Regional climate modeling over South America: challenges and perspectives

Silvina A. Solman CIMA (CONICET-UBA) DCAO (FCEN-UBA) UMI- IFAECI 2nd Meeting, Buenos Aires. Argentina April 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

AOGCM Regional Climate Model (RCM) es

Why do we need Regional Climate models?

How well do models represent regional climate over South America?

CLARIS-LPB

The EU FP7 CLARIS LPB project Main 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.

CORDEX

Initiative 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

NARCCAP ENSEMBLES CLARIS LPB

CORDEX: South America/CLARIS-LPB

Model Evaluation Framework

Climate Projection Framework ERA-Interim LBC 1989-2008 Regional Analysis Regional Databanks A1B Continuous runs & Timeslices (2010-2040 and 2070-2100) Multiple AOGCMs

HadCM3-Q0, ECHAM5OM-R3, IPSL

CLARIS-LPB coordinated experiments over South America: ERA-Interim boundary forcing RCM/Institution

RCA/SHMI MM5/CIMA RegCM3/USP REMO/MPI PROMES/UCLM LMDZ/IPSL ETA/INPE WRF/CIMA

Country

Sweden Argentina Brazil Germany Spain France Brazil Argentina

Contact person

Patrick Samuelsson Silvina Solman, Natalia Pessacg Rosmeri Porfirio da Rocha Armelle Reca Remedio, Daniela Jacob Enrique Sánchez , R. Ochoa Laurent Li Sin Chou, José Marengo Mario Nuñez

Mean Temperature (DJF) 1990-2006 BIAS RCMs Ensemble Warm / cold bias

DJF Ensemble spread

How large is the ensemble spread?

RATIO=spread/IV JJA

Temperature Annual cycle

Precipitation (DJF) 1990-2006 BIAS RCMs Ensemble Wet / dry bias

DJF Ensemble spread JJA RATIO=spread/IV

Precipitation Annual cycle

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

Diurnal cylce

Examples of precipitation variability over different time-scales

Mesoscale variability Intraseasonal variability Interannual to interdecadal variability

• • •

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?

Model Evaluation Framework

CLARIS-LPB CORDEX

Climate Projection Framework ERA-Interim LBC 1989-2008 A1B Continuous runs & Timeslices 2010-2040; 2070-2100 RCP4.5, RCP8.5

1951-2100 or timeslices Regional Analysis Regional Databanks

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.