According to an ********** survey on climate change conducted in the United States, approximately ** percent of the respondents claimed they heard about global warming in the media at least once a week. Just ***** percent of respondents stated that they had never heard about global warming in the media.

According to an April 2024 survey on climate change conducted in the United States, some ** percent of the respondents claimed they believed that global warming was happening. A much smaller share, ** percent, believed global warming was not happening.

This work combines global warming data from various publications and datasets, creating a new dataset covering a very long period - from the year 1 to 2100.

The dataset created in this work separates the actual records for the 1-2024 period from the forecast for the 2020-2100 period.

The work includes separate sets for land+ocean (GW), land only (GWL), and ocean only (GWO).

The online dataset is available on the site nowagreen.com.

This folder, titled "Data," contains the MATLAB code, final products, tables, and figures used in Parker, L.E., Zhang, N., Abatzoglou, J.T. et al. A variety-specific analysis of climate change effects on California winegrapes. Int J Biometeorol 68, 1559–1571 (2024). https://doi.org/10.1007/s00484-024-02684-8 Data Collection: Climatological data (daily maximum and minimum temperatures, precipitation, and reference evapotranspiration) were obtained from the gridMET dataset for the contemporary period (1991-2020) and from 20 global climate models (GCMs) for the mid-21st century (2040-2069) under RCP 4.5.Phenology Modeling: Variety-specific phenology models were developed using published climatic thresholds to assess chill accumulation, budburst, flowering, veraison, and maturity stages for the six winegrape varieties.Agroclimatic Metrics: Fourteen viticulturally important agroclimatic metrics were calculated, including Growing Degree Days (GDD), Cold Hardiness, Chilling Degree Days (CDD), Frost Damage Days (FDD), and others.Analysis Tools: MATLAB was used for data processing, analysis, and visualization. The MATLAB code provided in this dataset includes scripts for analyzing climate data, running phenology models, and generating visualizations.MATLAB Code: Scripts and functions used for data analysis and modeling.Processed Data: Results from phenology and agroclimatic analyses, including the projected changes in phenological stages and climate metrics for the selected varieties and AVAs.Tables: Detailed results of phenological changes and climate metrics, presented in a clear and structured format.Figures: Visual representations of the data and results, including charts and maps illustrating the impacts of climate change on winegrape development stages and agroclimatic conditions. Research Description: This study investigates the impacts of climate change on the phenology and agroclimatic metrics of six winegrape varieties (Cabernet Sauvignon, Chardonnay, Pinot Noir, Zinfandel, Pinot Gris, Sauvignon Blanc) across multiple California American Viticultural Areas (AVAs). Using climatological data and phenology models, the research quantifies changes in key development stages and viticulturally important climate metrics for the mid-21st century.

Raw figures providedRaw figures 1-4 accompanying paper on transient and equilibrium climate change. The scripts used to generate these figures may be found here: https://zenodo.org/record/3471030#.XcDSNTMzbIV. The underlying CMIP5 data are available in multiple repostitories (e.g. https://esgf-node.llnl.gov/projects/esgf-llnl/). The underlying population and GDP data used in Figures 2 and 4 are freely accessible here: http://www.cger.nies.go.jp/gcp/population-and-gdp.html.Example source data providedSource data for Figures 4a and 4b showing maps of probability ratios in netCDF format.Intermediate source data for years selected from each RCP8.5 model simulation equivalent to the level of global warming in the 23rd century in extended RCP4.5 simulations for the same model.For further data or data in different formats please contact andrew.king@unimelb.edu.au

Climate change is viewed as a major concern globally, with around 90 percent of respondents to a 2023 survey viewing it as a serious threat to humanity. developing nations often show the highest levels of concern, like in the Philippines where 96.7 percent of respondents acknowledge it as a serious threat. Rising emissions despite growing awareness Despite widespread acknowledgment of climate change, global greenhouse gas emissions continue to climb. In 2023, emissions reached a record high of 53 billion metric tons of carbon dioxide equivalent, marking a 60 percent increase since 1990. The power industry remains the largest contributor, responsible for 28 percent of global emissions. This ongoing rise in emissions has significant implications for global climate patterns and environmental stability. Temperature anomalies reflect warming trend In 2024, the global land and ocean surface temperature anomaly reached 1.29 degrees Celsius above the 20th-century average, the highest recorded deviation to date. This consistent pattern of positive temperature anomalies, observed since the 1980s, highlights the long-term warming effect of increased greenhouse gas accumulation in the atmosphere. The warmest years on record have all occurred within the past decade.

