Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

Occupations on the map: Using a super learner algorithm to downscale labor statistics, data

This repository contains all the input and output data (including maps) related to <a href="https://doi.org/10.1371/journal.pone.0278120">Van Dijk et al. (2022), Occupations on the map: Using a super learner algorithm to downscale labor statistics</a>. It does not contain several large (> 4GB) intermediate files, which summarize the results of the large number of machine learning models that were trained and tuned as part of the super learner algorithm. These  files can be created by running the scripts in the supplementary GitHub repository: https://github.com/michielvandijk/occupations_on_the_map. All input and output maps produced as part of  this study can also be accessed by means of an interactive web application: https://shiny.wur.nl/occupation-map-vnm. In this paper, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.

opencc-by-4.0Dec 2022View details →

Model input data for the FACETS downscaling simulation with the CAM-MPAS model

The archived file contains input data necessary to reproduce the set of simulations described in Sakaguchi et al., submitted to GWD, "Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM-MPAS variable-resolution model", using the experimental CAM-MPAS code further modified by Sakaguchi and Harrop (2022) for long-term AMIP-type simulations.

opencc-by-4.0Feb 2023View details →

Sample of high-resolution climate dataset based on ML downscaling.

The ClimateByte project created a high-resolution (downscaled) climate dataset for specific regions based on the CINECA MISTRAL observational dataset. AMIGO selected one region from the larger dataset and made that portion openly available to the other members of the EUH4D project for research and non-commercial purposes. Resolution of this dataset is 300m and covers the Adige Valley of Trentino Alto-Adige (Italy)

opencc-by-4.0Apr 2023View details →

A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR: accompanying dataset

Dataset containing surface temperature and surface mass balance datasets generated from the MAR regional climate model over Greenland over two test areas using statistical downscaling tools from 6 km to 100m. The abstract of the accompanying submitted paper follows:    The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise and this contribution is projected to accelerate over next decades. A crucial tool for studying the evolution surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs) which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. The results obtained in the case of the SMB, assessed through the comparison of the modeled outputs with in-situ SMB measurements, show a considerable improvement in the case of the downscaled product with respect to the original, coarse output. In the case of the downscaled MAR product, the coefficient of determination (R2) increases from 0.868 for the original MAR output to 0.935 for the downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the intercept and from the value -235mm (original) to -57 mm (downscaled) in the case of the slope, considerably improving upon results previously published in the literature.

opencc-by-4.0Jun 2023View details →

Dataset - Downscaling ERA5 Wind Speed Data: A Machine Learning approach considering Topographic Influences

This dataset provides three products: 1. The topographic data.   These data are provided as GeoTIFF files for Europe with 1km x 1km spatial resolution. These maps include: <ul> <li>Digital Elevation Model (DEM) map: Europe_DEM.tif</li> <li>Slope map: Europe_slope.tif</li> <li>Aspect map: Europe_aspect.tif</li> <li>Topographic Position Index (TPI) with a 5 km radius map: Europe_TPI_5.tif</li> <li>Topographic Position Index (TPI) with a 75 km radius map: Europe_TPI_75.tif</li> <li>Terrain Diversity Index (TDI) map: Europe_TDI.tif</li> </ul> These data can be used as input maps for the preprocessing step. In addition, the two TPI maps can also be used in the regression process. 2. The resulting map of the preprocessing step.  This map offers predictions on the quality of ERA5 data across Europe and is also provided as a GeoTIFF file with 1km x 1km spatial resolution under the name: <ul> <li> Europe_classification.tif</li> </ul> In this map, Class1 represents a good ERA5 quality with an RMSE of less than 1.5 m/s, Class2 represents a moderate ERA5 quality with an RMSE bigger than 1.5 m/s but less than 3 m/s, while Class 3 indicates a poor ERA5 quality with an RMSE greater than 3 m/s. 3. The downscaled wind speed time series data. Europe has been divided into 64 equal area blocks to accommodate the large data size. Each downscaled dataset is provided as a NetCDF file, offering hourly wind speed time series for a year (8760 hours) at approximately 1km x 1km spatial resolution. Each NetCDF file has three dimensions: 'lon' representing longitude, 'lat' representing latitude, and 'time' representing the hour. The variable name for wind speed in the NetCDF file is 'WindSpeed'. The 'WindSpeed' variable is stored as an Int32 data type in the NetCDF file, with values multiplied by 10000 in order to significantly reduce the data size. To utilize this variable, please divide it by 10000. For regions identified as Class1 and Class2, the downscaled wind speed is obtained through a simple nearest neighbour spatial interpolation of ERA5 due to the good quality of ERA5 in these regions. However, for the regions identified as Class3, the downscaled wind speed is derived using the machine learning-based regression approach described in the relevant publication. The geographic extent and the visual representation for each block are provided in 'Readme.pdf' document.   To cite this dataset, please cite our published paper in Environmental Research Letters (DOI: 10.1088/1748-9326/aceb0a)

