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Pagano, T, Pasteris P, Dettinger MD, Cayan DR, Redmond K.  2004.  Spring 2004: The heat is on Western US water supplies. EOS Trans. AGU. 85:385. Abstract
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Pan, LL, Chen SH, Cayan D, Lin MY, Hart Q, Zhang MH, Liu YB, Wang JZ.  2011.  Influences of climate change on California and Nevada regions revealed by a high-resolution dynamical downscaling study. Climate Dynamics. 37:2005-2020.   10.1007/s00382-010-0961-5   AbstractWebsite

In this study, the influence of climate change to California and Nevada regions was investigated through high-resolution (4-km grid spacing) dynamical downscaling using the WRF (Weather Research & Forecasting) model. The dynamical downscaling was performed to both the GFS (Global forecast model) reanalysis (called GFS-WRF runs) from 2000-2006 and PCM (Parallel Climate Model) simulations (called PCM-WRF runs) from 1997-2006 and 2047-2056. The downscaling results were first validated by comparing current model outputs with the observational analysis PRISM (Parameter-elevation Regressions on Independent Slopes Model) dataset. In general, the dominant features from GFS-WRF runs and PCM-WRF runs were consistent with each other, as well as with PRISM results. The influences of climate change on the California and Nevada regions can be inferred from the model future runs. The averaged temperature showed a positive trend in the future, as in other studies. The temperature increases by around 1-2A degrees C under the assumption of business as usual over 50 years. This leads to an upward shifting of the freezing level (the contour line of 0A degrees C temperature) and more rain instead of snow in winter (December, January, and February). More hot days (> 32.2A degrees C or 90A degrees F) and extreme hot days (> 37.8A degrees C or 100A degrees F) are predicted in the Sacramento Valley and the southern parts of California and Nevada during summer (June, July, and August). More precipitation is predicted in northern California but not in southern California. Rainfall frequency slightly increases in the coast regions, but not in the inland area. No obvious trend of the surface wind was indicated. The probability distribution functions (PDF) of daily temperature, wind and precipitation for California and Nevada showed no significant change in shape in either winter or summer. The spatial distributions of precipitation frequency from GFS-WRF and PCM-WRF were highly correlated (r = 0.83). However, overall positive shifts were seen in the temperature field; increases of 2A degrees C for California and 3A degrees C for Nevada in summer and 2.5A degrees C for California and 1.5A degrees C for Nevada in winter. The PDFs predicted higher precipitation in winter and lower precipitation in the summer for both California and Nevada.

Pandey, GR, Cayan DR, Georgakakos KP.  1999.  Precipitation structure in the Sierra Nevada of California during winter. Journal of Geophysical Research-Atmospheres. 104:12019-12030.   10.1029/1999jd900103   AbstractWebsite

Influences of upper air characteristics along the coast of California upon wintertime (November-April) precipitation in the Sierra Nevada are investigated. Precipitation events in the Sierra Nevada region occur mostly during wintertime, irrespective of station location (leeside or windside) and elevation. Most precipitation episodes in the region are associated with moist southwesterly winds (coming from the southwest direction) and also tend to occur when the 700-mbar temperature at the upwind direction is close to -2 degrees C. This favored wind direction and temperature signify the importance of both moisture transport and orographic lifting in augmenting precipitation in the region. By utilizing the observed dependency of the precipitation upon the upper air conditions, a linear model is formulated to quantify the precipitation observed at different sites as a function of moisture transport. The skill of the model increases with timescale of aggregation, reaching more than 50% variance explained at an aggregation period of 5-7 days. This indicates that upstream air moisture transport can be used to estimate the precipitation totals in the Sierra Nevada region.

