Impact and Science of Climate Change on Asia-Pacific region - - PowerPoint PPT Presentation

▶
impact and science of climate change on asia pacific
SMART_READER_LITE
LIVE PREVIEW

Impact and Science of Climate Change on Asia-Pacific region - - PowerPoint PPT Presentation

Aug 21, 2023 183 likes •602 views

Japan-UK Joint Research Project Impact and Science of Climate Change on Asia-Pacific region National Institute for Environmental Studies(NIES) Hideo Harasawa 1 Impact Research in AP Region 1) IPCC (Asia) 2) APN (CAPABLE) 3) AIACC (Assessment

slide-1
SLIDE 1

Impact and Science of Climate Change on Asia-Pacific region

National Institute for Environmental Studies(NIES) Hideo Harasawa

Japan-UK Joint Research Project

1

slide-2
SLIDE 2

Impact Research in AP Region

1) IPCC (Asia) 2) APN (CAPABLE) 3) AIACC(Assessment of Impacts and Adaptations to Climate Change ) 4) Global Warming Research Initiative (2002～2005FY, CSTP, Japan) 5) Global Environmental Research Fund (MoE, Japan) B-12 Prediction and Impacts of Extreme events S-4 Thresholds of Impacts / Comprehensive Impacts Assessment in Japan and E-Asia, etc. 6) Global Warming Research Project (1st: 2001～2005FY, 2nd: 2006FY～、 NIES)

  • AIM (Integrated Assessment Model, 1990～)

7) Other research activities

2

slide-3
SLIDE 3

Framework of Global Warming Research Initiative

3

slide-4
SLIDE 4

Achievement map for impacts and risk studies in Japan

4

slide-5
SLIDE 5

Reports of Global Warming Research Initiative

No.1, 2003 (Jp) No.2, 2004 (Eng) No.3, 2006 (Jp) No.4, 2006 (Eng)

?

5

slide-6
SLIDE 6

Global- ism Regional- ism econo my environ ment A1 high growth A2 pluralism B1 recycle-based B2 regional coexistent

Socio Socio-

  • economic

economic scenarios scenarios

Change of Potential Productivity, Winter Wheat, 2100-1990

+2000 (kg/ha)
  • 2000
  • 1000
+1000

Impact/adaptation Impact/adaptation assessment assessment

SO2

etc

CO2

Emission Scenarios Emission Scenarios

Carbon cycle Carbon cycle

Carbon

Sequestration

1990 2000 2010 2020 2030 2040

AFRICA+MIDDLE EAST LATIN AMERICA

ASIA PACIFIC

NORTH AMERICA EUROPE+FORMER USSR

10 20 30 40 50 1990 2000 2010 2020 2030 2040

AFRICA+MIDDLE EAST LATIN AMERICA

ASIA PACIFIC

NORTH AMERICA EUROPE+FORMER USSR

10 20 30 40 50

Sustainable Sustainable development development

Mitigation cost

Climate scenarios Climate scenarios

NIES GW Research Project 6

slide-7
SLIDE 7

Collaboration with climate model

CCSR/NIES CGCM

AIM

(Asian-Pacific Integrated Model)

Emission model Climate model Impact model

Atmosphere Land Surface Ocean Adaptation Socio-Econ. & Emission Scenario Water Resource Landuse Crop Productivity Food Demand And Supply Socio-Econ. Factors

7

slide-8
SLIDE 8

Observed Impacts of Global Warming

8

slide-9
SLIDE 9

Observed Impacts of Global Warming

Retreat of Glaciers Tianshan Glaciers (disappeared by 22% for the past 40 years) Tibettian Glaciers (disappeared 4420km2 (9%) for the past 30 years) Himalayan Glaciers (500,000 km2 to 100,000 km2 by 2035) Heat Wave 45-49oC in May, 2003 in India (1600 death) 2-3 oC increase in July, 2004 in Japan (heat stroke patients more than 600 in Tokyo) Typhoon 10 typhoon landed in 2004 in Japan (>200 death, 120 billion $ damage) Increasing damage in Philippines (900 death, Nov. 2004, >500 , Dec. 2004) Wind Storm Increasing wind storm in Mongolia

9

slide-10
SLIDE 10

Observed Impacts in China and Japan

China ・ Sea level rise: 1.4 – 2.5 mm per year ・ Change in plant growing period 2-3.5 days in temperature increase 1oC ・ Coral reef appearing in Gungxi and Haian Provinces Japan ・ Sea level rise 2.03mm/year (1970- 2003)

  • Lake Ice: Lake Suwa in Nagano Prefecture

Omiwatari, “the divinity’s pathway,” Omiwatari has not been seen very often in recent years

  • Decreased alpine flora in Hokkaido, the north island in Japan and other high

mountains

  • Expanded distribution of southern broad-leaved evergreen trees such as the

Chinese Evergreen Oak

  • Nagasakiageha butterfly (Papilio memnon thunbergii), the northern border for

which has been Kyushu and Shikoku Islands, appeared in Mie Prefecture in the 1990s, then in Tokyo area in early 2000s

  • Appearance of the southern tent spider, seen only in western Japan in the 1970s,

in the Kanto Region in the 1980s.

