Literature review and synthesis of recent climate change impacts research

Transcription

1 Literature review and synthesis of recent climate change impacts research August 2015 Authors: Rachel Warren 1, Nigel Arnell 2, Sally Brown 3, Tord Kjellstrom 4, Robert J. Nicholls 3 and Jeff Price 1 1 Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ 2 Walker Institute, University of Reading, Reading RG6 6AR 3 Faculty of Engineering and the Environment and Tyndall Centre for Climate Change Research, University of Southampton, Highfield, Southampton SO17 1BJ 4 Umea University, Sweden Version 1.0 first submission to DECC Reference: DECC: /AVOID2 WPB.1b Report 1 Funded by 1

2 Contents Summary Introduction and Scope Water resources scarcity River flood risk Coastal Zones Biodiversity Human health Agriculture Heating and cooling energy demands Conclusion References

3 Executive Summary This report presents an assessment of the evidence on the global and regional impacts of climate change that was presented in AR5 together with research that has been published subsequently. It draws together results from several independent studies across a number of sectors, and seeks to present them in a consistent form as far as the literature permits, given that the studies are independent and use a variety of different approaches. Projections of future climate change in the IPCC s Fifth Assessment Report (AR5) published in 2014 were based on a set of Representative Concentration Pathways (RCPs), corresponding to different levels of anthropogenic forcing on the climate system. However, to date few studies have assessed the global and regional impacts under these RCPs, so little evidence was reviewed in AR5. This report contains an updated quantitative synthesis of available estimates of the global and regional impacts of climate change under the new Representative Concentration Pathways (RCPs) as far as the current literature permits. Many of the recent publications derive from the ISI-MIP2 climate change impacts model inter-comparison project. Where simulations have yet to be published using RCPs literature from older scenarios is used. The future impacts of climate change depend of course not only on the climate forcing, but also on the future socio-economic conditions. A small number of studies have used new Shared Socio-economic Pathways (SSPs) to characterise alternative future socio-economic conditions, but more have used earlier SRES scenarios. Where possible, impacts projections reported here correspond to combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). However, for some impacts sectors, such as biodiversity and health, impacts simulations based on RCP/SSP combinations have yet to be published, and this case, the synthesis is based on earlier SRES or other scenarios. For this reason a comprehensive synthesis across RCP/SSP combinations is not yet feasible. A small number of studies have considered not only the effects of climate model uncertainty on projected impacts, but also the effects of impact model uncertainty. These too are reviewed here. For all impacts studied, impacts of different climate change scenarios begin to diverge in 2050 (for a given SSP (Shared Socio-economic Pathway). There is a clear difference between the different climate forcing scenarios in As stated in AR5, impacts become increasingly severe as global temperature rise, with impacts for RCP8.5 exceeding those for RCP6, 4.5 and 2.6 in that order. Future impacts are dependent not only on amount of forcing, but also on assumed future socio-economic conditions. When expressed as a change from a reference case without climate change, instead of in absolute terms, there is much less difference between socioeconomic scenarios. For example, the high population scenario SSP3 produces the largest number of projected people exposed to water stress, and both coastal and river flooding. For biodiversity, impacts described here are not influenced by socioeconomic scenario, although in practise land use change (which will be strongly affected by socioeconomic scenario) will impact on biodiversity, so there is an indirect effect not included here. The findings of this report are in general consistent with the IPCC AR5 assessment but provide additional quantitative assessment which was either not available for assessment at the time AR5 was produced, or was mentioned only briefly in AR5. 3

4 Technical Summary WATER SCARCITY AND RIVER FLOOD RISK Estimates of the future populations exposed to water scarcity depend not only on the rate of climate forcing and socio-economic assumptions, but also on the future spatial pattern of change in rainfall as represented by different climate models. For example, in 2050 the number of people exposed to increased water resources stress under high forcing RCP8.5 varies between 216 and 3416 million, depending on how rainfall is projected to change in south and east Asia. The estimated range in projected impacts therefore depends on which climate models are used to construct climate scenarios; the studies using fewer models show a smaller range in future impact. Additional uncertainty is introduced through the use of different hydrological models to simulate future river flows, and this is largely because different models imply different reference conditions. Figure 1 shows the projected numbers of people presently water stressed who are exposed to further increases in water stress in one of the studies (Arnell & Lloyd Hughes 2014). The figure shows the average impact across the various climate models, the full range is given in the main text. By the 2020s there is little difference between the changes in climate under the different climate forcings, so there is little difference in impacts on water resources stress. By the 2050s there is still little difference between the forcings with the exception of a clearly larger forcing under RCP8.5 but there is a difference in impact between different socio-economic scenarios. By the 2080s, there is a clear distinction not only between socioeconomic scenarios but also the different RCP forcings. However, when a large number of climate models are used to construct climate scenarios, the range in estimates for a given year between climate model patterns is considerably greater than the range between either different rates of forcing or different socio-economic scenarios. Several different indicators of exposure to water resources stress have been used in various studies, and these give different indications of the absolute impacts of climate change; however, the relative differences between climate forcings are consistent across different indicators. There have been only two studies into the global-scale effects of climate change on future river flood risk, using different indicators of flood risk. One study projected that the average annual number of people flooded each year would increase by a factor of 7.5 by 2080 over the 2010 figure under a medium population scenario with a low forcing (RCP2.6), and by a factor of over 25 under a high forcing (RCP8.5). Using a different metric and a similar medium growth socio-economic scenario, the other study projected that around 280 million people would be exposed to a doubling of flood risk by 2080 under RCP2.6 (compared with the situation with no climate change), with 420 million people exposed under RCP8.5. Figure 2 shows this study s projections of numbers of people exposed to doubling of flood frequency, based on mean of 18 climate models. The full range of results is provided in the main text. Both studies projected a wide range of estimates due to variations in the geographic distribution of changes in precipitation. Hydrological model uncertainty adds to the range in projected impacts, but the effects are smaller than for exposure to increased water resources stress. 4

5 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 Persons living in water stressed watersheds where increase in water stress projected s (millions people / year) 2080s (millions people / year) 2050s (millions people / year) Figure 1. Projected numbers of people presently water stressed who are exposed to further increases in water stress, based on Arnell & Lloyd Hughes Persons living in watersheds where flood frequency is projected to double relative to baseline SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP s (millions people / year) 2050s (millions people / year) 2080s (millions people / year) Figure 2. Projected numbers of people exposed to doubling of flood frequency, based on mean of 18 climate models, based on Arnell & Lloyd Hughes,

6 No change SSP1 SSP2 SSP3 SSP4 SSP5 No change SSP1 SSP2 SSP3 SSP4 SSP5 No change SSP1 SSP2 SSP3 SSP4 SSP5 COASTS In the 2050s actual numbers of people flooded are more dependent on socioeconomic scenario than climate scenario as sea-levels remain relatively low (as there is a delayed response between an increase in global mean temperatures and subsequent sea-level rise), and have not yet reached a critical elevation level where people live. Between the 2050s and the 2080s, numbers at risk of flooding range between 19.4 million (RCP2.6, SSP5) and million (RCP8.5, SSP3) assuming there is no additional adaptation to raise the standard of flood defences. Estimates of exposure to flood risk in the 2080s is influenced most strongly by the choice of RCP, and second most strongly by the rate of ice melt. Socio-economics become less important. The increase of those people flooded between the 2050s and the 2080s is faster than the increase between the 2020s and the 2050s as sea-level rise accelerates affecting elevations where people live. By the 2080s, between 19.4 million and million people could be flooded per year. Costs of coastal flooding due to surges are presently small (US $0.01 billion annually) but are projected to rise to up to $4.7 billion annually by the 2050s (RCP8.5, SSP5) and up to $22.7 billion annually (RCP8.5, SSP5) by the 2080s, if no further adaptation is undertaken. AR5 did not produce global impact projections of people flooded, damage and adaptation coasts to the coastal zone. However the sea-level rise scenarios in AR5 are comparable to the ones used in this analysis Annual projected exposure to coastal flooding across RCPs and SSPs Present day (millions people / year) 2050s (millions people / year) 2020s (millions people / year) 2080s (millions people / year) Figure 3. Projected exposure to coastal flooding in 2020s (orange), 2050s (red) and 2080s (black) under future combinations of RCPs and SSPs. 6

7 BIODIVERSITY AND ECOSYSTEMS Previous estimates found that of the thousands of species studied, across numerous taxa, are projected to be at an increasingly high level of climate vulnerability with temperature increases of 2-3 C above pre-industrial levels (Figure 4). Biodiversity is impacted by climate change at levels below 2 C above pre-industrial levels with ~20 of species/ecosystems vulnerable. The magnitude of the impacts approximately doubles between 2 and 3 C. The magnitude of impacts approximately doubles again between 3 and 4 C (up to a loss/transformation of of the world s species and ecosystems). With approximately 4 C warming above pre-industrial levels >50 species lose >50 These large ecosystem transformations and species range losses will significantly reduce ecosystem functioning and services thus affecting humans The results presented here are consistent with IPCC AR5 but extends it with more recent literature. Notably, IPCC AR5 did not quantify the potential impacts of increasing levels of warming on extinction risk. Major review papers appearing at the end of the AR5 process gave a much better indication of the potential quantifiable risks using a wide range of very different analytical techniques (including results from the Inter-sectoral Impact Model Intercomparison Project (ISIMIP). A recent meta-analysis specifically looking at extinction risk (Urban 2015) found that extinction risk nearly doubled between current and 2 C pre-industrial (2.8 to 5.2), rose to 8.5 with by 3 C above pre-industrial and then nearly doubled again, to 16 by 4.3 C above pre-industrial (or a nearly six-fold increase over current estimated extinction rates. This study looked at 131 published projections, spanning a range of modelling techniques, dispersal scenarios, taxonomic groups, and range sizes. At a minimum, this meta-analysis looked at a MINIMUM of ~68,000 species. However, the author found the results were robust and may be broadly applicable. In line with previous studies, endemic or restricted range species were found to be at a higher risk of extinction than more widespread species. Within the studies examined, South America and Australia/New Zealand were found to have the highest extinction risks and North America and Europe the lowest. It is important to note the numbers listed above refer to increasing climatic vulnerability (one definition being range losses of >50 up to >75) while the metaanalysis defined extinction risk and extinction debt being at >80 range loss. 7

8 Impacts on biodiversity and ecosystems accrues rapidly with temperature rise < 2 C [RCP2.6] 2 C - 3 C above p.i. [RCP4.5,6, 2050s; RCP4.5, 2080s] 3 C 4 C above p.i. [RCP8.5, 2050s; RCP6, 8.5, 2080s] Figure 4. Frequency of impacts on biodiversity and ecosystems with temperature rise. 8

9 Additional cases per annum HUMAN HEALTH Grid cell based analysis of climate change shows increased heat and longer heat periods particularly in tropical and sub-tropical areas. There may be 22,000 more occupational heat stroke fatalities in 2030 than in 1975; to this should be added many thousand cases of non-fatal heat strokes and other clinical effects (Figure 5). Under SRES A1B it is projected that an average of 1.4 of productive annual daylight work hours will be lost globally by 2030 relative to 2010, and the global economic costs of the lost labor productivity may be 2 trillion USD per year. The losses at country level will be at multi-billion dollar level for many low and middle income countries. The full range of projections is given in the main text. Climate change is projected to induce approximately 880,000 additional cases of occupational heat stroke fatalities by 2030 (relative to 2010) Climate change is projected to cause a labour productivity loss of 70 million work life years by 2030 (relative to 2010) Exposure to climate change related increased heat exposures is associated with an increase in cardiac events. The assessment is consistent with AR5 and extends it No. additional cases fatal occupational heat stroke Global Annual Mean Temperature Rise above pre-industrial levels C Figure 5. Projected additional cases globally of fatal occupational heat stroke 9

10 AGRICULTURE The results of the agriculture section are largely consistent with the overall findings of the AR5. The results presented here go into much greater detail and include a more complete summary of the meta-analysis performed by Challinor (2014) using the IPCC AR5 agricultural impact database. The results presented here also present more recent information from the Agriculture Model Intercomparison Project (AgMIP), especially on model uncertainties. Under RCP8.5 by the 2080s without CO2 fertilisation yields of wheat/rice/maize/soybean are variously projected to fall globally by (range 5 to 55) according to 6 crop models (Figure 6). RCP8.5 by the 2080s with CO2 fertilisation yields of wheat/rice/maize/soybean are variously projected to change globally by +10 to -11 (range +35 to -30) according to 6 crop models (Figure 6) Increased CO2 concentrations of between ppm (that level found between RCP2.6 and RCP4.5 in the 2080s) induce reductions of 2-9 in the nutrient levels of a wide range of crops (Table 1). With 5 C of local temperature rise, without adaptation, crop yields are projected to fall by in temperate regions and in tropical regions. Thus, as concluded by IPCC AR5, climate change presents a large threat to food security, particularly at higher levels of change and especially in the tropics. Mean projected yield change across 7 climate models and 6 global crop models in RCP8.5, showing estimates including (w) and excluding (w/o) CO2 fertilisation Wheat w/o Wheat w Maize w/o Maize w Rice w/o Rice w Soy w/o Soy w Figure 6. Projected impacts of climate change upon crop yields adapted from Rosenzweig et al RCP8.5 median projections correspond to global mean temperature rise of 1.74 (2040s) 2.66 (2060s) 3.72 (2080s) C above pre-industrial levels. 10

11 Baseline CO 2 (ppm) Elevated CO 2 (ppm) Zinc (ppm) -9.3 (-12.7, -5.9) Iron (ppm) -5.1 (-6.5, -3.7) Phytate (mg/g) Nutrient Wheat Rice Maize Field Peas Soybean Sorghum -4.2 (-7.5, -0.8) Protein -6.3 (-7.5, -5.2) -3.3 (-5.0, -1.7) -5.2 (-7.6, -2.9) (-9.8, -3.8) -5.8 (-10.9, -0.3) -4.1 (-6.7, -1.4) -5.1 (-6.4, -3.9) -4.1 (-5.8, -2.5) (-8.9, -6.8) Mn (ppm) * -7.5 (-12.0, -2.8) Mg () * (-9.9, -1.3) Cu (ppm) * (-13.8, -7.1) (-4.0, -0.1) (-4.2, -0.8) -9.9 (-19.3, 0.7) (-4.3, -2.8) -2.7 (-5.1, -0.3) -5.7 (-8.0, -3.4) Ca () * (-7.3, -4.2) S (ppm) * -7.8 (-8.8, -6.8) K () * (-3.1, -2.2) B (ppm) * 5.1 (1.9, 8.4) P () * (-9.0, -5.1) No data (*); Change not statistically significant (-) (-3.6, -0.7) 2.2 (0.6, 3.8) -2.9 (-3.5, -2.2) (-9.1, -3.6) -3.7 (-6.8, -0.5) Table 1. Percent change (with 95 confidence interval) in nutrient level at elevated CO 2 relative to ambient CO 2 for wheat, rice, field peas, soybeans, maize and sorghum. Adapted from Myers et al (Table cannot be published on the internet without permission.) 11

