Errata: Due to a coding error, monthly files with "dma8epax" statistics were wrongly aggregated. This concerns all gridded files of this metric as well as the monthly aggregated csv files. All erroneous files were replaced with corrected versions on Jan, 16th, 2018. Each updated file contains a version label "1.1" and a brief description of the error. If you have made use of previous TOAR data files with the "dma8epax" metric, please exchange your data files.

This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for the analysis, Experiment A, Experiment B, Experiment C, Experiment D, and Experiment AuxIn. Experiment A tested multi-task learning for predicting streamflow with 25 years of training data and using a different model for each of 101 sites. Experiment B tested multi-task learning for predicting streamflow with 25 years of training data and using a single model for all 101 sites. Experiment C tested multi-task learning for predicting streamflow with just 2 years of training data. Experiment D tested multi-task learning for predicting water temperature with over 25 years of training data. Experiment AuxIn used water temperature as an input variable for predicting streamflow. These experiments and their results are described in detail in the WRR paper. Data from a total of 101 sites across the US was used for the experiments. The model input data and streamflow data were from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset (Newman et. al 2014, Addor et. al 2017). The water temperature data were gathered from the National Water Information System (NWIS) (U.S. Geological Survey, 2016). The contents of this item are broken into 13 files or groups of files aggregated into zip files:

1. input_data_processing.zip: A zip file containing the scripts used to collate the observations, input weather drivers, and catchment attributes for the multi-task modeling experiments
2. flow_observations.zip: A zip file containing collated daily streamflow data for the sites used in multi-task modeling experiments. The streamflow data were originally accessed from the CAMELs dataset. The data are stored in csv and Zarr formats.
3. temperature_observations.zip: A zip file containing collated daily water temperature data for the sites used in multi-task modeling experiments. The data were originally accessed via NWIS. The data are stored in csv and Zarr formats.
4. temperature_sites.geojson: Geojson file of the locations of the water temperature and streamflow sites used in the analysis.
5. model_drivers.zip: A zip file containing the daily input weather driver data for the multi-task deep learning models. These data are from the Daymet drivers and were collated from the CAMELS dataset. The data are stored in csv and Zarr formats.
6. catchment_attrs.csv: Catchment attributes collatted from the CAMELS dataset. These data are used for the Random Forest modeling. For full metadata regarding these data see CAMELS dataset.
7. experiment_workflow_files.zip: A zip file containing workflow definitions used to run multi-task deep learning experiments. These are Snakemake workflows. To run a given experiment, one would run (for experiment A) 'snakemake -s expA_Snakefile --configfile expA_config.yml'
8. river-dl-paper_v0.zip: A zip file containing python code used to run multi-task deep learning experiments. This code was called by the Snakemake workflows contained in 'experiment_workflow_files.zip'.
9. random_forest_scripts.zip: A zip file containing Python code and a Python Jupyter Notebook used to prepare data for, train, and visualize feature importance of a Random Forest model.
10. plotting_code.zip: A zip file containing python code and Snakemake workflow used to produce figures showing the results of multi-task deep learning experiments.
11. results.zip: A zip file containing results of multi-task deep learning experiments. The results are stored in csv and netcdf formats. The netcdf files were used by the plotting libraries in 'plotting_code.zip'. These files are for five experiments, 'A', 'B', 'C', 'D', and 'AuxIn'. These experiment names are shown in the file name.
12. sample_scripts.zip: A zip file containing scripts for creating sample output to demonstrate how the modeling workflow was executed.
13. sample_output.zip: A zip file containing sample output data. Similar files are created by running the sample scripts provided.

