uantifying the sensitivity of shallow cumulus cloud fields to a broad range of environmental perturbations

Despite significant progress being made over previous decades of work, climate projections are still marred by large uncertainties, particularly in the Equilibrium Climate Sensitivity (ECS), a measure of the global equilibrium temperature response to a doubling of CO2. (Schneider et al., 2017)One of the key causes for this spread in predictions - for a given emissions scenario - emanates from the response of shallow, marine clouds to climate change, termed the `shallow cloud feedback`. These clouds are of particular importance for the earth's radiation budget as they have a net cooling influence due to their low, warm cloud tops and high albedo. (Bony et al., 2004; Bony and Dufresne, 2005; Spill et al., 2019)Recent work has shown that over half the variance of ECS between CMIP5 models can be explained by changes in the shortwave reflectance of tropical low clouds. (Brient et al., 2016) Additionally, early work has demonstrated that the single greatest source of uncertainty within the shallow cloud feedback is the sensitivity of warm, boundary layer clouds to changes in their environment. (Bony and Dufresne, 2005)Hence, it is critical to understand and quantify the sensitivity of warm, shallow cloud field properties to changes in their environment, so as to better constrain their uncertain behaviour in global climate models. (Schneider et al., 2017)
this project proposes to extend the analysis of previous sensitivity studies in two ways:Firstly, we will conduct a suite of high-resolution simulations of transient, warm shallow cloud fields which explicitly consider the possible range of covariation between both environmental and microphysical perturbations. (Dagan et al., 2017, 2018; Spill et al., 2019) To do this we will use Latin Hypercube sampling to select the maximally informative simulations to conduct in order to adequately sample the uncertainty space of possible combinations of external perturbations. Possible parameters to perturb could include temperature, subsidence, CDNC, free-tropospheric relative humidity or windspeed. (McKay, Beckman and Conover, 1979; Lee et al., 2011) Secondly, using the space-filling, maximally informative set of simulations conducted, we will be able to train and verify a Gaussian process (GP) emulator which can be used to estimate the cloud response across the high-dimensional parameter space. This technique of combining Latin hypercube sampling with GP emulation has already been used to great effect in the quantification of uncertainty in global aerosol-climate models, and the approach is ripe for use in cloud feedback studies. (Lee et al., 2011, 2012) Once the emulator is constructed and verified, we will be able to easily sample a wide range of environmental perturbations, allowing us to identify the most sensitive parameters in the modelled response of shallow cumuli to changes in their environment. We will also be able to comprehensively explore to what extent the cloud response to a combination of external parameters is linear or non-linear, which has not been possible in previous studies. Finally, it is expected that the emulator approach will also enable a better understanding of how CDNC changes and the "precipitation governor" mechanism act as a control on cloud feedbacks.