Research on climate changeand its impactsin the Institute

of Atmospheric PhysicsRadan HUTH,

Martin DUBROVSKÝ, Jan KYSELÝ(with contributions from R.BERANOVÁ, L.POKORNÁ, M.TRNKA)

Institute of Atmospheric Physics,Prague, Czech Republic

email: huth@ufa.cas.cz

Dept. of Climatology

• small– 5 scientists– 5 doctorands / postdocs

• research in– statistical climatology– climate variability– climate change

Our national cooperations• within projects funded by national grant agencies

– Mendel University for Agriculture and Forestry, Brno –clim. change impacts on agriculture, drought stress

– Hydrobiological Institute, České Budějovice – clim.change impacts on water reservoirs

– Institute of Hydrodynamics, Prague – clim. changeimpacts on hydrology

– Technical University, Liberec – statistical analyses– National Health Institute, Prague – climate effects on

human health, morbidity, mortality– Dept. of Meteorology, Charles University, Prague – climate

variability, scenarios– Czech Hydrometeorological Institute, Prague – climate

variability, drought

Our international cooperations• EU-funded (FP6)

– ENSEMBLES (ENSEMBLE-based predictions ofclimate change and its impacts) – integratedproject – only a minor participation in statisticaldownscaling

– CECILIA (Central and Eastern Europe ClimateChange Impact and Vulnerability Assessment) –STREP – validation of climate models, climatechange scenarios, analysis of extremes, …

• NATO– Drought as the Limiting Factor of Cereal

Production - together with National DroughtMitigation Center, Lincoln, Nebraska, USA

1. Recent trends in Czechia• 21 stations• 11 climate elements• 1961-2000• by seasons (DJF, MAM, …)

DAILY MEAN TEMPERATURE

4.0

4.9

5.15.5

5.34.8

4.5

2.6

4.6

5.0

4.4

5.55.0 5.8

5.05.5 4.8

5.0

5.5 5.36.5

3.5

3.3

4.22.4

3.51.7

2.4

1.9

3.2

2.6

3.0

2.23.0 3.0

1.5

2.61.7

2.7

2.5 2.11.4

2.8

2.7

3.11.5

3.00.5

2.2

2.8

1.6

1.7

3.1

1.92.8

3.3

2.02.9

3.13.7

2.4 2.13.0

-1.8

-2.0

-1.0-2.9

-1.8-2.8

-2.1

-1.8

-1.6

-2.0

-2.1

-1.8-0.9

-2.4

-2.4-2.0

-2.2-1.6

-3.1 -2.5-2.7

ZIMA JARO

LÉTO PODZIM

WINTER

AUTUMNSUMMER

SPRING

SUNSHINE DURATION

1.0

0.6

1.21.4

1.6

0.7

-0.6

2.0

0.7

2.6

2.0

1.11.4

1.01.6

2.11.42.3

1.5

1.8

2.61.8

1.4

0.8

0.5

2.5

1.7

2.6

1.7

1.70.5

0.92.4

1.1 0.50.9

0.4

0.6

1.20.1

-0.2

-0.4

0.0

1.8

1.1

1.5

1.4

1.40.1

2.52.8

1.0 1.52.1

-1.6

-2.6

-0.9-1.5

-1.7

-2.0

-2.0

-1.3

-2.0

-1.1

-2.3

-1.9

-1.8 -0.7-1.4

-2.1-1.2-2.1

ZIMA JARO

LÉTO PODZIM

WINTER

AUTUMNSUMMER

SPRING

OVERVIEW OF ALL TRENDS

• trends normalized by the half-width ofthe 95% confidence interval zero trend = 0 statistically significant trend > 1 / < -1

-3

-2

-1

0

1

2

T TX TN DTR ZW MW RH CL SUN PR PRO-3

-2

-1

0

1

2

T TX TN DTR ZW MW RH CL SUN PR PRO

-3

-2

-1

0

1

2

T TX TN DTR ZW MW RH CL SUN PR PRO-2

-1

0

1

2

3

T TX TN DTR ZW MW RH CL SUN PR PRO

ZIMA JARO

LÉTO

PODZIM

WINTER SPRING

SUMMER

AUTUMN

2. CLIMATIC EFFECTS ONHUMAN MORTALITY

• heat-related mortality summer is analyzed• population of Czech Rep.• mortality

– total– cardio-vascular diseases (CVD)

• effects described by 2 approaches– classical (mortality vs. temperature)– synoptic-climatological (days classified by weather

conditions – described by temperature, humidity,cloudiness, wind,…)

-80

-60

-40

-20

0

20

40

60

80

100

120

June

1

June

5

June

9

June

13

June

17

June

21

June

25

June

29

July

3

July

7

July

11

July

15

July

19

July

23

July

27

July

31

Aug

4

Aug

8

Aug

12

Aug

16

Aug

20

Aug

24

Aug

28

Dai

ly e

xces

s to

tal m

orta

lity

8

10

12

14

16

18

20

22

24

26

28

30

TAVG

/ H

IAVG

[°C

]

heat index (HIAVG) temperature (TAVG)

1994

Air temperature, heat index and excessmortality in summer 1994

+456 excess deaths (+10.3%) June 17-30,+598 excess deaths (+12.3%) July 24-August 8