These four NetCDF databases constitute the bulk of the spatial and spatiotemporal environmental covariates used in a latent health factor index (LHFI) model for assessment and prediction of ecosystem health across the MDB. The data formatting and hierarchical statistical modelling were conducted under a CSIRO appropriation project funded by the Water for a Healthy Country Flagship from July 2012 to June 2014. Each database was created by collating and aligning raw data downloaded from the respective state government websites (QLD, NSW, VIC, and SA). (ACT data were unavailable.) There are two primary components in each state-specific database: (1) a temporally static data matrix with axes "Site ID" and "Variable," and (2) a 3D data cube with axes "Site ID", "Variable," and "Date." Temporally static variables in (1) include geospatial metadata (all states), drainage area (VIC and SA only), and stream distance (SA only). Temporal variables in (2) include discharge, water temperature, etc. Missing data (empty cells) are highly abundant in the data cubes. The attached state-specific README.pdf files contain additional details on the contents of these databases, and any computer code that was used for semi-automation of raw data downloads. Lineage: (1) For NSW I created the NetCDF database by (a) downloading CSV raw data from the NSW Office of Water real-time data website (http://realtimedata.water.nsw.gov.au/water.stm) during February-April 2013, then (b) writing computer programs to preprocess such raw data into the current format. (2) The same was done for QLD, except through the Queensland Water Monitoring Data Portal (http://watermonitoring.derm.qld.gov.au/host.htm). (3) The same was also done for SA, except through the SA WaterConnect => Data Systems => Surface Water Data website (https://www.waterconnect.sa.gov.au/Systems/SWD/SitePages/Home.aspx) during April 2013 as well as May 2014. (4) For Victoria I created the NetCDF database by (a) manually downloading XLS raw data during November and December in 2013 from the Victoria DEPI Water Measurement Information System => Download Rivers and Streams sites website (http://data.water.vic.gov.au/monitoring.htm), then (b) writing computer programs to preprocess such raw data into CSV format (intermediate), then into the current final format.

Additional details on lineage are available from the attached README.pdf files.

This script extracts hydrological variables (e.g., qtot, qs, qg, airruse) from NetCDF (.nc) files and processes them into annual water resource indicators at the provincial level in China. The workflow includes:Reading NetCDF files and selecting the target variable (qtot, qs, qg, airruse).Converting monthly data into annual sums.Mapping grid-based values to Chinese provinces using a GeoJSON boundary file.Aggregating results by province and adjusting with provincial area data.Exporting the final annual provincial dataset to CSV format.

Annual mean (taken over 2000-2019) SST data from ECMWF ERA5 driven simulation of S2P3Rv2.0 in waters between 10 and 100m depth between 65 S and 65 N.

Data provided as netcdf (NETCDF4) and CSV files

Warm season* rainfall for the eight Natural Resource Management (NRM)** regions ---------------------------------------------------------------------------File format provided: netcdf and csv format

  ---------------------------------------------------------------------------1) Instrumental regional averages for each NRM region (1900-2015) based on the gridded Australian Water Availability Project (AWAP)2) Reconstructed regional averages for each NRM region (1200-2015) based on a network of palaeoclimate records from the Southern Hemisphere---------------------------------------------------------------------------* Warm season (average precipitation in: Oct, Nov, Dec, Jan, Feb, Mar)** NRM regions are defined by the CSIRO and Bureau of Meteorology (https://www.climatechangeinaustralia.gov.au/en/climate-projections/about/modelling-choices-and-methodology/regionalisation-schemes/) and should cited as followed: CSIRO and Bureau of Meteorology 2015, Climate Change in Australia Information for Australia's Natural Resource Management Regions: Technical Report, CSIRO and Bureau of Meteorology, Australia,2015.Regions: CS-Central SlopesEC-East CoastMB-Murray BasinMN-Monsoonal NorthR-RangelandsSS-Southern SlopesSSWF-Southern and South-Western FlatlandsWT-Wet TropicsReference and details in: Freund, M., Henley, B. J., Karoly, D. J., Allen, K. J. and Baker, P. J.: Multi-century cool- and warm-season rainfall reconstructions for Australia's major climatic regions, Clim. Past, 13(12), 1751–1770, doi:10.5194/cp-13-1751-2017, 2017.

Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format

TL;DR: this is a tidy and friendly version of a recreation of ENTSO-E's PECD 2021.3 data by using ERA5: hourly capacity factors for wind onshore, offshore, solar PV and hourly electricity demand are provided. All the data is provided for 28-71 climatic years (1950-2020 for wind and solar, 1982-2010 for demand).

