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

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.

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.

(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 x the 15th power of 10 bytes).

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

According to an April 2024 survey on climate change conducted in the United States, some 36 percent of respondents thought that global warming is affecting the weather a lot. Only eight percent of respondents claimed that global warming was affecting the weather just a little.

Introduction

Carbon Emission Statistics: Carbon emissions are a major cause of climate change. They mostly come from burning coal, oil, and gas. Cars, factories, power stations, and even homes release carbon into the air. Over time, the amount of carbon in the atmosphere has increased significantly. This leads to hotter weather, rising ocean levels, and stronger storms.

​In 2023, global carbon dioxide (CO₂) emissions from fossil fuels and industry reached approximately 37.01 billion metric tons, marking a continued upward trend in greenhouse gas emissions. China remained the largest contributor, accounting for over 31% of global CO₂ emissions. The United States and India followed, emitting significant portions of the total. The power sector was the leading source, responsible for 15 billion metric tons of CO₂ equivalent emissions. These escalating emissions have elevated atmospheric CO₂ concentrations to 419.3 parts per million, a 50% increase from pre-industrial levels.

The rise in greenhouse gas emissions contributes to global warming, resulting in increased temperatures, rising sea levels, and more frequent extreme weather events. Addressing these challenges necessitates comprehensive strategies to mitigate emissions across all sectors.

Introduction

Climate Change Impact Statistics: Climate change hurts our world in many ways. The Earth is getting hotter, leading to stronger storms and different weather. People, animals, and nature are all being affected. There are now more floods, fires, and dry areas than before. Most of this happens because of things people do, like using oil and gas and cutting down trees.

Scientists have found clear numbers that show how fast things are changing. These facts help us see why climate change is a big problem and why we must take action soon. This article shares facts about how climate change shapes our world and future.

Financial overview and grant giving statistics of Global Climate Change Foundation

We provide a novel, high-resolution hydrological modelling dataset using pseudo-global warming climate data as forcing to the Community Water Model (CWatM). CWatM is a state-of-the-art large-scale rainfall-runoff and channel routing water resources model that is process-based and used to quantify water supply, as well as human water withdrawals from different sectors (industry, domestic, agriculture) and multiple sources representing the effects of water infrastructure, including reservoirs, groundwater pumping and irrigation canals. CWatM is forced by a pseudo-global warming (PGW) experiment from 1981 to 2010. PGW simulations resemble historical weather patterns and events under globally warmer conditions (here, 2 K global warming) by perturbing historical, reanalysis-driven regional climate simulations. We performed simulations considering regular incremental adjustments of the historical water withdrawals (ranging between +/- 50% of historic water withdrawals) under PGW conditions. That range represents an ad hoc and simplified representation of multiple possible future water management scenarios across Southwestern and Central Europe. The approach allows us to investigate the effects of changing water withdrawals under 2 K global warming. Especially in Western and Central Europe, the projected impacts on low flows highly depend on the chosen water withdrawal assumption. The data highlights the importance of accounting for future water withdrawals in discharge projections.

Discharge statistics based on daily output from CWatM within 1981-2010:

Q1 - 1st percentile

Q5 - 5th percentile

Q10 - 10th percentile

Qavg - average discharge

Q90 - 90th percentile

Q95 - 95th percentile

Q99 - 99th percentile

Files:

Qxx_reference: CWatM considering historical water use forced by RACMO-ERA5

Qxx_PGW: CWatM considering historical water use forced by RACMO-ERA5 + climate pertubations under 2 K global warming. In the reference experiment, RACMO is forced at the lateral and sea surface boundaries of the model domain by unperturbed ERA5 reanalysis data, while in the pseudo-global warming experiment, the forcing data consist of perturbed reanalysis data. Perturbations are added to the ERA5 reference data corresponding to climate change patterns of surface pressure and sea surface temperature, and atmospheric profiles of temperature, relative humidity, and wind speed components that are retrieved from a 16-member single model initial condition ensemble of EC-EARTH global climate simulations.

Qxx_PGW_adjusted_demand: We have performed 11 additional hydrological simulations adjusting the historical water demand (ranging between +/- 50% of historic water withdrawals) to enable sensitivity assessments of low and high flows under 2 K global warming.

An upcoming publication will be made available and linked to this research very soon.

