Changes in wintertime 10 m winds due to the El Niño-Southern Oscillation are examined using a 6 km resolution climate simulation of Southern California covering the period from 1959 through 2001. Wind speed statistics based on regional averages reveal a general signal of increased mean wind speeds and wind speed variability during El Niño across the region. An opposite and nearly as strong signal of decreased wind speed variability during La Niña is also found. These signals are generally more significant than the better-known signals in precipitation. In spite of these regional-scale generalizations, there are significant sub-regional mesoscale structures in the wind speed impacts. In some cases, impacts on mean winds and wind variability at the sub-regional scale are opposite to those of the region as a whole. All of these signals can be interpreted in terms of shifts in occurrences of the region’s main wind regimes due to the El Niño phenomenon. The results of this study can be used to understand how interannual wind speed variations in regions of Southern California are influenced by the El Niño phenomenon.

The depth and timing of snowpack in the Sierra Nevada Mountains are of fundamental importance to California water resource availability, and recent studies indicate a shift toward earlier snowmelt consistent with projected impacts of anthropogenic climate change. In order for future studies to assess snowpack variability on seasonal to centennial time scales, physically based models of snowpack evolution at high spatial resolution must be improved. Here we evaluate modeled snowpack accuracy for the central Sierra Nevada in the Weather Research and Forecasting regional climate model coupled to the Noah land surface model. A simulation with nested domains at 27, 9, and 3 km grid spacings is presented for November 2001 to July 2002. Model outputs are compared with daily snowpack observations at 41 locations, air temperature at 31 locations, and precipitation at 10 locations. Comparison of snowpack at different resolutions suggests that 27 km simulations substantially underestimate snowpack, while 9 and 3 km simulations are closer to observations. Regional snowpack accumulation is accurately simulated at these high resolutions, but model snowmelt occurs an average of 22–25 days early. Some error can be traced to differences in elevation and observation scale between point‐based measurements and model grid cells, but these factors cannot explain the persistent bias toward early snowmelt. A high correlation between snowmelt and error in modeled surface air temperature is found, with melt coinciding systematically with excessively cold air temperatures. One possible source of bias is an imbalance in turbulent heat fluxes, erroneously warming the snowpack while cooling the surface atmosphere.

A study of the California Sierra Nevada snowpack has been conducted using snow station observations and reanalysis surface temperature data. Monthly snow water equivalent (SWE) measurements were combined from two datasets to provide sufficient data from 1930 to 2008. The monthly snapshots are used to calculate peak snow mass timing for each snow season. Since 1930, there has been an overall trend toward earlier snow mass peak timing by 0.6 days per decade. The trend toward earlier timing also occurs at nearly all individual stations. Even stations showing an increase in 1 April SWE exhibit the trend toward earlier timing, indicating that enhanced melting is occurring at nearly all stations. Analysis of individual years and stations reveals that warm daily maximum temperatures averaged over March and April are associated with earlier snow mass peak timing for all spatial and temporal scales included in the dataset. The influence is particularly pronounced for low accumulation years indicating the potential importance of albedo feedback for the melting of shallow snow. The robustness of the early spring temperature influence on peak timing suggests the trend toward earlier peak timing is attributable to the simultaneous warming trend (0.1°C decade⁻¹ since 1930, with an acceleration in warming in later time periods). Given future scenarios of warming in California, one can expect acceleration in the trend toward earlier peak timing; this will reduce the warm season storage capacity of the California snowpack.

Observational studies have shown that mesoscale variations in sea surface temperature may induce mesoscale variations in wind. In eastern subtropical upwelling regions such as the California coast, this mechanism could be of great importance for the mean state and variability of the climate system. In coastal regions orography also creates mesoscale variations in wind, and the orographic effect may extend more than 100 km offshore. The respective roles of SST/wind links and coastal orography in shaping mesoscale wind variations in nearshore regions is not clear. We address this question in the context of the California Upwelling System, using a high-resolution regional numerical modeling system coupling the WRF atmospheric model to the ROMS oceanic model, as well as additional uncoupled experiments to quantify and separate the effects of SST/wind links and coastal orography on mesoscale wind variations. After taking into account potential biases in the representation of the strength of SST/wind links by the model, our results suggest that the magnitude of mesoscale wind variations arising from the orographic effects is roughly twice that of wind variations associated with mesoscale SST anomalies. This indicates that even in this region where coastal orography is complex and leaves a strong imprint on coastal winds, the role of SST/winds links in shaping coastal circulation and climate cannot be neglected.