  • Expansion of the wintering spot of the White-Fronted Goose to Hokkaido
  • Appearance of tropical fish in Osaka Bay.
  • Breaching of Coral Reef in Okinawa islands
  • Shifting habitats of ermine and grouse on mountains such as Hakusan and

Tateyama to higher elevations. There is some danger of complete disappearance.

10

slide-11
SLIDE 11

Significance 1%

5% 10%

  • N. S.
  • N. Test

Earlier Later ※Change in Days in about 50 years (Minus means earlier flowering) ※Stat. test if the number of data is longer than 48 years

Earlier Flowering of Cherry Trees in Japan

11

slide-12
SLIDE 12

Daily max. temperature and number of heat stroke patients

who were transported to hospitals in Tokyo suburban areas(May – Sep, 2004）

Cumulative number of Heat Stroke Patients in 2000～2003

http://www.nies.go.jp/impact/index.html

Number of heat stroke patients transported to hospitals Number of heat stroke patients transported to hospitals Daily Max. Temp.

12

slide-13
SLIDE 13

気温別熱中症患者発生数

5 1 1 5 2 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0 3 1 3 2 3 3 3 4 3 5 日最高気温（℃） 熱中症患者平均搬送数

東京２３区 東京都下 川崎市 名古屋市

2 4 6 8 1 1 2 1 4 1 6 1 8 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 日平均気温（℃） 熱中症患者平均搬送数

東京２３区 東京都下 川崎市 名古屋市

（ａ）

気温別熱中症患者発生数

5 1 1 5 2 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0 3 1 3 2 3 3 3 4 3 5 日最高気温（℃） 熱中症患者平均搬送数

東京２３区 東京都下 川崎市 名古屋市

2 4 6 8 1 1 2 1 4 1 6 1 8 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 日平均気温（℃） 熱中症患者平均搬送数

東京２３区 東京都下 川崎市 名古屋市

（ａ）

気温別熱中症患者発生数

（ｂ）補正済み

. . 5 1 . 1 . 5 2 . 2 . 5 3 . 2 0 2 1 2 22 3 2 42 5 2 6 2 72 8 2 9 3 03 1 3 23 3 3 4 3 5 日最高気温（℃） 熱中症患者平均搬送数 東京２３区 東京都下 川崎市 名古屋市 . . 5 1 . 1 . 5 2 . 2 . 5 3 . 3 . 5 4 . 1 5 1 6 1 71 8 1 92 0 2 1 2 22 3 2 4 2 52 6 2 72 8 2 9 3 日平均気温（℃） 熱中症患者平均搬送数 東京２３区 東京都下 川崎市 名古屋市

気温別熱中症患者発生数

（ｂ）補正済み

. . 5 1 . 1 . 5 2 . 2 . 5 3 . 2 0 2 1 2 22 3 2 42 5 2 6 2 72 8 2 9 3 03 1 3 23 3 3 4 3 5 日最高気温（℃） 熱中症患者平均搬送数 東京２３区 東京都下 川崎市 名古屋市 . . 5 1 . 1 . 5 2 . 2 . 5 3 . 3 . 5 4 . 1 5 1 6 1 71 8 1 92 0 2 1 2 22 3 2 4 2 52 6 2 72 8 2 9 3 日平均気温（℃） 熱中症患者平均搬送数 東京２３区 東京都下 川崎市 名古屋市

Number of Heat Stroke Patients transported to hospitals

Standalized

Number of Heat Stroke Patients transported to hospitals

Number of heat stroke patients transported to hospitals Number of heat stroke patients transported to hospitals Number of heat stroke patients transported to hospitals Number of heat stroke patients transported to hospitals

Daily max. temp(oC) Daily max. temp(oC) Daily ave. temp(oC) Daily ave. temp(oC)

Tokyo (center) Tokyo (suburban) Kawasaki Nagoya Tokyo (center) Tokyo (suburban) Kawasaki Nagoya Tokyo (center) Tokyo (suburban) Kawasaki Nagoya Tokyo (center) Tokyo (suburban) Kawasaki Nagoya