MOTIVATION

-20

-10

0

10

20

30

40

50

60

70

1 5 9 13 17 21 25 29 33 37

TMAX [°C]

exce

ssm

orta

lity

total mortality

CVD mortality

TRADITIONAL APPROACH

SYNOPTIC APPROACH

• all examined classifications: at least 1 offensive airmass (AM)associated with significantly enhanced mortality

+20-30 deaths daily on average (7-10% relative increase)

• offensive airmass characterized byelevated temperature – mean TMAX (TAVG) exceeds30°C (23°C) – hottest among all AMs,small cloud cover – 2-4 tenths – usually least cloudyamong all AMsrelatively strong wind, with southerly component – 2.6-3.7 m/s on average

• high humidity (dew-point temperature) not typical

k-means, 10 AMs

average-linkage, 14 AMs

-20-15-10-505

1015202530

. 1 2 3 4 5 6 7 8 9 10 .

air mass

exce

ss m

orta

lity

0

5

10

15

20

25

30

35

TMAX

/ Td

[°C

]

total mortality CVD mortality TMAX [°C] Td14 [°C]

-15-10-505

1015202530

. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 .

air mass

exce

ss m

orta

lity

0

5

10

15

20

25

30

35

TMAX

/ Td

[°C

]

total mortality CVD mortality TMAX [°C] Td14 [°C]

3. CLIMATE CHANGE EFFECTS ONEXTREME PRECIPITATION

• 24 future climate runs of 10 RCMs (PRUDENCE project):horizontal resolution ~ 50 km; the only exceptions are the

high-resolution runs of HIRHAM with a 25 km gridmost RCMs driven by Hadley Centre HadAM3 GCM

• emission scenarios: SRES-A2 (B2) scenario leads to amore rapid (slower) increase in GHG concentrationscompared to the A1B scenario

• ‘peaks-over-threshold’ (POT) analysis with increasingthreshold censoring

RESULTS: SCENARIOS OF CHANGESIN 50-yr DAILY PRECIPITATION, JJA

Summer (JJA)

spatial patternsstrongly influenced byrandom samplingvariability (heavydaily amounts areoften related toconvective clouds thatmay affect relativelysmall areas)

very little coherentmixture of positiveand negativechanges appears insome RCMs

RESULTS: SCENARIOS OF CHANGESIN 50-yr DAILY PRECIPITATION, JJA

Summer (JJA)

projected changesmostly insignificant

BUT not true for thehigh-resolutionHIRHAM output:coherent areas withlarge and significantincreases in thewestern part of thearea, the meanrelative increase overthe whole area +40%

RESULTS: SCENARIOS OF CHANGESIN 50-yr DAILY PRECIPITATION, JJA

Summer (JJA)

regions of significantincreases appear in thewestern part of the area(the Elbe river basin) inseveral other model runs

they are absent in anyRCM in the eastern part(the Odra and Danuberiver basins)→ positive changes inJJA more likely in theElbe than Odra/Danuberiver basins→ may have implicationsinto decisions on floodprevention measures

RESULTS: SCENARIOS OF CHANGESIN 50-yr DAILY PRECIPITATION, DJF

Winter (DJF)

projected increasesmore uniform andgeneral compared toJJA

mean relativeincreases exceed +40%in RCAO and they areabove +20% in 14 outof the 24 future runs

BUT there are stillmodel runs in whichdecreases prevail(HadRM and ARPEGEunder B2 scenario)

RESULTS: SCENARIOS OF CHANGESIN 50-yr DAILY PRECIPITATION, DJF

Winter (DJF)

significant declinesdo not appear in anyRCM output

opposite to JJA, thecoherent areas ofsignificant positivechanges occur largelyin the eastern part(the Odra and Danuberiver basins)

the regions ofsignificant positivechanges tend to bemuch larger and morecoherent in DJFcompared to JJA

4. DROUGHT IN FUTURE CLIMATE• analysis of 7 global climate models (GCMs)• SRES-A2 emission scenario• 2 future periods

– 2031-2060– 2071-2100

• relative to “present” = 1991-2020• for variables

– temperature– precipitation– drought: Palmer drought severity index (PDSI), Z-

index

∆T - winter

∆T - summer

∆T - year

7 GCMs:∆TEMP

(2070-2099)vs (1991-2020)

∆prec - summer

∆prec - autumn

∆prec - spring

∆prec - winter

7 GCMs: PREC (2070-2099) vs (1991-2020)

avg(PDSI) :: 2031-2060 (7 GCMs)

avg(Z-index) :: 2031-2060 (7 GCMs)

spring (MAM)

autumn (SON)

summer (JJA)

winter (DJF)

avg(PDSI) :: 2070-2099 (7 GCMs)

spring (MAM)

avg(Z-index) :: 2070-2099 (7 GCMs)

autumn (SON)

summer (JJA)

winter (DJF)

5. CLIMATE CHANGE IMPACTS ON THEDISTRIBUTION OF CORN BORER

(Ostrinia nubilalis)

• moth harmful to maize• simulation of its potential distribution over the

Czech territory• of its 1st and 2nd generation• based on GCM outputs; 3 reasonable

emission scenarios (low, moderate, high)

Distribution of the first generationDistribution of the first generation

Distribution of the second generationDistribution of the second generation