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Courtenay Strong and Robert E. Davis

Abstract

Numerous teleconnections have been identified based upon spatial variability in sea level pressure or lower-tropospheric geopotential height fields. These teleconnections, which are commonly strongest in winter when the mean meridional temperature gradient is large, typically are neither derived from nor linked to changes in the jet stream. Here, winter tropospheric jet stream cores over the Northern Hemisphere (NH) are recovered from 6-hourly gridded data and interannual variability in winter jet core position, speed, and pressure are investigated in the context of NH teleconnections. Common methods for researching jet stream speed and position variability may yield unrepresentative results because jet core pressure variability is ignored (only one isobaric surface is evaluated) or pressure variability effects are smoothed (values are vertically averaged across several isobaric surfaces). In this analysis, data are extracted at the surface of maximum wind, thus controlling for jet core pressure variability and allowing for a more representative tracking of three-dimensional jet core variations.

In the extratropics, the leading pattern of variability in jet core frequency is correlated with the Arctic Oscillation index (AOI) and appears as an oscillation about the spiral-shaped mean configuration of the winter jet stream. In contrast to previous research, the authors find no evidence of Pacific jet deceleration during positive AOI. The second leading mode of variability appears as a split (merged) winter-mean jet stream in the east Pacific together with a merged (split) winter-mean jet stream over North America, a pattern of change that correlates with the Pacific–North American pattern and is reflected in the amplitude of the long-wave ridge over western North America.

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Robert E. Davis and David R. Walker

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An automated, year-round synoptic climatology is developed for the western United States from rawinsonde observations from 1979 to 1988. The classification uses thermal, moisture, and flow parameters to characterize seasonal and interannual synoptic-scale variations in hydrodynamic and thermodynamic conditions. Based on twice daily observations for a network of 21 stations from the Pacific coast to the Rockies, a synoptic climatology is developed using air temperature, dewpoint temperature, geopotential height, and the east-west and north-south components of the wind vector at 800-, 700-, 500-, and 250-mb constant pressure surfaces. The 798 variable by 3620 day matrix is reduced to six orthogonal principal components, and the resulting component scores are grouped using a two-stage clustering technique. The 13 synoptic situations represent days experiencing homogeneous weather conditions. These synoptic situations exhibit marked seasonality and interannual variability and depict features observed in the general circulation of the atmosphere. Examples include both summer monsoonal and dry situations, zonal flow situations with a strong polar or subtropical jet, or both and strong ridging or troughing, and meridional flow.

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David R. Walker and Robert E. Davis

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A climatology of the once-daily (0000 UTC) 1000-hPa error fields of the National Meteorological Center's 80-wave Medium-Range Forecast (MRF) model is studied. An analysis of the error field has been conducted over the contiguous United States and over the Northern Hemisphere from 20° to 80°N for three warm and four cool seasons (9 September 1987 to 6 March 1991). Temporal and spatial mean error fields over various integration lengths are presented.

The skill, as measured by the anomaly correlation, has not significantly changed over the lifetime of the 80-wave MRF model. Anomaly correlation values at 1000 hPa and 500 hPa show that the model is retaining useful information about the anomalies in the height field out to about one week. A reduction in the model biases may reflect an improvement in model physics (longwave radiational calculations, etc). The cool and warm seasons have distinctly different spatial error patterns. The 1000-hPa warm season shows spurious height falls over the southwestern United States that grow with increasing integration length. The 1000-hPa cool season underestimates the intensity of low pressure systems over and east of Hudson Bay and overestimates their strength over the Pacific Northwest.

Principal components analysis of the 429-variable error covariance matrices for the cool and warm seasons identifies 6 orthogonal variables that explain over 60% of the original error variance. MRF model problems appear to be related to problems the model has with simulating the atmosphere's interaction with orographic features (Alberta and Colorado Rockies), storm tracks and baroclinic zones (Gulf Stream region and United States-Canadian border), and persistent atmospheric features (Hudson Bay low, eastern Pacific subtropical high, and desert Southwest heat low).

