Human dynamics of climate change Technical report

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1 Human dynamics of climate change Technical report human_dynamics_of_climate_change_technical_info_v7-1 Crown copyright 2008

2 This document is published by the Met Office on behalf of the Secretary of State for Business, Innovation and Skills, HM Government, UK. Its content is covered by Crown Copyright The Met Office aims to ensure that the content of this document is accurate and consistent with its best current scientific understanding. However, the science which underlies meteorological forecasts and climate projections is constantly evolving. Therefore, any element of the content of this document which involves a forecast or a prediction should be regarded as our best possible guidance, but should not be relied upon as if it were a statement of fact. To the fullest extent permitted by applicable law, the Met Office excludes all warranties or representations (express or implied) in respect of the content of this document. Use of the content of this document is entirely at the reader s own risk. The Met Office makes no warranty, representation or guarantee that the content of this document is error free or fit for your intended use. Before taking action based on the content of this document, the reader should evaluate it thoroughly in the context of his/her specific requirements and intended applications. To the fullest extent permitted by applicable law, the Met Office, its employees, contractors or subcontractors, hereby disclaim any and all liability for loss, injury or damage (direct, indirect, consequential, incidental or special) arising out of or in connection with the use of the content of this document including without limitation any and all liability: relating to the accuracy, completeness, reliability, availability, suitability, quality, ownership, non-infringement, operation, merchantability and fitness for purpose of the content of this document; relating to its work procuring, compiling, interpreting, editing, reporting and publishing the content of this document; and resulting from reliance upon, operation of, use of or actions or decisions made on the basis of, any facts, opinions, ideas, instructions, methods, or procedures set out in this document. This does not affect the Met Office s liability for death or personal injury arising from the Met Office s negligence, nor the Met Office s liability for fraud or fraudulent misrepresentation, nor any other liability which cannot be excluded or limited under applicable law. If any of these provisions or part provisions are, for any reason, held to be unenforceable, illegal or invalid, that unenforceability, illegality or invalidity will not affect any other provisions or part provisions which will continue in full force and effect. This report was prepared in July 2014 by Dr Katy Richardson, Applied Climate Scientist, and scientifically reviewed by Kirsty Lewis, Climate Security Team Leader. 1

3 Contents Introduction...3 Present-day human dynamics data...4 Population density...4 Global trade dynamics...5 Water stress...7 Fragile States...8 Maritime choke points and shipping routes...9 Transport infrastructure...10 Fish catch...12 Tropical cyclone regions and major glaciers...13 Projections of future change...14 Mean changes in climate...15 Methods...17 Future change data layers...20 Water run-off...20 Water demand for irrigation...22 Crop yield...24 Drought...34 Temperature of the warmest days...36 Flood hazard...37 Coastal flooding...39 Sea surface temperature...40 Population change...41 Alternative integrated version of the HDCC poster...58 References

4 Introduction The human dynamics of climate change (HDCC) poster aims to illustrate some of the impacts of climate and population change in the context of a globalised world. There are two types of information included in the poster present-day human dynamics, and projected future changes in climate and population between a present day ( ) baseline and the end of the 21 st century ( ). Many data sources were considered for inclusion on the poster. Challenges included ensuring reliability of the data sources, and consistency of the data in terms of the time periods used and the compatibility of the data layers with each other. The aim of this technical report is to provide more detail on each of the data layers included in the HDCC poster. The report is divided into two sections; the first section describes the present-day human dynamics data, and the second describes future change data. Reasons for choosing to include each particular data layers, and references to the data sources and their reliability, are discussed. Tables and/or maps of the data are provided where appropriate. 3

5 Present-day human dynamics data The present-day human dynamics data used in the HDCC poster comes from the period as a time slice representative of the start of the 21 st century. Each of the present-day data layers included in the poster are described in turn below, including tables of the values and references to the data sources. Population density The present-day population density is displayed in order to show where people live in the world. There are many different estimates of global population. The data used in the HDCC poster is the 2010 estimated population as used in the middle of the road socioeconomic scenario (SSP2). This data was chosen to ensure consistency with the population projections shown at the end of the 21 st century, which also follow the SSP2 scenario (see the Future Change Data section later in this report, where links to accessing this data can be found). The data is provided as a gridded dataset of absolute population, and the population density was computed from this data. A map of the 2010 population density from SSP2 as used in the HDCC poster is shown in Figure 1. Figure estimate of population density from the middle of the road socio-economic scenario, SSP2. 4

6 Global trade dynamics In order to highlight countries that are important suppliers to the global food market, and those countries that depend upon that market, data for the top five importers and exporters of four important agricultural commodities (wheat, maize, rice and soybean), for , are marked on the map. These four commodities were chosen as these are the same four crops that projected average crop yields are shown for (see the Future Change Data section later in this report). The trade data comes from the United Nations Trade Statistics Database (UN Comtrade) 1, which stores standardised official annual trade statistics reported by countries and reflects international merchandise flows detailed by commodity and partner country with coverage reaching up to 99 percent of world merchandise trade. Data was not available from all countries, and the number of countries reporting varies from year to year. In order to gain a representation of the largest importers and exporters of these four crops for the beginning of the 21 st century, data from years 2001 to were averaged to reduce the influence of short term variability, for example variations in crop yield and price volatility. The top five importers and exporters were chosen as this allowed identification of the significant players in the global food market. The imports and exports data for each country were expressed as a percentage of the total exports for each crop, so that the quantities are comparable, for example the United States export 46.4% of total soybean exports to the global market, and China imports 46.1% of the soybeans available on the global market. The top five importing and exporting countries for each crop, ordered by their exports/imports as a percentage of total exports, are shown in Table 1. This is the data used to inform the scaled global trade dynamics arrows on the HDCC poster. 1 comtrade.un.org/ 2 Data for each year and commodity was compiled by Julian Shaw in January 2014 from the Trade and Investment Statistics team at the Department for Business, Innovation and Skills. 5

