Temperatures have risen in the last 100 years around the world. In the 1910s, global average temperatures were some 0.38 degrees Celsius lower than the average temperatures between 1910 and 2000. In the most recent decade, the world experienced temperatures that were 1.21 degrees Celsius over the average.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

Temperatures have risen in the last 100 years around the world. In the 1910s, North America had an average temperature some **** degrees Celsius lower than average temperatures between 1910 and 2000. In the most recent decade, this region experienced temperatures **** degrees Celsius over the average. All global regions (excluding Oceania) experienced an increased temperature over one degree Celsius in the 2010s, compared to the average between 1910 and 2000.

The Atlas shows regional climate conditions projected to occur if Earth’s long-term average temperature reaches specific levels of warming. These Global Warming Levels (GWLs) correspond to global average temperature increases of 1.5, 2, 3, and 4 degrees Celsius above pre-industrial levels measured from 1851 to 1900. On the Fahrenheit scale, these warming levels are 2.7, 3.6, 5.4, and 7.2 °F. As of the 2020s, global average temperature has already increased around 2 °F above pre-industrial levels. County projections for each global warming level were calculated by identifying the year when individual climate models reach that level. The projections were then averaged with the 9 previous years and the 10 subsequent years to represent the climatic state for that period. Using a 30-year average avoids focusing on a shorter period that may be warmer or cooler than the two-decade average. Projections for all GWLs are based on the same fossil-fuel intensive scenario, SSP5-8.5.Variables IncludedDaily temperature and precipitation projections were used to calculate more decision-relevant thresholds of climate exposure, which are available in this feature layer. The data variables and field names in this feature layer were used to create the maps found in the NCA Interactive Atlas Explorer. Name of Map Description of the variableVariable Field NameChange in Average Annual TemperatureAverage temperature (daily high + daily low/2) for the entire yearTemperature averageChange in Average Annual Maximum TemperatureAverage of the highest temperature of the day over a full yearTemp Annual MaxChange in Mean Summer TemperatureAverage temperature (daily high + daily low/2) during June, July, & AugustTemp mean summer Change in Temperature on the Hottest Day of the Year Highest temperature on the hottest day of the year Temp max 1-dayChange in Lowest Temperatures of SummerAverage of the lowest temperature of the day during June, July, AugustTemp min summerChange in the Number of Days Over 95°FDays per year with an afternoon high temperature of at least 95°FTemp Days 95 FChange in the Number of Days Over 100°FDays per year with an afternoon high temperature of at least 100°FTemp Days 100 FChange in the Number of Days Over 105°FDays per year with an afternoon high temperature of at least 105°FTemp Days 105 FChange in the Number of Warm NightsDays per year when the overnight low is 70°F or warmerTemp Days Min 70 FChange in the Number of Days Under 32 deg FDays per year when the lowest temperature is below freezingTemp Days Min 32 FChange in the Number of Days below 0°FDays per year when the lowest temperature is well below freezingTemp Days Min 0 FChange in Annual PrecipitationTotal precipitation over a full yearPrecip AnnualChange in Extreme Precipitation Total precipitation over a year that arrives on days when the daily total is in the top 1% of historical amountsPrecip Above 99th pctlChange in Days with Extreme PrecipitationDays per year when precipitation totals are in the top 1% of historical amountsPrecip Days 99 pctlChange in Precipitation on the Wettest Day of the YearHighest daily precipitation total of the yearPrecip 1-day maxChange in Precipitation on the Wettest Day in 5 YearsHighest daily precipitation total over five yearsPrecip 5-year maxCoastal Inundation related to Sea Level RiseAreas projected to be below sea level in 2100SLR_InundationDownscaled Climate ProjectionsProjections in the Atlas are from global climate models that participated in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). To make the CMIP6 projections more relevant at regional-to-local scales, results from global models were spatially downscaled using statistical methods documented by LOCA2 and STAR-ESDM. Note that climate projections are not weather forecasts for specific dates in the future—rather, they describe potential climate conditions for future decades based on plausible scenarios of human actions.Climate Projections for States and Territories Outside the Contiguous United StatesThe availability of downscaled climate projections for geographies outside of the contiguous United States is limited. For locations in Alaska, Hawai‘i, and Puerto Rico, the Atlas includes global data from CMIP6 and downscaled data from STAR-ESDM for selected weather stations. Specifically, where downscaled data are not available, the Atlas includes results from an ensemble of individual models in the scenario model intercomparison project (ScenarioMIP).More details are available from the NCA Interactive Atlas site.

This map plots the Change in Number of Days Over 100 °F if Earth’s long-term average temperature reaches specific levels of warming. These Global Warming Levels (GWLs) correspond to global average temperature increases of 1.5, 2, 3, and 4 °C above pre-industrial levels measured from 1851 to 1900. On the Fahrenheit scale, these warming levels are 2.7, 3.6, 5.4, and 7.2 °F. As of the 2020s, global average temperature has already increased around 2 °F above pre-industrial levels.Each layer of the map is style with the same range of data so that the spatial patterns of change can be compared across all scenarios. The projections are derived from downscaled climate models from LOCA2 and STAR-ESDM, and were used in the 5th National Climate Assessment. Click on the layers below to view more detailed descriptions of how the data was generated.

