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132 results for “climate, precipitation”
Monthly precipitation in mm at 1 km resolution (multisource average) based on SM2RAIN-ASCAT 2007-2021, CHELSA Climate and WorldClim
<p>Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2021 (<a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a>). <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/clim1km">Downscaled to 1 km resolution using gdalwarp</a> (cubic splines) and combined with WorldClim (<a href="https://worldclim.org/data/worldclim21.html">https://worldclim.org/data/worldclim21.html</a>) and CHELSA Climate (<a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a>) monthly values. Final values are estimated as a simple average between the three precipitation data sources; a more objective approach would be to use training points e.g. meteo-station monthly values, then train an ensemble model using the 3 data sources as independent variables. Another global data source of precipitation images is the <a href="https://gpm.nasa.gov/data/imerg">monthly IMERGE dataset</a>, however this requires transformation and is available only for limited span of years.</p> <p>Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Antarctica is not included. Standard deviation (sd) indicates a difference between the 3 data sources. To access and visualize maps use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a>.<strong> </strong>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>precipitation = variable: precipitation,</li> <li>wc.v2.1.chelsa.v2.1.sm2rain.oct = determination method: long-term average values for October,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1980..2020 = time reference: from 1980 to 2020,</li> <li>v0.3 = version number: 0.3,</li> </ul>
Antarctic time series of temperature, precipitation, and stable isotopes in precipitation from the ECHAM5/MPI-OM-wiso past1000 climate model simulation
<p>This data set contains time series of two-metre air temperature (tas), surface temperature (ts), total precipitation (pr), oxygen-18 isotopic composition in precipitation (oxy), and deuterium isotopic composition in precipitation (dtr) from the past-millennium (800-1999 CE) simulation of the fully coupled ECHAM5/MPI-OM-wiso atmosphere-ocean general circulation model equipped with stable isotope diagnostics (Sjolte et al., 2018, Werner et al., 2016) used in the publication of Münch et al. (2021).</p> <p>The data here are provided for the Antarctic region, i.e., all model grid cells south of 60° S. The model's atmospheric component was run with a T31 spectral resolution (3.75° x 3.75°) and with 19 vertical levels, resulting in a total of N = 768 model grid cells covered by this data set. Note, however, that all time series off the continent of Antarctica have been set to NA values, so that the effectively available number of model grid cells is N<sub>eff</sub> = 442.</p> <p>Time series are provided at the original monthly resolution of the model output and on annual resolution obtained from the monthly resolution data. At annual resolution, the temperature and isotopic composition data are available as normal time averages and as precipitation-weighted time averages. In addition to the time series, the spatial field of time-invariant means is supplied, also as normal and precipitation-weighted time averages.</p> <p>Data are available as netcdf files and as R data files. In addition, processing code (bash and R scripts) are provided to reproduce the processing from monthly to annnual and time-invariant resolution and to read the data into the R data format. To process the R data, you will need the CRAN packages "ncdf4" and "lubridate", and the package "pfields" available on GitHub (see References).</p>
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
NOAA's National Climatic Data Center including daily precipitation and USFS RDA datasets
This dataset was originally established as a subset of relevant NOAA daily precipitation data. This has been replaced with links to NOAA station websites which contain this data, please visit these links in the dataset file here. Previously, daily precipitation for 5 stations in or near the LEF were compiled from the NOAA National Climate Data Center and posted here. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Precipitation objects under the current and future climate: WRF 6-km hydroclimate simulation of the western US
<p>This folder includes the precipitation objects that are used in the following manuscript:</p> <p>Chen et al., Sharpening of Cold Season Storms over the Western US.</p> <p>It is generated using WRF V3.8 at PNNL. A historical simulation ("NARR") is done for 1981-2010, and five future simulations ("CanESM2", "CESM1-CAM5", "GFDL-ESM2M", "HadGEM2-ES", "MPI-ESM-MR") are done for 2041-2070 using the Pseudo Global Warming (PGW) approach. For the WRF model configuration and the simulation details, please refer to the abovementioned manuscript and Chen et al. (2018).</p> <p>This is the preliminary version of the dataset that contains the precipitation object features as analyzed in the manuscript. More data (including the WRF raw precipitation output) and the finalized scripts will be included here before the manuscript is published.</p> <p> </p> <p>Reference:</p> <p>Chen, X., L. R. Ruby, Y. Gao, Y. Liu, M. Wigmosta, and M. Richmond (2018), Predictability of Extreme Precipitation in Western U.S. Watersheds Based on Atmospheric River Occurrence, Intensity, and Duration, <em>Geophys. Res. Lett.</em> doi: <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018GL079831">10.1029/2018GL079831</a></p> <p>Chen, X., L. R. Ruby, Y. Gao, Y. Liu, and M. Wigmosta (2023), Sharpening of Cold Season Storms over the Western US, Nat. Clim. Change. doi: <a href="https://www.nature.com/articles/s41558-022-01578-0">10.1038/s41558-022-01578-0</a> </p>
Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"
<p>Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository.</p> <p>In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook <em>boxmodel.ipynb</em>.</p>
Processed model output of the climate simulation in the study: The effects of diachronous surface uplift of the European Alps on regional climate and the isotopic composition of precipitation (δ18Op) [Boateng et al.]