7 Table 1 Top five exporting and importing countries for for wheat, maize, rice and soybean. Data from UN Comtrade 1. Crop Wheat Maize Rice Soybean Exports Imports Country Percentage of Country Percentage of total exports total exports United States 23.2 Japan 5.8 Canada 14.6 Italy 5.8 France 12.7 Algeria 5.1 Australia 10.8 Egypt 4.7 Russian Federation 6.6 Brazil 4.3 United States 48.8 Japan 18.5 Argentina 11.2 South Korea 8.8 France 9.8 Mexico 6.8 Brazil 5.9 Spain 5.0 China 4.1 Egypt 4.3 Thailand 26.1 Saudi Arabia 6.4 India 13.5 Philippines 6.2 Vietnam 12.8 United Arab Emirates 5.5 United States 11.6 United States 3.3 Pakistan 9.8 United Kingdom 3.5 United States 46.4 China 46.1 Brazil 31.0 Japan 6.7 Argentina 11.5 Netherlands 5.4 Paraguay 3.5 Germany 5.1 Canada 2.5 Mexico 4.9 6

8 Water stress The measure of water stress used is the Adjusted Human Water Security (HWS) threat index by Vorosmarty et al. (2010) 3. This is a frequently used measure of the amount of water available per person. The Adjusted HWS index is derived from an incident HWS threat index, compiled of 23 geospatial drivers under four themes; catchment disturbance, pollution, water resource development and biotic factors. This incident HWS index is then adjusted to reflect technological investments that can improve human water security through an investment benefits factor ; this includes supply stabilisation, improved water resources and access to waterways. This measure highlights the benefits that investments in water infrastructure can have. For example, some highly developed regions, such as United States and Western Europe, score highly in the incident index, but reduce their exposure by 95% through large investments in water infrastructure. Conversely, lower investment in developing countries means that billions of people remain vulnerable to water security threats. For example, most of Africa scores better than the developed world in the incident index, but little investment in water infrastructure means that the water is not used efficiently, resulting in this region scoring highly in the adjusted index. A map of the adjusted HWS threat index is shown in Figure 2. Only the highest water stress levels are shown on the poster, this corresponds to values of 0.75 and 1. This is the chronically high water scarcity category, as defined by Vorosmarty et al. (2010), and it is estimated that 1 billion people live in these areas. Figure 2 Map of the Adjusted Human Water Security Threat from Vorosmarty et al. (2010). Only values 0.75 to 1 were shown on the HDCC poster. 3 Also available at 7

9 Fragile States Countries that have appeared in the top ten of the Fund For Peace s Fragile States Index 4 between 2005 and 2013 are marked on the poster as a potential indicator of governance challenges within those states. The Fund For Peace is a non-profit and nongovernmental research and education institution, and the Fragile States Index is widely used in research and by governments internationally. The Fragile States Index uses 12 social, economic, and political indicators of pressure, which include over 100 sub-indicators, to assess 178 countries. The index was first computed in 2005 (then known as the Failed States Index), and countries that appear in the top ten of the index since then are shown. These are listed in Table 2 along with their rank in the Fragile States Index for each year from 2005 to Table 2 Countries that have appeared in the top ten of the Fund For Peace s Fragile States Index since it began in 2005, and their rank for each year. The countries are listed in order of their 2013 ranking, and the boxes highlighted in grey show the top ten ranks for each year. *South Sudan is included for the first time in 2012, but as independence does not constitute a full year it cannot be accurately compared to the other countries, and is therefore ranked with Sudan. Country Somalia Congo (D. R.) Sudan South Sudan 4 3* Chad Yemen Afghanistan Haiti Central African Republic Zimbabwe Iraq Cote d Ivoire Pakistan Guinea Liberia Sierra Leone ffp.statesindex.org 8

10 Maritime choke points and shipping routes Seven of the largest maritime choke points are marked on the map, indicating strategically important locations for the transport of goods and trade. These choke points are categorised by the volume of daily transit of oil using data from the U.S. Energy Information Administration (EIA) 5 who collects, analyzes, and disseminates independent and impartial energy information to promote sound policymaking, efficient markets, and public understanding of energy and its interaction with the economy and the environment. Data from 2011 was used as this was the most recent data available from EIA, although data for the Turkish and Danish Straits was not available for 2011, so the 2010 values were used. The data used to inform the scaled choke point icons on the poster is shown in Table 3. Table 3 Maritime choke points ranked by transit of oil (millions of barrels of oil per day). Location Oil transported through choke point in 2011 (million barrels) Strait of Hormuz 17.0 Strait of Malacca 15.2 Suez Canal and SUMED Pipeline 3.8 Bab el_mandab 3.4 Danish Straits 3.0 in 2010 (2011 data not available) Turkish Straits 2.9 in 2010 (2011 data not available) Panama Canal 0.8 More than 90% of global trade is carried by sea, and therefore popular shipping routes over the ocean are shown on the map. The data comes from the National Center for Ecological Analysis and Synthesis s Global Map of Human Impact on Marine Ecosystems 6 (Halpern et al. (2008)). The data shows ship tracks collected as part of the World Meteorological Organization Voluntary Observing Ships Scheme for 12 months beginning in The data is shown in Figure 3, and was stylistically represented on the poster. Figure 3 - Popular shipping routes from Halpern et al.. (2008). Dark colours show low density and lighter colours show high density

