In this study, the authors compare six different rainfall datasets for South America with a focus on their representation of extreme rainfall during the monsoon season (December–February): the gauge-calibrated TRMM 3B42 V7 satellite product; the near-real-time TRMM 3B42 V7 RT, the GPCP 1° daily (1DD) V1.2 satellite–gauge combination product, the Interim ECMWF Re-Analysis (ERA-Interim) product; output of a high-spatial-resolution run of the ECHAM6 global circulation model; and output of the regional climate model Eta. For the latter three, this study can be understood as a model evaluation. In addition to statistical values of local rainfall distributions, the authors focus on the spatial characteristics of extreme rainfall covariability. Since traditional approaches based on principal component analysis are not applicable in the context of extreme events, they apply and further develop methods based on complex network theory. This way, the authors uncover substantial differences in extreme rainfall patterns between the different datasets: (i) The three model-derived datasets yield very different results than the satellite–gauge combinations regarding the main climatological propagation pathways of extreme events as well as the main convergence zones of the monsoon system. (ii) Large discrepancies are found for the development of mesoscale convective systems in southeastern South America. (iii) Both TRMM datasets and ECHAM6 indicate a linkage of extreme rainfall events between the central Amazon basin and the eastern slopes of the central Andes, but this pattern is not reproduced by the remaining datasets. The authors’ study suggests that none of the three model-derived datasets adequately captures extreme rainfall patterns in South America.
- extreme events
- climate classification/regimes
- pattern detection
- statistical techniques
- time series
Boers, N., Bookhagen, B., Marengo, J., Marwan, N., von Storch, J-S., & Kurths, J. (2015). Extreme rainfall of the South American monsoon system: A dataset comparison using complex networks. Journal of climate, 28(3), 1031-1056. https://doi.org/10.1175/JCLI-D-14-00340.1