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Regional climate modeling over South America: challenges and perspectives

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?

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Regional climate modeling over South America: challenges and perspectives

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  1. Regional climate modeling over South America: challenges andperspectives Silvina A. Solman CIMA (CONICET-UBA) DCAO (FCEN-UBA) UMI- IFAECI 2nd Meeting, Buenos Aires. Argentina April 25-27- 2011

  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. 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 predictthe regional climatechangeimpactson La Plata Basin (LPB) in South America, and at designingadaptationstrategies • Toprovideanensemble of regional hydroclimatescenarios and theiruncertaintiesforclimateimpactstudies.

  6. ENSEMBLES NARCCAP CLARIS LPB CORDEX Domains

  7. CORDEX: South America/CLARIS-LPB Model Evaluation Framework Climate Projection Framework ERA-Interim LBC 1989-2008 A1B Continuous runs & Timeslices (2010-2040 and 2070-2100) Regional Analysis Regional Databanks Multiple AOGCMs HadCM3-Q0, ECHAM5OM-R3, IPSL

  8. CLARIS-LPB coordinated experiments over South America: ERA-Interim boundary forcing

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

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

  11. Temperature Annual cycle

  12. Precipitation (DJF) 1990-2006 BIAS RCMs Ensemble Wet/drybias

  13. DJF JJA Ensemble spread 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? Mesoscalevariability Diurnalcylce Intraseasonalvariability Examples of precipitation variability over different time-scales Interannualtointerdecadalvariability

  16. What do weknow? • 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. Internalvariability 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 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

  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.

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