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.

The United States contributed roughly 17 percent of global warming from 1851 to 2023. By contrast, India contributed five percent of warming during this period, despite the country having a far larger population than the United States. In total, G20 countries have contributed approximately three-quarters of global warming to date, while the least developed countries are responsible for just six percent.

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'.

(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 majority of U.S. adults believe that non-government scientists and educators are the most trustworthy sources for information about climate change, with **** percent of respondents in 2022. By comparison, nearly ** percent of respondents said they considered environmental groups trustworthy, and some ** percent said they considered college professors/educators trustworthy.

We provide a novel, high-resolution hydrological modelling dataset using pseudo-global warming climate data as forcing to the Community Water Model (CWatM). CWatM is a state-of-the-art large-scale rainfall-runoff and channel routing water resources model that is process-based and used to quantify water supply, as well as human water withdrawals from different sectors (industry, domestic, agriculture) and multiple sources representing the effects of water infrastructure, including reservoirs, groundwater pumping and irrigation canals. CWatM is forced by a pseudo-global warming (PGW) experiment from 1981 to 2010. PGW simulations resemble historical weather patterns and events under globally warmer conditions (here, 2 K global warming) by perturbing historical, reanalysis-driven regional climate simulations. We performed simulations considering regular incremental adjustments of the historical water withdrawals (ranging between +/- 50% of historic water withdrawals) under PGW conditions. That range represents an ad hoc and simplified representation of multiple possible future water management scenarios across Southwestern and Central Europe. The approach allows us to investigate the effects of changing water withdrawals under 2 K global warming. Especially in Western and Central Europe, the projected impacts on low flows highly depend on the chosen water withdrawal assumption. The data highlights the importance of accounting for future water withdrawals in discharge projections.

Discharge statistics based on daily output from CWatM within 1981-2010:

• Q1 - 1st percentile
• Q5 - 5th percentile
• Q10 - 10th percentile
• Qavg - average discharge
• Q90 - 90th percentile
• Q95 - 95th percentile
• Q99 - 99th percentile

Files:

• Qxx_reference: CWatM considering historical water use forced by RACMO-ERA5
• Qxx_PGW: CWatM considering historical water use forced by RACMO-ERA5 + climate pertubations under 2 K global warming. In the reference experiment, RACMO is forced at the lateral and sea surface boundaries of the model domain by unperturbed ERA5 reanalysis data, while in the pseudo-global warming experiment, the forcing data consist of perturbed reanalysis data. Perturbations are added to the ERA5 reference data corresponding to climate change patterns of surface pressure and sea surface temperature, and atmospheric profiles of temperature, relative humidity, and wind speed components that are retrieved from a 16-member single model initial condition ensemble of EC-EARTH global climate simulations.
• Qxx_PGW_adjusted_demand: We have performed 11 additional hydrological simulations adjusting the historical water demand (ranging between +/- 50% of historic water withdrawals) to enable sensitivity assessments of low and high flows under 2 K global warming.

An upcoming publication will be made available and linked to this research very soon.

The Climate Change Mitigation Solutions market is evolving rapidly, driven by an urgent need to combat the escalating impacts of global warming and environmental degradation. This market encompasses a wide range of strategies and technologies aimed at reducing carbon emissions, enhancing energy efficiency, and promo

The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. www.climate-solutions-explorer.eu

Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges.

Updated gridded global climate and impact model data are based on CMIP6 and CMIP5 projections, using a subset of models from the ISIMIP project that have been consistently downscaled and bias-corrected. The data includes various indicators (~42) relating to extremes of precipitation and temperature (e.g. from Expert Team on Climate Change Detection and Indices), hydrological variables including runoff and discharge, heat stress (from wet bulb temperature) events (multiple statistics and durations), and cooling degree days, as well as further indicators relating to air pollution (PM2.5 from the GAINs model), and crop yields and natural habitat land-use change (biodiversity pressure) from the GLOBIOM model.

Indicators were calculated at a spatial resolution of 0.5° (approximately 50km at the equator), and subsequently spatially aggregated to the country level – from which population and land area exposure to the impacts were calculated. This has enabled the country-by-country comparison of national climate impacts and avoided exposure. Impacts were calculated at global mean temperature intervals, i.e. 1.2, 1.5, 2, 2.5, 3, and 3.5 °C, compared to a pre-industrial climate.

The dataset includes:

Global gridded projections (in netCDF format) of all the climate impact indicators at 0.5° spatial resolution, at global warming levels of 1.2, 1.5, 2, 2.5, 3, and 3.5 °CFor each GWL, maps for the absolute indicator values, the relative difference, and the scores are provided. The naming format is: cse_[short_indicator_name]_[ssp]_[gwl]_[metric].nc4. Please note that the Greenland ice sheet and the desert areas have been masked out for the hydrology indicators for these datasets.