Description
Country averages of energy-climate variables generated using the Python scripts, based on the ENTSO-E's TYNDP 2020 study. For the following scenario's data is available

• National trends 2025 (NT 2025)
• National trends 2030 (NT 2030)
• National trends 2040 (NT 2040)
• Distributed Energy 2030 (DE 2030)
• Distributed Energy 2040 (DE 2040)
• Global Ambitions (GA 2030)
• Global Ambitions (GA 2040)

The time-series are at hourly resolution and the included variables are:

• Generation wind offshore (aggregated for all years per scenario in a .zip)
• Generation wind onshore (aggregated for all years per scenario in a .zip)
• Generation solar photovoltaic (aggregated for all years per scenario in a .zip)
• Total energy demand (all zones combined in single file per scenario)

The Files are provided in CSV (.csv) & NetCDF (.nc). The data is given per ENTSO-E's bidding zone as used within the TYNDP2020.

DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies.

This dataset contains aerosol sub- and super- micron nss sulfate, MSA, ammonium and other major ion measurements, using 7-stage multi-jet cascade impactors, taken aboard the Ron Brown ship during the ACE-Asia field project. This dataset contains the netCDF data files. Data can also be downloaded in a comma delimited (.csv) format. A readme in PDF format accompanies the dataset when ordered.

Published empirical algorithms for oceanic total alkalinity (TA) and dissolved inorganic carbon (DIC) are used with monthly sea surface salinity (SSS) and temperature (SST) derived from satellite (SMOS, Aquarius, SST CCI) and interpolated in situ (CORA) measurements and climatological (WOA) ancillary data to produce monthly maps of TA and DIC at one degree spatial resolution. Earth system model TA and DIC (HADGEM2-ES) are also included. Results are compared with in situ (GLODAPv2) TA and DIC and results analysed in five regions (global, Greater Caribbean, Amazon plume, Amazon plume with in situ SSS < 35 and Bay of Bengal). Results are presented in three versions, denoted by 'X' in the lists below: using all available data (X = ''); excluding data with bathymetry < 500m (X = 'Depth500'); excluding data with both bathymetry < 500m and distance from nearest coast < 300 km (X = 'Depth500Dist300'). Datasets S1 to S5 are .csv lists of matchups in each region - date and location, in situ TA and DIC measurements and estimated uncertainties, all input datasets, estimates of TA and DIC from all outputs, and the best available output estimates of TA and DIC for each matchup. S1_GlobalAlgorithmMatchupsX.csv S2_GreaterCaribbeanAlgorithmMatchupsX.csv S3_AmazonPlumeAlgorithmMatchupsX.csv S4_AmazonPlumeLowSAlgorithmMatchupsX.csv S5_BayOfBengalAlgorithmMatchupsX.csv Datasets S6 to S10 are .csv statistical analyses of the performance of each combination of algorithm and input data - carbonate system variable, algorithm, input datasets used, (MAD, RMSD using all available data, output score, RMSD estimated from output score, output and in situ mean and standard deviation, correlation coefficient), all items in brackets presented both unweighted and weighted, number of matchups, number of potential matchups, matchup coverage, RMSD after subtraction of linear regression, percentage reduction in RMSD due to subtraction of linear regression and weighted score divided by number of matchups). S6_GlobalAlgorithmScoresX.csv S7_GreaterCaribbeanAlgorithmScoresX.csv S8_AmazonPlumeAlgorithmScoresX.csv S9_AmazonPlumeLowSAlgorithmScoresX.csv S10_BayOfBengalAlgorithmScoresX.csv Datasets S11 to S15 are zipped netCDF files containing error analyses of all outputs in each region, including the squared error of each output at each matchup, the weight of each squared error (1/squared uncertainty), weight * squared error, number of matchups available to each output, number of matchups available to each combination of two outputs, (score of each output in a given comparison of two outputs, overall output score and RMSD estimated from output score), all items in the last brackets presented both unweighted and weighted. S11_GlobalSquaredErrorsX.nc S12_GreaterCaribbeanSquaredErrorsX.nc S13_AmazonPlumeSquaredErrorsX.nc S14_AmazonPlumeLowSSquaredErrorsX.nc S15_BayOfBengalSquaredErrorsX.nc Datasets S16 to S20 are zipped netCDF files containing global maps of the mean and standard deviation of each of: in situ data; output data; output data - in situ data and number of matchups. Regional files show the same maps, but only including data within the region. S16_GlobalmapsX.nc S17_GreaterCaribbeanmapsX.nc S18_AmazonPlumemapsX.nc S19_AmazonPlumeLowSmapsX.nc S20_BayOfBengalmapsX.nc Datasets S21 and S22 are .csv files containing the effect on estimated RMSD of excluding various combinations of algorithms and/or inputs for TA and DIC in each region. For a given variable and region, the first line shows the algorithm, input data sources, estimated RMSD and bias of the output with lowest estimated RMSD. Subsequent lines show the effect of excluding combinations of algorithms and/or inputs, ordered first by the number of algorithms/inputs excluded (fewest first), then by effect on lowest estimated RMSD. So the first line(s) consist of the effects of excluding the best algorithm and each of the input sources to that algorithm, most important first. Each line consists of the item excluded, ratio of resulting estimated RMSD to original estimated RMSD, resulting bias and number of items excluded. Some exclusions are equivalent, for instance exclusion of WOA nitrate (the only nitrate source) is equivalent to excluding all algorithms using nitrate. Dataset S21 contains a comprehensive list of all possible exclusions, and so is rather hard to read and interpret. To mitigate this, Dataset S22 contains only those exclusion sets with effect greater than 1% and at least 0.1% greater than any subset of its exclusions. S21_importancesX.csv S22_importances2X.csv Dataset S23 is a .csv file containing like-for-like comparisons of RMSD between TA and DIC in each region. Bear in mind that the RMSD shown here is not the same as the estimated RMSD (RMSDe) shown elsewhere. S23_TA_DICcomparisonX.csv

