Download Potential Impacts of Climate Change on the Broads

 The Potential Impacts of Climate Change on the Norfolk Broads Dr. Jeff Price Tyndall Centre for Climate Change Research and School of Environmental Sciences, University of East Anglia, Norwich, U.K. October 2013 Executive Summary ................................................................................................................................. 6 Introduction ............................................................................................................................................ 8 Potential Climate Changes in the Broads .............................................................................................. 10 Average Maximum Temperature ...................................................................................................... 11 Table 1a, Change in Average Monthly Maximum Temperature, 2020s. ...................................... 12 Table 1b, Change in Average Monthly Maximum Temperature, 2050s. ...................................... 12 Table 1c, Change in Average Monthly Maximum Temperature, 2080s. ...................................... 12 Figure 1a, Average Monthly Maximum Temperature (summary), 2020s. ................................... 13 Figure 2a, Average Monthly Maximum Temperature, 2020s. ...................................................... 13 Figure 1b, Average Monthly Maximum Temperature (summary), 2050s. ................................... 14 Figure 2b, Average Monthly Maximum Temperature, 2050s....................................................... 14 Figure 1c, Average Monthly Maximum Temperature (summary), 2080s..................................... 15 Figure 2c, Average Monthly Maximum Temperature, 2080s. ...................................................... 15 Average Temperature ....................................................................................................................... 16 Table 2a, Change in Average Monthly Temperature, 2020s. ....................................................... 17 Table 2b, Change in Average Monthly Temperature, 2050s. ....................................................... 17 Table 2c, Change in Average Monthly Temperature, 2080s. ........................................................ 17 Figure 3a, Average Monthly Temperature (summary), 2020s. ..................................................... 18 Figure 4a, Average Monthly Temperature, 2020s. ....................................................................... 18 Figure 3b, Average Monthly Temperature (summary), 2050s. .................................................... 19 Figure 4b, Average Monthly Temperature, 2050s. ....................................................................... 19 Figure 3c, Average Monthly Temperature (summary), 2080s. ..................................................... 20 Figure 4c, Average Monthly Temperature, 2080s. ....................................................................... 20 Average Minimum Temperature ...................................................................................................... 21 Table 3a, Change in Average Monthly Minimum Temperature, 2020s. ....................................... 22 Table 3b, Change in Average Monthly Minimum Temperature, 2050s........................................ 22 Table 3c, Change in Average Monthly Minimum Temperature, 2080s. ....................................... 22 2 Figure 5a, Average Monthly Minimum Temperature (summary), 2020s. .................................... 23 Figure 6a, Average Monthly Minimum Temperature, 2020s. ...................................................... 23 Figure 5b, Average Monthly Minimum Temperature (summary), 2050s. .................................... 24 Figure 6b, Average Monthly Minimum Temperature, 2050s. ...................................................... 24 Figure 5c, Average Monthly Minimum Temperature (summary), 2080s. .................................... 25 Figure 6c, Average Monthly Minimum Temperature, 2080s. ....................................................... 25 Average Precipitation ........................................................................................................................ 26 Table 4a, Change in Average Monthly Precipitation (mm), 2020s. .............................................. 27 Table 4b, Change in Average Monthly Precipitation (mm), 2050s. .............................................. 27 Table 4c, Change in Average Monthly Precipitation (mm), 2080s. .............................................. 27 Figure 7a, Average Monthly Precipitation (summary), 2020s. ..................................................... 28 Figure 8a, Average Monthly Precipitation, 2020s. ........................................................................ 28 Figure 7b, Average Monthly Precipitation (summary), 2050s. ..................................................... 29 Figure 8b, Average Monthly Precipitation, 2050s. ....................................................................... 29 Figure 7c, Average Monthly Precipitation (summary), 2080s. ..................................................... 30 Figure 8c, Average Monthly Precipitation, 2080s. ........................................................................ 30 Average Wet Day Frequency............................................................................................................. 31 Table 5a, Change in Number of Wet Days, 2020s. ........................................................................ 32 Table 5b, Change in Number of Wet Days, 2050s. ....................................................................... 32 Table 5c, Change in Number of Wet Days, 2080s. ........................................................................ 32 Figure 9a, Wet Day Frequency (summary), 2020s. ....................................................................... 33 Figure 10a, Wet Day Frequency, 2020s. ....................................................................................... 33 Figure 9b, Wet Day Frequency (summary), 2050s. ....................................................................... 34 Figure 10b, Wet Day Frequency, 2050s. ....................................................................................... 34 Figure 9c, Wet Day Frequency (summary), 2080s. ....................................................................... 35 Figure 10c, Wet Day Frequency, 2080s. ........................................................................................ 35 3 Average Cloud Cover ......................................................................................................................... 36 Table 6a, Change in Average Cloud Frequency, 2020s. ................................................................ 37 Table 6b, Change in Average Cloud Frequency, 2050s. ................................................................ 37 Table 6c, Change in Average Cloud Frequency, 2080s. ................................................................ 37 Figure 11a, Average Cloud Cover Frequency (summary), 2020s. ................................................. 38 Figure 12a, Average Cloud Cover Frequency, 2020s. ................................................................... 38 Figure 11b, Average Cloud Cover Frequency (summary), 2050s. ................................................. 39 Figure 12b, Average Cloud Cover Frequency, 2050s. ................................................................... 39 Figure 11c, Average Cloud Cover Frequency (summary), 2080s. ................................................. 40 Figure 12c, Average Cloud Cover Frequency, 2080s. .................................................................... 40 Change in Average Stream Temperature (derived) .......................................................................... 41 Table 7a, Change in Average Stream Temperature, 2020s. ......................................................... 42 Figure 13a, Change in Average Stream Temperature, 2020s. ...................................................... 42 Table 7b, Change in Average Stream Temperature, 2050s. ......................................................... 43 Figure 13b, Change in Average Stream Temperature, 2050s. ...................................................... 43 Table 7c, Change in Average Stream Temperature, 2080s. .......................................................... 44 Figure 13c, Change in Average Stream Temperature, 2080s. ...................................................... 44 Change in Average Sea Surface Temperature .................................................................................. 45 Table 8b, Change in Average Sea Surface Temperature, 2020s. .................................................. 46 Table 8b, Change in Average Sea Surface Temperature, 2050s. .................................................. 46 Table 8c, Change in Average Sea Surface Temperature, 2080s. ................................................... 46 Figure 14a, Average Sea Surface Temperature (summary), 2020s. .............................................. 47 Figure 15a, Average Sea Surface Temperature, 2020s. ................................................................ 47 Figure 14b, Average Sea Surface Temperature (summary), 2050s. ............................................. 48 Figure 15b, Average Sea Surface Temperature, 2050s. ................................................................ 