Intermediate output data, including gridded maps of absolute values, relative differences, and scores for all ensemble members, as well as gridded maps of the multi-model ensemble statistics for the global warming levels and the reference period For the ensemble member data, the naming format is [gcm]_[ssp/rcp]_[gwl]_[short_indicator_name]_global_[start_year]_[end_year].nc4 or [ghm]_[gcm]_[ssp/rcp]_[gwl]_[soc]_[short_indicator_name]_global_[start_year]_[end_year]_[metric].nc4 for the hydrology indicators.

Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weightedThe .zip archives ‘table_output_climate_exposure_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_climate_exposure_land_air_pollution.zip’ contains the table data for theland and air pollution indicators.

Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure)The .zip archives ‘table_output_avoided_impacts_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_avoided_impacts_land_air_pollution.zip’ contains the table data for the land and air pollution indicators.

Further details are available on the Data Story page – www.climate-solutions-explorer.eu/story/data. A detailed description of the methodology and the calculation of the ISIMIP-derived indicators has been published in Werning, M. et al. (2024).

Release notes (v1.1)

Changes in this version:

Only table output data for the land and air pollution indicators have been changed, all other indicator data remain unchanged from v1.0

Updated land and air pollution indicators to use scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023

Fixed issue with the region mask for the EU

Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions

Release notes (v1.0)

Changes in this version:

Fixed calculation of the indicator “Drought intensity” (both for the version using discharge and run-off)

Masked out the Greenland ice sheet and the desert areas for the global gridded projections for the hydrology indicators in the final output files

Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions

Used scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023

Added the indicator ‘Heatwave days’

Added intermediate outputs for all ensemble members for energy, hydrology, precipitation, and temperature indicators

Release Notes (v0.4)

Changes in this version:

Removed ssp and metric from variable name in netCDF files

Removed obsolete coordinates in netCDF files for 'Drought intensity'

Added intermediate outputs for energy, hydrology, precipitation, and temperature indicators

Financial overview and grant giving statistics of The Global Warming Foundation Inc

Based on policies and actions in place as of November 2024, the global temperature increase is estimated to reach a median of 2.7 degrees Celsius in 2100. In the best-case scenario, where all announced net-zero targets, long-term targets, and Nationally Determined Contributions (NDCs) are fully implemented, the global temperature is still expected to rise by 1.9 degrees Celsius, when compared to the pre-industrial average. In 2015, Paris Agreement parties pledged to limit global warming to well below two degrees Celsius above pre-industrial levels, with the aim of reaching a maximum of 1.5 degrees. As of 2024, a warming of 1.3 degrees above the pre-industrial average was recorded.

The Climate Change Consulting market is rapidly evolving as organizations across various sectors increasingly recognize the need to address the pressing challenges posed by climate change. This niche industry provides vital services aimed at helping businesses and governments create strategies to mitigate environmen

The Climate Change Mitigation Technologies market is a dynamic sector that addresses the urgent need to combat climate change through innovative solutions aimed at reducing greenhouse gas emissions and enhancing sustainability. As industries grapple with the consequences of climate change, the demand for effective m

The Low Global Warming Potential (GWP) Refrigerants market is currently experiencing significant growth, driven by increasing global awareness about climate change and the urgent need for sustainable alternatives in refrigeration and air conditioning. These refrigerants are crucial in various industries, including c

Data for Figure 3.9 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.9 shows global, land, ocean and continental annual mean near-surface air temperatures anomalies in CMIP6 models and observations. --------------------------------------------------- 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: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. 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.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has ten panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d, panel_e, panel_f, panel_g, panel_h, panel_i and panel_j. --------------------------------------------------- List of data provided --------------------------------------------------- - Observed global near-surface air temperature change (1850-2020). - CMIP6 historical anthropogenic and natural global warming (1850-2020). - CMIP6 historical natural-only global warming (1850-2020). - CMIP6 historical greenhouse gas only global warming (1850-2020). - CMIP6 historical aerosol only global warming (1850-2020). --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - panel_a/fig_3_9_a.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_b/fig_3_9_b.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_c/fig_3_9_c.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_d/fig_3_9_d.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_e/fig_3_9_e.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_f/fig_3_9_f.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_g/fig_3_9_g.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_h/fig_3_9_h.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_i/fig_3_9_i.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_j/fig_3_9_j.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) Plotted data corresponds to the following "exp" and "stat" indices: brown line: exp = 0, stat = 0 green line: exp = 1, stat = 0 grey line: exp = 2, stat = 0 blue line: exp =3, stat = 0 black line: exp = 4, stat = 0 shaded regions: stat = 1 and 2, exp = 0, 1, 2 and 3 The ensemble spread (shaded regions) of CMIP6 data shown in figure 3.9 are the mean, 5th and 95th percentiles. The in-file metadata labels the same ensemble spread as the mean, min and max. panel_a: Global Ocean panel_b: Global panel_c: Global Land panel_d: North America panel_e: Central and South America panel_f: Europe and North Africa panel_g: Africa panel_h: Asia panel_i: Australasia panel_j: Antarctica Acronyms - CMIP6 - The sixth phase of the Coupled Model Intercomparison Project. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1