12 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 HEATING AND COOLING DEMAND During the 21 st century residential energy demands are projected to increase due to social and economic changes, even with no climate change. Climate change results in even larger increases. By the 2020s cooling demands would be higher with climate change than they would be with a constant climate, and by the 2080s the increase is between 47 and 97. The proportional change is consistent across different socio-economic scenarios, but the absolute effect of climate change varies with the different absolute amounts of cooling energy demanded under the different socio-economic scenarios. During the 21 st century residential heating demands also increase, but at a lower rate than cooling energy demands (because there is already greater penetration of heating than cooling equipment). However, climate change has the effect of reducing future heating demands. By the 2020s, global residential heating demands would be 5-20 lower than without climate change, and by the 2080s the reduction is 32-51, again relative to the situation without climate change. As with cooling demands, the proportional effects of climate change are relatively insensitive to socio-economic assumptions, but the absolute effects are not. Projections of future heating and cooling energy use are sensitive to assumptions about adaptation to climate change and the implementation of climate mitigation policies which seek to curb energy use. Figures 7 and 8 show the mean projected changes in global residential heating and cooling energy demand, relative to demand in the absence of climate change. The full range of projections is given in the main chapter. Percentage decrease in global residential cooling energy demand, relative to demand in the absence of climate change s 2050s 2080s Figure 7. Projected increase in residential cooling demand 12

13 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 Percentage decrease in global residential heating energy demand, relative to demand in the absence of climate change s 2050s 2080s Figure 8. Projected decrease in residential heating demand. 13

14 Million persons Millions exposed The below Figures 9a-f relate impacts in all sectors to global mean temperature rise above pre-industrial levels, harmonising the data in spite of the fact that inconsistent sets of climate change and socioeconomic scenarios are synthesised. In the case of agriculture, the best synthesis of the literature to date relates yield loss to local mean temperature rise. Since local mean temperature rise is almost always larger than the global mean, it means that an assumption that the curves shown apply also to global mean temperature rise is a conservative assumption. a. 80 Projected annual exposure to coastal flooding Annual global mean temperature rise above pre-industrial levels C SSP1 SSP2 SSP3 SSP4 SSP5 b Millions living in water stressed watersheds where increase in water stress projected Annual global mean temperature rise above pre-industrial levels C SSP1 SSP2 SSP3 SSP4 SSP5 c 14

15 Millions 600 Persons living in watersheds where flood frequency is projected to double relative to baseline Annual global mean temperature rise above pre-industrial levels C SSP1 SSP2 SSP3 SSP4 SSP5 d decrease in global residential heating energy demand Annual global mean temperature rise above pre-industrial levels C SSP1 SSP2 SSP3 SSP4 SSP5 e increase inglobal residential cooling demand Annual global mean temperature rise above pre-industrial levels C SSP1 SSP2 SSP3 SSP4 SSP5 f 15

16 Additional cases per annum g No. additional cases fatal occupational heat stroke Global Annual Mean Temperature Rise above pre-industrial levels C 16

17 at risk globally Impacts on biodiversity and ecosystems accrues rapidly with temperature rise < 2 C [RCP2.6] 2 C - 3 C above p.i. [RCP4.5,6, 2050s; RCP4.5, 2080s] 3 C 4 C above p.i. [RCP8.5, 2050s; RCP6, 8.5, 2080s] 17

18 Introduction and Scope This report contains a quantitative synthesis of existing estimates of the global and regional impacts of climate change under the current generation of climate scenarios created from the CMIP5 climate models forced by Representative Concentration Pathways (Moss et al. 2012).These are the climate projections which underpin Working Group I s contribution to the IPCC s Fifth Assessment Report. The estimates are taken from the small number of published studies, many of which derive from the ISI-MIP intercomparison project (Warszawski et al., 2014), and some are based on unpublished results. Not all sectors are covered, because impacts for many under RCP forcings have not yet been assessed. Some of the studies have used the new scenario matrix approach to impact assessment (van Vuuren et al., 2014), which estimates impacts under different combinations of rate of climate forcing (as represented by RCPs) and assumed socio-economic future. These socioeconomic futures are defined by a series of five Shared Socio-economic Pathways (SSPs: O Neill et al., 2014), which are differentiated on their basis of the extent of challenges to climate change mitigation and adaptation. Each SSP has a core quantitative characterisation (defining high-level characteristics such as population and GDP), and it is assumed that each SSP can in principle be combined with each RCP (although in practice some combinations actually might be rather unlikely). Where possible, impacts projections correspond to combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) (Moss et al. 2012). However, for some impacts sectors, such as biodiversity and health, impacts simulations based on RCP/SSP combinations have yet to be published, and this case, re-analysis is based on earlier SRES or other scenarios, and outputs are synthesised using global temperature rise as a metric in order to provide the most up to date post-ar5 synthesis possible. In these cases, outputs may be compared to RCP outcomes for temperature rise. The report is based on information that is either published or is easily accessible in public or privately owned databases. Some contributors have voluntarily included some output from new model runs in order to fill gaps in the picture. Table 1a summarises the changes in global mean temperature under the four RCPs, and Table 1b summarises global population in 2050 and 2100 under the five SSPs. Table 1a. RCPs: year 2100 parameters Radiative forcing Approximate (W/m 2 ) CO 2 equivalent concentration Rate of change in radiative forcing (ppm) RCP Rising 4.3+/-0.7 RCP Stabilizing 2.8+/-0.5 RCP Stabilizing 2.4+/-0.5 RCP Declining 1.6+/-0.4 Temperature rise in relative to pre-industrial times according to IPCC AR5, WGI, Table 12.3 Table 1b. Global population in 2050 and 2100 under the five SSPs 18

19 Shared Socio-economic 2050 population (billion) 2100 population (billion) pathway SSP SSP SSP SSP SSP We consider impacts upon coastal zones, water resources, agriculture, biodiversity, health, and energy demand. An expert in each field was selected from the team described in the project proposal. Table 1c. Experts assigned to analyse the impacts sectors in this report Chapter Topic Expert 2 Water resources Prof Nigel Arnell 3 Fluvial flooding Prof Nigel Arnell 4 Coastal Zones Dr Sally Brown and Prof Robert Nicholls 5 Biodiversity & Ecosystems Dr Jeff Price 6 Human Health Dr Tord Kjellstrom 7 Agriculture Dr Jeff Price 8 Heating and Cooling Demand Prof Nigel Arnell 19

20 Water resources scarcity Three journal articles have, to October 2014, been published estimating the future changes in global water scarcity using the new Representative Concentration Pathways (RCPs) and new Shared Socio-economic Pathways (SSPs). These papers have used different indicators of water scarcity (Table 2.1), and different numbers of climate models to construct climate change scenarios. Only one of the studies (Arnell & Lloyd-Hughes, 2014) considered impacts under all combinations of RCP and SSP. Hanasaki et al. (2013) and Portmann et al. (2013) are cited in AR5 WG2 (Jimenez Cisneros et al., 2014), but quantitative results are only presented from Portmann et al. (2013). Table 2.1. Global-scale assessments of the impacts of climate change on water scarcity using Representative Concentration Pathways and Shared Socio-economic Pathways. Study Indicator Number of climate models Hanasaki et al. Number of people living in Three CMIP5 (2013) grid cells with a low ratio of models abstraction to demand, where climate change Arnell & Lloyd- Hughes (2014) Portmann et al. (2013) reduces availability Number of people living in grid cells with withdrawals>40 availability, where climate change reduces availability Number of people living in water-stressed watersheds (<1000m 3 /capita/year) with a significant reduction in runoff Number of people living in grid cells where average annual groundwater recharge decreases by more than 10 Three CMIP5 models Number of RCPs CMIP5 models 4 Five CMIP5 models 4 Tables 2.2a to d summarise the estimated impacts of climate change by RCP, socioeconomic scenario and time horizon, and they are also shown in Figures 2.1 to 2.3. It is possible to draw a number of conclusions: The different indicators give different impressions of the magnitude of the impacts of climate change (the Hanasaki et al. indicators show much larger numbers of people apparently adversely affected than the other indicators). There is virtually no difference between the different climate forcings in 2020, and little difference in 2050 (for a given SSP): impacts under RCP2.6 are perhaps slightly lower than under the other forcings. There is a more clear difference between the different forcings in 2080 There are some differences in the magnitude of impact in 2050 between different socio-economic projections, and greater differences in

21 The greatest variation in impact, however, is between different climate models (the length of the lines in Figures 2.1 to 2.3). This is particularly the case with the Arnell & Lloyd-Hughes estimates, which use a much larger range of climate models. The ISI-MIP intercomparison project involved the simulation of river flows using consistent climate and forcing scenarios with a number of global hydrological models. These results are consistent with those presented qualitatively in AR5 (Jimenez Cisneros et al., 2014), but are based on a more comprehensive assessment of the effects of climate model uncertainty. Schewe et al. (2014) calculated a simple index of climate change impact (population living in 0.5x0.5 o grid cells where average annual runoff decreases by specified amounts) for all models and scenarios, but summarised results in terms of change in global mean temperature rather than climate and forcing scenario. However, the runoff data from the different hydrological models are available and can be used to construct consistent water resources impact indicators. Figure 2.4 shows the effect of hydrological model uncertainty on exposure to increased water resources stress, for one climate model (HadGEM2-ES), one forcing (RCP8.5) and one socio-economic scenario (SSP2), using the same indicator as in Arnell & Lloyd-Hughes (2014); impacts are calculated using eight of the global hydrological models in the ISI-MIP intercomparison. The right-hand bar suggests that hydrological model uncertainty means that the range in projected impacts is large (between 216 and 1945 million people), which is only slightly smaller than the range across climate models, for a given RCP, SSP and impact model (Table 2.2c). Part of the range in hydrological model uncertainty arises because different hydrological models can produce different estimates of change in average annual runoff for a given climate scenario, but a large part arises because the different models give different indications of exposure to water resources stress in the absence of climate change. This is shown in the left hand bar of Figure 2.4, which shows that the different models produce estimates of this baseline stress ranging between 1.8 and 4.5 billion (mostly due to differences in the estimated water balance in populous parts of south and east Asia). Table 2.2a. Number of people (millions) living in grid cells with a low ratio of abstraction to demand, where climate change increases scarcity (Hanasaki et al., 2013). The table shows the range across three climate models. There are no data for the empty cells, because not all scenario combinations were assessed Climate scenario Socioeconomi c scenario Baseline 2020s 2050s 2080s RCP2.6 none SSP SSP2 SSP3 SSP SSP5 RCP4.5 None SSP1 SSP SSP3 SSP4 21

22 SSP5 RCP6.0 None 2147 SSP SSP2 SSP SSP SSP RCP8.5 None SSP1 SSP SSP SSP4 SSP Table 2.2b. Number of people (millions) living in grid cells where withdrawals are greater than 40 of availability, where climate change increases scarcity (Hanasaki et al., 2013). The table shows the range across three climate models. Climate scenario Socioeconomi c scenario Baseline 2020s 2050s 2080s RCP2.6 none SSP SSP2 SSP3 SSP SSP5 RCP4.5 None SSP1 SSP SSP3 SSP4 SSP5 RCP6.0 None 1716 SSP SSP2 SSP SSP SSP RCP8.5 None SSP1 SSP SSP SSP4 SSP

23 Table 2.2c. Number of people (millions) living in water-stressed watersheds with a significant reduction in runoff (increase in water stress) (Arnell & Lloyd-Hughes, 2014). The table shows the mean and range across 19 climate models in 2020 from unpublished data. Climate scenario RCP2.6 RCP4.5 RCP6.0 RCP8.5 Socioeconomi c scenario Baseline 2020s 2050s 2080s none SSP ( ( ( ) 2997) 2575) SSP ( ( ( ) 3434) 3559) SSP ( ( ( ) 4088) 5264) SSP ( ( ( ) SSP ( ) None SSP ( ) SSP ( ) SSP ( ) SSP ( ) SSP ( ) None SSP1 941 ( ) SSP2 990 ( ) SSP ( ) SSP4 954 ( ) SSP5 954 ( ) None SSP ( ) SSP ( ) SSP ( ) SSP ( ) 3481) 1375 ( ) 1514 ( ) 1794 ( ) 2157 ( ) 1867 ( ) 1566 ( ) 1602 ( ) 1878 ( ) 2244 ( ) 1920 ( ) 1649 ( ) 1754 ( ) 2027 ( ) 2421 ( ) 2105 ( ) 4553) 1397 ( ) 1493 ( ) 2097 ( ) 3042 ( ) 2737 ( ) 1649 ( ) 1573 ( ) 2196 ( ) 3194 ( ) 2853 ( ) 1742 ( ) 1887 ( ) 2620 ( ) 3867 ( ) 3419 ( ) 23

24 SSP ( ) 1810 ( ) 2060 ( ) 24

25 Table 2.2d: Number of people (millions) living in grid cells where average annual groundwater recharge decreases by more than 10 (Portmann et al., 2013)). The table shows the mean and range across 5 climate models. Climate scenario RCP2.6 RCP4.5 RCP6.0 RCP8.5 Socioeconomi c scenario Baseline 2020s 2050s 2080s none SSP1 SSP ( ) SSP3 SSP4 SSP5 None SSP1 SSP ( ) SSP3 SSP4 SSP5 None SSP1 SSP ( ) SSP3 SSP4 SSP5 None SSP1 SSP ( ) SSP3 SSP4 SSP5 25

26 Figure 2.1. Global-scale impacts on water resources scarcity in 2020, by RCP and SSP. The lines show the range between climate models. Figure 2.2. Global-scale impacts on water resources scarcity in 2050, by RCP and SSP. The lines show the range between climate models. 26

