This map is intended for use by the Berkshire Taconic Regional Conservation Partnership. The data used in the application are compiled from available data on ArcGIS Online. Data sources include the Litchfield Hills Greenprint, the State of Connecticut Department of Energy and Environmental Protection, Massachusetts Deparmtent of Conservation and Recreation, State of Vermont, New York State Office of Parks and Historic Preservation, New York State DEC, the United States Census Bureau, the University of Connecticut, and the United States Department of Agriculture, Natural Resources Conservation Service. This application is powered by ESRI ArcGIS Server technology.This application uses the best available data and all efforts are made to ensure accuracy, however, data may be inaccurate or incomplete. Please use this data for reference purposes only. HVA/Berkshire Taconic RCP assumes no liability for inaccurate information.

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

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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

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.

A pre-configured, multi-layer web map for viewing all Total Summer Precipitation 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 total summer (Apr-Sep) precipitation and projected changes in total summer precipitation. 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.

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.

The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf. This dataset represents heat zones, or the mean number of days over 30 C, in 4 time periods (1980-2009, 2010-2039, 2040-2069, and 2070-2099), using two emissions scenarios (RCP 4.5 and 8.5, the medium and high scenarios, respectively).

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.

Greatest number of consecutive summer (May-Sept) dry days (<1/10 inch of rain) was calculated for each year over the historical (1985-2004) and future (RCP 8.5 2071-2090) time periods; absolute and percent change between these was then calculated. This includes three versions: one based on the 20-year average of summer maxima, one based on the overall maximum, and one based on the 90th percentile value of 20-year maxima.

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.

MAT and MAP and their changes in future scenarios.

IndicatorDescriptionWildfire, BaselineAnnual hectares burned, 30-year average for 1976-2005Wildfire, RCP 4.5 Mid-CenturyAnnual hectares burned, 30-year average for 2036-2065Wildfire, RCP 8.5 Mid-CenturyAnnual hectares burned, 30-year average for 2036-2065Wildfire, RCP 4.5 Late-CenturyAnnual hectares burned, 30-year average for 2066-2095Wildfire, RCP 8.5 Late-CenturyAnnual hectares burned, 30-year average for 2066-2095Source: Cal-AdaptData: Wildfire Simulations for California’s Fourth Climate Change Assessment, University of California, Merced + Wildfire Simulations Derived Products, Geospatial Innovation Facility - University of California, Berkeley.

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_v20230410

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: v20230410.

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.

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. To cite the maps and the methodology, it is possible to refer to the scientific publication:

Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 https://doi.org/10.7717/peerj.15593

This repository contains wildfire risk maps generated by replicating the methodology described in the study “Climate Change Risk Indicators for Central Banking: Explainable AI in Fire Risk Estimations” by Burger et al. (2024):

Burger, Csaba and Herzberg, Julika and Nuvoli, Thaïs, Climate Change Risk Indicators for Central Banking: Explainable AI in Fire Risk Estimations (February 01, 2024). Available at SSRN: https://ssrn.com/abstract=4865384 or http://dx.doi.org/10.2139/ssrn.4865384

While we are not the authors of the original study, we provide geospatial outputs based on the same modeling pipeline and input data sources as described in the paper.

These maps were generated within the Alpha-Klima, in collaboration with OS-Climate. This is the first version of the maps; a GitHub repository with the full code to replicate the results will be published soon.

The dataset consists of three netCDF files covering Europe with a 2.5 x 2.5 km resolution. These maps represent estimated probabilities of wildfire occurrence and are intended for use in climate risk analysis, particularly in finance and regional planning contexts.

1. Dataset Descriptions

(a) Historical wildfire risk map (2001–2023)[alpha_klima_historical_fire_probability_2010.nc]

This raster file contains annual average wildfire occurrence probabilities for the period 2001–2023. Values are derived using a constrained XGBoost model trained on fire flags, Fire Weather Index (FWI), land cover, and proximity to critical infrastructure, following the methodology outlined in Burger et al. The model enforces monotonicity with respect to key variables to ensure physical interpretability.

(b) Forecast wildfire risk map under RCP 4.5 (2024–2050)[alpha_klima_rcp_4p5_fire_probability_2035.nc]

This file presents projected fire occurrence probabilities under the RCP 4.5 climate scenario. The forecasts assume fixed land cover and infrastructure as of 2022, with only climate variables (e.g. FWI) evolving according to scenario data from the Copernicus Climate Data Store. Probabilities are computed using a recursive Monte Carlo simulation incorporating lagged fire risk.

(c) Forecast wildfire risk map under RCP 8.5 (2024–2050)[alpha_klima_rcp_8p5_fire_probability_2035.nc]

Similar to (b), this raster provides projected fire probabilities under the more extreme RCP 8.5 scenario. The map highlights regions expected to see the greatest increase in fire risk due to intensifying climatic conditions.

This dataset is the 1 km global cropland dataset from 10000 BCE to 2100 CE. It contains a total of 131 global cropland maps at 1 km resolution from past to future. The time-step intervals are 1000 years for 10000 BCE-1 CE, 100 years for 1 CE-1700 CE, and 10 years for 1700 CE-2100 CE. After 2010 CE, eight future SSP-RCP scenarios are provided. The map values indicate the proportion of cropland within 1×1 km grid cell.

This dataset can also be viewed online at https://cbw.users.earthengine.app/view/globalcroplanddataset

Citations:

When using this dataset, please cite both the dataset and the following data description article:

Cao, B., Yu, L., Li, X., Chen, M., Li, X., Hao, P., and Gong, P.: A 1 km global cropland dataset from 10 000 BCE to 2100 CE, Earth Syst. Sci. Data, 13, 5403–5421, https://doi.org/10.5194/essd-13-5403-2021, 2021.

A pre-configured, multi-layer web map for viewing all Total Winter Precipitation 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 total winter (Oct-Mar) precipitation and projected changes in total winter precipitation. 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.

A. current conditions; B. RCP 2.6 by 2050; C. RCP 8.5 by 2050; D. RCP 2.6 by 2070; E. RCP 8.5 by 2070. Warm areas: suitable; Cold areas: unsuitable, for tiger mosquito. The maps were built using the free and open source QGIS software version 3.10.11 (https://www.qgis.org/en/site/about/index.html) and shapefiles were obtained from the free and open source DIVA-GIS site (https://www.diva-gis.org/gdata). (ZIP)

Projected changes in total summer (Apr-Sep) precipitation for the 2050s, RCP 8.5. 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.

Levels of rice yield reduction ratio of MRD’s provinces due to negative impacts of El Niño in WS season and La Niña in AW season.