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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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Projection of Changes in Extremes by
Very High Resolution Atmospheric Models
Meteorological Research Institute, Tsukuba, Japan
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
CMIP3 modelsProjected 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
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
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
Orographic rainfall is successfully reproduced
Rajendran and Kitoh (2008) Current Science
In forecast mode
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
between observation and each resolution models
more wetday in low-res model
better Typhoons/Baiu in high-res model
more bias in low-res model
• 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)
• tropical cyclone
• extratropical cyclone
AGCM/RCM is a climate model version of the JMA operational NWP models
Mizuta et al. (2008)
20-km AGCM (AMIP run 1979-2003)
###:99% significance level
##:95% significance level
#:90% significance level
There is a skill for TC frequency interannual variability associated with ENSO
Annual global average
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.
In the simulation mode, TCs in the 20-km model are still weak.
Lower resolution models have more difficulty to interpret results.
Total cyclone number
“Strong” cyclone number (<970hPa)
Lambert and Fyfe (2006)
Frequency of cyclones as a function of threshold pressure
(60km model, 3 ensembles)
[Future] / [Present]
[Future] / [Present]
Mizuta et al. (2009)
higher resolution models better represents Atlantic blockings
Matsueda et al. (2009) submitted to JGR
long duration blockings decrease
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)
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
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)
Future (end of 21c）
Daily maximum precipitation greater than 110 mm/day increases significantly
Future (end of 21c)
• Uncertainty among observations
The areal average precipitation (Asia Land; June-August mean)
Error-bar shows amplitude of interannual variability
Arakawa et al. (2009)
Can we use satellite-based daily precipitation data to study extreme events?
Daily Precipitation for June to August, 2000 at Kagoshima (mm/day)
Satellite-based “observation” underestimates
heavy precipitation compared to rain-gauge-based
Satellite-based rainfall estimation is not sufficient to validate extreme precipitation events simulated by high-resolution models
GHCN (a global dataset）
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!