Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking

TL;DR

This study evaluates high-resolution convection-permitting regional climate models using a novel precipitation event tracking algorithm, finding they most effectively reduce biases in precipitation event characteristics compared to other configurations.

Contribution

It introduces a novel rainstorm identification and tracking method to evaluate the added value of convection-permitting models in climate simulations.

Findings

Convection-permitting models most effectively reduce precipitation bias.

Wet bias is driven by excessive areal extent of rainfall events.

Biases are time-dependent, with increased diurnal cycle amplitude.

Abstract

Dynamical downscaling with high-resolution regional climate models may offer the possibility of realistically reproducing precipitation and weather events in climate simulations. As resolutions fall to order kilometers, the use of explicit rather than parametrized convection may offer even greater fidelity. However, these increased model resolutions both allow and require increasingly complex diagnostics for evaluating model fidelity. In this study we use a suite of dynamically downscaled simulations of the summertime U.S. (WRF driven by NCEP) with systematic variations in parameters and treatment of convection as a test case for evaluation of model precipitation. In particular, we use a novel rainstorm identification and tracking algorithm that allocates essentially all rainfall to individual precipitation events (Chang et al. 2016). This approach allows multiple insights, including that, at least in these runs, model wet bias is driven by excessive areal extent of precipitating events. Biases are time-dependent, producing excessive diurnal cycle amplitude. We show that this effect is produced not by new production of events but by excessive enlargement of long-lived precipitation events during daytime, and that in the domain average, precipitation biases appear best represented as additive offsets. Of all model configurations evaluated, convection-permitting simulations most consistently reduced biases in precipitation event characteristics.