48 Figure 14c, Average Sea Surface Temperature (summary), 2050s. .............................................. 49 Figure 15c, Average Sea Surface Temperature, 2080s. ................................................................ 49 4 Discussion of Projected Climate Changes ............................................................................................. 50 Potential Impacts of Climate Change on the Terrestrial Biodiversity in the Broads Region ................ 52 Potential Impacts on Selected Taxa .................................................................................................. 52 Birds .............................................................................................................................................. 52 Mammals ...................................................................................................................................... 54 Reptiles.......................................................................................................................................... 54 Amphibians ................................................................................................................................... 54 Selected Trees (based on list of species from Woodland Trust website) ..................................... 54 Potential Changes in Species Richness in Selected Plant Families ................................................... 55 Methodology ......................................................................................................................................... 56 Climate modelling ............................................................................................................................. 56 Biodiversity Data ............................................................................................................................... 57 Climate envelope modelling ............................................................................................................. 58 Dispersal Scenarios ........................................................................................................................... 58 References ............................................................................................................................................ 60 5 Executive Summary This report builds on a report from 2003 that provided a brief overview of the potential climate changes projected over the Broads catchment basin. This report goes into substantially more detail, using more models, for more variables, at a finer scale, and using more recent scenarios for future climate change. While the report’s annex contains information on all scenarios, models and variables, the main report concentrates on information that may be most of use in adaptation planning. Thus the main report provides information on a ~2°C warmer world (the minimum amount of expected climate change adaptation practitioners should realistically be considering, and the goal set by both the EU and UNFCCC to avoid dangerous anthropogenic influence on the climate) by looking at the RCP8.5 scenario for the 2050s (2040-­‐2069). It also provides information on the upper end of projected climate change in order to provide information on the magnitude of changes to potentially prepare for (RCP8.5 in the 2080s; 2070-­‐2099). Overall, the climate model patterns for the Broads region are mostly consistent, subject to their own internal sensitivities to climate forcing. This is especially important for variables linked to precipitation as it makes interpretation and understanding potential impacts easier. One way of thinking about the projected temperature increases by the 2080s (RCP8.5) is that temperatures in winter, spring and fall will be more like the temperatures currently observed 2-­‐3 months later (1961-­‐1990). Thus, May/June temperatures of the future are projected to be more like current August temperatures, and average future winter temperatures are projected to be more like those currently seen in April. However, it is more difficult to compare summer temperatures in the same way. One comparison is that future projected average August temperature would be most similar to the current average maximum temperature. Similarly, projected average January stream temperature will be more like those currently occurring in April, and May/June stream temperatures more like those currently occurring in August. Additionally, winter sea surface temperature is projected to be more similar to that currently found in May. Precipitation is projected to increase slightly in winter but decrease in summer and this pattern is also seen in number of wet days and in cloud cover. On the surface, the potential impacts of climate change on some Broads’ sectors would appear beneficial. For example, the length of the tourism season would appear to potentially benefit as temperatures warm, and summer cloud cover and precipitation potentially decrease. Increases in winter temperatures could potentially extend the boating season by reducing the number of days boats were winterized. Even if projected August temperature increases led to a slight reduction in tourism in that month, the potential climate-­‐related increase in the length of the season could potentially offset it. However, the relationships between these average changes and extreme events, as well as with ecological processes and functions and surrounding land uses also need to be considered. For example, while an average August maximum temperature of 26°C is not 6 that great, it does mean that the number of days exceeding 30°C will also increase (and days that would have been 30°C could potentially be 34-­‐36°C). The combination of increased maximum temperature, increased stream temperature and reduced precipitation will likely increase the probabilities of blue-­‐green algae blooms, eutrophication and the growth of surface aquatic vegetation – all of which would likely impact not only the overall health of the Broads freshwater ecosystems but also potentially impact tourism. Increases in stream temperature may also affect the overall distribution and abundance pattern of freshwater species (favouring warm water over cooler water species). Reduced summer precipitation, coupled with increases in temperature, will potentially lead to reductions in soil moisture and drought risk and thus potentially influencing surrounding land use choices, especially which species are planted in agricultural operations, potentially impacting the water quality of The Broads. Finally, current projections are for a global sea-­‐level rise of up to 0.98m by the end of the century. Even a 0.5m rise in sea-­‐level will mean that saltwater will move farther upstream in extreme tides, the differentiation (halocline) between salt-­‐, brackish, and fresh-­‐ water will change in Breydon Water and elsewhere, and potential passage heights under bridges will be further restricted during high tides. The changes in water quality brought about with the influx of saltwater will impact the distribution of the flora and fauna of the broads, both terrestrial and aquatic. 7 Introduction In 2003, a report was prepared that provided an overview of the potential impacts of climate change on 23 living lakes (Hulme, Conway and Lu 2003). This report contained a preliminary analysis on the potential impacts on the Norfolk Broads catchment basin looking at a few state of the art models (then), a few key annual variables, and a single medium-­‐high emission scenario. In 2013, the Broads Authority made a request for a more in depth analysis of the potential climate changes projected to occur in the region containing the Norfolk Broads. This analysis could then be used for looking at climate change impacts and potential adaptation strategies that may be necessary to allow the Broads Authority to meet their goals over the coming decades. In the original 2003 report, the Broads were examined as an entire catchment basin and the climate changes considered were only provided for the 2080s and for only two seasons. This report expands on the 2003 report by looking at seventeen climate model patterns, monthly, for three time periods (2020s, 2010-­‐2039; 2050s, 2040-­‐2069; 2080s, 2070-­‐2099), for the four latest IPCC scenarios (the Representative Concentration Pathways (RCP 3PD, 4.5, 6 and 8.5)), for minimum, average, and maximum temperature, total monthly precipitation, wet day frequency, cloud cover and sea-­‐surface temperature (for a single 0.5° x 0.5° grid cell). A further analysis derived potential changes in water temperature based on changes in air temperature. The results of these analyses are described in the main report for a single emission scenario and a single grid cell covering much of the Northern Broads. Graphs providing the terrestrial climate information for all four of the 0.5° x 0.5° grid cells covering the Broads, and for all four of the RCP scenarios can be found in an extensive annex. The information presented in this report is providing as changes from a 1961-­‐1990 observed climate (CRU TS2.1) in tabular format, and in graphs summarizing the absolute values, and depicting the range of values for the seventeen climate model patterns. Finally, a summary of potential impacts on terrestrial biodiversity in the Broads region, using fewer models and slightly different emission scenarios (the U.K. Government AVOID scenarios) is included after the sections describing the climate changes (Price, Warren and Vanderwal, 2013). Complete details on the models and methods used can be found at the end of this document. While there may be uncertainty over the rate and magnitude of change over the coming decades, uncertainty paralysis needs to be avoided. Changes are already occurring and will continue to occur. The decision to be made should not be one of whether to begin preparing for change, but one of deciding how much change to prepare for. The model uncertainties, even for precipitation driven changes, are less for The Broads region than for other places in the world. Realistically, planning for a minimum of a 2°C average global temperature increase, and its concomitant local climate changes would seem to be prudent. Considering higher levels of change should also be considered, especially when considering 8 capital improvement projects having multi-­‐decadal lifespans, as it is often cheaper to design for a larger change than it is to retrofit for the change later. The changes presented here are for the monthly averages. Extreme events will continue to occur and these extreme events will then be on top of the changes shown here. Weather-­‐