Version 2 of the dataset has been superseded by a newer version. Users should not use version 2 except in rare cases (e.g., when reproducing previous studies that used version 2). The International Best Track Archive for Climate Stewardship (IBTrACS) dataset was developed by The NOAA National Climatic Data Center, which took the initial step of synthesizing and merging best track data from all official Tropical Cyclone Warning Centers (TCWCs) and the WMO Regional Specialized Meteorological Centers (RSMCs) who are responsible for developing and archiving best track data worldwide. In recognizing the deficiency in global tropical cyclone data, and the lack of a publically available dataset, the IBTrACS dataset was produced, which, for the first time, combines existing best track data from over 10 international forecast centers. The dataset contains the position, maximum sustained winds, minimum central pressure, and storm nature for every tropical cyclone globally at 6-hr intervals in UTC. Statistics from the merge are also provided (such as number of centers tracking the storm, range in pressure, median wind speed, etc.). The dataset period is from 1848 to the present with dataset updates performed semi-annually--in the boreal spring following the completion of the Northern Hemisphere TC season and in the boreal autumn following the completion of the Southern Hemisphere TC season. The dataset is archived as netCDF files but can be accessed via a variety of user-friendly formats to facilitate data analysis, including netCDF and CSV formatted files. Version 2 changes include source data updates, bug fixes, adjustments and corrections as well as additional source datasets.

Surface meteorological data at five minute temporal resolution from the weather stations that comprise the New York State Mesonet. Data are available in either NetCDF or CSV (comma-delimited ASCII) format for the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms 2020 (IMPACTS 2020) campaign.

On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the _location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point _location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point _location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States

This resource contains "RouteLink" files for version 2.1.6 of the National Water Model which are used to associate feature identifiers for computational reaches to relevant metadata. These data are important for comparing NWM feature data to USGS streamflow and lake observations. The original RouteLink files are in NetCDF format and available here: https://www.nco.ncep.noaa.gov/pmb/codes/nwprod

This resource includes the files in a human-friendlier CSV format for easier use, and a machine-friendlier file in HDF5 format which contains a single pandas.DataFrame. The scripts and supporting utilities are also included for users that wish to rebuild these files. Source code is hosted here: https://github.com/jarq6c/NWM_RouteLinks

Data and code for "Spatial and temporal expansion of global wildland fire activity in response to climate change" by Martin Senande-Rivera, Damian Insua-Costa and Gonzalo Miguez-Macho Data formats: netcdf and csv Codes: Python v3.8

General circulation model simulations have been performed over the Phanerozoic (from 540 to 0 million years ago, every 20 million years) using the FOAM model. Simulated continental climatic fields and Köppen-Geiger climatic zones are provided in the form of individual files (1 per geological age, 2 formats: NetCDF and CSV), and corresponding .zip archives containing NetCDF and CSV files for every geological age.

