km simulations of the same geographical location, but with different forest cover: i) afforestation, ii) deforestation, iii) current forest cover. This is only possible because the nested grids are one-way coupled, i.e. the global model only feeds information to the 12 km grid, and the 12 km only feeds information to the 4 km grid – neither the 4 km nor the 12 km domains feed information back to the global model. To ensure consistency, the forest cover is only modified in the 4 km simulations.

Despite the benefits of this approach, there are certain approximations that limit the conclusions that can be drawn. One-way coupling means that changes to weather systems caused by modified forest cover cannot propagate outside the 4 km domain, meaning that influences on larger spatial scales cannot be captured. This is also a consequence of only changing the forest cover within the 4 km domain, and not in the 12 km and global models. In general, this approximation is probably not a significant source of error, at least for short simulation times on the order of days, since the downstream impact of forest cover change diminishes rapidly with distance. Nevertheless, it would be useful to have two-way coupling between the nested models and to investigate the influence of altered forest cover beyond the spatial extent of the 4 km domain. With more powerful computers, this may become feasible in the future.

Aside from the limitations described above, the numerical solutions produced by the UM also display uncertainties that are inherent to the implementation of the model itself. For example, in the simulations described in A3_D1, the Istria peninsula in the north east of

the Adriatic Sea is notable for exhibiting clear differences between the afforestation / deforestation and control simulations for meteorological variables such as temperature, precipitation and wind speeds. These differences occur almost instantaneously at the start of the simulation, whenever forest cover is modified anywhere within the 4 km computational domain. This means that changes in the meteorology at this location cannot be responding directly to the change in land surface, but are caused by small differences in the simulated climate which modify the frequency of events like convection. Consequently, any modification in the initial conditions used to start the model, including changes to the land surface hundreds of kilometres away, can result in detectable perturbations to the weather at this sensitive location. Several other regions also persistently illustrate this particular instability, including the Balkans, the Mediterranean Sea between the Balearic Islands and Corsica/Sardinia, and the Alps.

In general terms, these perturbations can be considered to be ‘noise’, and not indicative of real weather events. If so, the best way to eliminate the noise would be to generate an ensemble of simulations, each of which would have slightly different initial conditions. These ensembles could be generated by starting the simulations on different days, or changing the position of the 4 km model domain slightly. All the results from the ensemble could then be combined, and any noise should reduce in magnitude, allowing the true impact of forests on weather outside of the area deforested or afforested to be identified. It would also be advisable to simulate a wide range of different weather events so that the model response to a broad spectrum of weather patterns can be investigated. Statistical analysis could then be used to distinguish between the noise and more meaningful signals.

As described in Task A1_D1, studies of deforestation and afforestation in temperate regions do not agree on the impacts. Indeed, some studies report little or no change in surface temperature and rainfall following deforestation, whereas others simulate either a warming or a cooling (Pitman et al., 2009). Some of these differences will be caused by the different numerical models and the representations of plant physiological processes. In addition, those models which simulate a drying of the soils during summer project increases in surface temperatures (e.g., Heck et al., 2001) whereas other models which simulate wetter soils allow the forests to continue to transpire during the summer months and maintain cooler surface temperatures.

Many of the studies of the impacts of forests on weather and climate have only examined changes in long-term average or seasonal average climate over a region. Very few have considered the impacts on extreme temperatures and rainfall, variability of climate over a region, and inter-annual variability of climate. For example, the magnitude of heat waves could be amplified by land surface preconditioning, such as below-average rainfall in the preceding winter and spring leading to dry soils (Vautard et al., 2007; Ferranti and Viterbo 2006). In general, the effects of land cover changes on extreme events have been poorly investigated, with exceptions being the studies by Clark et al. (2010) and Anav et al. (2010).

5. Lack of geographically explicit forest data

From the findings presented for Task A2_D2 it should be clear that, while there is a proliferation of literature on the subject, there is a lack of historical data on the geographical extent of forests. Indeed, some of the best data sets in current use (e.g. HYDE (Klein Goldewijk, 2001) and SAGE (Ramankutty and Foley, 1999)) are generated by numerical models that estimate historical forest cover using assumptions about the geographical distribution of human land use, or use vegetation distributions predicted by ecosystem models. Some of the ecosystem models are driven with climate data for the periods 1931-1960 or 1950-2000, which are not appropriate for other time periods.

6. Lack of meteorological data at forest locations

As noted in Tasks A2_D2 and A2_D3, it is extremely difficult to ascertain direct links between forests and weather patterns without long-term records of important meteorological variables at locations where forest cover has changed. However, there have been several recent studies (e.g. Ferrez et al., 2011) that have compared temperatures in forested locations with those at comparable open locations. Unfortunately, while informative, such investigations highlight the absence of coherent data on other useful weather variables such as rainfall, cloudiness and wind speeds. In addition, the FLUXNET project3 - a "network of regional networks" - coordinates regional and global analyses of observations from micrometeorological tower sites. These networks measure the exchanges of carbon dioxide, water vapour, and energy between terrestrial ecosystems and the atmosphere. However, rainfall and temperature have not

3 http://fluxnet.ornl.gov/

been collated in all of the available data sets, and it is not immediately clear whether the data recorded in grasslands and crops could be used to assess the effects of the forests, i.e., are the grasslands sufficiently close to the forested sites? Most of the published studies using FLUXNET data have focused on carbon and energy exchange between the surface and the atmosphere.

It would be extremely useful to record meteorological data in regions where forest cover has changed recently, or where forest cover could be artificially changed. The effect of forest cover changes could then be assessed. Clearly, this would necessarily be a long- term project, but it would be an invaluable resource in assessing the influence of forests on both local and regional scales.

Currently, two initiatives “EarthTemp4” and “GlobTemperature5” have been started to improve the use of land surface temperatures derived from satellite data by the research community. These data series are available at high spatial resolution, and some are 30 years long. These data sets could be used to validate climate model simulations of above-canopy temperatures, and might also be used to identify where forests are and where forest coverage has changed. An example product from the satellite data is shown in Figure 1, which illustrates the surface temperatures in London on 17th July 2006 during a heat wave. The parks in the city have lower temperatures than the surrounding urban areas and can be seen clearly in the image.

4 http://www.earthtemp.net/themes/1_in_situ_satellite/workshop_programme.html/ 5 http://due.esrin.esa.int/meetings/meetings278.php

Figure 1. Example of surface temperatures for the 18th July 2006 over London derived from satellite data. The temperature data have a resolution of approximately 1 km. This figure was created by Michael Saunby and Lizzie Good.

Summary

The report for Task B2_D2 has identified several major gaps in current knowledge and science capabilities that limit our understanding of the links between forests and weather in the European Union. These gaps are summarised below.

1. Any computational scheme which parameterises land surface processes must necessarily rely on certain assumptions and approximations, which can strongly influence simulated weather predictions and climate projections. In order to improve current understanding of the impact of vegetation cover on weather related events, it will be necessary to include more plant functional types, such as broad leaf evergreen and needle leaf deciduous trees, and to account for vegetation growth such that forest roughness and root depth change over time. Some of the approximations used in the land surface scheme of global and regional models may not be applicable at higher resolutions.

2. Reliable long-term observations of a range of meteorological variables, such as rainfall, temperature, humidity, cloud cover and surface energy fluxes (e.g., sensible and latent heat) are needed at a variety of locations to allow improvements in the understanding of forest-weather interactions. Observations at locations where forest cover has changed are also scarce, but would provide useful data on the effects on local climate. Soil moisture can also be of key importance in determining weather, especially in the summer. However, widespread observations of soil moisture are unavailable.

3. Recent findings suggest that the emission of organic compounds from boreal forests, and the resulting formation and growth of aerosol particles could double regional cloud condensation nuclei concentrations such that these forests have an overall cooling impact on the global climate. However, the rate at which these compounds are emitted by forests and the variation of emissions with local climate are poorly understood. Nevertheless, if the hypothesis is correct, it would have significant implications for climate and weather predictions, especially at high latitudes.

