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Feng, DM, Beighley E, Raoufi R, Melack J, Zhao YH, Iacobellis S, Cayan D.  2019.  Propagation of future climate conditions into hydrologic response from coastal southern California watersheds. Climatic Change. 153:199-218.   10.1007/s10584-019-02371-3   AbstractWebsite

As a biodiverse region under a Mediterranean climate with a mix of highly developed and natural watersheds, coastal Santa Barbara County (SB), located in southern California, is susceptible to the hydrologic impacts of climate change. This study investigates the potential changes in hydro-meteorological variables in this region as well as their societal and ecological implications for projected climate conditions during the twenty-first century. Daily streamflow ensembles from 135 coastal watersheds for the period 2021-2100 are developed using the Hillslope River Routing (HRR) model forced with downscaled precipitation and temperature projections derived from 10 climate models in the Coupled Model Inter-Comparison Project, Phase 5, and two emission scenarios (Representative Concentration Pathways, RCP, 4.5 and 8.5). Analysis of the projected ensemble precipitation and streamflow series relative to historical conditions (1961-2000) shows (i) minimal change in annual precipitation (median change within +/- 3%); (ii) an altered seasonal rainfall distribution with a decrease in rainfall at the beginning of the rainy season (Oct-Dec), an increase during the Jan-Mar period, and a decrease at the end of the season (Apr-Jun); (iii) increases in the magnitude and frequency of large storms (>36mm/day) which combined with a shorter rainy season, lead to increases in annual peak flows; and (iv) the propagation of the altered precipitation characteristics resulting in nonlinear changes in the magnitude and variability of annual maximum discharges (i.e., mean, standard deviation, skew) impacting estimated return period discharges (e.g., estimated 100-year flood discharges for the period 2061-2100 under 8.5 increase by up to 185%). While these results are specific to southern coastal California, the nature of nonlinear hydrologic response to altered precipitation characteristics underscores the value of regional studies investigating potential impacts of climate projections on streamflow dynamics.

Roche, JW, Rice R, Meng XD, Cayan DR, Dettinger MD, Alden D, Patel SC, Mason MA, Conklin MH, Bales RC.  2019.  Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA. Earth System Science Data. 11:101-110.   10.5194/essd-11-101-2019   AbstractWebsite

sWe present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010-2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2-6) from four locations spanning the rain-snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (

Knowles, N, Cronkite-Ratcliff C, Pierce DW, Cayan DR.  2018.  Responses of unimpaired flows, storage, and managed flows to scenarios of climate change in the San Francisco Bay-Delta Watershed. Water Resources Research. 54:7631-7650.   10.1029/2018wr022852   AbstractWebsite

Projections of meteorology downscaled from global climate model runs were used to drive a model of unimpaired hydrology of the Sacramento/San Joaquin watershed, which in turn drove models of operational responses and managed flows. Twenty daily climate change scenarios for water years 1980-2099 were evaluated with the goal of producing inflow boundary conditions for a watershed sediment model and for a hydrodynamical model of the San Francisco Bay-Delta estuary. The resulting time series of meteorology, snowpack, unimpaired flow, reservoir storage, and managed flow were analyzed for century-scale trends. In the Sacramento basin, which dominates Bay-Delta inflows, all 20 scenarios portrayed warming trends (with a mean of 4.1 degrees C) and most had precipitation increases (with a mean increase of 9%). Sacramento basin snowpack water equivalent declined sharply (by 89%), which was associated with a major shift toward earlier unimpaired runoff timing (33% more flow arriving prior to 1 April). Sacramento basin reservoirs showed large declines in end-of-September storage. Water-year averaged outflows increased for most scenarios for both unimpaired and impaired flows, and frequency of extremely high daily unimpaired and impaired flows increased (increases of 175% and 170%, respectively). Managed Delta inflows were projected to experience large increases in the wet season and declines in the dry season. Changes in management strategy and infrastructure can mitigate some of these changes, though to what degree is uncertain.

Pierce, DW, Cayan DR.  2016.  Downscaling humidity with Localized Constructed Analogs (LOCA) over the conterminous United States. Climate Dynamics. 47:411-431.   10.1007/s00382-015-2845-1   AbstractWebsite

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 degrees/16 degrees grid over the conterminous U.S. and the results compared to observations. LOCA reproduces observed monthly climatological values with a mean error of similar to 0.5 % and RMS error of similar to 2 %. Extreme (1-day in 1- and 20-years) maximum values (relevant to human health and energy demand) are within similar to 5 % of observed, while extreme minimum values (relevant to agriculture and wildfire) are within similar to 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 similar to 1 % and RMS error of similar to 3 %. LOCA increases spatial coherence in the final downscaled field by similar to 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.

