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. The LOCA downscaling was applied to multi-model mean weighted averages, using the following 32 CMIP5 model ensemble:ACCESS1-0, ACCESS1-3, bcc-csm1-1, bcc-csm1-1-m, CanESM2, CCSM4, CESM1-BGC, CESM1-CAM5, CMCC-CM, CMCC-CMS, CNRM-CM5, CSIRO-Mk3-6-0, EC EARTH, FGOALS-g2, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H-p1, GISS-E2-R-p1, HadGEM2-AO, HadGEM2-CC, HadGEM2-ES, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM-CHEM, MIROC-ESM, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, NorESM1-M.All of the LOCA variables used in NCA4 are presented here. Many are thresholded to provide 47 actionable statistics, like days with precipitation greater than 3", length of the growing season, or days above 90 degrees F. Time RangesStatistics for each variables were calculated over a 30-year period. Four different time ranges are provided:Historical: 1976-2005Early-Century: 2016-2045Mid-Century: 2036-2065Late-Century: 2070-2099Climate ScenariosClimate models use estimates of greenhouse gas concentrations to predict overall change. These difference scenarios are called the Relative Concentration Pathways. Two different RCPs are presented here: RCP 4.5 and RCP 8.5. The number indicates the amount of radiative forcing(watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but RCP 4.5 aligns with the international targets of the COP-26 agreement, while RCP 8.5 is aligns with a more "business as usual" approach. 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 NCA Atlas. Variable DefinitionsCooling Degree Days: Cooling degree days (annual cumulative number of degrees by which the daily average temperature is greater than 65°F) [degree days (degF)]Consecutive Dry Days: Annual maximum number of consecutive dry days (days with total precipitation less than 0.01 inches)Consecutive Dry Days Jan Jul Aug: Summer maximum number of consecutive dry days (days with total precipitation less than 0.01 inches in June, July, and August)Consecutive Wet Days: Annual maximum number of consecutive wet days (days with total precipitation greater than or equal to 0.01 inches)First Freeze Day: Date of the first fall freeze (annual first occurrence of a minimum temperature at or below 32degF in the fall)Growing Degree Days: Growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F) [degree days (degF)]Growing Degree Days Modified: Modified growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F; before calculating the daily average temperatures, daily maximum temperatures above 86°F and daily minimum temperatures below 50°F are set to those values) [degree days (degF)]growing-season: Length of the growing (frost-free) season (the number of days between the last occurrence of a minimum temperature at or below 32degF in the spring and the first occurrence of a minimum temperature at or below 32degF in the fall)Growing Season 28F: Length of the growing season, 28°F threshold (the number of days between the last occurrence of a minimum temperature at or below 28°F in the spring and the first occurrence of a minimum temperature at or below 28°F in the fall)Growing Season 41F: Length of the growing season, 41°F threshold (the number of days between the last occurrence of a minimum temperature at or below 41°F in the spring and the first occurrence of a minimum temperature at or below 41°F in the fall)Heating Degree Days: Heating degree days (annual cumulative number of degrees by which the daily average temperature is less than 65°F) [degree days (degF)]Last Freeze Day: Date of the last spring freeze (annual last occurrence of a minimum temperature at or below 32degF in the spring)Precip Above 99th pctl: Annual total precipitation for all days exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]Precip Annual Total: Annual total precipitation [inches]Precip Days Above 99th pctl: Annual number of days with precipitation exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]Precip 1in: Annual number of days with total precipitation greater than 1 inchPrecip 2in: Annual number of days with total precipitation greater than 2 inchesPrecip 3in: Annual number of days with total precipitation greater than 3 inchesPrecip 4in: Annual number of days with total precipitation greater than 4 inchesPrecip Max 1 Day: Annual highest precipitation total for a single day [inches]Precip Max 5 Day: Annual highest precipitation total over a 5-day period [inches]Daily Avg Temperature: Daily average temperature [degF]Daily Max Temperature: Daily maximum temperature [degF]Temp Max Days Above 99th pctl: Annual number of days with maximum temperature greater than the 99th percentile, calculated with reference to 1976-2005Temp Max Days Below 1st pctl: Annual number of days with maximum temperature lower than the 1st percentile, calculated with reference to 1976-2005Days Above 100F: Annual number of days with a maximum temperature greater than 100degFDays Above 105F: Annual number of days with a maximum temperature greater than 105degFDays Above 110F: Annual number of days with a maximum temperature greater than 110degFDays Above 115F: Annual number of days with a maximum temperature greater than 115degFTemp Max 1 Day: Annual single highest maximum temperature [degF]Days Above 32F: Annual number of icing days (days with a maximum temperature less than 32degF)Temp Max 5 Day: Annual highest maximum temperature averaged over a 5-day period [degF]Days Above 86F: Annual number of days with a maximum temperature greater than 86degFDays Above 90F: Annual number of days with a maximum temperature greater than 90degFDays Above 95F: Annual number of days with a maximum temperature greater than 95degFTemp Min: Daily minimum temperature [degF]Temp Min Days Above 75F: Annual number of days with a minimum temperature greater than 75degFTemp Min Days Above 80F: Annual number of days with a minimum temperature greater than 80degFTemp Min Days Above 85F: Annual number of days with a minimum temperature greater than 85degFTemp Min Days Above 90F: Annual number of days with a minimum temperature greater than 90degFTemp Min Days Above 99th pctl: Annual number of days with minimum temperature greater than the 99th percentile, calculated with reference to 1976-2005Temp Min Days Below 1st pctl: Annual number of days with minimum temperature lower than the 1st percentile, calculated with reference to 1976-2005Temp Min Days Below 28F: Annual number of days with a minimum temperature less than 28degFTemp Min Max 5 Day: Annual highest minimum temperature averaged over a 5-day period [degF]Temp Min 1 Day: Annual single lowest minimum temperature [degF]Temp Min 32F: Annual number of frost days (days with a minimum temperature less than 32degF)Temp Min 5 Day: Annual lowest minimum temperature averaged over a 5-day period [degF]For For freeze-related variables:The first fall freeze is defined as the date of the first occurrence of 32degF or lower in the nine months starting midnight August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 32degF or lower are excluded from the analysis.No freeze occurrence, value = 999The last spring freeze is defined as the date of the last occurrence of 32degF or lower in the nine months prior to midnight August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 32degF or lower are excluded from the analysis.No freeze occurrence, value = 999The growing season is defined as the number of days between the last occurrence of 28degF/32degF/41degF or lower in the nine months prior to midnight August 1 and the first occurrence of 28degF/32degF/41degF or lower in the nine months starting August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 28degF/32degF/41degF or lower are excluded from the analysis.No freeze occurrence, value = 999