related impacts like flooding are ultimately a combination of extreme precipitation events (potentially enhanced by drought-­‐hardened soils), or storm surges, coupled with human influences on the surrounding area (building in flood plains, increase in impervious surfaces, etc.). It is likely that precipitation events will change with a warming climate leading to more extremes – both more intense precipitation over shorter periods and longer dry periods. Sea level rise will exacerbate storm surge events and both the distance into The Broads, and the water levels within The Broads will likely be greater than previously planned for. This report only examines the projected climate changes. It does not consider the wide ranges of impacts potentially tied to these changes. However, it does provide the Broads Authority managers, scientists and stakeholders with information that can help them perform their own analyses, and guide them in their decision-­‐making activities. For example, increasing air and water temperature will influence thermal stratification in the surrounding broads, especially in those farther from the rivers and more isolated from tidal influences. Warmer winters will influence decay and mixing rates. A likely reduction in the number of winter freezing events, and a reduction in summer precipitation, will potentially further impact stratification as well as increase the probability of eutrophication. Exceeding critical thermal thresholds will likely increase with time (depending on the threshold). Hotter summer temperatures and reduced cloudiness will increase evaporation. When coupled with reduced summer precipitation water levels could be affected. Increases in water temperature will likely impact the freshwater ecology of the Broads through shifts in species distributions and food webs. Increases in water temperature will also likely affect the health of the Broads ecosystem, and, should blue-­‐green algae blooms increase, human health as well. There will likely be climate related indirect impacts as well. For example, the climate changes projected here indicate a potential lengthening of the tourism season by one or more months. This could increase impacts through greater usage of the waterways for longer periods. Changes in surrounding land use, or agricultural cropping patterns, linked to the changes in climate may also impact the broads through changes in runoff patterns and nutrient flows. 9 Potential Climate Changes in the Broads The main body of this report provides results from seventeen climate model patterns using the RCP 8.5 scenario (see Methodology for more details). While this is currently the highest scenario typically used in these types of studies it was chosen for several reasons: 1) it is the scenario that current emissions are most closely tracking; 2) in the 2050s the average global climate change is approximately 2°C warmer, thus providing guidance for adaptation planning for 2°C regardless of the timing of when it may occur; and 3) it also provides information on the greatest magnitude of changes the Broads Authority may need to prepare for in the 21st century. Results from the other RCP scenarios can be found in the annex. The results come from the Community Integrated Assessment System (CIAS; Warren et al. 2008), downscaled to a 0.5° x 0.5° latitude-­‐longitude grid, set to produce results for the region from 52° to 53° of latitude and 1° and 2° of longitude. Thus, information for four 0.5° x 0.5° grid cells was produced. In practice, the differences between the grid cells are not great. Nevertheless, the information for all four of the cells is presented in the annex. For this report the NE grid cell, covering much of the Northern Broads was chosen. 10 Average Maximum Temperature The average monthly maximum temperature is projected to increase, on average, by 0.94°C by the 2020s, 2.1°C by the 2050s, and 3.5°C by the 2080s. Temperature increases are projected to be slightly greater in the period July-­‐October that the rest of the year. The greatest projected change is an increase of >4° C in August/September in the 2080s (range 2.0° -­‐ 6.3°). For the 2050s this translates to an average August maximum temperature of 23°C (compared to just over 20°C 1961-­‐1990) and an average August maximum temperature of 24.5°C by the 2080s. Table 1 (a-­‐c) shows changes in average monthly maximum temperature for the NE grid cell (4) under the RCP8.5 scenario for the early (2020s, 2010-­‐2039), middle (2050s, 2040-­‐2069) and late (2080s, 2060-­‐2099) 21st century, averaged for the seventeen climate model patterns used in this study. Figure 1 (a-­‐c) provides summary data for the 17 climate model patterns including the mean (x), median (point where the bars change colour), quartiles, standard deviation (the line ending with a bar) and the minimum and maximum (end of line with no bar). Figure 2 (a-­‐c) shows the actual data for each of the 17 climate model patterns in relation to the observed climate of 1961-­‐1990. 11 Table 1a, Change in Average Monthly Maximum Temperature, 2020s. MEAN JAN FEB MAR APR MAY JUN JUL 0.9 0.9 0.9 0.9 0.8 0.8 1.1 AUG SEP OCT NOV DEC 1.2 1.1 1.0 0.9 0.9 SD 0.2 0.2 0.2 0.2 0.2 0.3 0.4 0.4 0.4 0.3 0.3 0.3 MEDIAN 0.9 0.9 0.9 0.9 0.8 0.8 1.0 1.1 1.1 1.0 0.9 0.9 MIN 0.6 0.6 0.5 0.6 0.4 0.3 0.5 0.5 0.5 0.6 0.4 0.5 MAX 1.3 1.4 1.4 1.2 1.1 1.5 1.7 1.8 1.8 1.5 1.3 1.4 Table 1b, Change in Average Monthly Maximum Temperature, 2050s. JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC MEAN 2.0 2.0 1.9 1.9 1.8 1.9 2.3 2.6 2.5 2.2 2.0 2.0 SD 0.5 0.6 0.5 0.4 0.5 0.7 0.8 0.9 0.8 0.6 0.6 0.5 MEDIAN 2.0 1.9 1.9 1.9 1.8 1.7 2.2 2.5 2.5 2.2 2.0 1.9 MIN 1.2 1.2 1.0 1.2 0.9 0.7 1.1 1.2 1.2 1.2 0.9 1.1 MAX 2.9 3.2 3.0 2.6 2.5 3.3 3.7 3.9 3.9 3.4 2.9 3.0 Table 1c, Change in Average Monthly Maximum Temperature, 2080s. MEAN JAN FEB 3.3 3.4 MAR APR MAY JUN 3.2 3.1 2.9 3.1 JUL 3.9 AUG SEP 4.3 4.2 OCT NOV DEC 3.7 3.3 3.3 SD 0.8 0.9 0.9 0.7 0.8 1.2 1.3 1.5 1.4 1.1 0.9 0.9 MEDIAN 3.3 3.2 3.1 3.2 2.9 2.8 3.7 4.2 4.4 3.5 3.2 3.2 MIN 2.1 2.1 1.9 2.1 1.4 1.2 1.9 2.0 2.0 2.1 1.7 2.0 MAX 4.9 5.4 5.0 4.4 4.3 5.4 6.1 6.3 6.3 5.7 4.8 4.8 12 Figure 1a, Average Monthly Maximum Temperature (summary), 2020s. Figure 2a, Average Monthly Maximum Temperature, 2020s. 13 Figure 1b, Average Monthly Maximum Temperature (summary), 2050s. Figure 2b, Average Monthly Maximum Temperature, 2050s. 14 Figure 1c, Average Monthly Maximum Temperature (summary), 2080s. Figure 2c, Average Monthly Maximum Temperature, 2080s. 15 Average Temperature The average monthly temperature is projected to increase, on average, by 0.92°C by the 2020s, 2.03°C by the 2050s, and 3.4°C by the 2080s. Temperature increases are projected to be slightly greater in the period July-­‐October that the rest of the year. The greatest projected change is an increase of >4° C in August/September in the 2080s (range 1.8° -­‐ 6.1°). For the 2050s this translates to an average August temperature of 18.5°C (compared to just over 16°C 1961-­‐1990) and an average August temperature of just over 20°C by the 2080s – thus an average August temperature similar to the current (1961-­‐1990) average maximum temperature. Table 2 (a-­‐c) shows changes in average monthly temperature for the NE grid cell (4) under the RCP 8.5 scenario for the early (2020s, 2010-­‐2039), middle (2050s, 2040-­‐2069) and late (2080s, 2060-­‐2099) 21st century averaged for the seventeen climate model patterns used in this study. Figure 3 (a-­‐c) provides summary data for the 17 climate model patterns including the mean (x), median (point where the bars change colour), quartiles, standard deviation (the line ending with a bar) and the minimum and maximum (end of line with no bar). Figure 4 (a-­‐c) shows the actual data for each of the 17 climate model patterns in relation to the observed climate of 1961-­‐1990. 