assessing the risk of heat-stressed mortality due to ... · 1 assessing the risk of heat-stressed...

1

Assessing the Risk of HeatAssessing the Risk of Heat--stressed stressed Mortality due to Global Warming Mortality due to Global Warming

Using MultiUsing Multi--GCM ApproachGCM Approach

H.

Jung1, K.Takahashi1, S.Emori1, Y.Honda2

1 National Institute for Environmental Research2 Tsukuba University

([email protected])

15th

AIM international workshop S-II-1(2010/02/20@NIES,Tsukuba)

2

IntroductionWarming impact on heat-stressed mortality

Climate change and variability will bring the higher death probability of heat-stressed mortalityV-shape relation with daily Tmax and mortality rate

Heat-stressed impact assessment using Multi-GCMsRepresenting the level of confidence in impact caused by GCM predictionsFinding the responses of impact to climate change and variabilityFinding the risk probabilities with different futureFinding the threshold temperature for assessing adaptation capacity

3

“V”-shaped relation between daily Tmax and Mortality (Honda et al. 1998, 2006) Using daily mortality data of 47 prefectures in Japan during 1972-1995

Daily Tmax vs. Heat Mortality (1)

-10 0 10 20 30 40

0.9

1.0

1.1

1.2

1.3

1.4

Tmax

MR

R

Daily Maximum Temperature, Tmax （℃）

Tokyo Pref.

Hokkaido Pref.

Optimal Temperature (OT)

Relativ

e Mortality Risk

Okinawa Pref.

HotCold

-10 0 10 20 30 40

0.9

1.0

1.1

1.2

1.3

1.4

Tmax

MR

R

Daily Maximum Temperature, Tmax （℃）

Tokyo Pref.

Hokkaido Pref.

Optimal Temperature (OT)

Relativ

e Mortality Risk

Okinawa Pref.

HotCold

20

22

24

26

28

30

32

34

5 10 15 20 25Average temperature

Opt

imum

tem

pera

ture

Okinawa

Opt

imal

tem

pera

ture

, OT

Average temperature, Tavg

Relative mortality vs. OT Tavg vs. OT (47 pref. in Japan)

4

20 25 30 35

2025

3035

Tmax80

OT

tt

t

c c

c

k

k

k kkk

Estimation of OT from daily Tmax Relative excess mortality

The 80th percentile of daily Tmax

C: ChinaK: KoreaT: Taiwan

: Japan OT

Mor

talit

y ra

te (d

eath

s/da

y)

Daily maximum temperature, Tmax

Average mortality during the period

Daily mortality at OT

20 25 30 35

2025

3035

Tmax80

OT

tt

t

c c

c

k

k

k kkk

Estimation of OT from daily Tmax Relative excess mortality

The 80th percentile of daily Tmax

C: ChinaK: KoreaT: Taiwan

: Japan OT

Mor

talit

y ra

te (d

eath

s/da

y)

Daily maximum temperature, Tmax

Average mortality during the period

Daily mortality at OT

OT in East Asian countries (the 85th percentile of daily Tmax as OT)Define relative excess mortality using 30-yr (1971-2000) daily Tmax for base period


5

Defining excess mortality, (Takahashi et al., 2007):


365

deathpopyr

deathpop dayday yr

⎡ ⎤⎢ ⎥⎣ ⎦

⎡ ⎤ ⎡ ⎤×⎢ ⎥ ⎢ ⎥

⎣ ⎦ ⎣ ⎦

, ,grid y grid grid y base cntDenADNE DenPN RelADNEADNO RelADNOADN ADR= × × ×

2

deathpopkm yr

⎡ ⎤⎢ ⎥⋅⎣ ⎦

= 2

personskm

⎡ ⎤⎢ ⎥⎣ ⎦

x xdeathpoppop daydeathpoppop day

⎡ ⎤⎢ ⎥⋅⎣ ⎦⎡ ⎤⎢ ⎥⋅⎣ ⎦

x

OPTbase

deathpoppop day

⎡ ⎤⎢ ⎥⋅⎣ ⎦

DDNO

( 1 1 2 2) / 365a N a N= × + ×

ADNE

ANLbase

DenPN: Population densityRelADNEADNO: Relative excess mortalityDDNO: Daily mortality at TOADNE: Annual sum of daily excess mortality, DDNERelADNOADN: Ratio of mortality at TO

to the annual average mortality of JapanOPTbase : Daily mortality at TO in JapanANLbase : Annual average daily mortality in Japan

