This dataset provides weekly average temperature data for all U.S. counties from 2013 to 2023. Each row in the dataset represents a specific county, and the columns correspond to the weekly average temperatures over the ten-year period. The dataset is structured to facilitate time series analysis, climate trend studies, and machine learning applications related to environmental and climate change research.

Key Features: - County-Level Data: Temperature data is provided for each county in the United States, allowing for detailed, localized climate analysis. - Weekly Time Intervals: The data is aggregated on a weekly basis, offering a finer temporal resolution that captures seasonal and short-term temperature fluctuations.

• 10-Year Span: Covers a significant period from 2013 to 2023, enabling long-term trend analysis and comparison across different periods.

• Temperature Units: All temperature values are presented in Kelvin (K).

Potential Uses:

• Climate Research: Investigate climate change impacts at the county level, identify trends, and assess regional climate variability. Geospatial Analysis: Integrate with other spatial datasets for comprehensive environmental and geographical studies.

• Machine Learning: Suitable for training models on temporal climate data, predictive analytics, and anomaly detection.

• Public Policy and Planning: Useful for policymakers to study historical climate trends and support decision-making in areas such as agriculture, disaster management, and urban planning.

This dataset is ideal for researchers, data scientists, and analysts interested in exploring U.S. climate data at a granular level.

Average Rainfall (mm) and average Temperature (centigrade) for the North East England and East England Met Office Climate district, which includes Lincolnshire. This dataset shows the average Rainfall in millimetres and average Temperature in centigrade, by month, meteorological season, and annual calendar year. The data is sourced from the UK Met Office website. See the Source link for more information about the data and the area it covers.

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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.

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. On an annual basis, approximately one year of "final" nClimGrid will be submitted to replace the initially supplied "preliminary" data for the same time period. Users should be sure to ascertain which level of data is required for their research.

EpiNOAA is an analysis ready dataset that consists of a daily time-series of nClimGrid measures (maximum temperature, minimum temperature, average temperature, and precipitation) at the county scale. Each file provides daily values for the Continental United States. Data are available from 1951 to the present. Daily data are updated every 3 days with a preliminary data file and replaced with the scaled (i.e., quality controlled) data file every three months. This derivative data product is an enhancement from the original daily nClimGrid dataset in that all four weather parameters are now packaged into one file and assembled in a daily time-series format. In addition to a direct download option, an R package and web interface has been developed to streamline access to the final data product. These options allow end users three separate access modes to arrive at a customized dataset unique to each end user’s application. Users should be sure to review the data documentation to inform which level of data is required for their research.

The Daily Air Temperature and Heat Index data available on CDC WONDER are county-level daily average air temperatures and heat index measures spanning the years 1979-2010. Temperature data are available in Fahrenheit or Celsius scales. Reported measures are the average temperature, number of observations, and range for the daily maximum and minimum air temperatures, and also percent coverage for the daily maximum heat index. Data are available by place (combined 48 contiguous states, region, division, state, county), time (year, month, day) and specified maximum and minimum air temperature, and heat index value. The data are derived from the North America Land Data Assimilation System (NLDAS) through NLDAS Phase 2, a collaboration project among several groups: the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC), the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Princeton University, the National Weather Service (NWS) Office of Hydrological Development (OHD), the University of Washington, and the NCEP Climate Prediction Center (CPC). In a study funded by the NASA Applied Sciences Program/Public Health Program, scientists at NASA Marshall Space Flight Center/ Universities Space Research Association developed the analysis to produce the data available on CDC WONDER.

Monthly totals of precipitation in millimeters (mm), monthly means of daily maximum air temperature in degrees Celsius (C), and monthly means of daily minimum air temperature (C) were developed at the 5 arc minute grid level for the conterminous United States (US) for the 1940-2006 period. Also, included are computed monthly mean of daily potential evapotranspiration (mm) and mean grid elevation in meters (m). These data were developed from PRISM (Parameter-elevation Regressions on Independent Slopes Model) data at the 2.5 arc minute scale and aggregated to the 5 arc minute grid scale. The county means were computed using a weighted mean of the 5 arc minute grids within the county.The USDA Forest Service (USFS) produces a periodic assessment of the condition and trends of the Nation's renewable resources as required by the Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974. This RPA Assessment provides a snapshot of current US forest and rangeland conditions and trends on all ownerships, identifies drivers of change, and projects 50 years into the future (//www.fs.fed.us/research/rpa/, accessed 8/16/2009). For 2010 RPA Assessment, an integrated modeling framework is being used in which the potential implications of climate change can be analyzed across some resource areas (Langner in review). The nature of the climate variables needed to address climate change impacts for these resource analyses in the 2010 RPA Assessment were determined to be monthly precipitation and temperature variables at the county level spatial scale and for some resource analyses at the 5 arc minute grid scale.Original metadata date was 08/02/2010. Metadata modified on 04/22/2011 to adjust citation to include the addition of a DOI (digital object identifier). Minor metadata updates on 02/20/2013. Metadata modified on 07/22/2015 to update cross-reference citations and other minor updates. Additional minor metadata updates on 12/13/2016 and 04/19/2018.