The recent experiments performed at the Hadley Centre have used the new Unified Model (Cullen, 1993). These experiments represent a large step forward in the way climate change is modelled by GCMs and raises new possibilities for scenario construction. This experiment has overcome some of the major difficulties that were associated with the previous generations of equilibrium (circa IPCC 1990) and cold-start transient (circa IPCC 1992) climate change experiments. HadCM2 has a spatial resolution of 2.5 degrees x 3.75 degrees (latitude by longitude) and the representation produces a grid box resolution of 96 x 73 grid cells. This produces a surface spatial resolution of about 417km x 278 km reducing to 295 x 278km at 45 degrees North and South (comparable to a spectral resolution of T42). The equilibrium climate sensitivity (DT2x) of HadCM2, that is the global-mean temperature response to a doubling of effective CO2 concentration, is approximately 2.5 degrees C, although, this quantity varies with the time-scale considered. This is somewhat lower than most other GCMs (IPCC, 1992). In order to undertake a 'warm-start' experiment it is necessary to perturb the model with a forcing from an early historical era, when the radiative forcing was relatively small compared to the present. The Hadley Centre started their experiments performed with HadCM2 with forcing from the middle industrial era, about 1860 Mitchell et al., 1995 and Johns et al., 1995. The greenhouse gas only integrations, HadCM2GG, used the combined forcing of all the greenhouse gases as an equivalent CO2 concentration. A further series of integrations, HadCM2GS, used the combined equivalent CO2 concentration plus the negative forcing from sulphate aerosols. The HadCM2GG integrations simulated the change in forcing of the climate system by greenhouse gases since the early industrial period (taken by HadCM2 to be 1860). The addition of the negative forcing effects of sulphate aerosols represents the direct radiative forcing due to anthropogenic sulphate aerosols by means of an increase in clear-sky surface albedo proportional to the local sulphate loading (refer to Mitchell et al., 1995 for details of this method). The indirect effects of aerosols were not simulated. The modelled control climate shows a negligible long term trend in surface air temperature over the first 400 years. The trend is about +0.04 degrees C per century, which is comparable to other such experiments. HadCM2CON represents an improvement over previous generations of GCMs that have been used at the Hadley Centre (Johns et al., 1995 and Airey et al., 1995). The experiments performed have simulated the observed climate system using estimated forcing perturbations since 1860. Johns et al., (1995) and Mitchell et al., (1995) have established that HadCM2's sensitivity is consistent with the real climate system. The agreement between the observed global-mean temperature record and that produced in these experiments is better for HadCM2GS than for HadCM2GG. This implies that HadCM2Gs has captured the observed signal of global-mean temperature changes better than HadCM2GG for the recent 100-year record. The climate sensitivity of HadCM2 is about 2.5 degrees C For the A2 emissions scenario the main emphasis is on a strengthening of regional and local culture, with a return to family values in many regions. The A2 world consolidates into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attention on contributing to the local community. Elsewhere, the trend is towards increased investment in education and science and growth in economic productivity. Social and political structures diversify, with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities. The A2 world sees more international tensions and less cooperation than in A1 or B1. People, ideas and capital are less mobile so that technology diffuses slowly. International disparities in productivity, an... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.203.2 for complete metadata about this dataset.

Led by the Massachusetts Executive Office of Energy and Environmental Affairs (EEA), in partnership with Cornell University, U.S. Geological Survey and Tufts University, the Massachusetts Climate and Hydrologic Risk Project (Phase 1) has developed new climate change projections for the Commonwealth. These new temperature and precipitation projections are downscaled for Massachusetts at the HUC8 watershed scale using Global Climate Models (GCMs) and a Stochastic Weather Generator (SWG) developed by Cornell University.

Stochastic weather generators provide a computationally efficient and complementary alternative to direct use of GCMs for investigating water system performance under climate stress. These models are configured based on existing meteorological records (i.e., historical weather) and are then used to generate large ensembles of simulated daily weather records that are similar to but not bound by variability in past observations. Once fit to historical data, model parameters can be systematically altered to produce new traces of weather that exhibit a wide range of change in their distributional characteristics, including the intensity and frequency of average and extreme precipitation, heatwaves, and cold spells.

The Phase 1 SWG was developed, calibrated, and validated across all HUC8 watersheds that intersect with the state of Massachusetts. A set of climate change scenarios for those watersheds were generated that only reflect mechanisms of thermodynamic climate change deemed to be most credible. These thermodynamic climate changes are based on the range of temperature projections produced by a set of downscaled GCMs for the region. The temperature and precipitation projections presented in this dashboard reflect a warming scenario linked to the Representation Concentration Pathway (RCP) 8.5, a comparatively high greenhouse gas emissions scenario.

