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.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

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.

Atmospheric Climate Data Records are measurements of several global variables to help characterize the atmosphere at or just above the land and ocean surface as well as other upper air composition variables.

Documentation

hhttps://www.ncdc.noaa.gov/cdr

Update Frequency

Climate Data Records are updated independently. For update frequency for a specific CDR, please refer to the Climate Data Record website.

License

Open Data. There are no restrictions on the use of this data.

The table Global Temperatures by City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://redivis.com/datasets/1e0a-f4931vvyg. It contains 8599212 rows across 7 variables.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. Geospatial data provided in an ArcGIS shapefile are described herein. The shapefile contains polygons of climate regions including their acreages. As part of this project, best models from each downscaled climate dataset are determined for climate regions in South Florida and South-Central Florida based on how well they reproduce historical climate extreme indices. Table 1 of Datasets_station_information.xlsx lists the NOAA Atlas 14 stations located within each climate region. See Best_model_lists.xlsx for a list of best models for each climate region in each downscaled climate dataset and when all downscaled climate datasets are considered together.

The preferred citation when using this dataset is: Stevens, A., & Lamie, C., Eds. (2024). New York State Climate Impacts Assessment: Understanding and preparing for our changing climate. The New York State Climate Impacts Assessment is an investigation into how climate change will affect New York State’s communities, ecosystems, and economy. The data and information presented will help New Yorkers plan and prepare for the impacts of climate change. The assessment also strives to show how addressing climate change provides opportunities to enhance equity and reduce the vulnerability of those most at risk. As part of the assessment, Columbia University developed climate change projections for temperature and precipitation, extreme events, degree days, and sea level rise, downscaled to 12 regions of New York State. This dataset includes those projections of future climate conditions in New York State, for the 2030s through 2100. For more information on these projections or to read the full NYS Climate Impacts Assessment, visit the assessment website at https://nysclimateimpacts.org/. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on Twitter, Facebook, YouTube, or Instagram.

The table Global Temperatures by Country is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://redivis.com/datasets/1e0a-f4931vvyg. It contains 577462 rows across 4 variables.

The NOAA Weather and Climate Toolkit is an application that provides simple visualization and data export of weather and climatological data archived at NCDC. The Toolkit also provides access to weather and climate web services provided from NCDC and other organizations. The Viewer provides tools for displaying custom data overlay, Web Map Services (WMS), animations and basic filters. The export of images and movies is provided in multiple formats. The Data Exporter allows for data export in both vector point/line/polygon and raster grid formats. Current data types supported include: CF-compliant Fridded NetCDF; Generic CF-compliant Irregularly-Spaced/Curvilinear Gridded NetCDF/HDF; GRIB1, GRIB2, GINI, GEMPAK, HDF(CF-compliant) and more gridded formats; GPES Satellite AREA Files; NEXRAD Radar Data(Level-II and Level-III); U.S. Drought Monitor Service from the National Drought Mitigation Center (NDMC); OPeNDAP support for Gridded Datasets

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

The U.S. Daily Climate Normals for 2006 to 2020 are 15-year averages of meteorological parameters that provide users supplemental normals for specialized applications for thousands of locations across the United States, as well as U.S. Territories and Commonwealths, and the Compact of Free Association nations. The stations used include those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the U.S. Climate Reference Network (USCRN) and other automated observation stations. In addition, precipitation normals for stations from the U.S. Snow Telemetry (SNOTEL) Network and the citizen-science Community Collaborative Rain, Hail and Snow (CoCoRaHS) Network are also available. The Daily Climate Normals dataset includes various derived products such as air temperature normals (including maximum and minimum temperature normals, heating and cooling degree day normals, and others), precipitation normals (including precipitation and snowfall totals, and percentiles, frequencies and other statistics of precipitation, snowfall, and snow depth), and agricultural normals (growing degree days (GDDs)). All data utilized in the computation of the 2006-2020 Climate Normals were taken from the Global Historical Climatology Network-Daily, but the Daily Normals are adjusted so that they are consistent with the Monthly Normals. The source datasets (including intermediate datasets used in the computation of products) are also archived at NOAA NCEI. A comparatively small number of station normals sets (~50) have been added as Version 1.0.1 to correct quality issues or because additional historical data during the 1991-2020 period has been ingested.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

The downloadable ZIP file contains an Esri grid. These data were created as part of a graduate thesis at the University of Idaho. The Koppen Climate Classification was produced using gridded estimates of precipitation, temperature, and elevation from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM was developed at Oregon State University and information about the gridded ASCII data sets can be obtained from: https://prism.oregonstate.edu/.These data were created as part of this thesis: https://alliance-primo.hosted.exlibrisgroup.com/permalink/f/m1uotc/CP71174200670001451These data were contributed to INSIDE Idaho at the University of Idaho Library in 1999.

This database contains historical temperature and precipitation data aggregated from 2-degree gridded data to the country and basin levels.