Pandey, GR, Cayan DR, Dettinger MD, Georgakakos KP.  2000.  A hybrid orographic plus statistical model for downscaling daily precipitation in northern California. Journal of Hydrometeorology. 1:491-506.   10.1175/1525-7541(2000)001<0491:ahopsm>2.0.co;2   AbstractWebsite

A hybrid (physical-statistical) scheme is developed to resolve the finescale distribution of daily precipitation over complex terrain. The scheme generates precipitation by combining information from the upper-air conditions and From sparsely distributed station measurements: thus, it proceeds in two steps. First, an initial estimate of the precipitation is made using a simplified orographic precipitation model. It is a steady-state, multilayer, and two-dimensional model following the concepts of Rhea, The model is driven by the 2.5 degrees x 2.5 degrees gridded National Oceanic and Atmospheric Administration-National Centers for Environmental Prediction upper-air profiles, and its parameters are tuned using the observed precipitation structure of the region, Precipitation is generated assuming a forced lifting of the air parcels as they cross the mountain barrier following a straight trajectory. Second, the precipitation is adjusted using errors between derived precipitation and observations from nearby sites. The study area covers the northern half of California, including coastal mountains, central valley, and the Sierra Nevada. The model is run for a 5-km rendition of terrain for days of January-March over the period of 1988-95. A jackknife analysis demonstrates the validity of the approach. The spatial and temporal distributions of the simulated precipitation field agree well with the observed precipitation, Further, a mapping of model performance indices (correlation coefficients, model bias, root-mean-square error, and threat scores) from an array of stations from the region indicates that the model performs satisfactorily in resolving daily precipitation at 5-km resolution.

Peterson, DH, Cayan DR, Festa JF.  1986.  Interannual variability in biogeochemistry of partially-mixed estuaries: dissolved silicate cycles in northern San Francisco Bay. Estuarine variability : Proceedings of the Eighth biennial international estuarine research conference, University of New Hampshire, Durham, July 28-August 2, 1985. ( Wolfe DA, Ed.).:123-128., Orlando, Fla.: Academic Press Abstract
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Peterson, DH, Cayan DR, Festa JF, Nichols FH, Walters RA, Slack JV, Hager SE, Schemel LE.  1989.  Climate variability in an estuary: effects of reverflow on San Francisco Bay. Aspects of climate variability in the Pacific and the western Americas. ( Peterson DH, Ed.).:419-442., Washington, DC, U.S.A.: American Geophysical Union Abstract
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Peterson, DH, Cayan DR, DiLeo J, Noble M, Dettinger M.  1994.  The role of climate in estuarine variability. American Scientist. 83:58-67. Abstract
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Peterson, DH, Smith RE, Dettinger MD, Cayan DR, Riddle L.  2000.  An organized signal in snowmelt runoff over the western United States. Journal of the American Water Resources Association. 36:421-432.   10.1111/j.1752-1688.2000.tb04278.x   AbstractWebsite

Daily-to-weekly discharge during the snowmelt season is highly correlated among river basins in the upper elevations of the central and southern Sierra Nevada (Carson, Walker, Tuolumne, Merced, San Joaquin, Kings, and Kern Rivers). In many cases, the upper Sierra Nevada watershed operates in a single mode (with varying catchment amplitudes). In some years, with appropriate lags, this mode extends to distant mountains. A reason for this coherence is the broad scale nature of synoptic features in atmospheric circulation which provide anomalous insolation and temperature forcings that span a large region, sometimes the entire western U.S. These correlations may fall off dramatically, however, in dry years when the snowpack is spatially patchy.

Peterson, DH, Cayan DR, Dettinger MD, Noble M, Riddle LG, Schemel LE, Smith RE, Uncles R, Walters R.  1996.  San Francisco Bay: observations, numerical simulation and statistical models. San Francisco Bay: the ecosystem. Further investigations into the natural history of San Francisco Bay and Delta with reference to the influence of man. ( Hollibaugh JT, Ed.).:9-34., San Francisco, Calif.: Pacific Division of the American Association for the Advancement of Science Abstract
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Pierce, DW, Das T, Cayan DR, Maurer EP, Miller NL, Bao Y, Kanamitsu M, Yoshimura K, Snyder MA, Sloan LC, Franco G, Tyree M.  2013.  Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling. Climate Dynamics. 40:839-856.   10.1007/s00382-012-1337-9   AbstractWebsite

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.

