Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, 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
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

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.

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.

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.

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

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

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

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

This data visualizes the Climate Action Tracker (CAT) expected warming degrees based on pledges and current policies.CAT is a science-based project that tracks government climate action and measures it against the globally agreed Paris Agreement aim of "holding warming well below 2°C, and pursuing efforts to limit warming to 1.5°C. CAT monitors what governments are doing to reduce climate change. It compares their actions to the goals of the Paris Agreement, which aims to limit global warming to well below 2 degrees Celsius.The temperatures shown are mid-point estimates (medians) of what the Earth's temperature could be in 2100, based on current government pledges and policies (NDCs). There's a 50% chance the actual temperature could be higher if these actions are followed. The CAT uses a climate model called MAGICC7 to make these estimates.Data originally from Climate Action Tracker.

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

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.

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.

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

A simulation of the projected changes in December to February mean temperatures from the period 1975 to 1995 to the period 2040 to 2060 is shown on this map. According to this projection, the Arctic would experience the greatest warming followed by other areas in northern Canada and central and northern Asia. Temperatures would generally increase as a result of the projected increases in greenhouse gas concentration in the atmosphere. The results are based on climate change simulations made with the Coupled Global Climate Model developed by Environment Canada.

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.

Scientists state unequivocally that the earth is warming. Climate change is happening, it is caused in large part by human activity, and it will have many serious and potentially damaging effects in the decades ahead. Greenhouse gas emissions from cars, power plants, and other human activities—rather than natural variations in climate—are the primary cause of contemporary global warming. Due largely to the combustion of fossil fuels, atmospheric concentrations of carbon dioxide (CO2), the principal greenhouse gas, are at a level unequaled for at least 800,000 years. The greenhouse gases from human activities are trapping more of the sun’s heat in the earth’s atmosphere, resulting in warming. Over the last century, average global temperatures rose by more than 1°F and some regions warmed by as much as 4°F. The oceans have also warmed, especially in the upper layers.Available onlineCall Number: [EL]Physical Description: 86 p.

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'.

The problem being investigated involves analyzing global land temperature changes for major cities as well as globally. This topic was chosen as it is representative of the international issue of global warming. The severity of the issue is reflected in The Paris Agreement, which is a legally binding treaty with the goal of limiting global warming to the target rate of 1.5°C (degrees Celsius), or at least 2°C within this century (United Nations, 2021). Global warming has been linked to intensification of extreme weather phenomena, which lead to fatalities, environmental damage, community devastation, and financial costs. For example, extreme heat can lead to drought, wildfires, and create the urban heat island effect (Center for Climate and Energy Solutions, 2018). The temperature of the ocean will be explored as well as this factor is significant since theoretically, changes in the ocean would take longer – which in turn provides clarity on the severity of climate change.

Overview of the set-up of the different scenarios for testing the framework to estimate the impact of GHG emissions on global temperature change.

This is an output from ccc-gistemp. It is a an estimate of temperature change over the global land surface (of Earth). The units are in centikelvin.

A simulation of projected changes in June to August mean temperatures from the period 1975 to 1995 to the period 2040 to 2060 is shown on this map. According to this projection, there would be typically more warming over land than over oceans, at higher latitudes than at lower latitudes, and in winter compared to summer. Temperatures would generally increase 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.

This dataset contains the tweet ids of 39,622,026 tweets related to climate change. They were collected between September 21, 2017 and May 17, 2019 from the Twitter API using Social Feed Manager. There is a gap in data collection between January 7, 2019 and April 17, 2019. Tweets were collected using the POST statuses/filter method of the Twitter Stream API, using the track parameter with the following keywords: #climatechange, #climatechangeisreal, #actonclimate, #globalwarming, #climatechangehoax, #climatedeniers, #climatechangeisfalse, #globalwarminghoax, #climatechangenotreal, climate change, global warming, climate hoax Because of the size of the collection, the list of identifiers is split into files of 10 million lines each, with a tweet identifier on each line. There is a README.txt file containing additional documentation on how the tweets were collected. The GET statuses/lookup method supports retrieving the complete tweet for a tweet id (known as hydrating). Tools such as Twarc or Hydrator can be used to hydrate tweets. Per Twitter’s Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Questions about this dataset can be sent to sfm@gwu.edu. George Washington University researchers should contact us for access to the tweets.

The dataset includes results of Global Warming forecast using four methods.

The methods include a parabolic trendline of the last 61 years of global warming and cumulated CO2 emissions.

Two other methods apply the velocity and the acceleration of global warming and cumulative CO2 emissions.

The relation between the global surface temperature change and the change in the cumulative CO2 emissions was determined in previous publications as 0.000745°C/GtCO2.

The average result from all four methods for the business as usual CO2 mitigation scenario is 4.4°C (4.1°C -5.0°C).

According to this forecast, the global temperature change will reach 1.5°C in 2031 (9 years from now) and 2.0°C in 2047 (25 years from now).