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This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create high-resolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey’s distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them. [ABSTRACT FROM AUTHOR]

Regional frequency analysis based on the index flood method has been utilized to analyze short duration annual maximum precipitation (AMP) for Northeastern Colorado. An extraordinary storm and flood that occurred in the City of Fort Collins, Colorado, in July 1997 prompted the study. The main objective of the study was to develop regional frequency curves that may be useful for that part of Colorado. In addition, we wanted to answer the question whether the City’s design criteria for storm drainage as of 1997 was adequate, and to determine to what degree the referred storm was indeed an extraordinary storm. We utilized up-to-date technology that has been widely popularized in literature, with some minor modifications. We conclude that the regional growth curves developed may be useful for the subregions specified. However, they should be used with caution for sites located near the boundaries of subregions close to the Colorado Front Range. This has become evident in determining the precipitation quantiles for Fort Collins, for which it was necessary to further narrow down the subregions. The study also showed that the city’s storm drainage design criteria were underestimated. Furthermore, the 1997 2 and 3 h storms appear to be 100 yr+ events, while the return period of the 6 h storm may have been of the order of 400 years. While the study provided us with some practical answers, it nevertheless also brought a number of questions concerning the assumptions underlying the index flood approach and several steps regarding the definition of regions and testing criteria thereof. These are discussed at length in the final section of the paper. [ABSTRACT FROM AUTHOR]

Statistical evidence supports a relationship between the warm-season thermodynamic environment in northeastern Colorado and the antecedent snow conditions in the upstream higher elevations. Above-normal snow storage and longevity reduces the sensible heating of the elevated terrain by increasing the albedo and exerting a stabilizing influence upon the surface layer. It follows that the residual spring snowpack of the Colorado Rockies represents an energy sink to the prevailing west to southwesterly winds of the free atmosphere, which traverse the uplift of the Western Plateau. The abrupt drop in elevation along the Front Range provides for a setting in which a thermal perturbation induced by mountain snow may be communicated to the downstream plains by cooling and perhaps stratifying the mobile elevated residual layer. Statistical and observational evidence suggest that the May and June months that follow a robust and extended snowmelt season are characterized by cooler 700-mb temperatures over Denver, Colorado. The apparent moist convective response to a positive snowmelt anomaly in the lee plains region is for severe weather to develop on average 45 to 50 min earlier, during May and June, than observed during seasons having a negative snow anomaly. Conversely, late springs, which harbor below-average snow, subject the elevated terrain to greater sensible heating. During the May and June months of these “low snow years,” 700-mb environmental conditions over Denver tend to be warmer and drier. Occurring in tandem with these trends are a reduction in May and June rainfall and a lower number of severe hail days on the lee plains. Observations further suggest that during low snow years substantial precipitation deficits extend to synoptic-scale distances downstream. [ABSTRACT FROM AUTHOR]

The feasibility of reconstructing total spring precipitation for the South Platte River basin from treering chronologies using artificial neural networks is explored. The use of artificial neural networks allows a comparison of reconstructions resulting from both linear and nonlinear models. Both types of models produced reconstructions that explained more than 40% of the variation in spring precipitation and were well verified with independent data. Although the nonlinear models produced higher R 2 values than did the linear model for the calibration period, they performed less well in the independent period. This result and other model evaluation statistics suggest that, in this study, the nonlinear models contain a greater degree of overfit than the linear model, and thus, do not offer a clear improvement over the linear model for the reconstruction of spring precipitation in this region. However, neural networks offer an alternative approach to linear regression techniques and may provide improved dendroclimatic reconstructions in other areas. [ABSTRACT FROM AUTHOR]

The National Center for Atmospheric Research (NCAR) Land Surface Model (LSM, version 1.0) provides a comprehensive treatment of land surface processes for the NCAR Community Climate Model version 3 (CCM3). It replaces the prescribed surface wetness, prescribed snow cover, surface albedo, and surface flux parameterizations used in the CCM2. A 15-yr simulation of the coupled atmosphere (CCM3) and land (LSM1.0) models using observed sea surface temperatures for the period December 1978--September 1993 is used to document the model's land surface climate. The model simulates many of the observed geographic and seasonal patterns of surface air temperature, precipitation, and soil water. In general, the transition seasons (spring, autumn) are better simulated than winter and summer. Annual precipitation and runoff are well simulated for some river basins and poorly simulated for others. In general, precipitation is better simulated than runoff. The inclusion of net land-atmosphere CO2 exchange is an important component of the land model, allowing it to be used for studies of the global carbon cycle. The model simulates annual net primary production that is consistent with other estimates of annual production. The model also simulates a clearly defined growing season based on temperature and soil water. [ABSTRACT FROM AUTHOR]