The statistics presented in this series of map layers are expressed as either a percent change or absolute change (see list of layers with units and definitions below). These changes are referenced to baseline values that are calculated based on the median value across the 50 model ensemble members associated with the 0°C temperature change scenario derived from observational data (1950-2013) from Livneh et al. (2015). The temperature projections derived from the downscaled GCMs for the region, which are used to drive the SGW, are averaged across 30 years and centered on a target decade (i.e., 2030, 2050, 2070). Projections for 2090 are averaged across 20 years.Definitions of climate projection metrics (with units of change):Total Precipitation (% change): The average total precipitation within a calendar year. Maximum Precipitation (% change): The maximum daily precipitation in the entire record. Precipitation Depth – 90th Percentile Storm (% change): The 90th percentile of non-zero precipitation. Precipitation Depth –99th Percentile Storm (% change): The 99th percentile of non-zero precipitation. Consecutive Wet Days (# days): The average number of days that exist within a run of 2 or more wet days. Consecutive Dry Days (# days): The average number of days that exist within a model run of 2 or more dry days. Days above 1 inch (# days): The number of days with precipitation greater than 1 inch. Days above 2 inches (# days): The number of days with precipitation greater than 2 inches.Days above 4 inches (# days): The number of days with precipitation greater than 4 inches.Maximum Temperature (°F): The maximum daily average temperature value in the entire recordAverage Temperature (°F): Daily average temperature.Days below 0 °F (# days): The number of days with temperature below 0 °F.Days below 32 °F (# days): The number of days with temperature below 32 °F.Maximum Duration of Coldwaves (# days): Longest duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Average Duration of Coldwaves (# days): Average duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Number of Coldwave Events (# events): Number of instances with ten or more consecutive days with temperature below 20 °F.Number of Coldstress Events (# events): Number of instances when a 3-day moving average of temperature is less than 32 °F. Days above 100 °F (# days): The number of days with temperature above 100 °F.Days above 95 °F (# days): The number of days with temperature above 95 °F.Days above 90 °F (# days): The number of days with temperature above 90 °F.Maximum Duration of Heatwaves (# days): Longest duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Average Duration of Heatwaves (# days): Average duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Number of Heatwave Events (# events): Number of instances with three or more consecutive days with temperature over 90 °F.Number of Heatstress Events (# events): Number of instances when a 3-day moving average of temperature is above 86 °F.Cooling Degree Days (# degree-day): Cooling degree days assume that when the outside temperature is below 65°F, we don't need cooling (air-conditioning) to be comfortable. Cooling degree-days are the difference between the daily temperature mean and 65°F. For example, if the temperature mean is 85°F, we subtract 65 from the mean and the result is 20 cooling degree-days for that day. (Definition adapted from National Weather Service).Heating Degree Days (# degree-day): Heating degree-days assume that when the outside temperature is above 65°F, we don't need heating to be comfortable. Heating degree days are the difference between the daily temperature mean and 65°F. For example, if the mean temperature mean is 25°F, we subtract the mean from 65 and the result is 40 heating degree-days for that day. (Definition adapted from National Weather Service).Growing Degree Days (# degree-day): A growing degree day (GDD) is an index used to express crop maturity. The index is computed by subtracting a base temperature of 50°F from the average of the maximum and minimum temperatures for the day. Minimum temperatures less than 50°F are set to 50, and maximum temperatures greater than 86°F are set to 86. These substitutions indicate that no appreciable growth is detected with temperatures lower than 50° or greater than 86°. (Adapted from National Weather Service).Please see additional information related to this project and dataset in the Climate Change Projection Dashboard on the Resilient MA Maps and Data Center webpage.

The recent experiments performed at the Hadley Centre have used the new Unified Model (Cullen, 1993). These experiments represent a large step forward in the way climate change is modelled by GCMs and raises new possibilities for scenario construction. This experiment has overcome some of the major difficulties that were associated with the previous generations of equilibrium (circa IPCC 1990) and cold-start transient (circa IPCC 1992) climate change experiments. HadCM2 has a spatial resolution of 2.5 degrees x 3.75 degrees (latitude by longitude) and the representation produces a grid box resolution of 96 x 73 grid cells. This produces a surface spatial resolution of about 417km x 278 km reducing to 295 x 278km at 45 degrees North and South (comparable to a spectral resolution of T42). The equilibrium climate sensitivity (DT2x) of HadCM2, that is the global-mean temperature response to a doubling of effective CO2 concentration, is approximately 2.5 degrees C, although, this quantity varies with the time-scale considered. This is somewhat lower than most other GCMs (IPCC, 1992). In order to undertake a 'warm-start' experiment it is necessary to perturb the model with a forcing from an early historical era, when the radiative forcing was relatively small compared to the present. The Hadley Centre started their experiments performed with HadCM2 with forcing from the middle industrial era, about 1860 Mitchell et al., 1995 and Johns et al., 1995. The greenhouse gas only integrations, HadCM2GG, used the combined forcing of all the greenhouse gases as an equivalent CO2 concentration. A further series of integrations, HadCM2GS, used the combined equivalent CO2 concentration plus the negative forcing from sulphate aerosols. The HadCM2GG integrations simulated the change in forcing of the climate system by greenhouse gases since the early industrial period (taken by HadCM2 to be 1860). The addition of the negative forcing effects of sulphate aerosols represents the direct radiative forcing due to anthropogenic sulphate aerosols by means of an increase in clear-sky surface albedo proportional to the local sulphate loading (refer to Mitchell et al., 1995 for details of this method). The indirect effects of aerosols were not simulated. The modelled control climate shows a negligible long term trend in surface air temperature over the first 400 years. The trend is about +0.04 degrees C per century, which is comparable to other such experiments. HadCM2CON represents an improvement over previous generations of GCMs that have been used at the Hadley Centre (Johns et al., 1995 and Airey et al., 1995). The experiments performed have simulated the observed climate system using estimated forcing perturbations since 1860. Johns et al., (1995) and Mitchell et al., (1995) have established that HadCM2's sensitivity is consistent with the real climate system. The agreement between the observed global-mean temperature record and that produced in these experiments is better for HadCM2GS than for HadCM2GG. This implies that HadCM2Gs has captured the observed signal of global-mean temperature changes better than HadCM2GG for the recent 100-year record. The climate sensitivity of HadCM2 is about 2.5 degrees C For the A2 emissions scenario the main emphasis is on a strengthening of regional and local culture, with a return to family values in many regions. The A2 world consolidates into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attention on contributing to the local community. Elsewhere, the trend is towards increased investment in education and science and growth in economic productivity. Social and political structures diversify, with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities. The A2 world sees more international tensions and less cooperation than in A1 or B1. People, ideas and capital are less mobile so that technology diffuses slowly. International disparities in productivity, an... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.218.4 for complete metadata about this dataset.

Some of the SNK rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This set of files includes downscaled projections of decadal means of monthly mean temperatures (in degrees Celsius, no unit conversion necessary) for each month of decades 2020-2029, 2050-2059, and 2060-2069 at 2x2 kilometer spatial resolution. Each file represents a mean monthly mean in a given decade.

The spatial extent is clipped to a Seward REA boundary bounding box.

