Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km‐Scale Simulations

Abstract Global kilometer‐scale models represent the future of Earth system modeling, enabling explicit simulation of organized convective storms and their associated extreme weather. Here, we comprehensively evaluate tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) simulations for both summer and winter phases. Using 10 different feature trackers applied to simulations and satellite observations, we assess MCS frequency, precipitation, and other key characteristics. Substantial differences (a factor of 2–3) arise among trackers in observed MCS frequency and their precipitation contribution, but model‐observation differences in MCS statistics are more consistent across trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi‐model mean biases ranging from −2%–8% over land and −8%–8% over ocean (summer vs. winter). However, most DYAMOND models underestimate MCS precipitation amount (23%) and their contribution to total precipitation (17%). Biases in precipitation contributions are generally smaller over land (13%) than over ocean (21%), with moderate inter‐model variability. While models better simulate MCS diurnal cycles and cloud shield characteristics, they overestimate MCS precipitation intensity and underestimate stratiform rain contributions (up to a factor of 2), particularly over land, albeit observational uncertainties exist. Additionally, models exhibit a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, with many models showing exaggerated sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed here provide process‐oriented diagnostics to guide future model development.

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Publication Details

Journal
Journal of Geophysical Research Atmospheres
Published
2025-04-12
DOI
https://doi.org/10.1029/2024jd042204
Citations
20
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
21.00

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article

Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km‐Scale Simulations

Manikandan Rajagopal, William K. Jones, Zhe Feng, Ran Feng et al.
20 citations
Journal of Geophysical Research Atmospheres
Climate variability and models
21.00
article

Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km‐Scale Simulations

Manikandan Rajagopal, William K. Jones, Zhe Feng, Ran Feng, Rémy Roca, María J. Molina, Thomas Fiolleau, Ben Maybee, Kelly M. Núñez Ocasio, Julia Kukulies, Fengfei Song, Andreas F. Prein, Mary Grace Albright, John F. Mejía, Wenhao Dong, Cornelia Klein, Zachary Moon, Adam C. Varble, Lai-Yung Ruby Leung, Jinyan Song, Vanessa Robledo
article en
20 citations

Abstract

Abstract Global kilometer‐scale models represent the future of Earth system modeling, enabling explicit simulation of organized convective storms and their associated extreme weather. Here, we comprehensively evaluate tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) simulations for both summer and winter phases. Using 10 different feature trackers applied to simulations and satellite observations, we assess MCS frequency, precipitation, and other key characteristics. Substantial differences (a factor of 2–3) arise among trackers in observed MCS frequency and their precipitation contribution, but model‐observation differences in MCS statistics are more consistent across trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi‐model mean biases ranging from −2%–8% over land and −8%–8% over ocean (summer vs. winter). However, most DYAMOND models underestimate MCS precipitation amount (23%) and their contribution to total precipitation (17%). Biases in precipitation contributions are generally smaller over land (13%) than over ocean (21%), with moderate inter‐model variability. While models better simulate MCS diurnal cycles and cloud shield characteristics, they overestimate MCS precipitation intensity and underestimate stratiform rain contributions (up to a factor of 2), particularly over land, albeit observational uncertainties exist. Additionally, models exhibit a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, with many models showing exaggerated sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed here provide process‐oriented diagnostics to guide future model development.

Journal of Geophysical Research AtmospheresVol. 130(8)
NSF National Center for Atmospheric Research (US), University of Iowa (US), Centre National de la Recherche Scientifique (FR), University of Leeds (GB), Université Toulouse III - Paul Sabatier (FR), University of Connecticut (US), Pacific Northwest National Laboratory (US), Université Fédérale de Toulouse Midi-Pyrénées (FR), NOAA Geophysical Fluid Dynamics Laboratory (US), University of Utah (US), University Corporation for Atmospheric Research (US), ETH Zurich (CH), University of Oxford (GB), Earth Resources Technology (United States) (US), UK Centre for Ecology & Hydrology (GB), Laboratoire d’Études en Géophysique et Océanographie Spatiales (FR), Institut de Recherche pour le Développement (FR), Laoshan Laboratory, Ocean University of China (CN), Desert Research Institute (US), University of Maryland, College Park (US), Texas A&M University (US)
U.S. Department of Energy
Life below water
Openalex Percentile: Top 0%
Climate variability and models
21.00
DOIOpen Access PDFOpenAlex
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