4. Many feedbacks between ecosystems and the atmosphere are poorly understood. For example, it is unclear whether forests increase or decrease the availability of water; the effect is likely to be regionally dependent. More intensive observation and modelling

studies of these feedbacks at a variety of different locations within Europe are needed to improve weather and climate models.

5. As described in Tasks A3 and A4 the nested UM suite and the COSMO_CLM regional model are efficient ways to simulate the effect of multiple forest cover regimes on local climate. Nevertheless, there are notable limitations. For example, the grids are one-way coupled, meaning that changes in weather patterns occurring in the highest resolution model do not influence weather patterns in the coarser scale models. In addition, there are clear instabilities that appear in the results which obscure any signal from the changes in forest cover on weather in the surrounding area.

6. As highlighted in Task A2_D2, our ability to determine links between historical weather and forest cover is partially limited by the lack of geographically explicit forest cover data. Indeed, some of the most widely used vegetation cover data sets are derived using numerical models which make assumptions about human population size and land used for crops, or use ecosystem models that are driven by climate data that are not necessarily representative of the period of interest. The HILDA data set represents a significant improvement, since it provides a historical record of forest cover change in Europe since 1900 based on historical maps.

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dynamics, plant geography and terrestrial carbon cycling in the LPJ Dynamic Global Vegetation Model. Glob. Change Biol. 9, 161-185.

Spracklen, D.V., Bonn, B. and Carslaw, K. (2008). Boreal forests, aerosols and the impacts on clouds and climate. Phil. Trans. Roy. Soc. A, 366, 4613–4626.

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Met Office Tel: 0870 900 0100 FitzRoy Road, Exeter Fax: 0870 900 5050 Devon EX1 3PB [email protected] United Kingdom www.metoffice.gov.uk Tasks C1 and C2: EU examples and representative case studies European Commission (DG Environment) July 2012 Edward Pope, Michael Sanderson and Monia Santini

Contents

Introduction ...... 4

TASK C1_D1: List of regions where weather is particularly influenced by forests .. 6 Western Mediterranean ...... 6 Northern Mediterranean ...... 7 Central Europe ...... 8 Eastern Europe ...... 9 Northern Europe ...... 10 Urban areas ...... 10

TASK C1_D2: Identify and rank hotspots of forest cover influence ...... 11

TASK C2_D1: Assessments of EU locations where weather is strongly influenced by climate...... 14 Global and regional modelling studies ...... 14 Boreal Forests ...... 14 Temperate forests of mid-latitude Europe ...... 16

Regional and National Studies ...... 18 Case Study R1: Hungary (a national example) ...... 19 Case Study R2: South West Germany (a sub-national example) ...... 20 Case Study R3: Deforestation in the Mediterranean Basin ...... 22 Case Study R4: Southern Europe (northern Mediterranean Basin) ...... 22 Case Study R5: Valencia and surrounding areas ...... 23

Observations within forests ...... 25 Case Study F1: Tuntsa, Finland ...... 25 Case Study F2: Southern Sweden ...... 26 Case Study F3: Switzerland ...... 26

Urban Areas ...... 26 Case Study U1: Lisbon (Atlantic-Mediterranean) ...... 27 Case Study U2: Paris (Atlantic) ...... 27 Case Study U3: London (Atlantic) ...... 27 Case Study U4: Vienna (Continental) ...... 28 Case Study U5: Freiburg (continental) ...... 29 Case Study U6: Urban examples in Greece (Mediterranean) ...... 29

Summary ...... 30

References ...... 32

Executive Summary

• A wide range of case studies examining the regional and local scale impacts of forests on European weather and climate have been summarised. The locations include the Mediterranean (Spain, Italy), central Europe (Germany, Switzerland, Austria), northern Europe (Sweden, Finland), eastern Europe (Romania, Hungary), and the cities of Lisbon, Paris, London, Vienna, Freiburg, Florence, Chania (Crete) and Athens.

• These studies show that forests have a large influence on summer rainfall in central and Eastern Europe, as well as parts of the western Mediterranean coast. However, they are less important near the Atlantic coast where rainfall mostly originates from synoptic-scale circulation and weather fronts.

• The loss of boreal forests in northern Europe can lead to colder temperatures, higher wind speeds and increase the depth of frozen soil, making it harder for forests to recover.

• The loss of forests in the Mediterranean Basin since the Roman period has probably contributed to the present-day dry summer climate in much of the region. This result is significant because winter precipitation deficits in the Mediterranean are linked to summer heat waves and drought in the rest of Europe.

• Trees planted in and around urban areas act to reduce temperatures within cities, and can partly mitigate the effects of heat waves. However, humidity levels can be slightly higher which could increase heat stress in humans.

• Reforestation in certain areas, such as the Mediterranean coast of Spain, could enhance the formation of summer storms and associated rainfall, which would be beneficial to water resources in the region and, potentially, much of Europe. However, existing desertification and soil degradation means that reforestation would be difficult, if not impossible, in some areas. If reforestation was successful, its beneficial effects could take several decades to be fully realised, owing to the slow growth rate of the trees.

Introduction

The objective of this task is to identify and describe regions or cities, from a range of landscapes, where weather and/or climate are particularly influenced by forests, and these influences are understood. To achieve this, we have made use of both literature reviews (including Tasks A1 and A2) and results from our simulations described in Tasks A3 and A4. In Tasks C1_D1 and C1_D2, we present and rank a list of forest complexes and hotspots that have significant impacts on regional or local weather. To provide a representative sample of EU forests the examples are taken from northern, central and southern Europe, and include a range of urban environments. For Task C2_D1, we have provided detailed case studies which help to quantify the extent of the influence exerted by forests on weather and climate across Europe. Lastly, the key findings from tasks C1 and C2 are reviewed in the Summary section.

In principle, the location of regions in which forests particularly affect weather and climate could be identified objectively using the approach advocated by Findell and Eltahir (2003). These authors used observations of surface air temperature and soil moisture content to highlight the approximate ranges of these variables over which soil moisture influences precipitation. However, without knowing the average monthly atmospheric conditions and soil moisture contents it is difficult to apply the criteria of Findell and Eltahir (2003), particularly as soil moisture data are seldom recorded, especially over long periods of time.

The presence of forests will always influence local and regional weather to some degree, depending on the spatial extent of tree cover. Indeed, our simulations (see Tasks A3 and A4) indicate that increasing forest cover consistently decreases wind speeds above the canopy (e.g., 10 m). Other influences of forests will be present and measurable, though may not be as predictable because there can be compounding influences, such as changes in albedo and moisture flux from the land surface. For example, surface temperatures are generally increased when forest cover is increased (due to reduced albedo), which may lead to reduced low cloud formation. Furthermore, increasing the forest cover may reduce the moisture flux from the land surface if soil moisture is high (potentially decreasing low cloud cover), or increase the moisture flux if soil moisture is low (potentially increasing low cloud cover). As such it can be difficult to identify regions in which weather is particularly strongly influenced by the presence of forests.

A study by Seneviratne et al. (2006) used two sets of regional climate model simulations (a control run, and a second where land-atmosphere interactions were disabled) to identify regions in which there are particularly strong soil moisture-atmosphere feedbacks. Since forests strongly influence soil moisture, the results of this study could be used to identify regions of strong forest-atmosphere interaction. In general, the regions with the strong interaction in the study by Seneviratne et al. (2006) seem to be central and eastern Europe. However, it is also important to note that other models could produce different results, and in the future, different regions may be more significantly influenced by the presence of forests, for example, Hungary, as described below.