16 Table 2a, Change in Average Monthly Temperature, 2020s. MEAN JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 0.9 0.9 0.8 0.9 0.8 0.8 1.0 1.1 1.1 1.0 0.9 0.9 SD 0.2 0.2 0.2 0.2 0.2 0.3 0.4 0.4 0.4 0.3 0.3 0.2 MEDIAN 1.0 0.9 0.9 0.9 0.8 0.8 0.9 1.0 1.0 1.0 0.9 0.8 MIN 0.6 0.6 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.5 0.4 0.5 MAX 1.3 1.4 1.3 1.2 1.1 1.5 1.7 1.7 1.6 1.5 1.3 1.3 Table 2b, Change in Average Monthly Temperature, 2050s. MEAN JAN 2.0 FEB 2.0 MAR APR MAY JUN 1.9 1.9 1.8 1.8 JUL 2.2 AUG SEP 2.4 2.4 OCT NOV DEC 2.1 1.9 2.0 SD 0.5 0.6 0.5 0.4 0.5 0.7 0.8 0.8 0.8 0.6 0.6 0.6 MEDIAN 2.1 1.9 1.9 1.9 1.8 1.6 2.1 2.4 2.4 2.1 1.9 1.9 MIN 1.2 1.2 1.0 1.2 1.0 0.8 1.0 1.1 1.1 1.2 0.9 1.1 MAX 2.8 3.2 3.0 2.6 2.4 3.2 3.6 3.7 3.5 3.4 2.9 2.9 Table 2c, Change in Average Monthly Temperature, 2080s. MEAN JAN 3.3 FEB 3.4 MAR APR MAY JUN 3.2 3.1 2.9 3.0 JUL 3.7 AUG SEP 4.1 4.0 OCT NOV DEC 3.5 3.2 3.3 SD 0.8 0.9 0.9 0.7 0.8 1.2 1.3 1.4 1.3 1.0 1.0 0.9 MEDIAN 3.3 3.2 3.0 3.2 2.9 2.7 3.5 4.0 4.3 3.4 3.2 3.1 MIN 2.1 2.1 1.9 2.0 1.7 1.2 1.7 1.8 1.9 2.0 1.6 1.9 MAX 4.8 5.3 5.0 4.4 4.1 5.3 5.9 6.1 5.8 5.6 4.8 4.8 17 Figure 3a, Average Monthly Temperature (summary), 2020s. Figure 4a, Average Monthly Temperature, 2020s. 18 Figure 3b, Average Monthly Temperature (summary), 2050s. Figure 4b, Average Monthly Temperature, 2050s. 19 Figure 3c, Average Monthly Temperature (summary), 2080s. Figure 4c, Average Monthly Temperature, 2080s. 20 Average Minimum Temperature The average monthly minimum temperature is projected to increase, on average, by 0.9°C by the 2020s, 2.0°C by the 2050s, and 3.3°C by the 2080s. Temperature increases are projected to be slightly greater in the period July-­‐October that the rest of the year. The greatest projected change is an increase of 3.9° C in August/September in the 2080s (range 1.7° -­‐ 5.9°). More importantly are the temperature increases in winter months. For the 2050s this translates to an average minimum winter temperature of 3.5-­‐4°C (compared to just over 1-­‐2°C 1961-­‐1990) and an average minimum winter temperature of 4.5 – 5.5°C by the 2080s. Thus, by the 2080s, the average winter minimum temperatures will be similar to the April temperatures of 1961-­‐1990. Table 3 (a-­‐c) shows changes in average monthly temperature for the NE grid cell (4) under the RCP 8.5 scenario for the early (2020s, 2010-­‐2039), middle (2050s, 2040-­‐2069) and late (2080s, 2060-­‐2099) 21st century averaged for the seventeen climate model patterns used in this study. Figure 5 (a-­‐c) provides summary data for the 17 climate model patterns including the mean (x), median (point where the bars change colour), quartiles, standard deviation (the line ending with a bar) and the minimum and maximum (end of line with no bar). Figure 6 (a-­‐c) shows the actual data for each of the 17 climate model patterns in relation to the observed climate of 1961-­‐1990. 21 Table 3a, Change in Average Monthly Minimum Temperature, 2020s. MEAN JAN 0.9 FEB 0.9 MAR APR MAY JUN 0.8 0.9 0.8 0.8 JUL 1.0 AUG SEP 1.1 1.0 OCT NOV DEC 0.9 0.9 0.9 SD 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.4 0.3 0.3 0.3 0.2 MEDIAN 0.9 0.9 0.8 0.9 0.8 0.7 0.9 1.0 1.0 0.9 0.9 0.8 MIN 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.5 0.5 0.4 0.5 MAX 1.3 1.4 1.3 1.2 1.1 1.5 1.6 1.6 1.6 1.5 1.3 1.3 Table 3b, Change in Average Monthly Minimum Temperature, 2050s. JAN FEB MAR APR MAY JUN JUL AUG SEP OCT MEAN 2.0 2.0 1.9 1.9 1.8 1.8 2.1 2.3 2.3 2.0 NOV 1.9 DEC 1.9 SD 0.5 0.6 0.5 0.4 0.4 0.7 0.8 0.8 0.8 0.6 0.6 0.5 MEDIAN 2.0 2.0 1.8 1.9 1.8 1.6 2.0 2.3 2.3 2.0 1.9 1.9 MIN 1.2 1.2 1.0 1.1 1.1 0.8 0.9 1.0 1.0 1.1 0.9 1.1 MAX 2.8 3.1 3.0 2.6 2.4 3.2 3.5 3.6 3.5 3.4 3.0 2.9 Table 3c, Change in Average Monthly Minimum Temperature, 2080s. JAN FEB MAR APR MAY JUN JUL AUG SEP OCT MEAN 3.3 3.3 3.2 3.1 2.9 2.9 3.5 3.9 3.8 3.4 NOV 3.2 DEC 3.2 SD 0.8 0.9 0.9 0.7 0.7 1.1 1.3 1.4 1.2 1.0 1.0 0.9 MEDIAN 3.2 3.1 3.0 3.1 2.9 2.6 3.4 3.8 4.1 3.3 3.1 3.1 MIN 2.0 2.0 1.9 1.9 1.9 1.3 1.6 1.7 1.7 1.9 1.5 1.8 MAX 4.8 5.3 5.0 4.4 4.0 5.2 5.7 5.9 5.7 5.6 4.9 4.8 22 Figure 5a, Average Monthly Minimum Temperature (summary), 2020s. Figure 6a, Average Monthly Minimum Temperature, 2020s. 23 Figure 5b, Average Monthly Minimum Temperature (summary), 2050s. Figure 6b, Average Monthly Minimum Temperature, 2050s. 24 Figure 5c, Average Monthly Minimum Temperature (summary), 2080s. Figure 6c, Average Monthly Minimum Temperature, 2080s. 25 Average Precipitation The average monthly precipitation is projected to increase, on average, by 0.2 mm by the 2020s, 0.62 mm by the 2050s, and 1.4 mm by the 2080s. However, this is only part of the story. The climate models are reasonably consistent with almost all showing a wetter winter, and all but two showing a drier summer. The two outliers in summer are variants of the same model (Miroc Hi Res and Miroc Med Res) so the similarities are not entirely unexpected. The precipitation increases in winter are projected to be on the order of 5-­‐8 mm by the 2050s and 8-­‐13 mm by the 2080s. The precipitation decreases in summer (July-­‐
Sept) are projected to be on the order of 7-­‐9 mm by the 2050s and 10-­‐13.7 mm by the 2080s. Table 4 (a-­‐c) shows changes in average monthly precipitation for the NE grid cell (4) under the RCP 8.5 scenario for the early (2020s, 2010-­‐2039), middle (2050s, 2040-­‐2069) and late (2080s, 2060-­‐2099) 21st century averaged for the seventeen climate model patterns used in this study. Figure 7 (a-­‐c) provides summary data for the 17 climate model patterns including the mean (x), median (point where the bars change colour), quartiles, standard deviation (the line ending with a bar) and the minimum and maximum (end of line with no bar). Figure 8 (a-­‐c) shows the actual data for each of the 17 climate model patterns in relation to the observed climate of 1961-­‐1990. 26 Table 4a, Change in Average Monthly Precipitation (mm), 2020s. MEAN JAN 3.6 FEB 2.3 MAR APR MAY JUN JUL 1.8 0.9 0.1 -­‐1.3 -­‐3.5 AUG SEP -­‐4.8 -­‐3.7 OCT NOV DEC 0.5 2.7 3.5 SD 2.6 1.7 1.6 1.5 2.4 2.9 6.2 3.6 2.2 1.7 2.5 MEDIAN 4.5 2.9 1.7 1.0 -­‐0.2 -­‐0.4 -­‐2.1 -­‐4.2 -­‐1.9 0.6 2.9 2.5 MIN -­‐1.6 -­‐0.8 -­‐1.2 -­‐1.8 -­‐5.0 -­‐9.0 -­‐13.3 -­‐14.4 -­‐12.2 -­‐3.6 -­‐2.1 0.4 MAX 7.0 5.7 4.4 3.5 2.2 5.1 8.6 4.7 4.8 3.3 8.3 0.2 5.2 Table 4b, Change in Average Monthly Precipitation (mm), 2050s. MEAN JAN FEB 8.0 5.2 MAR APR MAY JUN 4.1 2.0 0.3 -­‐2.7 JUL -­‐7.1 AUG SEP -­‐9.4 -­‐7.7 OCT NOV DEC 1.1 6.0 7.9 SD 5.6 3.7 3.4 3.3 5.1 6.0 9.7 12.4 7.2 4.8 3.7 5.6 MEDIAN 10.2 6.3 3.7 2.2 -­‐0.5 -­‐1.0 -­‐4.4 -­‐8.9 1.3 6.3 5.6 -­‐4.1 MIN -­‐3.5 -­‐1.8 -­‐2.6 -­‐4.0 -­‐10.1 -­‐17.7 -­‐25.1 -­‐28.0 -­‐23.6 -­‐7.6 -­‐4.4 0.9 MAX 15.0 12.2 9.5 7.7 10.4 4.7 7.4 18.5 0.5 11.7 11.0 19.1 Table 4c, Change in Average Monthly Precipitation (mm), 2080s. JAN FEB MAR APR MAY JUN JUL AUG SEP OCT MEAN 13.4 8.7 6.8 3.3 0.5 -­‐4.3 -­‐10.6 -­‐13.7 -­‐11.8 1.9 NOV DEC 10.1 13.0 SD 9.3 5.6 5.5 8.1 9.4 14.5 18.5 10.7 7.8 6.1 9.4 MEDIAN 16.7 10.5 6.0 3.6 -­‐0.8 -­‐1.6 -­‐6.9 -­‐14.3 -­‐6.8 2.1 10.8 9.5 MIN -­‐5.8 -­‐6.4 -­‐15.3 -­‐25.9 -­‐35.2 -­‐40.5 -­‐33.9 -­‐12.0 -­‐7.1 1.5 MAX 24.1 19.6 15.3 6.0 -­‐2.8 -­‐4.2 13.0 17.1 7.9 12.2 30.5 0.8 19.7 17.6 31.5 27 Figure 7a, Average Monthly Precipitation (summary), 2020s. Figure 8a, Average Monthly Precipitation, 2020s. 28 Figure 7b, Average Monthly Precipitation (summary), 2050s. Figure 8b, Average Monthly Precipitation, 2050s. 29 Figure 7c, Average Monthly Precipitation (summary), 2080s. Figure 8c, Average Monthly Precipitation, 2080s. 30 Average Wet Day Frequency Wet days are defined as days receiving more than 0.1 mm of precipitation. The average monthly wet day frequency is projected to undergo no change by the 2020s and 2050s, and increase only slightly (0.1 days) by the 2080s. However, as with precipitation, this is only part of the story. The climate models are reasonably consistent with almost all showing more wet days in winter, and all but two showing fewer wet days in August. The two August outliers are variants of the same model (Miroc Hi Res and Miroc Med Res) so the similarities are not entirely unexpected. The number of wet days in winter is projected to increase by 0.7-­‐1 by the 2050s and 1.2-­‐1.6 by the 2080s. The number of wet days in summer is projected to decline by 0.9-­‐1.2 (July-­‐Sept) by the 2050s and 1.4-­‐1.9 by the 2080s. Table 5 (a-­‐c) shows changes in average monthly wet day frequency for the NE grid cell (4) under the RCP 8.5 scenario for the early (2020s, 2010-­‐2039), middle (2050s, 2040-­‐2069) and late (2080s, 2060-­‐2099) 21st century averaged for the seventeen climate model patterns used in this study. Figure 9 (a-­‐c) provides summary data for the 17 climate model patterns including the mean (x), median (point where the bars change colour), quartiles, standard deviation (the line ending with a bar) and the minimum and maximum (end of line with no bar). Figure 10 (a-­‐c) shows the actual data for each of the 17 climate model patterns in relation to the observed climate of 1961-­‐1990. 