ADR: Annual average mortality of the countryN1: Annual number of days on which Tmax > TO and Tmax < TO+5 (̊ C)N2: Annual number of days on which Tmax > TO+5 (̊ C)

365dayyr

⎡ ⎤⎢ ⎥⎣ ⎦

x

6

Extreme CC Effect on MortalityChanges in distribution ?

Bias correction:Statistical method (Piani et al.2009)Quintile mapping (Wood et al.2004)Morphing (Belcher et al.,2005)Rank matching, histogram equalization, daily scaling, delta method, relative ratio etc.

Current minimum morality at OTbase

Future OTfutureBest adaptation

( )pmpofmfo .... μμμμ −+=

pm

pofmfo

.

...

σσσσ =

o: observation,

m：model, f： future

p：present

ImpactAdaptable

range

7

AR4 Climate ProjectionsDownscaling of monthly Tmax with Morphing method

‘shift’ (μ change) and ‘stretch’ (σ change) of monthly CMIP3c: 1980s(1971-2000): Monthly CRU (0.5) + Daily ECMWF_ERA40 (1.0)f: 2080s(2071-2100), [2020s (2011-2040), 2050s(2041-2070)]

( ) ( ) [ ( ) ( )] [ ( ) ( )]f

f o f c o o

c

T d T m T m T m T d T mσ

σ= + − + ⋅ −

GCMOriginating group(s) Model name A1B (20 GCMs) A2 (17 GCMs) B1 (20 GCMs)Beijing Climate Center BCC‐CM1 ‐ O OBjerknes Centre for Climate Research BCCR‐BCM2.0 ‐ O OCanadian Centre for Climate Modelling & Analysis CGCM3.1 (T47) O O OCanadian Centre for Climate Modelling & Analysis CGCM3.1 (T63) O ‐ OMe´ te´ o‐France/Centre National de Recherches Me´ te´ orologiques CNRM‐CM3 O O OCSIRO Atmospheric Research CSIRO‐Mk3.0 O O OUS Dept. of Commerce/NOAA/Geophysical Fluid Dynamics Laboratory GFDL‐CM2.0 O O OUS Dept. of Commerce/NOAA/Geophysical Fluid Dynamics Laboratory GFDL‐CM2.1 O O ONASA/Goddard Institute fr Space Studies GISS‐AOM O ‐ ONASA/Goddard Institute fr Space Studies GISS‐EH O ‐ ‐NASA/Goddard Institute fr Space Studies GISS‐ER O O OLASG/Institute of Atmospheric Physics FGOALS‐g1.0 O ‐ OInstitute for Numerical Mathematics INM‐CM3.0 O O OInstitut Pierre Simon Laplace IPSL‐CM4 O O OCenter for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, andFrontier Research Center for Global Change (JAMSTEC)

MIROC3.2 (hires) O ‐ O

Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, andFrontier Research Center for Global Change (JAMSTEC)

MIROC3.2 (medres) O O O

Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data ECHO‐G O O OMax Planck Institute for Meteorology ECHAM5/MPI‐OM O O OMeteorological Research Institute MRI‐CGCM2.3.2 O O ONational Center for Atmospheric Research PCM O O OHadley Centre for Climate Prediction and Research/Met Office UKMO‐HadCM3 O O OHadley Centre for Climate Prediction and Research/Met Office UKMO‐HadGEM1 O O ‐

