Findings Our dataset comprised 451 locations in 23 countries across nine regions of the world, including 85 879 895 deaths. Results indicate, on average, a net increase in temperature-relatedexcessmortalityunder high-emission scenarios, although with important geographical differences. In temperate areas such as northern Europe, east Asia, and Australia, the less intense warming and large decrease in cold-relatedexcess would induce a null or marginally negative net effect, with the net change in 2090–99 compared with 2010–19 ranging from −1·2% (empirical 95% CI −3·6 to 1·4) in Australia to −0·1% (−2·1 to 1·6) in east Asia under the highest emission scenario, although the decreasing trends would reverse during the course of the century. Conversely, warmer regions, such as the central and southern parts of America or Europe, and especially southeast Asia, would experience a sharp surge in heat-related impacts and extremely large net increases, with the net change at the end of the century ranging from 3·0% (−3·0 to 9·3) in Central America to 12·7% (−4·7 to 28·1) in southeast Asia under the highest emission scenario. Most of the health effects directly due to temperature increase could be avoided underscenarios involving mitigation strategies to limit emissions and further warming of the planet.
The effects of urbanization on the climate of cities have been discussed in the literature and it has been shown that these effects can generate both large warming and cooling trends. However, the urban- ization processes can be limited by different factors such as physical constraints and urban planning and policy (i.e., when a city is completely urbanized, or an urbanization goal is reached). Consequently, the warming/cooling caused by these factors would tend to stabilize at some level instead of showing a constant growth. Clearly, extrapolating their effects by means of trends with constant slope parameters would be unrealistic and in many cases physically impossible when long horizons are considered (as is the case of climatechangeprojections). Thus, the cooling shown by E09029 or the large warming shown by E09014 could be overestimated as they assume constant rates of urbanization and air pollution, which are probably impossible in reality. Likewise, it could be argued that the warming in E09020 may be underestimated given that the areas at piedmont may show in the future high- er rates of urbanization than what has been previously observed. However, how to integrate these changes for producing local climatechangescenarios is currently a subject of discussion in the downscaling literature and most of the current downscaling applications simply ignore this problem (IPCC, 2007).
The second aim of this study was to make glacier volume projections for the future under a range of high-end climatechangescenarios. The ensemble mean volume loss ± 1 stan- dard deviation is − 64 ± 5 % for all glaciers excluding those on the periphery of the Antarctic ice sheet. The small uncer- tainties in the multi-model mean are caused by the sensitivity of HadGEM3-A to the boundary conditions supplied by the CMIP5 models. Our end-of-the-century global volume loss is 215 ± 20 mm, which is higher than values reported by other studies. This is because we used a subset of CMIP5 mod- els with the highest warming levels to drive the model and glacier dynamics not included, which results in more mass loss than other studies that include dynamics. Including para- metric uncertainty in the calibration procedure results in an upper bound global volume loss of 281.1 mm of sea level equivalent by the end of the century. The projected ice losses will have an impact on sea level rise and on water availability in glacier-fed river systems.
Figure 9. Regional temperature and snowfall changes relative to the present day (2011–2015) from the HadGEM3-A ensemble over glaciated grid points. The ensemble mean is shown in the solid line and the range of model projections are shown in the shaded regions.
mass balance is relatively small. As expected, there is more melting in the lower elevation ranges and more accumula- tion at the higher elevation ranges. The refreezing compo- nent, which includes refreezing of meltwater and elevated adjusted rainfall, shows no clear variation with height. This is because the refreezing component can both increase and decrease with height. Refreezing can increase towards lever elevations because there is more rain and melted water. It can also decrease if the snowpack is depleted or if there is not enough pore space to hold water because previous re- freezing episodes have converted the firn into solid ice. The largest accumulation rates occur in Alaska (5.3 m w.e. yr −1 ) and western Canada and the US (7.3 m w.e. yr −1 ) between 4250 and 9000 m and the largest melt rates are found in the Caucasus and Middle East (−7.4 m w.e. yr −1 ) and the low latitudes (−7.6 m w.e. yr −1 ).