27 Figure 2.3. Global-scale impacts on water resources scarcity in 2080, by RCP and SSP. The lines show the range between climate models. Figure 2.4. The effect of hydrological model uncertainty on exposure to water scarcity in the absence of climate change (left bar) and the estimated effect of climate change (right bar). HadGEM2 climate model pattern, RCP8.5 and SSP2, The colours represent eight different global hydrological models. The left hand bar shows absolute numbers of people exposed without climate change, while the right hand bar shows how much that value is 27

28 increased. The left hand bar shows absolute numbers of people exposed without climate change, while the right hand bar shows how much that value is increased by climate change.by climate change. 28

29 River flood risk By October 2014, three journal papers have produced assessments of the potential changes in flood risk under different climate forcings (Representative Concentration Pathways), using different indicators (Table F1); other studies have used SRES scenarios and are not reported here. One of the papers (Dankers et al., 2014) focuses on hydrological change (change in return period) and does not consider socio-economic impacts. Hirabayashi et al. (2013) was cited in AR5 (Jimenez Cisneros et al., 2014). Table 3.1. Global-scale assessments of the impacts of climate change on water scarcity using Representative Concentration Pathways and Shared Socio-economic Pathways. Study Indicator Number of climate models Hirabayashi et al. (2013) Arnell & Lloyd- Hughes (2014) Dankers et al. (2014) Number of people living in grid cells in which the annual maximum flood is greater than the baseline 100-year flood, averaged over 30 years Number of people living in floodplains where the frequency of flooding at least doubles Proportion of land area with a change in the return period of the baseline 30- year flood 5-11 CMIP5 models, depending on RCP Number of RCPs 4 19 CMIP5 models 4 Five CMIP5 models 4 Tables 3.2a and 3.2b and Figure 3.1 summarise the global-scale impacts as estimated by Hirabayashi et al. (2013) and Arnell & Lloyd-Hughes (2014). Hirabayashi et al. (2013) did not use SSP socio-economic projections, but instead used a UN medium population projection alongside an assumption of no population change. The Hirabayashi et al. (2013) indicator of course produces a smaller value for the numbers of people affected by climate change than the Arnell & Lloyd-Hughes (2014) indicators. There is little clear difference between forcings in 2020 or 2050, but a clear distinction between impacts under RCP8.5 and under the other forcings by 2080 using both sets of indicators. Differences between SSPs appear by the 2050s, especially with the climate models which produce the greatest increase in flood frequency. However, as with the water scarcity indicators, the uncertainty in projected impacts is dominated by the range across different climate models. Figure 3.2 shows the effect of hydrological model uncertainty on the estimated numbers of people exposed to a doubling of flood risk, using eight of the hydrological models in the ISI- MIP intercomparison, with one climate model, one forcing (RCP8.5) and one socio-economic scenario (SSP2) in Unlike for water scarcity, there is relatively little difference in impact between the eight hydrological models, and the range is much smaller than the range between climate models (Figure 3.1). This is largely because the baseline no climate 29

30 change exposed population is defined by geographic location alone, and does not depend unlike for water scarcity on simulated hydrology. AR5 (Jimenez Cisneros et al., 2014) presents relatively little information on the potential effects of climate change on flood risk at catchment, regional or global scales. The results shown here extend the AR5 discussion, through the use of additional metrics and by explicitly considering the effects of hydrological model uncertainty. 30

31 Table 3.2a. Number of people (millions) exposed to a flood greater than the baseline 100- year flood (Hirabayashi et al., 2014). The table shows the mean and standard deviation across 5-11 climate models. Climate scenario Socioeconomi c scenario Baseline 2020s 2050s 2080s RCP2.6 none 5.7 ( ) 23 (16-30) medium 4.3 ( ) 30 (20-40) RCP4.5 None 5.6 ( ) 38 (27-49) medium 4.2 ( ) 49 (34-64) RCP6.0 None 5.8 ( ) 43 (27-59) medium 4.4 ( ) 62 (38-86) RCP8.5 None 5.6 ( ) 77 (55-99) medium 4.2 ( ) 106 (75-137) Table 3.2b. Number of people (millions) living in floodplains exposed to a doubling of river flood frequency (Arnell & Lloyd-Hughes, 2014). The table shows the mean and range across 19 climate models in 2020 from unpublished data. Climate scenario RCP2.6 RCP4.5 RCP6.0 RCP8.5 Socioeconomi c scenario Baseline 2020s 2050s 2080s none SSP1 190 (54-426) 253 (83-473) 228 (61-451) SSP2 194 (55-436) 280 (93-525) 282 (76-556) SSP3 199 (56-447) 317 ( ) 371 ( ) SSP4 192 (55-431) 268 (90-503) 268 (77-526) SSP5 189 (53-426) 250 (83-468) 225 (61-444) None SSP1 180 (51-433) 279 (77-478) 276 (81-497) SSP2 184 (52-443) 309 (84-530) 341 ( ) SSP3 189 (54-455) 351 (93-602) 449 ( ) SSP4 182 (53-438) 297 (81-507) 324 ( ) SSP5 180 (51-432) 276 (77-473) 272 (84-450) None SSP1 197 (78-438) 238 (62-511) 275 (80-497) SSP2 201 (79-448) 264 (69-567) 342 ( ) SSP3 207 (81-459) 301 (79-645) 452 ( ) SSP4 199 (79-442) 256 (68-546) 331 ( ) SSP5 196 (78-437) 253 (62-505) 271 (81-488) None SSP1 189 (48-411) 302 (93-519) 340 ( ) SSP2 194 (49-421) 336 ( ) 420 ( ) SSP3 199 (50-431) 381 ( ) 553 ( ) SSP4 192 (49-415) 322 (99-559) 399 ( ) SSP5 189 (48-411) 299 (93-515) 335 ( ) 31

32 Figure 3.1. Implications of climate change for global river flood risk. Arnell & Lloyd-Hughes: indicator is the number of flood-prone people exposed to a doubling of flood risk. Hirabayashi et al.: indicator is the average number of people flooded in floods greater than the baseline 100-year flood. The bars with the Arnell & Lloyd-Hughes indicators represents the full range across 19 climate models, but the bars with the Hirabayashi et al indicators represents the standard deviation across between 5 and 11 models. Figure 3.2. Numbers of people exposed to a doubling of river flood frequency, with eight global hydrological models. HadGEM2 RCP8.5 climate and SSP population. The colours represent the different hydrological models. 32

33 33

34 Coastal Zones This section contains quantitative re-analysis of existing climate change scenarios using the new Representative Concentration Pathway (RCP) and Share Socio-economic Pathway (SSP) scenarios. Data is extracted from Hinkel et al. (2014), which reports on results generated from the ISI-MIP project. The Dynamic Interactive Vulnerability Assessment (DIVA) model has been used (Hinkel et al. 2005; Vafeidis et al. 2008) to project impacts and costs due to sea-level rise and socio-economic change. Some additional model runs for the RCP/SSP scenarios other than those in Hinkel et al. (2014) have been undertaken to reflect scenarios of no socio-economic and/or no climate change. AR5 did not produce global impact results as presented here. However the AR5 sea-level rise scenarios are comparable to the ones used in this analysis. 4.1 Climate change scenarios Hinkel et al. (2014) presented four GCMs for three RCPs (2.6, 4.5 and 8.5) and five SSPs. In this report, only the results from HadGEM2-ES are reported. Sea-level rise is formed from a number of components, one of these being ice melt from glaciers, ice caps and the large ice sheets of Greenland and Antarctica. To represent uncertainty associated with sea-level rise projections, ice melt is considered for high, mid and low projections which reflect uncertainties. These uncertainties include (1) missing processes in surface-mass-balance glacial modelling, feedbacks in the thinning of ice and ice dynamics for ice caps and glaciers; and (2) the combined uncertainties in climate, ocean, basal melting and ice melting processes in ice sheets (Hinkel et al. 2014). Additionally, a scenario of no socio-economic and no climate change are considered. In DIVA, the climate scenarios are downscaled and combined with projections of uplift and subsidence (due to glacier isostatic adjustment and anthropogenic delta subsidence) to give relative sea-level rise. HadGEM2-ES was selected as it was a middle of the range model: For RCP2.6 and RCP4.5 (mid-range uncertainty), its total sea-level rise in 2100 was the same as the multi-model mean. For RCP8.5, projections for HadGEM2-ES are 2cm lower than the multi-model mean of 74cm. There is a greater variation in ice melt uncertainty than the mean of the mid-range value across all models. 4.2 Parameters reported Using data from Hinkel et al. (2014), the following impact parameters are reported at a global scale: a) Expected number of people flooded annually (people / year); b) Costs of sea floods (billions dollars / year) (1995 US dollars); Both these parameters assume that standards of coastal defences, such as dikes and beach nourishment have not increased over the time period of the study. The base line for this is It is often helpful to compare the damages against the cost of the potential building of any defences, to ascertain if it is economically worthwhile. The cost of building sea-dikes has 34

35 been modelled. No global database of dikes and other forms of coastal protection exist, so it was assumed that places with higher population densities had higher standards of protection. Projected costs are also subject to regional economic costings. Thus, the following parameter is also reported: c) Capital costs of building sea dikes to protect against flooding (thousand million dollars / year) (1995 US dollars). Data is presented in the following time steps: Present day, 2020s, 2050s and 2080s, where the latter three time steps are based on thirty year means. Figure 4.1. Global mean sea-level rise scenarios for HadGEM2-ES. Error bars represent the high, medium and low uncertainties associated with ice melt. Each level of uncertainty is continued throughout the time periods measured. 35

36 Figure 4.2. Expected number of people flooded per year (data extracted from Hinkel et al. 2014, plus new model runs reflecting no climate or socio-economic change) for RCP 2.6, 4.5 and 8.5 for SSP1-5, plus a scenario of no climate or socio-economic change. No upgrade to adaptation since the baseline period (1995) is assumed. 36

37 Figure 4.3. Cost of sea floods per year (data extracted from Hinkel et al. 2014, plus new model runs reflecting no climate or socio-economic change) for RCP 2.6, 4.5 and 8.5 for SSP1-5, plus a scenario of no climate or socio-economic change. No upgrade to adaptation since the baseline period (1995) is assumed. 37

38 Figure 4.4. Capital costs of building sea dikes per year assuming upgrade to adaptation (data extracted from Hinkel et al. 2014, plus new model runs reflecting no climate or socio-economic change) for RCP 2.6, 4.5 and 8.5 for SSP1-5, plus a scenario of no climate or socio-economic change. 38

39 Table 4.1a. Expected global number of people at risk from flooding per year. Values indicate sea-level rise with a low level of ice sheet melting from HadGEM2-ES, assuming no additional adaptation. Data extracted from Hinkel et al. (2014), except for the scenarios of no socio-economic change, which have been compiled especially for this report. Climate scenario (RCP) Socioeconomic scenario (SSP) Present day (millions people / year) 2020s (millions people / year) 2050s (millions people / year) 2080s (millions people / year) 2.6 No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP

40 Table 4.1b. Expected global number of people at risk from flooding per year. Values indicate sea-level rise with a mid level of ice sheet melting from HadGEM2-ES, assuming no additional adaptation. Data extracted from Hinkel et al. (2014), except for the scenarios of no socio-economic change, which have been compiled especially for this report. Climate scenario (RCP) Socioeconomic scenario (SSP) Present day (millions people / year) 2020s (millions people / year) 2050s (millions people / year) 2080s (millions people / year) No change No change No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP

41 Table 4.1c. Expected global number of people at risk from flooding per year. Values indicate sea-level rise with a high level of ice sheet melting from HadGEM2-ES, assuming no additional adaptation. Data extracted from Hinkel et al. (2014), except for the scenarios of no socio-economic change, which have been compiled especially for this report. Climate scenario (RCP) Socio-economic scenario (SSP) Present day (millions people / year) 2020s (millions people / year) 2050s (millions people / year) 2080s (millions people / year) 2.6 No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP

42 Table 4.2a. Global projected sea flood costs per year. Values indicate sea-level rise with a low level of ice sheet melting, from HadGEM2-ES assuming no additional adaptation. Data extracted from Hinkel et al. (2014), except for the scenarios of no socio-economic change, which have been compiled especially for this report. Climate scenario (RCP) Socioeconomic scenario (SSP) Present day (billions US$ / year) 2020s (billions US$ / year) 2050s (billions US$ / year) 2080s (billions US$ / year) 2.6 No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP Table 4.2b. Global projected sea flood costs per year. Values indicate sea-level rise with a mid level of ice sheet melting, from HadGEM2-ES assuming no additional 42

43 adaptation. Data extracted from Hinkel et al. (2014), except for the scenarios of no socio-economic change, which have been compiled especially for this report. Climate scenario (RCP) Socioeconomic scenario (SSP) Present day (billions US$ / year) 2020s (billions US$ / year) 2050s (billions US$ / year) 2080s (billions US$ / year) No change No change No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP

44 Table 4.2c. Global projected sea flood costs per year. Values indicate sea-level rise with a high level of ice sheet melting, from HadGEM2-ES assuming no additional adaptation. Data extracted from Hinkel et al. (2014), except for the scenarios of no socio-economic change, which have been compiled especially for this report. Climate scenario (RCP) Socioeconomic scenario (SSP) Present day (billions US$ / year) 2020s (billions US$ / year) 2050s (billions US$ / year) 2080s (billions US$ / year) 2.6 No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP

45 Table 4.3a. Global projected capital sea dike costs per year. Values indicate sealevel rise with a low level of ice sheet melting from HadGEM2-ES. Data extracted from Hinkel et al. (2014), except for the scenarios of no socio-economic change, which have been compiled especially for this report. Climate scenario (RCP) Socioeconomic scenario (SSP) Present day (thousands millions US$ / year) 2020s (thousands millions US$ / year) 2050s (thousands millions US$ / year) 2080s (thousands millions US$ / year) 2.6 No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP

46 Table 4.3b. Global projected capital sea dike costs per year. Values indicate sealevel rise with a mid level of ice sheet melting from HadGEM2-ES. Data extracted from Hinkel et al. (2014), except for the scenarios of no socio-economic change, which have been compiled especially for this report. Climate scenario (RCP) Socioeconomic scenario (SSP) Present day (thousands millions US$ / year) 2020s (thousands millions US$ / year) 2050s (thousands millions US$ / year) 2080s (thousands millions US$ / year) No change No change No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP

