Publications Published in Journal Of Hydrometeorology

1. A multiyear regional climate hindcast for the western United States using the mesoscale atmospheric simulation model. Kim, Jinwon; Lee, Jung-Eun.
   Journal Of Hydrometeorology: 2003
   Notes
   In preparation for studying the effects of increased CO2 on the hydrologic cycle in the western United States, an 8-yr hindcast was performed using a regional climate model (RCM) driven by the large-scale forcing from the NCEP–NCAR reanalysis. The simulated precipitation characteristics agree well with observations, especially in the winter. The simulated precipitation compares with rain gauge data at similar accuracy as the NCEP reanalysis, but the RCM-generated precipitation is more accurate than the reanalysis data at the scales of individual basins. Important characteristics of the hydrologic cycle of the region, such as seasonal snowfall, frequency of heavy and extreme daily precipitation events, and interannual variations of precipitation associated with the North American monsoon are also well represented in the hindcast. Compared to the Climate Research Unit, University of East Anglia (CRU), analysis, the simulated low-level air temperatures show cold biases except in summer. The temperature biases are difficult to quantify, however, due to suspected warm biases in the CRU data. The RCM overestimates surface insolation and outgoing longwave radiation at the top of the atmosphere (OLR-TOA). The errors in the simulated radiation are smaller over the land than the ocean. Both simulated and observed OLR-TOA suggest strong influence of low-level temperatures on the seasonal variations of OLR-TOA in the region. The results suggest that the RCM employed in this study possesses reasonable skill for studying regional climate change signals in the western United States.
   
   
   
2. Sources of variability of evapotranspiration in California. Hidalgo, Hugo G; Cayan, Daniel R; Dettinger, Michael D.
   Journal Of Hydrometeorology: 2005
   Notes
   The variability (1990–2002) of potential evapotranspiration estimates (ETo) and related meteorological variables from a set of stations from the California Irrigation Management System (CIMIS) is studied. Data from the National Climatic Data Center (NCDC) and from the Department of Energy from 1950 to 2001 were used to validate the results. The objective is to determine the characteristics of climatological ETo and to identify factors controlling its variability (including associated atmospheric circulations). Daily ETo anomalies are strongly correlated with net radiation (Rn) anomalies, relative humidity (RH), and cloud cover, and less with average daily temperature (Tavg). The highest intraseasonal variability of ETo daily anomalies occurs during the spring, mainly caused by anomalies below the high ETo seasonal values during cloudy days. A characteristic circulation pattern is associated with anomalies of ETo and its driving meteorological inputs, Rn, RH, and Tavg, at daily to seasonal time scales. This circulation pattern is dominated by 700-hPa geopotential height (Z700) anomalies over a region off the west coast of North America, approximately between 328 and 448 latitude, referred to as the California Pressure Anomaly (CPA). High cloudiness and lower than normal ETo are associated with the lowheight (pressure) phase of the CPA pattern. Higher than normal ETo anomalies are associated with clear skies maintained through anomalously high Z700 anomalies offshore of the North American coast. Spring CPA, cloudiness, maximum temperature (Tmax), pan evaporation (Epan), and ETo conditions have not trended significantly or consistently during the second half of the twentieth century in California. Because it is not known how cloud cover and humidity will respond to climate change, the response of ETo in California to increased greenhousegas concentrations is essentially unknown; however, to retain the levels of ETo in the current climate, a decline of Rn by about 6% would be required to compensate for a warming of 138C.
   
   
   
3. The Sensitivity of Precipitation and snowpack simulations to Model resolution via nesting in regions of complex terrain. Leung, L Ruby; Qian, Yun.
   Journal Of Hydrometeorology: 2003
   Notes
   This paper examines the sensitivity of regional climate simulations to increasing spatial resolution via nesting by means of a 20-yr simulation of the western United States at 40-km resolution and a 5-yr simulation at 13- km resolution for the Pacific Northwest and California. The regional simulation at 40-km resolution shows a lack of precipitation along coastal hills, good agreement with observations on the windward slopes of the Cascades and Sierra Nevada, but overprediction on the leeside and the basins beyond. Snowpack is grossly underpredicted throughout the western United States when compared against snowpack telemetry (snotel) observations. During winter, higher spatial resolution mainly improves the precipitation simulation in the coastal hills and basins. Along the Cascades and the Sierra Nevada range, precipitation is strongly amplified at the higher spatial resolution. Higher resolution generally improves the spatial distribution of precipitation to yield a higher spatial correlation between simulations and observations. During summer, higher resolution improves not only the spatial distribution but also the regional mean precipitation. In the Olympic Mountains and along the Coastal Range, increased precipitation at higher resolution reflects mainly a shift from light to heavy precipitation events. In the Cascades and Sierra Nevada, increased precipitation is mainly associated with more frequent heavy precipitation at higher resolution. Changes in precipitation from 40- to 13-km resolution depend on synoptic conditions such as wind direction and moisture transport. The use of higher spatial resolution improves snowpack more than precipitation. However, results presented in this paper suggest that accuracy in the snow simulation is also limited by factors such as deficiencies in the land surface model or biases in other model variables.