Explore Health and climate change : modelling the impacts of global warming and ozone depletion through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

Indicator data for the Blue Pacific 2050 Thematic Area 5: Climate Change And Disasters.

  "Our ambition is that all Pacific peoples remain resilient to the impacts of climate change and disasters and are able to lead safe, secure and prosperous lives. In addition, the region continues to play a leadership role in global climate action."

Find more Pacific data on PDH.stat.

GCOM-C/SGLI L2 Statistics-Snow and ice surface temperature (8-Days,1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data.This dataset includes SIST: Snow and ice surface temperature based on a model snow.Using the Level2 product as input, it calculates and outputs the temporal statistics of 8-days. The region definition and spatial resolutions of the output product are kept those of input data.Physical quantity unit is Kelvin. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 8 days also 1 month is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.

The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. www.climate-solutions-explorer.eu

Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges.

Updated gridded global climate and impact model data are based on CMIP6 and CMIP5 projections, using a subset of models from the ISIMIP project that have been consistently downscaled and bias-corrected. The data includes various indicators (~42) relating to extremes of precipitation and temperature (e.g. from Expert Team on Climate Change Detection and Indices), hydrological variables including runoff and discharge, heat stress (from wet bulb temperature) events (multiple statistics and durations), and cooling degree days, as well as further indicators relating to air pollution (PM2.5 from the GAINs model), and crop yields and natural habitat land-use change (biodiversity pressure) from the GLOBIOM model.

Indicators were calculated at a spatial resolution of 0.5° (approximately 50km at the equator), and subsequently spatially aggregated to the country level – from which population and land area exposure to the impacts were calculated. This has enabled the country-by-country comparison of national climate impacts and avoided exposure. Impacts were calculated at global mean temperature intervals, i.e. 1.2, 1.5, 2, 2.5, 3, and 3.5 °C, compared to a pre-industrial climate.

The dataset includes:

Global gridded projections (in netCDF format) of all the climate impact indicators at 0.5° spatial resolution, at global warming levels of 1.2, 1.5, 2, 2.5, 3, and 3.5 °CFor each GWL, maps for the absolute indicator values, the relative difference, and the scores are provided. The naming format is: cse_[short_indicator_name]_[ssp]_[gwl]_[metric].nc4. Please note that the Greenland ice sheet and the desert areas have been masked out for the hydrology indicators for these datasets.

Intermediate output data, including gridded maps of absolute values, relative differences, and scores for all ensemble members, as well as gridded maps of the multi-model ensemble statistics for the global warming levels and the reference period For the ensemble member data, the naming format is [gcm]_[ssp/rcp]_[gwl]_[short_indicator_name]_global_[start_year]_[end_year].nc4 or [ghm]_[gcm]_[ssp/rcp]_[gwl]_[soc]_[short_indicator_name]_global_[start_year]_[end_year]_[metric].nc4 for the hydrology indicators.

Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weightedThe .zip archives ‘table_output_climate_exposure_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_climate_exposure_land_air_pollution.zip’ contains the table data for theland and air pollution indicators.

Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure)The .zip archives ‘table_output_avoided_impacts_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_avoided_impacts_land_air_pollution.zip’ contains the table data for the land and air pollution indicators.

Further details are available on the Data Story page – www.climate-solutions-explorer.eu/story/data. A detailed description of the methodology and the calculation of the ISIMIP-derived indicators has been published in Werning, M. et al. (2024).

Release notes (v1.1)

Changes in this version:

Only table output data for the land and air pollution indicators have been changed, all other indicator data remain unchanged from v1.0

Updated land and air pollution indicators to use scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023

Fixed issue with the region mask for the EU

Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions

Release notes (v1.0)

Changes in this version:

Fixed calculation of the indicator “Drought intensity” (both for the version using discharge and run-off)

Masked out the Greenland ice sheet and the desert areas for the global gridded projections for the hydrology indicators in the final output files

Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions

Used scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023

Added the indicator ‘Heatwave days’

Added intermediate outputs for all ensemble members for energy, hydrology, precipitation, and temperature indicators

Release Notes (v0.4)

Changes in this version:

Removed ssp and metric from variable name in netCDF files

Removed obsolete coordinates in netCDF files for 'Drought intensity'

Added intermediate outputs for energy, hydrology, precipitation, and temperature indicators

