Monday, 23 January 2017: 4:15 PM
602 (Washington State Convention Center )
Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle and meteorological/hydrological extremes. To date, there have been very limited broad characterizations of AR representations in global weather and climate models despite the increasing awareness of ARs’ global signatures and impacts in all continents. Part of the challenge in AR-related global model evaluation has been the lack of automated AR detection algorithms suitable for such applications. One such algorithm was recently developed, evaluated, and applied to reanalysis products to provide a baseline characterization of the global climatology of ARs (Guan and Waliser, 2015). In this work, the above algorithm is applied to 20-year, 6-hourly simulations by 24 global weather/climate models from the GASS-YoTC Multi-model Experiment. Multiple reanalysis products are used as references. Model performance is examined for key characteristics of ARs (frequency, geometry, intensity, climate variations, etc.), with the focus on identifying and understanding systematic biases in simulated ARs. The results highlight the range of model performances relative to reanalysis uncertainties in representing the most basic features of ARs. Possible connections between AR simulation qualities and model configurations (e.g., spatial resolution, air-sea coupling) will be discussed. The work will contribute to the development of a suite of AR simulation diagnostics and model performance metrics and associated software packages.
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