The United States is responsible for almost 20 percent of global historical cumulative fossil and LULUCF carbon dioxide emissions from 1850 to 2021. During this period, the North American country contributed roughly 17 percent of global warming, despite representing just four percent of the current world population. The United States is the biggest contributor to global warming from 1850 to 2021.

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.

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.

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

This folder, titled "Data," contains the MATLAB code, final products, tables, and figures used in Parker, L.E., Zhang, N., Abatzoglou, J.T. et al. A variety-specific analysis of climate change effects on California winegrapes. Int J Biometeorol 68, 1559–1571 (2024). https://doi.org/10.1007/s00484-024-02684-8 Data Collection: Climatological data (daily maximum and minimum temperatures, precipitation, and reference evapotranspiration) were obtained from the gridMET dataset for the contemporary period (1991-2020) and from 20 global climate models (GCMs) for the mid-21st century (2040-2069) under RCP 4.5.Phenology Modeling: Variety-specific phenology models were developed using published climatic thresholds to assess chill accumulation, budburst, flowering, veraison, and maturity stages for the six winegrape varieties.Agroclimatic Metrics: Fourteen viticulturally important agroclimatic metrics were calculated, including Growing Degree Days (GDD), Cold Hardiness, Chilling Degree Days (CDD), Frost Damage Days (FDD), and others.Analysis Tools: MATLAB was used for data processing, analysis, and visualization. The MATLAB code provided in this dataset includes scripts for analyzing climate data, running phenology models, and generating visualizations.MATLAB Code: Scripts and functions used for data analysis and modeling.Processed Data: Results from phenology and agroclimatic analyses, including the projected changes in phenological stages and climate metrics for the selected varieties and AVAs.Tables: Detailed results of phenological changes and climate metrics, presented in a clear and structured format.Figures: Visual representations of the data and results, including charts and maps illustrating the impacts of climate change on winegrape development stages and agroclimatic conditions. Research Description: This study investigates the impacts of climate change on the phenology and agroclimatic metrics of six winegrape varieties (Cabernet Sauvignon, Chardonnay, Pinot Noir, Zinfandel, Pinot Gris, Sauvignon Blanc) across multiple California American Viticultural Areas (AVAs). Using climatological data and phenology models, the research quantifies changes in key development stages and viticulturally important climate metrics for the mid-21st century.

(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.

The dataset contains family- and class-wise globally compiled data on the number of plant species which are in the red list category, as per International Union of Conservation of Nature (IUCN). The different types of red list categories of species covered in the dataset include species which are extinct, extinct in the wild, critically endangered (possibly extinct), critically endangered (possibly extinct in the wild), endangered, vulnerable, lower risk/conservation dependent, near threatened, etc.

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

Climate change is a potent threat to human society, biodiversity, and ecosystem stability. Yet a 2021 Gallup poll found that only 43% of Americans see climate change as a serious threat over their lifetimes. In this study, we analyze college biology textbook coverage of climate change from 1970 to 2019. We focus on four aspects for document analysis: 1) the amount of coverage, determined by counting the number of sentences within the climate change passage, 2) the start location of the passage in the book, 3) the categorization of sentences as addressing a description of the greenhouse effect, impacts of global warming, or actions to ameliorate climate change, and 4) the presentation of data in figures. We analyzed 57 textbooks. Our findings show that coverage of climate change has continually increased. However, the greatest increase occurred during the 1990s, despite the growing threats of climate change. The position of the climate change passage moved further back in the book, from ...

The Climate Change Mitigation in Agriculture Statistics publication brings together statistics on agriculture which track progress on greenhouse gas (GHG) performance. The publication summarises available evidence and interprets it in the context of GHGs. It also incorporates emerging statistics which inform understanding of GHGs in agriculture as research.

Source agency: Environment, Food and Rural Affairs

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Greenhouse gases from agriculture

This dataset has all the summary tables for the Figures and supplementary information in the Phelan et al. publication.

Financial overview and grant giving statistics of Global Climate Change Foundation

