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Extreme Precipitation Features and Their Large-Scale Environments

Funding Program Area(s)
Project Type
University Grant
Project Term
Project Team

Principal Investigator

Co-Principal Investigator

Project Participant

Precipitation remains among the most challenging variables for weather and climate models to simulate accurately, but is of crucial importance for societal impacts. This project pursues exploratory diagnostics and metrics to inform the improvement of these models. It aims to bridge the gap between phenomenon-based diagnostics and process-oriented diagnostics. The former involve the identification of features associated with extreme weather. These including monsoon low-pressure systems (LPS), mesoscale convective systems (MCS), frontal systems, and atmospheric rivers. We propose two leading diagnostic approaches (1) constructing precipitation statistics such as probability distributions of precipitation intensity for each feature type; and (2) asking which physical process in a climate model is responsible for coupling precipitation to its large-scale environment. The work is organized into the following tasks:

Task 1: Create a framework to provide feature information from the phenomenon-based diagnostics as a catalog for input to the spatiotemporal and process-oriented diagnostics. We will further leverage the combination of different areas of expertise to define and create catalogs for feature-environment variables. This framework will be implemented compatible with the DOE Coordinated Model Evaluation Capabilities (CMEC) and NOAA Model Diagnostics Task Force (MDTF) common standard for Earth system diagnostics so that the capabilities generalize to future diagnostics from other groups. We will conduct feature analysis on an observational reanalysis, the European Center for Medium-Range Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and on the Department of Energy Energy Exascale Earth System Model (E3SM), and high-resolution climate model output from the High Resolution Model Intercomparison Project (HighResMIP) archive to complement feature catalogs produced by other efforts.

Task 2: For key precipitation spatiotemporal characteristics, evaluate: the characteristics for each phenomenon and whether the models can represent these; and the contributions by different phenomena to total precipitation. For instance, for probability distributions of precipitation intensity and spatial cluster feature-integrated precipitation, the ability of E3SM and HighResMIP models to correctly capture contributions of phenomena such as LPS (including tropical monsoon depressions and tropical cyclones), MCS, fronts, and ARs will be evaluated. Differences in precipitation probability distribution shape for contributions by different phenomena will be examined for implications for changes in probability of extremes under warming. Systematic evaluation of contributions by each phenomena as a function of space, season and environmental factors in models versus observations will be used to provide observational constraints and information about which processes are contributing to uncertainty and biases.

Task 3: For each of the major phenomena classes, we will create process-oriented diagnostics for the relation of feature statistics to hypothesized leading factors in the large-scale thermodynamic and dynamic environment. For convective phenomena, we will leverage an empirical buoyancy measure that provides strong relationships to precipitation, while evaluating factors in the vorticity-shear environment for all phenomena.

This proposal addresses the RGMA topic “Water cycle and Associated Extremes” with respect to the following aspects: influence of the large-scale environment on extreme events; interactions between convective scales and the large-scale circulation, including environmental influence on mesoscale organization; and process-level identification of sources of uncertainty and feedbacks to help reduce biases in global model projections. It will expand the sustainable software capabilities of CMEC both in terms of standards and of hypothesis-driven process diagnostics.

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