The National Climate Database (NCDB) is a high resolution, bias-corrected climate dataset consisting of the three most widely used variables of solar radiation- global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI)- as well as other meteorological data. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling. The NCDB is modeled using a statistical downscaling approach with Regional Climate Model (RCM)-based climate projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX; linked below). Daily climate projections simulated by the Canadian Regional Climate Model 4 (CanRCM4) forced by the second-generation Canadian Earth System Model (CanESM2) for two Representative Concentration Pathways (RCP4.5 or moderate emissions scenario and RCP8.5 or highest baseline emission scenario) are selected as inputs to the statistical downscaling models. The National Solar Radiation Database (NSRDB) is used to build and calibrate statistical models.

This dataset replaces the previous Time Bias Corrected Divisional Temperature-Precipitation Drought Index. The new divisional data set (NClimDiv) is based on the Global Historical Climatological Network-Daily (GHCN-D) and makes use of several improvements to the previous data set. For the input data, improvements include additional station networks, quality assurance reviews and temperature bias adjustments. Perhaps the most extensive improvement is to the computational approach, which now employs climatologically aided interpolation. This 5km grid based calculation nCLIMGRID helps to address topographic and network variability. This data set is primarily used by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) to issue State of the Climate Reports on a monthly basis. These reports summarize recent temperature and precipitation conditions and long-term trends at a variety of spatial scales, the smallest being the climate division level. Data at the climate division level are aggregated to compute statewide, regional and national snapshots of climate conditions. For CONUS, the period of record is from 1895-present. Derived quantities such as Standardized precipitation Index (SPI), Palmer Drought Indices (PDSI, PHDI, PMDI, and ZNDX) and degree days are also available for the CONUS sites. In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set. As of November 2018, NClimDiv includes county data and additional inventory files.

The Pan-European Climate Database (PECD) provides information on climate and renewable energy variables for both historical and future time periods. For historical data, the ERA5 global reanalysis serves as the underlying climate data, while future projections are based on selected CMIP6 global climate models. The raw climate model data are further processed through downscaling to achieve higher spatial and temporal resolution and by applying bias adjustment. Each energy variable is derived from the underlying climate data, providing datasets for temperature, total precipitation, surface solar radiation downwards and wind speed, as well as energy-related variables such as wind and solar capacity factors, and hydropower inflows and generation. The PECD dataset has been planned, designed, and produced by the Copernicus Climate Change Service (C3S) in collaboration with the European Network of Transmission System Operators for Electricity (ENTSO-E). This collaboration aims to increase the resilience of energy systems and optimize their performance in response to climate change. The PECD dataset existed prior to this collaboration between C3S and ENTSO-E but did not include future climate change signals. The current PECD dataset produced by C3S is the first to include climate change projections, providing energy analysts, planners, and decision-makers with essential tools for energy planning in the coming decades. The dataset is available in two formats: NetCDF for gridded indicators and CSV for area-averaged indicators. Note: The current dataset includes both PECDv4.1 and PECDv4.2 versions. PECDv4.1 was not extended beyond 2021 as it was frozen leading up to the 2023 European Resource Adequacy Assessment (ERAA) in agreement with ENTSO-E. Additionally, PECDv4.1 version only includes data from three climate models and one emission scenario. In contrast, PECDv4.2 version includes data from six climate models and four emission scenarios, improving uncertainty representation. Best practice favours the use of larger ensembles of models to more thoroughly capture the range of uncertainties present in climate projections. The latter also includes new bias adjustments for wind speed, enhanced energy modelling, extended temporal coverage, and new spatial aggregation zones. It is therefore strongly recommended to use the version PECDv4.2.

SILO is a Queensland Government database containing continuous daily climate data for Australia from 1889 to present, in a number of ready-to-use formats, suitable for modelling and research applications. The SILO database contains two major classes of data: point (station) time series and spatial grids, both based on observed data from the Bureau of Meteorology ADAM (Australian Data Archive for Meteorology) database. For point data, interpolated or derived values are used where observations are missing. Gridded data are spatially interpolated from observations.

In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set. In January 2025, the National Centers for Environmental Information (NCEI) began summarizing the State of the Climate for Hawaii. This was made possible through a collaboration between NCEI and the University of Hawaii/Hawaii Climate Data Portal and completes a long-standing gap in NCEI's ability to characterize the State of the Climate for all 50 states. NCEI maintains monthly statewide, divisional, and gridded average temperature, maximum temperatures (highs), minimum temperature (lows) and precipitation data for Hawaii over the period 1991-2025. As of November 2018, NClimDiv includes county data and additional inventory files In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set.

