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Diagnosing S2S Precipitation Biases and Errors Associated with Extratropical Cyclones and Storm Tracks over the Continental United States Using the GFDL SPEAR Model

Extratropical cyclones, which make up the mid-latitude storm tracks, are the key driver producing cool season precipitation in the CONUS. Hence, biases and errors in cyclone and storm track prediction, or in the structure of cyclone related precipitation, can give rise to biases in model predicted precipitation. S2S prediction models can have biases either in the mean climate or in the prediction of climate variability. Hence, precipitation biases and errors over the CONUS can arise due to model biases and errors in: 1) the prediction of climate drivers such as ENSO, MJO, polar vortex and the QBO; 2) the mid-latitude teleconnection patterns associated with these climate modes; 3) the response of extratropical cyclones to these large scale teleconnections; and 4) the precipitation structure associated with extratropical cyclones. Preliminary results by the PIs’ groups have shown that S2S model simulations exhibit several of these biases. This project will diagnose extratropical cyclone related precipitation biases, including biases in extreme precipitation, using GFDL’s SPEAR model simulations. We will evaluate the model bias in winter cyclone frequency/intensity using reanalysis data and examine the linkage between cyclone frequency/intensity bias and precipitation bias at the S2S time scale. Precipitation biases will be assessed using rain gauge- and satellite-based precipitation estimates. Model biases will be stratified according to geographical regions, cyclone paths and evolution, as well as cyclone intensity. Biases in model cyclone and precipitation response to the modes of climate variability discussed above will be quantified. We will diagnose the cyclone related synoptic precipitation structural errors in model simulations using observations and identify the key processes causing these structural errors.
Model sensitivity studies will be conducted to provide insights on how these biases may be reduced. Sensitivity to model resolution, sea surface temperature biases, biases in tropical forcing, biases in the large-scale circulation, and biases in model dynamics and physics will be assessed using model intervention experiments. The outcome from this work will inform GFDL’s model development team on how to improve cool season precipitation simulation and prediction. On top of that, our results will provide a detail account of the contribution of different kinds of extratropical cyclones to the mean and extreme precipitation over CONUS, as well as how well SPEAR simulates these contributions. Selected analyses will be conducted to examine biases exhibited by CFSv2 and GEFSv12 subseasonal predictions.
This project aims to identify and understand model precipitation biases and systematic errors associated with extratropical cyclones and storm tracks, which are the key physical drivers for precipitation over the CONUS during the cool season, through data analysis and global modeling experiments. While the numerical experiments will be conducted using the GFDL SPEAR model, our diagnostic studies will also be conducted on CFSv2 and GEFS simulations to identify biases and systematic errors in these models. Diagnostic tools developed in this project can also be applied to diagnose model errors and biases in future model simulations and predictions, including those of the subseasonal UFS. Our results will provide insights to model developers on the sources of cool season precipitation biases, informing future model development, thus this project is clearly relevant to the competition and to NOAA’s mission.

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