This data package contains information on atmospheric CO2 trends, surface temperature, absolute sea levels, surface temperature analysis, average mass balance of glaciers and temperature anomalies all at a global level.

(1) This is the dataset simulated by high resolution atmospheric model of which horizontal resolution is 60km-mesh over the globe (GCM), and 20km over Japan and surroundings (RCM), respetively. The climate of the latter half of the 20th century is simulated for 6000 years (3000 years for the Japan area), and the climates 1.5 K (*2), 2 K (*1) and 4 K warmer than the pre-industrial climate are simulated for 1566, 3240 and 5400 years, respectivley, to see the effect of global warming. (2) Huge number of ensembles enable not only with statistics but also with high accuracy to estimate the future change of extreme events such as typoons and localized torrential downpours. In addtion, this dataset provides the highly reliable information on the impact of natural disasters due to climate change on future societies. (3) This dataset provides the climate projections which adaptations against global warming are based on in various fields, for example, disaster prevention, urban planning, environmetal protection, and so on. It would realize the global warming adaptations consistent not only among issues but also among regions. (4) Total size of this dataset is 3 PB (3 x the 15th power of 10 bytes).

(*1) Datasets of the climates 2K warmer than the pre-industorial climate is available on 10th August, 2018. (*2) Datasets of the climates 1.5K warmer than the pre-industorial climate is available on 8th February, 2022.

The Climate Change Mitigation in Agriculture Statistics publication brings together statistics on agriculture which track progress on greenhouse gas (GHG) performance. The publication summarises available evidence and interprets it in the context of GHGs. It also incorporates emerging statistics which inform understanding of GHGs in agriculture as research.

Source agency: Environment, Food and Rural Affairs

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Greenhouse gases from agriculture

Context

Data description

The FAOSTAT Temperature Change domain disseminates statistics of mean surface temperature change by country, with annual updates. The current dissemination covers the period 1961–2023. Statistics are available for monthly, seasonal and annual mean temperature anomalies, i.e., temperature change with respect to a baseline climatology, corresponding to the period 1951–1980. The standard deviation of the temperature change of the baseline methodology is also available. Data are based on the publicly available GISTEMP data, the Global Surface Temperature Change data distributed by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS).

Content

Statistical concepts and definitions

Statistical standards: Data in the Temperature Change domain are not an explicit SEEA variable. Nonetheless, country and regional calculations employ a definition of “Land area” consistent with SEEA Land Use definitions, specifically SEEA CF Table 5.11 “Land Use Classification” and SEEA AFF Table 4.8, “Physical asset account for land use.” The Temperature Change domain of the FAOSTAT Agri-Environmental Indicators section is compliant with the Framework for the Development of Environmental Statistics (FDES 2013), contributing to FDES Component 1: Environmental Conditions and Quality, Sub-component 1.1: Physical Conditions, Topic 1.1.1: Atmosphere, climate and weather, Core set/ Tier 1 statistics a.1.

Statistical unit: Countries and Territories.

Statistical population: Countries and Territories.

Reference area: Area of all the Countries and Territories of the world. In 2019: 190 countries and 37 other territorial entities.

Code - reference area: FAOSTAT, M49, ISO2 and ISO3 (http://www.fao.org/faostat/en/#definitions). FAO Global Administrative Unit Layer (GAUL National level – reference year 2014. FAO Geospatial data repository GeoNetwork. Permanent address: http://www.fao.org:80/geonetwork?uuid=f7e7adb0-88fd-11da-a88f-000d939bc5d8.

Code - Number of countries/areas covered: In 2019: 190 countries and 37 other territorial entities.

Time coverage: 1961-2023

Periodicity: Monthly, Seasonal, Yearly

Base period: 1951-1980

Unit of Measure: Celsius degrees °C

Reference period: Months, Seasons, Meteorological year

Acknowledgements

Documentation on methodology: Details on the methodology can be accessed at the Related Documents section of the Temperature Change (ET) domain in the Agri-Environmental Indicators section of FAOSTAT.