We carried out a content analysis of images (photographs, illustrations and graphics) posted on Twitter, during five randomly selected weeks between 28 November 2019 and 29 November 2020. The random process of selecting five weeks, performed using the website random.org, yielded the following weeks: 3, 11, 22, 32 and 45. These weeks correspond to the following dates: Week 3: from 11 to 17 November 2019 Week 11: from 6 to 12 January 2020 Week 22: from 23 to 29 March 2020 Week 32: from 8 to 14 June 2020 Week 45: from 7 to 13 September 2020 The sample was selected using the Twitter API (twitter.com) by selecting the “top tweets” that included photos or videos and were posted during the periods mentioned. The sample was chosen on 30 January 2021. We considered that the time interval between the tweet dates and the date the sample was chosen allowed enough time for each image to reach its full interaction potential. The searches carried out using the Twitter API were as follows: 1. “climate change” since:2019-11-25 until:2019-11-17 filter:media 2. “climate change” since:2020-01-12 until:2020-01-06 filter:media 3. “climate change” since:2020-03-29 until:2020-03-23 filter:media 4. “climate change” since:2020-06-14 until:2020-06-08 filter:media 5. “climate change” since:2019-09-13 until:2019-09-07 filter:media Each of these searches yielded a result of between 90 and 100 tweets. The results were saved on a spreadsheet and all of the fixed images were selected (photographs, graphs, illustrations, etc.). When several images appeared on the same post, we considered each one of them independently. Besides the images, we saved the following information for each tweet: date, user, number of likes, number of retweets, number of comments and text associated with each tweet. The interactions (number of likes, number of retweets and number of comments) were considered indicators of interest in the content of the message and therefore an indication of the potential of that image (along with the text associated) to foster public involvement in climate change. Of the 419 total images included in the initial sample, 39 contained text only (the image showed only a sign, press cutting or similar), so these were excluded, leaving a final sample (n) of 380 images. Coding After putting the selected images in chronological order in a database, we developed the codebook based on examples from previous studies. To classify the types of images, we used the classification system proposed by O’Neill (2017) for the most common images in traditional media: - Identifiable people: i.e. politicians, businesspeople and celebrities. - Non-identifiable people. - Impacts of climate change: i.e. episodes of extreme weather, ice melting, desertification and endangered animal species. - Energy, emissions and pollution: i.e. factory smokestacks, renewable energy sources and traffic. - Protests: i.e. demonstrations and other protest actions. - Scientific images: i.e. graphics on greenhouse gas emissions and maps of global warming. - Other images. Basing our work on the principles outlined by Climate Visuals (2018), we propose seven factors that lend effectiveness to images as a means to foster climate change engagement: - Showing real people, avoiding staged images. Images that show people expressing identifiable emotions are especially effective. Politicians, due to their low credibility and the fact that they’re perceived as not being authentic, are not very effective. - Telling stories. Images that tell a story by themselves, especially the newest ones, tend to be more effective at fostering public involvement. - Showing the causes of climate change on the appropriate scale. For example, showing a gridlocked motorway could be more effective than showing a single driver. Images that show individual behaviour (such as eating meat) can trigger defensive reactions and may not be effective. - Showing powerful climate impacts. For example, floods and the effects of extreme weather, which can have a huge emotional impact. - Showing solutions. The levels of involvement and the ideology determine the response to the images. However, images that show “solutions” to climate change tend to generate positive emotions. - Establishing local connections. It’s a good idea to use images that connect climate change with a local environment. However, at the same time, they should connect with the problem on a global level. - Showing people who are directly affected. Although images of protests tend to generate scepticism among most observers, protests by people who are directly affected by climate change are usually perceived as more authentic and emotionally moving.

According to a 2024 survey conducted among UK residents, almost 80 percent had some concern about climate change. In comparison, 19 percent were not concerned, with four percent of those having no concerns at all. The survey was conducted by the Department for Business, Energy & Industrial Strategy (BEIS) as part of its Net Zero and Climate Change Public Attitudes Tracker. Climate change causesIn a recent BEIS survey, it was found that 38 percent of respondents believed climate change is mainly caused by human activity. 13 percent believed it is caused entirely by human activity, whilst one percent felt that there is no such thing as climate change. Climate change is the term used for global weather phenomena which results in new weather patterns, increasing global temperatures. This term also includes the climate effects these increasing temperatures cause. A move towards green energyOver the last decade, electricity generation from renewable sources in the UK has increased significantly, surpassing 122 terawatt-hours in 2021. In the same period of time, the UK has seen its greenhouse gas emissions decrease by nearly 30 percent – from approximately 609 MtCO2e in 2010 to 427 MtCO2e in 2021.

This data corresponds to the 'burning ember' diagrams from IPCC reports and related publications (IPCC TAR, Smith et al. 2009 for AR4-related embers, AR5 and SR15). It was used to build figure 3 of Zommers et al. 2020 (Burning Embers: Towards more transparent and robust climate change risk assessments. Accepted for publication in Nature Reviews Earth & Environment). The data provided here is the result of extraction of information from the original figures, as presented in the related technical document 10.5281/zenodo.3992856. As explained in the Supplementary Information of Zommers et al. 2020 and the technical document, this is not data from the IPCC. The provided values are approximations of the global mean temperature increase corresponding to each change in risk in the original diagrams. The rigour of the preparation process and the limitations of the dataset are explained in the technical document.

This dataset contains the Twitter IDs of all ~20M tweets containing the phrase "climate change" 2018-2021. Additionally, it contains the topical annotations and 2D semantic representation of our thematic analysis based on ~980 topic clusters that are grouped by hand into seven themes (COVID-19, Politics, Contrarian, Movements, Solutions, Impacts, Causes) as well as "non-relevant/spam", "others", and highlighting of potentially interesting topics.

