There is considerable seasonal-to-interannual variability in the flow of major watersheds in the Sierra Nevadas. This study examines that variability in terms of seasonal average surface weather variables, including atmospheric circulation, temperature, precipitation, and snow.

Of particular importance is an apparent decline in spring-early summer runoff from the Sierra Nevadas first pointed out by M. Roos of the California Department of Water Resources. While measured October-September (water year) and April-July (AMJJ) runoff have increased, the AMJ J / annual fractional runoff has decreased by approximately 10% over the 80+ years of record.

Further inspection shows that many streams in the West have a significant decline in spring-early summer fractional runoff as seen from a network of river gauging stations from Alaska south to Arizona and from California east to the Rockies. The cause of the trends at these stations is complex, involving both precipitation and temperature. For many basins the fractional spring-early summer runoff is affected by climatic behavior across all the seasons. In the Sierra, the decreased AMJJ fraction appears to have been produced by increased precipitation in the late summer, fall, and winter with decreased precipitation in spring. In addition, temperature along the West Coast has increased during the non-summer seasons, enhancing earlier runoff, and possibly evapotranspiration in spring.

Other combinations of temperature and precipitation were involved in fractional runoff trends in other regions. Reversals in temperature and precipitation trends in remote regions over the eastern part of North America suggest that much of these changes were produced by shifts in the long wave patterns of atmospheric circulation, perhaps discounting a greenhouse effect scenario.

Additional studies partially funded by this grant involve the influence of large scale atmospheric features usually emanating from the North Pacific Ocean during winter. These winter atmospheric patterns have strong connections to fluctuations in streamflow in watersheds in California and elsewhere in western North America.

An important part of the water supply in California and the western United States is derived from runoff fed by mountain snow melt. Snow accumulation responds to both precipitation and temperature variations, and forms an interesting climatic index, since it integrates these influences over the entire late fall-spring period. The study area includes the Sierra Nevada, which accumulates most of the snow pack and comprises a major portion of the water-bearing region of California. The purpose of this study is to shed light upon the link between climate and year-to-year variability in the snow pack. Specifically, we wanted to determine (a) the dependence of snow pack and streamflow upon natural climate variability, and (b) if the relationships linking snow and streamflow to climate variations are stable over the history of instrumental records by using pre-1948 historical records to test the results from 1948-present. The basis of the study will be several long series of historical observations containing observed variability over daily-toseasonal time scales. Our focus is on measurements on/around April 1, when snow accumulation is typically greatest. The primary data is from a network of mountainous snow courses; many have records of six decades or more. For any given year, the spring snow water equivalent (SWE) anomaly at a particular snow course is likely to be 25-60% of its long-term average. Although effects vary with region and with elevation, in general, the anomalous winter precipitation has the strongest influence on spring SWE fluctuations. Anomalous temperature has a weaker effect overall, but it has great influence in lower elevations such as in the coastal Northwest, and during spring in higher elevations. Patterns of the precipitation, temperature, and snow anomalies extend over broad regional areas, much larger than individual watersheds. These surface anomalies are organized by the atmospheric circulation, with primary anomaly centers over the North Pacific Ocean as well as over western North America. For most of the regions, anomalously low SWE is associated with a winter circulation resembling the PNA pattern. With a strong low in the central North Pacific and high pressure over the Pacific Northwest, this pattern diverts North Pacific storms northward, away from the region. Both warm and cool phases of ENSO tend to produce regional patterns with out-of-phase SWE anomalies in the Northwest and the Southwest.

This dissertation demonstrates the uses of satellite and surface observations, in tandem with hydrologic modeling, to characterize daily-to-interannual cloudiness variability and its influence on spring-summer snowmelt and streamflow fluctuations over the mountains of the western United States from 1996 to 2015.

Daily cloudiness variations can exceed 50% of long-term averages during the springtime. When aggregated over three-month periods, cloudiness varies by ±10% of long-term averages in many locations. Rotated empirical orthogonal functions (REOFs) analysis indicates the first five REOFs account for ~67% of the total variance, each of which has distinct regional and seasonal emphases. Each of the REOF modes associates with anomalous large scale atmospheric circulation patterns and one or more large-scale teleconnection indices, which helps to explain why anomalous cloudiness patterns take on regional spatial scales and contain substantial variability over seasonal time scales.

Cloud cover indices (CC) are, to some extent, related linearly to snowmelt (ΔSWE) and snow-fed streamflow (ΔQ) fluctuations. Local CC-ΔSWE and CC-ΔQ associations vary with time and location, with the dominance of negative correlations between CC and ΔSWE, exemplifying the cloud-shading (or clear-sky) effect on snowmelt. The magnitude of CC-ΔSWE association (R2) amounts to 5%-56%, typically peaking in May. These associations fade earlier in summer during dry years than wet years, indicating the differing responses of higher vs. lower snowpack. The CC-ΔQ association displays less consistent arrangement, with R2 amounting to 2%-47%. The ΔSWE and ΔQ fluctuations exhibit spatially extensive patterns of correlations with daily CC anomalies, indicating the effects of cloudiness often operate over regional scales.