11 Transport infrastructure The location of fixed transport infrastructure is marked to highlight the position of critical global assets. As previously mentioned, more than 90% of global trade is carried by sea, making ports critical for global trade, and due to their coastal locations they are potentially vulnerable to damage from the effects of sea level rise, particularly those located in tropical cyclone regions. Airports are used for both passenger and cargo traffic, and so both of these categories were considered when choosing which airports to show. Due to space restrictions on the map, only ten airport and ten port locations were chosen to be shown. The top five busiest airports for passenger traffic and the top five busiest airports for cargo traffic for 2010 are marked on the poster using data from the Airports Council International 7 (ACI). The ACI provides annual comprehensive overviews of passenger and cargo traffic and is the only global trade representative of the world s airports. These airports, along with the values of passenger and cargo traffic are shown in Table 4 and Table 5 respectively. Table 4 Top 5 busiest airports by passenger traffic in 2010 from Airports Council International City Country Airport code Passengers in 2010 Atlanta GA United States ATL 89,331,622 Beijing China PEK 73,948,113 Chicago IL United States ORD 66,774,738 London Great Britain LHR 65,884,143 Tokyo Japan HND 64,211,074 Table 5 Top 5 busiest airports by cargo traffic in 2010 from Airports Council International City Country Airport code Total cargo in 2010 Hong Kong Hong Kong HKG 4,165,852 (metric tonnes) Memphis TN United States MEM 3,916,811 Shanghai China PVG 3.228,081 Incheon South Korea ICN 2,684,499 Anchorage AK United States ANC 2,646,695 The top ten busiest ports for 2012 are marked on the poster using data from the World Shipping Council 8 which represents the liner shipping industry in its work with policymakers and other industry groups with an interest in international transportation. These ports, along with the values the volume of traffic in 2012, are shown in Table

12 Table 6 Top 10 busiest ports in 2012 from the World Shipping Council City Country Volume in 2012 (million TEUs (twenty-foot equivalent unit)) Shanghai China Singapore Singapore Hong Kong China Shenzhen China Busan South Korea Ningbo-Zhoushan China Guangzhou Harbor China Qingdao China Jebel Ali, Dubai United Arab Emirates Tianjin China

13 Fish catch In addition to terrestrial agriculture, fishing is an important component of global food security, so is also included on the poster. Volumes of fish catch for 2011 are shown for defined marine fishing regions. This data was sourced from the UN Food and Agriculture Organization (FAO) Yearbook of Fishery Statistics 9. The FAO provides data relating to hunger, food and agriculture for 245 countries from The data shows tonnes of fish caught in defined marine fishing regions, and is shown on the poster as scaled fish icons with the region boundaries shown as dashed blue lines. The data used in shown in Table 7, and a map of the corresponding numbered fishing regions is shown in Figure 4. Note that regions 48, 58 and 88 in the Antarctic were not included in the poster due to design reasons, but all values are included in Table 7. Table 7 Fish catch data in 2011 from FAOSTAT. Marine area codes relate to Figure 4. Marine Area 18 Arctic sea 589 Tonnes of catch 21 Atlantic, Northwest 2,059, Atlantic, Northeast 8,723, Atlantic, Western Central 1,269, Atlantic, Eastern Central 4,382, Mediterranean and Black Sea 1,434, Atlantic, Southwest 1,762, Atlantic, Southeast 1,316, Atlantic, Antarctic 215, Indian Ocean, Western 4,258, Indian Ocean, Eastern 6,858, Indian Ocean, Antarctic and Southern 11, Pacific, Northwest 2,0965, Pacific, Northeast 2,436, Pacific, Western Central 11,769, Pacific, Eastern Central 1,925, Pacific, Southwest 575, Pacific, Southeast 7,761, Pacific, Antarctic 3,387 9 ftp://ftp.fao.org/fi/stat/summary/default.htm 12

14 Figure 4 FAO marine areas as used for fish catch data. The numbers and marine area names are both listed in Table 7. Tropical cyclone regions and major glaciers In addition to data on human activity, current tropical cyclone regions and major glaciers are also included on the present-day human dynamics map on the HDCC poster. Tropical cyclone regions are included as they represent regions that are vulnerable to damage and devastation brought by tropical cyclones making landfall. The regions were marked using data from IBTrACS (Knapp et al. (2010)), which provides global tropical cyclone best track data from the National Climatic Data Center (NCDC) at the National Oceanic and Atmospheric Administration (NOAA). Glaciers are an important source of freshwater availability, and major glaciers are marked on the poster using data from Arendt et al. (2012). 13

