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Publication Date
8 November 2023

A Diagnostics Package to Evaluate Earth System Model Aerosol–Cloud Interactions with Field Campaign Measurements

Subtitle
The Earth System Model Aerosol–Cloud Diagnostics package version 2 uses aircraft, ship, ground, and satellite measurements to evaluate detailed physical processes in aerosols, clouds, and aerosol–cloud interactions.
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Science

Earth system model representations of aerosols, clouds, and their interactions are subject to large uncertainties. Model simulations need to be evaluated against real-world measurements to ensure their fidelity in representing the observed properties. However, many current model diagnostics packages do not include a detailed process-level evaluation of aerosols, clouds, and their interactions. Moreover, many recent field campaigns provide comprehensive measurements of aerosols and clouds that are currently underutilized. Researchers extended the Earth System Model Aerosol–Cloud Diagnostics package to add new capabilities for evaluating aerosol–cloud interactions (ACIs) with recent field campaign and long-term measurements.

Impact

The Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) v2 package generates various types of single-variable and multi-variable diagnostics, including percentiles, histograms, joint histograms, and heatmaps, to evaluate model representations of aerosols, clouds, and ACIs. Select examples highlighting ESMAC Diags capabilities are shown using the Energy Exascale Earth System Model version 2 (E3SMv2). E3SMv2 can reasonably reproduce 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 response to perturbed cloud droplet number concentration behaves differently in E3SMv2 and observations, which warrants further study to improve the detailed representation of aerosols, clouds, and their interactions in E3SMv2. ESMAC Diags is continually updated and being applied to evaluate the performances of newly developed physical components in E3SM.

Summary

ESMAC Diags package is a Python-based open-source package to quantify the performance of the Department of Energy’s E3SM atmospheric model using the Atmospheric Radiation Measurement (ARM) user facility and U.S. National Center for Atmospheric Research field campaign measurements. The current version of this diagnostics package focuses on process-level evaluation of aerosols, clouds, and aerosol–cloud interactions. Short-term field campaign measurements from ground, aircraft, and ship platforms and long-term measurements from ground and satellite platforms are used for evaluation. ESMAC Diags provides various types of single-variable and multi-variable diagnostics, including percentiles, histograms, joint histograms, and heatmaps to evaluate model representation of aerosols, clouds, and ACIs. Currently, the diagnostics cover six field campaigns over the Northeastern Atlantic, Continental U.S., Northeastern Pacific, and Southern Ocean. The code structure is designed to be flexible and modular to allow for future extensions to other field campaigns or the incorporation of additional datasets. ESMAC Diags is being applied to evaluate the performances of newly developed physical components in E3SM.

Point of Contact
Po-Lun Ma
Institution(s)
Pacific Northwest National Laboratory
Funding Program Area(s)
Publication
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
Tang, Shuaiqi, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma. 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”. Geoscientific Model Development 16 (21). Copernicus GmbH: 6355-6376. doi:10.5194/gmd-16-6355-2023.