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Webinar: AOS Summer Workshop on Cloud Climate Feedbacks: How can we use idealized model configurations to attempt to constrain cloud feedbacks?

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Tuesday, 18 August 2020, 12:00

Tuesday, August 18, 2020. 12:00 PM.Webinar: AOS Summer Workshop on Cloud Climate Feedbacks: How can we use idealized model configurations to attempt to constrain cloud feedbacks? Timothy Cronin, Princeton University. Sponsored by GFDL. More information here.

Abstract: I will talk about three angles of attack on questions tied to this broad subject that my work has recently pursued. First, how does convective aggregation affect climate sensitivity? Simulations of radiative-convective equilibrium in a long-channel geometry were found to have a somewhat realistic distribution of large-scale dynamical regimes (Cronin & Wing, 2017). This prompted us to look at how clouds and circulation change with surface temperature, and how these affect climate feedbacks. Using a novel approximate radiative kernel methodology, we found that cloud feedbacks are small and positive, but that the overall climate sensitivity is lower in the channel configuration with aggregated convection than a smaller domain with disaggregated convection. Second, precipitation efficiency has been found to be a key
parameter controlling cloud feedbacks in global climate models - can we constrain it and the direction of its change with warming in idealized simulations? We found that precipitation efficiency tends to increase with surface warming in small-domain simulations, mostly due to increasing cloud water content and the nonlinearity of precipitation formation by autoconversion (Lutsko & Cronin, 2018). Third, how do SST patterns influence cloud feedbacks? I will describe simulations of "Mock-Walker cells'' forced by sinusoidal patterns in long-channel geometry; preliminary results suggest that cloud feedbacks are much more strongly positive when the SST contrast between warm and cold pools is weak (El Nino-like) than when it is strong (La Nina-like). POC: Elizabeth Yankovsky

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