Overview:

Most of SNAP#8217;s climate projections come in multiple versions. There are 5 climate models, one 5 model average, 3 climate scenarios, 12 months, and 100 years. This amounts to 21,600 files per variable. Some datasets are derived products such as monthly decadal averages or specific seasonal averages, among others. This specific dataset is one subset of those.

Each set of files originates from one of five top ranked global circulation models or is calculated as a 5 Model Average. These models are referred to by the acronyms: cccma_cgcm31, mpi_echam5, gfdl_cm21, ukmo_hadcm3, miroc3_2_medres, or 5modelavg.

For a description of the model selection process, please see Walsh et al. 2008. Global Climate Model Performance over Alaska and Greenland. Journal of Climate. v. 21 pp. 6156-6174

Each set of files also represents one projected emission scenario referred to as: sresb1, sresa2, or sresa1b.

Emmission scenarios in brief:

The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) created a range of scenarios to explore alternative development pathways, covering a wide range of demographic, economic and technological driving forces and resulting greenhouse gas emissions. The B1 scenario describes a convergent world, a global population that peaks in mid-century, with rapid changes in economic structures toward a service and information economy. The Scenario A1B assumes a world of very rapid economic growth, a global population that peaks in mid-century, rapid introduction of new and more efficient technologies, and a balance between fossil fuels and other energy sources. The A2 scenario describes a very heterogeneous world with high population growth, slow economic development and slow technological change.

These files are bias corrected and downscaled via the delta method using PRISM (http:prism.oregonstate.edu) 1961-1990 2km data as baseline climate. Absolute anomalies are utilized for temperature variables. Proportional anomalies are utilized for precipitation variables. Please see http:www.snap.uaf.eduabout for a description of the downscaling process.

File naming scheme:

[variable]_[metric]_[units]_[format]_[assessmentReport] [groupModel][scenario]_[timeFrame].[fileFormat]

[variable] pr, tas, logs, dot, dof, veg, age, dem etc

[metric] mean, total, decadal mean monthly mean, etc

[units] mm, C, in, km

[format] optional, if layer is formatted for special use

[assessmentReport] ar4, ar5

[groupModel] cccma_cgcm31, mpi_echam5, gfdl_cm21, ukmo_hadcm3, miroc3_2_medres, 5modelavg, cru_ts30

[scenario] sresb1, sresa2, sresa1b

[timeFrame] yyyy or mm_yyyy or yyyy_yyyy or mm_yyyy_mm_yyyy

[fileFormat] txt, png, pdf, bmp, tif

examples:

tas_mean_C_ar4_cccma_cgcm3_1_sresb1_05_2034.tif

this file represents mean May, 2034 temperature from the 4th Assessment Report on Climate Change from the CCCMA modeling group, using their CGCM3.1 model, under the B1 climate scenario.

pr_total_mm_ar4_5modelAvg_sresa1b_09_2077.tif

this file represents total September, 2077 precipitation from the 4th Assessment Report on Climate Change from the 5 Model Average, under the A1B climate scenario.

tas = near-surface air temperature

pr = precipitation including both liquid and solid phases

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. Growing degree days address an important component to general patterns of plant growth by accumulating the degree days across the growing season. This metric provides a level of detail related to defining the growing season potential. Here, we evaluate the accumulation of growing degree days at or above 5 �C (41 �F), assuming that limited growth occurs below 5 �C.�Specifically, we calculate growing degree days by first calculating the average daily temperature, based on the maximum and minimum projected daily temperature. We then subtract 5 �C from each mean value and then accumulate the positive difference values for all days within each year. The mean GDD values for the conterminous United States during the baseline period ranged from less than 100 to over 7,000 degree days, increasing from north to south with highest values in the Florida panhandle, southern Texas, southwestern Arizona, and southeastern California. GDD projections throughout the century suggest a ubiquitous increase across the United States with slightly less change in the Northeast and much greater increases throughout the southern United States under the high scenario. Original data and associated metadata can be downloaded from this website:�https://www.fs.usda.gov/rds/archive/Product/RDS-2019-0001

This series is composed of five select physical marine parameters (water salinity and water temperature for surface and near bottom waters and sea ice) for two climate scenarios (RCP 45 and RCP 8.5) and three statistics (minimum, median and maximum) from an ensemble of five downscaled global climate models. The source data for this data series is global climate model outcomes from the Coupled Model Intercomparison Project 5 (CMIP5) published by the Intergovernmental Panel on Climate Change (Stocker et al 2013).

The source data were provided in NetCDF format for each of the downsampled climate models based on the five CMIP5 global climate models: MPI: MPI-ESM-LR, HAD: HadGEM2-ES, ECE: EC-EARTH, GFD: GFDL-ESM2M, IPS: IPSL-CM5A-MR. The data included monthly mean, maximum, minimum and standard deviation calculations and the physical variables provided with the climate scenario models included sea ice cover, water temperature, water salinity, sea level and current strength (as two vectors) as well as a range of derived biogeochemical variables (O2, PO4, NO3, NH4, Secci Depth and Phytoplankton).

These global atmospheric climate model data were subsequently downscaled from global to regional scale and incorporated into the high-resolution ocean–sea ice–atmosphere model RCA4–NEMO by the Swedish Meteorological and Hydrological Institute (Gröger et al 2019) thus providing a wide range of marine specific parameters. The Swedish Geological Survey used these data in the form of monthly mean averages to calculate change in multi-annual (30-year) climate averages from the beginning and end of the 21st century for the five select parameters as proxies for climate change pressures.

