Understanding and constraining extratropical clouds in Earth's present and changing climate

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The surface temperature-mediated response of clouds to global warming, known as "cloud feedback", continues to contribute the largest spread in climate projections by climate models. Previous work led by the PI has demonstrated using a single climate model that low biases in the proportion of supercooled liquid to total cloud water content in mixed-phase clouds causes underestimates in climate sensitivity --- a metric that quantifies the ultimate change in global mean surface air temperature in response to a doubling of atmospheric carbon dioxide concentrations. Recently, the newest generation of climate models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) predicts an increase in climate sensitivity that has been linked to general improvements to the same mixed-phase cloud biases identified by the PI's work. Establishing whether the higher climate sensitivity is realistic is of utmost importance for the development of climate change policy and mitigation strategies. The proposed five-year program will address the question of whether the higher climate sensitivity values of the latest generation of climate models are realistic by addressing the unresolved issues stemming from the PI's previous work in two distinct but parallel research themes. The first research theme strives to diagnose and constrain the extratropical cloud feedback in climate models using satellite remote sensing observations. This will be achieved by (i) applying the PI's methodology to evaluate the simulation of cloud optical depth in the CMIP6 models against observations from two different satellite instruments, (ii) performing single-model perturbed parameter ensemble simulations to identify the most important constrainable physical processes in a community climate model, and (iii) determining the impact of precipitation as a constrainable process rate in the extratropical cloud feedback based on satellite observations of precipitation. The second research theme strives to properly account for the most prominent long-standing mixed-phase cloud bias in climate models. This will be achieved by (i) characterizing the spatial distribution of cloud thermodynamic phase in mixed-phase clouds using two different approaches, (ii) calculating the covariability of ice nucleating particles with the spatial distribution of cloud thermodynamic phase, and (iii) using in situ observations to develop the first explicit parameterization of ice depositional growth in climate models that accounts for the spatial distribution of cloud thermodynamic phase in mixed-phase clouds based on observations. The overarching goal of the proposed five-year program is to constrain the extratropical cloud feedback in order to assess whether the higher climate sensitivity in the CMIP6 models is realistic. The implications are particularly pertinent to Canada, and especially our vulnerable Arctic, which has seen rapid climate change in the past few decades.