In order to illustrate the potential application of the above results to estimate future fire impact, we analysed the actual link between fire danger conditions (as represented by FWI90) and fire impact (burned area), calculated using the observational data from the WFDEI dataset (Sec. 2.4) and the burned area data from the European Fire database of the European Forest Fire Information System (EFFIS, Camia et al, 2010). The obtained results are shown in Fig. 5, where each of the regions represents a spatially continuous climatic (2001-2012) gradient encompassing a representative range of fire danger conditions (Fig. 4). The figure shows how and increase in FWI90 leads to larger burned areas, both along the whole FWI90 gradient —considering the 10- year averages of the different regions (full circles)—, and for the local FWI90 gradients within each particular region —coloured crosses—. Similar results are also found using different indices (particularly FOT30, FWI and SSR). It is therefore expectable an increased severity of fire impacts in the Mediterranean regions in the forthcoming decades, especially in those areas where the magnitude of projected changes is larger. In this sense, it must be also acknowledged the evidence that fire in resource-limited ecosystems, such as those in the most arid and hotter Mediterranean areas, is not so dependent on fire danger conditions but instead driven by fuel amount and structure (Krawchuk and Moritz, 2010; Pausas and Paula, 2012). This is reflected in the weak links attained in the hotter and drier regions with highest FWI90 records in Fig 5c, posing the question of how climate-induced changes in ecosystem properties will affect future fire regimes as a result of climate-vegetation feedbacks, an important subject out of the scope of this study.
Figure 1: Block diagram of the CFFWIS (Adapted from van Wagner, 1987)
1.2 Regional climatechangescenarios from RCMs
In Table 1 a summary of the RCMs used in this study is provided.
Table 1: Summary of the ENSEMBLES RCM simulations used in the study. Throughout the text, the different RCM-GCM couplings are named using the acronyms indicated in the first column, which correspond to the modelling cen- tres/institutions.
Species distribution models (SDMs) are valuable and increasingly used tools for analy- ses, such as conservation- or climate-change-related vulnerability analyses. However, SDMs must be optimised for study area, predictors, and presence-absence data to avoid false positive predictions. In stream ecosystems, for which such models were only re- cently adopted, this optimisation is particularly challenging, as false positive predictions may be projected in terrestrial areas and not in the stream network, with unknown ef- fects on habitat suitability simulations. To test for effects derived from the use of differ- ent study areas and predictors, we used consensus projections of a fixed set of 224 stream macroinvertebrate species, using five algorithms implemented in BIOMOD/R (GLM, GAM, BRT, ANN, CTA). Four modelling designs were applied: (1) a continu- ous study area without any discrimination between terrestrial and aquatic realms, (2) results from this design masked a posteriori with a stream network, (3) the stream net- work only considered as the study area during the model-building stage, and (4) same as (3) but with a corrected set of predictors. The true skill statistic (TSS) and accuracy of the consensus projections were not influenced by the different designs, as they were consistently high (TSS: 0.80 to 1.00, accuracy: 0.70 to 0.96). The models built on a stream network yielded a strong reduction in false positive predictions compared with those built on a continuous area, whereas the differences derived from non-corrected vs. corrected predictors were small. The models created in the stream network with cor- rected predictors were able to diminish the false positive predictions by an average of 56%, yielding the highest rate among the four designs. SDMs of stream macroinverte- brates should thus be built on a stream network rather than on a continuous area, and the predictors should be chosen carefully. We discuss several methods for developing pre- dictor accuracy to improve forecasts of potential climate-change effects on species’ ranges.
Arithmetic mean values of the precipitation for the periods 2010–2039, 2040–2069 and 2070– 2099 were estimated by using projected percentage changes according to the ECHAM4 (European Center – Hamburg 4), NCAR PCM (National Center for Atmospheric Research – Parallel Climate Model) and HADCM3 (Hadley Centre Coupled Model 3) models, which have the highest spatial resolution and the A1FI emission scenario (the last part of its name “FI” is by “fossil intensive”). This emission scenario was selected because it shows the most critical projections (Ruostenoja et al. 2003, IPCC 2007a).