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Adam Winstral, Kelly Elder, and Robert E. Davis

Abstract

Wind is widely recognized as one of the dominant controls of snow accumulation and distribution in exposed alpine regions. Complex and highly variable wind fields in rugged terrain lead to similarly complex snow distribution fields with areas of no snow adjacent to areas of deep accumulation. Unfortunately, these complexities have limited inclusion of wind redistribution effects in spatial snow distribution models. In this study the difficulties associated with physically exhaustive wind field modeling are avoided and terrain-based parameters are developed to characterize wind effects. One parameter, , was based on maximum upwind slopes relative to seasonally averaged winds to characterize the wind scalar at each pixel location in an alpine basin. A second parameter, , measured upwind breaks in slope from a given location and was combined with an upwind application of to create a drift delineator parameter, D 0, which was used to delineate sites of intense redeposition on lee slopes. Based on 504 snow depth samples from a May 1999 survey of the upper Green Lakes Valley, Colorado, the correlation of the developed parameters to the observed snow distribution and the effect of their inclusion in a spatial snow distribution model were quantified. The parameter was found to be a significant predictor, accounting for more of the variance in the observed snow depth than could be explained by elevation, solar radiation, or slope. Samples located in D 0-delineated drift zones were shown to have significantly greater depths than samples located in nondrift zones. A regression tree model of snow distribution based on a predictor variable set of , D 0, elevation, solar radiation, and slope explained 8%–23% more variance in the observed snow distribution, and performed noticeably better in unsampled areas of the basin, compared to a regression tree model based on only the latter three predictors.

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Oliver W. Frauenfeld, Robert E. Davis, and Michael E. Mann

Abstract

A new and distinctly interdecadal signal in the climate of the Pacific Ocean has been uncovered by examining the coupled behavior of sea surface temperatures (SSTs) and Northern Hemisphere atmospheric circulation. This interdecadal Pacific signal (IPS) of ocean–atmosphere interaction exhibits a highly statistically significant interdecadal component yet contains little to no interannual (El Niño scale) variability common to other Pacific climate anomaly patterns. The IPS thus represents the only empirically derived, distinctly interdecadal signal of Pacific Ocean SST variability that likely also represents the true interdecadal behavior of the Pacific Ocean–atmosphere system. The residual variability of the Pacific’s leading SST pattern, after removal of the IPS, is highly correlated with El Niño anomalies. This indicates that by simply including an atmospheric component, the leading mode of Pacific SST variability has been decomposed into its interdecadal and interannual patterns. Although the interdecadal signal is unrelated to interannual El Niño variability, the interdecadal ocean–atmosphere variability still seems closely linked to tropical Pacific SSTs. Because prior abrupt changes in Pacific SSTs have been related to anomalies in a variety of physical and biotic parameters throughout the Northern Hemisphere, and because of the persistence of these changes over several decades, isolation of this interdecadal signal in the Pacific Ocean–atmosphere system has potentially important and widespread implications to climate forecasting and climate impact assessment.

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James Montesi, Kelly Elder, R. A. Schmidt, and Robert E. Davis

Abstract

To determine how elevation affects the sublimation rate from intercepted snow within a subalpine forest canopy, a cut subalpine fir and an artificial conifer were weighed at each of two elevations (3230 and 2920 m) at a U.S. continental site (39°53′N, 105°54′W) from 1 January to 1 May 2001. Measured stand characteristics included canopy density (67% and 75%) and basal area (43.4 and 24.1 m2 ha−1) for the higher and lower elevations, respectively. Temperature, relative humidity, net radiation, wind speed, and mass of snow on suspended trees provided data to determine whether sublimation rates of intercepted snow are more rapid at higher elevations associated with increased wind speed. Measurements showed the unexpected result that wind speed during sublimation periods was lower at higher elevations, probably because of terrain sheltering. The analysis examined 21 storm-free periods ranging in duration from 9 to 53 h. Sublimation rates per unit mass of intercepted snow were significantly larger at the lower-elevation site associated with warmer temperatures, lower relative humidity, and greater wind speeds. Application of meteorological data to an ice sphere model indicated that predicted mean sublimation rates of an ice sphere index were 23% ± 7% more rapid at the lower elevation due to weather factors alone. However, greater snowfall at higher elevations produced greater interception, resulting in substantially more snow being sublimated back to the atmosphere at the upper site. Over the study period, sublimation of snow intercepted by the test trees amounted to 20%–30% of total snowfall accumulated at the sites during the 21 storms selected for analysis.

