Investigation of mb and MS formulas for the Western United States and their impact on the MS/mb discriminant

1987 ◽  
Vol 77 (3) ◽  
pp. 987-995
Author(s):  
Marvin D. Denny ◽  
Steven R. Taylor ◽  
Eileen S. Vergino

Abstract The impact of regional mb and MS formulas on regional MS/mb discrimination is investigated using a large number of Western United States earthquakes and explosions. Comparison of NEIS mb values with regional mb values shows a systematic error of 1.2. Additionally, a simple analysis of variance shows that the variance of the magnitude estimate is reduced when log(A) replaces log(A/T). These changes, along with a refinement of the distance correction, yield a new regional mb for the Western United States given by mb = log(A) + 2.4 log(Δ) − 3.95 + cj, where A = 0 to peak amplitude in nanometers, Δ is the distance in kilometers, and ci is a station correction. Usage of this formula improves the performance of regional MS/mb discrimination by a factor of 2 to 6.

2018 ◽  
Vol 31 (24) ◽  
pp. 9921-9940 ◽  
Author(s):  
N. Goldenson ◽  
L. R. Leung ◽  
C. M. Bitz ◽  
E. Blanchard-Wrigglesworth

In the coastal mountains of western North America, most extreme precipitation is associated with atmospheric rivers (ARs), narrow bands of moisture originating in the tropics. Here we quantify how interannual variability in atmospheric rivers influences snowpack in the western United States in observations and a model. We simulate the historical climate with the Model for Prediction Across Scales (MPAS) with physics from the Community Atmosphere Model, version 5 [CAM5 (MPAS-CAM5)], using prescribed sea surface temperatures. In the global variable-resolution domain, regional refinement (at ~30 km) is applied to our region of interest and upwind over the northeast Pacific. To better characterize internal variability, we conduct simulations with three ensemble members over 30 years of the historical period. In the Cascade Range, with some exceptions, winters with more atmospheric river days are associated with less snowpack. In California’s Sierra Nevada, winters with more ARs are associated with greater snowpack. The slope of the linear regression of observed snow water equivalent (SWE) on reanalysis-based AR count has the same sign as that arrived at using the model, but is statistically significant in observations only for California. In spring, internal variance plays an important role in determining whether atmospheric river days appear to be associated with greater or less snowpack. The cumulative (winter through spring) number of atmospheric river days, on the other hand, has a relationship with spring snowpack, which is consistent across ensemble members. Thus, the impact of atmospheric rivers on winter snowpack has a greater influence on spring snowpack than spring atmospheric rivers in the model for both regions and in California consistently in observations.


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.


2012 ◽  
Vol 12 (11) ◽  
pp. 29763-29800 ◽  
Author(s):  
A. R. Berg ◽  
C. L. Heald ◽  
K. E. Huff Hartz ◽  
A. G. Hallar ◽  
A. J. H. Meddens ◽  
...  

Abstract. Over the last decade, extensive beetle outbreaks in Western North America have destroyed over 100 000 km2 of forest throughout British Columbia and the Western United States. Beetle infestations impact monoterpene emissions through both decreased emissions as trees are killed (mortality effect) and increased emissions in trees under attack (attack effect). We use 14 yr of beetle mortality data together with beetle-induced monoterpene concentration data in the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) to investigate the impact of beetle mortality and attack on monoterpene emissions and secondary organic aerosol (SOA) formation in Western North America. Regionally, beetle infestations may have a significant impact on monoterpene emissions and SOA concentrations, with up to a 4-fold increase in monoterpene emissions and up to a 40% increase in SOA concentrations in some years (following a scenario where the attack effect is based on observed lodgepole pine response). Responses to beetle attack depend on the extent of previous mortality and the number of trees under attack in a given year, which can vary greatly over space and time. Simulated enhancements peak in 2004 (British Columbia) and 2008 (US). Responses to beetle attack are shown to be substantially larger (up to a 3-fold localized increase in SOA concentrations) when following a scenario based on bark-beetle attack in spruce trees. Placed in the context of observations from the IMPROVE network, the changes in SOA concentrations due to beetle attack are in most cases small compared to the large annual and interannual variability in total organic aerosol which is driven by wildfire activity in Western North America. This indicates that most beetle-induced SOA changes are not likely detectable in current observation networks; however these changes may impede efforts to achieve natural visibility conditions in the national parks and wilderness areas of the Western United States.


2013 ◽  
Vol 13 (6) ◽  
pp. 3149-3161 ◽  
Author(s):  
A. R. Berg ◽  
C. L. Heald ◽  
K. E. Huff Hartz ◽  
A. G. Hallar ◽  
A. J. H. Meddens ◽  
...  

