Surface Layer Transport of Sulfate Particles in the Western United States by the Large-Scale Wind Field

1984 ◽  
Vol 112 (5) ◽  
pp. 1067-1073 ◽  
Author(s):  
Lowell L. Ashbaugh ◽  
Leonard O. Myrup ◽  
Robert G. Flocchini
2017 ◽  
Vol 18 (5) ◽  
pp. 1227-1245 ◽  
Author(s):  
Edwin Sumargo ◽  
Daniel R. Cayan

Abstract This study investigates the spatial and temporal variability of cloudiness across mountain zones in the western United States. Daily average cloud albedo is derived from a 19-yr series (1996–2014) of half-hourly Geostationary Operational Environmental Satellite (GOES) images. During springtime when incident radiation is active in driving snowmelt–runoff processes, the magnitude of daily cloud variations can exceed 50% of long-term averages. Even when aggregated over 3-month periods, cloud albedo varies by ±10% of long-term averages in many locations. Rotated empirical orthogonal functions (REOFs) of daily cloud albedo anomalies over high-elevation regions of the western conterminous United States identify distinct regional patterns, wherein the first five REOFs account for ~67% of the total variance. REOF1 is centered over Northern California and Oregon and is pronounced between November and March. REOF2 is centered over the interior northwest and is accentuated between March and July. Each of the REOF/rotated principal components (RPC) modes associates with anomalous large-scale atmospheric circulation patterns and one or more large-scale teleconnection indices (Arctic Oscillation, Niño-3.4, and Pacific–North American), which helps to explain why anomalous cloudiness patterns take on regional spatial scales and contain substantial variability over seasonal time scales.


2014 ◽  
Vol 23 (2) ◽  
pp. 143-148 ◽  
Author(s):  
J. Daniel Oppenheimer ◽  
Stacy K. Beaugh ◽  
Julie A. Knudson ◽  
Peter Mueller ◽  
Nikki Grant-Hoffman ◽  
...  

2009 ◽  
Vol 24 (6) ◽  
pp. 1625-1643 ◽  
Author(s):  
Heather Dawn Reeves ◽  
David J. Stensrud

Abstract Valley cold pools (VCPs), which are trapped, cold layers of air at the bottoms of basins or valleys, pose a significant problem for forecasters because they can lead to several forms of difficult-to-forecast and hazardous weather such as fog, freezing rain, or poor air quality. Numerical models have historically failed to routinely provide accurate guidance on the formation and demise of VCPs, making the forecast problem more challenging. In some case studies of persistent wintertime VCPs, there is a connection between the movement of upper-level waves and the timing of VCP formation and decay. Herein, a 3-yr climatology of persistent wintertime VCPs for five valleys and basins in the western United States is performed to see how often VCP formation and decay coincides with synoptic-scale (∼200–2000 km) wave motions. Valley cold pools are found to form most frequently as an upper-level ridge approaches the western United States and in response to strong midlevel warming. The VCPs usually last as long as the ridge is over the area and usually only end when a trough, and its associated midlevel cooling, move over the western United States. In fact, VCP strength appears to be almost entirely dictated by midlevel temperature changes, which suggests large-scale forcing is dominant for this type of VCP most of the time.


2011 ◽  
Vol 20 (8) ◽  
pp. 982 ◽  
Author(s):  
Mary Ellen Miller ◽  
Lee H. MacDonald ◽  
Peter R. Robichaud ◽  
William J. Elliot

