Preprints
https://doi.org/10.5194/gmd-2024-142
https://doi.org/10.5194/gmd-2024-142
Submitted as: methods for assessment of models
 | 
20 Aug 2024
Submitted as: methods for assessment of models |  | 20 Aug 2024
Status: a revised version of this preprint is currently under review for the journal GMD.

Evaluation of atmospheric rivers in reanalyses and climate models in a new metrics framework

Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien

Abstract. We present a suite of new atmospheric river (AR) metrics that are designed for quick analysis of AR characteristics and statistics in gridded climate datasets such as model output and reanalysis. This package is expected to be particularly useful for climate model evaluation. The metrics include mean bias and spatial pattern correlation, which are efficient for diagnosing systematic AR biases in climate models. For example, the package identifies that in CMIP5 and CMIP6 models, AR tracks in the south Atlantic are positioned farther poleward compared to the ERA5 reanalysis, while in the south Pacific, tracks are generally biased towards the equator. For the landfalling AR peak season, we find that most climate models simulate a completely opposite seasonal cycle over western Africa. This tool is also useful for identifying and characterizing structural differences among different AR detectors (ARDTs). For example, ARs detected with the Mundhenk algorithm exhibit systematically larger size, width and length compared to the TempestExtremes (TE) method. The AR metrics developed from this work can be routinely applied for model benchmarking and during the development cycle to trace performance evolution across model versions or generations and set objective targets for the improvement of models. They can also be used by operational centers to perform near real-time climate and extreme events impact assessment as part of their forecast cycle.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2024-142', Anonymous Referee #1, 04 Sep 2024
    • AC1: 'Reply on RC1', Bo Dong, 24 Oct 2024
      • RC3: 'Reply on AC1', Anonymous Referee #1, 05 Nov 2024
        • AC4: 'Reply on RC3', Bo Dong, 21 Nov 2024
  • CEC1: 'Comment on gmd-2024-142', Astrid Kerkweg, 06 Sep 2024
    • AC2: 'Reply on CEC1', Bo Dong, 24 Oct 2024
  • RC2: 'Comment on gmd-2024-142', Anonymous Referee #2, 22 Sep 2024
    • AC3: 'Reply on RC2', Bo Dong, 24 Oct 2024
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien

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Short summary
1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the north and south Atlantic (south Pacific and Indian Ocean).