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IPCC Extremes-SR Scoping Meeting, 23 March 2009, Oslo, Norway Projection of Changes in Extremes by Very High Resolution Atmospheric Models Akio KITOH Meteorological Research Institute, Tsukuba, Japan Precipitation changes IPCC AR4 CMIP3 models White: <2/3 of models agree on sign of change

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slide1

IPCC Extremes-SR Scoping Meeting, 23 March 2009, Oslo, Norway

Projection of Changes in Extremes by

Very High Resolution Atmospheric Models

Akio KITOH

Meteorological Research Institute, Tsukuba, Japan

precipitation changes
Precipitation changes

IPCC AR4

CMIP3 models

White: <2/3 of models agree on sign of change

Stippled: >90% of models agree on sign of change

Precipitation increasesvery likely in high latitudes

Decreaseslikely in most subtropical land regions

Model agreement is not so high in other regions

projected changes in extremes

IPCC AR4

CMIP3 models

Projected changes in extremes

Intensity of precipitation events is projected to increase.

Even in areas where mean precipitation decreases, precipitation intensity is projected to increase but there would be longer periods between rainfall events.

“It rains less frequently, but when it does rain, there is more precipitation for a given event.” (Tebaldi et al. 2006)

Extremes will have more impact than changes in mean climate

frequency intensity

CMIP3 models

Frequency Intensity

All precip

Light precip 1-10 mm/d

Heavy precip 20-50 mm/d

Sun et al. (2007) JCLI

20 yr return values of 24 h precipitation

CMIP3 models

20-yr return values of 24-h precipitation

20-yr return values of annual extremes of 24-h precipitation rates (P20)

•Global average P20 increases about 6%/K of global warming, with the range of 4%/K-10%/K

•Large inter-model differences in precipitation extremes

Waiting times for P20

Kharin et al. (2007) JCLI

slide6

Needs for high resolution models• representation of topography depends on resolution (land-sea distribution, mountain height, snow-rain threshold, …)• low resolution models often fail to reproduce precipitation systems such as tropical cyclones, stationary front systems and blocking• high resolution models have better mean climate

indian summer monsoon rainfall
Indian summer monsoon rainfall

IMD observation

20-km model

Orographic rainfall is successfully reproduced

Rajendran and Kitoh (2008) Current Science

typhoon track and intensity 60km vs 20km
Typhoon track and intensity: 60km vs 20km

In forecast mode

JMA

60km mesh model

20km mesh model

60-km model forecasts shallower central pressures and weaker maximum winds. 20-km model represents typhoon development closer to the observations. NWP from Nov 2007 at JMA

slide9

Comparison of extremes indices

between observation and each resolution models

MRI AGCM

pav

wetday

r5d

cdd

obs(GPCP1DD)

1deg(100km)

model

TL63(270km)

model

TL95(180km)

model

TL959(20km)

  • pav: Annual mean precipitation (mm/day)
  • wetday: Number of days (> 1 mm/day)
  • r5d: The annual maximum 5-day total precipitation
  • cdd: The annual maximum consecutive dry-days

more wetday in low-res model

better Typhoons/Baiu in high-res model

more bias in low-res model

there are now some high resolution modeling results available for climate change projections
There are now some high-resolution modeling results available for climate change projections

• Regional climate model PRUDENCE, ENSEMBLES, NARCCAP other regions followmulti-model butregional (can apply to other regions)• Stretched-grid model CCAM, GEM, ARPEGE, GEOS• Super-high resolution models (=NWP model) MRI/JMA 20-km mesh AGCM + 5-km RCRCMglobal butsingle-model (can apply physics/b.c. ensemble)

daily summer temperature

PRUDENCE

Daily summer temperature

Over France, at least every second and third summer day exceeds the 95th percentile, and a considerable number of days even the maximum values of the present

Fischer and Schär (2009) ClimDyn

example by global 20 km mesh agcm
Example by global 20-km mesh AGCM

• tropical cyclone

• extratropical cyclone

• blocking

kakushin team extremes time slice experiments
Kakushin Team-Extremes Time-Slice Experiments

Boundary condition

Atmosphere

SST

CMIP3 AOGCMs

20km,60km AGCM

5km,2km,1km RCM

Projected SST

Atmosphere

SST

AGCM/RCM is a climate model version of the JMA operational NWP models

Lower B.C.