47 Table 4.3c. Global projected capital sea dike costs per year. Values indicate sealevel rise with a high level of ice sheet melting from HadGEM2-ES. Data extracted from Hinkel et al. (2014), except for the scenarios of no socio-economic change, which have been compiled especially for this report. Climate scenario (RCP) Socioeconomic scenario (SSP) Present day (thousands millions US$ / year) 2020s (thousands millions US$ / year) 2050s (thousands millions US$ / year) 2080s (thousands millions US$ / year) 2.6 No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP No change SSP SSP SSP SSP SSP

48 4.3 Summary statements based on data extracted from Hinkel et al. (2014) Global mean sea-level rise Global mean sea-level rise is projected to increase in the 2080s between 0.29m and 0.89m (with respect to ). Over the same time frame, global mean temperatures are projected to rise 1.4 C, 2.5 C and 4.4 C for RCP2.6, 4.5 and 8.5 respectively. Rates of sea-level rise are expected to accelerate, particularly in the second half of this century, due to a time lag between global mean surface temperature rise and oceanic warming / ice melt response. As with previous projections, ice melt uncertainty is greatest for the higher level of ice melt, rather than the lower. Expected number of people flooded annually The expected number of people flooded is similar between all scenarios in the present timeframe and 2020s as sea-level rise and socio-economic conditions remain similar. Impacts diverge from the 2050s, where people flooded ranges from 11.5 million (RCP2.6, SSP5) to 56.3 million (RCP8.5, SSP3) people per year. In the 2050s, actual numbers of people flooded are more dependent on socio-economic scenario than climate scenario. When considered across all socio-economic scenarios, there is a greater range of people flooded for the climate scenario associated with the highest level of ice melt. By the 2080s, the number of people at risk accelerates compared with the previous timestep, to between 19.4 million (RCP2.6, SSP5) and million (RCP8.5, SSP3) people per year. Note that fewer people could be flooded with the lowest range in 2080s (with 29cm of sea-level rise) compared to the highest range in the 2050s (with 49cm of sea-level rise). Compared with a scenario of no socio-economic change, the number of people flooded remains similar (apart from SSP3). This is partly because many people are already situated in low-lying areas, and large decreases in population are projected (e.g. China) following population trends, yet population increases elsewhere. Thus, both sea-level rise and socio-economic conditions are important. Only under conditions of no socioeconomic change and no sea-level rise are few additional people flooded each year. Cost of sea floods annually Present costs of global flooding are projected at US$0.01 billion annually. Even with socio-economic change, costs remain low as there is already large investment and infrastructure on global coasts. When taking account socio-economic and climate change, costs increase in the 2050s, up to US$4.7 billion per year (RCP8.5, SSP5), although this highest maximum is only experienced in this one combination of socioeconomic scenarios. Costs significantly increase in the 2080s, particularly for SSP5 (up to US$22.7 billion per year) regardless of the level of sea-level rise. Similar to people flooded, the lowest projected cost in the 2080s could be lower than the greatest flood cost in the 2050s. Apart from the RCP8.5 and/or SSP5, the magnitude of sea-level rise appears to have a greater influence on flood costs rather than socioeconomic conditions. Sea floods could be reduced by protective measures, such as dike building. 48

49 Capital costs of building sea dikes annually Dike costs in the present day for all scenarios are projected at US$15 thousand million per year, but this nearly halves under conditions of no socio-economic change. In the 2050s, annual costs range from US$7.0 thousand million to US$29.2 thousand million per year. The highest annual cost in the 2080s is US$33.3 thousand million per year. Decreasing costs are seen as these are annual costs, reflected by the rate of change of sea-level rise (due to stabilising rise or climate mitigation policies slowing the rate of rise). Costs continue to increase under conditions of no socio-economic change as sea-levels continue to rise, and locally land levels change. These costs are much lower than the costs of global flooding. From a policy perspective, it is often wise to consider protection before the risk occurs, particularly in highly urbanised or industrialised areas where infrastructure has long design lives. Hence protection costs may be higher than reported here. Additionally for efficient adaptation, dike maintenance costs (approximately 1 per year of capital costs) need to be accounted for Changes and improvements to the DIVA model since AVOID1. Many of the figures presented here show an increase in impacts or costs compared with earlier analyses. This is associated with the scenarios themselves, as well as the DIVA model used in these projections. Methods for generating sea-level rise scenarios has changed significantly between the AVOID1 (with sea-level rise scenarios based on Meehl et al. 2007) and those generated in the ISI-MIP project (Hinkel et al. 2014). Most importantly, changes to the way ice melt from glaciers, ice caps and ice sheets are modelled have changed. On many occasions, their contribution to sea-level rise has increased. In AVOID1 (Arnell et al. 2013), global mean uniform sea-level rise scenarios were used. In Hinkel et al. (2014), patterned scenarios were used, reflecting areas of the world where higher than average and lower than average sea-level rise could be expected. This is more realistic of actual oceanic change. Brown et al. (2013) undertook comparisons of uniform and patterned scenarios of global sea-level. They found that for most climate models on a global scale, impacts were of a similar magnitude regardless of whether a regional or uniform scenario was used. Therefore in this analyses, it is assumed that responses will be similar. Apart from changes to climate and socio-economic scenarios, significant changes have been undertaken in the coastal impacts model, DIVA. This reflects improved data availability and understanding of coastal and economic processes during the time elapsed between the two studies. These include a new topographic dataset (e.g. see Figure 1 of Hinkel et al. 2014) and new algorithms for projecting the costs of building dikes, which has led to significant increases in projected costs. 49

50 Biodiversity 5.1 Introduction The results presented here is consistent with IPCC AR5 but extends it with more recent literature and a meta-analysis. Notably, IPCC AR5 did not quantify the potential impacts of increasing levels of warming on extinction risk or increasing climate vulnerability. Major review papers appearing at the end of the AR5 process gave a much better cleared indication of the potential quantifiable risks using a wide range of very different analytical techniques (including results from the Inter-sectoral Impact Model Intercomparison Project (ISIMIP) and including adaptation potential). These results, and a comparison with the AR4, which did quantify the risk based on a much smaller sample size, are included below. The Intergovernmental Panel on Climate Change 4 th Assessment Report (AR4) estimated that of the species studied (thousands of species across multiple taxa) would be at an increasing high risk of extinction with temperature increases of 2 C 3 C above pre-industrial levels. While these figures have generated some controversy, the controversy has been less around the numbers than around the use of the term extinction. The statement in AR4 was framed to match statements in previous reports and did NOT specifically refer to species going extinct, but to increasing vulnerability to climate change. This is owing to a lack of consistent definition as to what level of range change (or equivalent) constitutes an extinction risk, and how much potential extinction debt needs to be included. The figure calculated in AR4 came from a meta-analyses of the major review papers included in that report. Since the AR4 there have been three major (both in terms of geographic coverage and/or in terms of species covered) modelling exercises, using three entirely different methodological suites. These examined vulnerability to climate change in large numbers of plants (both species and biome/ecosystems), birds (all), reptiles, amphibians (all), and mammals. While the expressed indicators of change are different, there are also substantial similarities. For example, the numbers in IPCC AR4, while not specifically referring to a range change (Fide, A. Fischlin, G. Midgley. J. Price, R. Warren (authors of that specific text in AR4)) refer to range changes of >50 (generally 50-70). This is similar to the approach taken by Warren et al. (2013) and NAS (2014). Foden et al. (2013) differed by examining the specific traits that might make a species more or less vulnerable to climate change, one of which was a substantial reduction in a species range. Finally, Warszawski et al. (2014) in the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) project examined global ecosystems, defining levels of moderate and severe ecosystem changes in determining the extent to which one ecosystem might transform to another. So, there are actually a great number of similarities in these approaches, all of which can then be defined under the category of high climate vulnerability. To facilitate such comparison, the original IPCC AR4 statement is changed here to read of the species studies would be at an increasing level of high climate vulnerability with temperature increases of 2 C 3 C above pre-industrial levels. This now makes it possible to compare these suites of studies. 50

51 An examination of the new large-scale studies, in comparison with the values in the AR4, shows that the AR4 numbers are generally conservative, especially as they span a large temperature range (for species). It can be difficult to identify the exact temperature ranges used in a study as there are large differences in the sensitivities of the climate models used. Table 5.1 categorizes the study as best as possible taking into account that some studies looked at defined temperature points that were on the threshold between two values. For example, the ecosystem study had ranges for 2 and 3 C and these were placed in the <2 C and 2-3 C categories. While bioclimatic models (where many of the numbers in AR4 came from) have sometimes been criticized, this analysis shows that they may be, in fact, conservative. Consider, for example, that traits-based analysis found that of the animals were vulnerable to climate change, while Warren et al. (2013), using bioclimatic models, reported For plants, Warren et al. (2013) found that of the plants were potentially vulnerable, while the results from physiological process based models found that of the ecosystems were vulnerable to moderate change, and 8-35 of them were vulnerable to severe change. Table 5.1. Taxa Climate # of < 2 C 2 C - 3 C 3 C 4 C GCMs Source scenario species in study above p.i. above p.i. Species 1 Varies Not Varies IPCC AR4 known Plants 2 A1B CMIP3 GCMs Warren et al. (2013) Animals 2 A1B CMIP3 GCMs Warren et al. (2013) Birds 3 A1B CMIP3 GCMs Foden et al. (2013) Amphibians 3 A1B CMIP3 GCMs Foden et al. (2013) Ecosystems 4 Various RCPs N/A 3-15 / / / CMIP5 GCMs Warszawski et al. (2014) 1 Numbers from IPCC AR4, which drew from a wide range of studies. 2 Ranges given include both no dispersal and realistic dispersal of the species. 3 No temperature change value is provided in Foden et al. 2013, the numbers here were calculated based on the climate models and scenarios used and corrected to pre-industrial. The temperature values are approximately 2 C and 3 C based on the climate models used. 4 The values given are for approximate percentage of moderate and severe ecosystem change or transformation. What these findings show is that the estimate of the potential impacts of climate change on biodiversity appear to largely be robust to modelling method, climate model and emission scenario. They also provide critical information on thresholds of change. Temperature changes of up to 2 C would still see potential impacts to or the plants, of the ecosystems (moderate change), and 5-50 of the terrestrial vertebrates studied. Between 2 and 3 C the potential impacts on 51

52 ecosystems and animals come close to doubling (up to a threshold), and then come close to doubling again between 3 and 4 C. The main exception to this seems to be that all of these models show an asymptotic effect with number of species/ecosystems impacted increasing up to approximately before the rate of change begins to level off, mostly owing to nearing complete transformation. Biodiversity is impacted by climate change at levels below 2 C (above preindustrial levels), with ~20 of the species/ecosystems vulnerable. The magnitude of impacts approximately DOUBLES between 2 C and 3 C (up to a loss/transformation of approximately 60-70). The magnitude of impacts approximately DOUBLES AGAIN between 3 C and 4 C (up to a loss/transformation of approximately 60-70). A recent meta-analysis (Urban 2015) specifically looked at the subject of extinction risk. The author defined extinction risk at occurring at >80 reduction in range (also looking at >90 and 100). This study found that extinction risk nearly doubled between current and 2 C above pre-industrial (2.8 to 5.2), rose to 8.5 with 3 C warming above pre-industrial and then nearly doubled again, to 16, by 4.3 C above pre-industrial (or a nearly six-fold increase over current estimated extinction rates). This study looked at 131 published projections, spanning a range of modelling techniques, dispersal scenarios, taxonomic groups, and range sizes. At a minimum, this meta-analysis looked at a MINIMUM of ~68,000 species. The number is almost certainly greater than this but as it spanned more than 100 studies, and the studies do not list the species actually examined, then it is impossible to know how many species have been examined, and there is the probability that some species were missed. This is a conservative estimate taken from looking at the greatest number of species in any one taxa across the studies. Overall, the author found the results were robust to potential study omission or species omissions and thus may be broadly applicable. In line with previous studies, endemic or restricted range species were found to be at a higher risk of extinction than more widespread species. Within the studies examined, South America and Australia/New Zealand were found to have the highest extinction risks and North America and Europe the lowest. It is important to note that species distribution models, sometimes been criticized as over-estimating extinction risk, actually have the lowest levels of extinction risk of any of the modelling techniques used and presented in the published literature. The differences between the findings of Urban (2015) and those of IPCC AR4 (and re-analysed with additional data) largely come from a difference in definition between extinction risk and climate vulnerability (>50-70 in earlier studies and >80 in Urban (2015)). An examination of the curves in Urban 2015 indicates that reducing the threshold to match the previous estimates would provide similar results across both range restricted and non-range restricted taxa. Further, the extinction risk modelled is the species specific rate and does not take into account the potential coextinctions (often in insects) that follow, meaning the actual number can be much higher. This includes hostplant-pollinator and parasite-host relationships for example (e.g. Koh et al. 2004). Most of the current models that examine the potential impacts of climate change on terrestrial biodiversity are based on the amount of climate change itself and do not take into account potential direct CO 2 effects. Furthermore, the rate of change is 52

53 usually only considered in the context of potential dispersal rates of species. For this reason, the actual dynamics of the emission scenario may not be as critical as it would be in some other studies (acidification, for example, or agriculture). The models themselves are based on the changes in temperature (and concomitant changes in other climate parameters) and therefore could potentially be matched to any time-scenario combination. The choice of model, however, is more critical as this can be the greatest source of regional uncertainty. To date, the majority of the large-scale species based biodiversity studies have still be done using the CMIP3 climate models or climate mode patterns. Some of the regional findings, therefore, may be different if they were re-run using CMIP5 climate model data. The lag between climate model data availability and peer-reviewed papers on impact models in certain sectors is fairly common. The ISI-MIP project has looked at ecosystem changes using Generalized Vegetation Models (GVMs or DGVMs) using the RCP climate scenarios, the SSP socio-economic scenarios and a small subset of the CMIP5 climate models. Pertinent specific projections from the individual studies are assembled in the tables below. 53

