2024-11-07 All-Hands Presentation Meeting Notes

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

Susannah Burrows
Pacific Northwest National Laboratory

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 (ERF_aer) are interdependent, implying that reducing uncertainty in ERF_aer 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.  However, 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 a targeted calibration of ERF_aer, resulting in a remarkably improved agreement with historical GMST trends.  Additionally, the partitioning of ERF_aer between aerosol-cloud interactions (ERF_aci) and aerosol-radiation interactions (ERF_ari) 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 ERF_aer.

Date Nov 7, 2024 

Time

• PT: 8:30 am

• ET: 11:30 am

Call Info

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