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Sarah Larson

Assistant Professor

Jordan Hall 5148


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Date: 08/01/20 - 7/31/23
Amount: $643,860.00
Funding Agencies: National Science Foundation (NSF)

Air-sea mechanisms are critical to establishing the timescales, patterns, and amplitudes of much of the climate variations we observe and simulate in models. And yet, the individual roles of different air-sea processes in driving climate variability and change are especially elusive due to sparse ocean observations and complicated dynamics. This issue is exacerbated by a wide gap in the model hierarchy, between fully coupled models with a complete dynamical representation of both the ocean and atmosphere and models that represent the ocean as a slab, a motionless layer that is only thermally coupled to the atmosphere. Due to this gap in the hierarchy, many open questions remain about the importance of winds versus buoyancy forcing (including radiative heat fluxes) as pathways through which the atmosphere communicates intrinsic and externally forced variations to the ocean. The proposed research will decompose these relative effects using rigorous climate model experiments, with emphasis on two features expected to substantially change in a future climate, surface temperature and the Atlantic Meridional Overturning Circulation (AMOC). This work will be implemented by closing said gap in the model hierarchy via leveraging a mechanically decoupled (MD) version of a climate model, which isolates variations in the ocean circulation that are driven solely by buoyancy from the wind-driven variability captured by fully coupled models. With the MD as an intermediate step between fully coupled and slab ocean models, a clearer mechanistic understanding of the drivers of climate variations is possible. These 3 model versions will be used together to complete the following objectives: identify the relative roles of buoyancy and wind variations in driving 1) intrinsic sea surface temperature variability, 2) anthropogenic trends in surface temperature, and 3) intrinsic and anthropogenically forced AMOC variations. The PI is ideally suited to perform such tasks, as she is the developer of the MD model.

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