| Abstract |
This study evaluates the ability of 15 CMIP6 global climate models (GCMs), dynamically downscaled to a 9-km grid, to effectively simulate the observed regional hydroclimate across the complex terrain of the western United States. The evaluation focuses on orographic precipitation, surface temperature, and snow water equivalent (SWE), evaluated over a 33-yr period (1981–2014) using gridded gauge- and station-based datasets and a snowpack reanalysis product. Additional comparisons are made against two ERA5-driven climate reconstructions: one at 9-km resolution, with the same physical choices, and one at 4-km resolution. The latter better captures the terrain and orographic processes and uses different physics. Model performance is evaluated in four geographic regions, and mountains are contrasted against the surrounding plains. The evaluation is challenged by the fact that gridded observational estimates of climate parameters in complex terrain have a poorly quantified uncertainty related to measurement and/or representativeness issues. The ensemble mean of the downscaled GCMs overestimates cold-season precipitation, its orographic enhancement, and peak SWE in the mountains. Its diurnal temperature cycle and its mountain–plain temperature contrast are suppressed, compared to observations: its temperature estimates err toward the mean on both sides of both distributions. But the same applies to the identically downscaled (9 km) ERA5, indicating that these biases are due to model physics, not a misrepresentation of the climate system. The 4-km ERA5-based reconstruction has smaller biases in terms of precipitation and temperature, especially over mountains, but underestimates SWE. Model performance differs between mountains and plains, and the differences vary by region. Significance Statement In the western United States, much of the precipitation contributing to streamflow falls over mountains. In addition, the mountain snowpack seasonally stores water for consumptive use in the warm season. Amid growing concerns about changing water availability associated with the changing hydrometeorological patterns in a warming climate, it is essential to evaluate how well climate models capture precipitation and the snowpack across the headwater regions. Dynamically downscaled CMIP6 models, on average, capture the historical climate quite well, but uncertainty remains about how well they capture key climate parameters over the mountains. This uncertainty is due, in part, to the paucity of measurements in the mountains. Here, we demonstrate that dynamic downscaling to finer grid resolutions reduces this uncertainty. |
, Bart Geerts 