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Jicheng Liu, Curtis E. Woodcock, Rae A. Melloh, Robert E. Davis, Ceretha McKenzie, and Thomas H. Painter

Abstract

Forest canopies influence the proportion of the land surface that is visible from above, or the viewable gap fraction (VGF). The VGF limits the amount of information available in satellite data about the land surface, such as snow cover in forests. Efforts to recover fractional snow cover from the spectral mixture analysis model Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area and grain size (MODSCAG) indicate the importance of view angle effects in forested landscapes. The VGF can be estimated using both hemispherical photos and forest canopy models. For a set of stands in the Cold Land Field Processes Experiment (CLPX) sites in the Fraser Experimental Forest in Colorado, the convergence of both measurements and models of the VGF as a function of view angle supports the idea that VGF can be characterized as a function of forest properties. A simple geometric optical (GO) model that includes only between-crown gaps can capture the basic shape of the VGF as a function of view zenith angle. However, the GO model tends to underestimate the VGF compared with estimates derived from hemispherical photos, particularly at high view angles. The use of a more complicated geometric optical–radiative transfer (GORT) model generally improves estimates of the VGF by taking into account within-crown gaps. Small footprint airborne lidar data are useful for mapping forest cover and height, which makes the parameterization of the GORT model possible over a landscape. Better knowledge of the angular distribution of gaps in forest canopies holds promise for improving remote sensing of snow cover fraction.

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Robert E. Davis, Bruce P. Hayden, David A. Gay, William L. Phillips, and Gregory V. Jones

Abstract

The semipermanent subtropical anticyclone over the North Atlantic basin (the “Azores high”) has a major influence on the weather and climate of much of North America, western Europe, and northwestern Africa. The authors develop a climatology of the Azores high by examining its spatial and temporal changes since 1899. Using gridded surface pressure values, anticyclones are identified when the daily pressure is ≥1020 mb and frequencies are tabulated for each half month from 1899 to 1990. Principal components analysis is applied to analyze the anticyclone’s spatial variance structure.

The Azores high is dominated by two spatial modes: a summer pattern, in which high pressure dominates the Atlantic basin, and a winter pattern, in which anticyclones are present over eastern North America and northwestern Africa. Century-long declines in these two modes indicate that there has been a net removal of atmospheric mass over the subtropical Atlantic. Other modes include a meridional versus zonal circulation pattern and omega blocks. Time series of the mean annual principal component scores indicate that meridional flow has been increasing over the Atlantic and that blocking anticyclones have become more prevalent over west-central Europe and less common over the northeastern Atlantic and the British Isles.

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Robert Nedbor-Gross, Barron H. Henderson, Justin R. Davis, Jorge E. Pachón, Alexander Rincón, Oscar J. Guerrero, and Freddy Grajales

Abstract

Standard meteorological model performance evaluation (sMPE) can be insufficient in determining “fitness” for air quality modeling. An sMPE compares predictions of meteorological variables with community-based thresholds. Conceptually, these thresholds measure the model’s capability to represent mesoscale features that cause variability in air pollution. A method that instead examines features could provide a better estimate of fitness. This work compares measures of fitness from sMPE analysis with a feature-based MPE (fMPE). Meteorological simulations for Bogotá, Colombia, using the Weather Research and Forecasting (WRF) Model provide an ideal case study that highlights the importance of fMPE. Bogotá is particularly interesting because the complex topography presents challenges for WRF in sMPE. A cluster analysis identified four dominant meteorological features associated with air quality driven by wind patterns. The model predictions are able to pass several sMPE thresholds but show poor performance for wind direction. The base simulation can be improved with alternative surface characterization datasets for terrain, soil classification, and land use. Despite doubling the number of days with acceptable specific humidity, overall acceptability was never more than 10%. By comparison, an fMPE showed that predictions were able to reproduce the air-quality-relevant features on 38.4% of the days. The fMPE is based on features derived from an observational cluster analysis that have clear relationships with air quality, which suggests that reproducing those features will indicate better air quality model performance. An fMPE may be particularly useful for high-resolution modeling (1 km or less) when finescale variability can cause poor sMPE performance even when the general pattern that drives air pollution is well reproduced.

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Robert E. Davis, Thomas H. Painter, Rick Forster, Don Cline, Richard Armstrong, Terry Haran, Kyle McDonald, and Kelly Elder

Abstract

This paper describes satellite data collected as part of the 2002/03 Cold Land Processes Experiment (CLPX). These data include multispectral and hyperspectral optical imaging, and passive and active microwave observations of the test areas. The CLPX multispectral optical data include the Advanced Very High Resolution Radiometer (AVHRR), the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Multi-angle Imaging Spectroradiometer (MISR). The spaceborne hyperspectral optical data consist of measurements acquired with the NASA Earth Observing-1 (EO-1) Hyperion imaging spectrometer. The passive microwave data include observations from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS; AMSR-E). Observations from the Radarsat synthetic aperture radar and the SeaWinds scatterometer flown on QuikSCAT make up the active microwave data.

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