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

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Title:	Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km‐Scale Simulations.
Authors:	Feng, Zhe¹ (AUTHOR) zhe.feng@pnnl.gov, Prein, Andreas F.^2,3 (AUTHOR), Kukulies, Julia² (AUTHOR), Fiolleau, Thomas⁴ (AUTHOR), Jones, William K.⁵ (AUTHOR), Maybee, Ben⁶ (AUTHOR), Moon, Zachary L.^2,7 (AUTHOR), Núñez Ocasio, Kelly M.^2,8 (AUTHOR), Dong, Wenhao^9,10 (AUTHOR), Molina, Maria J.^2,11 (AUTHOR), Albright, Mary Grace¹² (AUTHOR), Rajagopal, Manikandan¹³ (AUTHOR), Robledo, Vanessa¹⁴ (AUTHOR), Song, Jinyan¹⁵ (AUTHOR), Song, Fengfei^15,16 (AUTHOR), Leung, L. Ruby¹ (AUTHOR), Varble, Adam C.¹ (AUTHOR), Klein, Cornelia¹⁷ (AUTHOR), Roca, Remy⁴ (AUTHOR), Feng, Ran¹² (AUTHOR)
Source:	Journal of Geophysical Research. Atmospheres. 4/28/2025, Vol. 130 Issue 8, p1-29. 29p.
Subject Terms:	Extreme weather, Storms, Thunderstorms, Atmospheric water vapor, Mesoscale convective complexes
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. Plain Language Summary: Global storm‐resolving models represent a significant advancement in predicting extreme weather events, as they can directly simulate convective storms and their impacts. These models are crucial for understanding how extreme weather might change in a warming climate. This study evaluates the performance of these advanced models in predicting large tropical storms, known as mesoscale convective systems, which are key drivers of heavy rainfall and severe weather. The research used 10 different methods to track storms in model simulations and satellite observations. We found that different tracking methods can yield varying results regarding storm frequency and rainfall. However, when comparing simulated storms to observed ones, the results are more consistent across the different trackers. The models generally perform well in predicting the average frequency of tropical storms. But they underestimate the rainfall amount from these storms by about 23% and their contribution to total precipitation by about 17%. Additionally, most models predicted heavier rainfall for a given amount of atmospheric water vapor compared to observations. The multi‐method tracking analysis offers valuable insights for improving future model development efforts, helping them better predict extreme weather events and address societal needs in a changing climate. Key Points: Mesoscale convective systems (MCSs) in global km‐scale models are evaluated against satellite observations using 10 feature trackersMost models underestimate MCS precipitation amount and their contribution to total precipitation, with larger biases over oceanModels overestimate MCS precipitation intensity and its sensitivity to precipitable water while underestimating stratiform rain contribution [ABSTRACT FROM AUTHOR]
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Database:	GreenFILE

Description
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. Plain Language Summary: Global storm‐resolving models represent a significant advancement in predicting extreme weather events, as they can directly simulate convective storms and their impacts. These models are crucial for understanding how extreme weather might change in a warming climate. This study evaluates the performance of these advanced models in predicting large tropical storms, known as mesoscale convective systems, which are key drivers of heavy rainfall and severe weather. The research used 10 different methods to track storms in model simulations and satellite observations. We found that different tracking methods can yield varying results regarding storm frequency and rainfall. However, when comparing simulated storms to observed ones, the results are more consistent across the different trackers. The models generally perform well in predicting the average frequency of tropical storms. But they underestimate the rainfall amount from these storms by about 23% and their contribution to total precipitation by about 17%. Additionally, most models predicted heavier rainfall for a given amount of atmospheric water vapor compared to observations. The multi‐method tracking analysis offers valuable insights for improving future model development efforts, helping them better predict extreme weather events and address societal needs in a changing climate. Key Points: Mesoscale convective systems (MCSs) in global km‐scale models are evaluated against satellite observations using 10 feature trackersMost models underestimate MCS precipitation amount and their contribution to total precipitation, with larger biases over oceanModels overestimate MCS precipitation intensity and its sensitivity to precipitable water while underestimating stratiform rain contribution [ABSTRACT FROM AUTHOR]
ISSN:	2169897X
DOI:	10.1029/2024JD042204