Abstract

At least four different modeling studies indicate that variability in snow cover over Asia may modulate atmospheric circulation over the North Pacific Ocean during winter. Here, satellite data on snow extent for east Asia for 1971–95 along with atmospheric fields from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis are used to examine whether the circulation signals seen in model results are actually observed in nature. Anomalies in snow extent over east Asia exhibit a distinct lack of persistence. This suggests that understanding the effects of east Asian snow cover is more germane for short- to medium-range weather forecasting applications than for problems on longer timescales. While it is impossible to attribute cause and effect in the empirical study, analyses of composite fields demonstrate relationships between snow cover extremes and atmospheric circulation downstream remarkably similar to those identified in model results. Positive snow cover extremes in midwinter are associated with a small decrease in air temperatures over the transient snow regions, a stronger east Asian jet, and negative geopotential height anomalies over the North Pacific Ocean. Opposing responses are observed for negative snow cover extremes. Diagnosis of storm track feedbacks shows that the action of high-frequency eddies does not reinforce circulation anomalies in positive snow cover extremes. However, in negative snow cover extremes, there are significant decreases in high-frequency eddy activity over the central North Pacific Ocean, and a corresponding decrease in the mean cyclonic effect of these eddies on the geopotential tendency, contributing to observed positive height anomalies over the North Pacific Ocean. The circulation signals over the North Pacific Ocean are much more pronounced in midwinter (January–February) than in the transitional seasons (November–December and March–April).

Abstract

This paper provides a detailed description of the relationship between spring snow mass in the mountain areas of the western United States and summertime precipitation in the southwestern United States associated with the North American monsoon system and examines the hypothesis that antecedent spring snow mass can modulate monsoon rains through effects on land surface energy balance. Analysis of spring snow water equivalent (SWE) and July–August (JA) precipitation for the period of 1948–97 confirms the inverse snow–monsoon relationship noted in previous studies. Examination of regional difference in SWE–JA precipitation associations shows that although JA precipitation in New Mexico is significantly correlated with SWE over much larger areas than in Arizona, the overall strength of the correlations are just as strong in Arizona as in New Mexico. Results from this study also illustrate that the snow–monsoon relationship is unstable over time. In New Mexico, the relationship is strongest during 1965–92 and is weaker outside that period. By contrast, Arizona shows strongest snow–monsoon associations before 1970. The temporal coincidence between stronger snow–monsoon associations over Arizona and weaker snow–monsoon associations over New Mexico (and vice versa) suggests a common forcing mechanism and that the variations in the strength of snow–monsoon associations are more than just climate noise. There is a need to understand how other factors modulate monsoonal rainfall before realistic predictions of summertime precipitation in the Southwest can be made.

Abstract

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3°C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions.

Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases the accuracy of precipitation forecasts over the northeastern United States, but overall, the accuracy of MOS-based precipitation forecasts is slightly lower than the raw NCEP forecasts.

Four basins in the United States were chosen as case studies to evaluate the value of MRF output for predictions of streamflow. Streamflow forecasts using MRF output were generated for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado; East Fork of the Carson River near Gardnerville, Nevada; and Cle Elum River near Roslyn, Washington). Hydrologic model output forced with measured-station data were used as “truth” to focus attention on the hydrologic effects of errors in the MRF forecasts. Eight-day streamflow forecasts produced using the MOS-corrected MRF output as input (MOS) were compared with those produced using the climatic Ensemble Streamflow Prediction (ESP) technique. MOS-based streamflow forecasts showed increased skill in the snowmelt-dominated river basins, where daily variations in streamflow are strongly forced by temperature. In contrast, the skill of MOS forecasts in the rainfall-dominated basin (the Alapaha River) were equivalent to the skill of the ESP forecasts. Further improvements in streamflow forecasts require more accurate local-scale forecasts of precipitation and temperature, more accurate specification of basin initial conditions, and more accurate model simulations of streamflow.

Abstract

A snow data assimilation study was undertaken in which real data were used to update a conceptual model, SNOW-17. The aim of this study is to improve the model’s estimate of snow water equivalent (SWE) by merging the uncertainties associated with meteorological forcing data and SWE observations within the model. This is done with a view to aiding the estimation of snowpack initial conditions for the ultimate objective of streamflow forecasting via a distributed hydrologic model. To provide a test of this methodology, the authors performed experiments at 53 stations in Colorado. In each case the situation of an unobserved location is mimicked, using the data at any given station only for validation; essentially, these are withholding experiments. Both ensembles of model forcing data and assimilated data were derived via interpolation and stochastic modeling of data from surrounding sources. Through a process of cross validation the error for the ensemble of model forcing data and assimilated observations is explicitly estimated. An ensemble square root Kalman filter is applied to perform assimilation on a 5-day cycle. Improvements in the resulting SWE are most evident during the early accumulation season and late melt period. However, the large temporal correlation inherent in a snowpack results in a less than optimal assimilation and the increased skill is marginal. Once this temporal persistence is removed from both model and assimilated observations during the update cycle, a result is produced that is, within the limits of available information, consistently superior to either the model or interpolated observations.