31 Table 5a, Change in Number of Wet Days, 2020s. MEAN JAN 0.4 FEB 0.3 MAR APR MAY JUN 0.3 0.1 0.0 -­‐0.2 JUL -­‐0.4 AUG SEP -­‐0.6 -­‐0.4 OCT NOV DEC 0.1 0.3 0.5 SD 0.3 0.2 0.2 0.2 0.3 0.4 0.6 0.7 0.4 0.2 0.2 0.3 MEDIAN 0.5 0.4 0.3 0.1 0.0 0.0 -­‐0.2 -­‐0.5 -­‐0.2 0.1 0.3 0.3 MIN -­‐0.2 -­‐0.1 -­‐0.2 -­‐0.3 -­‐0.7 -­‐1.1 -­‐1.6 -­‐1.7 -­‐1.5 -­‐0.4 -­‐0.3 0.1 MAX 0.8 0.8 0.6 0.5 0.6 0.3 0.4 0.9 0.0 0.6 0.6 1.1 Table 5b, Change in Number of Wet Days, 2050s. MEAN JAN 0.9 FEB 0.7 MAR APR MAY JUN 0.6 0.3 0.0 -­‐0.3 JUL -­‐0.9 AUG SEP -­‐1.2 -­‐0.9 OCT NOV DEC 0.1 0.7 1.0 SD 0.6 0.5 0.5 0.5 0.7 0.8 1.2 1.5 0.9 0.6 0.4 0.7 MEDIAN 1.1 0.9 0.5 0.3 -­‐0.1 -­‐0.1 -­‐0.5 -­‐1.0 -­‐0.5 0.1 0.7 0.7 MIN -­‐0.4 -­‐0.3 -­‐0.4 -­‐0.6 -­‐1.5 -­‐2.4 -­‐3.2 -­‐3.7 -­‐3.0 -­‐0.9 -­‐0.5 0.1 MAX 1.7 1.6 1.3 1.1 1.4 0.6 0.8 1.9 0.0 1.3 1.2 2.3 Table 5c, Change in Number of Wet Days, 2080s. MEAN JAN 1.5 FEB 1.2 MAR APR MAY JUN 1.0 0.5 0.0 -­‐0.6 JUL -­‐1.4 AUG SEP -­‐1.9 -­‐1.5 OCT NOV DEC 0.2 1.1 1.6 SD 1.0 0.8 0.8 0.8 1.1 1.2 1.9 2.4 1.5 0.9 0.7 1.1 MEDIAN 1.9 1.4 0.9 0.5 -­‐0.1 -­‐0.2 -­‐0.8 -­‐1.7 -­‐0.8 0.2 1.2 1.2 MIN -­‐0.7 -­‐0.4 -­‐0.6 -­‐1.0 -­‐2.3 -­‐3.7 -­‐4.9 -­‐6.0 -­‐4.7 -­‐1.4 -­‐0.9 0.2 MAX 2.6 2.5 2.1 1.8 2.2 0.9 1.3 3.0 0.1 2.1 1.9 3.7 32 Figure 9a, Wet Day Frequency (summary), 2020s. Figure 10a, Wet Day Frequency, 2020s. 33 Figure 9b, Wet Day Frequency (summary), 2050s. Figure 10b, Wet Day Frequency, 2050s. 34 Figure 9c, Wet Day Frequency (summary), 2080s. Figure 10c, Wet Day Frequency, 2080s. 35 Average Cloud Cover The overall average monthly cloud percentage is projected to undergo only slight changes (-­‐.76%, -­‐1.72%, -­‐2.9% by the 2020s, 2050s, and 2080s respectively). However, as with precipitation and wet day frequency, this is only part of the story. The climate models are reasonably consistent, showing little change in cloudiness in winter and spring, potentially large decreases in summer and lesser decreases in fall. There is a lot of apparent variability in the box and whisker plots that is largely driven by a single outlier climate model pattern (CNRM) showing substantial projected reductions in July, August and September cloud cover. Removing this model from the list (not shown) leaves an overall reduction in the mean August cloud cover (2080s) from 70% to 63.6%. Table 6 (a-­‐c) shows changes in average monthly cloud cover for the NE grid cell (4) under the RCP 8.5 scenario for the early (2020s, 2010-­‐2039), middle (2050s, 2040-­‐2069) and late (2080s, 2060-­‐2099) 21st century averaged for the seventeen climate model patterns used in this study. Figure 11 (a-­‐c) provides summary data for the 17 climate model patterns including the mean (x), median (point where the bars change colour), quartiles, standard deviation (the line ending with a bar) and the minimum and maximum (end of line with no bar). Figure 12 (a-­‐c) shows the actual data for each of the 17 climate model patterns in relation to the observed climate of 1961-­‐1990. 36 Table 6a, Change in Average Cloud Frequency, 2020s. MEAN JAN -­‐0.1 FEB -­‐0.3 MAR APR MAY JUN -­‐0.2 -­‐0.3 -­‐0.7 -­‐0.7 JUL -­‐1.4 AUG SEP -­‐2.0 -­‐1.8 OCT NOV DEC -­‐1.0 -­‐0.4 -­‐0.3 SD 0.5 0.7 0.6 0.6 0.7 0.9 1.3 1.6 1.2 0.9 0.5 0.6 MEDIAN 0.0 -­‐0.1 -­‐0.1 0.0 -­‐0.8 -­‐0.6 -­‐1.0 -­‐1.9 -­‐1.6 -­‐0.7 -­‐0.4 -­‐0.2 MIN -­‐1.0 -­‐2.3 -­‐1.6 -­‐1.8 -­‐2.1 -­‐2.3 -­‐5.0 -­‐7.2 -­‐4.8 -­‐3.2 -­‐1.3 -­‐1.9 MAX 0.9 0.8 0.7 0.7 0.3 1.2 0.6 -­‐0.1 -­‐0.5 -­‐0.1 0.3 0.8 Table 6b, Change in Average Cloud Frequency, 2050s. MEAN JAN -­‐0.2 FEB -­‐0.6 MAR APR MAY JUN JUL -­‐0.5 -­‐0.5 -­‐1.5 -­‐1.5 -­‐3.2 AUG SEP -­‐4.5 -­‐4.0 OCT NOV DEC -­‐2.4 -­‐1.0 -­‐0.6 SD 1.2 1.6 1.4 1.4 1.5 2.0 3.6 2.7 2.1 1.1 1.3 MEDIAN 0.0 -­‐0.3 -­‐0.3 0.0 -­‐1.7 -­‐1.3 -­‐2.2 -­‐4.2 -­‐3.7 -­‐1.4 -­‐0.9 -­‐0.5 MIN -­‐2.2 -­‐5.4 -­‐3.6 -­‐4.0 -­‐4.6 -­‐5.1 -­‐11.3 -­‐16.5 -­‐10.9 -­‐7.3 -­‐2.9 -­‐4.3 MAX 1.9 1.7 1.4 1.5 2.5 1.6 0.7 3.0 1.3 -­‐0.1 -­‐1.1 -­‐0.3 0.6 Table 6c, Change in Average Cloud Frequency, 2080s. MEAN JAN FEB MAR APR MAY JUN JUL -­‐0.4 -­‐1.1 -­‐0.8 -­‐0.9 -­‐2.5 -­‐2.5 -­‐5.4 AUG SEP -­‐7.7 -­‐6.9 OCT -­‐4.1 NOV DEC -­‐1.7 -­‐1.1 SD 2.0 6.1 4.5 3.7 1.9 -­‐6.9 -­‐6.2 -­‐2.3 -­‐1.5 -­‐0.9 2.8 2.4 2.4 2.6 3.3 5.1 2.2 MEDIAN -­‐0.1 -­‐0.5 -­‐0.5 -­‐0.1 -­‐2.9 -­‐2.1 -­‐3.9 MIN -­‐3.8 -­‐9.5 -­‐6.2 -­‐6.7 -­‐7.6 -­‐8.4 -­‐19.1 -­‐27.7 -­‐18.6 -­‐12.5 -­‐4.9 -­‐7.5 MAX 3.0 2.5 4.0 2.6 2.2 1.2 2.2 -­‐0.2 -­‐1.9 -­‐0.4 1.0 2.6 37 Figure 11a, Average Cloud Cover Frequency (summary), 2020s. Figure 12a, Average Cloud Cover Frequency, 2020s. 38 Figure 11b, Average Cloud Cover Frequency (summary), 2050s. Figure 12b, Average Cloud Cover Frequency, 2050s. 39 Figure 11c, Average Cloud Cover Frequency (summary), 2080s. Figure 12c, Average Cloud Cover Frequency, 2080s. 40 Change in Average Stream Temperature (derived) While stream temperatures are not calculated in climate models it is possible to estimate the potential change in stream temperature from average air temperature using an equation originally derived from examining the relationship between air and stream temperature for 930 stations (including six in the U.K.) in the Köppen-­‐Geiger warm temperate climate zone (Punzet et al. 2012). There are many factors ultimately influencing stream temperature, including shading, rate of flow, and mixing with tidal waters. Thus, these estimates should be viewed as first approximations only. The overall change in average monthly stream temperature is projected to be an increase of 1.9°C in the 2050s (range 0.95° -­‐ 2.8°C) and 3°C in the 2080s (range 1.7° -­‐ 4.8°C) in the 2080s. The greatest temperature increase is projected for the August-­‐September period (2.6°C in the 2050s, 4.2°C in the 2080s but potentially greater than 5°C warmer). Overall, increases in winter stream temperature are not as pronounced. Table 7 (a-­‐c) shows changes in derived average stream temperature for the NE grid cell (4) under the RCP 8.5 scenario for the early (2020s, 2010-­‐2039), middle (2050s, 2040-­‐2069) and late (2080s, 2060-­‐2099) 21st century averaged for the seventeen climate model patterns used in this study. Figure 13 (a-­‐c) provides summary data for the 17 climate model patterns including the mean (x), median (point where the bars change colour), quartiles, standard deviation (the line ending with a bar) and the minimum and maximum (end of line with no bar). 41 Table 7a, Change in Average Stream Temperature, 2020s. MEAN JAN 0.6 FEB 0.6 MAR APR MAY JUN 0.6 0.7 0.8 0.8 JUL 1.0 AUG SEP 1.1 1.1 OCT NOV DEC 1.0 0.7 0.6 SD 0.1 0.2 0.2 0.2 0.2 0.3 0.4 0.4 0.4 0.3 0.2 0.2 MEDIAN 0.7 0.6 0.7 0.8 0.8 0.8 0.9 1.0 1.0 1.0 0.7 0.6 MIN 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.5 0.5 0.5 0.3 0.4 MAX 0.9 0.9 1.0 1.0 1.1 1.6 1.7 1.7 1.7 1.5 1.1 0.9 Figure 13a, Change in Average Stream Temperature, 2020s. 42 Table 7b, Change in Average Stream Temperature, 2050s. JAN FEB MAR APR MAY JUN JUL AUG MEAN 1.4 1.4 1.5 1.6 1.8 1.9 2.3 2.6 SEP 2.6 OCT 2.2 NOV 1.7 DEC 1.5 SD 0.3 0.4 0.4 0.4 0.5 0.7 0.8 0.9 0.8 0.6 0.5 0.4 MEDIAN 1.5 1.3 1.5 1.7 1.8 1.8 2.2 2.6 2.6 2.1 1.6 1.4 MIN 0.8 0.8 0.8 1.0 1.0 0.8 1.1 1.2 1.2 1.2 0.7 0.8 MAX 1.9 2.2 2.4 2.3 2.4 3.3 3.6 3.9 3.7 3.5 2.5 2.3 Figure 13b, Change in Average Stream Temperature, 2050s. 43 Table 7c, Change in Average Stream Temperature, 2080s. JAN FEB MAR APR MAY JUN JUL AUG MEAN 2.4 2.4 2.5 2.8 3.0 3.2 3.8 4.2 SEP 4.3 OCT 3.7 NOV 2.9 DEC 2.6 SD 0.6 0.8 0.8 0.7 0.8 1.2 1.2 1.4 1.3 1.1 0.9 0.8 MEDIAN 2.4 2.3 2.4 2.8 2.9 3.0 3.6 4.6 4.6 3.5 2.8 2.5 MIN 1.5 1.5 1.4 1.8 1.6 1.2 1.9 2.0 2.1 2.1 1.4 1.5 MAX 3.5 4.0 4.3 4.0 4.2 5.3 5.6 6.1 6.0 5.8 4.4 4.0 Figure 13c, Change in Average Stream Temperature, 2080s. 44 Change in Average Sea Surface Temperature The average sea surface temperature (SST) change was looked at for the coastal region offshore of Great Yarmouth and north. Rather than the CRU-­‐TS2.1, this analysis used the HadSST1 observed climate but still used projections of changes in SST from the same 17 climate model patterns. A slightly different 30-­‐year time series was also used in this analysis. The overall change in average monthly SST is projected to be an increase of 0.9°C in the 2020s, 1.9°C in the 2050s and 3.2°C in the 2080s. The greatest temperature increase is projected for the July-­‐October period (2.1°C in the 2050s, 3.5°C in the 2080s but potentially greater than 5°C warmer). Table 8 (a-­‐c) shows changes in sea surface temperature for the NE grid cell (4) under the RCP 8.5 scenario for the early (2020s, 2010-­‐2039), middle (2050s, 2040-­‐2069) and late (2080s, 2060-­‐2099) 21st century averaged for the seventeen climate model patterns used in this study. Figure 14 (a-­‐c) provides summary data for the 17 climate model patterns including the mean (x), median (point where the bars change colour), quartiles, standard deviation (the line ending with a bar) and the minimum and maximum (end of line with no bar). Figure 15 (a-­‐c) shows the actual data for each of the 17 climate model patterns in relation to the observed climate of 1961-­‐1990. 45 Table 8b, Change in Average Sea Surface Temperature, 2020s. JAN FEB MAR APR MAY JUN JUL AUG SEP MEAN 0.84 0.85 0.85 0.84 0.81 0.82 0.92 1.01 1.01 OCT 0.90 NOV DEC 0.85 0.83 SD 0.24 0.24 0.24 0.23 0.22 0.28 0.35 0.38 0.35 0.28 0.27 0.25 MEDIAN 0.9 0.9 0.9 0.9 0.8 0.8 0.9 0.9 1 0.9 0.9 0.8 MIN 0.4 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.4 0.4 0.4 0.4 MAX 1.3 1.4 1.4 1.3 1.2 1.3 1.5 1.6 1.6 1.4 1.3 1.2 Table 8b, Change in Average Sea Surface Temperature, 2050s. JAN FEB MAR APR MAY JUN JUL AUG SEP MEAN 1.8 1.9 1.9 1.8 1.8 1.8 2.1 2.2 2.2 OCT NOV DEC 2.0 1.8 1.8 SD 0.5 0.5 0.5 0.5 0.5 0.6 0.8 0.9 0.8 0.6 0.6 0.6 MEDIAN 1.9 1.9 1.9 1.9 1.8 1.8 1.9 2.1 2.1 1.9 1.9 1.8 MIN 1.0 1.1 1.1 1.2 1.1 0.9 0.7 0.7 0.8 0.8 0.9 0.8 MAX 2.9 3.1 3.1 2.9 2.7 2.8 3.4 3.6 3.5 3.1 2.9 2.7 Table 8c, Change in Average Sea Surface Temperature, 2080s. JAN FEB MAR APR MAY JUN JUL AUG SEP MEAN 3.0 3.1 3.1 3.1 3.0 3.0 3.4 3.7 3.6 OCT 3.2 NOV 3.0 DEC 3.0 SD 0.9 0.9 0.9 0.8 0.8 1.0 1.2 1.4 1.3 1.0 1.0 0.9 MEDIAN 3.1 3.1 3.1 3.1 3.1 2.8 3.2 3.5 3.5 3.1 3.0 2.9 MIN 1.7 1.8 1.9 2.0 1.9 1.5 1.2 1.2 1.4 1.3 1.5 1.5 MAX 4.8 5.2 5.1 4.9 4.5 4.6 5.6 6.0 5.7 5.1 4.7 4.4 46 Figure 14a, Average Sea Surface Temperature (summary), 2020s. Figure 15a, Average Sea Surface Temperature, 2020s. 47 Figure 14b, Average Sea Surface Temperature (summary), 2050s. Figure 15b, Average Sea Surface Temperature, 2050s. 48 Figure 14c, Average Sea Surface Temperature (summary), 2050s. Figure 15c, Average Sea Surface Temperature, 2080s. 49 Discussion of Projected Climate Changes Overall, the climate model patterns for the Broads region are mostly consistent, subject to their own internal sensitivities