1. Silvina A. Solman CIMA (CONICET-UBA) DCAO (FCEN-UBA)
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

2. 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

3. Why do we need Regional Climate models?
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)

4. Why do we need Regional Climate models?

5. How well do models represent regional climate over South America?
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
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.

6. CORDEX Domains
NARCCAP
CLARIS LPB
ENSEMBLES

7. HadCM3-Q0, ECHAM5OM-R3, IPSL
CORDEX: South America/CLARIS-LPB
Model Evaluation
Framework
Climate Projection
Framework
ERA-Interim LBC
A1B
Continuous runs &
Timeslices
( and )
Regional Analysis
Regional Databanks
Multiple AOGCMs
HadCM3-Q0, ECHAM5OM-R3, IPSL

8. CLARIS-LPB coordinated experiments over South America: ERA-Interim boundary forcing
RCM/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
Sin Chou, José Marengo
WRF/CIMA
Mario Nuñez

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

10. How large is the ensemble spread?
DJF
JJA
How large is the ensemble spread?
RATIO=spread/IV

11. Temperature Annual cycle

12. Precipitation (DJF)
BIAS
RCMs Ensemble
Wet/dry bias

13. DJF
Ensemble spread
JJA
RATIO=spread/IV

14. Precipitation Annual cycle

15. Up to date most RCMs evaluations have been focused on the mean climate, but what about higher order climate variability?
Mesoscale variability
Diurnal cylce
Intraseasonal variability
Examples of precipitation variability over different time-scales
Interannual to interdecadal variability

16. What do we know? But we don’t know much about …
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

17. 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?

18. CLARIS-LPB CORDEX
Model Evaluation
Framework
Climate Projection
Framework
ERA-Interim LBC
A1B
Continuous runs &
Timeslices ;
RCP4.5, RCP8.5
or timeslices
Regional Analysis
Regional Databanks
Need for a collaborative framework to provide CORDEX projections over South America

19. 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

20. 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.