Impact assessment considering extreme climate events Kiyoshi Takahashi (NIES)
（１） Comprehensive assessment of （２） Impact assessment climate change impact for discussing considering effects of long-term stabilization target extreme climate events CO2 排出経路 気温変化 大気中 CO2 濃度 4.0 25 1050 Temperature increase (1990=0) CO 2 eq concentration (ppmv) 20 CO2eq emission (Gt-C) 3.0 850 15 2.0 650 10 1.0 450 5 0.0 250 0 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Year Year Year 海面上昇 コムギ生産性変化 イネ生産性変化 0.3 0 0 Change of wheat productivity(%) Change of wheat productivity(%) -5 -5 Sea Level Rise (m) 0.2 -10 -10 -15 -15 0.1 -20 -20 -25 -25 インド インド 0.0 -30 -30 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 Year Year Year � SRES � SRES- -B2: Business as usual B2: Business as usual ▲ GHG ▲ GHG- -500ppmv: 500ppmv cap on total GHG concentrations 500ppmv: 500ppmv cap on total GHG concentrations ▲ GHG ▲ GHG- -600ppmv: 600ppmv cap on total GHG concentrations 600ppmv: 600ppmv cap on total GHG concentrations � To achieve around 2 ℃ temperature increase in 2100, 550ppmv cap on total GHG constraint is needed Climate change impact on crop productivity Optimal emission path for achieving 2 degC using daily climate scenario with high target and consequent temperature change, spatial resolution. SLR and crop impacts. Application to the Improvement of discussion of post-2012 impact model framework (Project B-12) Development of (Project S-3-2 and S-4) AIM/Ecosystem (Project B-52) （３） Impact assessment considering interaction between climate change, other environmental problems, and development target. Three main research directions of AIM impact study
Impact assessment considering effects of extreme climate events • Collaborative research project with NIES/CCSR • Collaborative research project with NIES/CCSR climate modeling team from FY2004. climate modeling team from FY2004. • Backgrounds of the project • Backgrounds of the project – Extreme climate events (hot summer, heavy rain, dry spell – Extreme climate events (hot summer, heavy rain, dry spell etc.) are expected to increase in frequency and/or severity, so etc.) are expected to increase in frequency and/or severity, so the severity of their impacts will also increase. the severity of their impacts will also increase. – Availability of climate model outputs more suitable for – Availability of climate model outputs more suitable for extreme event analysis is increasing. extreme event analysis is increasing. • Research objective of the project • Research objective of the project – Validation of recent climate model ’ s ability to reproduce – Validation of recent climate model ’ s ability to reproduce frequency and magnitude of extreme events frequency and magnitude of extreme events – Refinement and development of impact assessment models – Refinement and development of impact assessment models for considering extreme events for considering extreme events – More realistic impact assessment considering extreme events – More realistic impact assessment considering extreme events
Works in FY2005 • Impact assessment using daily outputs of • Impact assessment using daily outputs of general circulation model with high spatial general circulation model with high spatial resolution. resolution. – Estimation of change in mortality due to heat stress – Estimation of change in mortality due to heat stress – Estimation of change in crop productivity with – Estimation of change in crop productivity with considering negative effect of typhoon and heat considering negative effect of typhoon and heat wave. wave.
Works in FY2005 • Estimation of change in mortality due to heat • Estimation of change in mortality due to heat stress stress – With the increase in very hot day in a year, – With the increase in very hot day in a year, mortality due to heat stress is expected to increase. mortality due to heat stress is expected to increase. – Monthly climate scenario is not sufficient for – Monthly climate scenario is not sufficient for estimating heat stress mortality, thus estimation was estimating heat stress mortality, thus estimation was done using daily climate scenario based on the latest done using daily climate scenario based on the latest GCM with high resolution. GCM with high resolution. • Estimation of change in crop productivity with • Estimation of change in crop productivity with considering negative effect of typhoon and heat considering negative effect of typhoon and heat wave. wave.
