The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

\Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Annual average temperature and precipitation accumulation departures from climatology are plotted, using visualizations inspired by Ed Hawkins' Warming Stripes page. A temperature chart depicts years that are warmer (reds) and cooler (blue) than normal. In a similar fashion, precipitation graphs show wetter (greens) and drier (browns) conditions for a given year. Data is from 1895-present, using a climatology of 1901-2000. Alaska and Hawaii are not available.Description of DataData originates from NOAA NCEI's climate at a glance page, which uses a 5 kilometer gridded data set, known as nClimgrid. This data set provides temperature and precipitation information for each month back to 1895. Annual estimates since 1895 are derived from the monthly data and aggregated onto each county for the Contiguous United States (Alaska and Hawaii are not available at this time). To depict the long term change in temperature and precipitation, annual data are then compared to a 20th century average (1901-2000). (Note that this is different from Ed Hawkins' original project, which uses a 1971-2000 baseline. These differences in baseline mean that the graphics may not perfectly match: the general warming trends will be consistent). These differences from the century average (known as a departure from normal, or anomaly) are then used to produce the visual. For more information on anomalies, please refer to this FAQ page.This map is a copy of Jared Rennie's original map, published at https://arcg.is/19i1r90Data is from NOAA NCEI's climate at a glance page. Thanks to Ed Hawkins and Zeke Hausfather for inspiration. Plots and maps made by Jared Rennie (@jjrennie) Certified Consulting Meteorologist, North Carolina Institute for Climate Studies, Asheville, NC.

These rasters provide the local mean annual extreme low temperature from 1991 to 2020 in an 800m x 800m grid covering the USA (including Puerto Rico) based on interpolation of data from more than a thousand weather stations. Each location's Plant Hardiness Zone is calculated based on classifying that temperature into 5 degree bands.The classified rasters are then used to create print and interactive maps.Temperature station data for the 2023 edition of the USDA Plant Hardiness Zone Map (PHZM) came from many different sources. In the eastern and central United States, Puerto Rico, and Hawaii, data came primarily from weather stations of the National Weather Service and several state networks. In the western United States and Alaska, data from stations maintained by USDA Natural Resources Conservation Service, USDA Forest Service, U.S. Department of the Interior (DOI) Bureau of Reclamation, and DOI Bureau of Land Management also helped to better define hardiness zones in mountainous areas. Environment Canada provided data from Canadian stations, and data from Mexican stations came from the Mexico National Weather Service and the Global Historical Climate Network. The USDA PHZM was produced with PRISM, a highly sophisticated climate mapping technology developed at Oregon State University. The map was produced from a digital computer grid, with each cell measuring about a half mile on a side. PRISM estimated the mean annual extreme minimum temperature for each grid cell (or pixel on the map) by examining data from nearby stations; determining how the temperature changed with elevation; and accounting for possible coastal effects, temperature inversions, and the type of topography (ridge top, hill slope, or valley bottom). Information on PRISM can be obtained from the PRISM Climate Group website https://prism.oregonstate.edu. Once a draft of the map was completed, it was reviewed by a team of climatologists, agricultural meteorologists, and horticultural experts. If the zone for an area appeared anomalous to these expert reviewers, experts doublechecked the draft maps for errors or biases. A detailed explanation of the mapmaking process and a discussion of the horticultural applications of the 2012 PHZM (similar to 2023) are available from the articles listed below. Daly, C., M.P. Widrlechner, M.D. Halbleib, J.I. Smith, and W.P. Gibson. 2012. Development of a new USDA Plant Hardiness Zone Map for the United States. Journal of Applied Meteorology and Climatology, 51: 242-264.Widrlechner, M.P., C. Daly, M. Keller, and K. Kaplan. 2012. Horticultural Applications of a Newly Revised USDA Plant Hardiness Zone Map. HortTechnology, 22: 6-19.

Where is the data coming from?

