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