Average rainfall (mm) and average temperature (centigrade) for the North East England and East England Met Office Climate district, which includes Lincolnshire.

This dataset shows the average rainfall in millimetres and average temperature in centigrade by month, year, and meteorological season. It also has an annual figure for each year.

The data is sourced from the UK Met Office website. See the Source link for more information about the data and the area it covers.

From January to December 2018, the North Caucasian federal district of Russia was the warmest region with an average temperature of 10.2 degrees Celsius. The Far Eastern federal district was the coldest, with 5.54 degrees Celsius below zero on average.

Upon reactivation, movement of deep-seated landslides in the Greater Pittsburgh region may persist for long periods of time. Monitoring equipment was located at two sites on a deep-seated rockslide in Aleppo Township, Pennsylvania to establish relationships between precipitation and changes in the state of activity and velocity. Precipitation, snow depth, and air temperature are monitored at a weather station (ARS_WS) located in a relatively flat, open area in the northern part of the rockslide. Displacement and soil moisture are monitored at a second site located in the southern part of a graben (ARS_GR) in the head of the rockslide. At ARS_WS, instrumentation includes a tipping bucket rain gauge, temperature probe, and a sonic ranging sensor, and at ARS_GR instrumentation includes two cable extension transducers and a soil moisture probe.
This data release presents the time series data from this instrumentation for an initial monitoring period starting on November 6, 2013 and ending on December 31, 2018. The data release generally presents the output for each sensor type as recorded on the datalogger, except in one case, for which the output requires conversion to engineering units and the factor necessary to make this conversion is provided. ARS_WS instrumentation Rain gauge: The rain gauge at ARS_WS has a resolution of 0.01 inch (in.) and an accuracy of 1 percent for rainfall intensities up to 2 in/hr. Initially, rainfall was recorded by a Hobo event datalogger and each event was equal to 0.01 in. Beginning on November 18, 2016, 15-minute (min) rainfall totals were recorded by a Campbell Scientific CR1000 datalogger. The rain gauge collector is subject to periodic clogging due to vegetative debris and insect activity. This type of rain gauge is not designed to accurately measure the snow water equivalent of snow fall and likely underestimates precipitation during the winter. Maintenance of the rain gauge is only performed periodically during site visits to retrieve data, install or maintain equipment, or perform other work. No field calibration was performed on the rain gauge.
Snow depth sensor: Snow depth is measured using a sonic ranging sensor that has a resolution of 0.25 millimeters (mm) (0.01 in.) and a stated accuracy of 1 centimeter (cm) based on the height of the sensor, which was installed 1.68 meters (m) above the ground. Measurements are recorded on an hourly interval. Quality numbers (Q) provide additional information about snow depth measurement certainty. An explanation of the quality number ranges is included as a separate text file (QualityNos_SnowDepth_ReadMe.txt). In May 2015, a square meter (m2), flat measurement area covered with locally derived claystone fragments was constructed beneath the sensor. Periodic maintenance of the site entails removing vegetation from this and the surrounding area in the fall prior to the initial snow fall. Air temperature probe: Air temperature is measured using a temperature probe housed in 6-plate solar radiation shield. The probe consists of a thermistor in an epoxy-filled aluminum housing. The probe has a stated measurement range of -30oC to +50oC with no more than 0.01oC error over this range. Average air temperature is recorded at 15-min intervals.