<p><strong>The geodynamic evolution of the Alps suggests that the Alps did not rise monotonically due to the different post-collisional processes such as slab break-off. However, understanding such subsurface dynamics would require adequate knowledge about its surface uplift history. Stable isotope paleoaltimetry methods are widely used to infer past surface elevation using geologic archives. However, its accurate interpretation relies on attributing the extracted isotopic signal from proxies to surface uplift despite other influences such as climate. To resolve this issue, topographic sensitivity experiments across the Alps are used to investigate the impacts of the diachronous surface uplift on regional climate and δ18Op. The Atmospheric General Circulation Model ECHAM5 with water isotope tracking capabilities (ECHAM5-wiso) is used to simulate the climate with varied topographic scenarios. We present the processed (long-term means) model output of the relevant climate variables (i.e δ18Op, near-surface temperature, precipitation amount, near-surface meridional and zonal winds, mean sea level pressure, and elevation) in response to the changes in topography. The file names are representative of the topographic scenarios used for the simulations. For example, the file “W2E1.nc” is the model output produced by a topographic scenario in which the topography across the west-central Alps was set to 200% of its modern height, and the Eastern Alps were kept at 100%. The “CTL.nc” file contains model output from the control simulation that uses present-day topography. The datasets for instance can be used to select far-field sampling points for the δ-δ paleoaltimetry method that are not significantly affected by the topographic changes.</strong></p>
Regional climate simulations of surface precipitation and temperature for West Africa using COSMO-CLM based on MPI-LR (ECHAM6) and RCP4.5
<p>Regional climate model COSMO-CLM (CCLM) simulations with a horizontal resolution of 0.11° (approx. 12 km) for sub-Saharan West Africa under current and future climate conditions. The CCLM is driven by initial and lateral boundary conditions from the MPI-LR (ECHAM6), based on the emission scenario RCP4.5. The downscaled MPI-LR (ECHAM6) data for surface precipitation (P) and surface temperature (Tmin, Tmax) are provided for the baseline period (1981-2010) and two future time slices, i.e. the 2021–2050 and the 2071–2100 period. </p> <p> </p>
SCOPE Climate: precipitation
<p>SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year high-resolution reconstructions of precipitation, temperature and Penmann-Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate results from the statical downscaling of the global extended reanalysis 20CR V2 with the SCOPE method (Caillouet et al., 2016, 2017). SCOPE Climate provides an ensemble of 25 equally-plausible spatially-coherent gridded multivariate time series. Data are available at a daily time step on a 8 km grid over France as 25 files in NetCDF format. Reconstructed values cover grid cells located only within metropolitan France national borders (including Corsica). The SCOPE Climate dataset is fully described by Caillouet et al. (2019).</p> <p>This dataset provides reconstructions of precipitation as a 25-member ensemble of gridded time series. Precipitation values from member 1 should be used with reconstructed temperature/evapotranspiration values from member 1 (and so on). The corresponding temperature dataset can be found at http://doi.org/10.5281/zenodo.1299712, and the evapotranspiration dataset at http://doi.org/10.5281/zenodo.1251843.</p>
Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming
<p>Dataset used to analyize role of climate variability</p>
Supporting data for ``Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land'"
<p>Here we have the processed data used in the preprint ``'Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land''</p> <p>The README.md file includes explanations about the data in the repository.</p>
RACMO regional climate model data, postprocessed for winter precipitation and winter temperature
<p>This contains statistics of winter precipitation and winter temperature derived from the 16 model ensemble by RACMO2. In addition to the GCM driven runs, also a PGW (pseudo global warming) set is given. Data is used for a paper to be submitted.</p> <p>Reference on the RACMO2 runs: Aalbers EE, Lenderink G, van Meijgaard E, van den Hurk BJJM (2018) Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50:4745–4766. <a href="https://doi.org/10.1007/s00382-017-3901-9">https://doi.org/10.1007/s00382-017-3901-9</a></p>
Data and scripts for figures in Walton & Huntingford, "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation"
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation", submitted for publication to Environmental Research Letters. Scripts used for plotting and analysis are also included.</p>
Risk Assessment of Extreme Precipitation on to Low- and Medium-Voltage Electrical Infrastructure Under the Influence of Climate Change
Open the record for dataset details and reuse information.