[Note 02/2024: this resource is now decomissioned, link provides related maps, tools, and GIS layers.]The Dynamic Mapping Tool provides a spatial index to over 5,500 sites on streams and rivers in the U.S. and Canada where full year stream temperatures are currently being monitored by numerous agencies. You can filter stream temperature sites by state, agency, year and contact. The primary goal is to portray a comprehensive set of sites across all agencies to facilitate data sharing and avoid redundancies, as new monitoring sites are added to the regional network. Raw temperature data are not downloadable through this site, but typically reside with the local data stewards, whose contact information is displayed by clicking on a point in the map. In some instances, RMRS may have copies of the raw data and permission to distribute it, so we ask that you contact us before contacting the local data stewards. The map will be updated once each winter to maintain an accurate description of current monitoring locations. If interested in obtaining temperature data or adding temperature monitoring sites to this map, please contact Sherry Wollrab: 208.373.4371 or sherrywollrab@fs.fed.us.Resources in this dataset:Resource Title: Website Pointer for Stream Temperature Interactive Maps.File Name: Web Page, url: https://www.fs.usda.gov/rm/boise/AWAE/projects/stream_temp/maps.htmlThe Dynamic Mapping Tool provides a spatial index to over 5,500 sites on streams and rivers in the U.S. and Canada where full year stream temperatures are currently being monitored by numerous agencies. Users can filter stream temperature sites by state, agency, year and contact.

This map displays the Apparent and Expected Air Temperature forecast over the next 72 hours across the Contiguous United States, Alaska, Guam, Hawaii, and Puerto Rico in 3 hour increments. The original raster data has been processed into 1-degree contours.Two layers are included: apparent and expected temperature, both include a Time Series set to a 3-hour time interval. The apparent temperature is the perceived (or feels like) temperature derived from either a combination of temperature and wind (wind chill) or temperature and humidity (heat index) for the indicated hour. When the temperature at a particular grid point falls to 50 °F or less, wind chill will be used for that point for the apparent temperature. When the temperature at a grid point rises above 80 °F, the heat index will be used for apparent temperature.
Between 51 and 80 °F, the apparent temperature will be the ambient air temperature.The expected temperature is the forecasted ambient air temperature in °F.See sister data product for Min and Max Daily TemperaturesRevisionsApr 21, 2022: Added Forecast Period Number 'Interval' field for an alternate query method to the Timeline of data. Disabled Time Series by default to improve initial Map Viewer exprience and added a Filter for 'interval = 1' to display initial forecast time data (current time period).Apr 22, 2022: Set 'Apparent Temperature' layer visibility to True by default, so content is visible when initially viewed.Sep 1, 2022: Updated renderer Arcade logic on layers to correctly symbolize on values greater than 120 and less than -60 degrees.DetailService Data update interval is: HourlyWhere is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces gridded forecasts of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Apparent Temperature Source:CONUS: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.apt.binALASKA: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.alaska/VP.001-003/ds.apt.binHAWAII: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.hawaii/VP.001-003/ds.apt.binGUAM: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.guam/VP.001-003/ds.apt.binPUERTO RICO: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.puertori/VP.001-003/ds.apt.binExpected Temperature Source:CONUS: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.temp.binALASKA: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.alaska/VP.001-003/ds.temp.binHAWAII: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.hawaii/VP.001-003/ds.temp.binGUAM: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.guam/VP.001-003/ds.temp.binPUERTO RICO: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.puertori/VP.001-003/ds.temp.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This feature service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation or add a Filter using the 'Forecast Period Number'.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

We developed annual raster maps depicting spatiotemporal variation in mean winter temperature for the purposes of modeling the spread of the hemlock woolly adelgid. The maps are based on the PRISM and WorldClim datasets as described in methods.

Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The mean of the 12 months each year is displayed here. Each annual update is available around the 15th of the following January (e.g., 2020 is available Jan 15th, 2021). The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report summary report by NOAA NCEI is available here. GHCN monthly mean station averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here.What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each year since 1880. Be sure to configure the time settings in your web map to view the timeseries correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. For a more detailed temporal analysis, a monthly mean is available here.