Furthermore, technological advances in reducing heat effects such as improved building designs and green space, lifestyle changes, and health care delivery advancements may alter the health effects of heat and hence the projections. We only analysed public hospitals’ data. Data from private hospitals and general practitioners were not available. Therefore, the overall impact and costs of heat are likely to be underestimated. Weather patterns and population growth may not be the only drivers of future visits to EDs as other factors such as affordability and accessibility of alternative healthcare services may affect the demand for EDs. And finally, today’s costs and prices may not necessarily reflect the future, as other factors such as technological advancements may affect these costing estimates.
Individuals have adapted to and will likely continue to adapt to local climatechange, including increasing ambient temperature [ 16 , 29 ] through six levels of adaptation interven- tions ( Table 1 ), but only within certain limits [ 33 ]. These adaptation interventions could result in a lower rise in core temperature and a lower increase in heart rate at a given heat load [ 34 ]. Our findings show that, when we consider hypothetical adaptation related to heatwave thresh- olds (based on relative temperature), heatwave-relatedexcessmortality would level off for all countries/regions, with a lowered expected heatwave-relatedexcessmortality. This means ignoring adaptation in projections would result in a substantial overestimate of future heat- wave-relatedexcessmortality. In addition, there will be small differences in heatwave-relatedexcessmortality between RCPs. However, this hypothetical adaptation may be quite difficult to achieve in the short-term, because it depends on many factors ( Table 1 ), including the abil- ity of individuals, society, and populations to make modifications within a timeframe adequate to keep pace with changing temperatures. Furthermore, the ability of populations to adapt will differ dramatically by country and by subpopulations.
This study had several limitations. First, it was not possible to consider information about individual factors, such as demographic factors and socioeconomic status, in our analysis because of di ﬃcul- ties in collecting data. Thus, social and economic vulnerabilities were not accounted for in our projections of temperature-related morbidity associations for climatechangescenarios. Assessing the association between temperature and diarrhea and stratifying by these factors would provide a better understanding of the future impact of temperature –diarrhea relationships. Additionally, future climatechange –related diarrheal disease burden may depend on future emission scenarios as well as alterations in socioeconomic and demographic trends and public health interventions ( Gething et al. 2010 ; Hodges et al. 2014 ). Temperature-related social or be- havioral trends, such as treatment-seeking behaviors, the prevalence of coinfections, and infant care practices, may also alter the e ﬀect of climatechange on the epidemiology of acute infectious gastroenteri- tis ( Pitzer et al. 2011 ). Therefore, our results should not be inter- preted as predictions of future excess morbidity but rather of possible outcomes for each of the speci ﬁed but theoretical scenarios. Second, our projections are subject to uncertainty due to variation in the climate models and lack of precision in the estimated exposure – response association ( Benmarhnia et al. 2014 ). Moreover, our ﬁnd- ings are restricted to the range of observable temperature data for present climate conditions, and a key assumption for the projections of the infectious gastroenteritis burden into the future is that the shape of associations stay the same over time. Although this assumption is likely adequate for short-term estimations of the e ﬀect of climate on health, evidence suggests that temperatures will sur- pass currently observed ranges, the consequences of which on health will be di ﬃcult to estimate ( Mora et al. 2013 ). Third, there may be biases in our results because we could not adjust for immunity to and probability of person-to-person transmission and variations of infectious gastroenteritis in the susceptible population. Variations due to immunity to and the transmissibility of infectious diseases in the at-risk population may bias estimates of associations with tem- perature ( Imai et al. 2015 ; Imai and Hashizume 2015 ). Additionally, poor immunity and the chance of transmissibility could increase in the next decade mainly due to aging of the population, and this e ﬀect may o ﬀset the eﬀect of the increase in temperature in Japan. These potential biases may a ﬀect the interpretation of our results. Additional studies using more precise modeling methods are required to resolve these issues. Future studies should consider these important points.