As of November 2018, NClimDiv includes county data and additional inventory files.

NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).

Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.

Terrestrial CDRs are composed of sensor data that have been improved and quality controlled over time, together with ancillary calibration data.

Abstract

Compilation of Earth Surface temperatures historical. Source: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Documentation

Data compiled by the Berkeley Earth project, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures

%3C!-- --%3E

• LandAverageTemperature: global average land temperature in celsius

%3C!-- --%3E

• LandAverageTemperatureUncertainty: the 95% confidence interval around the average

%3C!-- --%3E

• LandMaxTemperature: global average maximum land temperature in celsius

%3C!-- --%3E

• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature

%3C!-- --%3E

• LandMinTemperature: global average minimum land temperature in celsius

%3C!-- --%3E

• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature

%3C!-- --%3E

• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius

%3C!-- --%3E

• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

%3C!-- --%3E

**Other files include: **

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)

%3C!-- --%3E

• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)

%3C!-- --%3E

• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)

%3C!-- --%3E

• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

%3C!-- --%3E

The raw data comes from the Berkeley Earth data page.

The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. In March 2015, new Alaska data was included in the nClimDiv dataset. The Alaska nClimDiv data were created and updated using similar methodology as that for the CONUS. It includes maximum temperature, minimum temperature, average temperature and precipitation. In January 2025, the National Centers for Environmental Information (NCEI) began summarizing the State of the Climate for Hawaii. This was made possible through a collaboration between NCEI and the University of Hawaii/Hawaii Climate Data Portal and completes a long-standing gap in NCEI's ability to characterize the State of the Climate for all 50 states. NCEI maintains monthly statewide, divisional, and gridded average temperature, maximum temperatures (highs), minimum temperature (lows) and precipitation data for Hawaii over the period 1991-2025.

This data set contains U.S. station surface observations that were digitized from the original forms by the Climate Database Modernization Program (CDMP). Data are available for more than 200 stations (mainly at airports, but also some city weather bureau offices) that made observations at hourly intervals at least during the daytime hours and often over the full 24-hour day. The general period of record for these stations is 1928-1948, though this varies by individual station. To find out what is available, see this inventory [https://rda.ucar.edu/datasets/ds506.0/inventories/sao_inventory.txt]. A significant effort was made by DSS to correct errors in the digitized data, especially dates, times, and pressures. For more information about this work, see this document [https://rda.ucar.edu/datasets/ds506.0/docs/qc-20040110.txt]. We have also received data for more than 130 U.S. stations that were digitized from Form 1001. These stations, which were usually city weather bureau offices, generally took observations once, twice, or four times daily. Some stations have data back as far as late 1892. An inventory [https://rda.ucar.edu/datasets/ds506.0/inventories/form1001_inventory.txt] can be viewed which shows stations and their date range coverage. These data will be made available to the community when errors in the digitized data have been corrected.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Other Collections. The data include parameters of database with a geographic _location of . The time period coverage is from Unavailable begin date to Unavailable end date in calendar years before present (BP). See metadata information for parameter and study _location details. Please cite this study when using the data.

Time series of monthly minimum and maximum temperature, precipitation, and potential evapotranspiration were derived for 1,469 watersheds in the conterminous United States for which stream flow measurements were also available from the national streamflow database, termed the Hydro-Climatic Data Network (HCDN), developed by Slack et al. (1993a,b). Monthly climate estimates were derived for the years 1951-1990.The climate characteristic estimates of temperature and precipitation were estimated using the PRISM (Daly et al. 1994, 1997) climate analysis system as described in Vogel, et al. 1999.Estimates of monthly potential evaporation were obtained using a method introduced by Hargreaves and Samani (1982) which is based on monthly time series of average minimum and maximum temperature data along with extraterrestrial solar radiation. Extraterrestrial solar radiation was estimated for each basin by computing the solar radiation over 0.1 degree grids using the method introduced by Duffie and Beckman (1980) and then summing those estimates for each river basin. This process is described in Sankarasubramanian, et al. (2001). Revision Notes: This data set has been revised to update the number of watersheds included in the data set and to updated the units for the potential evapotranspiration variable. Please see the Data Set Revisions section of this document for detailed information.

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KNMI operates automatic weather stations on land (incl. airports). These weather stations measures meteorological parameters such as temperature, precipitation, wind, air pressure and global radiation. On a daily basis all real-time collected observations and measurements (hourly) are validated on correctness and completeness. The validated data is archived in the Klimatologisch Informatie Systeem (KIS) of KNMI. The daily data is composed from hourly data and each day reference evaporation is calculated using the Makkink method. After the data has been processed and archived in KIS, changes are no longer possible. This assures data integrity.

