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Cayan, DR, Das T, Pierce DW, Barnett TP, Tyree M, Gershunov A.  2010.  Future dryness in the southwest US and the hydrology of the early 21st century drought. Proceedings of the National Academy of Sciences of the United States of America. 107:21271-21276.   10.1073/pnas.0912391107   AbstractWebsite

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.

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Graham, NE, Cayan DR, Bromirski PD, Flick RE.  2013.  Multi-model projections of twenty-first century North Pacific winter wave climate under the IPCC A2 scenario. Climate Dynamics. 40:1335-1360.   10.1007/s00382-012-1435-8   AbstractWebsite

A dynamical wave model implemented over the North Pacific Ocean was forced with winds from three coupled global climate models (CGCMs) run under a medium-to-high scenario for greenhouse gas emissions through the twenty-first century. The results are analyzed with respect to changes in upper quantiles of significant wave height (90th and 99th percentile H-S) during boreal winter. The three CGCMs produce surprisingly similar patterns of change in winter wave climate during the century, with waves becoming 10-15 % smaller over the lower mid-latitudes of the North Pacific, particularly in the central and western ocean. These decreases are closely associated with decreasing windspeeds along the southern flank of the main core of the westerlies. At higher latitudes, 99th percentile wave heights generally increase, though the patterns of change are less uniform than at lower latitudes. The increased wave heights at high latitudes appear to be due a variety of wind-related factors including both increased windspeeds and changes in the structure of the wind field, these varying from model to model. For one of the CGCMs, a commonly used statistical approach for estimating seasonal quantiles of H-S on the basis of seasonal mean sea level pressure (SLP) is used to develop a regression model from 60 years of twentieth century data as a training set, and then applied using twenty-first century SLP data. The statistical model reproduces the general pattern of decreasing twenty-first century wave heights south of similar to 40 N, but underestimates the magnitude of the changes by similar to 50-70 %, reflecting relatively weak coupling between sea level pressure and wave heights in the CGCM data and loss of variability in the statistically projected wave heights.

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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, 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, 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.

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.