et al. 2011; WHO 2009b). Scenario-based projections have been used as a key approach for
policymaking and planning in the context of uncertain future conditions (Varum and Melo
2010). The IPCC has developed a set of scenarios in its Special Report on Emissions
Scenarios (SRES). These scenarios are not assigned probabilities, but rather can be deemed
temperature to be reached by the year 2100, projected to reach a value 80% lower compared to RR calculated at each degree Celsius (˚C) during the 2000s; (2)Low Adaptation: Adaptation, as measured by RRmin or the minimal relative risk for a given temperature to be reached by the year 2100, projected to reach a value 20% lower compared to RR calculated at each degree Celsius (˚C) during the 2000s and (3)No Adaptation: Future adaptation does not occur. Adaptation, as measured by RRmin or the minimal relative risk for a given temperature to be reached by the year 2100, remains the same as the RR calculated at each degree Celsius (˚C) during the 2000s. Population scenarios include: (1) Baseline: assumed that all parameters of the model remain constant, that is age specific fertility and mortality rates and age characteristics of migration are all kept constant, but the population ages forward; (2) Decreased Mortality: assumed a decrease in age specific mortality rates such that the values reach to 2/3 of the 2010 values by 2100; (3) Increased In-Migration: assumed that the growth of the domestic in- migration (from other parts of the US to NYC) will be half of the growth of the US population and that the growth of the international in-migration (from outside of the US to NYC) will be half of the growth of the projected international in-migration nationwide; (4) Increased Out-
emissions floors, and compare them with a non-mitigation business-as-usual emissions scenario, for three time periods, the 2030s, 2050s and 2080s. Under an SRES A1B business-as-usual emissions scenario and using climateprojections from 21 GCMs, heat-relatedmortality rates (per 100,000 of the population) attributable to climatechange in the 2080s are simulated to be in the range 2-6 for London, 4-50 for Lisbon and 10-24 for Budapest. Whilst the policy scenarios serve to reduce the number of heat-related deaths attributable to climatechange, by up to 70% of the A1B impacts under an aggressive mitigation scenario that gives a 50% chance of avoiding a 2°C global-mean temperature rise from pre-industrial times, they do not eradicate the effects of climatechange on heat-relatedmortality. The magnitude of avoided impacts is minor in the early 21 st century but increases
Objectives We previously developed a model for pro- jection of heat-relatedmortality attributable to climatechange. The objective of this paper is to improve the fit and precision of and examine the robustness of the model. Methods We obtained daily data for number of deaths and maximum temperature from respective governmental organizations of Japan, Korea, Taiwan, the USA, and European countries. For future projection, we used the Bergen climate model 2 (BCM2) general circulation model, the Special Report on Emissions Scenarios (SRES) A1B socioeconomic scenario, and the mortality projection for the 65?-year-old age group developed by the World Health Organization (WHO). The heat-relatedexcessmortality was defined as follows: The temperature–mor- tality relation forms a V-shaped curve, and the temperature
It is also important to note that even for model TS, where a non-parametric mortality- temperature relationship is specified, the high number of deaths occurred during the 2003 heatwave is still not well predicted, as shown by the large residuals in Figs 4.5(a) to (c). There are a number of possible reasons for the anomalously high mortalities recorded during this event. Firstly, the effect of a day with extremely high temperature on mortality may persist for a longer period of time. However, the plot of autocorrelation function of deviance residuals [Fig. 4.9(a)] shows that the autocorrelation is close to zero up to lags of 40 days. This lack of autocorrelation does not support the existence of persistent effect of extreme heat on mortality. Secondly, part of the elevated mortality during the event might be attributed to increased concentration of air pollution. Stedman (2004) estimates that 21 to 38% of excessmortality in the UK during the first two weeks of August 2003 are related to the increased concentration of ambient ozone and particulate matters. As explained in Section 4.2.2, such an effect is not considered in this study. Thirdly, sustained high temperatures during the heatwave might cause the number of deaths to be significantly higher than that predicted by mortality models as the heat stress on humans could not be relieved for a long period of time. Hajat et al. (2006) compares the observed summer mortality in London from 1976 to 2003 and the mortality predicted by a model which specifies the mortality-temperature relationship to be a linear function above temperatures
aspects of the quality of life, well-beings, or impacts of one individual’s health conditions on other people and also mortality (20). In addition, the impacts of climatechange on the burden of typhoid fever are more complex, and it depends on variety numbers of factors (28). The results of our study may influence the limitations of YLDs estimation. However, despite the limitations, it increases our understanding of the implications of the climatechange on health impacts in Iran. The implication for the future analysis suggests that climatechange will cause more additional disabilities (in term of burden). While the climatechange is unavoidable, the YLDs estimations can help to take policy actions for adaptations. Taking actions would be considered based on WHO recommendation (30). We did not consider the mortality from typhoid fever because based on surveillance data; death rates are very low in the country (16). The sub-national estimation was not including for projection due to data availability. Despite there are different climate in the country, the control and prevention measures of disease are the same in whole part of Iran. One study projected Salmonella infection in temperate regions may cause the extra burden due to the future climatechange (18). Then projection may be useful if the studies consider sub-national estimation. Underreporting is a common problem of the surveillance system, and results may be underestimated. Other sources for underestimation are: (1) The diagnosis criteria of typhoid fever, which the blood culture is mostly common test for diagnosis, but it has only 50% sensitivity (17), (2) we did not consider the adverse sequels of disease.
