The table Global Temperatures by Country is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 577462 rows across 4 variables.

Abstract

Compilation of Earth Surface temperatures historical. Source: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Documentation

Data compiled by the Berkeley Earth project, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures

%3C!-- --%3E

• LandAverageTemperature: global average land temperature in celsius

%3C!-- --%3E

• LandAverageTemperatureUncertainty: the 95% confidence interval around the average

%3C!-- --%3E

• LandMaxTemperature: global average maximum land temperature in celsius

%3C!-- --%3E

• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature

%3C!-- --%3E

• LandMinTemperature: global average minimum land temperature in celsius

%3C!-- --%3E

• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature

%3C!-- --%3E

• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius

%3C!-- --%3E

• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

%3C!-- --%3E

**Other files include: **

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)

%3C!-- --%3E

• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)

%3C!-- --%3E

• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)

%3C!-- --%3E

• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

%3C!-- --%3E

The raw data comes from the Berkeley Earth data page.

This dataset includes processed climate change datasets related to climatology, hydrology, and water operations. The climatological data provided are change factors for precipitation and reference evapotranspiration gridded over the entire State. The hydrological data provided are projected stream inflows for major streams in the Central Valley, and streamflow change factors for areas outside of the Central Valley and smaller ungaged watersheds within the Central Valley. The water operations data provided are Central Valley reservoir outflows, diversions, and State Water Project (SWP) and Central Valley Project (CVP) water deliveries and select streamflow data. Most of the Central Valley inflows and all of the water operations data were simulated using the CalSim II model and produced for all projections.

These data were originally developed for the California Water Commission’s Water Storage Investment Program (WSIP). The WSIP data used as the basis for these climate change resources along with the technical reference document are located here: https://data.cnra.ca.gov/dataset/climate-change-projections-wsip-2030-2070. Additional processing steps were performed to improve user experience, ease of use for GSP development, and for Sustainable Groundwater Management Act (SGMA) implementation. Furthermore, the data, tools, and guidance may be useful for purposes other than sustainable groundwater management under SGMA.

Data are provided for projected climate conditions centered around 2030 and 2070. The climate projections are provided for these two future climate periods, and include one scenario for 2030 and three scenarios for 2070: a 2030 central tendency, a 2070 central tendency, and two 2070 extreme scenarios (i.e., one drier with extreme warming and one wetter with moderate warming). The climate scenario development process represents a climate period analysis where historical interannual variability from January 1915 through December 2011 is preserved while the magnitude of events may be increased or decreased based on projected changes in precipitation and air temperature from general circulation models.

2070 Extreme Scenarios Update, September 2020

DWR has collaborated with Lawrence Berkeley National Laboratory to improve the quality of the 2070 extreme scenarios. The 2070 extreme scenario update utilizes an improved climate period analysis method known as "quantile delta mapping" to better capture the GCM-projected change in temperature and precipitation. A technical note on the background and results of this process is provided here: https://data.cnra.ca.gov/dataset/extreme-climate-change-scenarios-for-water-supply-planning/resource/f2e1c61a-4946-4863-825f-e6d516b433ed.

Note: the original version of the 2070 extreme scenarios can be accessed in the archive posted here: https://data.cnra.ca.gov/dataset/sgma-climate-change-resources/resource/51b6ee27-4f78-4226-8429-86c3a85046f4

According to an April 2024 survey on climate change conducted in the United States, some 70 percent of the respondents claimed they believed that global warming was happening. A much smaller share, 13 percent, believed global warming was not happening.

This paper represents, to our knowledge, the first national-level (United States) estimate of the economic impacts of vibriosis cases as exacerbated by climate change. Vibriosis is an illness contracted through food- and waterborne exposures to various Vibrio species (e.g., non-V. cholerae O1 and O139 serotypes) found in estuarine and marine environments, including within aquatic life, such as shellfish and finfish. Data include all variables included in the regression models ("cleaned all"), climate variables ("cleaned climate vars"), county in which exposure occurred ("expcty"), county that reported diagnosis ("rptcty"), and sea surface temperature projections (identified by "SST"). Citation information for this dataset can be found in Data.gov's References section.

