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50 results for “atmospheric river”
SSP2-4.5 Data for paper "When will humanity notice its impacts on atmospheric rivers?"
<p>The repository contains the SSP2-4.5 scenario simulation (one ensemble member) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al.</p>
SSP5-8.5 Data for paper "When will humanity notice its impacts on atmospheric rivers?"
<p>The repository contains the SSP5-8.5 scenario simulation (one ensemble member) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al.</p>
Atmospheric River Database & Detection Code
<p>This database contains the global atmospheric river catalogs detected by the PanLu algorithm within ERA5 and CMIP6 models. The PanLu detection code is also included now.</p>
Atmospheric river contributions to ice sheet hydroclimate at the Last Glacial Maximum
<p>This dataset contains the atmospheric river catalogues and the associated precipitation and temperature data for the Preindustrial and Last Glacial Maximum CESM2 simulations presented in the GRL manuscript: Atmospheric river contributions to ice sheet hydro climate at the Last Glacial Maximum. The atmospheric river catalogue files (zipped) are in netcdf format and organized by year. There are 100 years of data for both simulations. The Preindustrial simulation catalogue begins in model year 41 and ends in model year 140. The LGM simulation catalogue begins in model year 1 and ends in year 100. Each yearly file has a temporal resolution of 6 hours (1460 time steps each file) and a spatial resolution of 0.9° x 1.25° (the native resolution of the CESM simulation). A variable in the file called "ar_binary_tag" indicates whether an atmospheric river is present at each grid cell and each tilmestep: 1 indicates an atmospheric river is present; 0 indicates an atmospheric river is not present. The precipitation and temperature files are 100-year annual or 100-year seasonal averages of atmospheric river precipitation/temperature. See the Methods section of the article for more details on the atmospheric river detection algorithm and precipitation/temperature calculations.</p> <p>Associated article abstract:</p> <p>Atmospheric rivers (ARs) are an important driver of surface mass balance over today’s Greenland and Antarctic ice sheets. Using paleoclimate simulations with the Community Earth System Model, we find ARs also had a key influence on the extensive ice sheets of the Last Glacial Maximum (LGM). ARs provide up to 53% of total precipitation along the margins of the eastern Laurentide ice sheet and up to 22-27% of precipitation along the margins of the Patagonian, western Cordilleran, and western Fennoscandian ice sheets. Despite overall cold conditions at the LGM, surface temperatures during AR events are often above freezing, resulting in more rain than snow along ice sheet margins and conditions that promote surface melt. The results suggest ARs may have had an important role in ice sheet growth and melt during previous glacial periods and may have accelerated ice sheet retreat following the LGM.</p>
Six hundred years of reconstructed atmospheric river activity on the US west coast
<p>These are the reconstruction data from the article: Six hundred years of reconstructed atmospheric river activity along the US West Coast (under review at the Journal of Geophysical Research: Atmosphere). The earliest start year of the reconstruction are mentioned in the files (either 1916 or 1400) and the corresponding models are mentioned as PCR (Regression) or NN (Neural Network). </p>
Visualization of Idealized Atmospheric-River-like Features
<p>In an idealized simulation of a shallow-water fluid, all the poleward transport of a water vapor-like tracer transport is produced by AR-like filamentary structures. This dry simulation is initialized with a barotropically unstable midlatitude jet stream and a passive tracer in the tropics. Due to the idealized nature of the simulation, we can unequivocally identify the formation of a Rossby wave in the jet stream. In the tracer field, this wave leads to turbulent stirring and generates long, narrow filamentary structures that have a similar aspect ratio as ARs. Based on our experiment setup, these structures should be responsible for all the meridional tracer transport in the extratropics. </p>
Ring Width Index (RWI) and Standard Precipitation Index (SPI) data for Atmospheric River reconstruction
<p>These are the two datasets used for reconstructing atmospheric river activity along the U.S. west coast (under review at the Journal of Geophysical Research: Atmosphere). The first file rwi.mat contains the ring-width-index for the chronologies used to develop the second dataset (SPI_recon.nc). This file is used as the covariate to develop the AR reconstructions (with Poisson regression) and goes back to 1400 CE. </p>
Measurement report: Atmospheric nitrate radical chemistry in the South China Sea influenced by the urban outflow of the Pearl River Delta
<p>Supporting dataset for Measurement report: Atmospheric nitrate radical chemistry in the South China Sea influenced by the urban outflow of the Pearl River Delta.</p>