Away from the active labour force, the most at-risk individuals are the young and elderly. The trends in heat-relatedexcessmortality analysis show that deaths due to global warming are estimated to rise from 11 people per million per year to about 700 people per million people per year underclimatechangescenarios, such as RCP 8.5 . Of those affected, the young and elderly are most at risk . This raises a range of public health policy issues. For instance, what would be the impact of high level of WBGT (exceeding 30 C) on infants, children, young people and the elderly? Could high level of fatigue among children affect their ability to undertake activities outside classrooms? What are the potential WBGT—premature mortality relationships and what are the implications for the health sector? How could high WBGT impact on morbidity and mortality among the older adults and what are the implications for the aged care? The population, particularly in developing countries, already struggles to deal with heat waves, because they lack access to basic infrastructure, such as clean drinking water or cooling centres. The analysis of these risks as well as their potential economic and societal impacts, including public health costs and funding, are the areas of further research. Understanding such costs and associated benefits will become of increasing importance for the formation of mitigation policies to limit global warming and reduce the associated public health risks and broader societal impacts.
Various studies have indicated that changes in the frequency and intensity of extreme climate events, such as heat waves, droughts, and Àoods, can be expected in several parts of the world due to global climatechange (IPCC, 2007). Changes in these ex- treme events are particularly important for society and the environment because, by de¿nition, they oc- cur outside the usual range of adaptability; therefore, they can have severe impacts and signi¿cant negative economic effects (Kharin et al., 2007). Variations in temperature extremes are of particular importance due to their relationship to biodiversity and human thermal comfort, as well as their use in climate vari- ability and climatechange impact assessments in sectors such as agriculture and energy demand. In the period from 1906 to 2005, the increase in average ter- restrial temperature was estimated at 0.74 ± 0.18 ºC, and although the value is small, visible effects were observed on many physical and biological systems (IPCC, 2007). According to some projections, ex- treme heat and cold events may increase during this century, resulting in increased mortality (Curriero et al., 2002; Qian and Lin, 2004).
Various studies have indicated that changes in the frequency and intensity of extreme climate events, such as heat waves, droughts, and floods, can be expected in several parts of the world due to global climatechange (IPCC, 2007). Changes in these ex- treme events are particularly important for society and the environment because, by definition, they oc- cur outside the usual range of adaptability; therefore, they can have severe impacts and significant negative economic effects (Kharin et al., 2007). Variations in temperature extremes are of particular importance due to their relationship to biodiversity and human thermal comfort, as well as their use in climate vari- ability and climatechange impact assessments in sectors such as agriculture and energy demand. In the period from 1906 to 2005, the increase in average ter- restrial temperature was estimated at 0.74 ± 0.18 ºC, and although the value is small, visible effects were observed on many physical and biological systems (IPCC, 2007). According to some projections, ex- treme heat and cold events may increase during this century, resulting in increased mortality (Curriero et al., 2002; Qian and Lin, 2004).