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Active CONTINUING GRANT National Science Foundation (US)

CAREER: The Large-scale Buffering of Shallow Cloud Perturbations

$5.96M USD

Funder National Science Foundation (US)
Recipient Organization University of California-San Diego Scripps Inst of Oceanography
Country United States
Start Date Feb 15, 2025
End Date Jan 31, 2030
Duration 1,811 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2441832
Grant Description

The shallow clouds that form over the ocean have a profound effect on Earth's climate as they reflect away solar energy that would otherwise be absorbed by the much darker ocean surface. Despite their controlling influence the clouds themselves are insubstantial, a collection of millimeter-sized droplets formed around micron-sized aerosol particles carried by small-scale turbulent motions.

Ideally the aggregate climatic effect of individually insubstantial clouds could be assessed by studying the microscale physics and dynamics that determe their reflectivity, which could then be used to estimate the amount of sunlight they reflect back to space over the whole globe. While appealing, this simple scaling up fails to account for the two-way interactions through which clouds affect the large-scale conditions in which they occur, which in turn affect the microscale processes that control the clouds.

Research under this award seeks to understand the net effect of clouds on climate, taking into account the interactions between clouds and climate that, taken as a whole, generate the equilibrated response of each to the other. The Principal Investigator (PI) of the award uses the term "buffering" to refer to the effect of the climate scale on the cloud scale based on recent studies, including Sherwood and Feingold (2009), arguing that the effect of fully equilibrated shallow cloud changes on global climate is often (though not always) less than what would be estimated by simply scaling up from cloud scale to global scale.

Much of the work in the project is devoted to the development of a model which can simulate the small scales relevant to clouds and turbulence while at the same time covering the whole globe. Existing models capable of simulating cloud motions are too computationally intensive to run on a global domain, thus the PI has devised a strategy in which cloud-scale motions are captured with machine learning-based emulators embedded in each grid column of a global model.

The emulators are trained on output from a hybrid model in which fine-scale cloud-resolving models are embedded in each grid column of a global model, a descendant of the model developed by the Center for Multi-scale Modeling of Atmospheric Processes under AGS-0425247.

The new emulator-based model configuration is used to address three questions regarding the equilibrated response of clouds and large-scale climate, the first of which is whether atmospheric adjustments do in fact serve to buffer the radiative effect of aerosols on clouds. The immediate effect of aerosols on clouds is to make them brighter by distributing cloud water over a larger number of smaller droplets (the Twomey effect), thus buffering would mean changes in liquid water content and areal coverage that counteract cloud brightening.

The second question is whether interaction with large-scale climate reduces the sensitivity of shallow clouds to sea surface temperature (SST) increases, as experiments with fine-scale models suggest strong reductions in cloudiness with ocean warming which would generate further warming. But the reduction in cloudiness found in fine-scale models may be buffered when large-scale equilibration is taken into account.

The third is whether the the adjustment of shallow clouds to aerosols depend systematically on changes in SST when the fully equilibrated cloud response is considered. The question is important as aerosols are expected to decrease as SST increases over the 21st century.

Research under this CAREER award is paired with a suite of educational activities built around the same themes of climate science and machine learning. One involves the development of a climate emulator with a user-friendly graphical interface for teaching climate science to a broad range of audiences including the general public. A second creates resources and training opportunities to help high school teachers show their students how scientists develop and use models to study climate and project future climate change.

A third involves public outreach at the Birch Aquarium, where the PI serves as an advisor and contributor to exhibits and public programs on climate and atmospheric science.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

All Grantees

University of California-San Diego Scripps Inst of Oceanography

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