Precipitation characteristics of the South American monsoon system derived from multiple datasets
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Carvalho LMV, Jones C, Posadas AND, Quiroz R, Bookhagen B, Liebmann B. 2012. Precipitation characteristics of the South American monsoon system derived from multiple datasets. Journal of Climate 25: 4600–4620.
Permanent link to cite or share this item: https://hdl.handle.net/10568/34933
The South American monsoon system (SAMS) is the most important climatic feature in South America and is characterized by pronounced seasonality in precipitation during the austral summer. This study compares several statistical properties of daily gridded precipitation from different data (1998–2008): 1) Physical Sciences Division (PSD), Earth System Research Laboratory [1.0° and 2.5° latitude (lat)/longitude (lon)]; 2) Global Precipitation Climatology Project (GPCP; 1° lat/lon); 3) Climate Prediction Center (CPC) unified gauge (CPC-uni) (0.5° lat/lon); 4) NCEP Climate Forecast System Reanalysis (CFSR) (0.5° lat/lon); 5) NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis (0.5° lat/0.3° lon); and 6) Tropical Rainfall Measuring Mission (TRMM) 3B42 V6 data (0.25° lat/lon). The same statistical analyses are applied to data in 1) a common 2.5° lat/lon grid and 2) in the original resolutions of the datasets. All datasets consistently represent the large-scale patterns of the SAMS. The onset, demise, and duration of SAMS are consistent among PSD, GPCP, CPC-uni, and TRMM datasets, whereas CFSR and MERRA seem to have problems in capturing the correct timing of SAMS. Spectral analyses show that intraseasonal variance is somewhat similar in the six datasets. Moreover, differences in spatial patterns of mean precipitation are small among PSD, GPCP, CPC-uni, and TRMM data, while some discrepancies are found in CFSR and MERRA relative to the other datasets. Fitting of gamma frequency distributions to daily precipitation shows differences in the parameters that characterize the shape, scale, and tails of the frequency distributions. This suggests that significant uncertainties exist in the characterization of extreme precipitation, an issue that is highly important in the context of climate variability and change in South America.