On a watershed scale, cloudiness variability redistributes the seasonal runoff and hastens the spring onset by 1-3 days. Higher elevation cloudiness exerts a greater influence on the basin runoff than lower elevation cloudiness does. Overall, cloudiness delays spring onset by 2-15 days regardless of the elevation. Lastly, the experiment on the intensification of cloudiness fluctuations suggests greater streamflow sensitivity to the “relatively cloudy periods get cloudier” scheme than to the “relatively clear periods get clearer” scheme, with the former producing 3-5 days later spring onsets.

Persistent drought presents one of the greatest risks of climate change faced by the western United States. Highlighting this risk is from paleo hydrological evidence that during the medieval period (~900-1400 AD) this region experienced two extensive droughts of greater duration than any experienced in recorded history. However, much remains to be learned about these “mega droughts,” including the severity of deficit in precipitation and snowpack in California’s Sierra Nevada Mountain range, which currently serve as a crucial source of fresh water to California agriculture, industry and domestic users. To possibly extract a record of the hydrological conditions in the Sierra Nevada during this era, cores of annually laminated sediment from Swamp Lake, in the northwest portion of Yosemite Park were collected. Stable hydrogen isotope (δD) ratios of plant leaf-wax lipid compounds preserved in the lake sediments and the thickness of the annual sedimentary laminations (varves) show promise as potential proxies for recording past hydroclimate variability in the Sierra Nevada Mountains. δD measurements at annual to interannual resolution were made for two time periods: the 20th century and the 13th-15th centuries while varve thickness was measured over the entire period 1150-2006 AD. We find significant negative correlations between 20th century δD fluctuations and instrumentally recorded variability in total precipitation, snow water equivalence (SWE) and Palmer Drought Severity Index (PDSI). Medieval δD values correspond to independent regional hydrologic reconstructions at the decadal to multi-decadal scale, which appear to delineate relatively dry medieval episodes. An issue that is still unclear is that the mean δD during this period is little different from that during the relatively moist contemporaneous period. This finding is at odds with evidence from submerged tree stumps for two prolonged droughts separated by about 200 years. Varve thickness measurements are on going, but results thus far reveal difficulties in using this proxy for reconstructing past Sierra Nevada hydroclimate variability, the most significant being the complexity of the seasonal deposition cycle and sub-millimeter thicknesses. Overall, geochemical and geomorphological information gleaned from Swamp Lake sediment cores provide an independent means of reconstructing hydroclimate in the Sierra Nevada and provide a unique perspective on the mega-drought intervals in the Sierra Nevada Mountains.

Visibility in coastal regions has a significant impact on government, commercial, and private sector activities. The primary phenomenon significantly affecting visibility along the western United States coastal regions is fog. Fog is a natural hazard to boating, commercial shipping, and other waterway activities.

The West Coast of the United States has been identified as one of the major fog producing regions of the world. Present accuracy in predicting marine coastal fog and low stratus clouds is limited. Although most weather forecasting has improved with recent advances in atmospheric circulation models and satellite observations, there is relatively little operational guidance for the prediction of marine and coastal fog.

Currently the forecasting of visibility relies mainly on the availability of local observations and experience with local weather tendencies. National Weather Service and other forecasters typically focus on selected local conditions which usually presage fog formation and if a sufficient number are present, they will forecast possible fog for the region. However, this level of information and experience is not available in all coastal areas. Results in this study suggest that attention to the large-scale circulation in addition to local conditions may lead to increased skill and accuracy in extended range forecasts.

The large-scale structure and interannual variability of fog have not been well described because data has been unavailable and a large volume of data is required. In the present analysis, several years of observations from a set of coastal weather stations and a set of comprehensive marine weather reports (primarily ships) is used to examine large-scale processes and local conditions which affect fog formation. A discussion of fog observation criteria is given. In addition, evidence is provided by a collection of observations of hours of fog-horn operations at several coastal sites over 15 years, starting in 1950.

The relationship between local and large-scale conditions, and fog and stratus formation may yield improved predictability of fog. This study indicates that large-scale conditions, especially regional-hemispheric circulation, are a vital component of both high and low fog occurrences; moreover, atmospheric circulation in the Eastern North Pacific Ocean and on the West Coast may contribute to improved forecasts for the development and persistence of fog.