15 Projections of future change The aim of the HDCC poster is to show some of the impacts of climate change in the context of the way we live today. To do this, a number of climate model projections, climate impact model projections and socio-economic projections are shown. The choice of model projections used was influenced by the need to show a coherent picture of the future climate, and to ensure consistency between each of the model projections. To ensure quality and credibility of the projections, it was decided that only publically available data and peer-reviewed work would be used. With this in mind, the climate impacts projections chosen are from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) 10 (Warszawski et al. (2013)); a database of climate impact projections from a number of climate impact models, driven by the same Global Climate Models (GCMs). The GCMs used to drive the ISI-MIP models are a subset of those in the Coupled Model Intercomparison Project phase 5 (CMIP5) archive, which were used in the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). These are: HadGEM2-ES (Met Office Hadley Centre); IPSL-CM5A-LR (Institute Pierre-Simon Laplace); MIROC-ESM-CHEM (Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies); GFDL-ESM2M (NOAA Geophysical Fluid Dynamics Laboratory); NorESM1-M (Norwegian Climate Centre). These five were chosen to span the space of global mean temperature from the CMIP5 ensemble (Warszawski et al. (2013)). Seven of the nine climate impacts data layers shown in the HDCC poster come from peer-reviewed analysis of data from the ISI-MIP archive, where each of the climate impacts models used in each study have been driven by all, or a selection of, the five GCMs listed above. The two layers that do not come from the ISI-MIP data are projections of the change in temperature of the warmest days, and projections of change in sea surface temperature. Both of these layers use the data from the GCMs listed above, to ensure consistency with the ISI-MIP data layers. Each of the future change data layers shown on the poster show projected changes for the end of the 21 st century ( ), with respect to a present-day baseline ( ). Individual impacts studies occasionally use slightly different years for the baseline and future periods (for example the crop yield projections shown here use a baseline of , and for the future period), but these differences have little impact on the results. The exact years included in each of the studies are detailed below. The projections shown on the poster follow a business as usual greenhouse gas concentration scenario (RCP8.5, see van Vurren et al. (2011) for an overview of all Representative Concentration Pathways (RCPs)), and a middle of the road socioeconomic scenario (SSP2, see O Neill et al. (2014) for an overview of all Shared Socioeconomic Pathways (SSPs)) for population change. An aggressive mitigation 10 ISI-MIP Fast Track funded by the German Federal Ministry of Education and Research (Project 01LS1201A); European Union Seventh Framework Programme through Project IMPACT2C (Quantifying projected impacts under 2 C warming). 14

16 greenhouse gas concentration scenario (RCP2.6) was also considered, and data from both RCP8.5 and RCP2.6 are provided below. Mean changes in climate An ensemble of multiple GCMs is used to give an indication of the uncertainty in climate model projections, but because only a limited sub-set of the total range available is applied in this study, the full range of uncertainty for all available models may be larger than the results shown in the poster. The impacts models add further uncertainty, and this is reflected in the range of results from across the models; for some impacts model projections disagree on even the direction (increase or decrease) of change. To gain a picture of the projected changes in mean climate from the five GCMs listed above, the ensemble mean (average across each of the models at each grid cell) of the change in average temperature is shown for RCP2.6 (Figure 5) and RCP8.5 (Figure 6) for compared to Similarly, the change in annual precipitation is shown for RCP2.6 (Figure 7) and RCP8.5 (Figure 8). Figure 5 Ensemble mean of the change in annual mean air surface temperature for with respect to for RCP

17 Figure 6 Ensemble mean of the change in annual mean air surface temperature for with respect to for RCP8.5 Figure 7 Ensemble mean of the percentage change in annual total precipitation for with respect to for RCP2.6 16

18 Figure 8 - Ensemble mean of the percentage change in annual total precipitation for with respect to for RCP8.5 Methods The future change data is displayed on the poster as scaled icons, and in some cases the spatial pattern of change is also shown on the map behind; this is the mean change across the ensemble of models, where the ensemble mean is taken per grid cell. The icons indicate the average impact across climatological regions, so that each of the impacts layers can be compared for consistent regions. The size of the icon is determined by the median value across the model ensemble for each region. The colour of the icon is determined by whether the median value refers to an increase or decrease in stress. For example, icons are coloured red for values representing an increase in stress, these are: a decrease in water run-off, an increase in water demand for irrigation, a decrease in average crop yield, an increase in the number of drought days, an increase in the temperature of the warmest days, an increase in flood frequency, an increase in the average number of people affected by coastal flooding, an increase in sea surface temperature; and icons are coloured green for values representing a decrease in stress, these are: an increase in water run-off, an increase in average crop yield, a decrease in flood frequency. The colours of the icons are determined by the rounded median value. In the case where the rounded value is zero, the icon has been coloured according to the more accurate median value with one decimal place. 17