13

slide-14
SLIDE 14

Heat Stroke Early Warning System

http://www.nies.go.jp/health/HeatStroke/index.html http://www.nies.go.jp/impact/inde x.html

14

slide-15
SLIDE 15

Future Climate Projection

15

slide-16
SLIDE 16

NIES/CCSR/JAMSTEC

Future Climate Projection by the Earth Simulator

Earth Simulator

16

slide-17
SLIDE 17

Temperature (1950～2100)

Temperature Increase (℃）

NIES/CCSR/JAMSTEC

17

slide-18
SLIDE 18

Precipitation (1950～2100)

NIES/CCSR/JAMSTEC

Change in Precipitation (%)

18

slide-19
SLIDE 19

2 4 6 8 1 1 2 1 4 1 6 1 9 0 1 9 2 0 1 9 4 0 1 9 6 0 1 9 8 0 2 0 2 2 0 2 4 0 2 6 0 2 8 0 2 1

Change in higher temperature days（1900～ 2100）

Daily maximum temperature 30

  • C without heat island effects

2 4 6 8 1 1 2 1 9 0 1 9 2 0 1 9 4 0 1 9 6 0 1 9 8 0 2 0 2 2 0 2 4 0 2 6 0 2 8 0 2 1

Change in summer heavy rain (June-August, 1990～ 2100）

Daily precipitation is more than 100mm

NIES/CCSR/JAMSTEC 19

slide-20
SLIDE 20

Regional Climate Model (JMA/MRI) To predict future regional climate change in spatially high resolution (20km) Nesting To use GCM output as boundary conditions for regional Climate Model

Global Climate Model（ 280km) Asian Climate Model(60km) Japan Regional Climate Model(20km) Japan Meteorological Agency (JMA)/ Metrological Research Institute (MRI)

20

slide-21
SLIDE 21

100 years （ 2081～2100 Ave.）

Predicted Average Temperature in January

Present （ 1981～2000 Ave.） 50 years （ 2031～2050 Ave.）

21

slide-22
SLIDE 22

100 years （ 2081～2100 Ave.） Present （ 1981～2000 Ave.） 50 years （ 2031～2050 Ave.）

Predicted Average Precipitation in January

22

slide-23
SLIDE 23

Fig Change in 100 years probability maximum daily precipitation using Gumbel Distribution (Future (100 years)/ present)

  • Dr. K. Wada

(National Institute for Land and Infrastructure Management) %

23

slide-24
SLIDE 24

Future Impacts

24

slide-25
SLIDE 25

0 1 2 3 4 (t/ha)

1) Potential Productivity of wheat in 2000

Based on CCSR/NIES Climate Model (A1B)

A: Potential productivity of wheat in 2000 and 2050

25

slide-26
SLIDE 26

0 1 2 3 4 (t/ha)

2) Potential Productivity of wheat in 2050 (No adaptation case) 26

slide-27
SLIDE 27

0 1 2 3 4 (t/ha)

3) Potential Productivity of wheat in 2050 (Adaptation case: changing variety and planting date) 27

slide-28
SLIDE 28

B) Heat Damage on Rice Production (Present Condition)

  • Damage due to heat wave occurred in some regions

Damage Index = [ Productivity without heat stress (A)－Productivity with heat stress (B) ] / (A)

3 6

%

28

slide-29
SLIDE 29

0 2 0 2

Change in Index＝ Index (2060)－Index (1990)

%

Heat Damage on Rice Production (2060)

  • Damage will increase in the whole world

29

slide-30
SLIDE 30

0 20 40 60 80 100 ～ (%) 1 5 10 0 20 40 60 80 100 ～ (%) 1 5 10

(B1) Water stress index in 2100 (Unsustainable society scenario) (A) Water stress index in 2000 (B2) Water stress index in 2100 (Sustainable society scenario)

“Water stress index” is defined as the ratio between water withdrawal and renewable water resource in a river basin (figure’s case), region, country or other boundaries. High value implies the higher risk of water shortage.