Shukla, S, Steinemann A, Iacobellis SF, Cayan DR.  2015.  Annual drought in California: Association with monthly precipitation and climate phases. Journal of Applied Meteorology and Climatology. 54:2273-2281.   10.1175/jamc-d-15-0167.1   AbstractWebsite

Annual precipitation in California is more variable than in any other state and is highly influenced by precipitation in winter months. A primary question among stakeholders is whether low precipitation in certain months is a harbinger of annual drought in California. Historical precipitation data from 1895 to 2013 are investigated to identify leading monthly indicators of annual drought in each of the seven climate divisions (CDs) as well as statewide. For this study, drought conditions are defined as monthly/annual (October-September) precipitation below the 20th/30th percentile, and a leading indicator is defined as a monthly drought preceding or during an annual drought that has the strongest association (i.e., joint probability of occurrence) with a statewide annual drought. Monthly precipitation variability and contributions to annual precipitation, along with joint probabilities of drought among the winter months, are first analyzed. Then the probabilities of annual drought and the variability in leading indicators are analyzed according to different climate phases and CDs. This study identified December within a water year as being the leading indicator that is most frequently associated with annual drought statewide (56%) and in most of the CDs (the highest was CD2 at 65%). Associated with its leading-indicator status, December drought was most frequently associated with drought in other winter months (joint probability > 30%). Results from this study can help stakeholders to understand and assess the likelihood of annual drought events given monthly precipitation preceding or early in the water year.

Pierce, DW, Cayan DR, Thrasher BL.  2014.  Statistical downscaling using Localized Constructed Analogs (LOCA). Journal of Hydrometeorology. 15:2558-2585.   10.1175/jhm-d-14-0082.1   AbstractWebsite

A new technique for statistically downscaling climate model simulations of daily temperature and precipitation is introduced and demonstrated over the western United States. The localized constructed analogs (LOCA) method produces downscaled estimates suitable for hydrological simulations using a multiscale spatial matching scheme to pick appropriate analog days from observations. First, a pool of candidate observed analog days is chosen by matching the model field to be downscaled to observed days over the region that is positively correlated with the point being downscaled, which leads to a natural independence of the downscaling results to the extent of the domain being downscaled. Then, the one candidate analog day that best matches in the local area around the grid cell being downscaled is the single analog day used there. Most grid cells are downscaled using only the single locally selected analog day, but locations whose neighboring cells identify a different analog day use a weighted combination of the center and adjacent analog days to reduce edge discontinuities. By contrast, existing constructed analog methods typically use a weighted average of the same 30 analog days for the entire domain. By greatly reducing this averaging, LOCA produces better estimates of extreme days, constructs a more realistic depiction of the spatial coherence of the downscaled field, and reduces the problem of producing too many light-precipitation days. The LOCA method is more computationally expensive than existing constructed analog techniques, but it is still practical for downscaling numerous climate model simulations with limited computational resources.

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.

Pierce, DW, Cayan DR.  2013.  The uneven response of different snow measures to human-induced climate warming. Journal of Climate. 26:4148-4167.   10.1175/jcli-d-12-00534.1   AbstractWebsite

The effect of human-induced climate warming on different snow measures in the western United States is compared by calculating the time required to achieve a statistically significant linear trend in the different measures, using time series derived from regionally downscaled global climate models. The measures examined include the water content of the spring snowpack, total cold-season snowfall, fraction of winter precipitation that falls as snow, length of the snow season, and fraction of cold-season precipitation retained in the spring snowpack, as well as temperature and precipitation. Various stakeholders may be interested in different sets of these variables. It is found that temperature and the fraction of winter precipitation that falls as snow exhibit significant trends first, followed in 5-10 years by the fraction of cold-season precipitation retained in the spring snowpack, and later still by the water content of the spring snowpack. Change in total cold-season snowfall is least detectable of all the measures, since it is strongly linked to precipitation, which has large natural variability and only a weak anthropogenic trend in the western United States. Averaging over increasingly wider areas monotonically increases the signal-to-noise ratio of the 1950-2025 linear trend from 0.15 to 0.37, depending on the snow measure.

Das, T, Dettinger MD, Cayan DR, Hidalgo HG.  2011.  Potential increase in floods in California's Sierra Nevada under future climate projections. Climatic Change. 109:71-94.   10.1007/s10584-011-0298-z   AbstractWebsite