ARS_WS_DATA.xlsx contains three worksheets labeled EVENT, 15MIN, and 1HR. The EVENT worksheet contains the following fields: TIMESTAMP_EST - records the timestamp in the format mm/dd/yyyy hh:mm, Eastern Standard Time; and EVENT – records precipitation, where each event equals 0.01 in. The 15MIN worksheet contains the following fields: TIMESTAMP_EST - records the timestamp in the format mm/dd/yyyy hh:mm, Eastern Standard Time; AT_Avg – records the average air temperature in degrees Celsius; PRCP_Tot – records the total precipitation in inches; and BV_Avg – records the average battery voltage in volts. The 1HR worksheet contains the following fields: TIMESTAMP_EST - records the timestamp in the format mm/dd/yyyy hh:mm, Eastern Standard Time; AT_Avg – records the average air temperature in degrees Celsius; DT_Avg – records the average distance to target in meters; Q_Avg -records the quality number; TCDT_Avg – records the average temperature corrected distance to target in meters; and SD_Avg – records the snow depth (when snow is present) in meters.
ARS_GR Instrumentation Cable Extension Transducers: Displacement and ground surface rotation is monitored at two locations near the margins of the graben using cable extension transducers (CETs) that have a 152-cm (60-in.) range and an accuracy of 0.1 percent of full stroke (potential deviations may range up to 1.5 mm). Both CETs are mounted above the ground surface on a 5.1-cm (2-in.) diameter stainless steel pole that is grouted approximately 90 cm deep into the slope colluvium and uppermost weathered/fractured claystone. An Invar wire attached to the factory-installed cable on each CET provides the required additional length to reach the anchor point. At the first location (CET01_MS), the anchor point consists of a stainless-steel eyebolt grouted into the sandstone exposed in the main scarp free face. At the second location on the western margin of the graben (CET02_AS), the cable is anchored to a stainless-steel pole grouted into the antithetic (upslope-facing) scarp slope. The CET data may be affected by falling tree limbs throughout the year and cable icing in winter. The average eyelet position (or amount of cable extension), measured in cm, is recorded at 15-min intervals.
Soil moisture probe: The volumetric water content (VWC) of the shallow colluvial deposit is measured using water content reflectometer. The probe derives VWC from its sensitivity to dielectric permittivity and has an accuracy of approximately ±3 percent. The probe was inserted into the upslope wall of shallow hand-excavated test pit using an insertion guide tool to create two parallel holes for the probe rods. The probe was installed at a depth of 20 cm, a depth approximately half the total thickness of the colluvium at that location. The pit is located approximately 4 m downslope from the stress relief joint that is the main scarp free face. The average slope of the ground surface is about 18 percent (10 degrees). Average volumetric water content in meters3/meters3 (m3/m3), electrical conductivity in deciSiemens/meter (dS/m), and soil temperature (°C) are recorded at either 15-min or hourly intervals. ARS_GR_DATA.xlsx contains two worksheets labeled 15MIN and 1HR. The 15MIN worksheet contains the following fields: TIMESTAMP_EST - records the timestamp in the format mm/dd/yyyy hh:mm, Eastern Standard Time; CET01_MS_Avg – records the average extension at cable extension transducer CET01_MS in cm; CET02_AS_Avg – records the average extension at cable extension transducer CET02_AS in cm; VWC_20_Avg – records the average volumetric water content at a depth of 20 cm in m3/m3; EC_20_Avg – records the average electrical conductivity at a depth of 20 cm in dS/m; ST_20_Avg – records average soil temperature at a depth of 20 cm in °C; and BV_Avg – records the average battery voltage in volts. The 1HR worksheet contains the following fields: TIMESTAMP_EST - records the timestamp in the format mm/dd/yyyy hh:mm, Eastern Standard Time; VWC_20_Avg – records the average volumetric water content at a depth of 20 cm in m3/m3; EC_20_Avg – records the average electrical conductivity at a depth of 20 cm in dS/m; and ST_20_Avg – records average soil temperature at a depth of 20 cm in °C. Details of this study are described in the journal article: Ashland, F. X., and Delano, H. L., 2015, Continuous monitoring of meteorological conditions and movement of a deep-seated, persistently moving rockslide along Interstate Route 79 near Pittsburgh: Pennsylvania Geology, v. 45, no. 2, p. 22–26.

The Far Eastern Federal District had the coldest average temperature in Russia in January 2023, at over 31 degrees Celsius below zero. In the Siberian Federal District, the average January temperature was 2.2 degrees Celsius below zero. The highest mean monthly temperature in July of the same year was observed in the Southern Federal District at 24.2 degrees Celsius above zero.