19 The climatological regions used to compute the regional values are Giorgi regions (Giorgi & Francisco (2000)); these are shown as numbered regions in Figure 9 and the numbers can be translated into region names in using Table 8. Note that the Greenland and Alaska Giorgi regions are not included in this map as they are also not included in the HDCC poster due to low population densities in these regions. Figure 9 Giorgi regions. These are the regions used to compute the regional icon values. The names of the numbered regions can be found in Table 8. Table 8 Giorgi region names associated with the numbered regions in Figure 9. Region number from Figure 9 Region name 1 Northern Europe 2 Mediterranean Basin 3 Sahara 4 Eastern Africa 5 Western Africa 6 Southern Africa 7 Northern Asia 8 Central Asia 9 Tibet 10 Eastern Asia 11 Southern Asia 12 Southeast Asia 13 Northern Australia 14 Southern Australia 15 Western North America 16 Central North America 17 Eastern North America 18 Central America 19 Amazon Basin 20 Southern South America A set of methods for computing the regional values of the scaled icons that are shown on the poster were used; these are detailed in the flow diagram in Figure 10. There are four different methods used. Routes 1 and 3 of Figure 10 take an average across the 18

20 region. Route 1 takes the median value across the region, this is used for data layers driven by precipitation that can be quite noisy, i.e. water run-off, water demand for irrigation and drought, and route 3 takes the mean across the region, this is used for more continuous data i.e. warm day temperature and sea surface temperature. Route 2 is used for the crop yield projections and involves summing the yield values across the region before computing the percentage change per region. Route 4 is used for the flooding projections as the data shows the number of model runs that show an increase or decrease in the frequency of flood events, and therefore the regional icons show the percentage of the grid points in each region that show an increase or decrease (only the strongest signal is shown). In the next section, each of the data layers are taken in turn and the methods used to compute the regional values will be discussed by referring to the flow diagram in Figure 10. Field of data 1. Take median value over region for each ensemble member Data layers using this method: Water run-off Water demand for irrigation Drought 2. Sum yield values across regions for each ensemble member in baseline and future period, then compute percentage change in yield per region Data layers using this method: Crop yield 3. Take mean value over region for each ensemble member Data layers using this method: Warm day temperature Sea surface temperature 4. Take percentage of region showing increase/decrease (only strongest signal) for each ensemble member Data layers using this method: Flood hazard Compute median value and interquartile-range across ensemble Bin median values into five categories to inform scaled icons Figure 10 - Flow diagram of the various processes for computing icon values All data has been processed and mapped with Iris 11 and Cartopy 11 ; open source Python libraries initially created by the Met Office to enable the visualisation of weather and climate data. The country border definitions come from the Natural Earth database

21 Future change data layers The future change data layers all come from either peer-reviewed analysis of the ISI-MIP data (see Projections of future change section above), analysis of the five GCMs listed above, or population projection data from the middle of the road socio-economic scenario (SSP2). The choice of ISI-MIP data analysis studies was restricted to those that considered similar time periods and Representative Concentration Pathways (RCPs) that were chosen for the poster, i.e. a 2080s future time period and both RCP2.6 and RCP8.5 scenarios considered. This section will take each of the future change projections on the HDCC poster in turn and give more detail such as the ensemble of models used to generate the projections, maps of the data, and how this data was represented on the poster. Water run-off The water run-off projections come from a comparison of projections of run-off from hydrological and biome models in the ISI-MIP ensemble by Davie et al. (2013). The data comes from an ensemble of 12 models; 12 hydrological and biome impact models each driven by HadGEM2-ES. The impacts models used in this study were: DBH; H08; MacPDM.09; MPI-HM; VIC; WBM; WaterGAP; PCR-GLOBWB; JeDi; VISIT; JULES; LPJmL. The ISI-MIP models define run-off as surface and sub-surface run-off, and do not include groundwater. The change in run-off is computed as the percentage change in the future period relative to the baseline period Maps of the data are shown for RCP2.6 (Figure 11) and RCP8.5 (Figure 12). 20

22 Figure 11 Ensemble mean of the percentage change in water run-off for relative to for RCP2.6. Data from Davie et al. (2013). Figure 12 Ensemble mean of the percentage change in water run-off for relative to for RCP8.5. Data from Davie et al. (2013). The data from Figure 12 was used for the water run-off icons in the HDCC poster. The data was computed using route 1 of the flow diagram in Figure 10 for each Giorgi region. The median values used for the icons and their inter-quartile ranges are shown in Table 10 for RCP2.6 and Table 11 for RCP8.5. The icons are coloured red for negative values i.e. a decrease in run-off, and green for positive values i.e. an increase in run-off. 21

23 Water demand for irrigation The water demand for irrigation projections come from a study of the change in irrigation water demand under climate change by Wada et al. (2013). The data comes from an ensemble of 25 models; 5 global hydrological models (GHMs) each driven by the five GCMs listed above. The GHMs used were: H08; LPJmL; PCR-GLOBWB; WaterGAP; WBMplus. Irrigation water demand (IWD) is defined as the amount of water that need needs to be supplied to ensure optimal crop growth considering losses during water transport and application. It is calculated in the models daily based on surface and soil water balance. The percentage change in IWD is computed for the future period relative to Maps of the data are shown for RCP2.6 (Figure 13) and RCP8.5 (Figure 14). Figure 13 - Ensemble mean of the percentage change in irrigation water demand for relative to for RCP2.6. Data from Wada et al. (2013). 22

