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M. Segal, Z. Pan, and W. J. Gutowski Jr.

Abstract

Snowfall occasionally occurs over bare soil with high thermal storage in its upper layer. Quantification and generalization of the potential impact of the thermal storage on episodic snowmelt is evaluated using a scaling approach and assuming negligible net thermal flux at the snow cover top. Soil thermal flux contribution to snowmelt is found to be affected significantly by the level of soil wetness. It is shown that, for a soil temperature of 10°C prior to the snowfall, the contribution of wet soil thermal flux is significant within the first 12 h when compared with intense surface moist enthalpy flux or solar radiation. Implications of these results to modeling of snowmelt using coupled soil–atmosphere models are elaborated.

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William J. Gutowski Jr., James W. Seidel, and Andrew B. Ervin

Abstract

A previous examination of water vapor layers in Project STORM-FEST is extended to include Project STORM-WAVE rawinsonde observations and assess the contribution of layers in these two datasets to atmospheric water transport. The observations indicate that the contribution of these layers to water transport climatology is only a few percent. However the analysis also shows that episodes occur fairly frequently where these layers contribute 20% or more of the horizontal transport. Instances when the layer’s moisture is an important part of the water transport tend to occur for relatively dry soundings. Numerical models that fail to resolve the layers during these episodes may thus miss condensation events leading to cloud formation and precipitation, and also give overly smooth vertical profiles of radiative heating and cooling. The layers thus appear to be important for numerical weather prediction.

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H. Wei, M. Segal, W. J. Gutowski Jr., Z. Pan, R. W. Arritt, and W. A. Gallus Jr.

Abstract

Under strong warm advection, sensible and latent heat fluxes may provide larger energy for surface snowmelt than does net radiation flux. With these thermally stable conditions, the height of the first model level may be well above the surface-layer depth and thus outside the range of applicability of the surface-layer similarity theory on which the models' surface thermal flux computation is based. This situation can strongly affect the magnitude of simulated surface thermal fluxes and snowmelt. To explore this issue, the impact of selected heights of the first model level on the simulated surface fluxes and snowmelt under stable surface stratification conditions was investigated. Simulations using a mesoscale atmospheric model considering two extreme contrasts in surface roughness were performed. Setting the first model level to 3 or 10 m, which typically was within the stable surface layer, yielded nearly the same contribution of simulated surface turbulent thermal fluxes to snowmelt. When the first model level height was set at about 40 m, as is used in many regional model simulations, it exceeded the depth of the stable surface layer over the snow cover. The surface turbulent thermal flux contribution in this case was smaller (by about 40%), with a directly proportional effect on the snowmelt. Pending observational support, results presented in this study imply that setting a model's lowest level to 10 m or less will likely improve simulated snowmelt accuracy during warm advection.

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W. J. Gutowski Jr., E. S. Takle, K. A. Kozak, J. C. Patton, R. W. Arritt, and J. H. Christensen

Abstract

Changes in daily precipitation versus intensity under a global warming scenario in two regional climate simulations of the United States show a well-recognized feature of more intense precipitation. More important, by resolving the precipitation intensity spectrum, the changes show a relatively simple pattern for nearly all regions and seasons examined whereby nearly all high-intensity daily precipitation contributes a larger fraction of the total precipitation, and nearly all low-intensity precipitation contributes a reduced fraction. The percentile separating relative decrease from relative increase occurs around the 70th percentile of cumulative precipitation, irrespective of the governing precipitation processes or which model produced the simulation. Changes in normalized distributions display these features much more consistently than distribution changes without normalization.

Further analysis suggests that this consistent response in precipitation intensity may be a consequence of the intensity spectrum’s adherence to a gamma distribution. Under the gamma distribution, when the total precipitation or number of precipitation days changes, there is a single transition between precipitation rates that contribute relatively more to the total and rates that contribute relatively less. The behavior is roughly the same as the results of the numerical models and is insensitive to characteristics of the baseline climate, such as average precipitation, frequency of rain days, and the shape parameter of the precipitation’s gamma distribution. Changes in the normalized precipitation distribution give a more consistent constraint on how precipitation intensity may change when climate changes than do changes in the nonnormalized distribution. The analysis does not apply to extreme precipitation for which the theory of statistical extremes more likely provides the appropriate description.