These data were compiled as a supplement to a previously published journal article (Bradford et al., 2019), that employed a ecosystem water balance model to characterize current and future patterns in soil temperature and moisture conditions in dryland areas of western North America. Also, these data are associated with a published USGS data release (Bradford and Schlaepfer, 2019). The objectives of our study were to (1) characterize current and future patterns in soil temperature and moisture conditions in dryland areas of western North America, (2) evaluate the impact of these changes on estimation of resilience and resistance among a representative set of climate scenarios. These data represent geographic patterns in simulated soil temperature and soil moisture conditions and underlying variables based on SOILWAT2 simulations under climate conditions representing historical (current) time period (1980-2010) and two future projected time periods (2020-2050, d40yrs) and (2070-2100, d90yrs) for two representative concentration pathways (RCP4.5, RCP8.5) as medians across simulation runs based on output from each of the available downscaled global circulation models that participated in CMIP5 (RCP4.5, 37 GCMs; RCP8.5, 35 GCMs; Maurer et al. 2007). Additional information about the SOILWAT2 simulation experiments can be found in Bradford et al. 2019. These data were created in 2018, 2019, and 2021 for the area of the sagebrush region in the western North America. These data were created by a collaborative research project between the U.S. Geological Survey, Marshall University and Yale University. These data can be used with the high-resolution matching as defined by Renne et al. (in prep.), and within the scope of Bradford et al. 2019. These data may also be used to evaluate the potential impact of changing climate conditions on geographic patterns in simulated soil temperature and soil moisture conditions.

Area-wide modeled near-surface temperature for 3-4 pm on July 27, 2020, based on temperature and humidity data collected for a one-day heat mapping project conducted by King County, Seattle Public Utilities, and the City of Seattle. Data collected on July 27, 2020 in partnership with project volunteers and CAPA Strategies. Data analysis and maps produced by CAPA strategies. This predictive temperature model was created from multi-band land cover rasters from Sentinel-2 satellite and raw heat data from sensor SD cards using the 70:30 holdout method.Heat maps also available for 6-7 am and 7-8 pm. Results can be viewed using this ArcGIS web app viewer. More information on the project available in Heat Watch Report for Seattle & King County. Contact CAPA Strategies for questions on the data, maps, and data analysis methods.

A simulation of projected changes in annual mean temperatures from the period 1975 to 1995 to the period 2080 to 2100 is shown on this map. Geographically, the temperature changes would not be evenly distributed. According to this projection, the Arctic would experience the greatest annual mean warming followed by other areas in northern Canada and central and northern Asia. Temperatures generally increase as the century progresses as a consequence of the projected increase in greenhouse gas concentrations in the atmosphere. The results are based on climate change simulations made with the Coupled Global Climate Model developed by Environment Canada.

The Mean Annual Temperature map was calculated by creating a contour map using compiled 10 meter firn temperature data from NSIDC and other mean annual temperature data from both cores and stations.

The 10 meter data contains temperature measurements dating back to 1957 and the International Geophysical Year, including measurements from several major recent surveys. Data cover the entire continental ice sheet and several ice shelves, but coverage density is generally low.

Data are stored in Microsoft Excel and Tagged Image File Format (TIFF), and are available sporadically from 1957 to 2003 via FTP.

Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data. Download this data or get more information

A new global map of climate classifications using the Koppen-Geiger system has been produced based on a large global data set of long-term monthly precipitation and temperature station time series.

To construct the new map, long-term station records of monthly precipitation and monthly temperature were obtained from the Global Historical Climatology Network (GHCN) version 2.0 data set (Peterson and Vose, 1997). Stations from this data set with at least 30 observations for each month were used in the analysis (12,396 precipitation and 4,844 temperature stations). The data are most representative from 1909 to 1991 for precipitation and 1923 to 1993 for temperature. Climatic variables were interpolated between stations in ESRI ArcMap version 9.1 using a two-dimensional (latitude and longitude) thin-plate spline with tension onto a 0.1 x 0.1 degree grid for each continent. The Koppen-Geiger criteria were then applied to the splined variables.

