CMIP6 High Resolution Model Intercomparison Project (HighResMIP) Bias in Extreme Rainfall Drives Underestimation of Amazonian Precipitation
Seasonal 3-hour mean of number of extreme events from TRMM 3h (thick black line) and HighResMIP models used.
Image credit authors
Our results showed that Eleven out of seventeen 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 correlation) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that resolution is necessary but not sufficient to improve extreme precipitation predictions. This is particularly true in cases where convective precipitation is the main contributor to extreme precipitation. It is important to also note that convective precipitation in HighResMIP is parameterized and these parameterizations are tuned to ensure that energy is balanced, but they are not designed to capture precipitation extremes. Measurements and understanding of extreme rainfall events in tropical forests is a research area that deserves further attention.
Improving model resolution is necessary but not sufficient to enhance the accuracy of rainfall modeling in the Amazon. Additionally, there is an urgent need for measurements to better understand extreme rainfall events.
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 in the High Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project, CMIP6) represent extreme rainfall events at annual, seasonal, and sub-daily time scales. TRMM 3B42 (Tropical Rainfall Measuring Mission) 3-hour data were used as observations. Our results showed that Eleven out of seventeen 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 correlation) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. 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.