to climate forcing. This is especially important for variables linked to precipitation as it makes interpretation and understanding potential impacts easier. One way of thinking about temperature increases by the 2080s in the RCP 8.5 scenario is that temperatures in winter, spring and fall will be more like the observed temperatures (1961-­‐1990) of two-­‐three months later. Thus, May/June temperatures of the future are projected to be more like current August temperatures, and average future winter temperatures projected to be more like those currently seen in April. It is more difficult to compare summer temperatures in the same way. One comparison is that future projected average August temperature would be most similar to the current average maximum temperature. While observed stream temperatures were not available for this study, the relationship between average temperature and stream temperature would suggest that, by the 2080s, the average January stream temperature will be more similar to that currently occurring in April, and the May/June stream temperatures more similar to those currently occurring in August. Similarly, the winter sea surface temperature is projected to be more similar to that currently found in May. This 2-­‐3 month shift in the timing of events may potentially lead to an equivalent 2-­‐3 month shift in the timing of temperature dependent biological events when possible, and a disruption in the events when not. Precipitation is projected to increase slightly in winter but decrease in summer and this pattern is also seen in number of wet days and in cloud cover. On the surface, the potential impacts of climate change on some Broads’ sectors would appear beneficial. For example, the length of the tourism season would appear to potentially benefit, lengthening as temperatures warm and summer cloud cover and precipitation decrease. Increases in winter temperatures could potentially extend the boating season by reducing the number of days boats were winterized. Even the projected increases in average monthly maximum temperature in the hottest months would not seem to be too great to discourage tourism. Even if August temperature increases led to a slight reduction in tourism the potential increase in the length of the season could potentially offset it. However, the relationships between these changes and extreme events, as well as with ecological processes and functions and surrounding land uses also need to be considered. For example, while an average August maximum temperature of 26°C (the higher end of the model results in the 2080s) is not that great, it does mean that the number of days exceeding 30°C will also likely increase (and days that would have been 30°C could potentially be 34-­‐36°C). Increased summer temperatures, even if overall precipitation is projected to decrease, could lead to an increase in thunderstorms as more heat energy is available to drive convective processes. The combination of increased maximum temperature, increased stream temperature and reduced precipitation will likely increase the probabilities of blue-­‐green algae blooms, eutrophication and the growth of surface 50 aquatic vegetation – all of which would likely impact not only the overall health of the Broads freshwater ecosystems but also potentially impact tourism. Increases in stream temperature may also impact the overall distribution and abundance pattern of freshwater species (favouring warm water over cooler water species). Reduced summer precipitation, coupled with increases in temperature, will potentially lead to reductions in soil moisture and drought risk and thus potentially impact surrounding land uses, especially which species are planted in agricultural operations, potentially impacting the water quality of The Broads. Finally, current projections of sea-­‐level rise (IPCC 5th Assessment Report) are for a global increase of 0.52 – 0.98m for RCP 8.5 by the end of the century. These highest levels are reported here in order to provide the information necessary for the Broads Authority to understand the greatest change, as understood at the moment, which they may need to prepare for. Even a 0.5m rise in sea-­‐level will mean that saltwater will move farther upstream in extreme tides, the delineation (halocline) between salt-­‐, brackish, and fresh-­‐ water will change in Breydon Water and elsewhere, and potential passage heights under bridges will be further restricted during high tides. The changes in water quality brought about with the influx of saltwater will impact the distribution of the flora and fauna of the broads, both terrestrial and aquatic. These factors were outside of the scope of this study but hopefully the data presented here will allow the Broads Authority to examine some of these factors themselves or in future studies. 51 Potential Impacts of Climate Change on the Terrestrial Biodiversity in the Broads Region The results presented here come from the report The role of mitigation in AVOIDing Potential Climate Change Impacts on Biodiversity, originally prepared for DECC and Defra as part of the AVOID project (Price, Warren and Vanderwal 2013). The methods used in this section are included in the overall methods section. The U.K.’s latitudinal position, and its island status, offers both benefits and challenges when it comes to comparing the potential impacts of climate change on U.K. biodiversity with that of Europe. On the benefits side, the projected impacts on U.K. biodiversity (as measured by the Wallace Initiative) are less than that elsewhere in southern Europe. This owes in part to its latitudinal position such that warming and drying projected to impact species in Spain and southern France rarely reach all the way north to the U.K. (the exception being the areas around Cornwall and Devon). Furthermore, the U.K., as an island, would be expected to potentially see slightly reduced temperature rises owing to the surrounding waters. The island status also introduces a number of challenges in attempting to assess the potential impacts of climate change on biodiversity, especially on animals. Even the short distance across the English Channel appears to act as a barrier to a number of species (including birds). There are a number of species that are found substantially farther north in Europe and whose climatic range is modelled as being suitable in the UK, but which do not occur as regular breeders (for example). Some of these species do occur regularly as vagrants, and occasionally breed, but are not established. Furthermore, the typical mechanism of change in a mobile species is thought to be by a series of colonization events when the climate is suitable with (usually) or without subsequent extirpations when the climate becomes unsuitable (variability) until the average climate remains within the tolerance zone of the species. Conversely, extirpations at the edge of a species range are thought to occur when the number of local extirpation events (owing to unsuitable climates, either through direct mortality, increased incidence of disease, or climatic reproductive tolerances exceeded) exceed the re-­‐colonization events (owing to return of suitable climates). The barrier of the English Channel means both that initial colonization as well as re-­‐
colonization after transient extirpation will likely be less than for the same species on mainland Europe. Thus, potential colonization (or re-­‐colonization) will be less likely in the U.K. than northward shifts in the same species on the continent. Similarly, climate events (extremes) leading to local extirpations in the U.K. will be less likely to recover owing to the ocean barrier to re-­‐colonization. In this latter case it means that a species may be lost from the U.K. sooner than it might be lost from an equivalent area in Europe. To look at the importance of the UK relative to Europe for biodiversity, maps of refugia (areas remaining climatically suitable for >75% of the species modelled in that grid cell) were compared for a number of scenarios and time periods across taxa. Of the five taxa studied (plants, birds, mammals, reptiles, and amphibians) only plants showed higher vulnerability (reptiles and amphibians slightly less so) in East Anglia and only in the 2080s under the highest emission scenario. However, the analyses do show the importance of the habitats in East Anglia, relative to Europe, for the maintenance of terrestrial biodiversity as the climate changes. Potential Impacts on Selected Taxa Birds Little Bittern (Ixobrychus minutus), Black-­‐crowned Night Heron (Nycticorax nycticorax), Cattle Egret (Bubulcus ibis), Squacco Heron (Ardeola ralloides), Purple Heron (Ardea purpurea) – The climate is currently suitable for these occasional visitors and becomes increasingly more so with warming. 