Average Extreme Heat Days in 2100 (projected with higher emission scenario). Higher emission 2100 scenario (RCP 8.5) and the Coupled Model Intercomparison Project Model (CMIP5).

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.0.]What does the data show? The Annual Count of Extreme Summer Days is the number of days per year where the maximum daily temperature is above 35°C. It measures how many times the threshold is exceeded (not by how much) in a year. Note, the term ‘extreme summer days’ is used to refer to the threshold and temperatures above 35°C outside the summer months also contribute to the annual count. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.The Annual Count of Extreme Summer Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of extreme summer days to previous values.What are the possible societal impacts?The Annual Count of Extreme Summer Days indicates increased health risks, transport disruption and damage to infrastructure from high temperatures. It is based on exceeding a maximum daily temperature of 35°C. Impacts include:Increased heat related illnesses, hospital admissions or death affecting not just the vulnerable. Transport disruption due to overheating of road and railway infrastructure.Other metrics such as the Annual Count of Summer Days (days above 25°C), Annual Count of Hot Summer Days (days above 30°C) and the Annual Count of Tropical Nights (where the minimum temperature does not fall below 20°C) also indicate impacts from high temperatures, however they use different temperature thresholds.What is a global warming level?The Annual Count of Extreme Summer Days is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Count of Extreme Summer Days, an average is taken across the 21 year period. Therefore, the Annual Count of Extreme Summer Days show the number of extreme summer days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named ‘ESD’ (where ESD means Extreme Summer Days, the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Extreme Summer Days 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Extreme Summer Days 2.5 median’ is ‘ExtremeSummerDays_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘ESD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Count of Extreme Summer Days was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

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

Average Extreme Heat Days in 2050 (projected with higher emission scenario). Higher emission 2050 scenario (RCP 4.5) and the Coupled Model Intercomparison Project Model (CMIP5).

This dataset includes climate change hazard projections and combined climate hazard index values for 42,786 drinking water utilities accross the continental United States (US). The projections are compiled from multiple sources, including the Climate Mapping for Resilience and Adaptation tool (CMRA) and Climate Risk and Resilience Portal (ClimRR), and use mid-century (2050) Representative Concentration Pathway 4.5 CMIP5 Localized Constructed Analogs (LOCA) CMIP5 Projections for North America. The included climate hazards are extreme heat, energy demand, freeze-thaw cycles, extreme precipitation, wildfires, water supply stress, and sea level rise. Each row of the dataset corresponds to a different community water system within the contiguous US, each identified using their assigned Public Water System Identification number More details about the data sources and modeled combined climate hazard index can be found in the publication: Lyle et al 2025, Environ. Res.: Climate, https://doi.org/10.1088/2752-5295/adab10. Code can be found here: https://github.com/zialyle/DW-climate-change-hazard-index

The columns in the database are as follows:

pwsid: Public Water System Identification Number

primacy_agency_code: Two character postal code for the state or territory having regulatory oversight for the water system.

pws_name: Name of the water system

State: State in which water system is located

city_served: City in which water system is located

County: County in which water system is located

population_served_count: Number of customers served by water system

service_connections_count: Number of service connections maintained by water system

service_area_type_code: Service area type code

owner_type_code: Code that dentifies the ownership category of the water system consisting of: F (Federal Government), L (Local Government), M (Public/Private), N (Native American), P (Private), or S (State Government)

is_wholesaler_ind: Indicates whether the system is a wholesaler of water

primacy_type: Code that indicates if the water system is regulated by a state, tribal, or territorial primacy program. Note that EPA direct implementation programs, except for Wyoming, are tribal primacy programs

primary_source_code: The code showing the differentiation between the sources of water: ground water (GW),groundwater purchased (GWP), surface water (SW), surface water purchased (SWP), groundwater under influence of surface water (GU), or purchased ground water under influence of surface water source (GUP)

centroid_lat: Latitude ocation of water system

centroid_lon: Longitude ocation of water system

NOAA.Region: NOAA Climate Region in which water system is located

heat_index: Extreme heat index value

historic_mean_maxtemp_5d: Annual highest maximum temperature averaged over a 5-day period [degF], historical mean

RCP4.5_mid_mean_maxtemp_5d: Annual highest maximum temperature averaged over a 5-day period [degF], RCP 4.5 mid-century

RC_maxtemp_5d: Relative change in annual highest maximum temperature averaged over a 5-day period [degF] from historical to RCP 4.5 mid-century

Diff_maxtemp_5d: Absolute change in annual highest maximum temperature averaged over a 5-day period [degF] from historical to RCP 4.5 mid-century

extremeprecip_index: Extreme precipitation index value

historic_mean_highest_precip_5d: Annual highest precipitation total over a 5-day period [inches] , historical mean