This NOAA Climate Data Record (CDR) of Zonal Mean Ozone Binary Database of Profiles (BDBP) dataset is a vertically resolved, global, gap-free and zonal mean dataset that was created with a multiple-linear regression model. The dataset has a monthly resolution and spans the period 1979 to 2007. It provides global product in 5 degree zonal bands, and 70 vertical levels of the atmosphere. The regression is based on monthly mean ozone concentrations that were calculated from several different satellite instruments and global ozone soundings. Due to the regression model that was used to create the product, various basis function contributions are provided as unique levels or tiers. To understand the different contributions of basis functions, the data product is provided in five different "Tiers". - Tier 0: raw monthly mean data that was used in the regression model - Tier 1.1: Anthropogenic influences (as determined by the regression model) - Tier 1.2: Natural influences (as determined by the regression model) - Tier 1.3: Natural and volcanic influences (as determined by the regression model) - Tier 1.4: All influences (as determined by the regression model, CDR variable)

This data set provides a Climate Data Record (CDR) of sea ice concentration from passive microwave data. The CDR algorithm output is a rule-based combination of ice concentration estimates from two well-established algorithms: the NASA Team (NT) algorithm (Cavalieri et al. 1984) and NASA Bootstrap (BT) algorithm (Comiso 1986). The CDR is a consistent, daily and monthly time series of sea ice concentrations from 25 October 1978 through the most recent processing for both the north and south polar regions. All data are on a 25 km x 25 km grid.Note: A near-real-time version of this data set also exists to fill the gap between the time that this data set is updated through to the present. The data set is called the Near-Real-Time NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration (https://nsidc.org/data/g10016).

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There is a growing need for past weather and climate data to support science and decision-making. This paper describes the compilation and the construction of a global multivariable (air temperature, pressure, precipitation sum, number of precipitation days) monthly instrumental climate database that encompasses a substantial body of the known early instrumental time series. The dataset contains series compiled from existing databases that start before 1890 (though continuing to the present) as well as a large amount of newly rescued data. All series underwent a quality control procedure and subdaily series were processed to monthly mean values. An inventory was compiled, and the collection was deduplicated based on coordinates and mutual correlations. The data are provided in a common format accompanied by the inventory. The collection totals 12452 meteorological records in 118 countries. The data has been merged from 18250 original data files. The data can be used for climate reconstructions and analyses. It is the most comprehensive global monthly climate data set for the preindustrial period. Other sources used:National weather services:Deutscher Wetterdienst: https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/Koninklijk Nederlands Meteorologisch Instituut: http://projects.knmi.nl/klimatologie/daggegevens/antieke_wrn/index.html MétéoFrance: https://meteofrance.com/comprendre-climat/etudier-climat-passe & https://nextcloud.meteo.fr/s/fbALNMQTasftcqiNorsk Meteorologisk Institutt: https://seklima.met.no/observations/Roshydromet Всероссийский научно-исследовательский институт: http://meteo.ru/english/climate/cl_data.phpSveriges meteorologiska och hydrologiska institut: https://www.smhi.se/data/meteorologi/temperatur---Others:Atmospheric Circulation Reconstructions over the Earth (R. Allen):Berkeley Earth data: https://climatedataguide.ucar.edu/climate-data/global-surface-temperatures-best-berkeley-earth-surface-temperaturesBrugnara, Y. (2021). Global early instrumental climate data digitized at the University of Bern. Bern Open Repository and Information System. https://doi.org/10.48620/7Brugnara, Y. Swiss Early Meteorological Observations (CHIMES), PAN-GAEA (2020) https://doi.pangaea.de/10.1594/PANGAEA.909141C3-EURO4M-MEDARE Mediterranean historical climate data (MEDARE): doi:10.5281/zenodo.7531Dove, H. W.: Temperaturtafeln nebst Bemerkungen¸ über die Verbreitung der Temperatur auf der Oberfläche der Erde und ihre jährlichen periodischen Schwankungen. Erste, Zweite, Dritte und Vierte Abhandlung. Berlin (1838, 1839, 1842).DWD - overseas data: https://www.dwd.de/EN/ourservices/overseas_stations/overseas_stations.htmlENOWA data (Rodrigo, 2021): http://repositorio.ual.es/handle/10835/10636European Climate Assessment & Dataset: https://www.ecad.eu/Forts and volunteer Observer Database: https://mrcc.purdue.edu/data_serv/cdmp/cdmp.jspGlobal Historical Climatology Network (GHCN): https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-monthly-version-4HARD 2.0 - Historical Arctic Database: http://www.hardv2.prac.umk.pl/Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region:http://www.met-acre.org/Homehttp://www.zamg.ac.at/histalp/index.phphttps://jefferson-weather-records.org/ ISTI data: http://www.surfacetemperatures.org/Japan-Asia Climate Data Program: https://jcdp.jp/instrumental-meteorological-data/Open Data Rescue (Canada): https://opendatarescue.orgPrecipitation data from Africa: https://doi.org/10.1175/BAMS-D-11-00212.1SEA – Historical Climate Data (Australia): https://lindenashcroft.com/research/Societas Meteorologica Palatina, Ephemerides Societatis Meteorologicae Palatinae: Observations anni 1781-92, vols. 12, edited by: Hemmer, 615J.J., Mannheim (1783-95).Southeast Asian Climate Assessment & Dataset (SACA&D): https://www.climateurope.eu/southeast-asian-climate-assessment-dataset-sacad/Stockholm Historical Weather Observations (A. Moberg): https://bolin.su.se/data/stockholmThe International Surface Pressure Databank (ISPD): https://rda.ucar.edu/datasets/ds132.2/Toaldo, G.: La Meteorologia applicata all'Agricoltura, Storti, Venice (1775).