Abstract. Over the last decade, extensive beetle outbreaks in western North America have destroyed over 100 000 km2 of forest throughout British Columbia and the western United States. Beetle infestations impact monoterpene emissions through both decreased emissions as trees are killed (mortality effect) and increased emissions in trees under attack (attack effect). We use 14 yr of beetle-induced tree mortality data together with beetle-induced monoterpene emission data in the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) to investigate the impact of beetle-induced tree mortality and attack on monoterpene emissions and secondary organic aerosol (SOA) formation in western North America. Regionally, beetle infestations may have a significant impact on monoterpene emissions and SOA concentrations, with up to a 4-fold increase in monoterpene emissions and up to a 40% increase in SOA concentrations in some years (in a scenario where the attack effect is based on observed lodgepole pine response). Responses to beetle attack depend on the extent of previous mortality and the number of trees under attack in a given year, which can vary greatly over space and time. Simulated enhancements peak in 2004 (British Columbia) and 2008 (US). Responses to beetle attack are shown to be substantially larger (up to a 3-fold localized increase in summertime SOA concentrations) in a scenario based on bark-beetle attack in spruce trees. Placed in the context of observations from the IMPROVE network, the changes in SOA concentrations due to beetle attack are in most cases small compared to the large annual and interannual variability in total organic aerosol which is driven by wildfire activity in western North America. This indicates that most beetle-induced SOA changes are not likely detectable in current observation networks; however, these changes may impede efforts to achieve natural visibility conditions in the national parks and wilderness areas of the western United States.


2015 ◽  
Vol 16 (2) ◽  
pp. 830-842 ◽  
Author(s):  
Scott Lee Sellars ◽  
Xiaogang Gao ◽  
Soroosh Sorooshian

Abstract This manuscript introduces a novel computational science approach for studying the impact of climate variability on precipitation. The approach uses an object-oriented connectivity algorithm that segments gridded near-global satellite precipitation data into four-dimensional (4D) objects (longitude, latitude, time, and intensity). These precipitation systems have distinct spatiotemporal properties that are counted, tracked, described, and stored in a searchable database. A case study of western United States precipitation systems is performed, demonstrating the unique properties and capabilities of this object-oriented database. The precipitation dataset used in the case study is the University of California, Irvine, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) from 1 March 2000 to 1 January 2011. A search of the database for all western United States precipitation systems during this time period returns 626 precipitation systems as objects. By analyzing these systems as segmented objects, joint interactions of the selected climate phenomena 1) Arctic Oscillation (AO), 2) Madden–Julian oscillation (MJO), and 3) El Niño–Southern Oscillation (ENSO) on precipitation can be shown. They directly show the increased/decreased likelihood of having precipitation systems occurring over the western United States (monthly count) during phases of these climate phenomena. It is found that specific climate phenomena impact the monthly count of the events differently, and that the joint interaction of climate phenomena of the AO–MJO and AO–ENSO is important, especially during certain months of the year. It is also found that these interactions impact the physical features of the precipitation systems themselves.


2008 ◽  
Vol 23 (1) ◽  
pp. 145-158 ◽  
Author(s):  
David T. Myrick ◽  
John D. Horel

Abstract Federal, state, and other wildland resource management agencies contribute to the collection of weather observations from over 1000 Remote Automated Weather Stations (RAWS) in the western United States. The impact of RAWS observations on surface objective analyses during the 2003/04 winter season was assessed using the Advanced Regional Prediction System (ARPS) Data Assimilation System (ADAS). A set of control analyses was created each day at 0000 and 1200 UTC using the Rapid Update Cycle (RUC) analyses as the background fields and assimilating approximately 3000 surface observations from MesoWest. Another set of analyses was generated by withholding all of the RAWS observations available at each time while 10 additional sets of analyses were created by randomly withholding comparable numbers of observations obtained from all sources. Random withholding of observations from the analyses provides a baseline estimate of the analysis quality. Relative to this baseline, removing the RAWS observations degrades temperature (wind speed) analyses by an additional 0.5°C (0.9 m s−1) when evaluated in terms of rmse over the entire season. RAWS temperature observations adjust the RUC background the most during the early morning hours and during winter season cold pool events in the western United States while wind speed observations have a greater impact during active weather periods. The average analysis area influenced by at least 1.0°C (2.5°C) by withholding each RAWS observation is on the order of 600 km2 (100 km2). The spatial influence of randomly withheld observations is much less.


2010 ◽  
Vol 10 (3) ◽  
pp. 6257-6278 ◽  
Author(s):  
B. Gantt ◽  
N. Meskhidze ◽  
A. G. Carlton

Abstract. The impact of marine organic emissions to the air quality in coastal areas of the western United States is studied using the latest version of the US Environmental Protection Agency (EPA) regional-scale Community Multiscale Air Quality (CMAQv4.7) modeling system. Emissions of marine isoprene, monoterpenes, and primary organic matter (POM) from the ocean are implemented into the model to provide a comprehensive view of the connection between ocean biology and atmospheric chemistry and air pollution. Model simulations show that marine organics can increase the concentration of PM2.5 by 0.1–0.3 μg m−3 (up to 5%) in coastal cities. This increase in the PM2.5 concentration is primarily attributed to the POM emissions, with small contributions from the marine isoprene and monoterpenes. When marine organic emissions are included, organic carbon (OC) concentrations over the remote ocean are increased by up to 50% (25% in coastal areas), values consistent with recent observational findings. This study is the first to quantify the air quality impacts from marine POM and monoterpenes for the United States, and highlights the need for inclusion of marine organic emissions in air quality models.


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