Process-Oriented Diagnosis of Tropical Cyclones in Reanalyses Using a Moist Static Energy Variance Budget

Caitlin A. Dirkes aDepartment of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida

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Allison A. Wing aDepartment of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida

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Suzana J. Camargo bLamont-Doherty Earth Observatory, Columbia University, Palisades, New York

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Daehyun Kim cDepartment of Atmospheric Sciences, University of Washington, Seattle, Washington

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Abstract

Global models are frequently used for tropical cyclone (TC) prediction and climate projections but have biases in their representation of TCs that are not fully understood. The objective of this work is to assess how well and how robustly physical processes that are important for TC development are represented in modern reanalysis products and to consider whether reanalyses can serve as an observationally constrained reference against which model representation of these physical processes can be evaluated. Differences in the representation of large-scale environmental variables relevant to TC development do not readily explain the spread in TC climatologies across climate models, as found in prior work, or across reanalysis datasets, as shown here. This motivates the use of process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled and can be used to identify areas to target for model improvement. Using the column-integrated moist static energy (MSE) variance budget, we analyze radiative and surface flux feedbacks across five different reanalyses. We construct an intensity-bin composite of the MSE variance budget to compare storms of similar intensity. Our results point to some fundamental differences across reanalyses in how they represent MSE variance and surface flux and radiative feedbacks in TCs, which could contribute to differences across reanalyses in how they represent TCs, but other factors also likely contribute. Any future work that evaluates these diagnostics in GCMs against reanalyses should do so cautiously, and efforts should be undertaken to provide a true observational estimate of these processes.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Process-oriented Diagnostics in CMIP6 and Beyond Special Collection.

Corresponding author: Allison A. Wing, awing@fsu.edu

Abstract

Global models are frequently used for tropical cyclone (TC) prediction and climate projections but have biases in their representation of TCs that are not fully understood. The objective of this work is to assess how well and how robustly physical processes that are important for TC development are represented in modern reanalysis products and to consider whether reanalyses can serve as an observationally constrained reference against which model representation of these physical processes can be evaluated. Differences in the representation of large-scale environmental variables relevant to TC development do not readily explain the spread in TC climatologies across climate models, as found in prior work, or across reanalysis datasets, as shown here. This motivates the use of process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled and can be used to identify areas to target for model improvement. Using the column-integrated moist static energy (MSE) variance budget, we analyze radiative and surface flux feedbacks across five different reanalyses. We construct an intensity-bin composite of the MSE variance budget to compare storms of similar intensity. Our results point to some fundamental differences across reanalyses in how they represent MSE variance and surface flux and radiative feedbacks in TCs, which could contribute to differences across reanalyses in how they represent TCs, but other factors also likely contribute. Any future work that evaluates these diagnostics in GCMs against reanalyses should do so cautiously, and efforts should be undertaken to provide a true observational estimate of these processes.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Process-oriented Diagnostics in CMIP6 and Beyond Special Collection.

Corresponding author: Allison A. Wing, awing@fsu.edu

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