Regional climate modeling over South America: challenges and

perspectives

Silvina A. SolmanCIMA (CONICET-UBA)

DCAO (FCEN-UBA)

UMI- IFAECI 2nd Meeting, Buenos Aires. ArgentinaApril 25-27- 2011

Outline

– Why do we need Regional Climate models?– How well do models represent regional climate

over South America?• Main shortcomings and strengths of RCMs over South

America: the CLARIS-LPB contribution.

– Sources of uncertainty in regional climate simulations

– Possible research topics

La información climática a escala regional es crítica para los estudios de impacto

Why do we need Regional Climate models?

AOGCM

Regional Climate Model (RCM)

Why do we need Regional Climate models?

How well do models represent regional climate over South America?

CLARIS-LPBThe EU FP7 CLARIS LPB projectMain goal: To predict the regional climate change impacts on La Plata Basin (LPB) in South America, and at designing adaptation strategies To provide an ensemble of regional hydroclimate scenarios and their uncertainties for climate impact studies.

CORDEXInitiative promoted by the TFRCD /WCRP Main goal: To Provide a quality-controlled data set of RCD-based information for the recent historical past and 21st century projections, covering the majority of populated land regions on the globe. To Evaluate the ensemble of RCD simulations. to provide a more solid scientific basis for impact assessments and other uses of downscaled climate information

CORDEX Domains

NARCCAPNARCCAP

CLARIS LPBCLARIS LPB

ENSEMBLESENSEMBLES

CORDEX: South America/CLARIS-LPBCORDEX: South America/CLARIS-LPB

Model Evaluation Model Evaluation FrameworkFramework

Climate ProjectionClimate ProjectionFrameworkFramework

ERA-Interim LBC ERA-Interim LBC 1989-20081989-2008

Multiple AOGCMsMultiple AOGCMsHadCM3-Q0, ECHAM5OM-R3, IPSL

A1BA1BContinuous runs & Continuous runs &

Timeslices Timeslices (2010-2040 and 2070-2100)(2010-2040 and 2070-2100)

Regional AnalysisRegional AnalysisRegional DatabanksRegional Databanks

CLARIS-LPB coordinated experiments over South America:

ERA-Interim boundary forcingRCM/Institution Country Contact person

RCA/SHMI Sweden Patrick Samuelsson

MM5/CIMA Argentina Silvina Solman, Natalia Pessacg

RegCM3/USP Brazil Rosmeri Porfirio da Rocha

REMO/MPI Germany Armelle Reca Remedio, Daniela Jacob

PROMES/UCLM Spain Enrique Sánchez , R. Ochoa

LMDZ/IPSL France Laurent Li

ETA/INPE Brazil Sin Chou, José Marengo

WRF/CIMA Argentina Mario Nuñez

Mean Temperature (DJF) 1990-2006 BIAS

RCMs Ensemble

Warm/cold bias

Ensemble spread DJF JJA

How large is the ensemble spread?

RATIO=spread/IV

Temperature Annual cycle

Precipitation (DJF) 1990-2006 BIAS

RCMs Ensemble

Wet/dry bias

Ensemble spread DJF JJA

RATIO=spread/IV

Precipitation Annual cycle

• Up to date most RCMs evaluations have been focused on the mean climate, but what about higher order climate variability?

Diurnal cylce Mesoscale variabilityIntraseasonal variability

Interannual to interdecadal variability

Examples of precipitation variability over different time-scales

What do we know?• Overall model performance of the mean climate• Systematic biases of the simulated mean climate

• Largest biases mainly over tropical South America• Warm and dry biases over tropical regions: Land surface?• Dry and bias over LPB: resolution?

• Uncertainty on simulating mean climate (inter-model spread)

– Largest biases mainly over tropical regions

But we don’t know much about …• Model performance on higher order variability

patterns• Systematic biases on higher order variability patterns• Uncertainty in simulating higher order variability

patterns

Internal variability of a RCM over South America

• MM5 model• OND 1986• 4 members(Solman and Pessacg, 2010)

•How large is the internal variability for long-term climate simulations?

•Annual cycle of the internal variability?

CLARIS-LPB CLARIS-LPB CORDEXCORDEX

Model Evaluation Model Evaluation FrameworkFramework

Climate ProjectionClimate ProjectionFrameworkFramework

ERA-Interim LBC ERA-Interim LBC 1989-20081989-2008

A1BA1BContinuous runs & Continuous runs &

TimeslicesTimeslices2010-2040; 2070-21002010-2040; 2070-2100

Regional AnalysisRegional AnalysisRegional DatabanksRegional Databanks

RCP4.5, RCP8.5RCP4.5, RCP8.51951-2100 1951-2100 or timeslicesor timeslices

Need for a collaborative framework to provide CORDEX projections over South America

RCM perspectives

• Need for evaluating RCMs in terms of variability patterns.

• Understanding the causes for the systematic biases of the simulated mean climate

• Need for evaluating the internal variability of RCMs to put the climate response patterns in the context of the noise level.

• Need for a collaborative framework to provide CORDEX projections over South America

Conclusions• South American climate is characterized by variability patterns on a

broad range of timescales and different spatial distributions.• Regional climate models are able to simulate the mean climatic

conditions, though large uncertainties and systematic biases can be identified over some regions /variables.

• Studies using Regional Climate models focused on the response of the regional climate to external forcings (increasing CO2; land use changes or soil moisture conditions) show that the climate response is very heterogeneous both spatially and temporally.

• Some particular regions of South America exhibit large responses, mainly in terms of changes in precipitation, temperature and moisture flux to these external forcings.