54 5.2 Biodiversity Table 5.2a Taken from reference: Warren et al. (2013). Indicator: Percentage of species (range in parentheses) losing more than 50 of their climatic range. The values for 2020s and 2050s represent the average ±5 as they have been read off the figures. Location: Global Taxa Plants ND Climate scenario Baseline # of species in study 2020s 2050s 2080s GCMs Source Type Notes A1B (51-63) 2030R2H (31-41) 2030R5L (31-41) 2016R2H ( R4L (20-28) 2016R5L (19-27) Plants A1B (51 - RD 63) 2030R2H (31-41) 2030R5L (31-41) 2016R2H ( R4L (20-28) 2016R5L (19-27) Plants A1B (47 - OD 59) 2030R2H (29-39) 2030R5L (26-34) 2016R2H ( R4L (18-26) 2016R5L (18-26) Anima A1B (35- ls 49) ND 2030R2H (20-30) 2030R5L (18-28) 2016R2H ( CMIP3 GCMs Warren et al. (2013) SDM MAX 7 CMIP3 GCMs Warren et al. (2013) SDM MAX 7 CMIP3 GCMs Warren et al. (2013) SDM MAX 7 CMIP3 GCMs Warren et al. (2013) SDM MAX

55 2016R4L (9-15) 2016R5L (9-15) Anima A1B (27- ls 41) RD 2030R2H (17-25) 2030R5L (14-22) 2016R2H ( R4L (10-16) 2016R5L (10- Anima ls OD 16) A1B (25-39) 2030R2H (15-23) 2030R5L (13-21) 2016R2H ( R4L (9-15) 2016R5L (9-15) ND No Dispersal RD Realistic OD Optimistic Dispersal 7 CMIP3 GCMs Warren et al. (2013) SDM MAX 7 CMIP3 GCMs Warren et al. (2013) SDM MAX 1 1 Table 5.2b Taken from reference: Warren et al. (2013) Indicator: Percentage of species (range in parentheses) losing more than 70 of their climatic range. The values for 2020s and 2050s represent the average ±5 as they have been read off the figures. Location: Global Taxa Plants ND Climate scenario 2030R2 H 2030R5 L 2016R2 H 2016R4 L 2016R5 L Baseline # of species in study 2020 s 2050 s A1B (27-37) (12-20) (10-18) ( (6-12) (5-11) 2080s GCMs Source Type Notes 7 CMIP3 GCMs Warren et al. (2013) SDM MAX 1 55

56 Plants RD A1B (27-37) 2030R2 H (12-20) 2030R (10 L - 18) 2016R (6- H R (6- L 12) 2016R (5- L 11) Plants A1B (25 OD - 35) 2030R2 H (11-19) 2030R (9 - L 17) 2016R (7- H R (5- L 11) 2016R (5- L 11) Animals A1B (16- ND 28) 2030R2 H (7-13) 2030R L (5-11) 2016R (3- H 9) 2016R (2- L 8) 2016R (2- L 8) Animals A1B (13- RD 23) 2030R2 H (5-11) 2030R (4- L 10) 2016R (2- H R (1- L 7) 2016R (1- L 7) Animals A1B (13- OD 21) 2030R2 H 2030R5 L (5-11) (4-10) 7 CMIP3 GCMs Warren et al. (2013) SDM MAX 7 CMIP3 GCMs Warren et al. (2013) SDM MAX 7 CMIP3 GCMs Warren et al. (2013) SDM MAX 7 CMIP3 GCMs Warren et al. (2013) SDM MAX 7 CMIP3 GCMs Warren et al. (2013) SDM MAX

57 2016R2 H ( R (2- L 6) 2016R (2- L 6) ND No Dispersal RD Realistic OD Optimistic Dispersal 57

58 Table 5.2c Taken from reference: Warren et al. (2013) Indicator: Percentage of species (range in parentheses) gaining more than 50 of their climatic range. Location: Global Taxa Plants ND Climate scenario Baseline # of species in study 2020 s 2050 s A1B CMIP3 GCMs 2080s GCMs Source Type Note s Warren et al. (2013) SDM MAX 2030R2H R5L R2H R4L R5L 0 Plants RD A1B CMIP3 GCMs Warren et al. (2013) SDM MAX 2030R2H R5L R2H R4L R5L 0 Plants OD A1B CMIP3 GCMs Warren et al. (2013) SDM MAX 2030R2H R5L R2H R4L R5L 0.1 Animals ND A1B CMIP3 GCMs Warren et al. (2013) SDM MAX 2030R2H R5L R2H R4L R5L 0 Animals RD A1B (3-5) 7 CMIP3 GCMs Warren et al. (2013) SDM MAX 2030R2H R5L R2H R4L R5L 3 Animals OD A1B (7-9) 2030R2H 7 ( ) 2030R5L 7 ( ) 7 CMIP3 GCMs Warren et al. (2013) SDM MAX

59 Table 5.3 Taken from reference: Foden et al. (2013). Indicator: Relative climate change vulnerability percent of species identified has highly climate change vulnerable based on their traits as measured against their individual sensitivity, exposure, and potential autonomous adaptive capacity. Range represents optimistic versus pessimistic extremes for missing data. Location: Global Taxa Climate scenario Number of Species 2020s (not done) Birds A1B, A ( ) 2050s 2090s GCMs Source Type Notes 2016R2H 6 ( ) 2016R4L 6 ( ) 2016R5L 6 ( ) ND No Dispersal RD Realistic OD Optimistic Dispersal HadCM3, ECHAM5, CSIRO35, GFDL21 Foden et al. (2013) TRAITS 2 B Values for B2 and 2090s are approximate as they have been read off of figures. Table 5.4 Taken from reference: National Audubon Society (2014) Indicator: Percentage of species (number in parentheses) losing more than 50 of their climatic range Location: United States and Canada Taxa Climate Baseline 2020s 2050s 2080s GCMs Source Type Notes scenario Birds B2, A1B, A2 B Amphibians ( ) (126) 53 (314) 9 CMIP3 GCMs NAS (2014) SDM BRT 3 59

60 5.3 Ecosystems Table 5.5a Taken from reference: Warszawski, et al. (2014) Indicator: Fraction of natural vegetation at moderate risk of ecosystem change. Location: Global Climate Baseline 2020s 2050s 2080s GCMs Source Type Notes scenario RCP HadGEM2- Warszawski Jules ES et al GVM RCP RCP RCP Table 5.5b Taken from Warszawski, et al. (2014) Indicator: Fraction of natural vegetation at severe risk of ecosystem change. Location: Global Climate Baseline 2020s 2050s 2080s GCMs Source Type Notes scenario RCP HadGEM2- Warszawski Jules ES et al GVM RCP RCP RCP Table 5.5c Taken from Warszawski, et al. (2014) Indicator: Fraction of natural vegetation at moderate risk of ecosystem change. Location: Global Climate Baseline 2020s 2050s 2080s GCMs Source Type Notes scenario RCP HadGEM2- Warszawski JeDi ES et al GVM RCP RCP RCP Table 5.5d Taken from Warszawski, et al. (2014) Indicator: Fraction of natural vegetation at severe risk of ecosystem change. Location: Global Climate Baseline 2020s 2050s 2080s GCMs Source Type Notes scenario RCP HadGEM2- Warszawski Jules ES et al GVM RCP RCP RCP Table 5.5e Taken from Warszawski, et al. (2014) Indicator: Fraction of natural vegetation at severe risk of ecosystem change. Location: Global Climate scenario RCP Baseline 2 C Median (range) 5 (2-10) 3 C 4 C GCMs Source Type Notes 12 (10-20) 25 (18-28) 60 HadGEM2- ES Warszawski et al GVMs 4

61 Table 5.5f Taken from Warszawski, et al. (2014). Indicator: Fraction of natural vegetation at severe/moderate risk of ecosystem change. Location: Global Climat e scenar io Baseli ne 2 C 3 C 4 C GCMs Source GVM Notes All RCPs /30 3/18 10/25 5/25 5/15 10/45 5/15 8/25 15/30 5/20 10/30 10/40 5/20 9/28 15/30 5/25 10/30 5/35 5/30 8/30 8/28 25/40 8/45 20/50 20/60 10/40 30/60 10/40 18/48 25/45 10/55 20/50 30/65 15/45 20/52 25/45 10/50 20/55 35/60 20/50 22/52 20/51 35/55 25/62 35/70 60/70 30/60 55/70 25/55 38/63 40/55 25/60 30/65 60/70 30/55 37/61 40/55 40/65 35/75 55/70 30/60 40/65 38/63 HadGE M2-ES Warszaw ski et al VISIT 4 JULES LPJmL SDGVM JeDi HYBRID ORCHID EE [MEAN] 5 LPJmL SDVGM JeDi [MEAN] IPSL- VISIT CM5A- LR JULES MIROC- VISIT ESM- CHEM JULES LPJmL SDGVM JeDi [MEAN] 3 GCMs 5-7 GVMs Types of models SDM Species Distribution Model (also known as climate envelope model) BRT Boosted Regression Trees, MAX Maximum Entropy TRAITS Assessment was based on species traits rather than climate envelope models GVM Global Vegetation Models [MEA N] 61

62 Notes 1 Examined multiple dispersal scenarios and this can be influenced by rate of climate change. These analyses used the AVOID I scenarios. 2 A novel approach that was based on species traits and not on direct bioclimatic modelling. This is also one of the few papers that has looked at potential adaptive capacity based on a species traits. However, direct comparison can be difficult in that the author s only looked at 20-year mean future climates and not the 30-year means more commonly used. The values for the impacts for A2 and A1B differed little in this analysis. 3 Only the major overview findings have been published and they are NOT in the peerreviewed literature. The primary results are currently under review. The numbers given here are the overall average for a single time point, across the GCMs and climate scenarios. 4 Part of the ISI-MIP results. These studies used Global Vegetation Models (including 3 Dynamic Generalized Vegetation Models, DGVMs) as proxies for ecosystem shifts (usually expressed as biome shifts). These models use changes in the biogeochemical state of vegetated land surfaces, as simulated by the seven models, as proxies for risk of change. Rather than model individual species, these models look at what are called Plant Functional Types (PFTs) that are representative of many of the species found in that ecosystem or biome. Experiment assumed that the entire land surface was covered with natural vegetation there was no anthropogenic land cover. Filters based on land cover classification were then used to remove cells that had less than 50 land cover. Results are presented as averages across the GCMs. The results from this study are largely independent of emission scenario and driven by the level of temperature changes. Values are approximate as they are read off of graphs. 5 The means were not part of the original papers but were calculated from the numbers as they were read from the figures in the Supplementary Material. 62

63 Human health 6.1 Introduction Overall health concerns in relation to climate conditions and climate change Climate change acts at many levels and with differing relative influence in its impacts on health outcomes. In general it plays a multiplier role, typically amplifying or extending a population s pre-existing health risks or problems. The observational evidence of the human health impacts of variations in climate conditions is now very substantial, particularly in relation to heat and cold exposure. The health impacts of heat have been a concern since the 19 th century, particularly among European colonial staff, military personnel and other working people, and mechanistic physiological research has elucidated the ill effects of excess heat. Thermal physiology, environmental ergonomics, biometeorology and other scientific disciplines continue to accumulate evidence of heat and cold impacts on human health and work productivity. Excessive daily heat exposures causes heat stroke, which may lead to deaths, heat exhaustion that reduces work productivity, and heat stress that interferes with daily household activities. Other extreme weather events including storms, floods and droughts, create direct injury risks and follow-on outbreaks of infectious diseases, lack of nutrition, and mental stress. Any heat, cold or weather related reduction of capacity to carry out daily activities should be considered a health effect, given the WHO s standing definition of health ( Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity ). Further, if actions taken to prevent adverse effects of extreme weather conditions inadvertently impair health or well-being, that too should be considered a climate-related health impact. The indirect effects of changes in climatic conditions are many and diverse. Changes in access to clean drinking water, particularly in conditions of crowding and poverty, can cause diarrhoeal diseases and other water-related diseases, including cholera. Other major examples include malnutrition and under-nutrition and impaired childhood development due to declines in local agriculture, altered ranges and rates of various vector borne diseases and other infectious diseases, and mental health disorders and conflict-prone tensions caused by forced migration from affected homes and workplaces. Of course, a variety of other factors influences these health problems and may have much greater impact than climate change. Examples of systemic impacts include food crises (sometimes causing famine), conflicts/wars over access to water, and large scale adverse economic effects due to reduced human and environmental productivity. Figure 7.1 summarizes the relationships between driving forces for local climate change, pressures on the environment, changes in the environment, human exposures to hazards and the health impacts using the DPSEEA framework developed at WHO. 63

64 The following information is consistent with IPCC AR5 but extends it by providing additional detailed quantitative information and projections about heat stress induced labour productivity loss and its economic consequences. Figure 6.1. DPSEEA framework for Climate Change and Global Public Health Specific concerns about heat exposures for working people Increasing heat exposure during the hottest seasons of each year is the most obvious outcome of global climate change, and the issue that greenhouse gas modeling can assess in the most predictable manner. Occupational heat stress is an important direct health hazard related to climate conditions and climate change. The physiological limits of a livable thermal environment are well defined, but naturally, the sensitivity to heat exposure has a substantial individual variation. Modern methods of analysis make possible quantitative estimations of the impacts of current climate and future climate change: mortality, non-fatal heat stroke, heat syncope and heat exhaustion, the latter linked to work capacity loss, which is often overlooked in climate change health impact analysis. For these direct health impacts of climate change, it is the local climate where people live and work that matters. Using a field change method for three climate model data provided by WHO under the scenario A1B, estimates of the heat stress index WBGT for 60,000 grid cells around the world were produced. These estimates included monthly values for the hottest four hours and other hours of each day. Using 30-year average estimates for baseline ( ), 2030 and 2050 we calculated the occupational health impacts for fatal 64