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Download this data or get more information

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Heat risk exposure for the RCP 8.5 emission scenario in the future period 2041-2060, expressed on a low-medium-high scale. For methodological details, see the attached file.

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. 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

Average historical annual total precipitation (mm) and projected relative change in total precipitation (% change from baseline) for Northern Alaska. 30-year averages. Handout format. Maps created using the SNAP 5-GCM composite (AR5-RCP 8.5) and CRU TS3.1.01 datasets.

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Evaluating multiple signals of climate change across the conterminous United States during three 30-year periods (2010�2039, 2040�2069, 2070�2099) during this century to a baseline period (1980�2009) emphasizes potential changes for growing degree days (GDD), plant hardiness zones (PHZ), and heat zones. These indices were derived using the CCSM4 and GFDL CM3 models under the representative concentration pathways 4.5 and 8.5, respectively, and included in Matthews et al. (2018). Daily temperature was downscaled by Maurer et al. (https://doi.org/10.1029/2007EO470006) at a 1/8 degree grid scale and used to obtain growing degree days, plant hardiness zones, and heat zones. Each of these indices provides unique information about plant health related to changes in climatic conditions that influence establishment, growth, and survival. These data and the calculated changes are provided as 14 individual IMG files for each index to assist with management planning and decision making into the future. For each of the four indices the following are included: two baseline files (1980�2009), three files representing 30-year periods for the scenario CCSM4 under RCP 4.5 along with three files of changes, and three files representing 30-year periods for the scenario GFDL CM3 under RCP 8.5 along with three files of changes.Heat zones map the distribution of potential heat stress for plants and animals, including humans. We define heat zones as the number of days with maximum daily temperature >30 �C (86 �F). Because species have unique adaptations and abilities to tolerate a wide variety of conditions, this metric is used merely as an indicator of change in �hot� conditions. The 30 �C value is set primarily for agricultural production and is a general temperature threshold at which photosynthesis can be negatively impacted for C3 plants (e.g., most species including trees), but it certainly also captures temperatures that induce stress in humans as well. In addition, increases in temperature above these thresholds for longer periods, especially when accompanied with prolonged dry conditions, are linked to reduced performance and likely mortality of trees. Each day surpassing the 30 �C threshold was tallied and summed for each year and reported as the mean number of days, per year, over each 30-year period: baseline, early, mid, and late century.�Original data and associated metadata can be downloaded from this website:�https://www.fs.usda.gov/rds/archive/Product/RDS-2019-0001

A pre-configured, multi-layer web map for viewing all Average Summer 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 summer (Jun-Aug) temperature and projected changes in average summer 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.

