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.

The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a "change consistent with warming" or a "change not consistent with warming", based on information from the underlying studies.

(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 × the 15th power of 10 bytes).

(*1) Datasets of the climates 2K warmer than the pre-industorial climate (d4PDF 2K) is available on 10th August, 2018. (*2) Datasets of the climates 1.5K warmer than the pre-industorial climate (d4PDF 1.5K) is available on 8th February, 2022.

Inland fishes provide important ecosystem services to communities worldwide and are especially vulnerable to the impacts of climate change. Fish respond to climate change in diverse and nuanced ways which creates challenges for practitioners of fish conservation, climate change adaptation, and management. Although climate change is known to affect fish globally, a comprehensive online, public database of how climate change has impacted inland fishes worldwide and adaptation or management practices that may address these impacts does not exist. We conducted an extensive, systematic primary literature review to identify peer-reviewed journal publications describing projected and documented examples of climate change impacts on inland fishes. From this standardized Fish and Climate Change database, FiCli, researchers and managers can query fish families, species, response types, or geographic locations to obtain summary information on inland fish responses to climate change and recommended management actions. The FiCli provides access to comprehensive published information to inform inland fish Inland fishes provide important ecosystem services to communities worldwide and are especially vulnerable to the impacts of climate change.

The Intergovernmental Panel on Climate Change Fifth Assessment Report (AR5) Observed Climate Change Impacts Database, Version 2.01 contains observed responses to climate change across a wide range of systems as well as regions. These responses include systems for which climate change has played a major role in observed changes, regional-scale impacts where climate change has played a minor role, and sub-regional impacts. Impacts on physical, biological, and human systems were differentiated, and the area impacted can vary from specific locations to broad areas such as a major river basin.

CCKP provides open access to a comprehensive suite of climate and climate change resources derived from the latest generation of climate data archives. Products are based on a consistent and transparent approach with a systematic way of pre-processing the raw observed and model-based projection data to enable inter-comparable use across a broad range of applications. Climate products consist of basic climate variables as well as a large collection (70+) of more specialized, application-orientated variables and indices across different scenarios. Precomputed data can be extracted per specified variables, select timeframes, climate projection scenarios, across ensembles or individual models, etc. CCKP adheres to data distributions standards defined under the Coupled Model Intercomparison Project (CMIP) and its contributions to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports and latest scientific methodologies identified by the World Meteorological Organization and climate science community. Climate products are available for the following collections. Downscaled CMIP6 global 0.25-degree – 1950-2100; ERA5 global 0.25-degree – 1950-2022; CRU global 0.50-degree – 1901-2022; Population global 0.25-degree – 1995-2100 (GPW v4).

The table Global Temperatures by Major City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 239177 rows across 7 variables.

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.

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).

Direct internet link to the Pacific Climate Change portal

The Climate Policy Database collects information on currently adopted policies related to climate change mitigation from countries worldwide. The objective of the website is to provide an open, collaborative platform for information access, policy analysis and good-practice sharing. All data can be downloaded via the policies page.

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, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Direct internet link to the one stop location for anyone searching for information and news relating to environment and climate change projects in the Federated States of Micronesia.

description: The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessement Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from about 80 studies (of which about 75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a change consistent with warming or a change not consistent with warming based on information from the underlying studies. The data is distributed by the Columbia University Center for Earth Science Information Network (CIESIN).; abstract: The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessement Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from about 80 studies (of which about 75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a change consistent with warming or a change not consistent with warming based on information from the underlying studies. The data is distributed by the Columbia University Center for Earth Science Information Network (CIESIN).

TOLNet_ECCC_Data is the lidar data collected by the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada as part of the Tropospheric Ozone Lidar Network (TOLNet). Data collection for this product is ongoing.In the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of six Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.TOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry Éxperiment (FRAPPÉ), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water–Land Environmental Transition Study (OWLETS).

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/.

USAID/Pacific Islands launched its Global Climate Change portfolio in 2011 in response to growing climate concerns. In 2016, Social Impact, Inc. conducted a performance evaluation on four of 11 portfolio activities (Coastal Community Adaptation Project, Pacific-American Climate Fund, Vegetation and Land Cover Mapping and Improving Food Security, and Climate Change Adaptation Program) across four of 12 portfolio countries (Fiji, Kiribati, Solomon Islands, and Papua New Guinea).

This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper:

Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. https://doi.org/10.5194/esd-10-741-2019

Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).

Climate change impact data

File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.

File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv

Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, https://tntcat.iiasa.ac.at/SspDb/, last accessed 15Nov 2019).

Climate change mitigation cost data

The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].

File 4: REMIND_scenario_results_economic_data.csv

File 5: REMIND_scenarios_climate_data.csv

Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature.

In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.

The first dimension specifies the climate policy regime (delayed action, baseline scenarios):

1xx: climate action from 2010
5xx: climate action from 2015
2xx climate action from 2020 (used in this study)
3xx climate action from 2030
4x1 weak policy baseline (before Paris agreement)

The second dimension specifies the technology portfolio and assumptions:

x1x Full technology portfolio (used in this study)
x2x noCCS: unavailability of CCS
x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed
x4x NucPO: phase out of investments into nuclear energy
x5x Limited SW: penetration of solar and wind power limited
x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)
x6x noBECCS: unavailability of CCS in combination with bioenergy

The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.).

xx1 0$/tCO2 (baseline)
xx2 10$/tCO2
xx3 30$/tCO2
xx4 50$/tCO2
xx5 100$/tCO2
xx6 200$/tCO2
xx7 500$/tCO2
xx8 40$/tCO2
xx9 20$/tCO2
xx0 5$/tCO2

For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).

[1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a.

[2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

Database containing the borehole temperature profiles used in the publication "Cuesta-Valero F.J., García-García A., Beltrami H., González-Rouco J.F., and García-Bustamante E. (2020). Long-Term Global Ground Heat Flux and Continental Heat Storage from Geothermal Data. Clim. Past Discuss. [preprint], doi:10.5194/cp-2020-65"

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.