opencc-by-4.0Aug 2023View details →

Selected near-bottom and other variables from NW European shelf physics-biogeochemistry downscaled ocean climate projections, 3-member ensemble.

Selected fields of physical and biogeochemical ocean variables from a 3-member ensemble of coupled physics-biogeochemistry downscaled climate runs on the North Western European Continental Shelf. All ensemble members use the NEMO-ERSEM model suite and cover the 1990-2099 period. Easch member is foced with a different set of atmospheric and oceanic boundary conditions from one of three CMIP5 ESMs that are: HADGEM2-ES, IPSL-CM5A-MR and GFDL-ESM2G. This dataset contains monthly average values saved as 2D fields either near-bottom, at the surface or depth integrated. The variables here saved are near-bottom oxygen, oxygen solubility, oxygen saturation state, temperature and bacterial respiration, surface salinity, depth integrated net primary production, and potential energy anomaly. Additionally the Western Norwegian Trench Current flux is provided (its values come smoothed with a gaussian filter). reference publication: https://doi.org/10.5194/egusphere-2023-1049. The complete set of variables is available from the authors upon request.

openCC (other)Mar 2018View details →

Downscaled climate grids at 30m for a variety of bioclimatic variables over the San Joaquin Experimental Range, CA: 2001-2099

Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.

openCC (other)Apr 2018View details →

Downscaled climate grids at 30m for a variety of bioclimatic variables over the Teakettle Experimental Forest, 2001-2099

openCC (other)Mar 2018View details →

Downscaled climate grids at 30m for a variety of bioclimatic variables over the Tejon Ranch, CA: 2001-2099

openCC (other)Mar 2024View details →

Statistically downscaled future precipitation for the Luquillo Mountains, Puerto Rico

This dataset contains climate predictions that serve as the basis for the analysis in Ramseyer et al. (2019), which projected a trend toward drier conditions in eastern Puerto Rico during the mid- and late-21st century. The analysis was informed by computing nine atmospheric variables, which had been shown by previous research to related to precipitation in Puerto Rico (Ramseyer and Mote 2016) from four GCMs. These nine variables were used to train an artificial neural network (ANN) to predict the binary occurrence of a wet (>= 5 mm of precipitation) versus dry (<5 mm) day using in-situ daily precipitation observations from El Verde Field Station in northeast Puerto Rico. The nine atmospheric variables used to train the ANN were: 1000- 850-, 700-, and 500-hPa daily specific humidity, 1000–700-hPa bulk wind shear (BWS), the Gálvez-Davison Index (GDI), and the GDI's three component terms (the column buoyancy index, mid-level warming index, and a trade-wind inversion index). These same nine variables were then extracted on a daily basis from four GCMs for the eastern Caribbean early rainfall season (April-July) between 2041-2060 and 2081-2100, and fed through the ANN. These data are the daily predicted values of wet (1) or dry (0) conditions for each of the four GCMs in the ensemble. Because ERS total precipitation at El Verde is strongly correlated with the percentage of ERS dry days (R2=0.95 for years with <10% missing data), the GCM predictions were used to estimate future ERS precipitation using the following formula: ERS precipitation (mm) = 3373-37.6*(ERS dry-day percentage) Applying this formula to each of the GCM dry-day projections yielded an ensemble mean ERS precipitation total of 771 mm by 2041-2060 and 974 mm by 2081-2100. See Ramseyer et al. (2019) for a complete description of the neural network and its predictions. Ramseyer, C., P. Miller, and T. Mote, 2019: Future precipitation variability during the early rainfall season in the El Yunque National Fore

opencc-by-4.0Mar 2020View details →

Model outputs for validation and inference of high‐resolution information (downscaling) of ENETwild abundance model for wild boar, January 2020 update

