Constraining Tropical Low Cloud Feedbacks Using Observations of the Fast Cloud Response

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Changes in the properties of low clouds in the tropics and subtropics in response to global warming have been identified as a key cause of the large uncertainty in global warming projections from climate models. In some models warming produces more prevalent or more reflective low clouds, thus reflecting more sunlight back to space and partially mitigating the warming effects of greenhouse gases (a negative cloud feedback to the imposed warming), while in other models the clouds become less reflective or less prevalent and enhanced sunlight at the surface exacerbates global warming (a positive cloud feedback). This research attempts to produce observationally-constrained estimates of the true low cloud feedback using a combination of satellite observations, reanalysis products, and climate model outputs. The method first uses a neural network to establish empirical relationships between low cloud reflectivity and other cloud properties (determined from satellite observations) and large-scale variables including sea surface temperature and atmospheric temperature and water vapor. These relationships are established using the fast (timescales of hours to a day) response of clouds to their environment. Once these relationships are established, cloud feedback will estimated by applying the empirical relationships represented by the neural network in combination with changes in the large-scale variables in the climate models which occur as a result of simulated global warming. In addition to estimates of the low cloud feedback to global warming, the method will provide uncertainty bounds for those estimates, and can be used to diagnose errors in model parameterizations of low cloud properties.The work has broader impacts due to the key role of tropical low cloud feedback in generating the large uncertainty in model projections of climate change. A reliable, observationally-based estimate of the low cloud feedback could help to reduce this uncertainty and provide better information to decision makers regarding the likely extent and physical consequences of greenhouse-gas induced global warming.