On the Verification and Comparison of Extreme Rainfall Indices from Climate Models

2008 ◽  
Vol 21 (7) ◽  
pp. 1605-1621 ◽  
Author(s):  
Cheng-Ta Chen ◽  
Thomas Knutson
Keyword(s):  
United States ◽  
High Resolution ◽  
Climate Models ◽  
Regional Distribution ◽  
Extreme Rainfall ◽  
The United States ◽  
Point Estimate ◽  
Intergovernmental Panel ◽  
Model Precipitation ◽  
Estimate Versus

Abstract The interpretation of model precipitation output (e.g., as a gridpoint estimate versus as an areal mean) has a large impact on the evaluation and comparison of simulated daily extreme rainfall indices from climate models. It is first argued that interpretation as a gridpoint estimate (i.e., corresponding to station data) is incorrect. The impacts of this interpretation versus the areal mean interpretation in the context of rainfall extremes are then illustrated. A high-resolution (0.25° × 0.25° grid) daily observed precipitation dataset for the United States [from Climate Prediction Center (CPC)] is used as idealized perfect model gridded data. Both 30-yr return levels of daily precipitation (P30) and a simple daily intensity index are substantially reduced in these data when estimated at coarser resolution compared to the estimation at finer resolution. The reduction of P30 averaged over the conterminous United States is about 9%, 15%, 28%, 33%, and 43% when the data were first interpolated to 0.5° × 0.5°, 1° × 1°, 2° × 2°, 3° × 3°, and 4° × 4° grid boxes, respectively, before the calculation of extremes. The differences resulting from the point estimate versus areal mean interpretation are sensitive to both the data grid size and to the particular extreme rainfall index analyzed. The differences are not as sensitive to the magnitude and regional distribution of the indices. Almost all Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) models underestimate U.S. mean P30 if it is compared directly with P30 estimated from the high-resolution CPC daily rainfall observation. On the other hand, if CPC daily data are first interpolated to various model resolutions before calculating the P30 (a more correct procedure in our view), about half of the models show good agreement with observations while most of the remaining models tend to overestimate the mean intensity of heavy rainfall events. A further implication of interpreting model precipitation output as an areal mean is that use of either simple multimodel ensemble averages of extreme rainfall or of intermodel variability measures of extreme rainfall to assess the common characteristics and range of uncertainties in current climate models is not appropriate if simulated extreme rainfall is analyzed at a model’s native resolution. Owing to the large sensitivity to the assumption used, the authors recommend that for analysis of precipitation extremes, investigators interpret model precipitation output as an area average as opposed to a point estimate and then ensure that various analysis steps remain consistent with that interpretation.

Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models

2012 ◽  
Vol 118 (2) ◽  
pp. 469-485 ◽  
Author(s):  
Jianting Zhu ◽  
William Forsee ◽  
Rina Schumer ◽  
Mahesh Gautam
Keyword(s):  
United States ◽  
Rainfall Intensity ◽  
Climate Models ◽  
Extreme Rainfall ◽  
The United States ◽  
Uncertainty Assessment ◽  
Future Projections

scholarly journals Evaluation of high-resolution simulations of daily-scale temperature and precipitation over the United States

2009 ◽  
Vol 33 (7-8) ◽  
pp. 1131-1147 ◽  
Author(s):  
Megan D. Walker ◽  
Noah S. Diffenbaugh
Keyword(s):  
United States ◽  
High Resolution ◽  
The United States ◽  
Temperature And Precipitation ◽  
Scale Temperature ◽  
Daily Scale

Assess 21st century Flash Drought in the United States using high resolution regional climate models

2021 ◽  
Author(s):  
Brandi Gamelin ◽  
Jiali Wang ◽  
V. Rao Kotamarthi
Keyword(s):  
Climate Change ◽  
United States ◽  
Soil Moisture ◽  
Climate Models ◽  
Wrf Model ◽  
The United States ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Regional Variability ◽  
Groundwater Levels