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 dataset contains monthly 1 degree averages of sea surface temperature data in Obs4MIPS format, from the European Space Agency (ESA)'s Climate Change Initiatve (CCI) Sea Surface Temperature (SST) v2.1 analysis.

The data covers the period from 1981-2017, with the data from 1981 to 2016 coming from the Sea Surface Temperature (SST) project of the ESA CCI project. The data for 2017 were generated using the same approach but under funding from the Copernicus Climate Change Service (C3S).

This particular product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons.

Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/

When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x

The model used here is a coupled ocean-atmosphere model that consists of the CCSR/NIES atmospheric GCM, the CCSR ocean GCM, a thermodynamic sea-ice model, and a river routing model (Abe-Ouchi et al., 1996). The spatial resolution is T21 spectral truncation (roughly 5.6 degrees latitude/longitude) and 20 vertical levels for the atmospheric part, and roughly 2.8 degrees horizontal grid and 17 vertical levels for the oceanic part. Flux adjustment for atmosphere-ocean heat and water exchange is applied to prevent a drift of the modelled climate. The atmospheric model adopts a radiation scheme based on the k-distribution, two-stream discrete ordinate method (DOM) (Nakajima and Tanaka, 1986). This scheme can deal with absorption, emission and scattering by gases, clouds and aerosol particles in a consistent manner. In the calculation of sulphate aerosol optical properties, the volumetric mode radius of the sulphate particle in dry environment is assumed to be 0.2 micron. The hygroscopic growth of the sulphate is considered by an empirical fit of d'Almeida et al. (1991). The vertical distribution of the sulphate aerosol is assumed to be constant in the lowest 2 km of the atmosphere. The concentrations of greenhouse gases are represented by equivalent-CO2. Three integrations are made for 200 model years (1890-2090). In the control experiment (CTL), the globally uniform concentration of greenhouse gases is kept constant at 345 ppmv CO2-equivalent and the concentration of sulphate is set to zero. In the experiment GG, the concentration of greenhouse gases is gradually increased, while that of sulphate is set to zero. In the experiments GS, the increase in anthropogenic sulphate as well as that in greenhouse gases is given and the aerosol scattering (the direct effect of aerosol) is explicitly represented in the way described above. The indirect effect of aerosol is not included in any experiment. The scenario of atmospheric concentrations of greenhouse gases and sulphate aerosols is given in accordance with Mitchell and Johns (1997). The increase in greenhouse gases is based on the historical record from 1890 to 1990 and is increased by 1 percent / yr (compound) after 1990. For sulphate aerosols, geographical distributions of sulphate loading for 1986 and 2050, which are estimated by a sulphur cycle model (Langer and Rodhe, 1991), are used as basic patterns. Based on global and annual mean sulphur emission rates, the 1986 pattern is scaled for years before 1990; the 2050 pattern is scaled for years after 2050; and the pattern is interpolated from the two basic ones for intermediate years to give the time series of the distribution. The sulphur emission rate in the future is based on the IPCC IS92a scenario. The sulphate concentration is offset in our run so that it starts from zero at 1890. The seasonal variation of sulphate concentration is ignored. Discussion on the results of the experiments will be found in Emori et al. (1999). Climate sensitivity of the CCSR/NIES model derived by equilibrium runs is estimated to be 3.5 degrees Celsius. Global-Mean Temperature, Precipitation and CO2 Changes (w.r.t. 1961-90) for the CCSR/NIES model. From the IPCC website: The A1 Family storyline is a case of rapid and successful economic development, in which regional averages of income per capita converge - current distinctions between poor and rich countries eventually dissolve. In this scenario family, demographic and economic trends are closely linked, as affluence is correlated with long life and small families (low mortality and low fertility). Global population grows to some nine billion by 2050 and declines to about seven billion by 2100. Average age increases, with the needs of retired people met mainly through their accumulated savings in private pension systems. The global economy expands at an average annual rate of about three percent to 2100. This is approximately the same as average global growth since 1850, although the conditions that lead to a global economic in productivity and per capita incomes are unparalleled in history. Income per capita reaches about US$21,000 by 2050. While the high average level of income per capita contributes to a great improvement in the overall health and social conditions of the majority of people, this world is not without its problems. In particular, many communities could face some of the problems of social exclusion encountered by the wealthiest countries in the 20th century and in many places income growth could come with increased pressure on the global commons. Energy and mineral resources are abundant in this scenario family because of rapid technical progress, which both reduce the resources need to produce a given level of output and increases the economically recoverable reserves. Final energy intensity (energy use per unit of GDP) decreases at a... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.310.1 for complete metadata about this dataset.

