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Recently the Southwest has experienced a spate of dryness, which presents a challenge to the sustainability of current water use by human and natural systems in the region. In the Colorado River Basin, the early 21st century drought has been the most extreme in over a century of Colorado River flows, and might occur in any given century with probability of only 60%. However, hydrological model runs from downscaled Intergovernmental Panel on Climate Change Fourth Assessment climate change simulations suggest that the region is likely to become drier and experience more severe droughts than this. In the latter half of the 21st century the models produced considerably greater drought activity, particularly in the Colorado River Basin, as judged from soil moisture anomalies and other hydrological measures. As in the historical record, most of the simulated extreme droughts build up and persist over many years. Durations of depleted soil moisture over the historical record ranged from 4 to 10 years, but in the 21st century simulations, some of the dry events persisted for 12 years or more. Summers during the observed early 21st century drought were remarkably warm, a feature also evident in many simulated droughts of the 21st century. These severe future droughts are aggravated by enhanced, globally warmed temperatures that reduce spring snowpack and late spring and summer soil moisture. As the climate continues to warm and soil moisture deficits accumulate beyond historical levels, the model simulations suggest that sustaining water supplies in parts of the Southwest will be a challenge.

Understanding global seasonal patterns of Kawasaki disease (KD) may provide insight into the etiology of this vasculitis that is now the most common cause of acquired heart disease in children in developed countries worldwide. Data from 1970-2012 from 25 countries distributed over the globe were analyzed for seasonality. The number of KD cases from each location was normalized to minimize the influence of greater numbers from certain locations. The presence of seasonal variation of KD at the individual locations was evaluated using three different tests: time series modeling, spectral analysis, and a Monte Carlo technique. A defined seasonal structure emerged demonstrating broad coherence in fluctuations in KD cases across the Northern Hemisphere extra-tropical latitudes. In the extra-tropical latitudes of the Northern Hemisphere, KD case numbers were highest in January through March and approximately 40% higher than in the months of lowest case numbers from August through October. Datasets were much sparser in the tropics and the Southern Hemisphere extra-tropics and statistical significance of the seasonality tests was weak, but suggested a maximum in May through June, with approximately 30% higher number of cases than in the least active months of February, March and October. The seasonal pattern in the Northern Hemisphere extra-tropics was consistent across the first and second halves of the sample period. Using the first global KD time series, analysis of sites located in the Northern Hemisphere extra-tropics revealed statistically significant and consistent seasonal fluctuations in KD case numbers with high numbers in winter and low numbers in late summer and fall. Neither the tropics nor the Southern Hemisphere extra-tropics registered a statistically significant aggregate seasonal cycle. These data suggest a seasonal exposure to a KD agent that operates over large geographic regions and is concentrated during winter months in the Northern Hemisphere extra-tropics.

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.

Climate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (>60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces California’s mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (>60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions.

This study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years’ (2000–06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality–related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047–53) and present (2000–06) simulation results driven by PCM data and is presented in a separate paper.

To investigate possible future climate changes in California, a set of climate change model simulations was selected and evaluated. From the IPCC Fourth Assessment, simulations of twenty-first century climates under a B1 (low emissions) and an A2 (a medium-high emissions) emissions scenarios were evaluated, along with occasional comparisons to the A1fi (high emissions) scenario. The climate models whose simulations were the focus of the present study were from the Parallel Climate Model (PCM1) from NCAR and DOE, and the NOAA Geophysical Fluid Dynamics Laboratory CM2.1 model (GFDL). These emission scenarios and attendant climate simulations are not “predictions,” but rather are a purposely diverse set of examples from among the many plausible climate sequences that might affect California in the next century. Temperatures over California warm significantly during the twenty-first century in each simulation, with end-of-century temperature increases from approximately +1.5°C under the lower emissions B1 scenario in the less responsive PCM1 to +4.5°C in the higher emissions A2 scenario within the more responsive GFDL model. Three of the simulations (all except the B1 scenario in PCM1) exhibit more warming in summer than in winter. In all of the simulations, most precipitation continues to occur in winter. Relatively small (less than ~10%) changes in overall precipitation are projected. The California landscape is complex and requires that model information be parsed out onto finer scales than GCMs presently offer. When downscaled to its mountainous terrain, warming has a profound influence on California snow accumulations, with snow losses that increase with warming. Consequently, snow losses are most severe in projections by the more responsive model in response to the highest emissions.

California's coastal observations and global model projections indicate that California's open coast and estuaries will experience rising sea levels over the next century. During the last several decades, the upward historical trends, quantified from a small set of California tide gages, have been approximately 20 cm/century, quite similar to that estimated for global mean sea level. In the next several decades, warming produced by climate model simulations indicates that sea level rise (SLR) could substantially exceed the rate experienced during modern human development along the California coast and estuaries. A range of future SLR is estimated from a set of climate simulations governed by lower (B1), middle-upper (A2), and higher (A1fi) GHG emission scenarios. Projecting SLR from the ocean warming in GCMs, observational evidence of SLR, and separate calculations using a simple climate model yields a range of potential sea level increases, from 11 to 72 cm, by the 2070-2099 period. The combination of predicted astronomical tides with projected weather forcing, El Niño related variability, and secular SLR, gives a series of hourly sea level projections for 2005-2100. Gradual sea level rise progressively worsens the impacts of high tides, surge and waves resulting from storms, and also freshwater floods from Sierra and coastal mountain catchments. The occurrence of extreme sea levels is pronounced when these factors coincide. The frequency and magnitude of extreme events, relative to current levels, follows a sharply escalating pattern as the magnitude of future sea level rise increases.

Global climate models (GCMs) are the fundamental drivers of regional climate-change projections (IPCC 2007). GCMs allow us to characterize changes in atmospheric circulation associated with human causes at global and continental scales. However, because of the planetary scope of the GCMs, their resolution, or level of detail, is somewhat coarse. A typical GCM grid spacing is about 62 miles (100 km) or greater, which is inadequate for creating projections and evaluating impacts of climate change at a regional scale. Thus, a “downscaling” procedure is needed to provide finer spatial detail of the model results.