A53J-3330:
Atmospheric River Model Simulation Diagnostics and Performance Metrics
Friday, 19 December 2014
Duane Edward Waliser1, Bin Guan2, Jinwon Kim2, L. Ruby Leung3 and F Martin Ralph4, (1)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (2)University of California Los Angeles, Los Angeles, CA, United States, (3)Pac NW National Lab, Richland, WA, United States, (4)Scripps Institute of Oceanography, La Jolla, CA, United States
Abstract:
Atmospheric Rivers (ARs) are narrow, elongated, synoptic jets of water vapor. These systems account for over 90% of the poleward transport of water vapor in mid-latitudes and thus are a key mechanism in help establish the water and energy cycles of the planet. Many of the intense wintertime hydrological (flood and drought-ending precipitation) events in the US western states (as well as in other continents) occur in conjunction with land-falling AR events. Despite the important role of the ARs in our climate and weather systems, there have been few broad characterizations of model performance of ARs for global weather and climate models (GCMs), in terms of their role in global climate or impacts associated with extreme weather. Part of the challenge has been the lack of a comprehensive set of observation-based model simulation diagnostics and performance metrics. Based on the objectives and support from three activities: 1) the CalWater 2 AR project, 2) the Year of Tropical Convection (YOTC) and GEWEX Atmospheric System Study (GASS) multi-model experiment on Vertical Structure and Physical Processes of Weather & Climate, and 3) a new NASA effort examining the value added by dynamic regional climate model (RCM) downscaling, we are working to develop a comprehensive set of AR simulation diagnostics and model performance metrics for RCMs and GCMs. Application of these diagnostics and metrics will afford: 1) a baseline characterization of model representations of synoptic features, impacts, and multi-scale interactions, 2) an ability to guide model development and assess proposed improvements, 3) quantify the evolution in forecast skill, as well as 4) estimate predictability of AR characteristics and impacts. The purpose of this presentation is to initiate a more formal dialogue of this activity with the community, present a preliminary set of diagnostics/metrics and illustrate their utility through application to the 27 GCMs that contributed simulations to the YOTC/GASS multi-model experiment highlighted above.