Ocean

  • Present-day climate experiment (1979-2003)
    • the observed sea surface temperature (SST) and sea-ice concentration
  • Future climate experiment (2075-2099)
    • the warming in the SST for the CMIP3 multi-model ensemble mean is added to the observed SST

Mizuta et al. (2008)

inter annual variability of tc frequency
Inter-annual variability of TC frequency

Observation

20-km AGCM (AMIP run 1979-2003)

0.04

0.17

0.55###

0.53###

0.35#

0.48##

0.17

###:99% significance level

##:95% significance level

#:90% significance level

There is a skill for TC frequency interannual variability associated with ENSO

number of tc generated in each latitude
Number of TC Generated in Each Latitude

Annual global average

Present =82

Future =66

20%

decrease

(Observation:84)

Observation

(20% decrease)

Present-day(25yr)

Future(25yr)

TC freqency

Latitude

intensity

Observation

Present Experiment

Future Experiment

Change in TC intensity and duration

Intensity

Duration when wind speed is over 17m/s

Longer lived TCs will increase

Stronger TCs will increase

radial profile change around tc
Radial Profile Change around TC

Precipitation

Surface Wind

Present Experiment

Future Experiment

Change rate

Radial Distance in km from Storm Center

・Large changes occur near inner-coreregion, 40-60% for precipitation and 15-20% for surface wind.

・A surface wind speed increase of more than 4% can be seen up to 500 km from storm center.

resolution dependency of tc intensity wind speed

Resolution dependency of TC intensity (wind speed)

In the simulation mode, TCs in the 20-km model are still weak.

Lower resolution models have more difficulty to interpret results.

slide20

CMIP3 models

~+10%

0

Extratropical Cyclones

Total cyclone number

“Strong” cyclone number (<970hPa)

Present 21002200

Present 21002200

  • When tracking extratropical cyclones..
    • Number of cyclones decreases
    • “Strong” cyclones increase

Lambert and Fyfe (2006)

same in high resolution models but with different threshold for or

MRI AGCM

Same in high-resolution models but with different threshold for + or -

Frequency of cyclones as a function of threshold pressure

(60km model, 3 ensembles)

(20km model)

[Future] / [Present]

[Future] / [Present]

Mizuta et al. (2009)

slide22

NH wintertime atmospheric blocking

MRI AGCM

20km

60km

higher resolution models better represents Atlantic blockings

120km

180km

Matsueda et al. (2009) submitted to JGR

slide23

Uncertainty in future projections of blocking

MRI AGCM

long duration blockings decrease

60km ensemble

Large warming

Medium warming

Small warming

frequencies of Euro-Atlantic and Pacific blockings are projected to decrease significantly.

The larger the warming is, the less blocking frequency

Matsueda et al. (2009)

slide24
Further regional downscaling is necessary to obtain quantitative assessment of future weather extremes
dynamical downscaling by rcm

Dynamical downscaling by RCM

5-km mesh cloud resolving RCM (summer)

Boundary condition from 20-km GCM

2-km mesh CRCM (summer)

1-km mesh CRCM (heavy precip events)

High resolution climate change information

three heaviest precipitation total ptop3
Three heaviest precipitation total (Ptop3)

(OBS)

Differences of 1-hour Ptop3 between 20km-AGCM and 5km-NHM are significantly large.

Differences become smaller for longer accumulated precipitations.

Wakazuki et al. (2008 JMSJ)

three heaviest precipitation total ptop327
Three heaviest precipitation total (Ptop3)

Present

Future (end of 21c)

Change ratio(F-P)/P

Present

Future/Present

Daily maximum precipitation greater than 110 mm/day increases significantly

Future (end of 21c)

slide29

What is real precipitation?

• Uncertainty among observations

The areal average precipitation (Asia Land; June-August mean)

Rain-gauges

+

Satellites

Rain-gauges

Satellites

Monthly

Daily

Error-bar shows amplitude of interannual variability

Arakawa et al. (2009)

slide30

Can we use satellite-based daily precipitation data to study extreme events?

Daily Precipitation for June to August, 2000 at Kagoshima (mm/day)

● Radar-AMeDAS

□ GPCP-1DD

× TRMM3B42

Satellite-based “observation” underestimates

heavy precipitation compared to rain-gauge-based

observation (Radar-AMeDAS)

Satellite-based rainfall estimation is not sufficient to validate extreme precipitation events simulated by high-resolution models

slide31

A QC tool with Google Earth in APHRODITE

GHCN (a global dataset)

http://www.chikyu.ac.jp/precip

Example: Annual precipitation in 1995 at Kathmandu AP.

GSOD (GTS base real-time data)

Directly obtained from Department of Hydrometorology, Nepal

GHCN and GSOD are 1/10 of real data.

⇒Errors in units-of-measurement exist in widely used global datasets!

summary
Summary
  • Resolution of climate models becomes finer; now we can use 60-km or even 20-km mesh global climate models
  • Topography is better represented by high resolution model
  • Large-scale features of model climate improve by increasing horizontal resolution
  • High resolution model is needed to better represent weather extremes and tropical cyclones
  • Resolution vs ensemble is an issue
  • Study to interpret and connect high-resolution and lower-resolution results (e.g. scaling) is needed
  • Further work for observation data itself
  • Collaboration with impact assessment group is important (e.g. to avoid misuse of GCM output)