Extreme temperatures can vary greatly across communities due to differences in land use, shade availability, proximity to water, and elevation. Spatially detailed estimates of temperature are difficult to find - often they are stations that are not regularly spaced or are from satellite observations, which estimate only the surface temperature, which can be quite different from air temperature. The PRISM Climate Group at the Oregon State University have developed an 800-meter resolution climatology of temperature for the United States that provides enough detail for intra-city temperature comparisons. It is created by a downscaling model, Parameter-elevation Regressions on Independent Slopes Model (PRISM).The 1991-2020 climate normal for maximum temperature for the month of July was downloaded and analyzed in ArcGIS Pro. Zonal Statistics provide min, max, and mean summaries for county and census tracts (2020 version) geometries. All temperatures were converted from degrees Celsius to Fahrenheit. Additionally, in each layer the mean of the maximum temperature analysis for the next order of geometry is provided (e.g., county data in the tracts layer), which allows comparison of the observed temperature to a larger geographic average. Data Source: https://www.prism.oregonstate.edu/normals/Citation: PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu, data created 10 June 2022, accessed 10 June 2022

This dataset consists of observed, modeled historic and modeled projected average daily maximum temperatures for Fulton County for the years 1950 to 2099. The source of the data is the U.S. Climate Resilience Toolkit Climate Explorer (https://crt-climate-explorer.nemac.org/). Additional details can be found at https://crt-climate-explorer.nemac.org/about/.

This item is in Mature Support as of September 2022 and will be retired in September 2023. Please switch to using this layer. The US Global Change Research Program sponsors the semi-annual National Climate Assessment, which is the authoritative analysis of climate change and its potential impacts in the United States. The 4th National Climate Assessment (NCA4), issued in 2018, used high resolution, downscaled LOCA climate data for many of its national and regional analyses. All of the LOCA variables used in NCA4 are presented here. A mean for each county was calculated for each climate variable using the high emissions scenario called RCP 8.5 over an average time period of 2036-2065. Baseline data from 1975-2000 are also provided. Each of the RCP 8.5 values are calculated as values above or below the baseline. For example:Average Temperature Historical = 70 deg FAverage Temperature (RCP 8.5) = 3.2 deg FAverage Temperature for 2036-2065 = 73.2 deg F Long field descriptions are provided for each variable.Detailed documentation and the original data from USGCRP, processed by NOAA's National Climate Assessment Technical Support Unit at the North Carolina Institute for Climate Studies, can be accessed from the LOCA-Viewer.

This data set includes daily, population-weighted mean values of various heat metrics for every county in the contiguous United States from 2000-2020. The dataset methodology, usage notes, and additional citations are published in Scientific Data (see reference below for Spangler et al. [2022]). Minimum, maximum, and mean ambient temperature, dew-point temperature, humidex, heat index, net effective temperature, wet-bulb globe temperature, and Universal Thermal Climate Index are included. Note that Monroe County, Florida (FIPS: 12087) and Nantucket County, Massachusetts (FIPS 25019) are missing due to unavailability of ERA5-Land data for Key West, Florida and Nantucket, MA. To use these data, assign the data from the .Rds file to a new data frame in R using the readRDS() function. Please cite the use of this data set with the following reference. Note that additional citations for specific variables can be found in Table 2.

K.R. Spangler, S. Liang, and G.A. Wellenius. "Wet-Bulb Globe Temperature, Universal Thermal Climate Index, and Other Heat Metrics for US Counties, 2000-2020." Scientific Data (2022). doi: 10.1038/s41597-022-01405-3

This data set contains modified Copernicus Climate Change Service information (2022), as described and cited in the manuscript referenced above. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. This data set is provided “as is” with no warranty of any kind.

Daily meteorological observations recorded at the Crooked Creek Station in the White Mountains for the period from 1949 through 1973 were summarized by month and by year for the entire period. High, low, and average values for temperature, diurnal temperature difference, barometric pressure, snowfall, and wind speed were calculated as were average degree days and relative humidity. The data provide a quantitative description of the climate at the Crooked Creek Station during a 25 year period.

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

A pre-configured, multi-layer web map for viewing all Average Winter Temperature scenarios. (To launch the map from the Climate Change Open Data site, select "View Metadata" under the "About" heading, then look for the button labeled "Open in Map Viewer" to the upper right.) The map layers depict historical average winter (Dec-Feb) temperature and projected changes in average winter temperature. Geographic units: HUC10. Map layer data include historical (1970-1999) values plus two projections each for two future time periods, 2050s (2040-2069) and 2080s (2070-2099), based on lower and higher greenhouse gas emission scenarios, RCP 4.5 and RCP 8.5. Data classes and symbology by Robert Norheim, Climate Impacts Group, based on the CMIP5 projections used in the IPCC 2013 report. Data source: Mote et al. 2015.

These datasets contain measures of weather by county in the United States for the years 2003-2016. Measures include average daily temperature, freezing days, cold days, hot days, rainy days, and snowy days.

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 (i.e.,