24 Figure 14 - Ensemble mean of the percentage change in irrigation water demand for relative to for RCP8.5. Data from Wada et al. (2013). The data from Figure 14 was used for the water demand for irrigation icons in the HDCC poster. The data was computed using route 1 of the flow diagram in Figure 10 for each Giorgi region. The median values used for the icons and their inter-quartile ranges are shown in Table 10 for RCP2.6 and Table 11 for RCP8.5. All icons are coloured red as the median values for every region indicate increases in water demand for irrigation, and therefore a stress on the availability of water. IWD is computed by both hydrological and crop models, however only hydrological models were used in this study, and therefore there is a low representation of the effects of CO 2 fertilisation in this ensemble as only one of the hydrological models used (LPJmL) includes CO 2 fertilisation. The differences between the projections of IWD in both hydrological and crop models with and without are CO 2 fertilisation studied in Elliott et al. (2013). 23

25 Crop yield The crop yield projections have been adapted from an intercomparison of projections of wheat, maize, rice and soybean yield from global gridded crop models by Rosenzweig et al. (2013). The data comes from an ensemble of 35 models; 7 global gridded crop models (GGCMs) each driven by the five GCMs listed above. One of the GGCMs did not simulate rice and therefore only a 30 member ensemble was used for projections of rice yield. The GGCMs used were: EPIC; LPJ-GUESS; GEPIC; LPJmL; pdssat; PEGASUS (this model did not simulate rice); IMAGE. As part of the ISI-MIP framework, each of the GGCMs produced runs that included the effects of CO 2 fertilisation. There were two irrigation scenarios; noirr where no irrigation was provided to the crops i.e. only rainwater can be used, and firr where currently irrigated areas have an unlimited supply of water for irrigation. The crop yield projections in Rosenzweig et al. (2013) included the effects of CO 2 fertilisation and used the noirr scenario. For the HDCC poster, the percentage change in total yield was computed by aggregating the irrigated and rainfed yield values. The irrigated yield values were computed by multiplying the irrigated model output (in yield/hectare) by the number of irrigated hectares per grid cell from the annual area harvested data in the MIRCA2000 dataset (Portmann et al., 2010). Similarly, the rainfed yield values were computed by multiplying the rainfed model output (in yield per hectare) by the number of rainfed hectares per grid cell from MIRCA2000. The irrigated and rainfed yield values were summed to produce a total yield value per grid cell for each crop. These values were then summed to Giorgi region boundaries to obtain a crop yield value for each Giorgi region. These were computed for both the baseline period ( ) and the future period ( ), and the percentage change calculated from these values 13. Maps of the rainfed only yield projections, irrigated only yield projections, and combined rainfed and irrigated yield projections are shown for wheat yield for RCP2.6 - Figure 15, Figure 16 and Figure 17, wheat yield for RCP8.5 - Figure 18, Figure 19 and Figure 20, maize yield for RCP2.6 - Figure 21, Figure 22 and Figure 23, maize yield for RCP8.5 - Figure 24, Figure 25 and Figure 26, rice yield for RCP2.6 - Figure 27, Figure 28 and Figure 29, rice yield for RCP8.5 - Figure 30, Figure 31 and Figure 32, soybean yield for RCP2.6 - Figure 33, Figure 34 and Figure 35, soybean yield for RCP8.5 - Figure 36, Figure 37 and Figure These adaptations to the crop yield projections from Rosenzweig et al. (2013) have been undertaken in collaboration with Joshua Elliott and Cynthia Rosenzweig the two lead authors. 24

26 Figure 15 Ensemble mean of the percentage change in rainfed wheat yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). Figure 16 Ensemble mean of the percentage change in irrigated wheat yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). Figure 17 Ensemble mean of the percentage change in total wheat yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). 25

27 Figure 18 Ensemble mean of the percentage change in rainfed wheat yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). Figure 19 Ensemble mean of the percentage change in irrigated wheat yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). Figure 20 Ensemble mean of the percentage change in total wheat yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). 26

28 Figure 21 Ensemble mean of the percentage change in rainfed maize yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). Figure 22 Ensemble mean of the percentage change in irrigated maize yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). Figure 23 Ensemble mean of the percentage change in total maize yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). 27

29 Figure 24 Ensemble mean of the percentage change in rainfed maize yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). Figure 25 Ensemble mean of the percentage change in irrigated maize yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). Figure 26 Ensemble mean of the percentage change in total maize yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). 28

30 Figure 27 Ensemble mean of the percentage change in rainfed rice yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). Figure 28 Ensemble mean of the percentage change in irrigated rice yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). Figure 29 Ensemble mean of the percentage change in total rice yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). 29

31 Figure 30 Ensemble mean of the percentage change in rainfed rice yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). Figure 31 Ensemble mean of the percentage change in irrigated rice yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). Figure 32 Ensemble mean of the percentage change in total rice yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). 30

32 Figure 33 Ensemble mean of the percentage change in rainfed soybean yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). Figure 34 Ensemble mean of the percentage change in irrigated soybean yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). Figure 35 Ensemble mean of the percentage change in total soybean yield for relative to for RCP2.6. Data adapted from Rosenzweig et al. (2013). 31

33 Figure 36 Ensemble mean of the percentage change in rainfed soybean yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). Figure 37 Ensemble mean of the percentage change in irrigated soybean yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). Figure 38 Ensemble mean of the percentage change in total soybean yield for relative to for RCP8.5. Data adapted from Rosenzweig et al. (2013). 32