Change in excess mortality per unit area (1990s and 2090s) 1990s 2090s 10 -5 10 -4 10 -3 10 -2 10 -1 (person/km 2 )
Estimation of excess mortality due to heat stress in future • Procedure to estimate – Model development and parameter estimation using mortality statistics in Japan – Development of daily climate scenario using GCM output – Application of the model to global scale assessment of excess mortality due to heat stress • What is “ excess mortality ” ?
“ Excess mortality due to heat stress ” - definition in this study - (/day) (/day) 590 590 580 580 570 570 560 30 560 30 20 20 10 10 ℃ ℃ Now Future 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Month Month Temperature ( ℃ ) Mortality (death/day) Optimal temperature To ( ℃ ) Mortality at To Mortality (Annual average) Excess mortality
Estimation flow of excess mortality Climate scenario development Model development Observed monthly Mortality statistics in Temperature data in 47 climate data (CRU) 47 prefectures (daily) prefectures (daily) GCM ’ s daily Formulation of model for estimating excess mortality projection (current and due to heat stress future) rat ed = f(t max [1:365],To,a 1 ,a 2 ) To = g(t max [1:365*30]) Optimal Daily climate scenario temperature T o (current and future) Number of days hotter than T o Country-wise annual mean mortality Excess mortality due to heat stress Population density Excess mortality number due to heat stress Country boundary map Country-wise excess mortality due to heat stress Model application Resolution 1.125 ° Resolution 0.5 ° Resolution 2.5 ′
Good correlation between 85%ile value of daily maximam temperature and optimal daily maximam temperature To Physiologic acclimatization to hot condition Tmax 85 %ile vs OT Social/cultural adaptation to hot condition 15,000 死亡率（日 -1 ）× 100,000,000 Mortality (/day) x 100,000,000 35 14,000 30 13,000 寒い地方 暑い地方 OT Cold Hot 12,000 25 prefecture prefecture 11,000 20 10,000 To To 20 25 30 35 １３＜＝＜１８ １８＜＝＜２３ ２３＜＝＜２８ ２８＜＝＜３３ ＜８ ８＜＝＜１３ Tmax 85 %ile ３３＜＝ It was found that optimal daily maximum temperature has a good correlation with Daily maximum temperature ( ℃ ) 日最高気温 （℃） 85%ile value of daily maximum temperature
Estimated optimal temperature ＝ 85%ile value of daily maximum temperature among 30years x 365 samples. 0 10 20 30 40 ( o C)
Estimated number of days with heat stress in 1990s （ Number of days when daily max temp exceeds optimal temperature To per year) Number of days with moderate heat stress To<T<To+5 Number of days with severe heat stress To+5<T 0 25 50 75 100 125 (days)
Population density in 2000 （ GPW3 ： http://beta.sedac.ciesin.columbia.edu/gpw/index.jsp ） 0.1 1 10 100 1000 (person/km 2 )
Density of excess mortality due to heat stress （ Total mortality - mortality assuming optimal temperature through whole year) 10 -5 10 -4 10 -3 10 -2 10 -1 (person/km 2 )
Scenarios for future projection • Climate change – Temperature increase projected by CCSR/NIES/FRCGS-MIROC (SRES-A1B) was multiplied with the factor of 2/3 and then it was added to CRU observed monthly climate data for creating daily climate scenario without model bias. • Population – Gridded Population of the World Ver.3 (GPW3) • Compatible with WB2000 estimates ； 2.5 ′ x 2.5 ′ – No change in future • Adaptation / Acclimatization – No change in future
Change in daily maximum temperature in 100 years (10year-mean ; (2090s – 1990s) × 2/3 ; SRES-A1B ; MIROC-hires) 1990s 2090s 0 10 20 30 40 ( o C) 2090s – 1990s 0 3 4 5 6 ( o C)
Change in number of days when daily maximum temperature exceeds optimal temperature To. 1990s 2090s To<T<To+5 To+5<T 0 25 50 75 100 125 (days)
Change in excess mortality per unit area (1990s and 2090s) 1990s 2090s 10 -5 10 -4 10 -3 10 -2 10 -1 (person/km 2 )
Percentage of change in excess mortality per area （ 2090s / 1990s × 100 － 100 ） -100 0 200 400 600 800 (%)
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