Convective Parameterization Model Bias in Extreme Rainfall Drives Underestimation of Amazonian Precipitation

The Science

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) represent extreme rainfall events at annual, seasonal, and subdaily time scales. Tropical Rainfall Measuring Mission 3B42 3-hour data were used as observations.

The Impact

Results suggest 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.

While improving model resolution is necessary, it alone is 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.

Summary

Eleven out of 17 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 subdaily timing of extreme rainfall, though some reproduced daily totals.

Results suggest resolution is necessary but not sufficient to improve extreme precipitation. This finding is particularly true in cases where convective precipitation is the main contributor to extreme precipitation. Convective precipitation in HighResMIP is parameterized, and these parameterizations are tuned to ensure energy is balanced. However, the parameterizations 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.

Principal Investigator

Robinson Negron-Juarez
Lawrence Berkeley National Laboratory
[email protected]

Program Manager

Brian Benscoter
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
[email protected]

Funding

This study was supported by the Biological and Environmental Research program within the U.S. Department of Energy (DOE). Support was also received from the Next-Generation Ecosystem Experiments Tropics project, Regional and Global Model Analysis program area, Agreement Grant DE-AC02-05CH11231, and Reducing Uncertainties in Biogeochemical Interactions through Synthesis Computation (RUBISCO) Science Focus Area.

References