34 The data from Figure 20, Figure 26, Figure 32 and Figure 38 were used for the wheat, maize, rice and soybean icons respectively in the HDCC poster. The data was computed using route 2 of the flow diagram in Figure 10 for each Giorgi region, i.e. the values were summed across the regions in the baseline and future periods and the percentage change per region was computed from these values. The icons are coloured red for negative values i.e. a decrease in average yield, and green for positive values i.e. an increase in average yield. Regional icons were only shown on the poster for regions that produce more than 1% of the present-day global total for each crop. Present-day yield values (year 2000) were taken from Monfreda et al. (2008), summed to the Giorgi region borders and expressed as a percentage of the global total. These values are shown for each crop in Table 9. The median values used for the icons and their inter-quartile ranges are shown in Table 10 for RCP2.6 and Table 11 for RCP8.5, only for the regions shown on the poster i.e. those in grey in Table 9. Table 9 Present-day (year 2000) crop yield values as a percentage of the global total for each Giorgi region. Boxes in grey indicate the regions that have yield values greater than 1% of the global total, and therefore the regions for which crop yield projection icons are shown on the poster. Data from Monfreda et al. (2008). Wheat (% of year 2000 global total) Maize (% of year 2000 global total) Rice (% of year 2000 global total) Soybean (% of global total) Giorgi region Amazon Basin Central America Central Asia Central North America Eastern Africa Eastern Asia Eastern North America Mediterranean Basin Northern Asia Northern Australia Northern Europe Sahara Southeast Asia Southern Africa Southern Asia Southern Australia Southern South America Tibet Western Africa Western North America

35 Drought The drought projections come from the analysis of hydrological droughts using the ISI- MIP ensemble of hydrological models by Prudhomme et al. (2013). The data comes from an ensemble of 35 models; seven impacts models each driven by the five GCMs listed above. The impact models used in this study were: H08; JULES; Mac-PDM.09; MPI-hm; PRCGlobWB; VIC; WBM. Prudhomme et al. (2013) studied the change in the occurrence of days under drought conditions for the future period relative to the baseline period At each grid cell, a day is defined as being under drought conditions if the daily run-off value is less than the daily drought threshold for that day and for that grid cell. The threshold is the 10 th percentile of run-off calculated over the baseline period. Maps of this data are shown for RCP2.6 (Figure 39) and RCP8.5 (Figure 40). Grid cells where there is no run-off for more than 90% of the time have been excluded from the calculation and are shown as white areas in the maps. Figure 39 - Ensemble mean of the percentage change in days under drought conditions for relative to for RCP2.6. Data from Prudhomme et al. (2013). 34

36 Figure 40 Ensemble mean of the percentage change in days under drought conditions for relative to for RCP8.5. Data from Prudhomme et al. (2013). The data from Figure 40 was used for the drought icons in the HDCC poster. The data was computed using route 1 of the flow diagram in Figure 10 for each Giorgi region. The median values used for the icons and their inter-quartile ranges are shown in Table 10 for RCP2.6 and Table 11 for RCP8.5. All icons are coloured red as the median values for every region indicate increases in the number of days in drought. 35

37 Temperature of the warmest days The warm day temperature projections come from the five member subset of CMIP5 GCMs listed above. The change in warm day temperature is the change in the 95 th percentile of daily maximum air surface temperature for the future period ( ) compared to the baseline period ( ). Maps of this data are shown for RCP2.6 (Figure 41) and RCP8.5 (Figure 42). Figure 41 Ensemble mean of the change in 95 th percentile of annual daily maximum air surface temperature for with respect to for RCP2.6 Figure 42 Ensemble mean of the change in 95 th percentile of annual daily maximum air surface temperature for with respect to for RCP8.5 The data from Figure 42 was used for the warm day temperature icons in the HDCC poster. The data was computed using route 3 of the flow diagram in Figure 10 for each Giorgi region. The median values used for the icons and their inter-quartile ranges are shown in Table 10 for RCP2.6 and Table 11 for RCP8.5. All icons are coloured red as the median values for every region indicate increases in the temperature of the warmest days. 36

38 Flood hazard The flood hazard projections come from the analysis of flood hazard in the ISI-MIP ensemble conducted by Dankers et al. (2013). The data comes from an ensemble of 45 models; nine impact models each driven by the five GCMs listed above. The impact models used in this study were the following global hydrology and land surface models: LPJmL; JULES; VIC; H08; Mac-PDM.09; WBM; MPI-HM; PCR-GLOBWB; MATSIRO. Dankers et al. (2013) studied the change in the frequency of a 1-in-30 year flood event (calculated using the 30-year return level of 5-daily peak river flows) between and Maps of this data are shown for RCP2.6 (Figure 43) and RCP8.5 (Figure 44). These maps show the change in frequency of flood events; blue shading shows an increase in the frequency of a 1-in-30 year flood event (defined as a shortening of the return period to a less than 20-year return period), and brown shading shows a decrease in the frequency of a 1-in-30 year flood event (defined as a lengthening of the return period to a greater than 40-year return period). The values associated with the various shading of blue and brown indicate the number of models that agree on the direction of change; only the strongest signal is shown for each grid cell, for example if 20 models show an increase and 5 models show a decrease, then the value plotted will be a value of 20 in blue. Not all models have data for every grid cell, and some may show no change. Figure 43 - The number of models showing an increase (blues) or decrease (browns) in a 1-in-30 year flood event for relative to for RCP2.6. Data from Dankers et al. (2013). 37