Each dataset uses only source data models based on an assumption of atmospheric climate gas concentrations in line with either the IPCCs representative concentration pathway RCP 4.5 or RCP 8.5. Changes were calculated as the difference between two multiannual (30 year) mean averages; one for a historical reference climate period (1976-2005) and one for an end of century projection (2070-2099). These data were extracted for each of the five downscaled CMIP5 models individually and then combined into ensemble summary statistics (ensemble minimum, median and maximum). In the Ensemble_Maximum/Median/Minimum_Rasters datasets, changes in mean (May-Sept) surface temperature and bottom temperature are given in Degrees Celsia (°C); changes in mean annual surface salinity and bottom salinity are given in Practical Salinity Units (PSU); changes in mean (October-April) sea ice are given in Percentage Points (pp).

In the Normalized_Rasters datasets, the changes are normalized using a linear stretch so that a cell value of zero represents no projected and a cell value of 100 represents a value equal to or above the mean change in Swedish national waters. The values representing 100 are: 4 °C for surface temperature; 3 °C for bottom temperature; -1.5 PSU for surface salinity; -2.0 PSU for bottom salinity; and -40 pp for sea ice. These were also the chosen reference values for determining, via expert review, the sensitivity of ecosystem components to changes in these parameters (for further information refer to the Symphony method).

Notes on interpretation. This dataset does not highlight inter-annual or inter-decadal climate variability (e.g. extreme events) or changes in biochemical parameters (e.g. O2, chlorophyll, secchi depth etc) resulting from change in surface temperature. Areas of no-data inshore were filled using extrapolating from nearby cells (using similar depths for benthic data) so data near the coast and particularly within archipelagos, bays and estuaries is not robust. Users should refer to the associated climemarine uncertainty map for this parameter. The uncertainty map shows the interquatile range from the climate ensemble and the area of no-data as 'interpolated values'. For any application which requires more temporally or spatially explicit information (e.g. at sub/national decision making) it is highly recommended that the user contact SMHI for access to the latest climate model source data (in NetCDF format) which contains much more detail and a far wider selection of parameters. For regional applications (e.g. at the scale of the Baltic Sea) - it should be noted that these data will likely require normalisation to regional rather than national values and that sensitivity scores used may differ.

ClimeMarine was selective in its choice of pressure parameters. SMHI have additional data available for other parameters such as O2, secchi depth and nutrients which could be included in future. This is complicated because many parameters are influenced by riverine discharge and therefore by decisions related to watershed management - disentanglement of impacts from climate vs river basin management becomes a complication. In a similar way, data on sealevel rise is also available which could be used to estimate impacts on the coast but likewise complicating factors such as isostatic uplift and coastal defence and management policies would need to be considered.

For simplicity and to reduce the amount of datasets to a manageable level for this assessment the source data were further limited and summarised in several ways:

Only the monthly mean averages of seawater temperature, salinity and sea ice (i.e. key physical parameters) were utilized.
For seawater salinity and temperature, the depth dimension (i.e. the water column) was summarised from 56 depth levels to just two: the surface and the deepest (bottom) waters.
Only two of the three climate periods were selected: a historical reference period: 1976-2005 (to represent the current status) and the projected end of century period: 2070-2099. Only two of the three available emission scenarios were selected detailing the consequence of intermediate and very high climate gas emissions : Representative Concentration Pathway (RCP) 4.5 and 8.5 (see SEDAC 2021).

Each dataset included in the series comes with extensive metadata.

The data processing followed the following steps:

Extraction of data for each parameter from NetCDF to TIFF Rasters for each model, emission scenario, depth level (using scripts in NCO, CDO and R). Calculation of climate ensemble statistics - Minimum, Mean, Median and Maximum (using Arcpy and Numpy)
Reprojection and resampling from the 2nm NEMO-RCO from Lat/Long WGS84 grid to the 250m ETRS89 LAEA Symphony grid (using Arcpy)
Extrapolation to fill no-data cells based on proximity and similar depths (using Arcpy script and the ArcGIS spatial analyst extension) Calculation of change for each parameter as the end of century multi-annual mean minus the reference multi-annual mean (using an Arcpy script)
Inversion of if negative (i.e. decreases) to positive (i.e. magnitude of change)
Normalisation as a linear stretch from 0 to 100 where zero equates to no change and 100 equates to the maximum pixel value in Swedish waters from the RCP 8.5 ensemble mean dataset with any values over this pixel value also set to 100 (Arcpy script)

NetCDF source data used in this analysis can be requested from the Swedish Meteorological and Hydrological Institute - kundtjanst@smhi.se

Processing scripts (R and arcpy) and interim raster data can be requested from the Geological Survey of Sweden - kundtjanst@sgu.se

Temperature in Japan increased to 13.11 celsius in 2024 from 13 celsius in 2023. This dataset includes a chart with historical data for Japan Average Temperature.

In 2024, the average air temperature in Japan's capital reached around **** degrees Celsius. Tokyo's annual mean air temperature increased by **** degrees Celsius since 1900, showing the progress of global warming. Weather in Tokyo Tokyo lies in the humid subtropical climate zone. It is affected by the monsoon circulation and has mild, sunny winters and hot, humid, and rainy summers. In most of Japan, the rainy season lasts from early June to mid-July. Furthermore, heavy rainfall is often caused by typhoons, which develop over the Pacific Ocean and regularly approach the archipelago between July and October. In recent years, the Kanto region, including Tokyo Prefecture, was approached by at least two typhoons each year. Since the winters are rather mild in Tokyo, the capital city does not often see snowfall and the snow rarely remains on the ground for more than a few days. Effects of global warming in Japan The increasing air temperature is one of the main consequences of global warming. Other effects are increased flooding frequency and a rise in sea levels due to melting ice caps. Global warming has already influenced Japan's climate in recent years, resulting in more frequent heat waves as well as increased annual rainfall. These weather changes can intensify natural disasters such as typhoons and inhibit the growth of crops. To counter global warming, Japan aims to reduce its greenhouse gas emissions by increasing its renewable and nuclear energy share.

