cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5067/oq6b1rhohbi8
title: tavg1_2d_mld_Nx: MERRA Simulated 2D Incremental Analysis Update (IAU) MERRA-Land reanalysis, GEOSldas-MERRALand, Time Average 1-hourly (2/3x1/2) Version 5.2.0
DOI: 10.5281/zenodo.4300780
title: Input and Output Files EMGC

description: This data set contains a Readme file and the input and output files used in the McBride et al., 2020 ESD manuscript entitled "Comparison of CMIP6 Historical Climate Simulations and Future Projected Warming to an Empirical Model of Global Climate".
DOI: 10.5281/zenodo.4441776
title: Three-dimensional hydrogeological and salinity model of the Mekong delta, Vietnam

description: The data in the file “<em>hydrogeology_Mekong_Vietnam.nc</em>” (in NetCDF – format) contains the 3D voxel model of the hydrogeological units for the unconsolidated sediments in the Mekong delta, Vietnam. The variable “hydrogeological_unit” represents the aquitards (uppercase), while the lowercase codes denote aquifers. The coding follows the Vietnamese nomenclature and is explained the accompanying paper. The data in the file “<em>TDS_Mekong_Vietnam.nc</em>” (in NetCDF – format) contains the 3D voxel model of: variable called “Etype_estimate_TDS”, representing the average concentration (in g/L) of Total Dissolved Solids (TDS) variable called “Probability_TDS_smaller_1g_L”, representing the probability of TDS &lt; 1g/L for each hydrogeological unit of the “hydrogeology_Mekong_Vietnam.nc file. Only the deepest units (N13 and n13) are not included due to lack of basic data. The coordinate system is WGS84m - UTM 48 N. The voxel size is 1000 m x 1000 m x 5 m. The accompanying paper (preprint, by the same authors) can be found at https://doi.org/10.5194/essd-2021-15 and is called "The 3D groundwater salinity distribution and fresh groundwater volumes in the Mekong Delta, Vietnam, inferred from geostatistical analyses"
DOI: 10.5281/zenodo.5585572
title: CAIRDIO simulation results and air-quality observations for Leipzig

description: This dataset contains results with the model CAIRDIO applied for a simulation case study of Leipzig from 1-3 March 2020, and measured concentration vs. time series of black carbon and particulate matter at 5 different air-monitoring sites for comparison.
DOI: 10.5281/zenodo.5602935
title: Sentinel-5p/TROPOMI SO2 Layer Height dataset covering the Raikoke volcanic eruption 2019

description: Sentinel-5p/TROPOMI SO2 Layer Height product generated by DLR as part of the INPULS project using the retrieval algorithm developed in the framework of the ESA Sentinel-5p Innovations: SO2 LH (S5P+I: SO2LH) project The dataset contains SO2LH results for the timeframe 2019-06-22 until 2019-07-30 covering the eruptive period of the Raikoke volcanic eruption. This dataset was used as input for the paper of Inness et al. "The CAMS volcanic forecasting system utilizing near-real time data assimilation of S5P/TROPOMI SO2 retrievals" (2021, submitted to GMD) The dataset contains modified Copernicus Sentinel data processed by DLR
DOI: 10.5281/zenodo.7187505
title: Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation"

description: Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation" Yuhang Zhang1, Aizhong Ye1*, Phu Nguyen2, Bita Analui2, Soroosh Sorooshian2, Kuolin Hsu2 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China. 2 Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, CA 92697, USA. ## Dataset Streamflow simulations from one observed precipitation (CMA) and three satellite precipitation products (PDIR, IMERG-F, and GSMaP) for 522 sub-basins. - Q-CMA (streamflow reference)<br> - Q-PDIR (uncorrected)<br> - Q-IMERGF (uncorrected)<br> - Q-GSMAP (uncorrected) ### Data structure - Head section (row1-row5)<br> - SubNO: 522 <br> - BeginT: 2003-01-01 00:00 <br> - EndT: 2019-12-31 00:00 <br> - Interval: 1440s (daily)<br> - Revise: 10 (scaling factor to keep int datatype)<br> - Point1 Point2 ... (Subbasin No.)<br> - Data section<br> - 6209 rows, 522 cols ## Results Two post-processing model results for test period (2015-1-1 to 2018-12-31). ### Data structure - 1462 rows, every row denotes each day from 2015-1-1 to 2018-12-31 - 100 columns, every column denotes each quantile from 0.005 to 0.995, total 100 quantiles. ### qrf-output - pdir (single input)<br> - imergf (single input)<br> - gsmap (single input)<br> - all (multiple inputs) ### lstm-output - pdir (single input)<br> - imergf (single input)<br> - gsmap (single input)<br> - all (multiple inputs)
DOI: 10.5281/zenodo.7612243
title: Output of several experiments with Fenics_ice over Smith, Pope, and Kohler Glaciers

