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Four dynamic regional climate models (RCMs) and one statistical downscaling approach were used to downscale 10 years of historical climate in California. To isolate possible limitations of the downscaling methods, we used initial and lateral boundary conditions from the NCEP global reanalysis. Results of this downscaling were compared to observations and to an independent, fine-resolution reanalysis (NARR). This evaluation is preparation for simulations of future-climate scenarios, the second phase of this CEC scenarios project. Each model has its own strengths and weaknesses, which are reported here. In general, the dynamic models perform as well as other state-of-the-art dynamical regional climate models, and the statistical model has comparable or superior skill, although for a very limited set of meteorological variables. As is typical, the dynamical models have the most trouble simulating clouds, precipitation, and related processes, especially snow. This suggests that the weakest aspects of the models are parameterized subgrid scale processes, the hydrological cycle, and land surface processes. However, the resulting probabilistic ensemble simulations result in reduced model uncertainty and a better understanding of model spread.

The extent to which winter precipitation is orographically enhanced within the Sierra Nevada of California varies from storm to storm, and season to season, from occasions when precipitation rates at low and high altitudes are almost the same to instances when precipitation rates at middle elevations ( considered here) can be as much as 30 times more than at the base of the range. Analyses of large-scale conditions associated with orographic precipitation variations during storms and seasons from 1954 to 1999 show that strongly orographic storms most commonly have winds that transport water vapor across the range from a more nearly westerly direction than during less orographic storms and than during the largest overall storms, and generally the strongly orographic storms are less convectively stable. Strongly orographic conditions often follow heavy precipitation events because both of these wind conditions are present in midlatitude cyclones that form the cores of many Sierra Nevada storms. Storms during La Nina winters tend to yield larger orographic ratios (ORs) than do those during El Ninos. A simple experiment with a model of streamflows from a river basin draining the central Sierra Nevada indicates that, for a fixed overall basin-precipitation amount, a decrease in OR contributes to larger winter flood peaks and smaller springtime flows, and thus to an overall hastening of the runoff season.

In terms of loss of life and property damage, storm induced coastal flooding has become the world's foremost natural hazard. As atmospheric weather systems pass over ocean areas, water level oscillations are induced both by the wind stress and the horizontal gradients in atmospheric pressure associated with such systems. Storm surge is by definition restricted to the storm induced oscillations of water level with periods ranging from a few minutes to a few days. This definition excludes the wind waves which typically have periods on the order of a few to several seconds. These high frequency waves do play a significant role in the coastal flooding problem and can contribute directly to high coastal sea levels due to wave set-up, but these effects are not considered in this thesis. It is the combination of large amplitude storm surge, large amplitude wind waves and high tides which can cause enormous devastation to coastal areas.Much of the work done on the study of storm surge has dealt with the large, highly destructive surges found in broad, shallow areas such as the continental shelf regions of the Bay of Bengal, the North Sea and the Gulf of Mexico. In those areas and many others storm surge can play a dominant role in coastal flooding. But often it is the coincidence of storm surge with high tides and other oceanographic factors which generates coastal flooding.It was the occurrence of many oceanographic and meteorological factors which caused the extensive (damage estimates totaled over $100,000,000) coastal floods of the winter of 1982-1983 along the California coast. In addition to wave attack, four factors were;1)High predicted astronomical tides;2) Storm surge due to strong North Pacific storms;3) High sea level associated with the EI Nino event of 1982-1983;4) The cumulative effect of slow, secular rise in sea level.Tides, which are the most predictable of all oceanic processes, contribute the largest amount of variance to the sea level signal. However, it has been suggested that more than half of the variance of sea level anomaly (observed level minus predicted tide) at short (hours to days) time scales can be related to atmospheric forcing by pressure and wind. Secular sea level rise, though important for its long term effects on coastal regions, is negligible for day to day or even year to year variations. Similarly, the increased ocean temperatures associated with El Ninos generally influence sea surface variability on seasonal time scales. Monthly mean sea levels, in 1983, were on the order of 10 cm above the long term monthly means. This sea level rise played a significant role in the coastal flooding seen that .year, but the influence of El Nino events on high frequency sea level anomalies is most likely due to enhanced storminess in the central North Pacific, which is attributed to El Nino episodes, not to above normal water temperatures.

Humidity is important to climate impacts in hydrology, agriculture, ecology, energy demand, and human health and comfort. Nonetheless humidity is not available in some widely-used archives of statistically downscaled climate projections for the western U.S. In this work the Localized Constructed Analogs (LOCA) statistical downscaling method is used to downscale specific humidity to a 1°/16° grid over the conterminous U.S. and the results compared to observations. LOCA reproduces observed monthly climatological values with a mean error of ~0.5 % and RMS error of ~2 %. Extreme (1-day in 1- and 20-years) maximum values (relevant to human health and energy demand) are within ~5 % of observed, while extreme minimum values (relevant to agriculture and wildfire) are within ~15 %. The asymmetry between extreme maximum and minimum errors is largely due to residual errors in the bias correction of extreme minimum values. The temporal standard deviations of downscaled daily specific humidity values have a mean error of ~1 % and RMS error of ~3 %. LOCA increases spatial coherence in the final downscaled field by ~13 %, but the downscaled coherence depends on the spatial coherence in the data being downscaled, which is not addressed by bias correction. Temporal correlations between daily, monthly, and annual time series of the original and downscaled data typically yield values >0.98. LOCA captures the observed correlations between temperature and specific humidity even when the two are downscaled independently. [ABSTRACT FROM AUTHOR]