The Weather Generator Gridded Data consists of two products:

[1] statistically perturbed gridded 100-year historic daily weather data including precipitation [in mm], and detrended maximum and minimum temperature in degrees Celsius, and

[2] stochastically generated and statistically perturbed gridded 1000-year daily weather data including precipitation [in mm], maximum temperature [in degrees Celsius], and minimum temperature in degrees Celsius.

The base climate of this dataset is a combination of historically observed gridded data including Livneh Unsplit 1915-2018 (Pierce et. al. 2021), Livneh 1915-2015 (Livneh et. al. 2013) and PRISM 2016-2018 (PRISM Climate Group, 2014). Daily precipitation is from Livneh Unsplit 1915-2018, daily temperature is from Livneh 2013 spanning 1915-2015 and was extended to 2018 with daily 4km PRISM that was rescaled to the Livneh grid resolution (1/16 deg). The Livneh temperature was bias corrected by month to the corresponding monthly PRISM climate over the same period. Baseline temperature was then detrended by month over the entire time series based on the average monthly temperature from 1991-2020. Statistical perturbations and stochastic generation of the time series were performed by the Weather Generator (Najibi et al. 2024a and Najibi et al. 2024b).

The repository consists of 30 climate perturbation scenarios that range from -25 to +25 % change in mean precipitation, and from 0 to +5 degrees Celsius change in mean temperature. Changes in thermodynamics represent scaling of precipitation during extreme events by a scaling factor per degree Celsius increase in mean temperature and consists primarily of 7%/degree-Celsius with 14%/degree-Celsius as sensitivity perturbations. Further insight for thermodynamic scaling can be found in full report linked below or in Najibi et al. 2024a and Najibi et al. 2024b.

The data presented here was created by the Weather Generator which was developed by Dr. Scott Steinschneider and Dr. Nasser Najibi (Cornell University). If a separate weather generator product is desired apart from this gridded climate dataset, the weather generator code can be adopted to suit the specific needs of the user. The weather generator code and supporting information can be found here: https://github.com/nassernajibi/WGEN-v2.0/tree/main. The full report for the model and performance can be found here: https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/All-Programs/Climate-Change-Program/Resources-for-Water-Managers/Files/WGENCalifornia_Final_Report_final_20230808.pdf

This dataset contains files that show the climate change velocity metrics calculated for three climate variables across Finland. The climate velocities were used to study the magnitude of projected climatic changes in a nation-wide Natura 2000 protected area (PA) network (Heikkinen et al., 2020). Using fine-resolution climate data that describes the present-day and future topoclimates and their spatio-temporal variation, the study explored the rate of climatic changes in protected areas on an ecologically relevant, but yet poorly explored scale. The velocities for the three climate variables were developed in the following work, where in-depth description of the different steps in velocity metrics calculation and a number of visualisations of their spatial variation across Finland are provided:

Risto K. Heikkinen 1, Niko Leikola 1, Juha Aalto 2,3, Kaisu Aapala 1, Saija Kuusela 1, Miska Luoto 2 & Raimo Virkkala 1 2020: Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678. https://doi.org/10.1038/s41598-020-58638-8

1 Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland

2 Department of Geosciences and Geography, University of Helsinki, FI-00014, Helsinki, Finland

3 Finnish Meteorological Institute, FI-00101, Helsinki, Finland

The dataset includes GIS compatible geotiff files describing the nine spatial climate velocity surfaces calculated across the whole of Finland at 50 m × 50 m spatial resolution. These nine different velocity surfaces consist of velocity metric values measured for each 50-m grid cell separately for the three different climate variables and in relation to the three different future climate scenarios (RCP2.6, RCP4.5 and RCP8.5). The baseline climate data for the study were the monthly temperature and precipitation data averaged for the period from 1981 to 2010 modelled at a resolution of 50-m, based on which estimates for the annual temperature sum above 5 °C (growing degree days, GDD, °C), the mean January temperature (TJan, °C) and the annual climatic water balance (WAB, the difference between annual precipitation and potential evapotranspiration; mm) were calculated. Corresponding future climate surfaces were produced using an ensemble of 23 global climate models for the years 2070–2099 (Taylor et al. 2012) and the three RCPs. The data for the three climate variables for 1981–2010 and under the three RCPs will be made available in separately via METIS - FMI's Research Data repository service (Aalto et al., in prep.).

The climate velocity surfaces included in the present data repository were developed using climate-analog approach (Hamann et al. 2015; Batllori et al. 2017; Brito-Morales et al. 2018), whereby velocity metrics for the 50-m grid cells were measured based on the distance between climatically similar cells under the baseline and the future climates, calculated separately for the three climate variables. In Heikkinen et al. (2020), the spatial data for the Natura 2000 protected areas were used to assess their exposure to climate change. The full data on N2K areas can be downloaded from the following link: https://ckan.ymparisto.fi/dataset/%7BED80465E-135B-4391-AA8A-FE2038FB224D%7D. However, note that the N2K areas including multiple physically separate patches were treated as separate polygons in Heikkinen et al. (2020), and a minimum size requirement of 2 hectares were requested. Moreover, the digital elevation model (DEM) data for Finland (which were dissected to Natura 2000 polygons to examine their elevational variation and its relationships to topoclimatic variation) can be downloaded from the following link: https://ckan.ymparisto.fi/en/dataset/dem25_astergdem25.

The coordinate system for the climate velocity data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). Summary of the key settings and elements of the study are provided below. A detailed treatment is provided in Heikkinen et al. (2020).

