Multiscale precipitation variability over South America: Analysis of the added value of CORDEX RCM simulations
This study is aimed to assess the added value of Regional Climate Models (RCMs) with respect to their driving Global Climate Models (GCMs) focused on the behavior of precipitation over South America (SA) at different temporal scales. RCMs from the CORDEX experiments available for the South American domain at 0.44° resolution were used together with their driving GCMs from the Coupled Models Intercomparison Project (CMIP5) dataset for the period 1979–2005. Observed data from the CPC-Global Unified Gauge-Based Analysis of Daily Precipitation was used to evaluate the simulations. Precipitation data were first filtered to retain the variability at the interannual, intraseasonal and synoptic timescales. Statistics of the daily precipitation data were also evaluated, including percentiles of light, moderate, heavy and extreme precipitation events. The added value was quantified in terms of two metrics accounting for the spatial distribution of the particular precipitation feature and the M-skill score, representing a measure of normalized square errors. Added value is strongly dependent on the RCM-GCM pair evaluated. RCMs with an overall good performance add value over their driving GCM for all metrics analyzed, from the climatological seasonal mean to the intensity of extreme events. The added value arises more clearly as long as the smaller the scale of the precipitation feature assessed is.
KeywordsAdded value Precipitation Temporal scale South America Regional climate modeling
The authors appreciate the comments and suggestions raised by two anonymous reviewers who helped improving the manuscript. This work was supported by UBACYT2014 Grant 20020130200233BA and UBACYT2018 Grant 20020170100117BA. The authors thank CMIP5 and CORDEX for making available the simulation data used in this work. CPC Global Unified Precipitation data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://www.esrl.noaa.gov/psd/.
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