A complete description of the dataset is given by Jones et al. (2023). Key information is provided below.

Background

A dataset describing the global warming response to national emissions CO₂, CH₄ and N₂O from fossil and land use sources during 1851-2021.

National CO₂ emissions data are collated from the Global Carbon Project (Andrew and Peters, 2024; Friedlingstein et al., 2024).

National CH₄ and N₂O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2024).

We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH₄ and N₂O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021).

Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST).

The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO₂, CH₄, N₂O or 3-GHG total) and source (fossil emissions, land use emissions or the total).

Data records: overview

The data records include three comma separated values (.csv) files as described below.

All files are in ‘long’ format with one value provided in the Data column for each combination of the categorical variables Year, Country Name, Country ISO3 code, Gas, and Component columns.

Component specifies fossil emissions, LULUCF emissions or total emissions of the gas.

Gas specifies CO₂, CH₄, N₂O or the three-gas total (labelled 3-GHG).

Country ISO3 codes are specifically the unique ISO 3166-1 alpha-3 codes of each country.

Data records: specifics

Data are provided relative to 2 reference years (denoted ref_year below): 1850 and 1991. 1850 is a mutual first year of data spanning all input datasets. 1991 is relevant because the United Nations Framework Convention on Climate Change was operationalised in 1992.

EMISSIONS_ANNUAL_{ref_year-20}-2023.csv: Data includes annual emissions of CO₂ (Pg CO₂ year^-1), CH₄ (Tg CH₄ year^-1) and N₂O (Tg N₂O year^-1) during the period ref_year-20 to 2023. The Data column provides values for every combination of the categorical variables. Data are provided from ref_year-20 because these data are required to calculate GWP* for CH₄.

EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2023.csv: Data includes the cumulative CO₂ equivalent emissions in units Pg CO₂-e₁₀₀ during the period ref_year+1 to 2023 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables.

GMST_response_{ref_year+1}-2023.csv: Data includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period ref_year+1 to 2023 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables.

Accompanying Code

Code is available at: https://github.com/jonesmattw/National_Warming_Contributions .

The code requires Input.zip to run (see README at the GitHub link).

Further info: Country Groupings

We also provide estimates of the contributions of various country groupings as defined by the UNFCCC:

• Annex I countries (number of countries, n = 42)
• Annex II countries (n = 23)
• economies in transition (EITs; n = 15)
• the least developed countries (LDCs; n = 47)
• the like-minded developing countries (LMDC; n = 24).

And other country groupings:

• the organisation for economic co-operation and development (OECD; n = 38)
• the European Union (EU27 post-Brexit)
• the Brazil, South Africa, India and China (BASIC) group.

See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group.

This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x

The Long Beach Climate Action Plan (LB CAP) was adopted by the City Council on August 16, 2022. The implementation of the LB CAP is an ongoing, collaborative process between the City, its partners, and the community to make Long Beach a safer, healthier, and more sustainable place to live, work, and play. The City aims to accomplish this by implementing LB CAP action items that work to reduce greenhouse gas emissions, mitigate the effects of climate change, enhance economic vitality, and improve the quality of life in Long Beach.Powering the Climate Portal2021 GHG Inventory Report2023 GHG Inventory Report

This data set provides the supplemental code, data and shape-files for Simpson et al. 2021. Climate Change Literacy in Africa, Nature Climate Change. Data set includes the following:Code for cleaning and merging the Afrobarometer data, as well as to run the analyses.Data set of national and sub-national climate change literacy rates for Africa.Data set of gender differences of national climate change literacy rates for Africa.Shape files presenting national and sub-national climate change literacy rates for Africa.Code and computed output files for climate trends extracted from ERA-5 experienced by Afrobarometer survey respondents for:the number of months per year in the past ten- and thirty-year periods in which temperature was above the 95th percentile (PPT),Standardized Precipitation-Evapotranspiration Index (SPEI),3-month Standardized Precipitation Index (SPI),the duration of the longest dry spell (Max CDD) of the year. Original datasets analysed during the current study are available from: the Afrobarometer repository, https://www.afrobarometer.org/data (all geolocation data has been removed from respondents in accordance with Afrobarometer data use protocols but can be accessed from the Afrobarometer).The ERA5-Land monthly data from the Copernicus Climate Data Store, https://cds.climate.copernicus.eu/cdsapp#!/home,EM-DAT – the international disaster database https://www.emdat.be/.