description: Output of several experiments with Fenics_ice over Smith, Pope, and Kohler Glaciers. The code to produce this output can be found in the following repository; Smith_glacier More information on how to read and plot this data can be found in the smith glacier repository wiki: Citation for the smith glacier repository used to produce this data: <pre>https://zenodo.org/badge/latestdoi/101511241</pre> Citation for the Fenics_ice version used to produce this data: <pre>https://zenodo.org/badge/latestdoi/417440075</pre>
description: Eigenvectors from all the experiments in the repository are not shared here, due to limited storage but they can be requested from the authors without conditions.
DOI: 10.5281/zenodo.8282997
title: Dataset supporting the article: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e

description: This dataset contains the measurement data, model outputs and Python (v3.10) scripts that are used to produce figures and tables in the paper: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e. The newly created module providing the interface with Cloud-J in the EMEP MSC-W and BoxChem models is called "CloudJ_mod.f90." The modules from the Cloud-J code itself are gathered in the "ModsCloudJ_mod.f90" file in the CloudJ sub-folders. The measurement data and supporting MATLAB scripts used to create the ATom-1 data files read in by the Python scripts can be downloaded from https://doi.org/10.3334/ORNLDAAC/1651
DOI: 10.6084/m9.figshare.17206835.v3
title: Soil erosion and pioneer vegetation in temperate forests

description: The data contain the results on surface runoff, sediment discharge, and vegetation development in four different skid trails of a Central European temperate forest.<br>
DOI: 10.7278/s50d-bpx8-gmtt
title: Dataset for: Natural marine cloud brightening in the Southern Ocean
DOI: 10.1594/pangaea.910176
title: Map of Ecosystems Types in Sierra Nevada mountain (southern Spain)

description: Map showing the spatial distribution of ecosystems identified in Sierra Nevada high mountain (southern Spain). Several sources were used to generate spatial distribution of ecosystem types. The Land Cover and Use Information System of Spain (SIOSE)(https://www.siose.es/) was used as base spatial information. Other technical reports also supported the spatialization and identification of the ecosystem types (e.g. Molero-Mesa, J.; Pérez Raya, F.; López Nieto, J.M.; El Aallali, A. & Hita Fernández, J.A. (2001). Cartografía y evaluación de la vegetación del Parque Natural de Sierra Nevada [Mapping and assessment of the vegetation of the Sierra Nevada Nature Reserve]. Department of the Environment. Regional Government of Andalusia). The main ecosystem types identified were: natural forests (Holm oak and Pyrenean oak forests [Quercus ilex and Q. pyrenaica], Scot pine forests [Pinus sylvestris var. nevadensis]), high-mountain shrublands (Juniperus communis), pine plantations, high-mountain grasslands ("borreguiles) among others. The data generated is stored as a vectorial file (shapefile). The coordinate reference system is UTM Zone 30N WGS84) (epsg:23030).
DOI: 10.21947/1373097
title: Owner Reports - Airfoil Performance Degradation due to Roughness and Leading-edge Erosion, data and plots - Raw Data

description: Airfoil Performance Degradation due to Roughness and Leading-edge Erosion. The zip file contains analysis, charts, and photos.
DOI: 10.25925/20180817
title: Multi-laboratory compilation of atmospheric sulfur hexafluoride data for the period 1983-2017; obspack_sf6_1_v2.1.1_2018-08-17