These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results. Objectives: - Validation of previously produced hunting yield maps and new ones - Downscaling to 10x10 km grid >>> file  "January_2020_HY_nut01_10x10.tif" - Downscaling to 2x2 km grid   >>> file "January_2020_HY_nut00_2x2.tif" Model settings and predictors:     - Assuming cells as municipality in 10x10 km grid downscaling - Assuming cells as hunting grounds in 2x2 km grid downscaling     Conclusions guiding future methodological steps: - To update wild boar hunting yield data for some specific regions - To increase hunting yield data resolution - To explore model independent parametrization for each bioregion For further details and methodological approach see the paper: ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A.  Cohen, R. Soriguer, J. Vicente (2020) Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar. EFSA supporting publication 2020:EN-1787. 23pp. doi:10.2903/sp.efsa.2020.EN-1787. Permission for reuse hunting yield outputs is granted under the terms indicated  by EFSA.

opencc-by-4.0Jul 2020View details →

Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"

Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>. The R code was written for R version 3.6.3. References Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]

opencc-by-4.0Dec 2020View details →

Cyclone tracks from 1901 to 2010 in dynamically downscaled ERA-20C reanalysis (COSMO-CLM+NEMO)

The database contains two files: one with all cyclone trajectories from 1901 to 2010, and another one only with the so-called Vb-cyclones that propagate from the Mediterranean Sea north-eastward to Central Europe. We detected the cyclone trajectories with the method of Wernli and Schwierz (2006) and Sprenger et al. (2017) and classified all cyclone trajectories that crossed the 47°N latitude between 12°E and 22°E as Vb-cyclones following Hofstätter and Blöschl (2019). The cyclone tracking was based on mean sea level pressure data of dynamically downscaled ERA-20C reanalysis. The downscaling was performed over Europe [including MED-CORDEX (Somot et al. 2018) and EURO-CORDEX (Giorgi et al. 2009)] from 1901 to 2010 with an interactively coupled high-resolution atmosphere-ocean model (COSMO-CLM+NEMO) by Cristina Primo. More details on the data basis can be found in Primo et al. (2019) and Krug et al. (2020).   Giorgi, F., Jones, C. & Asrar, G. Addressing climate information needs at the regional level: the CORDEX framework. WMO Bulletin 58, 175–183 (2009). Hofstätter, M. & Blöschl, G. Vb Cyclones Synchronized With the Arctic-/North Atlantic Oscillation. J. Geophys. Res. Atmos. 124, 3259–3278 (2019). Krug, A., Primo, C., Fischer, S., Schumann, A. & Ahrens, B. On the temporal variability of widespread rain-on-snow floods. Meteorol. Zeitschrift 29, 147–163 (2020). Primo, C., Kelemen, F. D., Feldmann, H., Akhtar, N. & Ahrens, B. A regional atmosphere-ocean climate system model (CCLMv5.0clm7-NEMOv3.3-NEMOv3.6) over Europe including three marginal seas: on its stability and performance. Geosci. Model Dev. 12, 5077–5095 (2019). Somot, S. et al. Editorial for the Med-CORDEX special issue. Clim. Dyn. 51, 771–777 (2018). doi: 10.1007/s00382-018-4325-x Sprenger, M. et al. Global climatologies of Eulerian and Lagrangian flow features based on ERA-Interim. Bull. Am. Meteorol. Soc. (2017). doi:10.1175/BAMS-D-15-00299.1 Wernli, H. & Schwierz, C. Surface Cyclones in the ERA-40 Dataset (1958–2001). Part I: Novel Identification Method and Global Climatology. J. Atmos. Sci. 63, 2486–2507 (2006).

opencc-by-4.0Jun 2021View details →

Downscaled surface mass balance in Antarctica: impacts of subsurface processes and large-scale atmospheric circulation

Here is the surface mass balance calculated from a offline subsurface model, that is used in the paper Downscaled surface mass balance in Antarctica: impacts of subsurface processes and large-scale atmospheric circulation. More data are available by contacting nichsen@space.dtu.dk

dryad40/100

High-resolution CONUS-wide downscaled rainfall estimates (HRCDRE)