Note: This dataset version has been superseded by a newer version. It is highly recommended that users access the current version. Users should only use this version for special cases, such as reproducing studies that used this version. This NOAA Climate Data Record (CDR) from Colorado State University (CSU) contains brightness temperatures that have been improved and quality-controlled over the observation time period. The temperature data are from the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) series of passive microwave radiometers carried onboard the Defense Meteorological Satellite Program (DMSP) satellites. This dataset encompasses data from a total of nine satellites including the SSM/I sensors on board DMSP satellites F08, F10, F11, F13, F14, and F15 as well as the SSMIS sensors on board DMSP satellites F16, F17, and F18. The data record covers the time period from July 1987 through the present with a 7 to 10 day latency. The spatial and temporal resolutions of the FCDR files correspond to the original resolution of the source TDR observations. There are roughly 15 orbits per day with a swath width of approximately 1400 km resulting in nearly global daily coverage. The spatial resolution of the data is a function of the sensor/channel and varies from approximately ~50 km for the lowest frequency channels to ~15km for the high-frequency channels. The processing of the CDR from the BASE Temperature Data Record (TDR) (also produced by CSU) includes a rigorous quality control of the original TDR data, updated geolocation information, corrections for known issues/problems, and adjustments for residual intercalibration differences between sensors. The output parameters include the observed brightness temperatures for each of the seven SSM/I channels and 24 SSMIS channels at the original sensor channel resolution along with latitude and longitude for each pixel, time, quality flags, and view angle information. The file format is netCDF-4 with added metadata that follow the Climate and Forecast (CF) Conventions and Attribute Convention for Dataset Discovery (ACDD).

Daily precipitation and temperature data from 18 Global Climate Models (GCM) in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) that were downscaled using an analog regression approach in En-GARD (Gutmann et al. 2022) over the Colorado River Basin from 1950-2099. En-GARD is a statistical downscaling method designed to use information about upper level atmospheric processes (e.g. 500 mb winds) in addition to processes observed at the surface (e.g. precipitation and temperature). Each GCM was downscaled using training data from ERA-Interim reanalysis (Dee et al. 2011) and observations from the Livneh meteorological dataset (Livneh et al. 2015). Daily GCM precipitation and temperature were downscaled independently for each monthly basis (+/- 15 days for training) and on a grid-cell by grid cell basis. The GCM and ERA-Interim data were bilinearly interpolated to the Livneh 1/16 degree grid for input. Input data (Precipitation/Temperature, 500 mb zonal and meridional wind speeds) were quantile mapped to the corresponding ERA-Interim data and the closest 200 analog days, or days in which the input data matched the large-scale surface and upper atmospheric features, were selected independently for each day to be downscaled and used to train a multivariate linear regression to predict the Livneh data from those analog days. For precipitation, occurrence is modeled separately from magnitude by using a logistic regression with the same analog days to predict the probability of precipitation. To preserve realistic spatiotemporal variability, the residual term from the regression model is saved, and this residual is used to condition a stochastic sampling of the probability distribution for the prediction. Each output variable from En-GARD was quantile mapped to the Livneh meteorological data on a monthly basis to be used as input for a hydrological model that was calibrated using the Livneh meteorological data. More description of the En-GARD...

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Historical. The data include parameters of historical with a geographic location of China, Eastern Asia. The time period coverage is from 306 to 155 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.