The results presented by Seneviratne et al. (2006) are intuitive - as a general rule, it is reasonable to say that the presence of forests is of less importance for regions in which precipitation is determined by large-scale synoptic weather patterns, such as along the Atlantic coast, than for regions where convective precipitation is more prominent, such as the Mediterranean region and central Europe. Forests are likely to play an important role in propagating moisture from coastal regions towards the interior of the European land mass where, in summer months, precipitation primarily occurs from orographic uplift of air and convection. Soil moisture at deep levels which is accessible by tree root systems but not by shorter roots of grasses and crops permits high levels of evapotranspiration during the warmer summer months, enhancing the moisture for convective precipitation events that are characteristic of continental European summers. Forests may contribute to increased summer temperatures in the short term compared to open ground but may mitigate the impact of more extreme heat waves (Teuling et al., 2010), and water shortage for agricultural production in central and eastern Europe. Removing coastal European forests could, therefore, contribute to a warmer, drier climate in central Europe, which could impact on agricultural productivity and water resources in these regions. Over time, if the soil dried out, this may lead to decreased environmental suitability for native vegetation and agriculture in these regions. Given such conditions soil can be eroded more easily during heavy precipitation, encouraging landslides and desertification, as has been observed in parts of Spain and Italy.

TASK C1_D1: List of regions where weather is particularly influenced by forests

In this task we list and describe forest complexes around the European Union that have been shown to significantly affect weather and/or climate. We provide a description of the impact of forests on weather, and provide context by referring to results from the literature reviews presented in Tasks A1 and A2, and the simulations presented in Tasks A3 and A4.

Western Mediterranean

Deforestation is likely to have significantly modified the south European and north African landscapes surrounding the Mediterranean Sea during the last 2000 years (Reale and Shukla, 2000). According to climate model studies conducted by Reale and Shukla (2000) and Bonan (2004) the loss of forests has resulted in a drier climate. This impact could be explained by the presence of natural vegetation (i.e. forests) in the past, which typically has a lower albedo than non-forested land, leading to higher surface temperatures over the land, and consequently larger land/sea temperature differences. This larger temperature difference would encourage more intense local atmospheric circulation than at the present time, which, when combined with additional water transpired by the forests, would mean that precipitation in the Mediterranean area was greater in the past when natural vegetation cover was higher (Reale and Shukla, 2000). A study by Dumenil-Gates and Ließ (2001) is consistent with this hypothesis, concluding that complete deforestation in the Mediterranean region would lead to a cooler land surface owing to the albedo effect. Sanchez et al. (2007) performed a climate model simulation where they substituted trees with grass, and found a significant decrease of summer precipitation (up to 3 mm / day) and increased surface temperatures of up to 3°C, owing to reduced evapotranspiration. This indicates that the soil was comparatively dry.

For eastern Spain, Millán et al. (2005) and Millán (2008) have proposed that deforestation around the Mediterranean coast (notably in eastern Spain) has had two important consequences. First, moisture from the sea, typically in the form of fog, is no longer trapped along the coast. Second, increased land surface temperatures due to urbanisation and reduced evapotranspiration due to vegetation loss mean that the

condensation level of the Mediterranean Sea breeze no longer falls below the level of the mountain tops. As a result, rising air columns carry summer storms up and over the surrounding mountains, reducing orographic precipitation and leading to droughts in areas near the coasts. Much of the moisture is returned to the sea at higher levels. The second point is particularly relevant for explaining observations which indicate that there has been a significant reduction in precipitation over eastern Spain during the last 30 years (e.g. Millán, et al. 2004).

The investigation of afforestation and deforestation using the nested modelling suite over eastern Spain (presented in Task A3) indicates that greater forest cover leads to locally higher surface temperatures, but lower wind speeds and a decreased moisture flux from the land surface. This latter result suggests that soil moisture levels in the model are high, so that evaporative cooling is greater over grassland than forested areas (Teuling et al., 2010). However, only short periods (9 days) were simulated. Overall, rainfall was simulated to increase with larger forest cover, but the increases were very small (a few percent), and were not significant.

Northern Mediterranean

The impact of forest cover on weather and climate was investigated for Tasks A3 and A4 using detailed numerical models. For Task A3, forest cover was modified in the north east of the Italian peninsula. As with eastern Spain, the model indicates that greater forest cover leads to locally higher surface temperatures, slower winds speeds and slightly reduced moisture flux from the land surface. This trend is opposite to that described by Sanchez et al. (2007) probably because the soil was comparatively wet in our simulations. For Task A4, climate model simulations showed that for afforestation, higher and lower evapotranspiration values are produced in the wet period and dry period, respectively. In the case of deforestation, the models produced a significant reduction in evapotranspiration during a large part of the year, indicating that the soil must be dry. These model simulations also showed that greater forest cover led to decreases in the frequency of extreme temperatures, and lower mean and variability in the 10-m wind speed. In addition, they also highlight differences between the models and the influence of soil moisture on the behaviour of the vegetation

Central Europe

Forests undoubtedly play an important role in determining the weather and climate of central and eastern Europe. Being much further away from the Atlantic Ocean than western Europe, the eastern European land surface plays a much more significant role in determining local weather patterns. Precipitation in central and eastern Europe is often higher during the summer than the winter, suggesting that it is convective in origin. Indeed, studies show that evapotranspiration from the European land area provides the largest contribution to this moisture (Sodemann and Zubler, 2010). As described in Task A4_D1, these convective events can be extreme, leading to flash flooding. It is for this reason that the Convective and Orographic Precipitation (COPS) study (Wulfmejer et al., 2011) in south-west Germany was initiated.

In addition, it is important to remember that the moisture evaporated from the land and transpired by vegetation originates from the Atlantic or Mediterranean basins. Consequently, forests influence the amount of moisture available to central and eastern Europe in two main ways. Firstly, the evapotranspiration of moisture which precipitates near the Atlantic or Mediterranean coasts returns moisture to the atmosphere which can then be propagated further inland. This process ensures a supply of moisture to the interior of the European continent, helping to maintain productivity. Secondly, Teuling et al. (2010) described the impact of forests on regulating soil moisture levels compared to crops or grassland during heat waves. They note that the presence of forests ensures that moisture from soils can be transpired even during the warm summer months, owing to the deep roots of the trees. This moisture can contribute to summer precipitation, thereby helping to maintain land productivity in central and eastern Europe. As a result, the removal of forests in central Europe could induce the region to become drier and warmer, less suitable for existing agricultural and natural vegetation, and more vulnerable to droughts and desertification.

Studies of forests in Switzerland have illustrated the insulating properties of tree cover. Ferrez et al. (2011) analysed air temperature data from 14 sites, each with two weather stations in close proximity, one under a forest canopy and the other in the open. They found that daily maximum temperature values were warmer and daily minimum temperatures were colder in the open site than the forested site.

Wattenbach et al. (2007) examined the impacts of afforestation in a region of former East Germany caused by the abandonment of agricultural land on local hydrology. The biggest impacts were seen in ground water recharge and runoff rates, which were both reduced after afforestation. This lack of recharge meant summer droughts would be worsened. In addition, the conversion of the forest from Scots Pine to native deciduous and mixed forest reduced the overall loss of water via evapotranspiration. This study shows that the particular species used for afforestation is important. Simulation studies presented in Task A3 suggest that increased forest cover in Germany and Austria leads to locally higher temperatures, lower winds speeds, and slightly reduced surface moisture fluxes.

Eastern Europe

Drüszler et al. (2010) used mesoscale simulations over the Carpathian Basin to study the effects of land use change (especially urbanisation) during the 20th Century. They found that land surface changes resulted in generally increased temperature and decreased relative humidity across the region, but no significant changes in precipitation. Similarly, the results of other mesoscale model studies over Hungary suggest that land use change in this region during the 20th Century altered the weather and climate in Hungary (Drüszler et al., 2010). Their results showed a maximum warming and drying over the urban areas and no significant impact on average precipitation over Hungary.

The capacity of afforestation to moderate the effects of climate change in Hungary was investigated in model simulations by Gálos et al. (2007). They found that afforestation across the country increased evapotranspiration and rainfall by 10-15%, and decreased surface temperature by up to 1°C throughout all seasons. These results are in contrast to those of Heck et al. (2001) whose modelling results indicated that the largest effects of afforestation across Hungary were in the north-eastern part of the country, where half of the future projected rainfall decrease could be offset and the number of summer droughts could be reduced by afforestation.