RCP4.5_mid_mean_highest_precip_5d: Annual highest precipitation total over a 5-day period [inches] , RCP 4.5 mid-century

RC_highest_precip_5d: Relative change in annual highest precipitation total over a 5-day period [inches] from historical to RCP 4.5 mid-century

Diff_highest_precip_5d: Absolute change in annual highest precipitation total over a 5-day period [inches] from historical to RCP 4.5 mid-century

SLR_index: Sea level rise index value

SLR_indicator: Sea level rise indicator, where 0 indicates utility is not in a county expecting some amount of sea level rise by 2100 and 1 indicates utility is in a county expecting some amount of sea level rise by 2100.

wildfirerisk_index: Wildfire index value

RC_avg_wildfire: Relative change in Fire Weather Index from historical to RCP 4.5 mid-century

D_avg_wildfire: Absolute change in Fire Weather Index from historical to RCP 4.5 mid-century

FT_index: Freeze-Thaw cycle index value

RCP_mid_mean_FT: Number of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC), RCP 4.5

historical_mean_FT: Number of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC), historical mean

RC_FT: Relative change in the umber of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC) from historical to RCP 4.5 mid-century

Diff_FT: Absolute change in the umber of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC) from historical to RCP 4.5 mid-century

waterrisk_index: Water stress index value, using (Dickson & Dzombak, 2019)

water_stress: Change in water supply stress from historical to RCP 4.5 mid-century, using Water Supply Stress Index from (Dickson & Dzombak, 2019)

energydemand_index: Energy demand index value, using regression model developed by (Sowby & Burian, 2022)

energy_demand: Change in energy demand by mid-century under RCP 4.5 scenarios, using utility energy use model from (Sowby & Hales, 2022).

historic_mean_avg_temp: Daily average temperature [degF] , historical mean

RCP4.5_mid_mean_avg_temp: Daily average temperature [degF] , RCP 4.5 mid-century

RC_avg_temp: Relative change in daily average temperature [degF] from historical to RCP 4.5 mid-century

Diff_avg_temp: Absolute change in daily average temperature [degF] from historical to RCP 4.5 mid-century

historic_mean_avg_precip: Daily average precipitation [inches] , historical mean

RCP4.5_mid_mean_avg_precip: Daily average precipitation [inches] , RCP 4.5 mid-century

RC_avg_precip: Relative change in daily average precipitation [inches] from historical to RCP 4.5 mid-century

Diff_avg_precip: Absolute change in daily average precipitation [inches] from historical to RCP 4.5 mid-century

hazard_index: Combined climate change hazard index value, normalized from 0 to 1

hazard_index_group: Classification group for combined climate change hazard index value (minimal, low, moderate, high)

heat_threshold: Binary value indicating whether PWS exceeded risk threshold level for extreme heat (0 indicating no, 1 indicating yes)

precip_threshold: Binary value indicating whether PWS exceeded risk threshold level for extreme precipitation (0 indicating no, 1 indicating yes)

SLR_threshold: Binary value indicating whether PWS exceeded risk threshold level for sea level rise (0 indicating no, 1 indicating yes)

wildfire_threshold: Binary value indicating whether PWS exceeded risk threshold level for wildfires (0 indicating no, 1 indicating yes)

FT_threshold: Binary value indicating whether PWS exceeded risk threshold level for freeze-thaw cycles (0 indicating no, 1 indicating yes)

waterstress_threshold: Binary value indicating whether PWS exceeded risk threshold level for water stress (0 indicating no, 1 indicating yes)

energydemand_threshold: Binary value indicating whether PWS exceeded risk threshold level for enegery demand (0 indicating no, 1 indicating yes)

sum: Total number of climate hazard risk threshold values exceeded

exposure: Product of combined climate change hazard index value and population served

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.0.]What does the data show? The Annual Count of Tropical Nights is the number of days per year where the minimum daily temperature is above 20°C. It measures how many times the threshold is exceeded (not by how much). It measures how many times the threshold is exceeded (not by how much) in a year. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.The Annual Count of Tropical Nights is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of tropical nights to previous values. What are the possible societal impacts?The Annual Count of Tropical Nights indicates increased health risks and heat stress due to high night-time temperatures. It is based on exceeding a minimum daily temperature of 20°C, i.e. the temperature does not fall below 20°C for the entire day. Impacts include:Increased heat related illnesses, hospital admissions or death for vulnerable people.Increased heat stress, it is important the body has time to recover from high daytime temperatures during the lower temperatures at night.Other metrics such as the Annual Count of Summer Days (days above 25°C), Annual Count of Hot Summer Days (days above 30°C) and the Annual Count of Extreme Summer Days (days above 35°C) also indicate impacts from high temperatures, however they use different temperature thresholds.What is a global warming level?The Annual Count of Tropical Nights is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Count of Tropical Nights, an average is taken across the 21 year period. Therefore, the Annual Count of Tropical Nights show the number of tropical nights that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named ‘Tropical Nights’, the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Tropical Nights 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Tropical Nights 2.5 median’ is ‘TropicalNights_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘Tropical Nights 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Count of Tropical Nights was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

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-2019-0001This 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.