In support of the first Tropospheric Ozone Assessment Report (TOAR) a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone metrics products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote. Exploitation of these global metadata allow for new insights into the global distribution, and seasonal and long-term changes of tropospheric ozone. Cooperation among many data centers and individual researchers worldwide made it possible to build the world's largest collection of in-situ hourly surface ozone data covering the period from 1970 to 2015. Considerable effort was made to harmonize and synthesize data formats and metadata information from various networks and individual data submissions. Extensive quality control was applied to identify questionable and erroneous data, including changes in apparent instrument offsets or calibrations. Such data were excluded from TOAR data products. Limitations of a posteriori data quality assurance are discussed. As a result of the work presented here, global coverage of surface ozone data has been significantly extended. Yet, large gaps remain in the surface observation network both in terms of regions without monitoring, and in terms of regions that have monitoring programs but no public access to the data archive. Therefore future improvements to the database will require not only improved data harmonization, but also expanded data sharing and increased monitoring in data-sparse regions.

****** UPDATE 05/01/2025

ERA5-Land forcings can be downloaded here: https://doi.org/10.5281/zenodo.15264814

******

The current version of the CAMELSH dataset, containing data for 9,008 basins,. Due to the total data volume in the repository being approximately 57 GB, which exceeds Zenodo's size limit, we split it into two different links. The first link (https://doi.org/10.5281/zenodo.15066778) contains data on attributes, shapefiles, and time series data for the first set of basins. The second link (https://doi.org/10.5281/zenodo.14889025) contains forcing (time series) data for the the remaining basins. All data is compressed in 7zip format. After extraction, the dataset is organized into the following subfolders:

• The attributes folder contains 28 CSV (comma-separated values) files that store basin attributes with all files beginning with "attributes_" and one excel file. Of these, the 'attributes_nldas2_climate.csv' file contains nine climate attributes (Table 2) derived from NLDAS-2 data. The 'attributes_hydroATLAS.csv' file includes 195 basin attributes derived from the HydroATLAS dataset. 26 files with names starting with 'attributes_gageii_' contain a total of 439 basin attributes extracted from the GAGES-II dataset. The name of each file represents a distinct group of attributes, as described in Table S.1. The remaining file, named 'Var_description_gageii.xlsx', provides explanatory details regarding the variable names included in the 26 CSV files, with information similar to that presented in Table S.1. The first column in all CSV files, labeled 'STAID', contains the identification (ID) names of the stream gauges. These IDs are assigned by the USGS and are sourced from the original GAGES-II dataset.
• The shapefiles folder contains two sets of shapefiles for the catchment boundary. The first set, CAMELSH_shapefile.shp, is derived from the original GAGES-II dataset and is used to obtain the corresponding climate forcing data for each catchment. The second set, CAMELSH_shapefile_hydroATLAS.shp, includes catchment boundaries derived from the HydroATLAS dataset. Each polygon in both shapefiles contains a field named GAGE_ID, which represents the ID of the stream gauges.
• The timeseries (7zip) file contains a compressed archive (7zip) that includes time series data for 3,166 basins with observed streamflow data. Within this 7zip file, there are a total of 3,166 NetCDF files, each corresponding to a specific basin. The name of each NetCDF file matches the stream gauge ID. Each file contains an hourly time series from 1980-01-01 00:00:00 to 2024-12-31 23:00:00 for streamflow (denoted as "Streamflow" in the NetCDF file) and 11 climate variables (see Table 1). The streamflow data series includes missing values, which are represented as "NaN". All meteorological forcing data and streamflow records have been standardized to the +0 UTC time zone.
• The timeseries_nonobs (7zip) file contains time series data for the remaining 5,842 basins. The structure of each NetCDF file is similar to the one described above.
• The info.csv file, located in the main directory of the dataset, contains basic information for 9,008 stream stations. This includes the stream gauge ID, the total number of observed hourly data points over 45 years (from 1980 to 2024), and the number of observed hourly data points for each year from 1980 to 2024. Stations with and without observed data are distinguished by the value in the second column, where stations without observed streamflow data have a corresponding value of 0.

