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Climate extremes in changing climate
Grigory Nikulin
Rossby Centre Swedish Meteorological and Hydrological Institute
WERISK
Climate risks and nuclear power plants
Finnish Meteorological Institute
Ari VenäläinenSeppo SakuKirsti Jylhä
Swedish Meteorological and Hydrological Institute
Grigory Nikulin Erik KjellströmLars Bärring
Outline
• regional climate modelingdynamical downscalingSMHI-Rossby centre regional climate modelsuncertainties
• analysis of extreme eventsconcept of return values
• projected climate changes in extreme eventstemperatureprecipitation
Regional climate modelingglobal climate models (about 20 worldwide):100-400 km resolution - too coarse for regional applications
statistical or dynamical downscalingdriving global model as boundary conditions
regional model ( 50 km )global model
global model
global modelgl
obal
mod
el
Models at SMHI-Rossby Centre
• RCA - atmospheric model• RCO - oceanographic model• RCAO - coupled atmospheric-ocean model
RCA:land-surface model (forest, open land, snow)lake model (important for Scandinavia)
Regions: Europe, Arctic, Africa, South America
Resolution : 50 km (standard)25 km (moving)12.5 km (testing; ‘future standard’)
RCA: 50 km resolution
50 x 50 km
observations (one point) ≠ model (box average)
RCA: 25 km resolution
50 x 50 km25 x 25 km
RCA: 12.5 km resolution
50 x 50 km25 x 25 km12 x 12 km
Uncertainties in climate modelingforcingfuture CO2 concentrations ???
model formulation
natural variability
both global driving and regionalmodels
How good are models at representing today’s climate?
2000 2100
futureclimate
observedclimate Natural variability
Models
Emission scenarios
From Jouni Räisänen
How can we deal with uncertainties ?en ensemble approach
to sample uncertainties by performing many simulations
Ensembles:one model, one scenario, different initial conditionsone model, one scenario, perturbed physicsone model, several scenariosseveral models, one scenario
Regional ensemble matrixregional climate models
RCM1 RCM2 ……..GCM1 X X
GCM2 X X
…….driv
ing
glob
al
clim
ate
mod
els the Ensembles project
~12 RCMs and 5 GCMs
http://ensembles-eu.metoffice.com/
Extreme value analysis (EVA)
EVA → probability of rare events (unusually large or small)probabilities → in terms of T-year return values
Temperature: 50-year return value is 35oC:35oC occurs once in 50 years
Sampling extremes → Block maximum method:one max or min value for every year
Statistical model:Fitting Generalized Extreme Value (GEV) distribution to the sampled extremes
Return values and periodsAnnual maximum temperature in Stockholm (1971-2000)
longer periods → larger uncertainties in the return levels
low confidence on the 100-year and longer return values
Forcing modelsForcing global models (A1B scenario):
ECHAM5-r1 (MPI, Germany)ECHAM5-r2ECHAM5-r3HadCM3-ref (Hadley Centre, UK)HadCM3-lowHadCM3-highBCM (NERSC, Norway)CCSM3 (NCAR, USA)CNRM (CNRM, France)
different initialconditions
perturbed physics: different climatesensitivity
Observationsthe gridded ENSEMBLES data set (Haylock et al., 2008)
20-yr return values of annual T2max
RCA, 50 km, 1961-1990
20-yr return values of annual T2max
Regional climate models, 25 km, 1961-1990
20-yr ret. values of maximum precipitation
RCA, 50 km, 1961-1990
Climate change signal: T2max
RCA: 20-yr ret. val. of T2max ENSEMBLE MEAN
Climate change signal:max precipitation
RCA: 20-yr ret. val. of max precipitation (ENSEMBLE MEAN)
Climate change signal: precipitation
Winter Spring
Summer Fall
Seasonal mean20-year ret. values ofannual maximum
an decrease in seasonal meansbut
an increase in annual maximum
Summary
Climate change in extreme temperature:an increase over Europe the largest increase in southern Europe
Climate change in extreme precipitation:an increase over Europe the largest increase in Scandinavia
Extreme events vs seasonal meanssign can be oppositeIberian peninsula
an decrease in seasonal mean precipitationan increase in extreme precipitation