Northern Europe

Using climate model simulations, Clark et al. (2010) showed that forest roughness exerts a significant influence on the duration and severity of heat waves across Scandinavia. In particular, if the roughness was reduced, evapotranspiration (which acts to cool the surface) was also reduced. They also found that if soils remained moist, the severity of heat waves was reduced owing to evaporative cooling of the surface via increased evapotranspiration rates.

Observational results for southern Sweden, presented by Karlsson (2000), are consistent with those reported by Ferrez et al. (2011) for Switzerland. Both showed that measured minimum temperatures under the forest canopy were higher than in open areas, while maximum temperatures under the forest canopy were lower than in open areas. In addition, Vajda and Venalainen (2005) showed that forest loss in northern Finland during the 1960s led to colder winter temperatures, higher wind speeds and an increased depth of frozen soil, all of which inhibit the re-growth of the forest.

In Task A3, we investigated the impact on local weather of modifying forest cover in southern Sweden. As with the other regions studied for that task, greater forest cover led to locally higher surface temperature, lower wind speeds and decreased moisture flux from the surface. However, it was notable that the changes in these variables were largest in this region compared with the others studies, suggesting a strong influence of forest cover on weather and climate in the far north of Europe.

Urban areas

In and around cities, urban and peri-urban forests have been shown to play a role in moderating local climates, particularly in warmer, drier locations. This result is important because heat island effects can make cities uncomfortable places to live and work (Stewart et al., 2011). A recent study by Bowler et al. (2010) found that an urban non- green site would be, on average, around 1˚C warmer than an urban park, and that larger parks tend to be cooler. Furthermore, by providing shade and cooling buildings, trees can help to reduce energy usage. As an example, a study in Athens concluded that the cooling effects of trees could reduce summer time consumption of air conditioning during 10

the day by 2.6-8.6% and during peak hours by 2.9-9.7% (Tsiros, 2010). A study for Chania (Crete) gave comparable results (Georgi and Dimitrou, 2010). Similar observations have been recorded across Europe in London, Paris, Freiburg, Vienna, and Lisbon. These findings are discussed in more detail in Task C2_D1.

To reinforce the above findings, recent results from the BRIDGE project (http://www.bridge-fp7.eu/, deliverable 4.2) confirmed that urban areas have lower capacity to store water at the surface, meaning that they form a heat island manifested by a temperature that is higher than in the surroundings on the order of 2-3°C and 12°C during day and night time, respectively.

McCarthy et al. (2010) modelled the combined effects of future projected climate change

(with doubled atmospheric CO2 concentrations) and urbanisation on urban climates. They showed that in regions of high population growth significant increases in warming and extreme heat events, as a result of urban heat island effects, were noted.

TASK C1_D2: Identify and rank hotspots of forest cover influence

For this task, we present a table which ranks the forest hotspots described in the previous task according to their influence on local and regional weather patterns. To provide further information, we have provided a brief description of the main impact of forests as identified from literature review and our simulations. In addition, the table also provides general information about the climate, main forest types and the meteorological variables of interest for each location.

Table 1 (overleaf): List and ranking of EU-examples where forest cover has a particular influence on weather and climate. Climate zone (column 4) is defined by the Köppen- Geiger classification, biogeographical region classifications (column 5) are those used by the European Environment Agency (see http://www.eea.europa.eu/data-and- maps/data/biogeographical-regions-europe),and the main forest types are defined by the GLC2000 land cover dataset (column 6). The following abbreviations are used: T – air temperature, P – precipitation, GHG – greenhouse gases, NA – not applicable.

Rank Region (sub)Country Climate Zone* Biogeographical Main forest Variable of Description of main historical land use changes Comments and notes region** types*** (main) and observed / simulated effects on ranking interest

1 Western Eastern Spain, Mix of cold semi- Mediterranean Evergreen T, P Historical: General afforestation and reported Hotspot of temperature Mediterranean including Valencia arid and warm needleleaf, sparse deforestation along the coasts (M.Millan studies), changes based on Region Mediterranean deciduous broadleaf causing local droughts, but also having an influence historical dataset climate and mixed on flood frequency locally and in central Europe analysis. Proposed where moisture arrives after being collected by Vb strong influence of storm tracks. Drought in Mediterranean area can precipitation both locally increase likelihood of drought in the rest of Europe. and over other regions Simulated: Afforestation causes an increase in (e.g. floods in Central temperature and rainfall, reduction in wind speed Europe). Focus of and surface evaporation during summer. weather model simulation Deforestation causes the opposite effects. Doubling at two scales. forest size did not change magnitudes of changes significantly.

2 Central Europe Switzerland/ Temperate Continental /Alpine Deciduous T, P Historical: Afforestation, just local deforestation. Importance for extremes. Germany/ Austria /humid broadleaf; Mean climate: Forests may moderate daily Impacted by other (also focus of MO continental Evergreen temperature range, by increasing (decreasing) regions (West Med, see simulations) needleleaf; mixed surface minimum (maximum) temperatures. above). Partially, hotspot Extremes: Afforestation can increase local drought of temperature and because of reduced groundwater recharge. precipitation changes Simulated: Afforestation increases above-canopy (drying) based on temperatures and reduces wind speeds. historical dataset Deforestation leads to opposite effects. analysis. Focus of Extremes (previous studies): Afforestation increases weather model (reduces) severity of heat waves in the short (long) simulation. lasting events.

3 Eastern Europe Hungary Temperate/ Pannonian Evergreen T Historical: Afforestation. As a national average, Detailed country level humid needleleaf increased forest led to increased temperature but study available (Galòs, continental negligible changes in precipitation. Noteworthy 2009). Strong role of importance in terms of offsetting the foreseen drying forest to reduce drying due to climate change. risk in the future based on simulations

4 Northern Europe Southern Sweden Mix of Boreal Evergreen T, P Historical: Deforestation. Sweden: focus of and northern Temperate/ needleleaf; sparse Simulated: Afforestation increased temperature weather model Finland humid/ cool Deciduous above forested areas by 0.15°C, very small increase simulation. Likely hotspot continental & broadleaf and mixed in rainfall during summer, reduced wind speeds. of P changes based on subarctic Deforestation had opposite effect (lower historical dataset temperature, higher wind speed, reduction in rainfall analysis. negligible).

5 Northern Italy (northern and Mix of warm/ Mediterranean/ Deciduous T, P Historical: Afforestation. Focus of weather/climate Mediterranean southern peninsular temperate continental broadleaf; sparse Simulated: In case of climate simulation, modelling. Part of hotspot territory) Mediterranean mixed afforestation seems decreasing the frequency of of precipitation changes and humid temperature extremes, increasing water recycling based on historical subtropical (via ET) and decreasing maximum wind speed dataset analysis. (mean and variability). The opposite was found for deforestation. In case of weather simulation for summer, afforestation increased 1.5m temperature above canopy by 0.22° in summer, decreased wind speed by 0.51 m/s, slightly decreased surface moisture flux and slightly increased rainfall. Results of changes for spring are similar but less evident. 5 Eastern Europe Romania Mix of Continental /Alpine Deciduous T, P Historical: Deforestation. Hotspot of deforestation temperate/humid broadleaf; Simulated: Afforestation seems to decrease the from historical analysis. /cool continental Evergreen frequency of temperature extremes (lowering Focus of climate model needleleaf; mixed Tmax), as well as the maximum wind speed (mean simulations. and variability). The opposite was found for deforestation. 6 Urban Athens, Chania Warm Mediterranean NA T, GHG, For Florence, increasing urbanization shown to Recent EU-project (Mediterranean) (Greece), Florence Mediterranean heat flux raise sensible heat flux, CO2 and temperature, BRIDGE, also including (Italy) decrease latent heat flux. For Greece, urban trees GHG. But not including reduce T via shading effects and ET. precipitation. 7 Urban (Atlantic / London, Paris, Temperate Atlantic (London & NA T Tree planting reduces temperatures through Multiple studies among Atlantic- Lisbon oceanic and Paris) and shading and evapotranspiration, thereby reducing urban sites. Only T Mediterranean) warm Mediterranean sensible heat flux and increasing latent heat flux. addressed. Mediterranean (Lisbon) 8 Urban Freiburg, Vienna Mix of Continental NA T, soil Urban tree improve air quality and reduce heat. Also influence on air (Continental) Temperate/humi moisture Forest in the urban surrounding favour water quality and water d continental and upstream, air retention in the soil. quantity. Temperate quality. oceanic

TASK C2_D1: Assessments of EU locations where weather is strongly influenced by climate.