The dataset consists of 4 comma-separated value (csv) text files and 3 netCDF data files. Each csv file contains the observed and CMAQ modeled gas and aerosol concentrations collected during the SENEX field campaign. The netCDF files contain ground layer modeled single source contributions. The headers of each file contain the variable names for each column. An additional data dictionary with variable descriptions and units for the csv files is included with the data along with a file detailing the mapping of files to figures in the manuscript.

This dataset is associated with the following publication: Baker, K., and M. Woody. Assessing Model Characterization of Single Source Secondary Pollutant Impacts Using 2013 SENEX Field Study Measurements. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 51(7): 3833-3842, (2017).

March 2025 Update. Here we provide hourly observations of atmospheric carbon dioxide (CO2), methane (CH4), and (for a few sites) carbon monoxide (CO) from tower-based sites in the NIST Northeast Corridor network. Each *.tgz (tar/gzip) archive contains data files for a given site location, named by its 3-letter code. To extract on a unix platform, use "tar -xvzf ". Data files within the -1-hr-CSV.tgz archives are comma delimited (CSV) 1-hour averages; data files within the -1-hr-NC.tgz archives are 1-hour averages in NetCDF format; data files within the -1-min-CSV.tgz are CSV files with 1-minute averages (there is no NetCDF version for 1-minute data). Site locations, heights, and other information are in a separate ascii (CSV) file (NEC_sites.csv), and also within each data file (for the 1-hour averages files). Data in this archive is for January 1 2015-January 31 2025. ASCII Readme files (Readme_v20250319.txt and Readme_1minfiles_v20250319.txt) contain file information and an Updates_v20250319.pdf file that includes additional information on updates. Note about calibrations: CO2 data are reported on the NOAA/WMO X2019 calibration scale. CH4 data are reported on the NOAA/WMO X2004A calibration scale; CO data, where available, are reported on the NOAA/WMO X2014 scale. Note that previous archives of this data reported CO2 on the X2007 scale. This data is being freely distributed for research, academic and related non-commercial purposes consistent with NIST's mandate to further the science and the promulgation of appropriate standards. Current update: March 19, 2025.

Context

From arm.gov/about:

The Atmospheric Radiation Measurement (ARM) user facility is a multi-laboratory, U.S. Department of Energy (DOE) scientific user facility, and a key contributor to national and international climate research efforts.

ARM data are currently collected from three atmospheric observatories—Southern Great Plains, North Slope of Alaska, and Eastern North Atlantic—that represent the broad range of climate conditions around the world, as well as from the three ARM mobile facilities and ARM aerial facilities. Data from these atmospheric observatories, as well as from past research campaigns and the former Tropical Western Pacific observatory, are available at no charge through the ARM Data Center via Data Discovery.

Content

This dataset spans 1-month of data collected in January 2021 from a meteorological tower located at the Atmospheric Radiation Measurement (ARM) User Facility's Southern Great Plains (sgp) site in Oklahoma. This dataset is a reduced version of the sgp1twrmrC1.c1 ARM datastream, available here consisting of two (nearly) identical files: a netCDF (.nc) file, and a csv file. NetCDF is the native data format used by the ARM program and this file contains embedded variable-level and global provenance metadata. The csv file contains a subset of the variables of the netCDF file perceived to be to be useful for analysis but it does not include metadata – this is provided separately.

Each file contains variables reporting temperature, relative humidity, barometric pressure, water vapor pressure, and water vapor mixing ratios at the 2-meter, 25-meter, and 60-meter levels of the meteorological tower at the SGP C1 ARM facility. Data are reported in 1-minute intervals with each point representing the average data value over the preceding minute. The latitude of the tower is 36.605 degrees north and the longitude is -97.485 degrees east (this is useful for determining sunrise/sunset).

Acknowledgements

Data were obtained from the Atmospheric Radiation Measurement (ARM) user-facility, a U.S. Department of Energy (DOE) Office of Science user facility managed by the Biological and Environmental Research Program.

Goals

This dataset is provided without specific questions to answer, rather it is provided as an open-ended way for you to explore a small subset of ARM data and come up with questions or analyses of your own.

Feedback is appreciated – let us know if you would like to see more data like this, if there are certain properties you would like to see, etc.