Systematic temperature and precipitation biases in the CLARIS-LPB ensemble simulations over South America and possible implications for climate projections

Within the framework of the CLARIS-LPB EU Project, a suite of 7 coordinated Regional Climate Model (RCM) simulations over South America driven by both the ERA-Interim reanalysis and a set of Global Climate Models (GCMs) were evaluated. The systematic biases in simulating monthly mean temperature and...

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Detalles Bibliográficos
Autor principal: Solman, Silvina Alicia
Publicado: 2016
Materias:
La Plata Basin
Regional climate change
Regional Climate Models
South America
Systematic bias
amplification
climate change
climate modeling
climate signal
data set
ensemble forecasting
European Union
precipitation intensity
regional climate
sampling bias
temperature effect
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0936577X_v68_n2-3_p117_Solman
http://hdl.handle.net/20.500.12110/paper_0936577X_v68_n2-3_p117_Solman
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_0936577X_v68_n2-3_p117_Solman
record_format dspace
spelling paper:paper_0936577X_v68_n2-3_p117_Solman2023-06-08T15:53:18Z Systematic temperature and precipitation biases in the CLARIS-LPB ensemble simulations over South America and possible implications for climate projections Solman, Silvina Alicia La Plata Basin Regional climate change Regional Climate Models South America Systematic bias amplification climate change climate modeling climate signal data set ensemble forecasting European Union precipitation intensity regional climate sampling bias temperature effect La Plata Basin Within the framework of the CLARIS-LPB EU Project, a suite of 7 coordinated Regional Climate Model (RCM) simulations over South America driven by both the ERA-Interim reanalysis and a set of Global Climate Models (GCMs) were evaluated. The systematic biases in simulating monthly mean temperature and precipitation from the 2 sets of RCM simulations were identified. The Climate Research Unit dataset was used as a reference. The systematic model errors were more dependent on the RCMs than on the driving GCMs. Most RCMs showed a systematic temperature overestimation and precipitation underestimation over the La Plata Basin region. Model biases were not invariant, but a temperature-dependent temperature bias and a precipitation-dependent precipitation bias were apparent for the region, with the warm bias amplified for warm months and the dry bias amplified for wet months. In a climate change scenario, the relationship between model bias behaviour and the projected climate change for each individual model revealed that the models with the largest temperature bias amplification projected the largest warming and the models with the largest dry bias amplification projected the smallest precipitation increase, suggesting that models' bias behaviour may affect the future climate projections. After correcting model biases by means of a quantile-based mapping bias correction method, projected temperature changes were systematically reduced, and projected precipitation changes were systematically increased. Though applying bias correction method - ologies to projected climate conditions is controversial, this study demonstrates that bias correction methodologies should be considered in order to better interpret climate change signals. © Inter-Research 2016. Fil:Solman, S.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0936577X_v68_n2-3_p117_Solman http://hdl.handle.net/20.500.12110/paper_0936577X_v68_n2-3_p117_Solman
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic La Plata Basin
Regional climate change
Regional Climate Models
South America
Systematic bias
amplification
climate change
climate modeling
climate signal
data set
ensemble forecasting
European Union
precipitation intensity
regional climate
sampling bias
temperature effect
La Plata Basin
spellingShingle La Plata Basin
Regional climate change
Regional Climate Models
South America
Systematic bias
amplification
climate change
climate modeling
climate signal
data set
ensemble forecasting
European Union
precipitation intensity
regional climate
sampling bias
temperature effect
La Plata Basin
Solman, Silvina Alicia
Systematic temperature and precipitation biases in the CLARIS-LPB ensemble simulations over South America and possible implications for climate projections
topic_facet La Plata Basin
Regional climate change
Regional Climate Models
South America
Systematic bias
amplification
climate change
climate modeling
climate signal
data set
ensemble forecasting
European Union
precipitation intensity
regional climate
sampling bias
temperature effect
La Plata Basin
description Within the framework of the CLARIS-LPB EU Project, a suite of 7 coordinated Regional Climate Model (RCM) simulations over South America driven by both the ERA-Interim reanalysis and a set of Global Climate Models (GCMs) were evaluated. The systematic biases in simulating monthly mean temperature and precipitation from the 2 sets of RCM simulations were identified. The Climate Research Unit dataset was used as a reference. The systematic model errors were more dependent on the RCMs than on the driving GCMs. Most RCMs showed a systematic temperature overestimation and precipitation underestimation over the La Plata Basin region. Model biases were not invariant, but a temperature-dependent temperature bias and a precipitation-dependent precipitation bias were apparent for the region, with the warm bias amplified for warm months and the dry bias amplified for wet months. In a climate change scenario, the relationship between model bias behaviour and the projected climate change for each individual model revealed that the models with the largest temperature bias amplification projected the largest warming and the models with the largest dry bias amplification projected the smallest precipitation increase, suggesting that models' bias behaviour may affect the future climate projections. After correcting model biases by means of a quantile-based mapping bias correction method, projected temperature changes were systematically reduced, and projected precipitation changes were systematically increased. Though applying bias correction method - ologies to projected climate conditions is controversial, this study demonstrates that bias correction methodologies should be considered in order to better interpret climate change signals. © Inter-Research 2016.
author Solman, Silvina Alicia
author_facet Solman, Silvina Alicia
author_sort Solman, Silvina Alicia
title Systematic temperature and precipitation biases in the CLARIS-LPB ensemble simulations over South America and possible implications for climate projections
title_short Systematic temperature and precipitation biases in the CLARIS-LPB ensemble simulations over South America and possible implications for climate projections
title_full Systematic temperature and precipitation biases in the CLARIS-LPB ensemble simulations over South America and possible implications for climate projections
title_fullStr Systematic temperature and precipitation biases in the CLARIS-LPB ensemble simulations over South America and possible implications for climate projections
title_full_unstemmed Systematic temperature and precipitation biases in the CLARIS-LPB ensemble simulations over South America and possible implications for climate projections
title_sort systematic temperature and precipitation biases in the claris-lpb ensemble simulations over south america and possible implications for climate projections
publishDate 2016
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0936577X_v68_n2-3_p117_Solman
http://hdl.handle.net/20.500.12110/paper_0936577X_v68_n2-3_p117_Solman
work_keys_str_mv AT solmansilvinaalicia systematictemperatureandprecipitationbiasesintheclarislpbensemblesimulationsoversouthamericaandpossibleimplicationsforclimateprojections
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