Articles | Volume 16, issue 21
https://doi.org/10.5194/gmd-16-6355-2023
https://doi.org/10.5194/gmd-16-6355-2023
Methods for assessment of models
 | 
08 Nov 2023
Methods for assessment of models |  | 08 Nov 2023

Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations

Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma

Related authors

Atmospheric Radiation Measurement (ARM) airborne field campaign data products between 2013 and 2018
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024,https://doi.org/10.5194/essd-16-5429-2024, 2024
Short summary
Understanding aerosol–cloud interactions using a single-column model for a cold-air outbreak case during the ACTIVATE campaign
Shuaiqi Tang, Hailong Wang, Xiang-Yu Li, Jingyi Chen, Armin Sorooshian, Xubin Zeng, Ewan Crosbie, Kenneth L. Thornhill, Luke D. Ziemba, and Christiane Voigt
Atmos. Chem. Phys., 24, 10073–10092, https://doi.org/10.5194/acp-24-10073-2024,https://doi.org/10.5194/acp-24-10073-2024, 2024
Short summary
Evaluation of liquid cloud albedo susceptibility in E3SM using coupled eastern North Atlantic surface and satellite retrievals
Adam C. Varble, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Shuaiqi Tang, and Jerome Fast
Atmos. Chem. Phys., 23, 13523–13553, https://doi.org/10.5194/acp-23-13523-2023,https://doi.org/10.5194/acp-23-13523-2023, 2023
Short summary
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 1: assessing E3SM aerosol predictions using aircraft, ship, and surface measurements
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022,https://doi.org/10.5194/gmd-15-4055-2022, 2022
Short summary
The E3SM version 1 single-column model
Peter A. Bogenschutz, Shuaiqi Tang, Peter M. Caldwell, Shaocheng Xie, Wuyin Lin, and Yao-Sheng Chen
Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020,https://doi.org/10.5194/gmd-13-4443-2020, 2020
Short summary

Related subject area

Climate and Earth system modeling
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025,https://doi.org/10.5194/gmd-18-3081-2025, 2025
Short summary
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025,https://doi.org/10.5194/gmd-18-3157-2025, 2025
Short summary
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025,https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Baseline Climate Variables for Earth System Modelling
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025,https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025,https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary

Cited articles

AMWG Diagnostic Package: https://www.cesm.ucar.edu/working_groups/Atmosphere/amwg-diagnostics-package/, last access: 2 November 2021. 
ARM Research Facility: ARM Data Discovery, https://adc.arm.gov/discovery, last access: 3 March 2023. 
Bennartz, R.: Global assessment of marine boundary layer cloud droplet number concentration from satellite, J. Geophys. Res.-Atmos., 112, D02201, https://doi.org/10.1029/2006JD007547, 2007. 
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Share