Incorporating Snow Albedo Feedback into Downscaled Temperature and Snow Cover Projections for California’s Sierra Nevada

Abstract

California’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems.

Some Pitfalls in Statistical Downscaling of Future Climate

“…Previous studies employing high-resolution models have shown that snow albedo feedback will likely amplify warming in mountain areas (e.g., Salathé et al 2008;Walton et al 2017) and that typical GCMs do not have sufficient resolution to properly simulate snow cover in the complex topography of the mountainous western United States. However, the model used in this study, which has a higher resolution than most contemporaneous GCMs used in century time-scale studies, is able to sufficiently capture the relevant effects.…”

Simulating and Evaluating Atmospheric River‐Induced Precipitation Extremes Along the U.S. Pacific Coast: Case Studies From 1980–2017

“…Instead, we use the most recent version of the WRF model and implement a suite of parameterizations borrowed from recent AR-focused studies that themselves engaged in extensive parameterization choice validation. Hence, our parameterization selection is heavily informed by those that have been successfully used in previous California-based downscaling work (Leung & Qian, 2009;Hughes et al, 2014;Swain et al, 2015;Sun et al, 2016;Walton et al, 2017;Pontoppidan et al, 1271;Huang et al, 2018). Specifically, Hughes et al (2014) successfully used the same radiation schemes, microphysics, cumulus, and boundary schemes as we used here to comprehensively study a single historical AR event.…”

Sensitivity of Mountain Hydroclimate Simulations in Variable‐Resolution CESM to Microphysics and Horizontal Resolution

“…Understanding the physical processes that affect precipitation and snowpack accumulation in U.S.‐west mountainous regions and how they may be altered with anthropogenic climate change necessitates the use of climate models that can properly characterize land surface heterogeneity and synoptic‐scale storm systems (Huning & Margulis, ). An accurate representation of U.S.‐west orography is particularly important to realistically simulate the capture and storage of available precipitable water from the atmosphere (Ashfaq et al, ; Chen et al, ; Hughes et al, ; Ikeda et al, ; Liu et al, ; Musselman et al, ; Pierce et al, ; Rasmussen et al, ; Walton et al, ). This is due to the importance of mountain range orientation, the mountain slope variation impacts on orographic uplift, the corresponding alterations in the precipitation phase, the resultant transport and location of surface precipitation, the snow‐albedo feedback, and the life cycle of stored mountain snowpack.…”