In this task we draw examples from deliverables C1_D1 and C1_D2 and provide selected case studies which identify and describe the specific impacts of forest cover, together with its influence on both local and regional climate. In particular, we have focused on the main forest groups in Europe: Boreal forests, temperate forests and Mediterranean coastal forests. First, studies at the regional and national scales are described, with a focus on the Mediterranean basin. Next, observations within forested areas are summarised, and finally the effects of green spaces and trees in urban areas are presented.

Global and regional modelling studies

Starting from the general discussion in section 1.2, in this section results from studies using global climate models to understand the effects of boreal and temperate forests on climate are presented. These studies have used simulations where the forests in question have either been replaced with grassland and other land types, or vice-versa. The analyses of the results have focused on large-scale climate effects. Nevertheless, these studies provide a useful initial view of the impacts of forests on climate. More detailed studies of the effects of forest on European climate at higher spatial scales are described in sections 2 to 4.

Boreal Forests

Boreal forests are thought to be important in determining climate on both regional and global scales. Simulations using global climate models (e.g. Bonan et al. 1992; Snyder et al. 2004; Bala et al. 2007; Swann et al. 2010) have shown that, unlike tropical forests, the removal of boreal forest vegetation has a large impact on the amount of solar radiation absorbed by the land surface. Replacing the dark forests with bare ground that will be snow covered during winter increases the albedo considerably, meaning that less radiation is absorbed and air temperatures will be cooler. As a result, the loss of boreal forests would provide a positive feedback for increased ice cover and glaciation.

However, it is important to note that this mechanism assumes that boreal forests do not produce sufficient quantities of biogenic volatile organic compounds (BVOCs) to significantly modify cloud cover above the forest. This general picture is complicated somewhat by feedbacks that could occur if BVOCs do modify the concentration of cloud condensation nuclei (e.g. Spracklen et al., 2008). A more detailed description of these processes is provided by Brovkin (2002).

To illustrate the impact of boreal forest removal in Eurasia, we will now describe some changes in physical quantities such as albedo, temperature, and humidity that were specifically noted by Snyder et al. (2004). In their simulations, Snyder et al. (2004) found that, following removal of boreal forests, the annual average increase in albedo was 0.26, and was largest in the winter and spring seasons (being 0.51 and 0.37 respectively) owing to the removal of vegetation exposing more reflective snow underneath. As a result of large reductions in the solar radiation absorbed by the land surface, surface temperature responded quite dramatically throughout the biome, with average annual temperatures dropping by 2.8 ˚C. The spring season (March, April and May) experienced the largest temperature decrease of 6.2 ˚C. This result is consistent with an increase in the snow cover fraction due to a reduction in snow melt, even though precipitation (primarily falling as snow) decreases. Indeed, the annual average precipitation decreased by as much as 0.3 mm day-1 (15%), and the annual average humidity fell by 0.5 g kg-1. These reductions were largest during the summer months (June, July and August) and smallest during the winter months (December, January and February). Despite the reduction in humidity and precipitation, low level cloud cover increased, especially during spring and summer owing to lower near surface air temperatures. Snyder et al. (2004) found that removing boreal forests would cause a global average cooling of 0.77 ˚C, but the impact on global average precipitation was negligible.

While these results highlight the impact of boreal forests on climate in northern Eurasia and globally, it is important to remember that the values given are calculated by averaging over a very large area that extends far beyond northern Europe. As a result, the impact of removing boreal forests from just Europe may be different. It is also important to remember that these results were produced by one model, and that other climate models could produce different results. Nevertheless, these findings are generally consistent with other studies by Bonan et al. (1992), Bala et al. (2007) and Swann et al. (2010).

Temperate forests of mid-latitude Europe

In Europe, temperate forests occupy the region between the boreal forests of northern Europe and the scrub land of the Iberian Peninsula, roughly between latitudes of 40- 60°N. As a result of this geographical location and the prominent species across this zone, the interaction between temperate forests and the atmosphere varies considerably with season.

Several studies have investigated the impact of afforestation and deforestation at these latitudes. However, the results are model dependent (see for example South et al. (2011) and Bala et al. (2007) and references therein). Seneviratne et al. (2006) used climate simulations to investigate the strength of land – atmosphere coupling across Europe. They used two simulations where the land-atmosphere coupling was disabled in one simulation and active in the other. Notably, they found particularly strong land– atmosphere coupling in central and eastern Europe, and emphasized the importance of soil-moisture–temperature feedbacks (in addition to soil-moisture–precipitation feedbacks) in influencing summer climate. For example, in many regions of central and eastern Europe, precipitation increases during the summer months, primarily due to increased evaporation from the European land surface (Sodemann and Zubler, 2010). This result is in contrast to regions nearer the Atlantic coast for which precipitation is relatively consistent throughout the year, e.g. London and Paris, compared to more continental locations, e.g. Dresden and Vienna. However, the moisture evaporated from the land surface is recycled from its original source in the Atlantic or Mediterranean basins. Forests, therefore, influence the amount of moisture available for precipitation in central Europe in two ways. First, evapotranspiration of moisture which falls near the Atlantic or Mediterranean coasts returns moisture to the atmosphere, which can then be propagated further inland, thereby supplying moisture to the interior of the European continent. Secondly, Teuling et al. (2010) described the impact of forests on regulating and conserving soil moisture compared to crops or grassland. The presence of forests means that moisture at deep levels can be accessed, so that during the summer months this moisture can be evaporated to provide summer precipitation, thereby helping to maintain land productivity in central and Eastern Europe.

Snyder et al. (2004) used a global climate model to investigate the climatic effects of removing temperate forests. Unfortunately for our study, the changes in climate they quoted are averages for the entire global temperate zone, rather than simply considering 16

Europe. They found that removal of the temperate forests in winter and spring has similar effects to the removal of boreal forests. That is, after removal of the forests, the land surface has a higher albedo, leading to lower surface temperatures which results in greater snow coverage and increased low level cloud cover. For this reason, it may be expected that decreasing forest cover in temperate regions such as Germany and Austria should result in locally higher low cloud formation for our spring time simulations.

During the summer months, the simulations of Snyder et al. (2004) suggest that temperate forest removal produces higher surface temperatures owing to a reduction in evaporative cooling which had a greater effect than the increase in surface albedo. These results conflict with our simulation results, in which increased forest cover raised surface temperatures by decreasing the albedo and reducing the surface moisture flux. Nevertheless, the simulations performed by Snyder et al. (2004) show a decrease in annual average surface temperatures of 1.1°C where the temperate forest was removed. The largest decrease (2.4°C) occurs in the spring months of March, April and May. In contrast, surface temperatures were simulated to increase slightly during summer and autumn by 0.3°C and 0.6°C, respectively. However, spatial variability of the simulated temperature changes was large.

In contrast to the surface moisture flux results from Snyder et al (2004) our simulations showed that removal of temperate forests reduced the flux of moisture into the atmosphere leading to a reduction in the near-surface specific humidity by up to 2 g kg-1 during the summer (typical summer time humidity is 6-10 g kg-1, so this represents a significant percentage change), and a reduction in precipitation of up to 1.5 mm day-1 also in the summer. The reductions are greatest during the growing season. Consequently, these results strongly suggest that temperate forests keep summers in central Europe cooler and wetter, and winters milder than they would otherwise be. However, it is important to remember that our findings, and those of Bonan (1997, 1999) suggest that forests can cause summers to be warmer, but that they reduce the severity of heat waves (e.g. Teuling et al., 2010). Thus, the specific details of the effects of temperate forests on climate are somewhat unclear.

