Uncertainties in cloud and aerosol radiative effects are a principal contributor to climate model uncertainty, and remain so despite decades of model development (Boucher et al., 2013). These uncertainties arise from the difficulty of representing aerosol-cloud interactions and other key physical processes at the typical resolutions of global climate models (GCMs). Evaluations of available models from the Coupled Model Intercomparison Project Phase 6 (CMIP6; Eyring et al., 2016) indicate that changes in climate sensitivity relative to CMIP5 (Taylor et al., 2012) are mostly due to changes in cloud representation, specifically for extratropical low-level clouds (Zelinka et al., 2020). Using observations to reevaluate the representation of these clouds in the latest generation of GCMs is a vital part of testing the validity of these new predictions.In the Arctic, clouds mediate climate change through interactions with land and sea ice, and impacts on surface radiative fluxes (H. Morrison et al., 2012). As the thermodynamic phase of Arctic clouds shifts from ice to liquid in response to warming, the radiative effect they exert on the surface changes (Mitchell et al., 1989). This cloud phase feedback depends on cloud optical thickness and lifetime changes. In the Arctic, observations indicate that liquid and ice clouds exert very different radiative forcings on the surface

Mixed-phase clouds, i.e., clouds in which ice particles and supercooled liquid water can coexist in the temperature range of approximately −40°C to 0°C, are not fully understood yet and therefore not well represented in weather and climate models (Forbes & Ahlgrimm, 2014; McCoy et al., 2016). Several studies have shown that mixed-phase clouds occur irrespective of the season, can be found in diverse locations, and can be associated with various cloud types (Korolev et al., 2017). Observations of mixed-phase clouds include active (e.g.

No abstract

Uncertainties in cloud and aerosol radiative effects are a principal contributor to climate model uncertainty, and remain so despite decades of model development (Boucher et al., 2013). These uncertainties arise from the difficulty of representing aerosol-cloud interactions and other key physical processes at the typical resolutions of global climate models (GCMs). Evaluations of available models from the Coupled Model Intercomparison Project Phase 6 (CMIP6; Eyring et al., 2016) indicate that changes in climate sensitivity relative to CMIP5 (Taylor et al., 2012) are mostly due to changes in cloud representation, specifically for extratropical low-level clouds (Zelinka et al., 2020). Using observations to reevaluate the representation of these clouds in the latest generation of GCMs is a vital part of testing the validity of these new predictions.In the Arctic, clouds mediate climate change through interactions with land and sea ice, and impacts on surface radiative fluxes (H. Morrison et al., 2012). As the thermodynamic phase of Arctic clouds shifts from ice to liquid in response to warming, the radiative effect they exert on the surface changes (Mitchell et al., 1989). This cloud phase feedback depends on cloud optical thickness and lifetime changes. In the Arctic, observations indicate that liquid and ice clouds exert very different radiative forcings on the surface

No abstract