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Cloud microphysics is critical for weather and climate prediction. In this work, we document updates and corrections to the cloud microphysical scheme used in the Community Earth System Model (CESM) and other models. These updates include a new nomenclature for the scheme, now called Parameterization of Unified Microphysics Across Scales (PUMAS), and the ability to run the scheme on graphics processing units (GPUs). The main science changes include refactoring an ice number limiter and associated changes to ice nucleation, adding vapor deposition onto snow, and introducing an implicit numerical treatment for sedimentation. We also detail the improvements in computational performance that can be achieved with GPU acceleration. We then show the impact of these scheme changes on the (a) mean state climate, (b) cloud feedback response to warming, and (c) aerosol forcing. We find that corrections are needed to the immersion freezing parameterization and that ice nucleation has important impacts on climate. We also find that the revised scheme produces less cloud liquid and ice but that this can be adjusted by changing the loss process for cloud liquid (autoconversion). Furthermore, there are few discernible effects of the PUMAS changes on cloud feedbacks but some reductions in the magnitude of aerosol–cloud interactions (ACIs). Small cloud feedback changes appear to be related to the implicit sedimentation scheme, with a number of factors affecting ACIs.

Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

The McMurdo Dry Valleys (MDV) are home to a unique microbial ecosystem dependent on the availability of freshwater. It is a polar desert and freshwater originates almost entirely from surface and near-surface melt of cold-based glaciers. Understanding the future evolution of these environments requires the simulation of the full chain of physical processes-from net radiative forcing, surface energy balance, melt, runoff and the transport of meltwater in stream channels from the glaciers to the terminal lakes where the microbial community resides. We present the first application of the WRF-Hydro/Glacier model in the MDV. The model was tested for a 7-month period (1 August 2021 to 28 February 2022) at a point on Commonwealth Glacier and forced by automatic weather station observations. We found it was necessary to limit the percolation of meltwater through ice layers to represent near-surface runoff as observed in the field. We also tuned the parameters controlling the spectral albedo for snow and ice based on observations to model the evolution of broadband albedo over a melt season. With these modifications, we were able to accurately simulate surface and near-surface temperatures, surface height change, broadband albedo and runoff over a melt season. These modifications show that once the model is adapted to this extreme environment, the model is capable of accurately capturing the physical processes governing the meltwater generation of an MDV glacier. This will enable future efforts to model spatially distributed melt and streamflow in the MDV and will allow us to answer questions around the timing of meltwater transport and the present and future hydrological response of melt to atmospheric forcing.

Glacial melt is the primary source of freshwater for the fragile microbial ecosystem in the McMurdo Dry Valleys (MDV) of Antarctica. These glaciers are cold-based, with internal temperatures around -18°C, however, air temperatures hover around 0°C for several weeks in the summer and föhn wind events can rapidly raise ice surface temperatures to the melting point. Thus, episodical glacial melt is sensitive to small changes in the climate. The aim of this research is to adapt a detailed snowpack model embedded in a distributed hydrological model to simulate the surface energy balance and run-off of a glacier in the MDV. To do this, the snowpack model in the WRF-Hydro-Crocus modelling scheme, which has been used for avalanche forecasting and temperate glaciers, is adapted to the MDV. Several modifications are made to model calculations and parameters to allow the model to successfully simulate surface energy balance and runoff in this environment. For example, the parameters for the Crocus albedo scheme are adjusted to obtain band profiles for snow, firn and ice that replicate observed albedo and remain internally consistent between surface types. The modelling system is then validated against data from an automatic weather station, eddy covariance measurements and stream discharge. It is shown to be suitable for future efforts to model the full hydrological cycle of glacial meltwater in this region.