<p>Flash droughts are the rapid intensification of drought conditions generally associated with increased temperatures and decreased precipitation on short time scales.  Consequently, flash droughts are responsible for reduced soil moisture which contributes to diminished agricultural yields and lower groundwater levels. Drought management, especially flash drought in the United States is vital to address the human and economic impact of crop loss, diminished water resources and increased wildfire risk. In previous research, climate change scenarios show increased growing season (i.e. frost-free days) and drying in soil moisture over most of the United States by 2100. Understanding projected flash drought is important to assess regional variability, frequency and intensity of flash droughts under future climate change scenarios. Data for this work was produced with the Weather Research and Forecasting (WRF) model. Initial and boundary conditions for the model were supplied by CCSM4, GFDL-ESM2G, and HadGEM2-ES and based on the 8.5 Representative Concentration Pathway (RCP8.5). The WRF model was downscaled to a 12 km spatial resolution for three climate time frames: 1995-2004 (Historical), 2045-2054 (Mid), and 2085-2094 (Late).  A key characteristic of flash drought is the rapid onset and intensification of dry conditions. For this, we identify onset with vapor pressure deficit during each time frame. Known flash drought cases during the Historical run are identified and compared to flash droughts in the Mid and Late 21<sup>st</sup> century.</p>

scholarly journals A prognostic pollen emissions model for climate models (PECM1.0)

2017 ◽  
Author(s):  
Matthew C. Wozniak ◽  
Allison Steiner
Keyword(s):  
United States ◽  
Land Cover ◽  
Climate Models ◽  
Climate Model ◽  
Vegetation Type ◽  
Global Climate ◽  
The United States ◽  
Global Climate Models ◽  
Pollen Counts ◽  
Surface Pollen

Abstract. We develop a prognostic model of Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in a regional climate model (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model: (1) using a taxa-specific land cover database, phenology and emission potential, and (2) a PFT-based land cover, phenology and emission potential. The resulting surface concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model, however we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

scholarly journals Non-targeted Screening of Commercial CBD Products in the United States Reveals Common Contamination and Adulteration

2020 ◽  
Author(s):  
Ben Orsburn
Keyword(s):  
United States ◽  
Mass Spectrometry ◽  
High Resolution ◽  
The United States ◽  
Mass Spectrometry Data ◽  
Accurate Mass ◽  
Targeted Screening ◽  
Accurate Mass Spectrometry ◽  
High Concentrations ◽  
High Resolution Accurate Mass

AbstractThe production of hemp and products derived from these plants that contain zero to trace amounts of the psychoactive cannabinoid tetrahydrocannabidiol (THC) is a rapidly growing new market in the United States. The most common products today contain relatively high concentrations of the compound cannabidiol (CBD). Recent studies have investigated commercial CBD products using targeted assays and have found varying degrees of misrepresentation and contamination of these products. To expand on previous studies, we demonstrate the application of non-targeted screening by high resolution accurate mass spectrometry to more comprehensively identify potential adulterants and contaminants. We find evidence to support previous conclusions that CBD products are commonly misrepresented in terms of cannabinoid concentrations present. Specifically, we observe a wide variation in relative THC concentrations across the product tested, with some products containing 10-fold more relative signal than others. In addition, we find that several products appear to be purposely adulterated with over the counter drugs such as caffeine and melatonin. We also observe multiple small molecule contaminants that are typically linked to improper production or packaging methods in food or pharmaceutical production. Finally, we present high resolution accurate mass spectrometry data and tandem MS/MS fragments supporting the presence of trace amounts of fluorofentanyl in a single mail order CBD product. We conclude that the CBD industry would benefit from more robust testing regulations and that the cannabis testing industry, in general, would benefit from the use of non-targeted screening technologies.

scholarly journals The Regional Distribution and Correlates of an Entrepreneurship-Prone Personality Profile in the United States, Germany, and the United Kingdom: A Socioecological Perspective

2013 ◽  
Author(s):  
Martin Obschonka ◽  
Eva Schmitt-Rodermund ◽  
Rainer K. Silbereisen ◽  
Samuel D. Gosling ◽  
Jeff Potter
Keyword(s):  
United States ◽  
United Kingdom ◽  
Regional Distribution ◽  
The United States ◽  
Personality Profile ◽  
The United Kingdom

scholarly journals Precipitation and Fatal Motor Vehicle Crashes: Continental Analysis with High-Resolution Radar Data