This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat.

A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.

To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative Copernicus Climate Change Service (C3S). For the observations the e-OBS dataset is used and for the future projections the EURO-CORDEX dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the Copernicus Climate Data Store (CDS).

The database is organized in three datasets:

Regional_eobs_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:

region: unique identifier of the corresponding EUROSTAT NUTS_ID.

tmax: daily maximum temperature threshold.

tmin: daily minimum temperature threshold.

Historical_eobs_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:

region: unique identifier of the corresponding EUROSTAT NUTS_ID.

start: first date of the heatwave.

tmax: maximum temperature reached during the heatwave.

intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.

duration: duration of the heatwave.

Future_and_baseline_eobs_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are:

hazard_level: it can be a warning, an alert or an alarm.

region: unique identifier of the corresponding EUROSTAT NUTS_ID.

experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.

period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.

decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.

decade_frequency_best: only applicable to the future. It determines the best projection among the models.

decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.

year_days: average annual days.

year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.

year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.

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), heat zones (HeatZone), and cumulative drought severity (CDSI). 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. (2007) at a 1/8 degree grid scale and used to obtain growing degree days, plant hardiness zones, and heat zones. Monthly precipitation and temperature downscaled to 30 arc-seconds (~800 meters) by Daly et al. (2008) for the period 1980–2015 and Thrasher et al. (2013) for the period 2016–2099, were aggregated to a 10 square kilometer grid and used to calculate a self-calibrated palmer drought severity index that was then aggregated into 30-year cumulative drought severity index values. 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 13 (CDSI) or 14 (GDD, HeatZone, PHZ) 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: one [two for nonCDSI] baseline file (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.Forest managers planning for potential changes in precipitation, temperatures, and extreme events during the later parts of this century need information on how conditions are likely to change. The Research Map NRS-9 (Matthews et al. 2018), provides maps and regional summaries for four climatic indices across the conterminous United States that provide information on stressors related to tree establishment, growth, and survival. These climate data will support user specific evaluations and analyses.*These data are also available as a story map: https://usfs.maps.arcgis.com/apps/MapSeries/index.html?appid=96088b1c086a4b39b3a75d0fd97a4c40

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.�Plant hardiness zones provide a general indication of the extent of overwinter stress experienced by plants. PHZ are based on the average annual extreme minimum temperatures and have been used by horticulturists to evaluate the cold hardiness of plants. Specifically, the value used here is the absolute minimum temperature achieved for each year and reported as the 30-year mean. Because they reflect cold tolerance for many plant species, including woody ones, hardiness zones are most likely to reflect plant range limits. The zonal variations caused by warming temperatures in the future will therefore be useful to approximately delineate niche constraints of many plant species and hence their future range potential. Plant hardiness zones and subzones were delineated according to the USDA definitions, which break the geography into zones by 10 �F (5.56 �C) increments from zone 1 (-55 to -45.6 �C) to zone 13 (15.7 to 22 �C) of annual extreme minimum temperature. To define the coldest day per year, daily minimum temperatures were identified within the period July 1 to June 30, with the nominal year assigned to the first 6 months of the 12-month period.�Original data and associated metadata can be downloaded from this website:�https://www.fs.usda.gov/rds/archive/Product/RDS-2019-0001

This dataset includes the processing results used to create the interactive climate service Thermal Assessment Tool. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative Copernicus Climate Change Service (C3S). For the observations the e-OBS dataset is used and for the future projections the EURO-CORDEX dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the Copernicus Climate Data Store (CDS). The database is organized in three datasets: Regional_eobs_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID. tmax: daily maximum temperature threshold. tmin: daily minimum temperature threshold. Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID. start: first date of the heatwave. tmax: maximum temperature reached during the heatwave. intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds. duration: duration of the heatwave. Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are: hazard_level: it can be a warning, an alert or an alarm. region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID. experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5. period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future. decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models. decade_frequency_best: only applicable to the future. It determines the best projection among the models. decade_frequency_worst: only applicable to the future. It determines the worst projection among the models. year_days: average annual days. year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold. year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average percentage change between the 'lower' values before and after this update is -1%.]What does the data show? A Growing Degree Day (GDD) is a day in which the average temperature is above 5.5°C. It is the number of degrees above this threshold that counts as a Growing Degree Day. For example if the average temperature for a specific day is 6°C, this would contribute 0.5 Growing Degree Days to the annual sum, alternatively an average temperature of 10.5°C would contribute 5 Growing Degree Days. Given the data shows the annual sum of Growing Degree Days, this value can be above 365 in some parts of the UK.Annual Growing Degree Days are calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of GDD to previous values. What are the possible societal impacts?Annual Growing Degree Days indicate if conditions are suitable for plant growth. An increase in GDD can indicate larger crop yields due to increased crop growth from warm temperatures, but crop growth also depends on other factors. For example, GDD do not include any measure of rainfall/drought, sunlight, day length or wind, species vulnerability, or plant dieback in extremely high temperatures. GDD can indicate increased crop growth until temperatures reach a critical level above which there are detrimental impacts on plant physiology.GDD does not estimate the growth of specific species and is not a measure of season length.What is a global warming level?Annual Growing Degree Days are calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Growing Degree Days, an average is taken across the 21 year period. Therefore, the Annual Growing Degree Days show the number of growing degree days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named 'GDD' (Growing Degree Days), the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘GDD 2.5 median’ is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. ‘GDD 2.5 median’ is ‘GDD_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘GDD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, Annual Growing Degree Days were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