description: A full list of all contributors for this product can be found at www.esrl.noaa.gov/gmd/ccgg/obspack/providerlist/obspack_sf6_1_v2.1.1_2018-08-17.html
<br>
This product is constructed using the Observation Package (ObsPack) framework [Masarie et al., 2014; www.earth-syst-sci-data.net/6/375/2014/]. The framework is designed to bring together atmospheric greenhouse gas (GHG) observations from a variety of sampling platforms, prepare them with specific applications in mind, and package and distribute them in a self-consistent and well-documented product. ObsPack products are intended to support GHG budget studies and represent a new generation of cooperative value-added GHG data products. This product includes 224 atmospheric sulfur hexafluoride datasets derived from observations made by 12 laboratories. Data for the period 1983-2017 (where available) are included.
DOI: 10.26050/wdcc/pdrmip_2012-2021
title: Precipitation Driver Response Model Intercomparison Project data sets 2013-2021

description: Project: Precipitation Driver Response Model Intercomparison Project - PDRMIP is a climate model intercomparison project that ran from the end of 2013 until completion in 2021. In PDRMIP a number of different climate models have been used to explore whether differences in precipitation at present and future projections can be linked to differences in forcing mechanisms. The fact that various climate forcers (e.g. CO2, BC, sulphate) have different influences on precipitation was explored in e.g. Andrews et al. (2010) https://doi.org/10.1029/2010GL043991, Kvalevåg et al (2013) https://doi.org/10.1002/grl.50318, Ming et al (2010) https://doi.org/10.1029/2010GL042895 and Wu et al (2013) https://doi.org/10.1038/nclimate1932 . PDRMIP built upon these studies in trying to understand more of the differences in precipitation and extreme precipitation in climate models by the use of idealized experiments where the input data for each model has been nearly identical. Eleven climate models and modelling communities participated in the PDRMIP project. The models that performed the experiments and delivered data are: NCAR CESM1/CAM4, NCAR CESM1/CAM5, HadGEM2, HadGEM3, GISS-E2-R, MIROC-SPRINTARS, IPSL-CM5, NorESM1, CanESM2, MPI-ESM and ECHAM-HAM. Funding for PDRMIP was provided through the Norwegian research council projects SUPER and NAPEX and storage infrastructure for the project during operations was provided through the storage project NS9042K on UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway. Further details on the project can be found on the project web page https://cicero.oslo.no/en/PDRMIP . Summary: Data output from the Precipitation Driver Response Model Intercomparison Project (PDRMIP). A set of 6 core experiments (a base, co2x2, ch4x3, solar, bcx10, sulx5 where the solar experiment has increased incoming solar radiation), 5 regional experiments (bcx10asia, sulx10asia, sulx10eur, sulred, sulasiared) and 7 phase 2 experiments (base2, cfc12, cfc11, n2o1p, ozone, lndus, bcslt) have been run by one or more of the participating models; CanESM2, MPI-ESM, NorESM1, NCAR-CESM1-CAM4, NCAR-CESM1-CAM5, MIROC-SPRINTARS, HadGEM2, HadGEM3, GISS-E2-R, IPSL-CM5A, ECHAM-HAM. Each of the experiments has been run (for the most part) both in coupled and fixed sst ocean setups. Time designations varry from model to model, however, all models have ran the coupled ocean experiments for 100 years and 15 years in the fixed sst experiments. Outputs varry between models, but include 2D and 3D monthly variables, 2D daily variables and fixed 2D fields.
DOI: 10.4121/922d30de-a26b-4c81-8644-ad036182239c
title: Data and code underlying the publication: Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability has minor effects on the hydrological response in the Neckar basin, Germany

description: The semi-distributed hydrological model codes, which can be used to estimate the root zone storage capacity based on calibration. The model codes are also used to quantity the effects of time-variant root zone storage capacity on hydrological response.
DOI: 10.5067/2cix82huv88y
title: IceBridge BedMachine Greenland, Version 3
DOI: 10.5067/l0t5geg1nyfa
title: MERRA-2 tavg1_2d_lfo_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Forcings V5.12.4
DOI: 10.5281/zenodo.3611127
title: CLIMPY: Climate of the Pyrenees