65 and non-fatal heat stroke, as well as work capacity loss. The results depend on whether a person works outdoors in the sun or indoors (or in full shade), the level of exertion required for the work (metabolic rate), and the clothing worn while working. Occupational heat stress is already a significant problem in several regions, and more hot days will make the situation worse. The worst affected regions are East Asia; South Asia; South-East Asia; Oceania; Central America; Caribbean; Tropical Latin America; North Africa/Middle East; Central Africa; East Africa; and West Africa. Working populations in low and middle income countries are particularly vulnerable, but many people in high income countries in North America, Europe and Asia are also at risk. Taking estimates of potential changes in the future workforce distribution into account, in the most affected region, South Asia (major country is India), the annual number of fatal cases of occupational heat stress would increase from 14,000 in 1975 by 8,000 23,000 in 2030, and by 18,000 41,000 in 2050 (depending on the model used). The global number of additional occupational heat stress fatalities due to climate change may amount to 12,000 29,000 cases in 2030 and 26,000 54,000 in For nonfatal heat stroke cases we estimated 75,000 cases in 1975 and additional cases due to climate change may be 35,000 65,000 in 2030 and 40,000 73,000 in 2050 (taking changing workforce distribution into account). The loss of work capacity globally will affect possibly 2 billion working age people in agriculture and industry resulting in a loss of of global annual productive daylight work hours in 2030 (depending on climate model used) and in This assumes no change in the application of workplace cooling methods, but with the assumption of a change in workforce distribution away from heavy labor in extreme heat. If these work hour losses create equivalent reductions of global GDP, which has been estimated at 140 trillion US dollars in 2030, the global costs of increased occupational heat stress would be trillion US dollars in 2030 (mean = 2.1 trillion USD PPP). In the worst affected regions (South Asia and West Africa) the estimated annual work capacity losses at population level are at least twice as high. The worst affected people are those working outdoors in the sun in heavy labor jobs. They already lose approximately 10 of annual daylight work hours due to extreme heat in the hottest regions and this may increase to beyond 20 in People working in light jobs indoors are not so much affected, and air conditioning can of course prevent the high workplace heat exposures at high cost in certain occupations and countries. Many outdoor jobs, and jobs in workshops and factories, in low and middle income countries are paid at low rates and not likely to receive the protective benefit of air conditioning. The access to cooling systems for hundreds of millions of people is highly questionable as a recent estimate of the number of people lacking basic sanitation in 2050 was 1.4 billion people. Will they benefit from occupational heat stress prevention at work; - most likely not. Our analysis highlights the major negative impacts that climate change will have on millions of working people. More precise analysis is needed to quantify the costs and benefits of different adaptation and mitigation policies and programs in different countries. A summary of quantitative estimates for 2030 implies: Grid cell based analysis of climate change shows increased heat and longer heat periods particularly in tropical and sub-tropical areas There may be 22,000 more occupational heat stroke fatalities in 2030 than in 1975; to this should be added many thousand cases of non-fatal heat strokes and other clinical effects 65

66 Productive annual daylight work hours will be lost globally at 1.4 in 2030, and the global economic costs of the lost labor productivity may be 2 trillion USD per year. The losses at country level will be at multi-billion dollar level for many low and middle income countries. The work life years lost (similar notion as DALYs) due to occupational heat stroke fatalities in 2030 will be approximately 880,000, while the work life years lost due to labor productivity loss may be 70 million years, indicating that the labor productivity loss could be 70 times more damaging to healthy, productive and disability-free life years than the fatalities. For a country that will lose an estimated 100 billion USD per year in 2030 due to climate change related increasing heat exposures, it may be a good investment to spend one or a few million USD on research and analysis to develop policies and programs to reduce the economic impacts of occupational heat stress. Even if the cost estimate (100 billion USD) is 10 times too high, and the research and analysis only reduces the actual cost by 10, the savings could still be 1 billion USD per year. Thus the benefit/cost ratio for this analysis work could be 1000 to Climate related clinical health risks Table 6.1. Number of cases of fatal heat stroke in selected regions among working age people (assuming they all work in sun or in shade exposures) at baseline and after climate change in 2030 and 2050 (differences between climate model estimates and baseline), based on person-months of exposure to in sun and in shade WBGTmax; Pop = working age population in millions in 2030; Rates = assuming moderate physical intensity work as in Wyndham (1969). BL = baseline (1975) climate values. Additional cases of fatal heat stroke per year due to occupational Region Year Cases in sun (outdoor) exposure Baseline (BL) climate heat exposure increase during climate change Climate 2030; Three model results minus BL, 2030 BCM- BL EGMAM- BL IPCM- BL Climate 2050; Three model results minus BL, 2050 EGMAM- IPCM-BL BCM-BL BL 3 Asia, East, China Asia, South, India Asia, South East Lat-America, Central Lat-America, tropical North America North Africa, M. East Africa, East Africa, West World total, in sun Cases in shade and indoor exposure 3 Asia, East, China

67 4 Asia, South, India Asia, South East Lat-America, Central Lat-America, tropical North America North Africa, M. East Africa, East Africa, West World total, in shade Here we present the most detailed results for occupational fatal heat stroke. Fatal heat stroke For each region we calculated the health and productivity impacts per million people and used those to calculate estimated number of cases using assumptions about changes in population size and workforce distribution. We started with fatalities due to heat stroke at workplaces. The underlying exposure-response relationships present relatively low risks per degree of WBGT. Table 6.1 presents the risks in 2030 and 2050 for selected regions according to the three climate models and two different exposure situations. BCM generally produces the lowest estimates, but the estimates are reasonably close. In the regions with the highest occupational heat stroke risks the deviations between the three climate models and the average of the three models are generally within of the average. More detailed regional tables are included in the longer report (Kjellstrom et al., 2014a). If the full working population is working in the shade, at this work intensity level 5,000 fatal cases (4,950 in Table 6.1) would occur (Table 6.1). The additional number of heat fatalities, if all working age people work in sun with the modeled climate change heat exposure levels (based on the three climate models), would be between 27,000 and 59,000 in 2030, and between 61,000 and 123,000 in 2050 (Table 5.1). The equivalent numbers for the whole working age population working in shade would be between 5,600 and 11,400 in 2030 and between 13,000 and 25,000 in 2050 (Table 6.1). There are two factors unrelated to climate itself that influence the likely future impacts by region, whether these are clinical effects or work capacity loss. These factors are the size of the working age populations in each region and the workforce distribution in terms of work intensity and location of work (in sun or in shade, and with or without workplace cooling system). The detailed report (Kjellstrom et al., 2014a) shows that in the most affected regions the working age population in 2050 may be several times larger than in 1975 (baseline). On the other hand, associated with increased GDP in the most affected regions, it was assumed that less people will work in labor intensive agricultural work outdoors or in factory work indoors (Kjellstrom et al., 2014a). 67

68 Table 6.2. Fatal occupational heat stroke case numbers in 2030 and 1975 depending on climate estimates for 2030 (3 models and average), based on person-months of heat exposure; Cases = numbers of workplace heat stroke deaths; moderate work intensity; agricultural workers exposed outdoors; industrial workers exposed indoors; service workers not exposed to excessive workplace heat. Climate A, B, 2030 Model BCM EGMA M IPCM Average Population B - A Workforce distribution Region cases cases cases cases cases cases 1 Asia, High Income Asia, Central Asia, East Asia, South Asia, South East Australasia Caribbean Europe, Central Europe, East Europe, West Lat-America, Andean Lat-America, Central Lat-America, South Lat-America, Tropical North America Africa North Oceania Africa, Central Africa, East Africa, South Africa, West World total World, difference by model, as compared with 1975 (A) Differenc e The impact on calculated fatal cases due to occupational heat stress may be reduced in some regions in 2050 to 1/3 of the case numbers in 1975 because less people are working in the highly exposed occupations. These changes for each region are taken into account in the final calculations of the clinical and work capacity impacts (Tables 6.8 and 6.9 uses populations in 2030 and 2050 as a base). When we adjust the calculation for the impact of increasing population and changing workforce distribution and only look at differences in model outputs caused by the calculated climate change, we find increasing fatalities in most regions (Tables 6.2 and 6.3). The worst affected are South Asia, West Africa and South-East Asia. Few or no fatalities at all are estimated for Australasia, Europe and the southern parts of Latin America and Africa. For the three climate models the additional fatal occupational heat stroke cases in 2030 would be in the range 12,000 29,000 (average 22,000; Table 6.2). In 2050 the equivalent additional cases are in the range 26,000 54,000 cases (average 43,000; Table 6.3). 68

69 Table 6.3. Fatal occupational heat stroke case numbers in 2050 depending on climate estimates for 1975 and 2050 (3 models and average), based on personmonths of heat exposure; Cases = numbers of workplace heat stroke deaths; moderate work intensity; agricultural workers exposed outdoors; industrial workers exposed indoors; service workers not exposed to excessive workplace heat. Climate A, B, 2050 Model BCM EGMA M IPCM Average Population B - A Workforce distribution Region cases cases cases cases cases cases 1 Asia, High Income Asia, Central Asia, East Asia, South Asia, South East Australasia Caribbean Europe, Central Europe, East Europe, West Lat-America, Andean Lat-America, Central Lat-America, South Lat-America, Tropical North America Africa North Oceania Africa, Central Africa, East Africa, South Africa, West World total World, differences by model, as compared with 1975 (A) In the published estimate of the annual number of deaths due to climate change during the decade (McMichael, et al., 2004) the global number of heat stress deaths, principally among elderly people, was given as 3,000 per year in Our calculations of global occupational heat stress deaths due to climate change in 2030 and 2050 indicate 22,000 and 43,000 additional cases per year. This increase represents an addition of approximately 10,000 annual deaths each decade due to workplace exposures alone, higher than the previous estimate by McMichael et al. (2004) focusing on the elderly population. It is also interesting to note that in North America, high income region, the expected fatal cases due to occupational heat exposure with the baseline (1975) climate was 14, not far from the annual reported numbers in MMWR (2008) (423/15 per year = 28 fatalities per year). Climate change in 2030 and 2050 may increase these numbers by a factor of 3 to 5. However, these estimated numbers of occupational heat deaths are low compared to South Asia where approximately 16,000 (Table 6.2) and 32,000 (Table 6.3) additional occupational heat stroke fatalities are estimated for 2030 and Exposure to climate change related increased heat exposures is associated with an increase in cardiac events (de Blois et al. 2015) Differenc e 69

70 Additional cases per annum Tables 6.2 and 6.3 translate into the following relationship between increase in fatal occupational heat stroke due to climate change, and global mean temperature rise (Figure 6.1). Global mean temperature rise data corresponding to the use of models BCM, EGMAM and IPCM in the 2030s and 2050s were obtained from the UEA Climatic Research Unit. No. additional cases fatal occupational heat stroke Global Annual Mean Temperature Rise above C Figure 6.1 Relationship between global mean temperature and projected increases in fatal occupational heat stroke. For methodological detail, please see Tables 6.2 and 6.3 which contain identical data. Table 6.4. Non-fatal occupational heat stroke case numbers in 2030 and 1975 depending on climate estimates for 2030 (3 models and average); workforce distribution = agriculture workers assumed to be outdoors and carry out heavy work, and industry workers working indoors at moderate work intensity. Service workers assumed not to be affected by climate change related heat exposure. Climate A, B, 2030 EGMAM Differenc Model Baseline BCM IPCM Average e Population B - A Workforce distribution Region cases cases cases cases cases cases 1 Asia, High Income Asia, Central Asia, East Asia, South Asia, South East Australasia Caribbean Europe, Central Europe, East Europe, West Lat-America, Andean

71 12 Lat-America, Central Lat-America, South Lat-America, Tropical North America Africa North Oceania Africa, Central Africa, East Africa, South Africa, West World total The three climate models produce data that deviate from the average by up to 1/3. The exposure-response relationships also have uncertainties that cannot be exactly quantified. Thus, we find that the global number of additional occupational heat stress fatalities due to climate change may amount to 12,000 29,000 cases in 2030 and 26,000 54,000 cases in 2050 (Tables 6.2 and 6.3). Non-fatal heat stroke and heat exhaustion Just like for the fatal occupational heat stress impacts South Asia has the greatest risks and number of cases followed by West Africa, South-East Asia and East Asia (Tables 6.4 and 6.5). More details can be found in the full report (Kjellstrom et al., 2014a). At a global level climate change in 2030 (difference in Table 6.4) may cause an additional 55,000 cases of non-fatal occupational heat stroke, and in 2050 (difference in Table 6.5) the average estimate is at 61,000. Table 6.5. Occupational heat exhaustion case numbers (millions) in 2050 and 1975 depending on climate estimates for 2050 (3 models and average); workforce distribution = agriculture workers assumed to be outdoors and carry out heavy work, and industry workers working indoors at moderate work intensity. Service workers assumed not to be affected by climate change related heat exposure. Climate A, B, 2050 EGMAM Differenc Model BCM IPCM Average e Population B - A Workforce distribution Region cases cases cases cases cases Cases 1 Asia, High Income Asia, Central Asia, East Asia, South Asia, South East Australasia Caribbean Europe, Central Europe, East Europe, West Lat-America, Andean Lat-America, Central

72 13 Lat-America, South Lat-America, Tropical North America Africa North Oceania Africa, Central Africa, East Africa, South Africa, West World total A = 1975 B = 2030 C = South-East Asia, heavy outdoors South-East Asia, moderate outdoors South-East Asia, South-East Asia, heavy indoors moderate indoors Figure 6.2. Percentage work capacity loss due to workplace heat exposure in jobs with different exposure characteristics, South-East Asia in the SRES scenario A2, assuming linear trends in GDP between 1975 and Climate related work capacity loss All the details of the analysis can be seen in the longer report (Kjellstrom et al., 2014a), but Table 6.6 and 6.7 show the work capacity losses in 2030 and 2050 for the population sizes listed in Appendix table 1 and the workforce distributions listed in Appendix table 6.2. Heavy labor in the sun is most affected with 6.2 of annual hours lost for these work conditions in 2050, while work in the shade and moderate labor in sun and in shade have lower loss percentages. As an example, Figure 6.2 shows the situation for South-East Asia in 2030 and Light labor is even less affected, but in the hottest regions there will, be some work capacity loss also in this group. Figure 6.2 highlights the equality impact of climate change. People in heavy labor outdoors will be much more affected than people in moderate or light labor indoors. The first category includes mainly low income or poor working people, while those with less heat stress exposure are likely to have a higher income. The changes during climate change will thus affect low income people more than higher income people. Air conditioning or other cooling systems can eliminate the increasing indoor 72