Increased precipitation over land due to climate feedback of large-scale bioenergy cultivation
<p>Biophysical effects of different bioenergy crop cultivation scenarios on global water cycles simulated by coupled IPSL-CM model, with ORCHIDEE-MICT-BIOENERGY as the land component and LMDz as the atmosphere component. The spatial resolution of the coupled model was 1.26° latitude × 2.5° longitude (i.e., 143*144 grid cells globally).</p> <p>Datasets includes:</p> <p>1. Source data and plotting code for Figure 1, the global precipitation changes induced by bioenergy cultivation.</p> <p>2. Source data and plotting code for Figure 2, the diagnostic precipitation changes for global land area, in and outside of the bioenergy cultivation area.</p> <p>3. Source data plotting code for Figure 3, the changes in water balance for global land area, in and outside of bioenergy cultivation area, monsoon regions and four different humidity zones.</p>
Data for "Global Precipitation Correction Across a Range of Climates Using CycleGAN"
<p># Data for "Global Precipitation Correction Across a Range of Climates Using CycleGAN"</p> <p>This repository contains the data used in the paper "Global Precipitation Correction Across a Range of Climates Using CycleGAN" by J. McGibbon et al. (2023, *in review*).</p> <p>`train_val` contains the model outputs for the training and validation sets used in the paper. Model spinup data is not included. The C384 runs are stored as a single series along a concatenated time axes. All C384 data has been coarsened to C384 resolution.</p> <p>`ramping_data` contains the model outputs from the 4-year, 3-month ramping simulation, including spinup data.</p> <p>`predicted` is included for convenience, and contains the model outputs from the "best" model, as was used to create figures shown in the paper. This model output was created from the CycleGAN using the validation dataset (stored in `train_val`) as input, and years 2 and 3 of the ramping simulation in `ramping_data` (following the 3-month spinup period). Ramping predictions are stored as separate datasets for the C48 (backwards) prediction and the C384 prediction. Validation data is stored alongside its respective target data in a single netCDF file, labelled "processed-agg". These filenames are intentionally left unchanged so that they correspond with the names of files used in the `process_combined_aggregate.py` script in `projects/cyclegan` of the code DOI for this paper, which was used to create its figures.</p>
Changes in vegetation in northern Alaska under scenarios of climate change, 2003-2100: I - Trends in air temperature, precipitation, and cloudiness
These data contain estiamtes of changes in climate from northern AK based on a modeling study for the years 2003-2100. See Euskirchen et al., 2009 for more information. This file contains data for Figure 3.
Data for the publication "Reconciling compensating errors between precipitation constraints and the energy budget in a climate model"
<p>These data are a set of 6yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments (diagnostic and prognostic) of precipitation under the present-day (PD, aerosol emission at the year 2000) and preindustrial (PI, aerosol emission at the year 1850) conditions.<br> The data are used in the manuscript entitled "Reconciling compensating errors between<br> precipitation constraints and the energy budget in a climate model".</p>
High-resolution climate model output for selected extreme precipitation events in Cyprus
<p>This dataset consists of high-resolution model output for selected past and future extreme precipitation events for Cyprus. It was generated in the framework of the BINGO Research Project (http://www.projectbingo.eu/) . BINGO has received funding from the European Union’s Horizon 2020 Research and Innovation programme, under Grant Agreement number 641739. More details about the dataset and the design of the simulations in:</p> <p>G. Zittis, A. Bruggeman, C. Camera, P. Hadjinicolaou, J. Lelieveld,<br> The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean,<br> Atmospheric Research, Volume 191, 2017, Pages 20-33, https://www.sciencedirect.com/science/article/pii/S0169809516307153</p>
FYRE Climate: Precipitation
<p><strong>FYRE Climate</strong> (French hYdrometeorological REanalysis Climate dataset) is a 25-member ensemble of 142-year high-resolution reanalysis of precipitation and temperature over France, from 1 January 1871 to 29 December 2012. FYRE Climate results from the assimilation of historical daily station observations of temperature and precipitation into the SCOPE Climate reconstructions (Caillouet et al., 2019) through a Kalman filter ensemble approach (Devers et al., 2020). FYRE Climate provides an ensemble of 25 equally-plausible spatially-coherent gridded bivariate time series. Data are available at a daily time step on a 8 km grid over France as 25 files in NetCDF format. Values cover grid cells located only within metropolitan France national borders (including Corsica). The FYRE Climate dataset is fully described by Devers et al. (2021).</p>
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