Anav et al. (2010) have modelled the effects of deforestation and afforestation in central Europe. Their results suggested that while there would be little change in mean surface temperature, there would be large changes in the extremes. In particular, their deforestation scenario led to a decreased number of hot summer days, while

afforestation led to an increased number of hot summer days. Consequently, the general consensus for central Europe is that afforestation may lead to generally warmer summers, but less extreme heat waves. However, the impact is regionally dependent.

Teuling et al. (2010) studied the difference between the temporal responses of forest and grassland ecosystems during heat waves. They found that, initially, surface heating is twice as high over forest than grassland. Their simulations showed that over grass, heating is suppressed by increased evaporation in response to increased solar radiation and temperature. Ultimately, however, this process accelerates soil moisture depletion and induces a critical shift in the regional climate system that leads to increased heating. It has been proposed that this mechanism could explain the reduced ecosystem productivity which may have contributed to the extreme temperatures recorded in August 2003 heat wave (Reichstein et al. 2007). The conservative water use of forests contributes to increased temperatures in the short term, but mitigates the impact of the most extreme heat waves and/or long-lasting hot events. Gálos et al. (2011a) suggest that afforestation in Northern Germany, Poland and Ukraine can mitigate up to 20% of the temperature climate change signal, and up to 50% of the precipitation change.

While there are numerous studies investigating the impact of the land use on a global scale and in the United States (e.g. Bonan 1997; 1999; 2002), there seems to be a paucity of work investigating the influence of vegetation on regional and national scales specifically for Europe. However, a few of these studies are described below.

Regional and National Studies

In Section 4, results from global modelling studies investigating the impact of boreal and temperate forests on regional and global climate were summarised. These studies focused on the mean change in surface temperature across large areas. In this section, case studies of the effects of forest on regional climate are described. First, a modelling study of the impact of forests on the climate in Hungary are presented, followed by results from some of the participants in the COPS project (Convective and Orographically-induced Precipitation Study). The COPS project was located in south- western Germany and eastern France, and included the collection of high quality and high resolution data pertaining to the pre-convective environment, the formation of

clouds and the onset and development of rainfall as well as its intensity. Next, studies focusing on the effects of forests on Mediterranean climate are presented.

Case Study R1: Hungary (a national example)

Besides the historical assessment reported in section 1.1.3 for Hungary, the study of Gálos et al. (2011b) is noteworthy. They investigated the effects of maximum afforestation and complete deforestation on the Hungary’s climate for the end of the 21st century (2071-2100). For their deforestation scenario, they converted all of Hungary’s forested land to grass, whereas for the afforestion scenario, all of Hungary’s vegetated area was converted to forest. In addition, for the period 2021-2025, they also studied the climatic influence of potential afforestation based on a national survey. The potential forest cover for 2021-2025 was estimated based on plans to increase forest cover by 7% from the conversion of marginal agricultural land.

Climate simulations of the period 2071-2100 suggest that the south-western part of Hungary will be most affected by warming and drying. The projected temperature increase was up to 3.5°C, and the decrease of summer precipitation was just over 25%. Their results suggest that, across the entire country, the projected decrease of precipitation caused by climate change can be reduced by increasing forest cover. In the north east, more than half of the projected summer climate change signal could be relieved with enhanced forest cover. However, in the south-west region, which is most affected by climate change, the influence of increased forest cover is relatively small. Gálos et al. (2011b) noted that the most likely mechanism is an increase in humidity where precipitation occurs owing to orographic uplift.

The simulations also indicated that the reduction in summer precipitation is projected to significantly increase the probability of dry summers and droughts. While afforestation is shown to reduce the number of mild droughts, it is unable to reduce probability of severe droughts, characterised by a decrease in precipitation of 40% or more. Overall, these results suggest that projected reductions in summer precipitation over Hungary could be minimised by increasing forest cover, which has the added benefit of reducing the number of mild droughts.

Case Study R2: South West Germany (a sub-national example)

To extend the general discussion on central Europe in section 1.1.2, focus is here given to south-west Germany, which experiences a typical mid-latitude moderate climate, characterised by a westerly flow with precipitation associated with frontal systems in winter and more convective processes in summer. Some of the intense summer precipitation events can bring devastating floods (e.g. Rotach et al., 2009a, 2009b), which are currently difficult to forecast accurately owing to the complexity of the terrain and the influence of small-scale processes that are not accurately captured by numerical weather prediction models. It is for this reason that the region has been studied extensively in the Convection and Orographically-induced Precipitation study (COPS) - the central issue being to determine why, where and when precipitating convection breaks out and develops in this region (Wulfmeyer et al., 2011). Through this work, it has become clear that moisture transpired by extensive forest cover plays a significant role in these phenomena.

The area studied under the COPS project is located in central Europe and covers south- western Germany and eastern France, between 6 and 11˚E and between 47 and 50˚N (Figure 3). This area is characterized by low mountains, and covers the Vosges Mountains, the Rhine Valley and the Black Forest Mountains. Measurements were recorded from 1 June to 31 August 2007. In the Rhine valley and smaller valleys extending into the orographic terrain, the main land-use classes are agricultural (~48%), woodland (~37%) and water (~1%). The other ~14% is composed of urban areas, vineyards, sand, fens, and moor. In the Black Forest the land use is more like 75% woodland, 22% pasture, and 3% urban (Uhlenbrook et al., 2007).

An early study by Kalthoff et al. (1998) demonstrated how local variations in the regional climate can be partly attributed to land-use differences, as a result of changes in the surface energy balance. The partitioning between sensible and latent heat fluxes is particularly important because, for the same insolation higher vegetation cover will increase the latent heat flux, thereby reducing the number of very hot days. However, while a higher vegetation cover fraction helps to reduce temperatures, it leads to high humidity in this region. High forest cover also means that heat waves are generally less severe in this region than they would be in the absence of forests (e.g. Teuling et al., 2010). The presence of high forest cover in this region undoubtedly plays a role in propagating moisture from the Atlantic Ocean and Mediterranean Sea further inland, 20

thereby helping to maintain summer precipitation in central and eastern Europe. In addition, several studies (e.g. Meissner et al., 2008; Kalthoff et al., 2009) have indicated that the high humidity also plays a role in the convective and orographic precipitation for which the region is famous. The details are complex, due to the terrain and the formation of valley winds. However, when convection is initiated, high rates of evapotranspiration from the land surface increase the moisture content of the planetary boundary layer, which contributes to the precipitation.

Figure 3. Simulated daily rainfall (left-hand panel) and evapotranspiration (right-hand panel) by the COSMO-CLM regional climate model for the summer months from 2005 to 2009. The enhancement of rainfall and evapotranspiration over the Black Forest can clearly be seen. The forests make a large contribution to the total evapotranspiration rates and subsequent rainfall.

The importance of evapotranspiration from the land surface is further reinforced by Schnitter et al. (2008) who used high resolution observations of water vapour changes and rainfall rates made during the COPS project to validate a high-resolution climate model, which was then used to study the effects of the topography and land surface on rainfall. They found that, owing to the predominantly south-westerly winds that occurred during the measurement period, rainfall was enhanced on the western slopes of the Black Forest region (Figure 3). The air masses are lifted upwards over the mountain ridge on which the Black Forest is located, and the water vapour condenses and forms rain. Additionally, small-scale thermally induced winds can form which also induce

convection and rainfall. Enhancement of evapotranspiration by the forests was shown to be an important contribution to the overall evapotranspiration rates and formation of rainfall (Figure 3).