52 Little Egret (Egretta garzetta) – Already climatically suitable (though only marginally so in the models) as is evidenced by current population growth and range expansions. By 2020 (A1B) the climate becomes much more suitable, suggesting a potential climate influence on the current range expansion. European Spoonbill (Platalea leucorodia) -­‐ The climate envelope shows that the climate from East Anglia to S Scotland is currently suitable (and areas where recent breeding has occurred). The climate envelope becomes more suitable as the climate changes. Pink-­‐footed Goose (Anser brachyrhynchus) – East Anglia potentially loses its winter climatic suitability for Pink-­‐footed Goose with a shift in the primary suitable climate space north up the eastern coast of England under A1B, less so with mitigation. Black Kite (Milvus migrans) – Potential colonist. Southern England suitable by 2050s with the climatic suitability increasing over time. Montagu’s Harrier (Circus pygargus) – Current climate marginally suitable, becoming slightly better over time in both scenarios. Grey Partridge (Perdix perdix) – The climatic range of this species shows a major contraction with loss of climate suitability over most of England and Wales. Even with mitigation the climate over southern England and East Anglia is projected to become increasingly unsuitable. Stone Curlew (Burhinus oedicnemus) – Climate becomes increasingly suitable under A1B. Eurasian Eagle-­‐Owl (Bubo bubo) – Potential colonist as the climate in southern UK becomes increasingly suitable. Scops Owl (Otus scops) -­‐ Potential colonist as the climate in southern UK becomes increasingly suitable. European Bee-­‐eater (Merops apiaster) – The likelihood of a suitable climate (currently low and restricted to the southern coast) increases greatly with most of SE England becoming climatically suitable for this species. European Roller (Coracias garrulus) – Models project an expansion of climatic range in Europe and increasing probabilities of climatic suitability in the UK pointing to more potential sightings of this species in the UK. Hoopoe (Upupa epops) – The climate in parts of the UK is currently suitable for this species. Under both emission scenarios the area of suitable climate increases (especially towards the coast) suggesting the likelihood of an increase in sightings of this species. Eurasian Wryneck (Jynx torquilla) – Currently climatically suitable, climate becomes more suitable under both emission scenarios. Waxwing (Bombycilla garrulus) – Winter climatic suitability lost from East Anglia even with ~2°C of warming. Savi’s Warbler (Locustella luscinioides) – Climate suitability of this species potentially increases in East Anglia. Great Reed Warbler (Acrocephalus arundinaceus) – Parts of SE England are currently climatically suitable in the models and the climatic suitability potentially increases. 53 Red-­‐backed Shrike (Lanius collurio) – Much of the UK is currently suitable and remains so under both scenarios. Mammals Water Vole (Arvicola terrestris) -­‐ Climate potentially becomes less suitable in East Anglia in the 2080s in a high emission scenario. American Mink (Mustela vison) – Much of S England potentially becomes climatically unsuitable by the 2080s in a high emission scenario. Fallow Deer (Dama dama) – East Anglia becomes less climatically suitable by the 2080s in a high emission scenario. Reeve’s Muntjac (Muntiacus reevesi) – East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Chinese Water Deer (Hydropotes inermis) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Soprano Pipistrelle (Pipistrellus pygmaeus) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Reptiles Adder (Vipera berus) -­‐ East Anglia potentially becomes climatically unsuitable by the 2080s in a high emission scenario. Amphibians Great-­‐crested Newt (Triturus cristatus) – East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Smooth Newt (Lissotriton vulgaris) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Selected Trees (based on list of species from Woodland Trust website) Common Ash (Fraxinus excelsior) – East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Aspen (Populus tremula) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Bay Willow (Salix pentandra) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Bird Cherry (Prunus padus) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Downy Birch (Betula pubescens) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. European Larch (Larix decidua) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Sallow (Salix caprea) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. 54 Osier (Salix viminalis) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Silver Birch (Betula pendula) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Turkey Oak (Quercus cerris) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. Western Hemlock (Tsuga heterophylla) -­‐ East Anglia becomes climatically unsuitable by the 2080s in a high emission scenario. As trees are long-­‐lived species the loss of climate suitability would most likely occur first as a reduction of natural reproduction as well as in an increased susceptibility to disease and insects. Potential Changes in Species Richness in Selected Plant Families Figure 16. Percent of current Fagaceae species (includes oaks, beeches and chestnuts) retaining suitable climates in the 2080s under A1B. East Anglia becomes climatically unsuitable for 40-­‐50% of the Fagaceae species currently occuring there. Figure 17. Percent of current Salicaceae (willows) species retaining suitable climates in the 2080s under A1B. East Anglia becomes climatically unsuitable for 30-­‐40% of the Salicaceae species currently occuring there. The results from Price, Warren and Vanderwal (2013) showed the importance of the U.K. in the maintenance of European biodiversity (as measured by refugia likelihood), at least for shared species, under the baseline scenario (2080s, high emissions). Overall, the U.K. has higher adaptation indices (indicating reduced adaptation deficits) than much of southern Europe under this scenario. While the examination of potential impacts of climate change on specific plant families was limited, it clearly showed the importance of the U.K. to maintaining woodland biodiversity in Europe. The 55 reduced level of climatic impacts on some key tree families in the UK, relative to Europe, even under the baseline scenario, suggests that rehabilitation and restoration of woodlands in the UK is very important. Even with the projected impacts presented here the Norfolk Broads region potentially has a key role to play in the maintenance of European plant biodiversity. Methodology Climate modelling The climate data for this report came from the Community Integrated Assessment System (CIAS, Warren et al. 2008) which couples together IPCC scenarios (the Representative Concentration Pathways, RCPs), a Simple Global Climate Model, MAGICC6, tuned to the patterns of eighteen (seventeen used in this study) General Circulation Models, and a downscaling module, ClimGen. CIAS works by driving the MAGICC6 climate model with 21st century emissions time series to create a projection of 21st century climate change, based on an exploration of uncertainties in three key parameters: climate sensitivity, ocean mixing rate, and a climate-­‐carbon cycle feedback factor that amplifies the temperature dependent climate-­‐carbon cycle feedbacks in MAGICC6 (Meinshausen et al. 2011). The climate-­‐carbon cycle amplification parameter follows a normal distribution whose parameters were derived to allow MAGICC’s atmospheric carbon dioxide concentrations to closely match that of the earth system models in the C4MIP analysis, specifically the Bern carbon cycle in this study (Friedlingstein et al. 2006). MAGICC6 contains an improved representation of the carbon cycle. The resulting CO2 concentration depends on the forcing, the climate sensitivity and the ocean heat uptake efficiency. Sulphate aerosol forcing is scaled directly with the emissions because of the short residence time in the atmosphere. MAGICC6 has been previously tuned to emulate eighteen state-­‐of-­‐the-­‐art AOGCMs and used to create global temperature projections for the four IPCC RCPs (van Vuuren et al. 2011). Thus the model allows the user to emulate AOGCM output, specifically changes in CO2 concentration, and global-­‐mean surface air temperature between the years 2000 and 2100 resulting from anthropogenic emissions of CO2, CH4, N2O, HFCs, CFCs and PFCs, as well as SO2. Previous versions of MAGICC have been widely used in integrated modelling studies (e.g., Rotmans, Hulme and Downing 1994; van Vuuren et al. 2008; Warren et al 2013a, b). Use of MAGICC6 and ClimGen was necessary because GCMs have yet to be run for RCPs and consistently downscaled globally at the time of this study. The global temperature projections from MAGICC6 were then used to drive the pattern-­‐scaling model ClimGen (developed from Mitchell 2003); see also (Warren et al. 2008; Osborn 2009), in which scaled climate change patterns diagnosed from GCM simulations are combined with a baseline climate (CRU TS 2.1 for 1961-­‐1990, updated from(Mitchell and Jones 2005)). Spatially specific projections of monthly mean, minimum and maximum temperatures, total precipitation, wet day frequency and cloud cover were downscaled to a resolution of 0.5° x 0.5°. Patterns from seventeen GCMs from the CMIP3 archive were used, specifically UKMO-­‐HadCM3, CCCMA-­‐CGCM3.1, IPSL-­‐CM4, MPI-­‐ECHAM5, UKMO-­‐HadGEM1, CSIRO-­‐Mk3.0, NCAR-­‐CCSM3.0, CCSR-­‐MIROC3.2 HiRes, CCSR-­‐MIROC3.2 MedRes, CNRM-­‐CM3, GFDL-­‐CM2.0, GFDL-­‐CM2.1, GISS-­‐EH, GISS-­‐ER, INM-­‐CM3.0, MRI-­‐CGCM232a, NCAR-­‐PCM1 (Osborn 2009). ClimGen was used to produce projected monthly time series for 30-­‐year periods centred on the 2020s (2010-­‐2039), 2050s (2040-­‐2069) and 2080s (2070-­‐