Background and objectivesHeat related mortality is of great concern for public health, and estimates of future mortality under a warming climate are important for planning of resources and possible adaptation measures. Papers providing projections of future heat-related mortality were critically reviewed with a focus on the use of climate model data. Some best practice guidelines are proposed for future research.MethodsThe electronic databases Web of Science and PubMed/Medline were searched for papers containing a quantitative estimate of future heat-related mortality. The search was limited to papers published in English in peer-reviewed journals up to the end of March 2017. Reference lists of relevant papers and the citing literature were also examined. The wide range of locations studied and climate data used prevented a meta-analysis.ResultsA total of 608 articles were identified after removal of duplicate entries, of which 63 were found to contain a quantitative estimate of future mortality from hot days or heat waves. A wide range of mortality models and climate model data have been used to estimate future mortality. Temperatures in the climate simulations used in these studies were projected to increase. Consequently, all the papers indicated that mortality from high temperatures would increase under a warming climate. The spread in projections of future climate by models adds substantial uncertainty to estimates of future heat-related mortality. However, many studies either did not consider this source of uncertainty, or only used results from a small number of climate models. Other studies showed that uncertainty from changes in populations and demographics, and the methods for adaptation to warmer temperatures were at least as important as climate model uncertainty. Some inconsistencies in the use of climate data (for example, using global mean temperature changes instead of changes for specific locations) and interpretation of the effects on mortality were apparent. Some factors which have not been considered when estimating future mortality are summarised.ConclusionsMost studies have used climate data generated using scenarios with medium and high emissions of greenhouse gases. More estimates of future mortality using climate information from the mitigation scenario RCP2.6 are needed, as this scenario is the only one under which the Paris Agreement to limit global warming to 2°C or less could be realised. Many of the methods used to combine modelled data with local climate observations are simplistic. Quantile-based methods might offer an improved approach, especially for temperatures at the ends of the distributions. The modelling of adaptation to warmer temperatures in mortality models is generally arbitrary and simplistic, and more research is needed to better quantify adaptation. Only a small number of studies included possible changes in population and demographics in their estimates of future mortality, meaning many estimates of mortality could be biased low. Uncertainty originating from establishing a mortality baseline, climate projections, adaptation and population changes is important and should be considered when estimating future mortality.

Statistics such as the 99th percentile are useful for identifying and providing context for local extremes. The maximum daily temperatures across the United States were analyzed from 1976-2005. The top 1% of the warmest average daily values per year were used as a baseline to compare future temperatures in 2036-2065. This layer shows the number of days that daily high temperatures are expected to exceed those top 1% of the historic values. This information is sourced from the high resolution LOCA climate models used in the 4th National Climate Assessment. Specifically, we are showing the number of days under a high CO2 emissions scenario (RCP 8.5). The original 6.5 km resolution gridded data was summarized into means for each county in the U.S. Original data can be downloaded from the LOCA-Viewer, maintained by NOAA and the US Global Climate Research Program.

The Union of Concerned Scientists (UCS) analysis shows the number of projected days per year with heat index values above 100 degrees Fahrenheit. The number of projected high heat days is a 30-year average for the midcentury timeframe (from 2036 to 2065). The projected data is based on an average of 18 independent climate models that each provide daily maximum temperature and daily minimum relative humidity. The emissions scenario included in this report is Representative Concentration Pathway (RCP) 4.5, a “slow action” scenario where emissions start to decline at midcentury. Rapid, widespread increases in extreme heat are projected to occur across the country due to climate change. Historically there were an average of X[0136] high heat days per year in your area of interest. Without swift action to mitigate climate change there are projected to be an average of Y[0137] high heat days per year by mid-century. To learn more about high heat days and their associated impacts visit this site. [hyperlink this site to https://www.ucsusa.org/resources/killer-heat-united-states-0 ]