Quality documentation: For more information on the methods, coverage, accuracy and limitations of the Temperature Change dataset please refer to the NASA GISTEMP website: https://data.giss.nasa.gov/gistemp/

                                                                          Source: http://www.fao.org/faostat/en/#data/ET/metadata

Inspiration

Climate change is one of the important issues that face the world in this technological era. The best proof of this situation is the historical temperature change. You can investigate if any hope there is for stopping global warming :)

• Can you find any correlation between temperature change and any other variable? (Using ISO3 codes for merging any other countries' data sets possible.)

• Prediction of temperature change: there is also an overall world temperature change in the country list as 'World'.

This dataset provides statistics of mean surface temperature change by country, updated annually. It covers the period from 1961 to 2023, offering insights into historical temperature anomalies. The data represents temperature changes relative to a baseline climatology period of 1951–1980. This information is crucial for understanding global warming and climate change, allowing for the investigation of historical trends and the potential for mitigating future temperature increases. It is based on publicly available Global Surface Temperature Change (GISTEMP) data distributed by NASA-GISS.

Columns

• Area Code: An integer representing the numerical code for each geographic area.
• Area: The name of the countries and territories covered. This column is an object type, listing entities such as Afghanistan. As of 2019, it includes 190 countries and 37 other territorial entities.
• Months Code: An integer representing the numerical code for months.
• Months: The period covered, which can be individual months, seasons, or the meteorological year. This column is an object type.
• Element Code: An integer representing the numerical code for the type of measurement.
• Element: The type of data recorded, specifically 'Temperature change' or 'Standard Deviation'. This column is an object type.
• Unit: The unit of measurement for the temperature data, which is Celsius degrees (°C). This column is an object type.
• Y1961 through Y2023: These columns represent the temperature anomalies for each respective year from 1961 to 2023. These are numerical values indicating the change in temperature.

Distribution

The dataset is typically provided as a CSV data file, with a sample file named Environment_Temperature_change_E_All_Data_NOFLAG.csv. The file size is 6.26 MB and it contains 66 columns. While the exact number of rows or records is not explicitly stated, various column statistics indicate approximately 9656 entries are typically valid.

Usage

This dataset is ideal for: * Analysing historical global warming and climate change trends. * Investigating potential correlations between temperature changes and other environmental or socio-economic variables, particularly by merging with other country-level datasets using ISO3 codes. * Predicting future temperature changes based on historical patterns, including overall world temperature changes. * Academic research and environmental policy development focusing on atmospheric science and climate studies.

Coverage

The dataset's geographic scope encompasses the countries and territories of the world, covering 190 countries and 37 other territorial entities in 2019. The time range spans from 1961 to 2023, with data available for monthly, seasonal, and annual mean temperature anomalies. The baseline period for temperature anomaly calculations is 1951–1980. Notably, the dataset also includes an overall 'World' temperature change entry.

License

Attribution 3.0 IGO (CC BY 3.0 IGO)

Who Can Use It

• Climate scientists and researchers studying long-term temperature trends and climate phenomena.
• Environmental agencies and policymakers for monitoring climate change impacts and informing policy decisions.
• Data analysts and modellers interested in predictive analytics for environmental forecasting.
• Academics and students for educational purposes, exploring environmental statistics and global temperature patterns.

Dataset Name Suggestions

• Global Temperature Change 1961-2023
• FAOSTAT Worldwide Temperature Anomalies
• Historical Country Temperature Data
• Climate Change Indicators (1961-2023)

Attributes

Original Data Source: Historical Country Temperature Data

This dataset has all the summary tables for the Figures and supplementary information in the Phelan et al. publication.

