Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle and regional weather/hydrology. A recent study (Guan and Waliser, 2017) revealed considerable challenges and inter-model differences in simulating the phenomenology of ARs (e.g., geometry, frequency, intensity) with the state-of-the-art weather/climate models. The current work takes a step further to diagnose model errors at process levels, with a focus on quantifying the AR water vapor budget. An AR detection algorithm (Guan and Waliser, 2015) is applied to 20-year, 6-hourly simulations by 20+ global weather/climate models from the GASS-YoTC Multi-model Experiment. Water vapor budget terms are calculated for four distinctive sectors of each AR: post-frontal, frontal, AR, and pre-AR, with the dominant term(s) identified in each sector and compared to the ERA-Interim reanalysis. Systematic biases in individual models, as well as inter-model differences in these biases, are identified and will be illustrated. Possible connections between simulation qualities of AR water vapor budget and model configurations (e.g., spatial resolution, super-parameterization, air-sea coupling) will be discussed. The work will contribute to the ongoing development of a suite of AR simulation diagnostics and model performance metrics and associated software packages, and can help guide dedicated observational efforts for better constraining AR processes in weather and climate models.
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