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Reducing biases in the simulated historical temperature record through calibration of aerosol and cloud processes: Improvements to the aerosol forcing in E3SMv3

Presentation Date
Thursday, August 8, 2024 at 1:00pm - Thursday, August 8, 2024 at 1:45pm
Authors

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Co-Author

Abstract

Atmospheric aerosols significantly impact the Earth’s radiative balance and hydrological cycle through their interactions with radiation and clouds. Additionally, energy budget constraints on climate sensitivity and aerosol effective radiative forcing (ERFaer) are interdependent, implying that reducing uncertainty in ERFaer can improve confidence in future climate projections (e.g., Watson-Parris and Smith, 2022).

One of the primary methods by which the Earth system modeling community evaluates and builds confidence in the realism of a model’s response to major anthropogenic forcings is by simulating the historical changes in global mean surface temperature (GMST) since the preindustrial era.  Yet, the first two versions of the Department of Energy’s Energy Exascale Earth System Model (E3SM) failed to adequately reproduce observed historical trends in GMST, simulating a mid-twentieth century that was cooler than the preindustrial climate.  Golaz et al. (2022) used single-forcing experiments to attribute this bias to an overly strong aerosol forcing.

In the recently finalized E3SMv3 release, this critical bias has been corrected through targeted model development and calibration of ERFaer, resulting in a remarkably improved agreement with historical GMST trends.  Additionally, the partitioning of ERFaer between aerosol-cloud interactions (ERFaci) and aerosol-radiation interactions (ERFari) is consistent with recent community assessments, and key metrics characterizing the aerosol life cycle are improved, while simultaneously improving the climate mean state and variability.  This presentation will summarize and explain how these improvements were achieved, and provide insights into selected model processes with significant impacts on ERFaer.

Presentation File(s)
Category
Model Uncertainties, Model Biases, and Fit-for-Purpose
Water Cycle and Hydroclimate
Biogeochemistry (Processes and Feedbacks)
Funding Program Area(s)
Additional Resources:
ALCC (ASCR Leadership Computing Challenge)
NERSC (National Energy Research Scientific Computing Center)