Submitted as: methods for assessment of models
15 May 2023
Submitted as: methods for assessment of models |  | 15 May 2023
Status: this preprint is currently under review for the journal GMD.

Earth System Model Aerosol-Cloud Diagnostics Package (ESMAC Diags) Version 2: Assessments of Aerosols, Clouds and Aerosol-Cloud Interactions Through 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

Abstract. Poor representations of aerosols, clouds and aerosol-cloud interactions (ACI) in Earth System Models (ESMs) have long been the largest uncertainties in predicting global climate change. Huge efforts have been made to improve the representation of these processes in ESMs, and key to these efforts is evaluation of ESM simulations with observations. Most well-established ESM diagnostics packages focus on the climatological features; however, they are lack of the process-level understanding and representations of aerosols, clouds, and ACI. In this study, we developed an ESM aerosol-cloud diagnostics package (ESMAC Diags) to facilitate routine evaluation of aerosols, clouds and aerosol-cloud interactions simulated by the Department of Energy’s (DOE) Energy Exascale Earth System Model (E3SM). This paper documents its version 2 functionality (ESMAC Diags v2), which has substantial updates from its version 1 (Tang et al., 2022a). The simulated aerosol and cloud properties have been extensively compared with in-situ and remote-sensing measurements from aircraft, ship, surface and satellite platforms in ESMAC Diags v2. It currently includes six field campaigns and two permanent sites covering four geographical regions: Eastern North Atlantic, Central U.S., Northeastern Pacific and Southern Ocean, where frequent liquid or mixed-phase clouds are present and extensive measurements are available from the DOE Atmospheric Radiation Measurement user facility and other agencies. ESMAC Diags v2 generates various types of single-variable and multi-variable diagnostics, including percentiles, histograms, joint histograms and heatmaps, to evaluate model representation of aerosols, clouds, and aerosol-cloud interactions. Select examples highlighting ESMAC Diags capabilities are shown using E3SM version 2 (E3SMv2). E3SMv2 in general can reasonably reproduces many observed aerosol and cloud properties, with biases in some variables such as aerosol particle and cloud droplet sizes and number concentrations. The coupling of aerosol and cloud number concentrations may be too strong in E3SMv2, possibly indicating a bias in processes that control aerosol activation. Furthermore, the liquid water path adjustment to perturbed cloud droplet number concentration behaves differently in E3SMv2 and observations, which warrants a further study to improve the cloud microphysics parameterizations in E3SMv2.

Shuaiqi Tang et al.

Status: open (until 10 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Shuaiqi Tang et al.

Data sets

ESMAC Diags data Shuaiqi Tang, Jerome D. Fast, Adam C. Varble, Joseph C. Hardin, Po-Lun Ma

Model code and software

ESMAC Diags code Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Joseph C. Hardin, Po-Lun Ma

Shuaiqi Tang et al.


Total article views: 194 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
146 42 6 194 14 3 3
  • HTML: 146
  • PDF: 42
  • XML: 6
  • Total: 194
  • Supplement: 14
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 15 May 2023)
Cumulative views and downloads (calculated since 15 May 2023)

Viewed (geographical distribution)

Total article views: 187 (including HTML, PDF, and XML) Thereof 187 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 05 Jun 2023
Short summary
To assess the ability of Earth System Model (ESM) predictions, we developed a tool called ESMAC Diags to understand the details of how aerosols, clouds, and ACI are represented in ESMs, and this paper describes its version 2 functionality. We compared the model predictions with measurements taken by airplanes, ships, satellites, and ground instruments over four regions over the world. Results show that this new tool can help identify model problems and guide future development of ESMs.