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Extreme Precipitation Scaling With Temperature Across Weather-Climate Timescales and the Role of Land Surface Feedback

Funding Program Area(s)
Additional Resources:
NERSC (National Energy Research Scientific Computing Center)
Project Type
University Grant
Project Term
Project Team

Principal Investigator

Co-Principal Investigator

Project Participant

This project will investigate potential causes for model biases in reproducing the emergent extreme precipitation-temperature relationship, and how these biases may propagate to influence the projection of future extremes. This will be accomplished using sub-daily observational data from meteorological stations, gridded precipitation data derived from satellite remote sensing, global reanalysis products, an observation-based mesoscale convective system tracking dataset, existing simulations of Energy Exascale Earth System Model (E3SM) and other Earth System Models (ESMs) at different spatial resolutions, and new E3SM experiments.

The overarching goal is to better understand and predict future changes in extreme precipitation over land. Specifically, the team  will:

  1. Assess the performance of E3SM and other ESMs at different resolutions in reproducing the extreme precipitation scaling relationships with surface air temperature and saturation deficit and identify the sources of model biases;
  2. Quantify how model-projected future changes of extreme precipitation are related to the model’s scaling ratio under saturation in the present-day climate, and to develop emergent constraints for future projections;
  3. Investigate how local and regional land surface conditions and feedback may influence extreme precipitation intensity and its scaling relationship with environmental variables, and how the land surface impact may change in a warmer climate;
  4. Evaluate the role of mesoscale convective systems in the extreme precipitation scaling with temperature and saturation deficit, and how model biases in simulating mesoscale convective systems precipitation may contribute to model biases in the scaling relationships.   

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