Regional estimates are presented by industry and household for four gases - carbon dioxide, methane, nitrous oxide, and F-gases. The F-gases constitute of hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride and nitrogen trifluoride.Emissions are presented in Million metric tons of CO₂ equivalent (MTCO2e).Sources: Organisation for Economic Co-operation and Development (2022), Air Emission Accounts, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=AEA; Organisation for Economic Co-operation and Development (2022), Air Emission Accounts – OECD Estimates, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=OECD-AEA; Organisation for Economic Co-operation and Development (2022), Quarterly National Accounts, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=QNA%20; United Nations Framework Convention on Climate Change (UNFCCC). 2022. Greenhouse Gas Inventory Data - Detailed data by Party - Annex I. https://di.unfccc.int/detailed_data_by_party. Copyright 2022 United Nations Framework Convention on Climate Change; Crippa, M., Guizzardi, D., Solazzo, E., Muntean, M., Schaaf, E., Monforti-Ferrario, F., Banja, M., Olivier, J., Grassi, G., Rossi, S. and Vignati, E., GHG emissions of all world countries, EUR 30831 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-41547-3, doi:10.2760/074804, JRC126363; IEA (2022) Monthly electricity data, www.iea.org/statistics, All rights reserved; as modified by IMF; IEA (2022) Monthly oil statistics, www.iea.org/statistics, All rights reserved; as modified by IMF; IEA (2022) Monthly gas statistics, www.iea.org/statistics, All rights reserved; as modified by IMF; Country Authorities; IMF staff calculations.Category: Greenhouse Gas (GHG) EmissionsData series: Quarterly greenhouse gas (GHG) air emissions accountsMetadata:Quarterly greenhouse gas air emissions from production and household consumption are adjusted for seasonality. SEEA Air Emissions Accounts from official country sources have been accessed via the OECD Air Emissions Accounts database.Methodology:The OECD Air Emission Accounts database presents estimates that align with the classifications, concepts and methods consistent with the System of Environmental-Economic Accounting Central Framework (SEEA-CF). In addition to the OECD database, the estimation procedure uses the emission inventories sourced from UNFCCC, EDGAR and CAIT. Correspondence tables and industry output shares are used to concord the UNFCCC, EDGAR and CAIT estimates to their corresponding industrial and household activities. Annual estimates of greenhouse gas emissions by industry and for households are trended forward using the latest emission data available. They are temporally disaggregated using the best temporal aggregation method in conjunction with seasonally adjusted sub-annual indicators of economic activity highly correlated with the annual estimates, under a prior assumption on linkages with the annual estimates.Quarterly estimates for the most recent period (for which annual estimates do not exist) are extrapolated using the timelier sub-annual indicators.Disclaimer:The estimates are considered experimental. The sources and methods used to compile these estimates are still in development. Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.

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

Financial overview and grant giving statistics of Global Climate Change Foundation

The data files for figures in Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below: Figure 1 The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:

The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables.
The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables.
The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable. The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable. The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable. Figure 2

The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_.nc where is either 'north_america', 'australia', 'europe', or 'new_zealand'. The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_.txt where is either 'north_america', 'australia', 'europe', or 'new_zealand'. Figure 3

The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_.dat where is the latitude and is the longitude. Files for each region are zipped into .7z files named Figure3_.7z where is either 'north_america', 'australia', 'europe', or 'new_zealand'. The latitudes and longitudes for the upper panels can be inferred from the file names for each region. Figure 4 The data for each panel are provided in a netCDF file named Figure4_.nc where is either 'north_america', 'australia', 'europe', or 'new_zealand'. Figure 5 The data are provided as text files named Figure5_ where is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. Figure 6 The data are provided as text files named Figure6_ where is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.
Figure 7 The data for each panel are provided in a netCDF file named Figure7_.nc where is either 'north_america', 'australia', 'europe', or 'new_zealand'. Figure 8 The data are provided as text files named Figure8_.txt where is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood. Figure S1 and Figure S3 The data are provided as text files named Figure_S1_and_S3_.txt where is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents. Figure S2

The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_.nc where is either 'north_america', 'australia', 'europe', or 'new_zealand'. The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_.txt where is either 'north_america', 'australia', 'europe', or 'new_zealand'. Figure S4

The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents.
The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents.
Figure S5 There are no data associated with this figure. This figure was made using Microsoft Powerpoint. Figure S6 The data are provided as text files named FigureS6_.txt where is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.

The Climate Change Impact Assessment Tools Market Share size and share are expected to exceed USD 48.21 billion by 2034, with a compound annual growth rate (CAGR) of 25.1% during the forecast period.