Case Study R3: Deforestation in the Mediterranean Basin

As extensively assessed in section 1.1.1, the Mediterranean region of southern Europe and northern Africa has received particular attention because extensive deforestation during the past 2000 years is thought to have contributed to increasing aridity in the region (e.g. Ballif et al., 1998). Reale and Shukla (2000) constructed a vegetation map of the Mediterranean basin for the Roman period (approximately 2000 years ago) using fossil pollen maps and other historical records. Climate model simulations using this vegetation map, and with the current vegetation distribution show that the surface albedo change had a significant impact on the atmospheric circulation over northern Africa and the Mediterranean Sea. A northward shift of the inter-tropical convergence zone and a local circulation between the Mediterranean Sea and north-western Africa in the region of the Atlas Mountains occurs when the model is run using the appropriate vegetation distribution for the Roman period. This model produces a large increase in simulated rainfall over the Sahel, the Nile valley and north-western Africa, while a smaller increase occurs also over the Iberian Peninsula. These results suggest that the deforestation of the Mediterranean has contributed to the dryness of the present climate.

Case Study R4: Southern Europe (northern Mediterranean Basin)

Heck et al. (2001) have shown from simulations that the climate of southern Europe is especially sensitive to changes in leaf area index and rooting depth. Replacement of forests by cropland and grassland greatly reduced leaf area index and rooting depth compared to natural vegetation. The greater leaf area and deeper rooting depth of natural vegetation leads to a moister and cooler spring followed by a warmer and drier summer compared to present vegetation. Evapotranspiration increases from April until mid-July, cooling the surface, moistening the boundary layer, and enhancing precipitation. Specifically, the models predict that the Iberian Peninsula would be ~2˚C cooler in May than it presently is, if the vegetation cover had not been modified by human activities. The specific humidity is predicted to be higher by ~1g / kg owing to increased evapotranspiration. In contrast, for August, the models predict that potential

vegetation cover would increase the temperature by ~1˚C in Spain, and decrease the specific humidity by ~0.5g/kg, owing to reduced evapotranspiration caused by limited soil moisture availability. Interestingly, for the months April to August, the models find that precipitation (P) minus evapotranspiration (ET) is less than zero (i.e. P – ET < 0), meaning that the atmospheric circulation exports water away from the region. This result is consistent with other studies of the Mediterranean hydrological cycle (e.g. Mariotti et al 2002). In contrast, precipitation and evapotranspiration approximately balance for central and northern Europe.

Case Study R5: Valencia and surrounding areas

Observations (M. Millán, 2008) indicate that there has been a reduction in precipitation from summer storms forming above the hills west of Valencia, and other coastal areas of Spain. As shown in Figure 4, the east coast of Spain is strongly influenced by sea breezes during summer. Indeed, the summer storms used to occur in the late afternoon as a consequence of moisture carried inland by a combination of sea and up-slope breezes.

Such storms should form as long as the lifted condensation level (LCL) of the combined breeze is below the heights of the mountains. If this is not the case, the water vapour will be carried back out to sea, as part of a return flow. Analysis by Millán has indicated that the LCL of the sea breeze is initially below the height of the mountain tops. However, the moist sea air can be heated by as much as 15˚C as it flows inland, thereby raising the LCL above the mountain tops. Consequently, the LCL can only be brought back below the elevation of the mountain tops if moisture is added to the air.

Figure 4. Development of sea breezes and subsequent return flows over coastal areas of Spain.

Figure 5. Illustration of sea breezes, and cloud formation in coastal areas of Spain. Top panel: When the marshes and forests were still in existence, sufficient water was added to the air for clouds to form and produce rainfall. Bottom panel: following drainage of the marshes and deforestation, insufficient water is added to the air for clouds to form, and any evaporated water from the land forms clouds at higher altitudes that do not produce rain, and is carried aloft back to the sea.

In the past, this moisture was provided by evaporation from coastal marshes, irrigated land and, importantly, evapotranspiration from forests (Figure 5, top panel). However, the drainage of marshland and the replacement of forests with urban areas and other vegetation has simultaneously increased land surface temperatures and reduced the quantity of moisture added to the sea breeze as it advances. Deforestation further inland, partly caused by fires, has also been suggested to have exacerbated this problem. Moisture currently evaporated from the land surface is transported aloft to the sea (Figure 5, lower panel). Indeed, there are regions near Valencia which, due to continued drought, now require bottled drinking water to be delivered daily, as the streams and springs are now dry.

Observations within forests

There have been several studies where the temperature within forested sites and nearby open areas has been monitored to identify the key differences and understand the effects of the forests. However, a variety of analyses techniques have been used, which makes a direct comparison difficult.

Case Study F1: Tuntsa, Finland

Vajda and Venalainen (2005) compared the weather and climate in forested and open areas in northern Finland, focusing on the Tuntsa region which is situated near the tree line (between 67˚ and 68˚N). The main causes of damage to forests which creates the open areas in this region are strong winds, snowfall and fire (Vajda, 2007). The Tuntsa wilderness was affected by a widespread forest fire in 1960, during which 20,000 ha of forest was lost. The area was regenerated from 1961 onwards by seeding with Scots pine. However, regeneration with pine failed on sites formerly covered by spruce, with large areas still being treeless.

The impact of changes in forest cover on the climate of the Tuntsa region was estimated by measuring snow depths and wind velocities at forested and open locations. In addition, temperature, pressure, humidity, and precipitation were measured at two sites: one site surrounded by Norway spruce, the other an open, formerly forested site. From these measurements, Vajda and Venalainen (2005) found that winds were 60-70% stronger in open areas compared with forested sites. The lowest snow depths were recorded over open land, which can lead to abrasion of the surface by ice particles blown by the winds and deep levels of frozen soil, all of which impede the establishment and growth of seedlings. In contrast, deep insulating snow within the forested areas provides greater protection from winter desiccation and wind abrasion. Their results also show that, during the summer, forested land provided a sensible heating rate that was 5- 60 W m-2 greater than for the open land. According to these measurements, the loss of forest in the Tunsta region has caused the climate to become more severe, impeding the re-establishment of tree cover (Vajda, 2007).

Case Study F2: Southern Sweden

Several studies have compared nocturnal air temperatures in forested and open sites in southern Sweden (Gustavsson, 1995; Gustavsson et al., 1998; Karlsson, 2000). Karlsson (2000) examined a series of nocturnal temperature measurements made in south-western Sweden, located at 58°N, 14°E between November 1995 and April 1996. The results showed that temperatures in small clearings within the forest were colder than the forest itself, in agreement with other studies. However, as a whole, temperatures within the forest were 3°C cooler than the surrounding open area, which was attributed to sheltered conditions within the forest, with very little turbulence. An earlier study by Gustavsson (1995) reached similar conclusions. On windy nights, the sheltering effect of the forest was very important, such that the temperature differences between open areas and the forests were greatest.

Case Study F3: Switzerland

Ferrez et al. (2011) analysed maximum and minimum temperature data recorded at 14 sites in Switzerland. At each site, two sets of measurements made within the forest and at a nearby open site were available. The temperature data were detrended and deseasonalised so that the dependence of extreme maximum and minimum temperatures on forest cover could be ascertained. Forest cover had a greater effect on temperature minima than maxima, and conifers provided more efficient insulation than other tree species. Overall, the insulating effect of the forests acted to reduce maximum temperatures and increase minimum temperatures.

Urban Areas

There are many studies of the impacts of green spaces and trees in urban areas. Results from studies in seven major European cities are summarised here, ranging from Atlantic to Mediterranean to Continental zones.

Case Study U1: Lisbon (Atlantic-Mediterranean)

The city of Lisbon lies near the centre of Portugal’s west coast, and experiences an average annual temperature of 16.6°C and average annual total rainfall of 708 mm. Oliveira et al. (2011) analysed the influence of a small green space (0.24 ha) in the surrounding environment of a densely urbanised area in Lisbon. It was found that the green space was cooler than the surrounding areas, either in the sun or in the shade, with the differences greater on hotter days. They attributed these differences to the shade inside the garden due to the tall trees and the tall buildings around it, and the intense evapotranspiration.