Code to the files (four files per each velocity layer: *.tif, *.tfw. *.ovr and .tif.aux.xml) in the dataset:

(a) Velocity of GDD with respect to RCP2.6 future climate (Fig 2a in Heikkinen et al. 2020). Name of the file: GDDRCP26.

(b) Velocity of GDD with respect to RCP4.5 future climate (Fig. 2b in Heikkinen et al. 2020). Name of the file: GDDRCP45.*

(c) Velocity of GDD with respect to RCP8.5 future climate (Fig. 2c in Heikkinen et al. 2020). Name of the file: GDDRCP85.*

(d) Velocity of mean January temperature with respect to RCP2.6 future climate (Fig. 2d in Heikkinen et al. 2020). Name of the file: TJanRCP26.*

(e) Velocity of mean January temperature with respect to RCP4.5 future climate (Fig. 2e in Heikkinen et al. 2020). Name of the file: TJanRCP45.*

(f) Velocity of mean January temperature with respect to RCP8.5 future climate (Fig. 2f in Heikkinen et al. 2020). Name of the file: TJanRCP85.*

(g) Velocity of climatic water balance with respect to RCP2.6 future climate (Fig. 2g in Heikkinen et al. 2020). Name of the file: WABRCP26.*

(h) Velocity of climatic water balance with respect to RCP4.5 future climate (Fig. 2h in Heikkinen et al. 2020). Name of the file: WABRCP45.*

(i) Velocity of climatic water balance with respect to RCP8.5 future climate (Fig. 2i in Heikkinen et al. 2020). Name of the file: WABRCP85.*

Note that velocity surfaces e and f include disappearing climate conditions.

Summary of the study:

Climate velocity is a generic metric which provides useful information for climate-wise conservation planning to identify regions and protected areas where climate conditions are changing most rapidly, exposing them to high rates of climate displacement (Batllori et al. 2017), causing potential carry-over impacts to community structure and ecosystem functions (Ackerly et al. 2010). Climate velocity has been typically used to assess the climatic risks for species and their populations, but velocity metrics can also be used to identify protected areas which face overall difficulties in retaining ecological conditions that promote present-day biodiversity.

Earlier climate velocity assessments have focussed on the domains of the mesoclimate (resolutions of 1–100 km) or macroclimate (>100 km scales), and fine-grained (<100 m) local climatic conditions created by variation in topography ('topoclimate'; Ackerly et al. 2010; 2020) have largely been overlooked (Heikkinen et al. 2020). This omission may lead to biased exposure assessments especially in rugged terrain (Dobrowski et al. 2013; Franklin et al. 2013), as well as a limited ability to detect sites decoupled from the regional climate (Aalto et al. 2017; Lenoir et al. 2017). This study provided the first assessment of the climatic exposure risks across a national PA (Natura 2000) network based on very fine-grained velocities of three established drivers of high latitude biodiversity.

The produce fine-grain climate velocity measures, 50-m resolution monthly temperature and precipitation data averaged for 1981–2010 were first developed, and based on it, the three bioclimatic variables (growing degree days, mean January temperature and annual climatic water balance) were calculated for the whole study domain. In the next phase, similar future climate surfaces were produced based on data from an ensemble of 23 global climate models, extracted from the CMIP5 archives for the years 2070–2099 and the three RCP scenarios (RCP2.6, RCP4.5 and RCP8.5)26. In the final step, climate velocities for each the 50 x 50 m grid cells were measured using climate-analog velocity method (Hamann et al. 2015) and based on the distance between climatically similar cells under the baseline and future climates.

The results revealed notable spatial differences in the high velocity areas for the three bioclimatic variables, indicating contrasting exposure risks in protected areas situated in different areas. Moreover, comparisons of the 50-m baseline and future climate surfaces revealed a potential wholesale disappearance of current topoclimatic temperature conditions from almost all the studied PAs by the end of this century.

Calculation of climate change velocity metrics for the three climate variables

The overall process of calculation of climate velocities included three main steps.

(1) In the first step, we developed high-resolution monthly average temperature and precipitation data averaged over the years 1981–2010 and across the study domain at a spatial resolution of 50 × 50 m. This was done by building topoclimatic models based on climate data sourced from 313 meteorological stations (European Climate Assessment and Dataset [ECA&D]) (Klok et al. 2009). Our station network and modelling domain covered the whole of Finland with an additional 100 km buffer. However, it was also extended to cover large parts of northern Sweden and Norway for areas >66.5°N, as well as selected adjacent areas in Russia (for details see Heikkinen et al. 2020). This was done to capture the present-day climate spaces in Finland which are projected to move in the future beyond the country borders but have analogous climate areas in neighbouring areas; this was done to avoid developing a large number of velocity values deemed as infinite or unknown in the data for Finland.

The 50-m resolution average air temperature data were developed for the study domain using generalized additive modelling (GAM), as implemented in the R-package mgcv version 1.8–7 (R Development Core Team 2011; Wood 2011). In this modelling we utilised variables of geographical location (latitude and longitude, included as an anisotropic interaction), topography (elevation, potential incoming solar radiation, relative

Temperature in India increased to 25.43 celsius in 2024 from 25.03 celsius in 2023. This dataset includes a chart with historical data for India Average Temperature.

