2024 Abstracts

CMIP6 HighResMIP Bias in Extreme Rainfall Drives Underestimation of Amazonian Precipitation


Robinson Negron-Juarez1* (robinson.inj@lbl.gov), Michael Wehner1, Maria Assunção F. Silva Dias2, Paul Ullrich3,4, Jeffrey Chambers1,5, William J. Riley1


1Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA; 2Departamento de Ciências Atmosféricas, Universidade de São Paulo, São Paulo, SP, Brazil; 3Physical and Life Sciences Division, Lawrence Livermore National Laboratory, Livermore, CA; 4Department of Land, Air, and Water Resources, University of California–Davis, CA; 5Department of Geography, University of California–Berkeley, CA



Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3-hourly predictions from 17 models in the High-Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project Phase 6) represent extreme rainfall events at annual, seasonal, and subdaily time scales. Tropical Rainfall Measuring Mission (TRMM 3B42) 3-hour data were used as observations. 11 HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher r values) than the other models. None of the models captured the subdaily timing of extreme rainfall, though some reproduced daily totals. Therefore, higher model resolution may be necessary but not sufficient to accurately reproduce the number and time of extreme rainfall events in the Amazon. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.