2099). Future scenarios used in this study are from the suite of IPCC Representative Concentration Pathways (RCPs; van Vuuren et al. 2011). The RCP scenarios differ from the previous emission scenarios (SRES) in that they focus on radiative forcing, which potentially could be reached in many different ways. The four RCPs used in this study encompass a mitigation pathway in which radiative 56 forcing is reduced to 2.6 W/m2 (RCP 2.6, also known as RCP3PD) by 2100, a business as usual pathway in which radiative forcing increases to 8.5 W/m2 (RCP 8.5) by 2100, and two stabilisation pathways in which forcing levels out at 4.5 W/m2 (RCP 4.5) and 6.0 W/m2 (RCP 6.0) by 2100. In practice, RCP3PD is currently unlikely in that it requires a world with negative emissions. However, this scenario can achieve a mean temperature with only 2°C of warming above pre-­‐industrial (as can scenarios that have emissions peak in 2016 and are then reduced by 5% per year). The scenarios used in this study used a baseline of 1961-­‐1990, so the temperatures will be approximately 0.4°C lower than those here. Table 9. Global Mean Surface Temperature Change for the RCP scenarios (based on IPCC 5th Assessment Report and then corrected to pre-­‐industrial). Scenario 2046-­‐2065 2081-­‐2100 3PD 1.61 (1.01 – 2.21) 1.61 (.91 – 2.31) 4.5 2.01 (1.51 – 2.61) 2.41 (1.71 – 3.21) 6 1.91 (1.41 – 2.41) 2.81 (2.01 -­‐ 3.71) 8.5 2.61 (2.01 – 3.21) 4.31 (3.21 – 5.41) Deriving Stream Temperature – Stream temperature is not an output from climate models yet is an important parameter in the health of aquatic ecosystems. For that reason stream temperature changes were derived using techniques previously used for climate change studies (Punzet et al. 2012). As no observed stream temperature data was available at the time of the study the results are presented as a change derived by calculating the derived stream temperature for the observed average monthly temperature and subtracting that from the derived stream temperature from the projected average monthly temperatures. The formula used was: ଷଶ
Twater= [ଵା௘ షబ.భయ೅ೌ೔ೝశభ.వర ]
Where 32, -­‐0.13 and 1.94 are coefficients developed for the Köppen-­‐Geiger warm temperate region. Biodiversity Data Methodology used for the biodiversity analysis comes from Warren et al. 2013b). Primary biodiversity data were obtained from the Global Biodiversity Information Facility (GBIF; (Yesson et al. 2007). GBIF facilitates discovery of data from many datasets worldwide, exposed via the Internet, indexed centrally and accessible through a common portal (Global Biodiversity Information Facility (GBIF) 2010). At the time of this analysis the data used totalled 170 million occurrence records from 200 data providers. Whilst there are some gaps in the available data, GBIF provides a source allowing researchers to identify potential patterns of change across the widest range of species and areas possible. The data were then checked for locational consistency using an established automated process (Ramirez-­‐Villegas et al. 2012). Specifically, records were removed that lacked locational data or that did not fall within land areas using a high resolution (~90m) coastal layer derived from the SRTM Digital Elevation Model (Jarvis et al. 2008). The location of each record was then compared with the reported country of collection in the database and all records with conflicting values were discarded. The environmental niche of each of the 48,786 species remaining in our database were then examined using the 19 MaxEnt Bioclimatic Indices plus elevation and a Tukey outlier test was performed, discarding all occurrences being considered as outliers in 80% or more of the 20 indices for each species (Tukey 1977). 57 Climate envelope modelling MaxEnt bioclimatic were developed by first performing a training procedure to estimate a probabilistic representation of the current geographic distribution of each species using the observed species distributions held in the GBIF network and by deriving a relationship between the observations and the observed current climate. We then calculated the present-­‐day (climate of 1961-­‐1990) geographic distribution of the species for all global land areas at a spatial resolution of 0.5 degrees. During the training process, we only used AMT, TS, ATR and RS as driving variables on taxa with 10 to 40 records and species with fewer than 10 records were not analyzed. Ten cross-­‐
validated runs were performed to assess MaxEnt’s accuracy for all models. All default settings were used in MaxEnt as these were optimized for broad groups of species, globally (Phillips et al. 2006; Phillips and Dudik 2008). The Area under the Receiver Operating Characteristic (ROC) curve (AUC) was used as a model performance indicator to select species models for projection over all climate scenarios. An average cross-­‐validated AUC >0.7 is generally an indicator of good performance in MaxEnt. The predicted ‘current’ distributions were clipped by two factors: the eight bio-­‐geographic realms (defined by Olson et al. 2001) for which all occurrence information for an individual species came, and a 2000 km buffer around the occurrence records, to help compensate for the limited amount of data in some regions in the GBIF network. Oceanic islands within this 2000 km range and within the relevant biogeographic region were included. The current distribution was defined as the climatically suitable areas within this buffer region given a threshold defined as the minimum ROC distance. The projected climates and trained models were then used to derive the potential future climate space for each species in our future climate scenarios for 30-­‐year periods centered on 2025, 2055 and 2085. We applied three dispersal scenarios to each future projection as a buffer (distance defined by the rate of movement and the number of years into the future) around this current distribution given a continuous land surface, allowing dispersal to contiguous land areas. Dispersal Scenarios In this study the dispersal rate refers to the average long-­‐term shift of an entire species’ range (taken from the published literature of current observed changes and paleoecological changes) taking into account potential repeated colonization and extinction events until a species’ entire range catches up with the new ‘environmental space’. Most previous studies of this kind only look at two dispersal scenarios – none and full. For the species richness portion of this study we included a zero dispersal rate as one scenario, in common with many previous studies. However, contrary to many studies, we did not look at a “full” dispersal scenario, where species are allowed to instantly move into the new available climate space, as we do not feel this is a realistic representation of what actually occurs in nature. The typical dynamics of range shifts (through multiple dispersal, colonization, extirpation and rescue events), barriers to movements, lack of instant availability of suitable soils or habitats, as well as lack of a stabilized climate (within the timeframes of this study) are just some of the factors making a ‘full’ dispersal scenario unlikely within the timeframe of this study in the absence of assisted movement. Instead, we looked at two further dispersal scenarios per taxa, based on dispersal rates found in the literature. In these scenarios, we allow species to move to fill their new climate envelope at the rate specified, provided they do not move across sea or ocean barriers (Eastaugh 2008). Hence, we focus on dispersal to contiguous areas in the same manner as Petersen et al.(2002) as well as constrain species to remain within their native biogeographic zones. For example, if a species can disperse at a rate of 1.5 km/yr, then in 100 years it could move 150 km. As grid cell size is approximately 30-­‐50 km depending on location, this species would be simulated to cross 2-­‐4 grid cell boundaries to track the climate. Our approach does not include the potential response of species movements to changes in the frequency of magnitude extremes resulting from 58 inter-­‐decadal and inter-­‐annual climate variability, which can constrain dispersal rates (Early and Sax 2011) by introducing gaps in climate suitable pathways even in barrier-­‐free situations. Dispersal can also be strongly affected by a species’ ability to persist during short periods of unfavourable climate (op cit.), whilst dispersal rates can vary significantly within taxa. Nevertheless, our approach is a balanced compromise between the more extreme approaches of no and full dispersal that are typically used in this field. The dispersal rates chosen for this study come from a review of the literature (see Warren et al. 2013b). Based on the literature for birds we chose a realistic dispersal rate of 1.5 km/year, and an optimistic one of 3 km/year. The projected velocity of temperature change over the earth’s surface is estimated to be 0.42km/yr (0.11-­‐0.46 km/yr) for the 2050s, whilst observations over the period 1950-­‐2009 already show median velocities of 2.7 km/yr (on land) and 2.2 km/yr (in the ocean) over the period 1950-­‐2009. While these dispersal rates suggests that birds may be able to keep up with climate change, with dispersal rates of 1.7km/yr reported, recent work has found that even birds are lagging behind the climate change in some areas. The literature suggests that mammals have similar dispersal rates as birds, for example a rate of 2 km/year was estimated for deer. Therefore the same dispersal rates for mammals were used as for birds. For reptiles and amphibians, the literature on dispersal rates is sparse. Based on the very limited information available the ‘realistic’ dispersal rates for amphibians and reptiles was set to 0.1 km/year and in the ‘optimistic’ scenario a rate of 0.5 km/year was used. 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