description: CLIMPY (Characterisation of the evolution of climate and provision of information for adaptation in the Pyrenees) is a transboundary project that aimed to perform a detailed analysis of recent trends in temperature, precipitation and snow cover in the Pyrenees, and their future projection. The three files provided contain daily precipitation and maximum and minimum temperature in a high-resolution gridded dataset of 1x1 km spatial resolution for the 1981-2015 period. Daily estimates for each grid point were computed from the raw data of 1,343 stations located in Spain, France and Andorra, provided by national and regional meteorological services. 7 institutions worked together (University of Zaragoza (UNIZAR), Servei Meteorològic de Catalunya (SMC), Andorran Research Institut (IEA-CENMA), Instituto Pirenaico de Ecología (IPE-CSIC), Centre d’Études Spatiales (CESBIO), Agencia Estatal de Meteorología (AEMET), Météo-France (MF)) using the daily precipitation and temperature reconstruction (quality control, reconstruction and gridding) from Serrano-Notivoli et al. (2017 and 2019) (https://doi.org/10.5194/essd-9-721-2017 and https://doi.org/10.5194/essd-11-1171-2019).
DOI: 10.5281/zenodo.3957826
title: Emissions changes in 2020 due to Covid19

description: <strong>Emissions and climate effects due to the COVID-19 response</strong> Both sector and national emission files are given as well as the code to create them from mobility data Sector csv files have aviation and shipping by nation. National csv files have international aviation and shipping as a separate nation. See https://github.com/Priestley-Centre/COVID19_emissions for more details Monthly emissions and concentrations for the effects of the Covid-19 pandemic and resulting society response. All scenarios should be compared to the input4MIPS SSP245 baseline https://esgf-node.llnl.gov/search/input4mips/ Four scenarios are given for departures from the SSP245 baseline. The baseline is also provided for convenience. Scenarios depart after the start of 2020. Monthly concentrations of CO2, CH4 and N2O are provided over 2015-2050. For short-lived species emissions are provided for BC, CO, OC, SO2, NH3, NOx, & NMVOCs. Monthly values for years:2015,2019,2020,2021,2023,2030,2040 and 2050 are provided for the four scenarios and baseline. The SSP245 baseline is also available from the input4MIPS website. NOx emissions from aviation is provided in monthly values for years:2015,2019,2020, 2021, 2022, 2023,2030,2040 and 2050. Well-mixed greenhouse gas emissions are provided for North, South and global average concentration of CO2, CH4 and N2O for all months 2015-2050 Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) http://constrain-eu.org/ Contact Piers Forster (p.m.forster@leeds.ac.uk) for more details. www.climate.leeds.ac.uk Paper reference is <strong>Forster, P.M., Forster, H.I., Evans, M.J. <em>et al.</em> Current and future global climate impacts resulting from COVID-19. <em>Nat. Clim. Chang.</em> (2020). https://doi.org/10.1038/s41558-020-0883-0</strong>
description: Version 4 has smoother treatment of oceans, using a global value for locations not overlapping with the coast of a country (coastal locations us the country-specific modifiers). From Version 2, more years are provided via interpolation. The baseline data is also provided for convenience and interpolated onto same years. There is also a bug fix for the aerosol precursor files that affected July 2020-Dec 2021
DOI: 10.5281/zenodo.4288883
title: The consolidated European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2018

description: This dataset contains all data from figures (in csv format) and figures from the ESSD submitted paper: "<strong>The consolidated European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2018"</strong>, essd-2020-376 Petrescu, A. M. R., McGrath, M. J., Andrew,<sup> </sup>R. M., Peylin, P., Peters, G. P., Ciais, P., Broquet, G., Tubiello, F. N., Gerbig, C., Pongratz, J., Janssens-Maenhout, G., Grassi, G, Nabuurs, G.-J., Regnier, P., Lauerwald, R., Kuhnert, M.,<sup> </sup>Balkovič, J., Schelhaas, M. J., Denier van der Gon, H. A. C., Solazzo, E., Qiu, C., Pilli, R., Konovalov,<sup> </sup>I. B., Houghton, R., Günther, D., Perugini, L., Crippa, M., Ganzenmüller, R., Luijkx, I. T., Smith, P., Munassar, S., Thompson, R.L., Conchedda, G., Monteil, G., Scholze, M., Karstens, U., Brokmann, P. and Dolman, A. J.: The consolidated European synthesis of CO<sub>2</sub> emissions and removals for EU27 and UK: 1990-2018, Earth Syst. Sci. Data Discuss., essd-2020-376, in review, 2020.
DOI: 10.5439/1046211
title: ARM: Microwave Water Radiometer (MWR): water liq. and vapor along line of sight (LOS) path