The Atmospheric Layer Temperature Climate Data Record (CDR) dataset is a monthly analysis of the tropospheric and stratospheric data using temperature sounding microwave radiometers flown on polar-orbiting weather satellites providing an important record of upper atmosphere temperatures by merging data from the older Microwave Sounding Unit (MSU) and the new Advanced Microwave Sounding Units (AMSUs). The instruments measure the brightness temperature (Tb) for each channel and corresponds to an average temperature of the atmosphere averaged over that channel's weighting function. Global average and global anomaly maps on a 2.5 x 2.5 degree resolution are produced from each month's data from both the MSU and the AMSU for over a 31 year period (1978-2011) for each channel (layer); the Temperature Lower Troposphere (TLT); the Temperature Middle Troposphere (TMT); the Temperature Troposphere/Stratosphere (TTS) ; the Temperature Lower Stratosphere (TLS). UAT 4 Layer MW data is important to continue into the future to ensure the existence of a high-quality record of atmospheric temperatures for climate change detection and climate model verification activities. The data are available in netCDF-4 file format with variables containing standard deviation, quality flags, and projection information and will be updated monthly.

Although climate change has become a major political topic, most people have never explored climate data or models of possible future climates. The data is often stored in scientific file formats that require specialized software and can seem unintelligible to those unfamiliar with climate terms and concepts. In this lesson, you'll map historical and projected climate data in ArcGIS Pro. You'll learn about climate at both local and global levels, as well as how climate might change in the future. Overall, you'll gain understanding of major climate concepts and familiarity with real climate data.

In this lesson you will build skills in the these areas:

• Creating layers and tables from NetCDF files
• Mapping projected climate scenarios
• Using Raster Functions

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

Contains global weather station locations with data for monthly means from 1981 through 2010 for: Daily Mean Temperature °C Daily Maximum Temperature °C Daily Minimum Temperature °C Precipitation in mm Highest Daily Temperature °C Lowest Daily Temperature °C Additional monthly fields containing the equivalent values in °F and inches are available at the far right of the attribute table. GHCND stations were included if there were at least fifteen average daily values available in each month for all twelve months of the year, and for at least ten years between 1981 and 2010. 3,197 of the 7,480 stations did not collect or lacked sufficient precipitation data. These data are compiled from archived station values which have not undergone rigorous curation, and thus, there may be unexpected values, particularly in the daily extreme high and low fields. Esri is working to further curate this layer and will make updates as improvements are found. If your area of study is within the United States, we recommend using the U.S. Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer because the data in that service were compiled from web services produced by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc., thus the data in the U.S. service is of higher quality. Revision History: Initially Published: 6 Feb 2019 Updated: 12 Feb 2019 - Improved initial extraction algorithm to remove stations with extreme values. This included values higher than the highest temperature ever recorded on Earth, or those with mean values that were considerably different than adjacent neighboring stations.Updated: 18 Feb 2019 - Updated after finding an error in initial processing that excluded a 2,870 stations. Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 635 of 7,452 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as:

Esri, 2019: World Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed April 2019. https://www.arcgis.com/home/item.html?id=ed59d3b4a8c44100914458dd722f054f Source Data: Station locations compiled from: Initially compiled using station locations from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24 Amended to use the most recent station locations from Russell S. Vose, Shelley McNeill, Kristy Thomas, Ethan Shepherd (2011): Enhanced Master Station History Report of March 2019. NOAA National Climatic Data Center. Access Date: April 10, 2019 doi:10.7289/V5NV9G8D. Station Monthly Means compiled from Daily Data: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd_all.tar.gz Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24

This dataset is the underlying data described in Nolte et al., "The potential effects of climate change on air quality across the conterminous U.S. at 2030 under three Representative Concentration Pathways", Atmos. Chem. Phys., in press, 2018. The paper describes simulated changes in U.S. air quality (ozone and particulate matter) between 2000 and 2030 under three scenarios of climate change. Ozone data are in parts per billion by volume, particulate matter are in micrograms per cubic meter, temperature changes are in degrees Celsius, and precipitation has units of millimeters of accumulated precipitation per month. This dataset is associated with the following publication: Nolte, C., T. Spero, J. Bowden, M. Mallard, and P. Dolwick. The potential effects of climate change on air quality across the conterminous US at 2030 under three Representative Concentration Pathways. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 18(20): 15471-15489, (2018).

The Climate Data Analytics Market Report is Segmented by Type (Climate Model Evaluation, Climate Data Processing and Visualization, Climate Data Formats, and Statistical Methods), End-User Industry (Government and Public Sector, Energy and Utilities, Agriculture, Insurance and Risk Management, Infrastructure and Transportation, and Healthcare), and Geography (North America, Europe, Asia Pacific, Middle East and Africa, and Latin America). The Market Sizes and Forecasts Regarding Value (USD) for all the Above Segments are Provided.