The spatiotemporal character of rainfall is particularly important for hydrologic modeling, as well as hydroclimatic risk estimation and impact assessment. Existing atmospheric reanalysis datasets offer extensive record lengths and global coverage, but usually their spatial resolution is coarse for distributed hydrologic simulations at small spatial scales. On the other hand, the temporal coverage of high-resolution radar-based rainfall estimates can be rather short for risk applications. To address these shortcomings, we simultaneously bias-correct and downscale a state-of-the-art atmospheric reanalysis (<a href="https://doi.org/10.24381/cds.adbb2d47">ERA5</a>) rainfall dataset, using the radar-based <a href="https://doi.org/10.5065/D6PG1QDD">Stage IV</a> precipitation product as fine resolution reference, to develop an hourly CONUS-wide precipitation product over a 4-km grid, which extends back to 1979. In this regard, we refine an existing parametric quantile mapping framework based on a two-component theoretical distribution model, where we impose continuity of the parametric forms via optimal threshold selection to transition between higher and lower rain rates. An evaluation over the probability frequency and time domains, using <a href="https://doi.org/10.25921/p7j8-2170">NOAA's raingauge</a> measurements as benchmark, reveals that the developed product benefits from the strengths of the calibration datasets, demonstrating good performance and robust behavior over all studied time periods and Köppen climate classification zones, including snow-prone regions or areas where mesoscale convective systems become dominant. The accuracy of the yielded high spatial-resolution rain rates, especially in low probability events, shows that the developed product can be effectively used for hydroclimatic risk applications and frequency analysis, while its high temporal and spatial resolution makes it particularly useful for distributed hydrologic modeling.

opencc-zeroJun 2021View details →

opencc-by-4.0Oct 2022View details →

Data for: Downscaled gridded global dataset for Gross Domestic Product (GDP) per capita at purchasing power parity (PPP) over 1990-2022

This dataset provides a gridded dataset for GDP per capita at purchasing power parity (PPP) downscaled to an admin 2 level (43,501 admin units). The dataset is based on reported subnational admin data (from 89 countries and 2,708 subnational units) and spans three decades from 1990 to 2022.  The dataset is presented in details in the following publication. Please cite this paper when using data.  Kummu, M., Kosonen, M. & Masoumzadeh Sayyar, S. 2025. Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022. Scientific Data 12: 178. <a href="https://doi.org/10.1038/s41597-025-04487-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-04487-x</a> Code is available at: <a href="https://github.com/mattikummu/griddedGDPpc" target="_blank" rel="noopener">https://github.com/mattikummu/griddedGDPpc </a>   The following data is given (formats in brackets) <ul> <li>GDP per capita (PPP) at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>GDP per capita (PPP) at admin 1 level (at the level of reporting, either admin 1 level or admin 0 level) (GeoTIFF, gpkg, csv)</li> <li>GDP per capita (PPP) at admin 2 level (downscaled from admin 1 level) (GeoTIFF, gpkg, csv)</li> <li>Total GDP (PPP), downscaled admin 2 level GDP per capita (PPP) multiplied by gridded population count, with three resolutions: 30 arc-sec, 5 arc-min, and 30 arc-min (GeoTIFF) </li> <li>Input data for the script that was used to generate the data above (code_input_data.zip). Code available at https://github.com/mattikummu/griddedGDPpc </li> </ul> Files are named as follows Format: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*.  Admin levels: adm0 for admin 0 level, adm1 for admin 1 level, and adm2 for admin 2 level Product type: GDP per capita at purchasing power parity (PPP): _gdp_perCapita_; and total GDP at purchasing power parity (PPP): _gdp_tot_   Metadata  Grids for GDP per capita data: Resolution: 5 arc-min (0.083333333 degrees)  (for admin 2 level also 30 arc-min, 0.5 degree, resolution is provided) Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)  Coordinate ref system: EPSG:4326 - WGS 84  Format: Multiband geotiff; each band for each year over 1990-2022  Unit: USD in 2017 international dollars   Grids for total GDP: Resolution: 30 arc-sec, 5 arc-min or 30 arc-min Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)  Coordinate ref system: EPSG:4326 - WGS 84  Format: Multiband geotiff; each band for each year over 1990-2022 (5 arc-min, 30 arc-min) or for each five years 1990, 1995, ... 2015, 2020 (30 arc-sec) Unit: USD in 2017 international dollars   Geospatial polygon (gpkg) files:  Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)  Temporal extent: annual over 1990-2022 Coordinate ref system: EPSG:4326 - WGS 84  Format: gkpk  Unit:  USD in 2017 international dollars