Dataset for manuscript "Contributions of Atmospheric Rivers to the Hydroclimatology of British Columbia's Nechako River Basin"
<p>This dataset is the foundation of the findings presented in the "Contributions of Atmospheric Rivers to the Hydroclimatology of British Columbia's Nechako River Basin".</p>
Atmospheric river variability over the last millennium driven by annular modes: Part 1 (AR tags for LME Members #2–7)
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Atmospheric river variability over the last millennium driven by annular modes: Part 2 (AR tags for LME Members #8–13)
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Atmospheric river variability over the last millennium driven by annular modes: Part 3 (AR tags for ERA5)
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Response of atmospheric river width and intensity to aquaplanet warming: A detection algorithm- and background moisture-independent approach
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Trajectories for 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport''
<p>This is the trajectories output for the article 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport''.</p> <p>The settings for FLEXPART-WRF is also included.</p>
Trajectory Movie for the article 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport'
<p>The supplement movie for the article 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport'.</p>
Atmospheric River (AR) Fused Identification Dataset from 1980 to 2016
<p>This dataset provides a comprehensive global resource for Atmospheric River (AR) identification, fused with twelve different atmospheric river detection tool datasets based on their identification conflicts. The data spans from 1980 to 2016, providing high spatio-temporal resolution AR identification results. It covers a global scale, with a spatial resolution of 0.5 degrees in latitude, 0.625 degrees in longitude, and a temporal resolution of three-hour intervals. The fusion of multiple datasets provides a robust and unified AR identification, offering a valuable tool for historical AR pattern analysis and future predictive modeling.</p>
Code and Data for Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0
<p>Includes the code used for all simulations and grid configurations for the paper entitled "Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0" submitted to Geoscientific Model Development. Also included are the model output files for all cases and grid configurations used to generate the analysis and figures in the paper. </p>
Data for "Back-to-back high category atmospheric river landfalls occur more often on the west coast of the United States"
<p>The repository contains data used in "(Zhou et al. 2024) Back-to-back high category atmospheric river landfalls occur more often on the west coast of the United States". We included the landfall flags used for AR cluster identification. The landfall flags are generated using TECA-BARD AR detection on datasets including ERA5 reanalysis and CMIP5/6 simulaitons. Large-scale fields of 500hPa geopotential height, 850hPa meridional wind, and 850hPazonal wind are also included. </p> <p>Landfall flags from ERA5 detected by TECA: TECA_IVTmax_landfall_yyyy_US.pkl (yyyy = year)</p> <p>Landfall flags from CESM2-LEN detected by TECA: TECA_IVTmax_landfall_xxxx_yy_US.pkl (xxxx=initical states, yy=ensemble member)</p> <p>Landfall flags from CMIP5/6 detected by TECA: TECA_IVTmax_landfall_{model_name}_US.pkl</p> <p>large-scale fields from ERA5: {VAR}_cluster_mean_cl_length_US.nc4</p> <p> </p>
Data for "Characteristics and Variability of Winter Northern Pacific Atmospheric River Flavors"
<p>The repository contains data used in "(Zhou et al. 2022) Characteristics and Variability of Winter Northern Pacific Atmospheric River Flavors", which are outputs from Zhou et al. (2018) tracking algorithm, including lifecycle-related variables such as area, precipitation, 850hPa wind speed, IVT, IWV, locations (latitude and longitude), AR frequency, and landfall precipitation. </p>
Kilometre-scale regional climate model simulations of two atmospheric river case studies in West Antarctica
<p>Regional climate model simulations produced using the MetUM, HCLIM and Polar-WRF models at 1 km horizontal grid spacing. The data span two case studies in which an atmospheric river made landfall over the Amundsen Sea Embayment and Thwaites / Pine Island ice shelves. The first is a winter case (23-30 June 2020) and the second a summer case (3-9 February 2020). </p> <p>Data are gridded, in native model coordinates, and saved as netcdf.</p> <p>Data produced by:</p> <p>HCLIM: José Abraham Torres</p> <p>MetUM: Ella Gilbert</p> <p>Polar-WRF: Denys Pishniak</p> <p>Data were produced to support the analysis presented in Gilbert et al. (2024) [preprint] . The research was funded by the PolarRES project, which is funded under the EU's Horizon 2020 programme call H2020-LC-CLA-2018-2019-2020 under grant agreement 101003590. MetUM simulations were performed on the ARCHER2 UK National Supercomputer. </p>
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International Brain Laboratory public data
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OpenNeuro
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