73 or in shade heat impacts shown here, but air conditioning cannot always be applied and it contributes to greenhouse gas emissions (Lundgren and Kjellstrom, 2013). The percentage work capacity loss estimates in Tables 6.6 and 6.7 may look limited, but the resulting economic impact may be considerable if the annual loss of economic output is similar to the losses of daylight work hours. The two tables use different estimates of working age population and workforce distributions (2030 and 2050), which creates different loss estimates for The most affected regions are South Asia (losses at 8.1 in 2030 and 7.3 in 2050) and West Africa (losses at 7.0 in 2030 and 6.2 in 2050). The total losses of productive daylight work hours in 2050 are in the range in these regions (Table 6.7). The only estimate to date of the economic consequences of labor productivity loss in different regions around the world due to increasing heat exposure during global climate change until 2030 has been presented in the Climate Vulnerability Monitor 2012 (DARA, 2012). Using a similar analysis approach based on global GDP (estimated at 140 trillion USD PPP), for example, the 1.36 loss of daylight working hours shown in Table 14 could amount to 1.9 trillion USD PPP losses due to climate change in The published estimate was a loss of 2.1 trillion (DARA, 2012). The economic aspects of the occupational heat stress effects during climate change will be analyzed further in the Discussion. The percentage losses in 2050 (Table 7.7) are higher than in 2030 (Table 6.6) for most regions, in spite of the expected reduced vulnerability to heat due to workforce changes (shown in Table 6.6). In 2030 the highest loss regions were in order South Asia, West Africa, Oceania and South-East Asia (Table 6.6), while in 2050 it was West Africa, South Asia, Oceania and Central Africa (Table 6.7). Table 6.6. Work capacity loss as percent of annual available daylight working hours. Differences between losses in 2030 and 1975 depending on climate estimates for 2030 (3 models and average); workforce distribution = agriculture workers assumed to be outdoors and carry out heavy work, and industry workers working indoors at moderate work intensity. Service workers assumed not to be affected by climate change related heat exposure. Climate A, B, EGMAM Differenc e Model BCM IPCM Average Population B - A Workforce distribution Region 1 Asia, High Income Asia, Central Asia, East Asia, South Asia, South East Australasia Caribbean Europe, Central Europe, East Europe, West Lat-America, Andean Lat-America, Central

74 13 Lat-America, South Lat-America, Tropical North America Africa North Oceania Africa, Central Africa, East Africa, South Africa, West World total The additional work capacity loss due to climate change in 2050 for the hottest regions varies between 1 and 5 (Table 6.7), assuming no change in heat adaptation takes place (apart from workforce distribution change). It should be emphasized that these numbers are averages at regional level for a mixed workforce and the work capacity losses for groups of workers carrying out heavy labor are much greater. Figure 6.4 showed an example of this type of assessment for one region (South-East Asia). (additional figures showing the equity impact are included in the longer report (Kjellstrom et al., 2014a). Table 6.7. Work capacity loss as percent of annual available daylight working hours. Differences between percentages in 2050 and 1975 depending on climate estimates for 2050 (3 models and average); workforce distribution etc. Climate A, B, 2050 EGMAM Differenc e Model BCM IPCM Average Population B - A Workforce distribution Asia, High Income Asia, Central Asia, East Asia, South Asia, South East Australasia Caribbean Europe, Central Europe, East Europe, West Lat-America, Andean Lat-America, Central Lat-America, South Lat-America, Tropical North America Africa North Oceania Africa, Central Africa, East Africa, South Africa, West World total

75 Work Capacity Loss Additonal work capacity loss due to climate change as annual daylight working hrs Global Annual Mean Temperature Rise above C Figure 6.3 Changes in the of world total work capacity loss relative to 1975, due to climate change, may then be summarised as a function of global temperature rise, as follows (Figure 4.3). Global annual mean temperature rise for 30 year periods surrounding 2030 and 2050 for BCM, EGMAM and IPCM were provided by the UEA Climatic Research Unit. Methodological detail is provided in Tables 6.6 and Daylight work time loss,, 2050 Figure 6.4. Additional losses of annual productive daylight work hours in 2050 compared with 1975 in the 21 regions ranked from highest to lowest losses. 75

76 The data in Table 6.7 can also be presented graphically with a ranking from highest to lowest losses in order to highlight the worst areas (Figure 6.5). Tropical low and middle income countries are most affected. The highest additional losses occur in 2050 in West Africa, South Asia (India), Oceania, Central Africa, South-East Asia, East Africa and East Asia (China). In terms of regional equity it is also important to note the number of workers likely to be affected in the different regions (Table 6.8). South Asia and East Asia are particularly populous regions, which implies that they will contribute much to the global impact. Oceania, Caribbean, Andean Latin America and Australasia have very few workers likely to be affected. Table 6.8. Millions of working people affected by work capacity loss by region in 2050, and the additional work capacity loss due to climate change. Region Agriculture Industry Agriculture +Industry Loss, Sub-Saharan Africa, West Asia, South, India Oceania Sub-Saharan Africa, Central Asia, South East Sub-Saharan Africa, East Asia, East, China North Africa, Middle East Caribbean Latin America, Central Latin, America, tropical Asia, central North America, high income Latin America, Andean Sub-Saharan Africa, South Asia-Pacific, high income Latin America, South Australasia Europe, East, Russia Europe, West Europe, central World, total Economic consequences 76

77 Just to give one example of the dollar numbers that may emerge from these calculation, we will put in some data for India, which is the largest country in the region Asia, South. The per capita GDPppp in 2000 was US$ According to GDP growth trends for the A1 greenhouse gas and future climate scenario the annual growth in GDPppp per caput (/year) would be: 2000s = 0.97; 2010s = 2.46; 2020s = 3.07; 2030s = 3.74; 2040s = 3.3 (IPCC estimates for scenario A1). From 2000 to 2050 this would result in a 3.8 times increase of GDP per caput, which for India would result at US$ In 2050 the working age population of the South Asia region is estimated at 1400 million, and the total population at approximately 2000 million. The total GDPppp for India in 2050 would then be US$ 6984 x 2000 million or billion. If 4.86 of the economic output is lost in this region in 2050, this would amount to US$ 679 billion/year. However, as the losses due to effects of climate change on work capacity and the resulting economic output is occurring each year and will accumulate, it is likely that the losses may become much greater. For example, if 2 of the GDP growth each year was lost because of the climate change induced work capacity loss, the final increase in 2050 would be 3.7 times instead of 3.8 times. The annual loss in 2050 would then be 0.1 x billion, or US$ 1370 billion, which is twice as big as the loss calculated above for a 4.86 loss of work capacity in 2050 (Table 6.9). Clearly, there will be an impact of adaptation measures, but they are not likely to eliminate all of these losses. If 0.5 of the IPCC GDP growth estimates above were lost each year the accumulated GDPppp in India in 2050 would have increased to 3 times the 2000 values. So, in 2050 the GDP would be US$ 5520 billion, which would be US$ 8177 billion less than the result without this work capacity related loss. (final calculations can be carried out when an agreed method has been established by the WHO team). So, even small changes in the economic performance of the population in a region can have major impacts on the resulting GDP. Table 6.9 Economic effects Region Occup. heat stress Economic effects * High income regions (all] 68 Asia, C Asia, E Asia, S * *4.86 Asia, SE L America and Ca 389 SSA, C SSA, E SSA, S SSA, W World (omits Africa, North with 1779 occupational heat stress deaths, and Oceania with 39). * At this stage just the percentage loss of work capacity ** The economic impact of this level of work capacity loss in the largest South Asia country, India, could be US$ 679 billion/year in 2050, if this percent loss is applied to the estimated total GDP for India in It could also be as high as US$ 8177 billion/year if 0.5 of the economic growth each year is lost due to the heat impacts. 77

78 Another way of estimating annual and accumulated economic impact is to calculate the growth curves for different levels of annual economic growth (Figure 6.5). This shows how the accumulated effect of even small differences in annual economic output leads to large differences after a few decades. In 30 years, annual income growth at 4 per year increases income from $ 2,000 per person to $ 6,500 per person. If the annual growth were 1 lower the income after 30 years would reach $ 4,900, and at 2 lower growth $ 3,500. The loss of economic improvement after 30 years will therefore be 36 ( = 100*(6,500 4,900)/(6,500-2,000)) with loss A (1) and 67 with loss B (2) Income, no loss Income, with loss A Income, with loss B Figure 6.5. Economic growth curves during 30 years for 4 per year (no loss), 3 per year (loss A) and 2 per year (loss B). Appendix to Section 6: Assumptions about trends in population It should be noted that these assumptions differ from the assumptions in SSPs: this is one of the sectors where SSP specific scenario output is not available. This appendix therefore is unique to this chapter. Projections are not available beyond Population - weighted average exposures (million person-months of specific level heat exposure) for each of the 21 regions (Figure A6.1), were based on the exposure estimates and the working age population (age range years) in each grid cell for each estimation year (1975, 2030 and 2050; with 2000 for comparison purposes) (Table 6A5). Age-specific population estimates were acquired by the WHO team for the age groups 0-4, 5-14, 15-64, 65+ years. The population data at specific grid cell level was downscaled from larger geographic areas than our grid cells by IIASA (2010) (data supplied by WHO from IIASA website), which creates uncertainties in the local estimates. 78

79 Figure A.7.1 Table 6.A.1 shows that the expected increase of working age population, with potential exposure to occupational heat stress, will be particularly great in parts of Africa, Latin America and Asia. These increases of the local populations at risk contribute to the estimated impacts of climate change. The changing distribution of workforce activities from labour intensive outdoor jobs to moderate intensive indoor jobs and air conditioned indoor jobs (including modern office jobs) is another essential aspect of future climate resilience trends and specific adaptation programs. Unfortunately, we did not have access to detailed workforce distribution estimates for the scenario A1B, but in order to test the impact of workforce changes we used a model that relates the percentage of the population in agriculture, industry or services to the country GDP PPP (Gross Domestic Product per person, based on Purchasing Power Parity; PPP). The estimates (Table 6A6) were calculated from World Bank data for This model was used for scenario A2 in Kjellstrom et al. (2009b) and we used the same 1975 and future (2050) estimates here. Estimates for 2030 assume linear trends between 1975 and Table 6.A.2 shows that in some high income regions with a small proportion of the population in agriculture (2.6 in North America and 5.6 in Australasia) no change is expected in the workforce distribution until Western Europe and High Income Asia (mainly Japan) are assumed to experience reductions of the agricultural and industrial workforce proportions down to the levels of North America. In most of the regions with low and middle income countries the reductions of the expected percentage of workforce in agriculture is dramatic (as in South-East Asia, Tropical Latin America, and South Africa), while the workforce proportion in industry changes less. Central Europe is also expected to experience these types of changes, while Central America shows no change at all (Table 6A6). 79

80 Table 6.A.1. Population (millions; men and women combined) in the year working age range in 1975, 2000, 2030 and 2050 by region (source IIASA = International Institute of Advanced Systems Analysis: Population, millions, age Ratio, 2050/1975 Region name Asia, High Income Asia, Central Asia, East Asia, South Asia, South East Australasia Caribbean Europe, Central Europe, East Europe, West Lat-America, Andean Lat-America, Central Lat-America, South Lat-America, Tropical North America Africa North Oceania Africa, Central Africa, East Africa, South Africa, West World total Table 6.A.2. Population proportions () working in agriculture, industry and services (workforce distribution) in (1975) (baseline), 2030 and agriculture industry services Region name Asia, High Income Asia, Central Asia, East Asia, South Asia, South East Australasia Caribbean Europe, Central Europe, East Europe, West Lat-America, Andean Lat-America, Central Lat-America, South

81 14 Lat-America, Tropical North America Africa North Oceania Africa, Central Africa, East Africa, South Africa, West

82 Agriculture There have been two significant meta-analyses on agricultural yields published in 2014 building on the results in the Intergovernmental Panel on Climate Change 5 th Assessment Report (AR5). While the results are largely consistent with the overall findings of the AR5, these reports go into much greater detail than present in the AR5 report (e.g., the meta-analysis of Challinor mentioned below). One uses CMIP5 climate models and RCP emission scenarios and represents the outputs of the Ag- MIP (Agriculture Model Intercomparison Project) part of ISI-MIP (Rosenzweig et al. 2014) and the other is a meta-analysis of more than 1700 published simulations that were used in the AR5 (Challinor et al. 2014). There has also been one meta-analysis on the impacts of increasing carbon dioxide levels on protein with its concomitant impacts on human nutrition. There are still a great many uncertainties in modelling the potential impacts of climate change on agriculture yields. Chief among these is the benefit, if any, of a carbon fertilization effect from rising CO 2 levels. These differences can clearly be seen in the tables summarizing the Ag-MIP results (Rosenzweig et al. 2014). However, it is these same potential yield benefits that can lead to the losses of proteins in the table from Myers et al. (2014) owing to changes in the C/N ratio and the plants ability to make proteins. These protein reductions can either mean that more of a crop needs to be consumed to make up for the difference (which could be viewed as a reduction in yield) or as potential nutrition issue. The C/N protein effect can potentially be offset, but usually requires increasing use of N fertilizer, which would impose its own set of issues relating to GHG emissions, costs, and other potential pollution issues (e.g., eutrophication). The studies presented here show that there is still a great deal of work that needs to be done on quantifying the potential impacts of climate change on agricultural yields. This includes a better understanding of the role of carbon fertilization, especially in conjunction with micronutrient needs, ground-level ozone pollution, and water availability. The ability, or need, to geographically shift crops with changing climates. The proper selection and modelling of cultivars to identify the best crops to plant for any given climate as well as to guide the development of new varieties. Given the projected yield gap in the absence of climate change, the data from these different modelling exercises, even with their uncertainties, demonstrate that climate change is a large threat to global food security, especially at higher levels of change and especially in the tropics. A set of summary tables follow, showing simulations from the key references discussed. 7.1 Changes in nutrient level Table 7.1a. Reference: Myers et al. (2014) Indicator: Percent change (with 95 confidence interval) in nutrient level at elevated CO 2 relative to ambient CO 2 for wheat, rice, field peas, soybeans, maize and sorghum. Adapted from Myers et al NOT FOR WEB PUBLICATION AS IT IS DRAWN FROM A PUBLISHED TABLE. Location: Global (countries most impacted owing to loss of iron and/or zinc Afghanistan, Algeria, Iraq, Bangladesh, Iran, Pakistan, Tunisia, Jordan, Morocco, 82