Under various scenarios, land use changes in Belgium are simulated at 10-meter resolution. Three SSP-RCP scenarios were used to model the land use trends in the present (2020) and the year 2050 at the national level in Belgium. Key inputs to the model include regional land use demand, quantification of the suitability of grid cells for different land use types, and a reference land cover map. The 10 meter-resolution baseline land use map of Belgium was sourced from the European Space Agency (ESA) WorldCover for the reference year 2020. The classification systems ESA is different from LUH2. To make these datasets comparable for land use simulations, we performed reclassification based on the guidelines provided by Pérez-Hoyos et al. (2012); Dong et al. (2018); Liao et al. (2020) to unify the land use classes, except water, into six general categories: 1) urban, 2) cropland, 3) pasture, 4) forestry, 5) bare/sparse vegetation, and 6) undefined.

Projected change in average summer (Jun–Aug) temperature in °F for the 2080s based on the RCP 8.5 greenhouse gas emission scenario. Geographic units: HUC10. These data are part of a set that includes 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. When rendered and displayed in Map Viewer (web map): 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.

Baseline (1961-1990) average winter temperature in and projected change in temperature for for the northern portion of Alaska. For the purposes of these maps, 'winter' is defined as December - February. The Alaska portion of the Arctic LCC's terrestrial boundary is depicted by the black line. Baseline results for 1961-1990 are derived from Climate Research Unit (CRU) TS3.1 data and downscaled to 2km grids; results for the other time periods (2010-2039, 2040-2069, 2070-2099) are based on the SNAP 5-GCM composite using the AR5-RCP 8.5, downscaled to 2km grids.

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Download this data or get more information

Date of thaw for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Download this data or get more information

MAT and MAP and their changes in future scenarios.

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. 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

Baseline (1961-1990) average winter temperature in and projected change in temperature for for the northern portion of Alaska. For the purposes of these maps, 'winter' is defined as December - February. The Alaska portion of the Arctic LCC's terrestrial boundary is depicted by the black line. Baseline results for 1961-1990 are derived from Climate Research Unit (CRU) TS3.1 data and downscaled to 2km grids; results for the other time periods (2010-2039, 2040-2069, 2070-2099) are based on the SNAP 5-GCM composite using the AR5-RCP 8.5, downscaled to 2km grids.

Degree days are based on the assumption that when the outside temperature is 65°F, we don't need heating or cooling to be comfortable. Cooling Degree Days (CDD) are the difference between the daily temperature mean (high temperature plus low temperature divided by two) and 65°F. In essence, it tells us how many degrees we need to cool our houses/buildings by each day to achieve that "comfortable" level. More information on CCDs can be found here. This layer shows the total number of CDDs needed per year over the average period of 2036-2065. This information is sourced from the high resolution LOCA climate models used in the 4th National Climate Assessment. Specifically, we are showing CDDs under a high CO2 emissions scenario (RCP 8.5), which is, at this point, the most realistic scenario. Time Extent: Annual average from 2036-2065Units: degree daysCell Size: 1/16th degree (~6 km)Source Type: StretchedPixel Type: 32 Bit floating pointData Projection: GCS WGS84Extent: United States plus some of Canada and MexicoSource: CMIP5 Localized Constructed Analogs (LOCA)What can this layer be used for?In addition to mapping, this ArcGIS Imagery for ArcGIS Online tile imagery layer supports spatial analysis, and contains 32-bit floating point values for CDD. Original data can be downloaded from the LOCA-Viewer.

Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080.

Files are named according to the following naming convention, e.g.:

• biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230110

with the following fields:

• generic theme: biomes,
• variable name: graminoid.and.forb.tundra.rcp85,
• variable type, e.g. probability ("p"), hard class ("c"), model deviation ("md")
• spatial resolution: 1km,
• depth reference, e.g. below ("b"), above ("a") ground or at surface ("s"),
• begin time (YYYYMMDD): 20610101,
• end time: 20801231,
• bounding box, e.g. global land without Antarctica ("go"),
• EPSG code: epsg.4326,
• version code, e.g. creation date: v20230110.

We provide probability and hard class layers using a revised classification system of the BIOME 6000 project explained in the work of Hengl et al. (2018). The 20 classes from this classification system have then been aggregated in 6 biome classes following the IUCN Global Ecosystem Typology classification system.

For probability layers, the uncertainty (model deviation: md) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel.

For hard class layers the uncertainty is calculated using the margin of victory (Calderón-Loor et al., 2021) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the md layers to properly interpret the results of the map.

Styling files are provided in both .SLD and .QML format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics.

A publication describing, in detail, all processing steps, accuracy assessment and general analysis of the biome maps is under preparation The R scripts and a tutorial will be uploaded to the PNVmaps Github repository, where previous versions of the biomes maps from Hengl et al. (2018) is currently hosted. In the meantime, to cite the maps and the methodology, it is possible to refer to the preprint:

Bonannella, C., Hengl, T., Parente, L., and de Bruin, S. (2023). "Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation". Preprint (Version 1) available on Research Square [https://doi.org/10.21203/rs.3.rs-2471847/v1]

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