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.

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 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 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.

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 Climate Change Mitigation Solutions market is evolving rapidly, driven by an urgent need to combat the escalating impacts of global warming and environmental degradation. This market encompasses a wide range of strategies and technologies aimed at reducing carbon emissions, enhancing energy efficiency, and promo

(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.

Financial overview and grant giving statistics of Global Climate Change Foundation

According to an April 2024 survey on climate change conducted in the United States, some ** percent of the respondents claimed that the issue of global warming is extremely/very/somewhat important to them. Another ** percent stated that the issue was not too or not at all important to them.

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

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.

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.

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

GCOM-C/SGLI L2 Statistics-Land surface temperature (LST) (1-Month,250m) 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 LST: Land surface temperature and QA_flag. 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 250 m. The statistical period is 1 month also 8 days statistics 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.

According to a survey conducted on climate change in Japan in September 2023, with over 87 percent, the majority of respondents stated that they were aware of the risks of global warming such as an increased risk of flooding due to frequent heavy rains, and increased risk of heat strokes. Only a few respondents stated that they did not know about these consequences.

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.

GCOM-C/SGLI L2 Statistics-Shadow Index (SI) (8-Days,250m) 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. This dataset includes SI: Shadow Index. SDI is the fraction of shadow generated by conformation of vegetation (areal occupation within a pixel) and is estimated with regression equation. The physical quantity unit is dimensionless. 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 250 m. The statistical period is 8 days also 1 month statistics 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.