description: Microwave Water Radiometer (MWR): water liq. and vapor along line of sight (LOS) path
DOI: 10.5439/1779709
title: ARM: LD

description: ldis
DOI: 10.15468/dl.4fxx5d
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bomolochus bramus Ho & Lin, 2009",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004556-250123221155621/datasets/export for details.
DOI: 10.1594/pangaea.900442
title: Aircraft measurements of broadband irradiance during the ACLOUD campaign in 2017

description: Solar and terrestrial broadband irradiance and nadir brightness temperature observed during the ACLOUD (Arctic CLoud Observations Using airborne measurements during polar Day) campaign in May / June 2017.
DOI: 10.5067/9t4epqxtjyw9
title: MEaSUREs Annual Antarctic Ice Velocity Maps, Version 1
DOI: 10.5067/jti5yg3s6vaj
title: ICESat-2 L4 Along-Track Sea Ice Thickness, Version 1
DOI: 10.5067/oe8bdp5ku30q
title: ICESat-2 L4 Monthly Gridded Sea Ice Thickness, Version 2
DOI: 10.5281/zenodo.14039035
title: Experimental data and processing scripts of chirp ENDOR spectra

description: Experimental data and MATLAB processing scripts for the manuscript:"Increased sensitivity in Electron Nuclear Double Resonance spectroscopy with chirped radiofrequency pulses"
DOI: 10.5281/zenodo.4297010
title: Carbonyl Sulfide (OCS/COS) and Carbon Disulfide (CS2): global modelled marine surface concentrations and emissions, 2000-2019

description: This dataset contains a global ocean emission inventory of the sulfur-containing trace gases carbonyl sulfide (OCS/COS) and carbon disulfide (CS2). It covers the period 2000-2019, and includes a monthly average and an average diel cycle for each month for sea surface concentrations and emissions to the atmosphere. The spatial resolution is 2.8° x 2.8° at the equator (T42 grid), the depth extends from the surface to the mixed layer depth. Carbonyl sulfide (OCS) is the most abundant, long-lived sulphur gas in the atmosphere and a major supplier of sulfur to the stratospheric sulfate aerosol layer. The short-lived gas carbon disulfide (CS<sub>2</sub>) is oxidized to OCS and constitutes a major indirect source to the atmospheric budget of OCS. We encourage the use of the data provided here as input for atmospheric modelling studies to further assess the atmospheric OCS budget and the role of OCS in climate.
DOI: 10.5281/zenodo.4737769
title: CEDS v_2021_04_21 Release Emission Data

description: Emissions data files by emission species (SO2, NOx, BC, OC, NH3, NMVOC, CO, CO2, CH4, N2O), country, and sector produced by the April-21-2021 release of CEDS. This release updates some emissions species for four isos (Australia, Canada, South Korea, and Taiwan). See the CEDS GitHub site for details including journal paper reference information and any known issues with this data.
description: See the May 6 version of this dataset, which adds files missing from this version.
description: {"references": ["Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q. (2018) Historical (1750\u20132014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369-408. doi: 10.5194/gmd-11-369-2018.", "Hoesly, R. P. O'Rourke, C. Braun, L. Feng, S. J. Smith, T. Pitkanen, J. J. Seibert, L. Vu, M. Presley, R. Bolt, B. Goldstein. (2019, December 23). JGCRI/CEDS: v-12-23-2019 (Version Dec-23-2019). Zenodo. http://doi.org/10.5281/zenodo.3592073"]}
DOI: 10.5281/zenodo.6958047
title: Passive gas plume database for metrics comparison

description: Plume database used for the evaluation of different metrics that are presented in the submitted paper "New plume comparison metrics for the inversion of passive gases emissions". The synthetical CO2 plumes presented in the NetCDF file entitled "Synthetical_CO2_plume_database.nc" are the results of chemical transport model simulations described in the preprint available here https://amt.copernicus.org/preprints/amt-2022-48/.