Data for Figure 3.9 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.9 shows global, land, ocean and continental annual mean near-surface air temperatures anomalies in CMIP6 models and observations. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has ten panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d, panel_e, panel_f, panel_g, panel_h, panel_i and panel_j. --------------------------------------------------- List of data provided --------------------------------------------------- - Observed global near-surface air temperature change (1850-2020). - CMIP6 historical anthropogenic and natural global warming (1850-2020). - CMIP6 historical natural-only global warming (1850-2020). - CMIP6 historical greenhouse gas only global warming (1850-2020). - CMIP6 historical aerosol only global warming (1850-2020). --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - panel_a/fig_3_9_a.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_b/fig_3_9_b.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_c/fig_3_9_c.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_d/fig_3_9_d.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_e/fig_3_9_e.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_f/fig_3_9_f.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_g/fig_3_9_g.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_h/fig_3_9_h.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_i/fig_3_9_i.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) - panel_j/fig_3_9_j.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas) Plotted data corresponds to the following "exp" and "stat" indices: brown line: exp = 0, stat = 0 green line: exp = 1, stat = 0 grey line: exp = 2, stat = 0 blue line: exp =3, stat = 0 black line: exp = 4, stat = 0 shaded regions: stat = 1 and 2, exp = 0, 1, 2 and 3 The ensemble spread (shaded regions) of CMIP6 data shown in figure 3.9 are the mean, 5th and 95th percentiles. The in-file metadata labels the same ensemble spread as the mean, min and max. panel_a: Global Ocean panel_b: Global panel_c: Global Land panel_d: North America panel_e: Central and South America panel_f: Europe and North Africa panel_g: Africa panel_h: Asia panel_i: Australasia panel_j: Antarctica Acronyms - CMIP6 - The sixth phase of the Coupled Model Intercomparison Project. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1

The Climate Change Mitigation Technologies market is a dynamic sector that addresses the urgent need to combat climate change through innovative solutions aimed at reducing greenhouse gas emissions and enhancing sustainability. As industries grapple with the consequences of climate change, the demand for effective m

Introduction: Though awareness of climate change rose globally with the release of former Vice President Al Gore’s movie and book An Inconvenient Truth in 2006, there has seemingly never been a connection drawn between Gore’s works and subsequent fertility trends in the United States, particularly along political lines. Objectives: The primary objective of this project is to determine whether the release of the movie and book An Inconvenient Truth in 2006 sparked an inflection point within a year or two in the United States for birth rates, and whether those rates differ between red and blue states. The secondary objective is to determine whether there was a drop in birth rates after that inflection point. Methods: This project used natality data – birth rates per state per year from 2003-2020 – from the Centers for Disease Control and Prevention, joined with state political party data from the 2020 Presidential election from Wisevoter. Data were cleaned using Excel and analyzed using Tableau visualizations. Results: The year 2007 was indeed an inflection point in the United States for birth rates, as both red and blue states recorded their highest birth rates at this point in the 2003-2020 span. The birth rate in red states was higher than that of blue states throughout the span but both rates had a positive correlation, running parallel throughout the span. Conclusions: The United States birth rate declined after 2007 in both red and blue states, but it is unclear whether the release of An Inconvenient Truth influenced this decline.

The Low Global Warming Potential (GWP) Refrigerants market is currently experiencing significant growth, driven by increasing global awareness about climate change and the urgent need for sustainable alternatives in refrigeration and air conditioning. These refrigerants are crucial in various industries, including c

GCOM-C/SGLI L2 Statistics-Snow and ice surface temperature (1-month,1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 2 statistics products are using the Level2 product of land and cryosphere (Daily, Tile, 250 m or 1 km resolution) as input, it calculates and outputs the temporal statistics of 8-days or 1-month. The region definition and spatial resolutions of the output product are kept those of input data. This dataset includes SIST: Snow and ice surface temperature based on a model snow. Using the Level2 product as input, it calculates and outputs the temporal statistics of 1 month. The region definition and spatial resolutions of the output product are kept those of input data. The physical quantity unit is Kelvin. The statistics values stored to product are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1 km. The statistical period is 1 month also 8 days is available. The projection method is EQA. The generation unit is Tile. The current version of the product is Version 3. The Version 2 is also available.