opencc-by-4.0Apr 2024View details →

A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation

<h1>Dataset Description for "A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</h1> This dataset accompanies the research paper titled "A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation", currently under review for the AGU Journal JAMES. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/A-Robust-Generative-Adversarial-Network-Approach-for-Climate-Downscaling" target="_new">GitHub repository</a>. <h2>Aims</h2> Our study's overarching goal was to assess the effectiveness of Generative Adversarial Networks (GANs) in a climate downscaling context and is structured around two aims. The first aim of our study is to examine whether GANs can overcome several important limitations of regression-based climate downscaling algorithms (i.e. underestimating the magnitude of extreme events). The second and most important aim of our study is to assess the robustness GAN performance to different training hyperparameters. Our robustness assessment thoroughly scrutinizes GANs for their application in climate downscaling contexts, ensuring that they can learn and capture regional climate processes <h2>Geographic Focus</h2> Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).   <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> The training data used in this study (for our RCM emulator) only spans the historical period of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables.  <ul> <li> Resolution: The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation.  </li> <li> Period Coverage: <ul> <li>Training Data: 1960-2014</li> <li>Validation Data: 1986-2005</li> </ul> </li> <li> Models: <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM</li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> Training Data: <ul> <li>Target/Ground Truth (Y): <code>predictor_ACCESS-CM2_hist.nc</code></li> <li>Predictor (X): <code>pr_ACCESS-CM2_hist.nc</code></li> </ul> </li> <li> Evaluation Data: <ul> <li>NorESM2-MM: <ul> <li>Target (Y): <code>NorESM2-MM_historical_precip_compressed.nc</code></li> <li>Predictor (X): <code>NorESM2-MM_histupdated_compressed.nc</code></li> </ul> </li> <li>EC-Earth3: <ul> <li>Target: <code>EC-Earth3_historical_precip_compressed.nc</code></li> <li>Predictor: <code>EC-Earth3_histupdated_compressed.nc</code></li> </ul> </li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> Regional Climate Model, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>. </li> <li> Predictor and Target Variables: Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository. </li> <li> Training Framework: Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a> </li> </ul>

opencc-by-4.0Dec 2021View details →

Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning"

Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning". Predictors from the ERA5 reanalysis and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rda" format, to be read from R.

opencc-by-4.0Mar 2022View details →

MASS2ANT Snowfall Dataset (Downscaling @5.5km over Dronning Maud Land, Antarctica, 1850 - 2014): Daily fields (Part 1)

We provide in this dataset maps at 5.5 km resolution of the daily and yearly accumulated snowfall over emerged land (Ice Sheet) of Dronning Maud Land (Antarctica) from 1850 to 2014. We used a statistical method to derive fine resolution maps from GCM runs (CESM2, 10 runs). In the method, we searched for analogs in a database we constructed from the association between re-analyses large-scale meteorological fields (ERA5 and ERA-Interim) and RCM daily accumulated snowfall (RACMO2.3p5.5). RACMO2.3p5.5 data are available freely on request (<a href="https://www.projects.science.uu.nl/iceclimate/models/antarctica.php">https://www.projects.science.uu.nl/iceclimate/models/antarctica.php</a>). CESM2 CMIP6 runs are also freely available (<a href="https://esgf-node.llnl.gov/search/cmip6/">https://esgf-node.llnl.gov/search/cmip6/</a>). The complete description of the algorithm and performance is described in: Ghilain N., Vannitsem S., Dalaiden Q., Goosse H., De Cruz L., Wei W., Reconstruction of daily snowfall accumulation at 5.5km resolution over Dronning Maud Land, Antarctica, from 1850 to 2014 using an analog-based downscaling technique, submitted to Earth System Science Data (ESSD). The MASS2ANT Snowfall dataset is composed of the annual estimations of snowfall over Dronning Maud Land, the daily time series for the total period for all the emerged grid points of the domain, the principal components time series and Empirical Orthogonal Functions (EOF) offering the possibility to analyze the synoptic weather patterns associated to snowfall over the ice sheet and the Principal Component weights (PCs) time series from the re-analysis in case one wants to extend or improve the database. Realistic weather patterns can be recomposed in associating (product of matrices) the PCs with the EOFs. Here (Daily fields - Part 1), we provide the daily snowfall time series resulting from the downscaling of the 7 first members of CESM2, using ERA5 and RACMO2.3p5.5 for training.