Mesoscale convective systems tracking method intercomparison (MCSMIP): application to DYAMOND global km‐scale simulations

Feng, Zhe; Prein, Andreas F.; Kukulies, Julia; Fiolleau, Thomas; Jones, William K.; Maybee, Ben; Moon, Zachary L.; Núñez Ocasio, Kelly M.; Dong, Wenhao; Molina, Maria J.; Albright, Mary Grace; Rajagopal, Manikandan; Robledo, Vanessa; Song, Jinyan; Song, Fengfei; Leung, L. Ruby; Varble, Adam C.; Klein, Cornelia; Roca, Remy; Feng, Ran; Mejia, John F.

Mesoscale convective systems tracking method intercomparison (MCSMIP): application to DYAMOND global km‐scale simulations

Before downloading, please read NORA policies.

Preview

Text
N539274JA.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.
Download (14MB) | Preview

Official URL: https://doi.org/10.1029/2024JD042204

Abstract/Summary

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.

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1029/2024JD042204

UKCEH and CEH Sections/Science Areas:

Water and Climate Science (2025-)

ISSN:

2169-897X

Additional Information:

Open Access paper - full text available via Official URL link.

NORA Subject Terms:

Meteorology and Climatology
Computer Science
Data and Information