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.

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 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'.

This dataset contains modeled temperature, ozone, and PM2.5 data for the United States over the 21st century, using two global climate model scenarios and two emissions datasets.

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

Annual averages of global surface temperature changes for land only based on Berkeley Earth monthly dataset above the 1951-1980 baseline. The dataset is from 1750 in °C, 3 decimal places.

Maximum temperature and rainfall observed data files were downloaded from the IRI Data Library as well as the model predicted 850-to-500 geopotential thickness fields (used to predict maximum temperature over southern Africa) and 850 circulation data fields (predictor for rainfall). Model Output statistics in CPT - climate predictability tool, was set up using CCA - canonical correlation analysis to produce retroactive forecasts. MATLAB was further utilized to post-process / fine-tune the output from CPT and to produce other results. The researcher used the output from the global climate model to develop a statistical model for maximum temperature seasonal forecasts for Southern Africa.

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.

Global warming vs climate change

Many people use these two terms interchangeably, but we think it’s important to acknowledge their differences. Global warming is an increase in the Earth’s average surface temperature from human-made greenhouse gas emissions. On the other hand, climate change refers to the long-term changes in the Earth’s climate, or a region on Earth, and includes more than just the average surface temperature. For example, variations in the amount of snow, sea levels, and sea ice can all be consequences of climate change.

Content

Worldwide Climate Change & Global Warming keyword / topic search in Google Search Engine from 2004 - present

Acknowledgements

Google Trends Lab

As part of this national strategy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) had launched a 5-year (FY2007 - 2011) initiative called the Innovative Program of Climate Change Projection for the 21st Century (KAKUSHIN Program), using the Earth Simulator (ES) to address emerging research challenges, such as those derived from the outcomes of the MEXT's Kyosei Project (FY2002 - 2006), that had made substantial contributions to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The KAKUSHIN Program was expected to further contribute to the Fifth Assessment Report (AR5).

The research items include the advancement and forecasting of global warming models, the quantification and reduction of model uncertainty, and the evaluation of the impacts of natural disasters based on forecast information. Much of the data submitted to CMIP5 from Japan were generated under this KAKUSHIN program using the global climate models and the Earth system models developed in Japan. This dataset is the result of using the Global Climate Model MIROC4h.

All CMIP5 data are collected, managed, and published by the Earth System Grid Federation (ESGF), and DIAS serves as an ESGF node. All public datasets, including this dataset, are available from ESGF. For information on how to use these datasets, including this dataset, see "CMIP5 Data - Getting Started" (URL is available in the online information below). Please note that an ESGF account is required to download the CMIP5 data.

Because the terms of use for CMIP5 data are different from CMIP6 in many respects, please check the following Terms of Use carefully: https://pcmdi.llnl.gov/mips/cmip5/terms-of-use.html Currently, all CMIP5 data, including this dataset, is classified as "unrestricted" within it.

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.

A simulation of projected changes in annual mean temperatures from the period 1975 to 1995 to the period 2080 to 2100 is shown on this map. Geographically, the temperature changes would not be evenly distributed. According to this projection, the Arctic would experience the greatest annual mean warming followed by other areas in northern Canada and central and northern Asia. Temperatures generally increase as the century progresses as a consequence of the projected increase in greenhouse gas concentrations in the atmosphere. The results are based on climate change simulations made with the Coupled Global Climate Model developed by Environment Canada.

A simulation of projected changes in mean annual precipitation from the period 1975 to 1995 to the period 2040 to 2060, is shown on this map. On average, precipitation increases, but it is not evenly distributed geographically. There are marked regions of decreasing, as well as increasing precipitation, over both land and ocean. Annual average precipitation generally increases over northern continents, and particularly during the winter. Warmer surface temperature would speed up the hydrological cycle at least partially, resulting in faster evaporation and more precipitation. The results are based on climate change simulations made with the Coupled Global Climate Model developed by Environment Canada.

This repository contains data used in figures in Samset et al. 2024, Communications Earth & Environmenthttps://www.nature.com/articles/s43247-024-01637-8https://doi.org/10.1038/s43247-024-01637-8Obs_GMST_GreensFunctionFiltered.zip:Global mean surface temperature data series for four observational reconstructions.Key fields:- tas_aa: Global mean surface temperature anomaly, relative to 1850-1899 or 1880-1899, depending on the coverage.- tas_fbr_aa: As tas_aa, but with SST pattern filering applied, as documented in the publicationSimilarly formatted CMIP6 data are available in a separate archive:Underlying data for "Steady global surface warming from 1973 to 2022 but increased warming rate after 1990" (10.1038/s43247-023-01061-4)

This survey shows the concerns of U.S. Americans about the environmental threat of global warming from 1989 to 2021. As of March 2021, 43 percent of the respondents were worried "a great deal" about global warming.

Derived climate model projections data produced as part of the UK Climate Projections 2018 (UKCP18) project. The data produced by the UK Met Office Hadley Centre provides information on changes in 21st century climate for the UK helping to inform adaptation to a changing climate.

The derived climate model projections are estimated using a methodology based on time shift and other statistical approaches applied to a set of 28 projections comprising of 15 coupled simulations produced by the Met Office Hadley Centre, and 13 coupled simulations from CMIP5. The derived climate model projections exist for the RCP2.6 emissions scenario and for 2°C and 4°C global warming above pre-industrial levels.

The derived climate model projections are provided on a 60km spatial grid for the UK region and the projections consist of time series for the RCP2.6 emissions scenario that cover 1900-2100 and a 50 year time series for each of the global warming levels.

This dataset contains realisations scenario with global warming stabilised at 4°C

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.13°C.]What does the data show? This dataset shows the change in annual temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Note, as the values in this dataset are averaged over a year they do not represent possible extreme conditions.The dataset uses projections of daily average air temperature from UKCP18 which are averaged to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a change (in °C) relative to the 1981-2000 value. This enables users to compare annual average temperature trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.

PeriodDescription 1981-2000 baselineAverage temperature (°C) for the period 2001-2020 (recent past)Average temperature (°C) for the period 2001-2020 (recent past) changeTemperature change (°C) relative to 1981-2000 1.5°C global warming level changeTemperature change (°C) relative to 1981-2000 2°C global warming level changeTemperature change (°C) relative to 1981-20002.5°C global warming level changeTemperature change (°C) relative to 1981-2000 3°C global warming level changeTemperature change (°C) relative to 1981-2000 4°C global warming level changeTemperature change (°C) relative to 1981-2000What is a global warming level?The Annual Average Temperature Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Average Temperature Change, an average is taken across the 21 year period.We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for the 1981-2000 baseline, 2001-2020 period and each warming level. They are named 'tas annual change' (change in air 'temperature at surface'), the warming level or historic time period, and 'upper' 'median' or 'lower' as per the description below. e.g. 'tas annual change 2.0 median' is the median value for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tas annual change 2.0 median' is named 'tas_annual_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tas annual change 2.0°C median’ values.What do the 'median', 'upper', and 'lower' values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Average Temperature Change was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.The ‘lower’ fields are the second lowest ranked ensemble member. The ‘higher’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and higher fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksFor further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.