High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7

Geoscientific Model Development Copernicus Publications 18:4 (2025) 1307-1332

Authors:

Malcolm J Roberts, Kevin A Reed, Qing Bao, Joseph J Barsugli, Suzana J Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S Fučkar, Shabeh ul Hasson, Helene T Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A Ullrich, Pier Luigi Vidale, Michael F Wehner, Colin M Zarzycki, Bosong Zhang, Wei Zhang, Ming Zhao

Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model

Journal of Advances in Modeling Earth Systems American Geophysical Union (AGU) 17:3 (2025)

Authors:

Bobby Antonio, Andrew TT McRae, David MacLeod, Fenwick C Cooper, John Marsham, Laurence Aitchison, Tim N Palmer, Peter AG Watson

Relationships Between Mesoscale Convective System Properties and Midlevel Dynamic Perturbations

Journal of Geophysical Research: Atmospheres American Geophysical Union (AGU) 130:4 (2025)

Authors:

James N Marquis, Zhe Feng, Sandro W Lubis, Zhixiao Zhang, L Ruby Leung, Huancui Hu

Environmental conditions affecting global mesoscale convective system occurrence

Journal of the Atmospheric Sciences American Meteorological Society 82:2 (2025) 391-407

Abstract:

The ERA5 environments of mesoscale convective systems (MCSs), tracked from satellite observations, are assessed over a 20-yr period. The use of a large set of MCS tracks allows us to robustly test the sensitivity of the results to factors such as region, latitude, and diurnal cycle. We aim to provide novel information on environments of observed MCSs for assessments of global atmospheric models and to improve their ability to simulate MCSs. Statistical analysis of all tracked MCSs is performed in two complementary ways. First, we investigate the environments when an MCS has occurred at different spatial scales before and after MCS formation. Several environmental variables are found to show marked changes before MCS initiation, particularly over land. The vertically integrated moisture flux convergence shows a robust signal across different regions and when considering MCS initiation diurnal cycle. We also found spatial scale dependence of the environments between 200 and 500 km, providing new evidence of a natural length scale for use with MCS parameterization. In the second analysis, the likelihood of MCS occurrence for given environmental conditions is evaluated, by considering all environments and determining the probability of being in an MCS core or shield region. These are compared to analogous non-MCS environments, allowing discrimination between conditions suitable for MCS and non-MCS occurrence. Three environmental variables are found to be useful predictors of MCS occurrence: total column water vapor, midlevel relative humidity, and total column moisture flux convergence. Such relations could be used as trigger conditions for the parameterization of MCSs, thereby strengthening the dependence of the MCS scheme on the environment.

Postprocessing East African rainfall forecasts using a generative machine learning model

Copernicus Publications (2025)

Authors:

Bobby Antonio, Andrew McRae, Dave MacLeod, Fenwick Cooper, John Marsham, Laurence Aitchison, Tim Palmer, Peter Watson