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 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.

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 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.

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'.

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 data package contains information on atmospheric CO2 trends, surface temperature, absolute sea levels, surface temperature analysis, average mass balance of glaciers and temperature anomalies all at a global level.

By 2099, climate change could be one of the leading causes of death in the world. With an increase of 3.5 degrees Celsius in mean surface temperature compared to a pre-industrial average, it was estimated that around 45 people per 100,000 population could die in that year due to effects caused by climate change. Only death rates from heart disease and strokes would surpass that value.

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and the CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset). Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios. A total of 31 climate indices have been calculated using the CanDCS-U6 and CanDCS-M6 datasets. The climate indices include 27 Climdex indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) and 4 additional indices that are slightly modified from the Climdex indices. These indices are calculated from daily precipitation and temperature values from the downscaled simulations and are available at annual or monthly temporal resolution, depending on the index. Monthly indices are also available in seasonal and annual versions. Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale. Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

(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.

We calculated monthly vapor pressure values for the conterminous United States from 1950 to 2100 from global climate models (GCM) output published by Coupled Model Intercomparison Project Phase 5 (CMIP5). These data include 28 GCMs under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 climate change scenarios. Vapor pressure data were then downscaled from their original spatial resolutions to 30 arcsecond using a statistical downscaling method called Bias Correction-Spatial Disaggregation (BCSD). These monthly vapor pressure data are provided as separate NetCDF files for each year (1950-2100), each of 28 GCM's, and each scenario (historical, RCP 4.5, and RCP 8.5).Vapor pressure (VPR) is the amount of water vapor held in the air. Vapor pressure deficit (VPD) is the difference between the total amount of water vapor air can hold at a given temperature and the actual amount of water held, expressed as partial pressure of water. VPD exerts a direct effect on plant transpiration by controlling the opening and closing of stomata (REF). VPD values are relevant for simulating vegetation response to climate, estimating drought conditions, and to simulate wildfire dynamics. Spatial vegetation or fire models require VPD dataset in a gridded format, along with other climate variables. Thus, these data may be used as input for vegetation, fire, drought or earth system models.Package was originally published on 02/22/23. On 03/20/2023 a subset of the data were made available for immediate download. Metadata updated on 04/28/2023 to include reference to newly published article.

Take urgent action to combat climate change and its impacts : Climate change is a critical development challenge for the region. The key threats are sea level rise, saltwater intrusion of freshwater lenses and ocean acidification and their impact on people, water and food security, livelihoods, and the Pacific region’s biodiversity and culture. Climate induced mobility and migration across the region may be a required adaptation strategy; Goal 13 indicators still require development for effective monitoring to take place.

Find more Pacific data on PDH.stat.

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

Abstract The impact of climate change on precipitation and maximum and minimum temperatures patterns in the Northeast region of Brazil is investigated based on the mean results of four global climate models, ECHAM5-OM from Germany, HADGEM2-ES from the UK, BCM2 from Norway and the CNRM-CM3 of France, for two scenarios of greenhouse gas emissions, A1B and A2, that had their future projections regionalized for the period 2021-2080 using the statistical downscaling model. The ability of the models to simulate present climate conditions was checked for the 1961-1990 control period, presenting very satisfactory results, and validated for the period 1991-2000. The analogues method was employed to perform statistical downscaling and to find predictor-prediting relationships. The results point to a significant reduction in rainfall in the respective rainy periods of the northeastern subregions, and the highest temperatures increase in the first semester, with a tendency to decrease in large areas of the northern Northeast sector in the second semester, mainly for scenario A2. For the minimum temperatures the results show a tendency of increase in all the year with highlight for the winter months.

