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Guzman-Morales, J, Gershunov A, Theiss J, Li HQ, Cayan D.  2016.  Santa Ana Winds of Southern California: Their climatology, extremes, and behavior spanning six and a half decades. Geophysical Research Letters. 43:2827-2834.   10.1002/2016gl067887   AbstractWebsite

Santa Ana Winds (SAWs) are an integral feature of the regional climate of Southern California/Northern Baja California region, but their climate-scale behavior is poorly understood. In the present work, we identify SAWs in mesoscale dynamical downscaling of a global reanalysis from 1948 to 2012. Model winds are validated with anemometer observations. SAWs exhibit an organized pattern with strongest easterly winds on westward facing downwind slopes and muted magnitudes at sea and over desert lowlands. We construct hourly local and regional SAW indices and analyze elements of their behavior on daily, annual, and multidecadal timescales. SAWs occurrences peak in winter, but some of the strongest winds have occurred in fall. Finally, we observe that SAW intensity is influenced by prominent large-scale low-frequency modes of climate variability rooted in the tropical and north Pacific ocean-atmosphere system.

Maurer, EP, Das T, Cayan DR.  2013.  Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction. Hydrology and Earth System Sciences. 17:2147-2159.   10.5194/hess-17-2147-2013   AbstractWebsite

When correcting for biases in general circulation model (GCM) output, for example when statistically down-scaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a "base" set and a 10 yr independent "projected" set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales.

Maurer, EP, Hidalgo HG, Das T, Dettinger MD, Cayan DR.  2010.  The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrology and Earth System Sciences. 14:1125-1138.   10.5194/hess-14-1125-2010   AbstractWebsite

Three statistical downscaling methods were applied to NCEP/NCAR reanalysis (used as a surrogate for the best possible general circulation model), and the downscaled meteorology was used to drive a hydrologic model over California. The historic record was divided into an 'observed' period of 1950-1976 to provide the basis for downscaling, and a 'projected' period of 1977-1999 for assessing skill. The downscaling methods included a bias-correction/spatial downscaling method (BCSD), which relies solely on monthly large scale meteorology and resamples the historical record to obtain daily sequences, a constructed analogues approach (CA), which uses daily large-scale anomalies, and a hybrid method (BCCA) using a quantile-mapping bias correction on the large-scale data prior to the CA approach. At 11 sites we compared three simulated daily flow statistics: streamflow timing, 3-day peak flow, and 7-day low flow. While all downscaling methods produced reasonable streamflow statistics at most locations, the BCCA method consistently outperformed the other methods, capturing the daily large-scale skill and translating it to simulated streamflows that more skillfully reproduced observationally-driven streamflows.