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
La información
a escala Climate
regional es
Why do weclimática
need Regional
crítica para los
estudios
de
impacto
models?
AOGCM
Regional Climate
Model (RCM)
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 qualitycontrolled 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
CLARIS LPB
ENSEMBLES
CORDEX: South America/CLARIS-LPB
Model Evaluation
Framework
ERA-Interim LBC
1989-2008
Climate Projection
Framework
A1B
Continuous runs &
Timeslices
(2010-2040 and 2070-2100)
Regional Analysis
Regional Databanks
Multiple AOGCMs
HadCM3-Q0, ECHAM5OM-R3, IPSL
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
Brazil
Sin Chou, José Marengo
WRF/CIMA
Argentina
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
RCMs
Ensemble
BIAS
Wet/dry bias
DJF
Ensemble spread
RATIO=spread/IV
JJA
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
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?
CLARIS-LPB
Model Evaluation
Framework
ERA-Interim LBC
1989-2008
Regional Analysis
Regional Databanks
CORDEX
Climate Projection
Framework
A1B
Continuous runs &
Timeslices
2010-2040; 2070-2100
RCP4.5, RCP8.5
1951-2100
or 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.