Case Study U2: Paris (Atlantic)

Strengthening the findings of Lion et al. (2009) mentioned in section 1.1.4, a study by Météo-France (2009) using a very high resolution (1 km) mesoscale climate model found that a 40% increase in forest cover, together with reflective paints on buildings in the suburbs around Paris, could reduce temperatures in the centre of Paris by 2°C during heat waves. Makhelouf (2009) investigated the effect of green spaces in urban climate in Paris. In this work, measurements of temperature and humidity in urban areas, gardens and parkland were compared. During periods of cold weather, temperatures were lower and humidity was higher in gardens and parkland than in urban areas. In the summer, temperatures were lower and humidity was higher in the parks than in urban areas. Results from Burian et al. (2002) also suggest that parklands increase precipitation due to the increased humidity.

Case Study U3: London (Atlantic)

Similar effects of green spaces in urban areas other than those noted above for Paris are seen for Richmond Park in London in the work of Boehnenstengel et al. (2011). They used a very high resolution climate model to simulate the urban heat island of London. They compared two simulations of May 2008, one which included the effects of the buildings and a second where the urban areas had been replaced by grass. Although forests were not considered, these results are still useful in illustrating the effect of vegetation cover on the urban heat island. The simulated urban heat island was strongly

dependent on the urban surface fraction, and small vegetated areas within densely built areas had almost no effect on the simulated surface temperatures. The non-urban land fraction had to be between 10% and 20% before a reduction in the urban heat island occurred. Advection of heat across the city by winds had a large effect on the heat island at downwind locations, such that even small urbanised areas lead to strong urban heat islands, suggesting that large areas of vegetation cover might not reduce the urban heat island by very much.

McCarthy et al. (2011) used a regional climate model which had a horizontal resolution of 25 km to study the urban heat island of London under a warming climate. Their results indicated the vegetation cover of London would have to be increased considerably to reduce the effects of the warming climate on the urban heat island. However, they note that increased vegetation cover could have larger effects at the street level.

Case Study U4: Vienna (Continental)

The Austrian capital of Vienna is located to the east of a forest which covers an area of more than 135,000 ha, and is often referred to as the “green lung” of the city (Weidinger, 2002). Vienna’s climate is described as humid continental, which is partly influenced by the relatively large forest cover in central Europe, and also the forest to the west of Vienna. Vienna’s precipitation is greatest during the summer months as a consequence of increased evaporation from the European land area. As described above, this is partly a consequence of the presence of forests which conserve soil moisture and return moisture to the atmosphere, especially in the warm summer months.

In addition, Vienna receives about 95% of its drinking water supply from karstic springs in the north-eastern Limestone Alps of Austria (Dirnböck and Grabherr, 2000). Vegetation cover in the uplands is considered to be critical for a sustainable supply of high quality drinking water and a balanced spring discharge. Forests on these soils require special attention, both because of the high levels of biodiversity associated with these soils and because of their hydrology (Koeck et al., 2005).

Case Study U5: Freiburg (continental)

The city of Freiburg is located south-east of the Black Forest in Germany, and also has a high degree of urban forest cover. Freiburg is one of Germany’s warmest and sunniest cities with a mean annual air temperature of 10.5 °C and average precipitation of 933 mm yr-1 (Streiling and Matzarakis 2003).

A study by Streiling and Matzarakis (2003) found that single trees and small clusters are able to affect the micro-climate of the city. In particular, they described the findings from an area of approximately 1700 m2 containing 12 horse chestnut trees of different ages. They found that temperatures in areas with trees were about 1.0°C cooler than areas without trees, with a maximum difference of 2.2°C. In the vicinity of the trees, the relative humidity was increased by 5-7%. In addition, the study revealed that horse chestnut trees do not produce terpenes, meaning that such trees have a very small ozone- forming potential. This result is important because ozone is known to be a respiratory irritant. Consequently, horse chestnut trees improve both the thermal comfort of urbanites, and provide health benefits.

Case Study U6: Urban examples in Greece (Mediterranean)

The city of Chania resides in the north east of Crete and has an annual mean temperature of 18.9°C, with about 5-6 months of hot, dry weather which can be the cause of discomfort especially in urban areas. As described previously, it is well known that trees planted in open spaces reduce urban temperatures through shading and via evapotranspiration (Streiling and Matzarakis, 2003). To evaluate the magnitude of this effect in Chania, Georgi and Dimitrou (2010) measured the temperature and relative humidity for shaded and sunlit pavements and under five different species of tree currently growing in Chania. They found that areas planted with trees were, on average, 3.1°C cooler than sunlit pavements. In addition, plants with a high level of evapotranspiration were the most effective in making the micro-climate more pleasant and reducing the discomfort caused by hot, dry summers. Their findings suggest that the Indian laurel fig trees were the most effective in reducing discomfort, as this species provided high evapotranspiration rates, the highest relative humidity and the lowest temperature under the tree canopies. These results are consistent with a study in Athens

which found that the cooling effects of trees could reduce summer time consumption of air conditioning during the day by 2.6-8.6% (Tsiros, 2010).

Summary

There have been many studies of the influence of forests at a wide range of spatial scales, from global effects to localised impacts within urban areas. At the global scale, boreal forests act to warm the climate owing to their relatively low albedo and subsequent transfer of sensible heat. Temperate forests can have a similar effect on climate, but the specific effect varies considerably between different regions and climates, and modelling studies do not show consensus in these responses.. It is particularly difficult to simulate accurately surface moisture fluxes in models. Soil moisture is (to a good approximation) the relatively small difference between two relatively large fluxes (precipitation and evapotranspiration). Modelled soil moisture levels and changes are prone to major errors, especially in sign over time. Many of the differences in model simulations of the effects of forest cover changes in Europe could therefore be caused by different behaviour of simulated soil moisture levels.

Although forests can increase temperatures during summer months, they can also reduce temperatures during heat waves. Forests can access water at deep levels within the soil, and continue to cool the surface even when the upper soil levels are dry.

One of the most important effects of forests is in the formation of clouds and associated production of precipitation. This effect is very important in central and eastern Europe. Evapotranspiration of moisture by forests increases the humidity of the air above the canopy, which enhances the formation of clouds. The canopy also increases turbulence which acts to increase evapotranspiration and encourage the formation of small-scale circulation patterns and convection, which can produce precipitation-bearing clouds. Consequently, the loss of forests often results in increasing aridity, as has been observed in parts of Spain. Modelling studies have shown that deforestation in the Mediterranean since the Roman period is likely to have resulted in reduced precipitation in much of the region. Restoration of the forests, if possible, could result in increased rainfall over the Sahel, Iberian Peninsula and other areas.

Once areas have been deforested, meteorological and environmental conditions may change such that it can prove extremely difficult to reforest them. A study in Finland showed that, following an extensive forest fire in 1960, the forest has not re-grown in some areas which were burned. In these new open areas, wind speeds are much higher than over the forested areas, and (during winter) abrasion by ice particles means seedlings cannot survive. In other areas (e.g., parts of Spain), desertification of areas formerly covered by trees means reforestation would be very difficult, if not impossible under natural conditions. Hence, even if reforestation of selected areas was desired to increase precipitation, it may prove extremely difficult to do so.

In urban areas, the presence of trees results in cooler temperatures during summer and, in some cases, reduced energy consumption for cooling of buildings. The effect of green spaces and trees in and around urban areas on temperature has been studied in a number of cities across Europe. Temperatures in areas with trees are cooler than other areas during the summer months as a result of the shading effect of the trees, and increased evapotranspiration. Increased tree cover in cities would, therefore, improve the local climate during warm periods, and lower temperatures may also help to reduce energy consumption by air conditioning. Selection of appropriate species, especially those which emit few terpenes means that negative effects of enhanced ozone levels can be avoided. As well as improving hydrological cycling and supporting biodiversity, increasing forest cover around a city may also help reduce temperatures within the city itself.

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