83 Syria, Libya, Yemen, Myanmar, Tajikistan, India, Egypt, Indonesia, Sierra Leone, Cambodia, Sri Lanka, Laos, Viet Nam) Baseline CO 2 (ppm) Elevated CO 2 (ppm) Zinc (ppm) -9.3 (-12.7, -5.9) Iron (ppm) -5.1 (-6.5, -3.7) Phytate (mg/g) Nutrient Wheat Rice Maize Field Peas Soybean Sorghum -4.2 (-7.5, -0.8) Protein -6.3 (-7.5, -5.2) -3.3 (-5.0, -1.7) -5.2 (-7.6, -2.9) (-9.8, -3.8) -5.8 (-10.9, -0.3) -4.1 (-6.7, -1.4) -5.1 (-6.4, -3.9) -4.1 (-5.8, -2.5) (-8.9, -6.8) Mn (ppm) * -7.5 (-12.0, -2.8) Mg () * (-9.9, -1.3) Cu (ppm) * (-13.8, -7.1) (-4.0, -0.1) (-4.2, -0.8) -9.9 (-19.3, 0.7) (-4.3, -2.8) -2.7 (-5.1, -0.3) -5.7 (-8.0, -3.4) Ca () * (-7.3, -4.2) S (ppm) * -7.8 (-8.8, -6.8) K () * (-3.1, -2.2) B (ppm) * 5.1 (1.9, 8.4) P () * (-9.0, -5.1) No data (*); Change not statistically significant (-) (-3.6, -0.7) 2.2 (0.6, 3.8) -2.9 (-3.5, -2.2) (-9.1, -3.6) -3.7 (-6.8, -0.5) Changes in crop yields Table 7.1b Taken from reference: Challinor et al. (2014) Indicator: Approximate percent change in maize, wheat and rice yield (values read off of figures) with no adaptation Location: Temperate regions, all temperatures are LOCAL temperatures, not global Climate Crop 1 C 2 C 3 C 4 C 5 C Source Notes scenario Sensitivity Maize Challinor et al Wheat Rice Table 7.1c Taken from reference: Challinor et al. (2014) Indicator: Approximate percent change in maize, wheat and rice yield (values read off of figures) with adaptation Location: Temperate regions, all temperatures are LOCAL temperatures, not global Climate scenario Crop 1 C 2 C 3 C 4 C 5 C Source Notes Sensitivity Maize Challinor et 1 al Wheat

84 Rice N.D. N.D. N.D. N.D. N.D. 1 Table 7.1d Taken from reference: Challinor et al. (2014) Indicator: Approximate percent change in maize, wheat and rice yield (values read off of figures) with no adaptation Location: Tropical regions, all temperatures are LOCAL temperatures, not global Climate Crop 1 C 2 C 3 C 4 C 5 C Source Notes scenario Sensitivity Maize Challinor et al Wheat Rice Table 7.1e Taken from reference: Challinor et al. (2014) Indicator: Approximate percent change in maize, wheat and rice yield (values read off of figures) with adaptation Location: Tropical regions, all temperatures are LOCAL temperatures, not global Climate Crop 1 C 2 C 3 C 4 C 5 C Source Notes scenario Sensitivity Maize Challinor et al Wheat Rice Table 7.2 Taken from Reference: Muller and Robertson (2014) Indicator: Percentage yield change in crops with no adaptation and no CO2 fertilization in 2050 (table modified from Muller and Robertson (2014), NOT FOR PUBLICATION ON THE WEB) Location: Global Climate scenari o Climate model RCP 8.5 HadGEM 2 ES RCP 8.5 HadGEM 2 ES RCP 8.5 IPSL CM5A- LR RCP 8.5 IPSL CM5A- LR Crop mode l DSSA T LPJm L DSSA T LPJm L Wheat Maize Rice Soybean Groundnu t Source Muller and Robertson (2014) RCP 8.5 Mean Mean Mean Table 7.3a Taken from Reference: Rosenzweig et al. (2014) Indicator: Percentage yield change in crops with and without nitrogen stress by REGIONAL temperature change (values approximated from figures, ±5) Location: Mid-high latitudes 84

85 Climate scenari o RCP 8.5 RCP 8.5 RCP 8.5 RCP 8.5 RCP 8.5 RCP 8.5 RCP 8.5 RCP 8.5 Climate model 7 CMIP5 GCMS 7 CMIP5 GCMS 7 CMIP5 GCMS 7 CMIP5 GCMS 6 CMIP5 GCMS 6 CMIP5 GCMS 7 CMIP5 GCMS 7 CMIP5 GCMS # Crop mode ls N Stres s Crop 1 C 2 C 3 C 4 C 5 C 6 C 4 Y Maize N Maize Y Wheat N Wheat Y Rice N Rice Y Soybean N Soybean Table 7.3b Taken from Rosenzweig et al. (2014) Indicator: Percentage yield change in crops with and without nitrogen stress by REGIONAL temperature change (values approximated from figures, ±5) Location: Low latitudes Climate scenari o RCP 8.5 RCP 8.5 RCP 8.5 RCP 8.5 RCP 8.5 RCP 8.5 RCP 8.5 RCP 8.5 Climate model 7 CMIP5 GCMS 7 CMIP5 GCMS 7 CMIP5 GCMS 7 CMIP5 GCMS 6 CMIP5 GCMS 6 CMIP5 GCMS 7 CMIP5 GCMS 7 CMIP5 GCMS # Crop mode ls N Stres s Crop 1 C 2 C 3 C 4 C 5 C 6 C 4 Y Maize N Maize Y Wheat N Wheat Y Rice N Rice Y Soybean N Soybean

86 Table 7.3c Taken from Rosenzweig et al. (2014) Indicator: Percentage yield change in Maize without/with carbon fertilization (values approximated from figures, ±5) Location: Global Climate Climate Crop model scenario model RCP CMIP5 EPIC -10/-5-15/-10-30/-20 GCMS RCP CMIP5 GEPIC -10/-5-20/-5-20/-5 GCMS RCP CMIP5 GAEZ-IMAGE -2/0 0/5-5/5 GCMS RCP CMIP5 LPJmL -5/0-10/0-20/-5 GCMS RCP CMIP5 LPJ-GUESS 7/10 10/15 10/25 GCMS RCP CMIP5 pdssat -7/0-15/-5-25/-10 GCMS RCP CMIP5 Pegasus -15/- -25/-25-35/-30 GCMS 15 RCP CMIP5 GCMS Mean* -8/-4-14/-7-22/-11 *Mean of models excluding LPJ-Guess which was an outlier Table 7.3d Taken from Rosenzweig et al. (2014) Indicator: Percentage yield change in Rice without/with carbon fertilization (values approximated from figures, ±5) Location: Global Climate Climate Crop model scenario model RCP CMIP5 EPIC -10/0-20/-5-30/-8 GCMS RCP CMIP5 GEPIC -10/0-20/-5-30/-5 GCMS RCP CMIP5 GAEZ-IMAGE 0/5-5/5-5/5 GCMS RCP CMIP5 LPJmL -10/10-20/10-30/15 GCMS RCP CMIP5 LPJ-GUESS 0/35-10/55-15/>75 GCMS RCP CMIP5 pdssat -5/5-10/2-15/0 GCMS RCP CMIP5 GCMS Mean* -7/4-15/1-22/1 *Mean of models excluding LPJ-GUESS which was an outlier 86

87 Table 7.3e Taken from Rosenzweig et al. (2014) Indicator: Percentage yield change in Wheat without/with carbon fertilization (values approximated from figures, ±5) Location: Global Climate Climate Crop model scenario model RCP CMIP5 EPIC -5/5-10/2-15/2 GCMS RCP CMIP5 GEPIC -2/5-5/5-8/4 GCMS RCP CMIP5 GAEZ-IMAGE -7/0-15/-5-22/-5 GCMS RCP CMIP5 LPJmL -7/0-12/5-22/4 GCMS RCP CMIP5 LPJ-GUESS -7/15-12/22-20/27 GCMS RCP CMIP5 pdssat -8/5-15/0-25/2 GCMS RCP CMIP5 Pegasus -12/0-28/10-42/-12 GCMS RCP CMIP5 GCMS Mean* -7/2.5-14/3-22/-1 *Mean of models excluding LPJ-Guess which was an outlier Table 7.3f Taken from Rosenzweig et al. (2014) Indicator: Percentage yield change in Soybean without/with carbon fertilization (values approximated from figures, ±5) Location: Global Climate Climate Crop model scenario model RCP CMIP5 EPIC -15/-5-20/-5-30/-8 GCMS RCP CMIP5 GEPIC -15/0-22/0-30/-4 GCMS RCP CMIP5 GAEZ-IMAGE 5/10 10/20 10/20 GCMS RCP CMIP5 LPJmL -15/15-25/25-30/35 GCMS RCP CMIP5 LPJ-GUESS -7/25-15/38-22/50 GCMS RCP CMIP5 pdssat -20/5-30/-5-30/0 GCMS RCP CMIP5 Pegasus -25/- -40/-20-55/-25 RCP 8.5 GCMS 7 CMIP5 GCMS 15 Mean -13/5-20/8-27/10 Note: The authors developed a statistical equation based on the results of more than 91 studies covering 1722 data points taking into account temperature, precipitation, adaptation and CO2 effects. These results, based largely on results driven by climate models, were then expressed in terms of regional climate change. This equation could, in theory, be applied regionally to the AVOIDII climate scenarios once they are completed. 87

88 Heating and cooling energy demands Global residential heating and cooling energy demands currently consume around a third of end-use energy. Demands will increase as population increases, and cooling energy demand will increase further as the penetration of cooling appliances increases with increasing wealth. Household composition and physical size will also alter total demands. Demands also depend on temperature regimes. The impacts of climate change on regional domestic heating and cooling energy requirements under different climate forcings, climate scenarios and socio-economic scenarios are here estimated using a simplified version of Isaac & van Vuuren s (2009) residential energy demand model. The model projects energy requirements from heating and cooling degree days, together with population, household size and assumptions about heating and cooling technologies and efficiencies (in the simplified version applied here these assumptions are global rather than regional). The model does not incorporate adaptation to climate change in terms of either preferences or technologies. Table 8.1 shows estimated global residential heating and cooling energy demand in the absence of climate change. Even in the absence of climate change, increases in cooling energy demands are very substantial, due largely to increased ownership of air conditioning in Asia, southern Africa and south America. However, by 2080 global cooling energy demand remains below global heating energy demand. Table 8.1: Global residential heating and cooling energy demand in the absence of climate change Heating energy demands Cooling energy demands Socioeconomic s 2050s 2080s s 2050s 2080s scenario SSP SSP SSP SSP SSP Tables 8.2 and 8.3 show the estimated change in global residential heating and cooling energy demands respectively, relative to demands in the absence of climate change (Table 8.1); these changes are also shown graphically in Figures 9.1 and 9.2. The estimates are constructed from 18 CMIP5 climate models and, given the generalised assumptions about changes in future heating and cooling technologies and penetration, are to be regarded as indicative only. By the 2020s, global heating energy demands decrease by 5 to 20 and cooling energy demands increase by 10 to 40, with little difference between climate forcing and socio-economic scenario; the range between the climate models is greater than the difference between forcings and socio-economic scenarios. By the 2050s the impacts under RCP8.5 are typically greater than under the other forcings, and this is exaggerated further by the 2080s. There is relatively little difference between the socio-economic scenarios. By the 2080s, global cooling energy demands can (under 88

89 some climate models) be greater than global heating energy demands. There is very considerable variability in impact between regions. Table 8.2: Percentage change in global residential heating energy demand, relative to demand in the absence of climate change. The table shows the average and the range across 18 climate models. Climate scenario RCP2.6 RCP4.5 RCP6.0 RCP8.5 Socioeconomi c scenario Baseline 2020s 2050s 2080s none SSP1-12 ( ) -17 ( ) -17 ( ) SSP2-12 ( ) -17 ( ) -17 ( ) SSP3-12 ( ) -17 ( ) -18 ( ) SSP4-12 ( ) -17 ( ) -18 ( ) SSP5-12 ( ) -17 ( ) -17 ( ) None SSP1-13 ( ) -21 ( ) -26 ( ) SSP2-13 ( ) -21 ( ) -26 ( ) SSP3-13 ( ) -21 ( ) -27 ( ) SSP4-13 ( ) -21 ( ) -27 ( ) SSP5-13 ( ) -21 ( ) -25 ( ) None SSP1-11 ( ) -19 ( ) -28 ( ) SSP2-11 ( ) -19 ( ) -29 ( ) SSP3-11 ( ) -19 ( ) -29 ( ) SSP4-11 ( ) -19 ( ) -29 ( ) SSP5-11 ( ) -19 ( ) -28 ( ) None SSP1-13 ( ) -26 ( ) -40 ( ) SSP2-13 ( ) -27 ( ) -40 ( ) SSP3-13 ( ) -27 ( ) -41 ( ) SSP4-13 ( ) -27 ( ) -41 ( ) SSP5-13 ( ) -26 ( ) -39 ( ) 89

90 Table 8.3. Percentage change in global residential cooling energy demand, relative to demand in the absence of climate change. The table shows the average and the range across 18 climate models. Climate scenario RCP2.6 RCP4.5 RCP6.0 RCP8.5 Socioeconomi c scenario Baseline 2020s 2050s 2080s none SSP1 26 ( 12-41) 23 ( 14-38) 22 ( 12-40) SSP2 26 ( 12-41) 23 ( 14-38) 21 ( 11-39) SSP3 26 ( 12-41) 25 ( 15-42) 22 ( 11-41) SSP4 26 ( 12-41) 24 ( 14-40) 22 ( 11-42) SSP5 26 ( 12-41) 23 ( 14-38) 22 ( 12-40) None SSP1 26 ( 13-39) 31 ( 21-47) 37 ( 26-56) SSP2 26 ( 13-39) 31 ( 21-47) 36 ( 25-55) SSP3 26 ( 13-39) 34 ( 22-52) 38 ( 26-59) SSP4 26 ( 13-39) 32 ( 21-49) 38 ( 26-59) SSP5 26 ( 13-39) 31 ( 21-47) 38 ( 26-57) None SSP1 23 ( 12-37) 27 ( 18-40) 41 ( 32-61) SSP2 23 ( 12-37) 27 ( 18-39) 40 ( 31-59) SSP3 23 ( 12-37) 29 ( 19-43) 42 ( 31-63) SSP4 23 ( 12-37) 28 ( 18-41) 42 ( 31-64) SSP5 23 ( 12-38) 27 ( 18-40) 42 ( 32-62) None SSP1 29 ( 16-43) 42 ( 32-59) 67 ( 49-93) SSP2 28 ( 15-43) 42 ( 32-59) 66 ( 47-91) SSP3 28 ( 15-43) 46 ( 34-65) 70 ( 49-96) SSP4 28 ( 15-43) 43 ( 32-62) 70 ( 49-97) SSP5 29 ( 16-43) 42 ( 32-60) 69 ( 50-94) 90

91 Figure 8.1. Percentage change in global residential heating energy demands, relative to situation with no climate change Figure 8.2. Percentage change in global residential cooling energy demands, relative to situation with no climate change 91