Climate change, that is a threat to ecosystems and the livelihoods of those that depend on them, is increasingly manifesting as an increased frequency and intensity of severe weather events such as droughts and floods (Déqué et al., 2017). Climate change has created an urgent need for early warning aids or models to enhance the sub-Saharan African health systems ability to prepare for, and cope with escalations in treatment needs of climate sensitive diseases (Nhamo & Muchuru, 2019). This dataset was created from the health and weather data of nine purposively selected study districts in Uganda, whose health and weather data were available for the development of an early warning health model (https://github.com/CHAIUGA/chasa-model) and an accompanying prediction web app (https://github.com/CHAIUGA/chasa-webapp). The districts were selected based on the following criteria: (a) were experiencing climate change and variability, (b) represented different climatologic, and agro-ecological zones, (c) availability of climate information and health information from a health facility within a 40 kilometres radius of a functional weather station. Historical weather data was retrieved from the Uganda National Meteorological Association databases, as monthly averages. The weather variables in this data included: atmospheric pressure, rainfall, solar radiation, humidity, temperature (maximum, minimum and mean), and wind (gusts and average wind speed). The monthly health aggregated data for the period starting September 2018 to December 2019, was retrieved from the National Health Repository (DHIS2) for referral hospitals within the selected districts. Only data for a selection of climate-sensitive disease aggregates was obtained. The dataset contains 436 complete matched disease and weather records. Ethical issues: Both the de-identified aggregate monthly disease diagnosis count data and weather data in this dataset are from national data available to the public on request.

According to an April 2024 survey on climate change conducted in the United States, approximately 28 percent of the respondents claimed they heard about global warming in the media at least once a week. Just seven percent of respondents stated that they had never heard about global warming in the media.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Analysis of ‘Climate Change Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sammyboy55/climate-change-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

This dataset contains coordinates for cities, and whether they are affected by climate change.

Acknowledgements

Thanks to Business Insider for some of the data.

Inspiration

What will you make with this data? Let me know!

--- Original source retains full ownership of the source dataset ---

The preferred citation when using this dataset is: Stevens, A., & Lamie, C., Eds. (2024). New York State Climate Impacts Assessment: Understanding and preparing for our changing climate. The New York State Climate Impacts Assessment is an investigation into how climate change will affect New York State’s communities, ecosystems, and economy. The data and information presented will help New Yorkers plan and prepare for the impacts of climate change. The assessment also strives to show how addressing climate change provides opportunities to enhance equity and reduce the vulnerability of those most at risk. As part of the assessment, Columbia University developed climate change projections for temperature and precipitation, extreme events, degree days, and sea level rise, downscaled to 12 regions of New York State. This dataset includes those projections of future climate conditions in New York State, for the 2030s through 2100. For more information on these projections or to read the full NYS Climate Impacts Assessment, visit the assessment website at https://nysclimateimpacts.org/. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on Twitter, Facebook, YouTube, or Instagram.

When studying the impacts of climate change, there is a tendency to select climate data from a small set of arbitrary time periods or climate windows (e.g., spring temperature). However, these arbitrary windows may not encompass the strongest periods of climatic sensitivity and may lead to erroneous biological interpretations. Therefore, there is a need to consider a wider range of climate windows to better predict the impacts of future climate change. We introduce the R package climwin that provides a number of methods to test the effect of different climate windows on a chosen response variable and compare these windows to identify potential climate signals. climwin extracts the relevant data for each possible climate window and uses this data to fit a statistical model, the structure of which is chosen by the user. Models are then compared using an information criteria approach. This allows users to determine how well each window explains variation in the response variable and compare model support between windows. climwin also contains methods to detect type I and II errors, which are often a problem with this type of exploratory analysis. This article presents the statistical framework and technical details behind the climwin package and demonstrates the applicability of the method with a number of worked examples.

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

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.