2019 ◽  
Vol 100 (8) ◽  
pp. 1453-1461 ◽  
Author(s):  
Scott E. Stevens ◽  
Carl J. Schreck ◽  
Shubhayu Saha ◽  
Jesse E. Bell ◽  
Kenneth E. Kunkel
Keyword(s):  
United States ◽  
High Resolution ◽  
Motor Vehicle ◽  
Radar Data ◽  
The United States ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
Fatal Crash ◽  
Fatal Crashes ◽  
High Resolution Radar

AbstractMotor vehicle crashes remain a leading cause of accidental death in the United States, and weather is frequently cited as a contributing factor in fatal crashes. Previous studies have investigated the link between these crashes and precipitation typically using station-based observations that, while providing a good estimate of the prevailing conditions on a given day or hour, often fail to capture the conditions present at the actual time and location of a crash. Using a multiyear, high-resolution radar reanalysis and information on 125,012 fatal crashes spanning the entire continental United States over a 6-yr period, we find that the overall risk of a fatal crash increases by approximately 34% during active precipitation. The risk is significant in all regions of the continental United States, and it is highest during the morning rush hour and during the winter months.

scholarly journals Impact of Anthropogenic Climate Change on United States Major Hurricane Landfall Frequency

2019 ◽  
Vol 7 (5) ◽  
pp. 135 ◽  
Author(s):  
Emma L. Levin ◽  
Hiroyuki Murakami
Keyword(s):  
Climate Change ◽  
United States ◽  
High Resolution ◽  
The United States ◽  
Anthropogenic Climate Change ◽  
Buffer System ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Long Duration ◽  
Major Hurricane ◽  
The U.S

Although anthropogenic climate change has contributed to warmer ocean temperatures that are seemingly more favorable for Atlantic hurricane development, no major hurricanes made landfall in the United States between 2006 and 2016. The U.S., therefore, experienced a major hurricane landfall drought during those years. Using the high-resolution Geophysical Fluid Dynamics Laboratory 25 km grid High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR) global climate model, the present study shows that increases in anthropogenic forcing, due to increases in greenhouse gasses, are associated with fewer long-duration major hurricane landfall droughts in the U.S., which implies an increase in major hurricane landfall frequency. We create six different fixed-distance ‘buffers’ that artificially circle the United States coastline in 100 km radial increments and can compensate for the bias in hurricane landfall calculations with six-hourly datasets. Major hurricane landfall frequencies are computed by applying the buffer zones to the six-hourly observed and simulated storm track datasets, which are then compared with the observed recorded major hurricane frequencies. We found that the major hurricane landfall frequencies generated with the 200 km buffer using the six-hourly observed best-track dataset are most correlated with the observed recorded major hurricane landfall frequencies. Using HiFLOR with an implemented buffer system, we found less frequent projections of long-duration major hurricane landfall drought events in controlled scenarios with greater anthropogenic global warming, which is independent on the radius of the coastal buffer. These results indicate an increase in U.S. major hurricane landfall frequencies with an increase in anthropogenic warming, which could pose a substantial threat to coastal communities in the U.S.

scholarly journals Detailed Transmission Network Analysis of a Large Opiate-Driven Outbreak of HIV Infection in the United States

2017 ◽  
Vol 216 (9) ◽  
pp. 1053-1062 ◽  
Author(s):  
Ellsworth M Campbell ◽  
Hongwei Jia ◽  
Anupama Shankar ◽  
Debra Hanson ◽  
Wei Luo ◽  
...  
Keyword(s):  
United States ◽  
High Resolution ◽  
Network Analysis ◽  
Hiv Infection ◽  
The United States ◽  
Intervention Strategies ◽  
Data Sources ◽  
Transmission Network ◽  
Community Needs ◽  
High Resolution Reconstruction

We demonstrate that integration of laboratory, phylogenetic, and epidemiologic data sources allow detailed reconstruction of an outbreak. High-resolution reconstruction of outbreak phylodynamics allows prevention and intervention strategies to be tailored to community needs.

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