39 Figure 44 The number of models showing an increase (blues) or decrease (browns) in a 1-in-30 year flood event for relative to for RCP8.5. Data from Dankers et al. (2013). The data used for the flood hazard Giorgi region icons in the HDCC poster are based on the data from Figure 44. The percentage of the Giorgi region that shows an increase or decrease is shown, and again only the strongest signal is shown, for example if 76% of the region shows an increase and 21% of the region shows a decrease then the 76% increase data is shown on the map. This is shown in route 4 of the flow diagram in Figure 10 for each Giorgi region. The icons are coloured red if the value refers to an increase in flood events, and green if the value refers to a decrease in flood events. The median values used for the icons and their inter-quartile ranges are shown in Table 10 for RCP2.6 and Table 11 for RCP

40 Coastal flooding The coastal flooding projections show the annual number of people flooded per year due to sea level rise, and come from an application of the impact model DIVA (Dynamic Interactive Vulnerability Assessment) to a range of sea level rise scenarios by Hinkel et al. (2014). They compute sea level rise projections for the end of the 21 st century relative to a baseline of from oceanic thermal expansion, mass changes from glaciers and ice caps, and the Greenland and Antarctic ice sheets using a range of GCMs 14 and ice models. Three sea level rise scenarios are considered which represent the uncertainty in the contributions to sea level rise from the three land-ice components (glaciers and ice caps, and the Greenland and Antarctic ice sheets). These are referred to as the low, medium and high ice-melt scenarios, representing the 5 th, 50 th and 95 th percentiles of the various contributions to the sea level rise estimates (see methods in Hinkel et al. (2014) for more detail). The average number of people flooded per year is then computed using the DIVA model. The number of people living along the coast below defined elevation levels is estimated, and the average number of people flooded per year is computed using the estimations of sea level rise for RCP2.6 and RCP8.5, projected changes in population from the middle of the road socio-economic scenario (SSP2) and extreme water level probability distributions. The computation is carried out along variable-length coastal segments, and then summed to country borders. Adaptation to rising sea levels is also considered. An estimation of the world s dikes in 1995 is used to form two adaptation scenarios; a no adaptation scenario, where dike heights are maintained but not raised, and an adaptation scenario, where dikes are raised following a demand function for safety (see methods in Hinkel et al. (2014) for more details). The number of people projected to be flooded along the coasts is significantly reduced in the adaptation scenario, however, only the no adaptation scenario is used in the HDCC poster as the aim is to show the stresses climate change could exert on present-day human dynamics, without adaptation. While this is illustrative, it is very unlikely that such a scenario will materialise in practice, because humans will adapt either by protecting themselves or by migrating out of the flood zone. The data used for the coastal flooding icons in the HDCC poster shows the average annual number of people flooded per country 15 due to sea level rise for RCP8.5 and the medium ice melting scenario. Only the top ten countries based on the absolute number of people flooded under the medium ice melting scenario, and the top ten countries based on the percentage of the 2085 population (from SSP2) flooded under the medium ice melting scenario are shown on the poster. Values for all countries 16, for the low, medium and high ice melting scenarios, and for both no adaptation and adaptation scenarios are shown in Table 12 for RCP2.6 and Table 13 for RCP8.5. The medium ice melting scenario values are also expressed as a percentage of the 2085 population for the no adaptation scenarios. The countries and their values that are included in the poster are highlighted in grey in Table The oceanic thermal expansion changes are taken from four of the five GCMs listed above; these are HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M. For specific details of the other estimated contributions, please see Hinkel et al. (2014). 15 The country borders used in this study come from ESRI World Countries Countries without coastlines are not included. 39

41 Sea surface temperature The sea surface temperature projections also come from the five member subset of CMIP5 GCMs listed above. The change in sea surface temperature is the change in the mean daily sea surface temperature for the future period ( ) compared to the baseline period ( ). Maps of this data are shown for RCP2.6 (Figure 45) and RCP8.5 (Figure 46). Figure 45 - Ensemble mean of the change in annual sea surface temperature for with respect to for RCP2.6 Figure 46 - Ensemble mean of the change annual sea surface temperature for with respect to for RCP8.5 The data from Figure 46 was used for the sea surface temperature icons in the HDCC poster. The data was computed using route 3 of the flow diagram in Figure 10 for each marine area (defined in Figure 4). The median values used for the icons and their interquartile ranges for both RCP2.6 and RCP8.5 are shown in Table

42 Population change The population change projections come from the middle of the road socio-economic scenario (SSP2, see O Neill et al. (2014)) 17 and show the percentage change in population between 2010 and The percentage change data is aggregated to the Natural Earth country borders (as discussed earlier) in order to show the percentage change in population per country. The percentage change in population data for each country is shown in Table 15, and plotted in Figure 47 where the values are grouped into six categories. Figure 47 Population change per country between 2010 and 2085 from the SSP2 socio-economic scenario. 17 Available at https://secure.iiasa.ac.at/web-apps/ene/sspdb/dsd?action=htmlpage&page=about 41

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