California's mountainous topography, exposure to occasional heavily moisture-laden storm systems, and varied communities and infrastructures in low lying areas make it highly vulnerable to floods. An important question facing the state-in terms of protecting the public and formulating water management responses to climate change-is "how might future climate changes affect flood characteristics in California?" To help address this, we simulate floods on the western slopes of the Sierra Nevada Mountains, the state's primary catchment, based on downscaled daily precipitation and temperature projections from three General Circulation Models (GCMs). These climate projections are fed into the Variable Infiltration Capacity (VIC) hydrologic model, and the VIC-simulated streamflows and hydrologic conditions, from historical and from projected climate change runs, allow us to evaluate possible changes in annual maximum 3-day flood magnitudes and frequencies of floods. By the end of the 21st Century, all projections yield larger-than-historical floods, for both the Northern Sierra Nevada (NSN) and for the Southern Sierra Nevada (SSN). The increases in flood magnitude are statistically significant (at p <= 0.01) for all the three GCMs in the period 2051-2099. The frequency of flood events above selected historical thresholds also increases under projections from CNRM CM3 and NCAR PCM1 climate models, while under the third scenario, GFDL CM2.1, frequencies remain constant or decline slightly, owing to an overall drying trend. These increases appear to derive jointly from increases in heavy precipitation amount, storm frequencies, and days with more precipitation falling as rain and less as snow. Increases in antecedent winter soil moisture also play a role in some areas. Thus, a complex, as-yet unpredictable interplay of several different climatic influences threatens to cause increased flood hazards in California's complex western Sierra landscapes.

Cayan, DR, Maurer EP, Dettinger MD, Tyree M, Hayhoe K.  2008.  Climate change scenarios for the California region. Climatic Change. 87:S21-S42.   10.1007/s10584-007-9377-6   AbstractWebsite

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 degrees C under the lower emissions B1 scenario in the less responsive PCM1 to +4.5 degrees 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 similar to 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.

Franco, G, Cayan D, Luers A, Hanemann M, Croes B.  2008.  Linking climate change science with policy in California. Climatic Change. 87:S7-S20.   10.1007/s10584-007-9359-8   AbstractWebsite

Over the last few years, California has passed some of the strongest climate policies in the USA. These new policies have been motivated in part by increasing concerns over the risk of climate-related impacts and facilitated by the state's existing framework of energy and air quality policies. This paper presents an overview of the evolution of this increased awareness of climate change issues by policy makers brought about by the strong link between climate science and policy in the state. The State Legislature initiated this link in 1988 with the mandate to prepare an assessment of the potential consequences of climate change to California. Further interactions between science and policy has more recently resulted, in summer of 2006, in the passage of Assembly Bill 32, a law that limits future greenhouse gas emissions in California. This paper discusses the important role played by a series of state and regional climate assessments beginning in 1988 and, in particular, the lessons learned from a recently completed study known as the Scenarios Project.

Bonfils, C, Santer BD, Pierce DW, Hidalgo HG, Bala G, Das T, Barnett TP, Cayan DR, Doutriaux C, Wood AW, Mirin A, Nozawa T.  2008.  Detection and attribution of temperature changes in the mountainous western United States. Journal of Climate. 21:6404-6424.   10.1175/2008jcli2397.1   AbstractWebsite

Large changes in the hydrology of the western United States have been observed since the mid-twentieth century. These include a reduction in the amount of precipitation arriving as snow, a decline in snowpack at low and midelevations, and a shift toward earlier arrival of both snowmelt and the centroid (center of mass) of streamflows. To project future water supply reliability, it is crucial to obtain a better understanding of the underlying cause or causes for these changes. A regional warming is often posited as the cause of these changes without formal testing of different competitive explanations for the warming. In this study, a rigorous detection and attribution analysis is performed to determine the causes of the late winter/early spring changes in hydrologically relevant temperature variables over mountain ranges of the western United States. Natural internal climate variability, as estimated from two long control climate model simulations, is insufficient to explain the rapid increase in daily minimum and maximum temperatures, the sharp decline in frost days, and the rise in degree-days above 0 degrees C (a simple proxy for temperature-driven snowmelt). These observed changes are also inconsistent with the model-predicted responses to variability in solar irradiance and volcanic activity. The observations are consistent with climate simulations that include the combined effects of anthropogenic greenhouse gases and aerosols. It is found that, for each temperature variable considered, an anthropogenic signal is identifiable in observational fields. The results are robust to uncertainties in model-estimated fingerprints and natural variability noise, to the choice of statistical down-scaling method, and to various processing options in the detection and attribution method.

Bromirski, PD, Flick RE, Cayan DR.  2003.  Storminess variability along the California coast: 1858-2000. Journal of Climate. 16:982-993.   10.1175/1520-0442(2003)016<0982:svatcc>;2   AbstractWebsite

The longest available hourly tide gauge record along the West Coast (U. S.) at San Francisco yields meteorologically forced nontide residuals (NTR), providing an estimate of the variation in "storminess'' from 1858 to 2000. Mean monthly positive NTR (associated with low sea level pressure) show no substantial change along the central California coast since 1858 or over the last 50 years. However, in contrast, the highest 2% of extreme winter NTR levels exhibit a significant increasing trend since about 1950. Extreme winter NTR also show pronounced quasi-periodic decadal-scale variability that is relatively consistent over the last 140 years. Atmospheric sea level pressure anomalies (associated with years having high winter NTR) take the form of a distinct, large-scale atmospheric circulation pattern, with intense storminess associated with a broad, southeasterly displaced, deep Aleutian low that directs storm tracks toward the California coast.