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William J. Gutowski Jr., Francis O. Otieno, Raymond W. Arritt, Eugene S. Takle, and Zaitao Pan

Abstract

Precipitation from a 10-yr regional climate simulation is evaluated using three complementary analyses: self-organizing maps, bias scores, and arithmetic bias. Collectively, the three reveal a precipitation deficit in the south-central United States that emerges in September and lingers through February. Deficient precipitation for this region and time of year is also evident in other simulations, indicating a generic problem in climate simulation.

Analysis of terrestrial and atmospheric water balances shows that the 10-yr average precipitation error for the region results primarily from a deficit in horizontal water vapor convergence. However, the 10-yr average for fall only suggests that the primary contributor is a deficit in evapotranspiration. Evaluation of simulated temperature and soil moisture suggests the model has insufficient terrestrial water for evaporation during fall. Results for winter are mixed; errors in both evapotranspiration and lateral moisture convergence may contribute substantially to the precipitation deficit. The model reproduces well both the time-average and time-filtered large-scale circulation, implying that the moisture convergence error arises from an error in simulating mesoscale circulation.

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L. E. Hay, M. P. Clark, M. Pagowski, G. H. Leavesley, and W. J. Gutowski Jr.

Abstract

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.

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Kenneth E. Kunkel, Karen Andsager, Xin-Zhong Liang, Raymond W. Arritt, Eugene S. Takle, William J. Gutowski Jr., and Zaitao Pan

Abstract

A regional climate model simulation of the period of 1979–88 over the contiguous United States, driven by lateral boundary conditions from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis, was analyzed to assess the ability of the model to simulate heavy precipitation events and seasonal precipitation anomalies. Heavy events were defined by precipitation totals that exceed the threshold value for a specified return period and duration. The model magnitudes of the thresholds for 1-day heavy precipitation events were in good agreement with observed thresholds for much of the central United States. Model thresholds were greater than observed for the eastern and intermountain western portions of the region and were smaller than observed for the lower Mississippi River basin. For 7-day events, model thresholds were in good agreement with observed thresholds for the eastern United States and Great Plains, were less than observed for the most of the Mississippi River valley, and were greater than observed for the intermountain western region. The interannual variability in frequency of heavy events in the model simulation exhibited similar behavior to that of the observed variability in the South, Southwest, West, and North-Central study regions. The agreement was poorer for the Midwest and Northeast, although the magnitude of variability was similar for both model and observations. There was good agreement between the model and observational data in the seasonal distribution of extreme events for the West and North-Central study regions; in the Southwest, Midwest, and Northeast, there were general similarities but some differences in the details of the distributions. The most notable differences occurred for the southern Gulf Coast region, for which the model produced a summer peak that is not present in the observational data. There was not a very high correlation in the timing of individual heavy events between the model and observations, reflecting differences between model and observations in the speed and path of many of the synoptic-scale events triggering the precipitation.

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William J. Gutowski Jr., Steven G. Decker, Rodney A. Donavon, Zaitao Pan, Raymond W. Arritt, and Eugene S. Takle

Abstract

Precipitation intensity spectra for a central U.S. region in a 10-yr regional climate simulation are compared to corresponding observed spectra for precipitation accumulation periods ranging from 6 h to 10 days. Model agreement with observations depends on the length of the precipitation accumulation period, with similar results for both warm and cold halves of the year. For 6- and 12-h accumulation periods, simulated and observed spectra show little overlap. For daily and longer accumulation periods, the spectra are similar for moderate precipitation rates, though the model produces too many low-intensity precipitation events and too few high-intensity precipitation events for all accumulation periods. The spatial correlation of simulated and observed precipitation events indicates that the model's 50-km grid spacing is too coarse to simulate well high-intensity events. Spatial correlations with and without very light precipitation indicate that coarse resolution is not a direct cause of excessive low-intensity events. The model shows less spread than observations in its pattern of spatial correlation versus distance, suggesting that resolved model circulation patterns producing 6-hourly precipitation are limited in the range of precipitation patterns they can produce compared to the real world. The correlations also indicate that replicating observed precipitation intensity distributions for 6-h accumulation periods requires grid spacing smaller than about 15 km, suggesting that models with grid spacing substantially larger than this will be unable to simulate the observed diurnal cycle of precipitation.

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E. S. Takle, J. Roads, B. Rockel, W. J. Gutowski Jr., R. W. Arritt, I. Meinke, C. G. Jones, and A. Zadra
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E. S. Takle, J. Roads, B. Rockel, W. J. Gutowski Jr., R. W. Arritt, I. Meinke, C. G. Jones, and A. Zadra

A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.

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