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.

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.

Pierce, DW, Barnett TP, Hidalgo HG, Das T, Bonfils C, Santer BD, Bala G, Dettinger MD, Cayan DR, Mirin A, Wood AW, Nozawa T.  2008.  Attribution of declining western US snowpack to human effects. Journal of Climate. 21:6425-6444.   10.1175/2008jcli2405.1   AbstractWebsite

Observations show snowpack has declined across much of the western United States over the period 1950-99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D-A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean-atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D-A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols.

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.

Pierce, DW, Cayan DR, Das T, Maurer EP, Miller NL, Bao Y, Kanamitsu M, Yoshimura K, Snyder MA, Sloan LC, Franco G, Tyree M.  2013.  The key role of heavy precipitation events in climate model disagreements of future annual precipitation changes in California. Journal of Climate. 26:5879-5896.   10.1175/jcli-d-12-00766.1   AbstractWebsite

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.

Pierce, DW, Cayan DR, Maurer EP, Abatzoglou JT, Hegewisch KC.  2015.  Improved bias correction techniques for hydrological simulations of climate change. Journal of Hydrometeorology. 16:2421-2442.   10.1175/jhm-d-14-0236.1   AbstractWebsite

Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM's mean climate change signal, with differences of up to 2 degrees C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models' simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season's values at once.

Polade, SD, Pierce DW, Cayan DR, Gershunov A, Dettinger MD.  2014.  The key role of dry days in changing regional climate and precipitation regimes. Scientific Reports. 4   10.1038/srep04364   AbstractWebsite

Future changes in the number of dry days per year can either reinforce or counteract projected increases in daily precipitation intensity as the climate warms. We analyze climate model projected changes in the number of dry days using 28 coupled global climate models from the Coupled Model Intercomparison Project, version 5 (CMIP5). We find that the Mediterranean Sea region, parts of Central and South America, and western Indonesia could experience up to 30 more dry days per year by the end of this century. We illustrate how changes in the number of dry days and the precipitation intensity on precipitating days combine to produce changes in annual precipitation, and show that over much of the subtropics the change in number of dry days dominates the annual changes in precipitation and accounts for a large part of the change in interannual precipitation variability.

Polade, SD, Gershunov A, Cayan DR, Dettinger MD, Pierce DW.  2017.  Precipitation in a warming world: Assessing projected hydro-climate changes in California and other Mediterranean climate regions. Scientific Reports. 7   10.1038/s41598-017-11285-y   AbstractWebsite

In most Mediterranean climate (MedClim) regions around the world, global climate models (GCMs) consistently project drier futures. In California, however, projections of changes in annual precipitation are inconsistent. Analysis of daily precipitation in 30 GCMs reveals patterns in projected hydrometeorology over each of the five MedClm regions globally and helps disentangle their causes. MedClim regions, except California, are expected to dry via decreased frequency of winter precipitation. Frequencies of extreme precipitation, however, are projected to increase over the two MedClim regions of the Northern Hemisphere where projected warming is strongest. The increase in heavy and extreme precipitation is particularly robust over California, where it is only partially offset by projected decreases in low-medium intensity precipitation. Over the Mediterranean Basin, however, losses from decreasing frequency of low-medium-intensity precipitation are projected to dominate gains from intensifying projected extreme precipitation. MedClim regions are projected to become more sub-tropical, i.e. made dryer via pole-ward expanding subtropical subsidence. California's more nuanced hydrological future reflects a precarious balance between the expanding subtropical high from the south and the south-eastward extending Aleutian low from the north-west. These dynamical mechanisms and thermodynamic moistening of the warming atmosphere result in increased horizontal water vapor transport, bolstering extreme precipitation events.

Polade, SD, Gershunov A, Cayan DR, Dettinger MD, Pierce DW.  2013.  Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models. Geophysical Research Letters. 40:2296-2301.   10.1002/grl.50491   AbstractWebsite

Natural climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.