Many forests and their associated water resources are at increasing risk from large and severe wildfires due to high fuel accumulations and climate change. Extensive fuel treatments are being proposed, but it is not clear where such treatments should be focussed. The goals of this project were to: (1) predict potential post-fire erosion rates for forests and shrublands in the western United States to help prioritise fuel treatments; and (2) assess model sensitivity and accuracy. Post-fire ground cover was predicted using historical fire weather data and the First Order Fire Effects Model. Parameter files from the Disturbed Water Erosion Prediction Project (WEPP) were combined with GeoWEPP to predict post-fire erosion at the hillslope scale. Predicted median annual erosion rates were 0.1–2 Mg ha–1 year–1 for most of the intermountain west, ~10–40 Mg ha–1 year–1 for wetter areas along the Pacific Coast and up to 100 Mg ha–1 year–1 for north-western California. Sensitivity analyses showed the predicted erosion rates were predominantly controlled by the amount of precipitation rather than surface cover. The limited validation dataset showed a reasonable correlation between predicted and measured erosion rates (R2 = 0.61), although predictions were much less than measured values. Our results demonstrate the feasibility of predicting post-fire erosion rates on a large scale. The validation and sensitivity analysis indicated that the predictions are most useful for prioritising fuel reduction treatments on a local rather than interregional scale, and they also helped identify model improvements and research needs.


2020 ◽  
Author(s):  
Christopher A. Halsch ◽  
Aimee Code ◽  
Sarah M. Hoyle ◽  
James A. Fordyce ◽  
Nicolas Baert ◽  
...  

AbstractMonarch butterflies (Danaus plexippus) are in decline in the western United States and are encountering a range of anthropogenic stressors. Pesticides are among the factors that likely contribute to this decline, though the concentrations of these chemicals in non-crop plants is not well documented, especially in complex landscapes with a diversity of crop types and land uses. In this study, we collected 227 milkweed (Asclepias spp.) leaf samples from 19 sites representing different land use types across the Central Valley of California. We also sampled plants purchased from two stores that sell to home gardeners. We found 64 pesticides (25 insecticides, 27 fungicides, and 11 herbicides, as well as 1 adjuvant) out of a possible 262 in our screen. Pesticides were detected in every sample, even at sites with little or no pesticide use based on information from landowners. On average, approximately 9 compounds were detected per plant across all sites, with a range of 1 to 25 compounds in any one sample. For the vast majority of pesticides detected, we do not know the biological effects on monarch caterpillars that consume these plants, however we did detect a few compounds for which effects on monarchs have been experimentally investigated. Chlorantraniliprole in particular was identified in 91% of our samples and found to exceed a tested LD50 for monarchs in 58 out of 227 samples. Our primary conclusion is the ubiquity of pesticide presence in milkweeds in an early-summer window of time that monarch larvae are likely to be present in the area. Thus, these results are consistent with the hypothesis that pesticide exposure could be a contributing factor to monarch declines in the western United States. This both highlights the need for a greater understanding of the lethal and sublethal effects of these compounds (individually, additively, and synergistically) and suggests the urgent need for strategies that reduce pesticide use and movement on the landscape.Contribution to the FieldInsects are facing multifaceted stressors in the Anthropocene and are in decline in many parts of the world. The widespread use of pesticides is believed to be an important part of the problem. In particular, the monarch butterfly is in sharp decline in the western United States. Here we show that milkweeds in the Central Valley of California, a large urban and agricultural landscape that is part of the monarch breeding and migration route, are contaminated with a diverse array of pesticides. We found a few in high concentrations and many in trace amounts. We do not know how these compounds act together and with other large-scale stressors to cause declines, but it is clear that monarchs and other non-target insects are encountering these pesticides. These results provide critical insight into the growing literature on the impact of pesticides on butterflies specifically and non-target insects more broadly. We hope these field realistic concentrations will aid in the design of further experiments in the field and the lab.