Code and additional notes are available on GitHub: https://github.com/TimRepke/twitter-climate

The topics, including statistics and the annotator labels for broader themes (aka "super topics") are contained in the spreadsheet. This data is extrapolated to the tweets contained in the share.jsonl file containing one json object per line with the following fields:

'rel': true iff Tweet is contained in analysis

'filters': null if Tweet is not included, otherwise contains an object with "reasons" why this tweet was excluded

'dup': 1 iff this is a duplicate (excl first)

'lan': 1 iff language is English (and not None)

'txt': 1 iff status text is not None

'mit': 1 iff text has minimum number of tokens (>=4)

'mah': 1 iff text has less than maximum number of hashtags (<=5),

'pfd': 1 iff tweet was posted after 01.01.2018

'ptd': 1 iff tweet was posted before 31.12.2021

'cli': 1 iff tweet actually contains "climate change" (API matches some false positives)

'ann': null if Tweet is not included, otherwise contains an object with topic annotations

't_km': topic (based on "keep & majority vote" strategy)

't_kp': topic (based on "keep & closest topic centroid [proximity]" strategy)

't_fm': topic (based on "drop sample topic [fresh] & majority vote" strategy)

't_fp': topic (based on "drop sample topic [fresh] & closest topic centroid [proximity]")

'st_int': theme annotation "Interesting"

'st_nr': theme annotation "Non-relevant / spam"

'st_cov': theme annotation "COVID"

'st_pol': theme annotation "Politics"

'st_mov': theme annotation "Movements"

'st_imp': theme annotation "Impacts"

'st_cau': theme annotation "Causes"

'st_sol': theme annotation "Solutions"

'st_con': theme annotation "Contrarian"

'st_oth': theme annotation "Other"

'x': x position in 2D representation

'y': x position in 2D representation

'sample': true iff this tweet was in the original topic model sample

Regional estimates are presented by industry and household for four gases - carbon dioxide, methane, nitrous oxide, and F-gases. The F-gases constitute of hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride and nitrogen trifluoride.Emissions are presented in Million metric tons of CO₂ equivalent (MTCO2e).Sources: Organisation for Economic Co-operation and Development (2022), Air Emission Accounts, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=AEA; Organisation for Economic Co-operation and Development (2022), Air Emission Accounts – OECD Estimates, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=OECD-AEA; Organisation for Economic Co-operation and Development (2022), Quarterly National Accounts, OECD.Stat https://stats.oecd.org/Index.aspx?DataSetCode=QNA%20; United Nations Framework Convention on Climate Change (UNFCCC). 2022. Greenhouse Gas Inventory Data - Detailed data by Party - Annex I. https://di.unfccc.int/detailed_data_by_party. Copyright 2022 United Nations Framework Convention on Climate Change; Crippa, M., Guizzardi, D., Solazzo, E., Muntean, M., Schaaf, E., Monforti-Ferrario, F., Banja, M., Olivier, J., Grassi, G., Rossi, S. and Vignati, E., GHG emissions of all world countries, EUR 30831 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-41547-3, doi:10.2760/074804, JRC126363; IEA (2022) Monthly electricity data, www.iea.org/statistics, All rights reserved; as modified by IMF; IEA (2022) Monthly oil statistics, www.iea.org/statistics, All rights reserved; as modified by IMF; IEA (2022) Monthly gas statistics, www.iea.org/statistics, All rights reserved; as modified by IMF; Country Authorities; IMF staff calculations.Category: Greenhouse Gas (GHG) EmissionsData series: Quarterly greenhouse gas (GHG) air emissions accountsMetadata:Quarterly greenhouse gas air emissions from production and household consumption are adjusted for seasonality. SEEA Air Emissions Accounts from official country sources have been accessed via the OECD Air Emissions Accounts database.Methodology:The OECD Air Emission Accounts database presents estimates that align with the classifications, concepts and methods consistent with the System of Environmental-Economic Accounting Central Framework (SEEA-CF). In addition to the OECD database, the estimation procedure uses the emission inventories sourced from UNFCCC, EDGAR and CAIT. Correspondence tables and industry output shares are used to concord the UNFCCC, EDGAR and CAIT estimates to their corresponding industrial and household activities. Annual estimates of greenhouse gas emissions by industry and for households are trended forward using the latest emission data available. They are temporally disaggregated using the best temporal aggregation method in conjunction with seasonally adjusted sub-annual indicators of economic activity highly correlated with the annual estimates, under a prior assumption on linkages with the annual estimates.Quarterly estimates for the most recent period (for which annual estimates do not exist) are extrapolated using the timelier sub-annual indicators.Disclaimer:The estimates are considered experimental. The sources and methods used to compile these estimates are still in development. Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.