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59 results for “climate downscaling”
The Effects of Climate Downscaling Technique and Observational Dataset on Modeled Ecological Responses: Supporting Data Tables
These data have been prepared as a supplement to Pourmokhtarian et al. (2016; full citation below), where complete details on methods can be found. We evaluated three downscaling methods: the delta method (or the change factor method); monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD); and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8th degree resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (8 ensembles of 6 runs). This dataset consists of three files - 1) a zip archive file of all raw daily downscaled AOGCMs (csv format; years 1960-2099; delta method 2012-2099 only) which were used as input for PnET-BGC model, 2) a zip archive file of all PnET-BGC output files for each model run (csv format; years 1000-2100), and 3) a pdf document file that describes the content of the input and output files. Data were also used from the following Hubbard Brook longterm datasests: Daily Streamflow Watershed 6: http://dx.doi.org/10.6073/pasta/727ee240e0b1e10c92fa28641bedb0a3 Chemistry of Streamwater at the Hubbard Brook Experimental Forest, Watershed 6: http://dx.doi.org/10.6073/pasta/2ec152b0ab1d4e64aa40f4aa9bc492ac Daily Precipitation Watershed 6: http://dx.doi.org/10.6073/pasta/17c8ff8b160bf7893ef39f75a02652e5 Daily Maximum/Minimum Temperature Data: http://dx.doi.org/10.6073/pasta/2a4ab5522ce15f28196a6035802b09e8 Daily Solar Radiation Data: http://dx.doi.org/10.6073/pasta/2fa098a5aa191c64e622b253c0fee5af These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest
AWE-GEN-2d downscaled climate simulations for Omo-Turkana and Zambezi river basins
<p>AWE-GEN-2d downscaled climate simulations generated for the DAFNE project</p> <p>Refer to the README.txt for data access and description.</p>
Data from: Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD
Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950–2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850–2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.
Downscaled climate time series
<p>Downscaled time series developed for the following paper: Fernández, Manquehual-Cheuque & Somos_Valenzuela (2024): Impact of Solar Radiation Management on Andean glacier-wide surface mass balance, npj Climate and Atmospheric Science, doi:<strong> </strong>10.1038/s41612-024-00807-x</p>
MATLAB scripts for reproducing figures in "Atlantic Tropical Cyclones Downscaled from Climate Reanalyses Show Increasing Activity Through the Late 19th and 20th Centuries"
<p>A set of MATLAB scripts that contain the data for all 4 figures of "<strong>Atlantic Tropical Cyclones Downscaled from Climate Reanalyses Show Increasing Activity Through the Late 19<sup>th</sup> and 20<sup>th</sup> Centuries" </strong> and allows the user to plot these data. Please read the very short ReadMe file before using the scripts. </p>
Data and code: High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method
<p>Data and code supporting the research article:High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method - <br> Fasil M. Rettie, Sebastian Gayler, Tobias KD Weber, Kindie Tesfaye, Thilo Streck. Please, find detail description of the codes and datasets in readme file.</p>
Data for "Dynamical Downscaling of Climate Simulations in the Tropics"
<p>Precipitation, radiation and vertical mass flux data. 'MPI' indicates conventional downscaling results. 'biascor' indicates bias-corrected downscaling results. 'sstcor' indicates SST-corrected downscaling results.</p>
Data from: Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD
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Data from: Evaluation of downscaled, gridded climate data for the conterminous United States
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Local sea-level rise caused by climate change in the northwest Pacific marginal seas using dynamical downscaling
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Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Kortrijk Kennedy Park, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kenny Park (50° 48' 2"N 3°16'13" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Uncertainties Inherent from Large-Scale Climate Projections in the Statistical Downscaling Projection of North Atlantic Tropical Cyclone Activity
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High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 6
<p>Future projections of precipitation by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 10
<p>Extra data of the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia" that did not fit in their respective deposits:</p> <p>Future projections of:</p> <p>Precipitation by all CNN models (BMlinear, BM1, BM10, BMdense) forced by the UKESM1-0-LL GCM.</p> <p>2-meter maximum and minimum temperatures by the BM1 model forced by the NorESM2-MM and UKESM1-0-LL GCMs.</p> <p>2-meter mean temperature by the BMlinear and BM1 models forced by the NorESM2-MM and UKESM1-0-LL GCMs.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 4
<p>Future projections of 2-meter mean temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
Residual Matters: How Residual Learning Can Improve Climate Downscaling
<p><strong>Title</strong>: Residual Matters: How Residual Learning Can Improve Climate Downscaling</p> <p><strong>Description</strong>:<br>This repository contains the code and data used in the project "<em>Residual Matters: How Residual Learning Can Improve Climate Downscaling</em>." This project explores how residual networks, particularly models like EDSR and VDSR, can improve the downscaling of climate data. The goal is to enhance the spatial resolution of climate variables—such as ERA5 2m temperature data—using deep learning approaches. By leveraging residual learning techniques, this project achieves higher accuracy in climate downscaling, particularly in complex terrain regions, providing more reliable data for climate predictions and research.</p> <p>The repository includes:</p> <ul> <li>Python scripts implementing EDSR and VDSR architectures for temperature data downscaling.</li> <li>Requirements file (<code>requirements.txt</code>) listing all dependencies for reproducibility.</li> <li>Sample data preprocessing and model training scripts.</li> <li>Instructions for evaluating model performance.</li> <li><code>LICENSE</code> file under the GNU General Public License (GPL) v3, permitting reuse and modification.</li> </ul> <p><strong>License</strong>: GNU General Public License v3.0</p> <p><strong>Keywords</strong>: Climate Downscaling, Deep Learning, Super-Resolution, Residual Networks, EDSR, VDSR, ERA5</p> <p><strong>Usage Notes</strong>:<br>To run the code, set up a Python environment using the dependencies listed in <code>requirements.txt</code>. The provided data files can be used as input examples, and the README offers detailed instructions on running the models. For further usage guidance, please refer to the README or the paper associated with this project.</p> <p><strong>Acknowledgments</strong>: This work is conducted as part of research at IIT Mandi, focusing on improving climate downscaling techniques with deep learning.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Antwerp Berchem, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Amazon Web Services: Downscaled Climate Projections (NEX-DCP30)
The NASA Earth Exchange (NEX) Downscaled Climate Projections (NEX-DCP30) dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011] developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The dataset includes downscaled projections from 33 models, as well as ensemble statistics calculated for each RCP from all model runs available. The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions. Each of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.