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Assessment of South America summer rainfall climatology and trends in a set of global climate models large ensembles
Author(s) -
Díaz Leandro B.,
Saurral Ramiro I.,
Vera Carolina S.
Publication year - 2021
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.6643
Subject(s) - coupled model intercomparison project , climatology , precipitation , climate model , ensemble average , environmental science , dispersion (optics) , climate change , geography , meteorology , geology , oceanography , physics , optics
The purpose of this study is to assess the ability of a set of large ensembles (LE) of Global Climate Model (GCM) simulations from the Multi‐Model Large Ensemble Archive, Coupled Model Intercomparison Project Phase 6 (CMIP6) and database for Policy Decision making for Future climate change in reproducing the variability and change of the austral summer precipitation observed in South America along the second part of the 20th century and beginning of the 21st. LE show similar biases in the mean austral summer rainfall and interannual variability than those detected in previous model sets, such as the Coupled Model Intercomparison Project Phase 5 (CMIP5). The positive trends in south‐eastern South America (SESA) and negative ones in the southern Andes (SAn), that are the most significant observed regional features, are identified to some extent in LE simulations. The negative trend in SAn is a feature consistently shown among different models. For all models, mean trend is negative in that particular region and larger than the inter‐member dispersion for each large ensemble. Trend magnitudes in the SESA region show a larger dispersion between models than the SAn region. While the mean trend among models is consistent with the observations, models underestimate the observed trend. The multi‐model ensemble of selected models that best reproduce both mean and interannual variability features of rainfall in South America shows an 18% larger positive mean trend in SESA rainfall than those that have the worst performance. However, internal variability uncertainty is still higher than the mean trend for both sets of multi‐model ensembles [5% (18%) for selected (non‐selected) models]. Improvements in the representation of the main features of mean rainfall and variability are needed in order to better describe and reduce the uncertainties associated with the main climate change related signals in the continent.