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Aguado, E, Cayan D, Riddle L, Roos M.  1992.  Climatic fluctuations and the timing of West Coast streamflow. Journal of Climate. 5:1468-1483.   10.1175/1520-0442(1992)005<1468:cfatto>;2   AbstractWebsite

Since about 1950 there has been a trend in the California Sierra Nevada toward a decreasing portion of the total annual streamflow occurring during April through July, while the streamflow during autumn and winter has increased. This trend not only has important ramifications with regard to water management, it also brings up the question of whether this represents a shift toward earlier release of the snowpack resulting from greenhouse warming. Therefore, the observed record has been examined in terms of relative influences of temperature and precipitation anomalies on the timing of streamflow in this region. To carry out this study, the fraction of annual streamflow (called the fractional streamflow) occurring in November-January (NDJ), February-April (FMA), and May-July (MJJ) at low, medium, and high elevation basins in California and Oregon was examined. Linear regression models were used to relate precipitation and temperature to the fractional streamflow at the three elevations for each season. Composites of monthly temperature and precipitation were employed to further examine the fractional streamflow in its high and low tercile extremes. Long time series of climatic and hydrologic data were also looked at to infer the causes in the trend toward earlier runoff. For the low-elevation basins, there is a dominant influence of precipitation on seasonal fractional streamflow. Middle-elevation basins exhibit a longer memory of precipitation and temperature in relation to their fractional streamflow. In-season precipitation is still the most important influence upon NDJ and FMA fractional streamflow; however, the influence of temperature in melting the snowpack is seen on MJJ fractional streamflow, whose strongest influence is FMA temperature. At higher elevations, prior-season precipitation exerts a greater influence than at low and middle elevations, and seasonal temperature anomalies have an effect on all seasonal streamflow fractions. There are several causes for the trend toward decreasing fractional streamflow in the spring and summer. Concomitant with the trend in the timing of streamflow was an increase in NDJ (most notably November) precipitation. There also has been a trend toward higher spring temperatures over most of the western United States, but since there has also been a trend toward decreasing temperatures in the southeast, we do not interpret this as a signal of anthropogenic warming. Other factors in the trend toward earlier streamflow may include a decrease in MJJ precipitation and an increase in August-October precipitation.

Alfaro, EJ, Gershunov A, Cayan DR.  2004.  A method for prediction of California summer air surface temperature. EOS Trans. AGU. 85:553,557-558. Abstract
Alfaro, EJ, Gershunov A, Cayan D.  2006.  Prediction of summer maximum and minimum temperature over the central and western United States: The roles of soil moisture and sea surface temperature. Journal of Climate. 19:1407-1421.   10.1175/jcli3665.1   AbstractWebsite

A statistical model based on canonical correlation analysis (CCA) was used to explore climatic associations and predictability of June-August (JJA) maximum and minimum surface air temperatures (Tmax and Tmin) as well as the frequency of Tmax daily extremes (Tmax90) in the central and western United States (west of 90 degrees W). Explanatory variables are monthly and seasonal Pacific Ocean SST (PSST) and the Climate Division Palmer Drought Severity Index (PDSI) during 1950-2001. Although there is a positive correlation between Tmax and Tmin, the two variables exhibit somewhat different patterns and dynamics. Both exhibit their lowest levels of variability in summer, but that of Tmax is greater than Tmin. The predictability of Tmax is mainly associated with local effects related to previous soil moisture conditions at short range (one month to one season), with PSST providing a secondary influence. Predictability of Tmin is more strongly influenced by large-scale (PSST) patterns, with PDSI acting as a short-range predictive influence. For both predictand variables (Tmax and Tmin), the PDSI influence falls off markedly at time leads beyond a few months, but a PSST influence remains for at least two seasons. The maximum predictive skill for JJA Tmin, Tmax, and Tmax90 is from May PSST and PDSI. Importantly. skills evaluated for various seasons and time leads undergo a seasonal cycle that has maximum levels in summer. At the seasonal time frame, summer Tmax prediction skills are greatest in the Midwest, northern and central California, Arizona, and Utah. Similar results were found for Tmax90. In contrast, Tmin skill is spread over most of the western region, except for clusters of low skill in the northern Midwest and southern Montana, Idaho, and northern Arizona.

Auad, G, Miller AJ, Roads JO, Cayan D.  2001.  Pacific Ocean wind stress and surface heat flux anomalies from NCEP reanalysis and observations: Cross-statistics and ocean model responses. Journal of Geophysical Research-Oceans. 106:22249-22265.   10.1029/2000jc000264   AbstractWebsite

Wind stresses and surface heat fluxes over the Pacific Ocean from the National Center for Environmental Prediction (NCEP) reanalysis and the comprehensive Ocean-Atmosphere Data Set (COADS) (blended with FSU tropical wind stresses) are compared over a common time interval (1958-1997) in their statistics anal in the responses that they induce in sea surface temperature (SST) and heat storage when used to force an ocean model. Wind stress anomalies from the two data sets are well correlated in the midlatitude extratropics, especially in the highly sampled North Pacific. In the tropics and subtropics, low correlations were found between the two wind stress data sets. The amplitudes of the stress variations of the two data sets are similar in midlatitudes, but in the tropics NCEP wind stresses are weaker than the LOADS/FSU stresses, especially on interannual timescales. Surface heat flux anomalies from the two data sets are well correlated on interannual and shorter timescales in the North Pacific Ocean poleward of 20 degreesN, but they are poorly correlated elsewhere and on decadal timescales. In the extratropics the amplitudes of the heat flux variations of the two data sets are comparable, but in the tropics the NCEP heat fluxes are weaker than those of CORDS. Ocean model hindcasts driven by bath data sets are also compared: The midlatitude SST hindcasts were superior when using the NCEP flux anomalies while tropical SST hindcasts were equally skillful for the two hindcasts when considering all climatic timescales. The spatial and temporal sampling rates of the LOADS observations and their consequent impacts on constraining the NCEP reanalysis appear to be the main factors controlling the results found here.