2021 ◽  
Author(s):  
Nicolas Gauthier ◽  
Kevin J. Anchukaitis ◽  
Bethany Coulthard

AbstractThe decline in snowpack across the western United States is one of the most pressing threats posed by climate change to regional economies and livelihoods. Earth system models are important tools for exploring past and future snowpack variability, yet their coarse spatial resolutions distort local topography and bias spatial patterns of accumulation and ablation. Here, we explore pattern-based statistical downscaling for spatially-continuous interannual snowpack estimates. We find that a few leading patterns capture the majority of snowpack variability across the western US in observations, reanalyses, and free-running simulations. Pattern-based downscaling methods yield accurate, high resolution maps that correct mean and variance biases in domain-wide simulated snowpack. Methods that use large-scale patterns as both predictors and predictands perform better than those that do not and all are superior to an interpolation-based “delta change” approach. These findings suggest that pattern-based methods are appropriate for downscaling interannual snowpack variability and that using physically meaningful large-scale patterns is more important than the details of any particular downscaling method.


1980 ◽  
Vol 17 (9) ◽  
pp. 1125-1140 ◽  
Author(s):  
W. Douglas Stewart ◽  
Roger G. Walker

The Pennsylvanian Rocky Mountain Supergroup, in the Elk Range, B.C., includes three sandstone formations: Tyrwhitt (about 100 m thick), Storelk (about 95 m), and Tobermory (about 75). The Tyrwhitt consists mostly of structureless sandstones which prove to be bioturbated in X radiographs. There is an abundant trace fauna, as well as brachiopods. The second most abundant facies consists of medium scale (up to about 1 m) cross-bedded sandstones. The Tobermory is essentially similar to the Tyrwhitt but contains fewer structureless sandstones, and more beds with ripple cross-lamination and horizontal lamination. Both formations were probably deposited slowly in water deeper than fair-weather wave base (deeper than 10–15 m).The Storelk, sandwiched between Tyrwhitt and Tobermory, is dominated by large scale (up to 10.5 m) sets of cross-bedding. These occur in three members, separated by two structureless members. The Storelk structureless members are not mottled in X radiographs, and no trace fauna was found in the field. There is only one 1 m thick fossiliferous bed within the Storelk, which is otherwise barren of trace or body fossils. We reject fluvial and marine sandwave interpretations of the large scale cross-bedding, and suggest that the Storelk represents a coastal eolian dune complex. Paleoflow directions are dominantly toward south-southwest, similar to Pennsylvanian paleowind directions in the western United States. The Storelk structureless members possibly represent sand blown into standing bodies of water, one of which was connected to the open sea (the fossiliferous bed).


2006 ◽  
Vol 19 (8) ◽  
pp. 1407-1421 ◽  
Author(s):  
Eric J. Alfaro ◽  
Alexander Gershunov ◽  
Daniel Cayan

Abstract A statistical model based on canonical correlation analysis (CCA) was used to explore climatic associations and predictability of June–August (JJA) maximum and minimum surface air temperatures (Tmax and Tmin) as well as the frequency of Tmax daily extremes (Tmax90) in the central and western United States (west of 90°W). Explanatory variables are monthly and seasonal Pacific Ocean SST (PSST) and the Climate Division Palmer Drought Severity Index (PDSI) during 1950–2001. Although there is a positive correlation between Tmax and Tmin, the two variables exhibit somewhat different patterns and dynamics. Both exhibit their lowest levels of variability in summer, but that of Tmax is greater than Tmin. The predictability of Tmax is mainly associated with local effects related to previous soil moisture conditions at short range (one month to one season), with PSST providing a secondary influence. Predictability of Tmin is more strongly influenced by large-scale (PSST) patterns, with PDSI acting as a short-range predictive influence. For both predictand variables (Tmax and Tmin), the PDSI influence falls off markedly at time leads beyond a few months, but a PSST influence remains for at least two seasons. The maximum predictive skill for JJA Tmin, Tmax, and Tmax90 is from May PSST and PDSI. Importantly, skills evaluated for various seasons and time leads undergo a seasonal cycle that has maximum levels in summer. At the seasonal time frame, summer Tmax prediction skills are greatest in the Midwest, northern and central California, Arizona, and Utah. Similar results were found for Tmax90. In contrast, Tmin skill is spread over most of the western region, except for clusters of low skill in the northern Midwest and southern Montana, Idaho, and northern Arizona.


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