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12 results for “atmospheric drivers”
The role of atmospheric drivers in a sudden transition of California precipitation in the 2012/13 winter
<p>This contains the data, presented in a publication entitled "The role of atmospheric drivers in a sudden transition of California precipitation in the 2012/13 winter" (JGR: Atmospheres). See the paper for details. See 'Readme.pdf' for the file descriptions.</p>
META-DATA for IEA Wind Task 46 report: Atmospheric drivers of wind turbine blade leading edge erosion: Hydrometeors
<p>The objectives of the work summarized in the report that accompanies this dataset are to:</p> <ul> <li>Describe crucial meteorological parameters for wind turbine blade leading edge erosion</li> <li>Describe technologies appropriate to measurement of hydroclimates and specifically hydrometeor size distributions and phase</li> <li>Identify available data sets that are available to describe hydrometeor size distributions and phase and generate meta-data for data sets available for use in mapping wind turbine blade leading edge erosion potential. This dataset summarizes those meta-data. </li> <li>Identify priority geographic areas for geospatial mapping of wind turbine blade leading edge erosion potential <p> </p> </li> </ul>
Evaluating the Arabian Sea as a regional source of atmospheric CO2: seasonal variability and drivers
<p>The netCDF file included here corresponds to datasets used in the Biogeosciences paper entitled "Evaluating the Arabian Sea as a regional source of atmospheric CO2: seasonal variability and drivers" by Alain de Verneil, Zouhair Lachkar, Shafer Smith, and Marina Levy</p> <p>The data included here comprises of model output used in the paper to generate figures in the main manuscript. Many of the figures also contain data from publicly available sources, which is detailed in the "Data availability" section at the end of the paper.</p> <p>The data are in standard netCDF file format, readily readable using netCDF tools (i.e. netCDF4 package in Python, ncread function in Matlab, etc.).</p> <p>Variables names, dimensions, and units are described in the metadata within the netCDF file.</p> <p>Questions regarding this dataset and how it can be used to reproduce the results in the article can be forwarded to Alain de Verneil through email at ajd11@nyu.edu</p>
Data from: Patterns and drivers of atmospheric nitrogen deposition retention in global forests
<p>Forests are the largest carbon sink in terrestrial ecosystems, and the impact of nitrogen (N) deposition on this carbon sink depends on the fate of external N inputs. However, the patterns and driving factors of N retention in different forest compartments remain elusive. In this study, we synthesized 408 observations from global forest <sup>15</sup>N tracer experiments to reveal the variation and underlying mechanisms of <sup>15</sup>N retention in plants and soils. The results showed that the average total ecosystem <sup>15</sup>N retention in global forests was 63.04 ± 1.23%, with the soil pool being the main N sink (45.76 ± 1.29%). Plants absorbed 17.28 ± 0.83% of <sup>15</sup>N, with more allocated to leaves (5.83 ± 0.63%) and roots (5.84 ± 0.44%). In subtropical and tropical forests, <sup>15</sup>N was mainly absorbed by plants and mineral soils, while the organic soil layer in temperate forests retained more <sup>15</sup>N. Additionally, forests retained more <sup>15</sup>NH<sub>4</sub><sup>+</sup> than <sup>15</sup>NO<sub>3</sub><sup>−</sup>, primarily due to the stronger capacity of the organic soil layer to retain <sup>15</sup>NH<sub>4</sub><sup>+</sup>. The mechanisms of <sup>15</sup>N retention varied among ecosystem compartments, with total ecosystem <sup>15</sup>N retention affected by N deposition. Plant <sup>15</sup>N retention was influenced by vegetative and microbial nutrient demands, while soil <sup>15</sup>N retention was regulated by climate factors and soil nutrient supply. Overall, this study emphasizes the importance of climate and nutrient supply and demand in regulating forest N retention and provides data to further explore the impacts of N deposition on forest carbon sequestration.</p>
Large-Scale Atmospheric Drivers of Snowfall over Thwaites Glacier, Antarctica
<p>Monthly RACMO2 snowfall rates (1979-2015, in mm w.e. per month), as described in Lenaerts et al., 2018 (https://www.cambridge.org/core/journals/annals-of-glaciology/article/climate-and-surface-mass-balance-of-coastal-west-antarctica-resolved-by-regional-climate-modelling/E3DD6D0DA914C6031F96A548AF53603A), and used in this paper. </p>
Supporting data for: Oceanic and atmospheric drivers of post-El-Niño chlorophyll rebound in the equatorial Pacific
<p>The GFDL ESM2M, ESM4.1, ESM4.1-static simulation data used for ENSO related chlorophyll and iron analysis in the study are available in this repository. </p> <p>Please contact <a href="mailto:hyung-gyu.lim@noaa.gov">hyung-gyu.lim@noaa.gov</a> for further questions.. </p> <p>The project on "Oceanic and atmospheric drivers of post-El-Niño chlorophyll rebound in the equatorial Pacific" is implemented based on below dataset that is submitted to Geophysical Research Letters at September 2021</p> <p>regridded by 1 degree horizontal resolution for the tropical Pacific region [120E-60W, 20N-20S]</p> <p>ESM2M : chl, temp, fed, no3 for 1001-1100 years</p> <p>ESM4.1: chl, thetao, dfe, no3, od550dust, dep_dry_fed, dep_wet_fed for 501-650 years</p> <p>ESM4.1-static: chlos, dfeos, tos for 501-650 years</p> <p>The tar file in this submission holds the above data. </p>
META-DATA for IEA Wind Task 46. WP2: Atmospheric drivers of wind turbine blade leading edge erosion: Ancillary variables
<p>Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) created Task 46 to undertake cooperative research in the key topic of blade erosion.</p> <p>This report is a product of WorkPackage 2 <strong>Climatic conditions driving blade erosion. </strong></p> <p>The objectives of the work summarized in this report are to:</p> <ul> <li>Summarize efforts to elucidate critical atmospheric co-stressors that may accelerate leading edge erosion and hence for which meta-data regarding observations should be collated.</li> <li>Briefly describe and summarize additional data pertaining to those LEE co-stressors from sites that were the focus of analyses of hydrometeors in the report “Atmospheric drivers of wind turbine blade leading edge erosion: Hydrometeors” (Pryor et al. 2021)</li> </ul> <p>Accompanying this report is a detailed spreadsheet that summarizes the meta-data regarding these co-stressor variables.</p>
Data from: Patterns and drivers of atmospheric nitrogen deposition retention in global forests
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Data set for the manuscript "The Atmospheric Drivers of the Major Saharan Dust Storm in June 2020"
<p>This publication is under consideration at geophysical research letters.</p>
Data for "Martian oxygen and hydrogen upper atmospheres responding to solar and dust storm drivers: Hisaki space telescope observations"
<p>Data files (.npy) and python codes (.ipynb) to produce the figures in the paper.</p> <p>Download the zip file (dataforfigures_v2.zip), open plot_figX.ipynb with Jupyter notebook, and run it.</p> <p> </p>
Data from: "Injection strategy - a driver of atmospheric circulation and ozone response to stratospheric aerosol geoengineering" by Bednarz et al. (2023)
<p>Data from: "Injection strategy - a driver of atmospheric circulation and ozone response to stratospheric aerosol geoengineering" by Bednarz et al. (2023), which has been accepted for publication in Atmospheric Chemistry and Physics.</p>
Land and atmospheric drivers of the 2023 flood in India
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