In-situ observations suffer from large uncertainties from interpolation when comparing with gridded model outputs, and re-analysis or remote sensing datasets highly disagree from one another 12.
The snow water equivalent (SWE), the measure of water volume contained within the snowpack, has received less attention in part because of the lack of reliable observational datasets for comparison with models. However, the evaluation of snowpack in regional climate models has not received as much attention as other variables such as temperature, precipitation, and radiation.Ĭlimate model evaluations related to snow have often focused on total precipitation with no rain-snow distinction, or on snow cover for which accurate measurements exist for evaluation 10, 11. Therefore, snow simulations can serve as a crucial litmus test for climate model performance since their accuracy requires so many processes to be represented with fidelity. Moreover, radiation, temperature and moisture fluxes at the snow surface must be accurate to ensure that the residence time of snowpack is correct. The accurate simulation of seasonal snow dynamics in mountainous regions requires that models correctly represent a number of interacting processes including storm track location, landfall and timing, orographic uplift of storms and accurate dispersal of precipitation over the mountain.
Numerical models offer promise to predict future changes in snowpack and to inform the interactions between snowpack and other climate components. The depletion of snowpack increases fire potential 7, alters natural ecosystem 8, and affects the Earth’s energy budget 9. Observations have revealed a declining mountain snowpack in the western US over the last half-century due to climate warming 4, 5, 6. Rising air temperatures reduce snowfall and enhance snow melt in spring and summer, resulting in a decline in the volume and duration of mountain snowpack, earlier peak snow mass timing, and therefore a deficit of summer water supply 1, 2, 3. More than one-sixth of the world’s population relies on seasonal snow for water 1. It affects the earth’s energy balance through high albedo and low heat conductivity, and in the mountains acts as a natural reservoir to store water in winter and slowly release meltwater in summer. Snowpack is a key component of the Earth’s climate. This work reveals both positive and negative contributions to snow bias in climate models and provides guidance for future model development to enhance SWE simulation. To varying degrees across models, additional difference arises from spatial and elevational distribution of precipitation, cold biases revealed by topographic correction, uncertainties in the rain-snow partitioning threshold, and high ablation biases.
Refining resolution from 0.44° to 0.11° improves SWE simulation by 35%. Unresolved topography associated with model resolution contribute to dry and warm biases in models. Models generally predict less SWE compared to Landsat-Era Sierra Nevada Snow Reanalysis (SNSR) dataset. We apply this framework within the California Sierra Nevada to four regional climate models from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX) run at three spatial resolutions. To identify the driving forces behind SWE difference between model and reanalysis datasets, and guide model improvement, we design a framework to quantitatively decompose the SWE difference contributed from precipitation distribution and magnitude, ablation, temperature and topography biases in regional climate models. Accurate SWE simulation depends on complex interacting climate processes such as the intensity and distribution of precipitation, rain-snow partitioning, and radiative fluxes. The simulation of snow water equivalent (SWE) remains difficult for regional climate models.
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