Scenario

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Observation

Data

• Monthly CRU 0.5º• Daily ECMWF-R40 1.0º

Multi-GCMs•

CMIP3 Monthly/Daily with more than 2-3º

res.• Current-20C3M• Future-A1B, A2, B1

Bias Correction/

Downscaling

• higher spatial res.• Monthly to Daily

Bias Corrected Climate Inputs•

with GCM skill score for non-equal weighted impact

Heat Impact

(HIP) Assessment

•

Impact of equally weighted GCMs’

impact

Probabilistic representation of HIP

and Risk• for years 2020s, 2050s, 2080s

Daily mortality data for the 47 prefecture of Japan (DDNpref,y,d)

Daily maximum temperature data for the 47 prefecture of Japan (Tmaxpref,y,d)

Construction of excess mortality estimation model-Formula to estimate optimal temperature, relative excess mortality and density of excess mortality due to heat stress

Observed monthly climate data set (ObsMTmaxgrid,y,m)

Observed daily climate data set (ObsTmaxgrid,y,d)

Monthly climate model output (ModTmaxgrid,y,m)

Number of days on which Tmax is higher than TO (N1grid,y, N2grid,y)

Relative excess mortality due to heat stress (RelADNEADNOgrid,y)

Density of excess mortality due to heat stress (DenADNEgrid,y)

Optimal temperature (TO) Present and future daily maximum temperature (Tmaxgrid,y,d)

Model construction

Annual average mortality rate by country (ADRcnt)

Population density (DenPNgrid)

Preparation of climate scenarios

Excess mortality due to heat stress by country

Administration boundaries

Model application1.125º 0.5º 2.5 ́ Resolution of spatial data











Model construction







①

③ Impact Assessment

①Model Construction ② Climate DB Preparation











Model construction

















Model construction







①

③ Impact Assessment

①Model Construction ② Climate DB Preparation

Sim

plif

icat

ion

of p

roce

ss u

sing

Impa

ct r

espo

nse

func

tion

Research Framework

(ºC)0 10 20 30 40

Optimal Temperature

(85th Tmax during 1971-2000)

(deaths/km2)10-5 10-4 10-3 10-2 10-1

Excess mortality density due to heat stress in the existing condition (upper) and the future (lower)

Current (1971-2000)

Ensemble mean of SRES-A2 17 GCMs (2071-2100)

(%)-100 0 200 400 600 800

Rate of change of excess mortality due to hear stress (SRES-A2, 2071-2100)

Changes in ensemble mean of SRES-A2 17 GCMs

(deaths/km2)10-5 10-4 10-3 10-2 10-1 (deaths/km2)10-5 10-4 10-3 10-2 10-110-5 10-4 10-3 10-2 10-1

(%)10 20 30 40 50 (%)10 20 30 40 50

(ºC)0 3 4 5 6 (ºC)0 3 4 5 6

(%)10 20 30 40 50 (%)10 20 30 40 50

(d)(c)

(annual mean temperature change) (excess mortality change)

(CV of annual mean temperature change) (CV of excess mortality change)

Ensemble mean and normalized dispersion (Mean and Coefficient of variation, CV)

Changes in annual mean daily maximum temperature and excess mortality years from the existing condition (1971-2000) to the future (2071-2100) using the SRES A2 scenario (17 GCMs)

(b)(a)

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Future ConsiderationAssessing the extreme effect of temperature on impact

Comparison of BCDS methods to produce the daily extremes of TmaxComparison of AR4-GCMs mean and variability effects on mortality

Defining the impact responses with GCM uncertaintiesUncertainty of GCM projections (AR4) and scenarios (SRES A1b A2, B2)Defining skill of GCMs and scoring for impact with changes in mean and variation of Temperature.Transfer GCM uncertainty to impact and Skill scoring

Defining the risk by using the probabilistic distribution of impactDeveloping the impact response function with uncertaintyDefining the adaptability (risk threshold) based on OT Producing the impact probability and assessing the risk

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