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Vano, JA, Udall B, Cayan DR, Overpeck JT, Brekke LD, Das T, Hartmann HC, Hidalgo HG, Hoerling M, McCabe GJ, Morino K, Webb RS, Werner K, Lettenmaier DP.  2014.  Understanding uncertainties in future Colorado River streamflow. Bulletin of the American Meteorological Society. 95:59-78.   10.1175/bams-d-12-00228.1   AbstractWebsite

The Colorado River is the primary water source for more than 30 million people in the United States and Mexico. Recent studies that project streamf low changes in the Colorado River all project annual declines, but the magnitude of the projected decreases range from less than 10% to 45% by the mid-twenty-first century. To understand these differences, we address the questions the management community has raised: Why is there such a wide range of projections of impacts of future climate change on Colorado River streamflow, and how should this uncertainty be interpreted? We identify four major sources of disparities among studies that arise from both methodological and model differences. In order of importance, these are differences in 1) the global climate models (GCMs) and emission scenarios used; 2) the ability of land surface and atmospheric models to simulate properly the high-elevation runoff source areas; 3) the sensitivities of land surface hydrology models to precipitation and temperature changes; and 4) the methods used to statistically downscale GCM scenarios. In accounting for these differences, there is substantial evidence across studies that future Colorado River streamflow will be reduced under the current trajectories of anthropogenic greenhouse gas emissions because of a combination of strong temperature-induced runoff curtailment and reduced annual precipitation. Reconstructions of preinstrumental streamflows provide additional insights; the greatest risk to Colorado River streamf lows is a multidecadal drought, like that observed in paleoreconstructions, exacerbated by a steady reduction in flows due to climate change. This could result in decades of sustained streamflows much lower than have been observed in the ~100 years of instrumental record.

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.

Cayan, DR, Douglas AV.  1984.  Urban influences on surface temperatures in the southwestern United States during recent decades. Journal of Climate and Applied Meteorology. 23:1520-1530.   10.1175/1520-0450(1984)023<1520:uiosti>2.0.co;2   AbstractWebsite

Trends of surface temperature at rapidly growing urban sites during the last three to five decades are compared to those at non-urban sites, temperatures at 70 kPa, and sea surface temperature at a coastal Pacific station. Significant urban heat island effects have apparently taken hold, with urban-affected temperature increases of 1 to 2°C common over this period. In contrast, the trend of the non-urban records has been distinctly smaller over this period. The urban warming appears to be predominantly a nighttime phenomenon, with minimum temperatures displaying considerably more increase than maximum temperatures. No uniform seasonal preference for this increase emerged from these stations. Because of this increase, the distribution of observed temperatures shows a marked warm bias at several of the urban sites during recent years.

Maurer, EP, Hidalgo HG, Das T, Dettinger MD, Cayan DR.  2010.  The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrology and Earth System Sciences. 14:1125-1138.   10.5194/hess-14-1125-2010   AbstractWebsite

Three statistical downscaling methods were applied to NCEP/NCAR reanalysis (used as a surrogate for the best possible general circulation model), and the downscaled meteorology was used to drive a hydrologic model over California. The historic record was divided into an 'observed' period of 1950-1976 to provide the basis for downscaling, and a 'projected' period of 1977-1999 for assessing skill. The downscaling methods included a bias-correction/spatial downscaling method (BCSD), which relies solely on monthly large scale meteorology and resamples the historical record to obtain daily sequences, a constructed analogues approach (CA), which uses daily large-scale anomalies, and a hybrid method (BCCA) using a quantile-mapping bias correction on the large-scale data prior to the CA approach. At 11 sites we compared three simulated daily flow statistics: streamflow timing, 3-day peak flow, and 7-day low flow. While all downscaling methods produced reasonable streamflow statistics at most locations, the BCCA method consistently outperformed the other methods, capturing the daily large-scale skill and translating it to simulated streamflows that more skillfully reproduced observationally-driven streamflows.