C) Water stress index in 2000 and 2100

30

slide-31
SLIDE 31

D) Heat stress Impacts

Daily mortality data for the 47 prefectures of Japan (DDNpref,y,d) Daily maximum temperature data for the 47 prefectures 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 Number of days on which Tmax is higher than TO (N1grid,y,N2grid,y) Present and future daily maximum temperature scenarios (Tmaxgrid,y,d) Observed monthly climate data set (ObsMTmaxgrid,y,m) Daily climate model

  • utput (ModTmaxgrid,y,d)

Population density (DenPNgrid) Relative excess mortality due to heat stress (RelADNEADNOgrid,y) Annual average mortality rate by country (ADRcnt) Density of excess mortality due to heat stress (DenADNEgrid,y) Excess mortality due to heat stress by country Optimal temperature (TO) Administrative boundaries Preparation of climate scenarios Model construction Model application

0.5° 2.5′ 1.125°

Resolution of spatial data

31

slide-32
SLIDE 32

10-4 10-3 10-2 10-1 100 (death/km2) 1990s 2090s

Excess mortality density due to heat stress in the existing condition and the future

32

slide-33
SLIDE 33

1990s 2090s 10-4 10-3 10-2 10-1 100 (death/km2)

Excess mortality density due to heat stress in the existing condition and the future

33

slide-34
SLIDE 34

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

Changes in excess mortality density due to heat stress (Future excess mortality density - Existing excess mortality density)

34

slide-35
SLIDE 35
  • 100 0 200 400 600 800 (%)

Rate of change of excess mortality density due to heat stress (Future excess mortality density / Existing excess mortality density x 100 – 100 (%))

35

slide-36
SLIDE 36

To avoid serious CC impacts, it is necessary to stabilize temperature rise below 2 degree compared with pre- industrialized level

IPCC TAR, 2001

2 2 °

°C

C

36

slide-37
SLIDE 37

BaU GHG-475ppm GHG-500ppm GHG-550ppm GHG-650ppm

5 10 15 20 25 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 年

温室効果ガス排出量 (二酸化炭素換算： GtC/年)

0.0 1.0 2.0 3.0 4.0 5.0 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2110 2120 2130 2140 2150 年

気温上昇 (1990年=0.6℃)

Temperature raise (global average) Global GHG emissions

  • It is estimated that around 50% GHG

reductions in 2050 are required to control temperature raise below 2oC

  • Japan may be required more reduction (60-80%).

Another country-level 2050 scenarios have been studied (UK 60%, Germany 80%, France 75%, and so on).

GHG475ppm GHG: Greenhouse gases 50% reduction

Calculated by AIM/Impact[policy] Model

  • Impacts will be
  • ccurred even in

2oC temp control.

  • Adaptation is

necessary.

650 550 500

BaU

GHG 475ppm Temperature raise (above the pre-industrial level)

Year Year

GHG emissions (Gt-Ceq) 475

650 550 500

BaU

37

slide-38
SLIDE 38

Summary

Figure Asia and its subregions as used in generating the climate change projections for SRES emission scenarios based on seven A-O GCM experiments

38

slide-39
SLIDE 39

Table Vulnerability of key sectors to the impacts of climate change by subregions in Asia (Tentative)

  • 2 / H
  • 1 / M
  • 2 / H
  • 2 / H
  • 1 / H
  • 2 / H
  • 2 / H

Southeast Asia

  • 2 / H
  • 1 / M
  • 2 / M
  • 2 / H
  • 2 / H
  • 2 / H
  • 2 / H

South Asia

  • 2 / H
  • 1 / H
  • 1 / H
  • 2 / H
  • 2 / H
  • 2 / H
  • 2 / VH

East Asia

  • 1 / L

No Info No Info NA

  • 1 / M
  • 2 / M

+1/ L Tibetan Plateau

  • 2 / H
  • 1 / M
  • 2 / M
  • 1 / L
  • 2 / VH
  • 1 / M
  • 2 / H

Central Asia

  • 1 / M
  • 1 / M
  • 1 / M
  • 1 / M

+1 / M

  • 2 / M

+1 / H North Asia

Land Degradatio n Settlements Human Health Coastal Ecosystem Water Resource Biodiversity Food And Fiber

Sub-regions

VH - Very High H - High M - Medium L - Low VL - Very Low Level of Confidence:

  • 2 – Highly Vulnerable
  • 1 – Moderately Vulnerable

0 – Slightly or Not Vulnerable +1 – Moderately Resilient +2 – Most Resilient Vulnera- bility:

39

slide-40
SLIDE 40

Summary

1) Impacts of Global Warming have been observed in AP region. 2) Significant impacts will be predicted in all sub-regions and sectors. 3) Precise regional climate prediction is necessary to conduct regional vulnerability assessment. 4) Adaptation is key measures to mitigate current and future impacts. 5) Research Needs To identify Hot spots/sectors in AP region To assess long-term and short-term (extreme events) impacts and adaptation measures To identify thresholds of impacts To assist capacity building of Impact researchers in AP region

40