This special topic poll, conducted April 5-10, 2007, is part of a continuing series of monthly polls that solicit public opinion on the presidency and on a range of other political and social issues. The focus of this poll was environmental issues. Respondents were asked to rate the condition of the natural environment and to give their opinions about the biggest environmental problem the world faces today. Questions about weather patterns focused on whether the respondent thought the average temperature in the United States and in the world had increased over time, and whether they believed the earth's temperature had been increasing over the past 100 years. Respondent's opinions about global warming were collected and included information on how important global warming was to the respondent, how serious it was to them, how much could be done to reduce future global warming, how much could be done to reduce the effect of global warming on people and on the environment, and whether the federal government should do more to try to deal with global warming. Respondents of this poll were also asked a series of questions about national parks such as whether the respondent had ever visited a national park in the United States, whether the country's national parks were better compared to five years ago, whether they are well managed, and what respondents thought should have priority at national parks. Other questions asked whether respondents favored or opposed tax increases on electricity and gas, building cars that use less gas, building appliances that use less electricity, building homes and offices that use less energy for heating and cooling, and lowering the amount of greenhouse gases allowed into the air. Respondents were also asked who they trusted to do a better job, President Bush or the Congress, handling the overall environment, global warming, and the national parks. Demographic information includes respondent sex, age, race, income, marital status, religious preference, education level, type of residential area (e.g., urban or rural), political philosophy, political party affiliation, whether the respondent owed or rented their home, and whether there was any children under the age of 18 living at the respondent's home.

Data for Figure 6.SM.3 from the Chapter 6 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 6.SM.3 shows global mean temperature response 20 and 100 years following one year of present-day (year 2014) emissions.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers Supplementary Material. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from https://www.ipcc.ch/

Figure subpanels

The figure has three panels with data provided for all panels in the main directory

List of data provided

This dataset contains global-mean temperature response after 20 and 100 years following one year pulse of present-day (year 2014) for different emissions for:

• total anthropogenic emissions
• sectoral emissions
• regional emissions

Data provided in relation to figure

Top panel: - Data file dT20_100_CEDS_total_v2_wHFCs_v210214.txt (column 0, reference to 20 or 100 years, columns 1 to 13, coloured bars)

Left panel: - Data file dT20_CEDS_sectors_v2_wHFCs_v210214.txt (column 0, reference to several sectors, columns 1 to 13, coloured bars) - Data file dT100_CEDS_sectors_v2_wHFCs_v210214.txt (column 0, reference to several sectors, columns 1 to 13, coloured bars) - Data file errorbar_dT20_sectors.txt (column 1: Lower bound of the uncertainty in the 20-year sectoral global-mean temperature responses. column 3: Upper bound of the uncertainty in the 20-year sectoral global-mean temperature responses.) - Data file errorbar_dT100_sectors.txt (column 1: Lower bound of the uncertainty in the 100-year sectoral global-mean temperature responses. column 3: Upper bound of the uncertainty in the 100-year sectoral global-mean temperature responses.)

Right panel: - Data file dT20_CEDS_regions_v2_wHFCs_v210214.txt (column 0, reference to several regions, columns 1 to 13, coloured bars) - Data file dT100_CEDS_regions_v2_wHFCs_v210214.txt (column 0, reference to several regions, columns 1 to 13, coloured bars) - Data file errorbar_dT20_regions.txt (column 1: Lower bound of the uncertainty in the 20-year regional global-mean temperature responses. column 3: Upper bound of the uncertainty in the 20-year sectoral global-mean temperature responses.) - Data file errorbar_dT100_regions.txt (column 1: Lower bound of the uncertainty in the 100-year regional global-mean temperature responses. column 3: Upper bound of the uncertainty in the 100-year sectoral global-mean temperature responses.)

Acronyms: AGR: agriculture ENE_C: Fossil fuel combustion for energy ENE_P: Fossil fuel production and distribution IND: Industry TRA: Land transportation RES_FF: Residential and commercial RES_BF: Residential and commercial (biofuel use only) WST: Waste management SHP: Shipping BIO: Open biomass burning AVIA: Aviation

LAM: Latin America and Caribbean SAS: Southern Asia AFR: Africa EUR: Europe CAS: Eurasia MDE: Middle East SEA: Southeast Asia and Developing Pacific PAN: Asia-Pacific Developed NAM: North America EAS: Eastern Asia

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component related to the figure (Chapter 6) - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3

In 2023, the observed annual average mean temperature in Australia reached 22.32 degrees Celsius. Overall, the annual average temperature had increased compared to the temperature reported for 1901. Impact of climate change The rising temperatures in Australia are a prime example of global climate change. As a dry country, peak temperatures and drought pose significant environmental threats to Australia, leading to water shortages and an increase in bushfires. Western and South Australia reported the highest temperatures measured in the country, with record high temperatures of over 50°C in 2022. Australia’s emission sources While Australia has pledged its commitment to the Paris Climate Agreement, it still relies economically on a few high greenhouse gas emitting sectors, such as the mining and energy sectors. Australia’s current leading source of greenhouse gas emissions is the generation of electricity, and black coal is still a dominant source for its total energy production. One of the future challenges of the country will thus be to find a balance between economic security and the mitigation of environmental impact.

In 2023/2024, the average winter temperature in Germany was *** degrees Celsius. That winter was part of a growing list of warmer winters in the country. Figures had increased noticeably compared to the 1960s. Warmer in the winter Everyone has a different perception of what actually makes a cold or warm winter, but the fact is that winter temperatures are, indeed, changing in Germany, and its 16 federal states are feeling it. Also in 2022/2023, Bremen and Hamburg in the north recorded the highest average figures at around 4 degrees each. The least warm states that year, so to speak, were Thuringia, Saxony, and Bavaria. The German National Meteorological Service (Deutscher Wetterdienst or DWD), a federal office, monitors the weather in Germany. Global warming Rising temperatures are a global concern, with climate change making itself known. While these developments may be influenced by natural events, human industrial activity has been another significant contributor for centuries now. Greenhouse gas emissions play a leading part in global warming. This leads to warmer seasons year-round and summer heat waves, as greenhouse gas emissions cause solar heat to remain in the Earth’s atmosphere. In fact, as of 2022, Germany recorded **** days with a temperature of at least 30 degrees Celcius, which was more than three times the increase compared to 2021.