The Koppen-Geiger system includes 30 possible climate types. They are divided into 3 tropical (Af, Am and Aw), 4 arid (BWh, BWk, BSh and BSk), 9 temperate (Csa, Csb, Csc, Cfa, Cfb, Cfc, Cwa, Cwb and Cwc), 12 cold (Dsa, Dsb, Dsc, Dsd, Dfa, Dfb, Dfc, Dfd, Dwa, Dwb, Dwc and Dwd) and 2 polar (ET and EF) (The source document and metadata record define the subdivisions). All precipitation variables are in units of millimetres (mm) and all temperature variables are in units of degrees Celsius (C).

Koppen-Geiger climate type maps were constructed for each continent and the percentage of land area covered by the major climate types was calculated. Since the area of a 0.1 x 0.1 degree pixel changes with latitude, a map of 0.1 x 0.1 degree pixel area was constructed and then projected onto a Cylindrical Equal Area projection of the world to determine the area (in km2) of each 0.1 x 0.1 degree pixel. These pixel areas were then summed for each climate type to provide an estimate of the land area covered by each climate type. The continental maps are presented and discussed in Peel et al. (2007). The global map is available for download in ESRI Arc Grid.

Oak Ridge National Laboratory (ORNL) also provides the updated World Map of the Koppen-Geiger Climate Classification, but in GeoTiff format. ORNL Convert data from ESRI Grid format to GeoTIFF format. The processed GeoTIFF data were fed into ORNL DAAC Web Map Service v1.1.1 (WMS), Web Coverage Service v1.0.0 (WCS), and Spatial Data Access Tool (SDAT) to provide data visualization and distribution capabilities. References:

Koppen, W. 1936. Das geographisca System der Klimate, in: Handbuch der Klimatologie, edited by: K¨oppen, W. and Geiger, G., 1. C. Gebr, Borntraeger, 1â�“44.

Peel, M. C., B. L. Finlayson, and T. A. McMahon. 2007. Updated World Map of the Koppen-Geiger Climate Classification. Hydrol. Earth Syst. Sci., 11, 1633-1644. doi:10.5194/hess-11-1633-2007.

Peterson, T.C., and R.S. Vose. 1997. An overview of the Global Historical Climatology Network temperature database, Bull. Am. Meteorol. Soc., 78(12), 2837�2849.

The North America climate data were derived from WorldClim, a set of global climate layers developed by the Museum of Vertebrate Zoology at the University of California, Berkeley, USA, in collaboration with The International Center for Tropical Agriculture and Rainforest CRC with support from NatureServe.The global climate data layers were generated through interpolation of average monthly climate data from weather stations across North America. The result is a 30-arc-second-resolution (1-Km) grid of mean temperature values. The North American data were clipped from the global data and reprojected to the standard Lambert Azimuthal Equal Area projection used for the North American Environmental Atlas. Background information on the WorldClim database is available in: Very High-Resolution Interpolated Climate Surfaces for Global Land Areas; Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis; International Journal of Climatology 25: 1965-1978; 2005.Files Download

Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data. Download this data or get more informationThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The superficial sea temperature (SST) map package was built with daily-mean data at low spatial resolution (0.05 degree) remotely collected between 1982 and 2020. The maps were also built over the four quarters of the time period. These layers are provided with the suffixes Q1 to Q4. The SST trend maps show the increasing or decreasing trend of SST over the selected time period.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

This map features daily sea surface temperature from 2008 to present in degrees Celsius (⁰C) at 25 km resolution. Sea Surface Temperature is a key climate and weather measurement used for weather prediction, ocean forecasts, tropical cyclone forecasts, and in coastal applications such as fisheries, pollution monitoring and tourism. El Niño and La Niña are two examples of climate events which are forecast through the use of sea surface temperature maps.

The Naval Oceanographic Office sea surface temperature dataset is calculated from satellite-based microwave and infrared imagery. These data are optimally interpolated to provide a daily, global map of the midday (12:00 pm) sea surface temperature.

Learn more about the source data.