Abstract

This paper describes a flexible method to generate ensemble gridded fields of precipitation in complex terrain. The method is based on locally weighted regression, in which spatial attributes from station locations are used as explanatory variables to predict spatial variability in precipitation. For each time step, regression models are used to estimate the conditional cumulative distribution function (cdf) of precipitation at each grid cell (conditional on daily precipitation totals from a sparse station network), and ensembles are generated by using realizations from correlated random fields to extract values from the gridded precipitation cdfs. Daily high-resolution precipitation ensembles are generated for a 300 km × 300 km section of western Colorado (dx = 2 km) for the period 1980–2003. The ensemble precipitation grids reproduce the climatological precipitation gradients and observed spatial correlation structure. Probabilistic verification shows that the precipitation estimates are reliable, in the sense that there is close agreement between the frequency of occurrence of specific precipitation events in different probability categories and the probability that is estimated from the ensemble. The probabilistic estimates have good discrimination in the sense that the estimated probabilities differ significantly between cases when specific precipitation events occur and when they do not. The method may be improved by merging the gauge-based precipitation ensembles with remotely sensed precipitation estimates from ground-based radar and satellites, or with precipitation and wind fields from numerical weather prediction models. The stochastic modeling framework developed in this study is flexible and can easily accommodate additional modifications and improvements.

Abstract

The timing of snowmelt runoff (SMR) for 84 rivers in the western United States is examined to understand the character of SMR variability and the climate processes that may be driving changes in SMR timing. Results indicate that the timing of SMR for many rivers in the western United States has shifted to earlier in the snowmelt season. This shift occurred as a step change during the mid-1980s in conjunction with a step increase in spring and early-summer atmospheric pressures and temperatures over the western United States. The cause of the step change has not yet been determined.

Rain-on-snow events pose a significant flood hazard in the western United States. This study provides a description of the spatial and temporal variability of the frequency of rain-on-snow events for 4318 sites in the western United States during water years (October through September) 1949–2003. Rain-on-snow events are found to be most common during the months of October through May; however, at sites in the interior western United States, rain-on-snow events can occur in substantial numbers as late as June and as early as September. An examination of the temporal variability of October through May rain-on-snow events indicates a mixture of increasing and decreasing trends in rain-on-snow events across the western United States. Decreasing trends in rain-on-snow events are most pronounced at lower elevations and are associated with trends toward fewer snowfall days and fewer precipitation days with snow on the ground. Rain-on-snow events are more (less) frequent in the northwestern (southwestern) United States during La Niña (El Niño) conditions. Additionally, increases in temperature in the western United States appear to be contributing to decreases in the number of rain-on-snow events for many sites through effects on the number of days with snowfall and the number of days with snow on the ground.

Abstract

One of the hypothesized effects of global warming from increasing concentrations of greenhouse gases is a change in the frequency and/or intensity of extratropical cyclones. In this study, winter frequencies and intensities of extratropical cyclones in the Northern Hemisphere for the period 1959–97 are examined to determine if identifiable trends are occurring. Results indicate a statistically significant decrease in midlatitude cyclone frequency and a significant increase in high-latitude cyclone frequency. In addition, storm intensity has increased in both the high and midlatitudes. The changes in storm frequency correlate with changes in winter Northern Hemisphere temperature and support hypotheses that global warming may result in a northward shift of storm tracks in the Northern Hemisphere.

Abstract

Calculations of a thermal front parameter using NCEP–NCAR reanalysis data over the period 1979–98 reveal a relative maximum in frontal frequencies during summer along northern Eurasia from about 60° to 70°N, best expressed over the eastern half of the continent. A similar relative maximum is found over Alaska, which is present year-round although best expressed in summer. These high-latitude features can be clearly distinguished from the polar frontal zone in the midlatitudes of the Pacific basin and collectively resemble the summertime“Arctic frontal zone” discussed in several early studies. While some separation between high- and midlatitude frontal activity is observed in all seasons, the summer season is distinguished by the development of an attendant mean baroclinic zone aligned roughly along the Arctic Ocean coastline and associated wind maxima in the upper troposphere. The regions of maximum summer frontal frequency correspond to preferred areas of cyclogenesis and to where the summertime contribution to annual precipitation is most dominant. Cyclones generated in association with the Eurasian frontal zone often track into the central Arctic Ocean, where they may have an impact on the sea-ice circulation. Development of the summertime Eurasian frontal zone and the summertime strengthening of the Alaskan feature appear to be largely driven by differential heating between the cold Arctic Ocean and warm snow-free land. Frontal activity also shows an association with orography. Several studies have argued that the location of the summer Arctic frontal zone may be in part determined by discontinuities in energy exchange along the tundra–boreal forest boundary. While such a linkage is not discounted here, a vegetation forcing is not required in this conceptual model.

Abstract

Gridded meteorological estimates are essential for many applications. Most existing meteorological datasets are deterministic and have limitations in representing the inherent uncertainties from both the data and methodology used to create gridded products. We develop the Ensemble Meteorological Dataset for Planet Earth (EM-Earth) for precipitation, mean daily temperature, daily temperature range, and dewpoint temperature at 0.1° spatial resolution over global land areas from 1950 to 2019. EM-Earth provides hourly/daily deterministic estimates, and daily probabilistic estimates (25 ensemble members), to meet the diverse requirements of hydrometeorological applications. To produce EM-Earth, we first developed a station-based Serially Complete Earth (SC-Earth) dataset, which removes the temporal discontinuities in raw station observations. Then, we optimally merged SC-Earth station data and ERA5 estimates to generate EM-Earth deterministic estimates and their uncertainties. The EM-Earth ensemble members are produced by sampling from parametric probability distributions using spatiotemporally correlated random fields. The EM-Earth dataset is evaluated by leave-one-out validation, using independent evaluation stations, and comparing it with many widely used datasets. The results show that EM-Earth is better in Europe, North America, and Oceania than in Africa, Asia, and South America, mainly due to differences in the available stations and differences in climate conditions. Probabilistic spatial meteorological datasets are particularly valuable in regions with large meteorological uncertainties, where almost all existing deterministic datasets face great challenges in obtaining accurate estimates.