California’s Central Valley (CV) is one of the most productive agricultural regions in the world, enabled by the conjunctive use of surface water and groundwater. We investigated variations in the CV’s managed surface water diversions relative to climate variability. Using a historical record (1979−2010) of diversions from 531 sites, we found diversions are largest in the wetter Sacramento basin to the north, but most variable in the drier Tulare basin to the south. A rotated empirical orthogonal function (REOF) analysis finds 72% of the variance of diversions is captured by the first three REOFs. The leading REOF (35% of variance) exhibited strong positive loadings in the Tulare basin, and the corresponding principal component time-series (RPC1) was strongly correlated (ρ > 0.9) with contemporaneous hydrologic variability. This pattern indicates larger than average diversions in the south, with neutral or slightly less than average diversions to the north during wet years, with the opposite true for dry years. The second and third REOFs (20% and 17% of variance, respectively), were strongest in the Sacramento basin and San Francisco Bay−Delta. RPC2 and RPC3 were associated with variations in agricultural- and municipal-bound diversions, respectively. RPC2 and RPC3 were also moderately correlated with 7-year cumulative precipitation based on lagged correlation analysis, indicating that diversions in the north and central portions of the CV respond to longer-term hydrologic variations. The results illustrate a dichotomy of regimes wherein diversions in the more arid Tulare are governed by year-to-year hydrologic variability, while those in wetter northern basins reflect land-use patterns and low-frequency hydrologic variations.

Climate variations on seasonal to decadal time scales can have enormous social, economical and environmental impacts. As such, the ability to make skilful and reliable climate predictions at these time scales offers many benefits for climate preparedness, adaptation and resilience. In the recent years, major progress has been made in the development of such predictions with the advent of simulations with global climate models that are initialized from the current climate state. However, many challenges remain including an understanding of the underlying physical mechanisms for skilful predictions and whether such predictions could be improved. The purpose of this thesis is to establish new benchmarks for seasonal to decadal predictions in diverse components of the climate system and to provide some pieces of evidence that help to understand what are the drivers for these predictable patterns. Specifically, we use a suite of empirical models to perform predictions of oceanic and atmospheric variables together with initialized climate predictions to: 1. Understand the contribution of remote and local factors to the predictability of North and Tropical Pacific Oceans Sea Surface Temperature and Land Surface Temperature over Western North America; 2. Provide a higher baseline level skill for the state-of-art global prediction systems, from seasonal to decadal time scales; 3. Explore possible sources of errors in the global climate model simulations using statistical predictive models.

First, we isolate contributions to the forecast skill from different spatial and time scales in the Pacific Ocean using a Liner Inverse Modelling (LIM) approach, showing the importance of temporal scale interactions in improving the predictions on decadal time scales. Specifically, we show that the Extratropical North Pacific is a source of predictability for the tropics on seasonal to interannual time scales, while the tropics enhance the forecast skill for the decadal component. We then show that the skill for an empirically-built LIM is comparable to and sometimes better than that from two state-of-art global prediction systems, from seasonal to decadal timescales and for several regions around the globe. These results indicate that the evolution of the system in those areas may not be not fully driven by unpredictable dynamics and that there may be some room for improvement in the dynamical models predictions, given that a low-dimensional linear model is able to generate better skill than the fully-coupled nonlinear model. Bearing that in mind, we use the LIM linear feedback matrix to explore possible sources of errors in the dynamical model simulations and we find that some of the simulated atmospheric and oceanic local and remote feedbacks differ in several key regions from that obtained with observations. These results may indicate sources of error in the dynamical models and therefore in its prediction skill that merit focused attention.

We then investigate the role of remote and local predictors in seasonal predictors of minimum and maximum air temperatures over the Western North America, using a Canonical Correlation Analysis approach. We show that remote predictors, in the form of Pacific climate modes, provide the best predictive skill for temperature over land, particularly during wintertime. Lastly, considering that persistence is the widely-used measure when evaluating the predictive skill for dynamical models, we suggest the use of CCA as a much higher benchmark for seasonal predictions of land surface air temperatures.

During the past year, an investigation has been initiated regarding how future climate changes may impact the hydrology of the Tijuana River Watershed – a binational watershed. The study has used gridded observed daily precipitation and temperatures and downscaled daily precipitation and temperature projections from three global climate models (GCM) to drive the VIC macroscale hydrologic model. Sensitivity analysis using VIC suggests about 2% reduction of runoff for each 1% reduction in precipitation. A 1oC increase in average temperature produces about 3% reduction of runoff. All three GCM simulations yield annual warming, with end-of-century temperature increases from approximately +1oC under a lower emission scenario in the less responsive PCM1 to +3oC in a higher emission scenario with the more responsive GFDL model. Climate projections suggest greater warming in the spring and summer months ranging between 2oC to 3oC under the higher emission scenario. Two of the three GCM simulations yield more frequent summer drying as gauged by VIC simulated soil moisture in the twenty-first century under the higher emission scenario. Summer soil moisture declines most, and most rapidly, in the later part of the twenty-first century. This initial evaluation provides perhaps the first direct estimates of the climate change impacts on the Tijuana River Watershed. However, transforming these results into a more useable projections and impacts will be a task for the future and will require collaboration and interaction between local stakeholders and the researchers.