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

NOTICE: A significant issue with the precipitation variables in this dataset was found in January 2015. The precipitation data has two fewer columns than the temperature data, one from each edge. When merged into the same coordinate system, this caused the temperature data to be offset to the west by one pixel. The dataset is now broken into two sub-datasets, one for precipitation and one for temperature. This corrects the pixel _location. Any use of precipitation data from this dataset from September 2013, when new precipitation files containing the issue were introduced, should be considered slightly in error. For more information please contact gdp@usgs.gov.In this project, we used an advanced statistical downscaling method that combines high-resolution observations with outputs from 16 different global climate models based on 4 future emission scenarios to generate the most comprehensive dataset of daily temperature and precipitation projections available for climate change impacts in the U.S. The gridded dataset covers the continental United States, southern Canada and northern Mexico at one-eighth degree resolution and Alaska at one-half degree resolution. The high-resolution projections produced by this work have been rigorously quality-controlled for both errors and biases in the global climate and statistical downscaling models. We also calculated projected future changes in a broad range of impact-relevant indicators, from seasonal temperature to extreme precipitation days. The results of the error and bias tests and the indicator calculations are made available as part of this database. Additional information and raw data from this dataset can be found here: https://cida.usgs.gov/thredds/catalog.html Before using this dataset, please review the material summarized here: https://my.usgs.gov/confluence/display/GeoDataPortal/2014/04/16/Notice%3A+Evaluation+of+Maurer+gridded+observational+datasets+and+their+impacts+on+downscaled+products Note that the CONUS temperature and precipitation data were split into two sub datasets in January 2015. This was done because the precipitation data uses a slightly different longitude axis than the temperature data.

This series is composed of five select physical marine parameters (water salinity and water temperature for surface and near bottom waters and sea ice) for two climate scenarios (RCP 45 and RCP 8.5) and three statistics (minimum, median and maximum) from an ensemble of five downscaled global climate models. The source data for this data series is global climate model outcomes from the Coupled Model Intercomparison Project 5 (CMIP5) published by the Intergovernmental Panel on Climate Change (Stocker et al 2013).

The source data were provided in NetCDF format for each of the downsampled climate models based on the five CMIP5 global climate models: MPI: MPI-ESM-LR, HAD: HadGEM2-ES, ECE: EC-EARTH, GFD: GFDL-ESM2M, IPS: IPSL-CM5A-MR. The data included monthly mean, maximum, minimum and standard deviation calculations and the physical variables provided with the climate scenario models included sea ice cover, water temperature, water salinity, sea level and current strength (as two vectors) as well as a range of derived biogeochemical variables (O2, PO4, NO3, NH4, Secci Depth and Phytoplankton).

These global atmospheric climate model data were subsequently downscaled from global to regional scale and incorporated into the high-resolution ocean–sea ice–atmosphere model RCA4–NEMO by the Swedish Meteorological and Hydrological Institute (Gröger et al 2019) thus providing a wide range of marine specific parameters. The Swedish Geological Survey used these data in the form of monthly mean averages to calculate change in multi-annual (30-year) climate averages from the beginning and end of the 21st century for the five select parameters as proxies for climate change pressures.

Each dataset uses only source data models based on an assumption of atmospheric climate gas concentrations in line with either the IPCCs representative concentration pathway RCP 4.5 or RCP 8.5. Changes were calculated as the difference between two multiannual (30 year) mean averages; one for a historical reference climate period (1976-2005) and one for an end of century projection (2070-2099). These data were extracted for each of the five downscaled CMIP5 models individually and then combined into ensemble summary statistics (ensemble minimum, median and maximum). In the Ensemble_Maximum/Median/Minimum_Rasters datasets, changes in mean (May-Sept) surface temperature and bottom temperature are given in Degrees Celsia (°C); changes in mean annual surface salinity and bottom salinity are given in Practical Salinity Units (PSU); changes in mean (October-April) sea ice are given in Percentage Points (pp).

In the Normalized_Rasters datasets, the changes are normalized using a linear stretch so that a cell value of zero represents no projected and a cell value of 100 represents a value equal to or above the mean change in Swedish national waters. The values representing 100 are: 4 °C for surface temperature; 3 °C for bottom temperature; -1.5 PSU for surface salinity; -2.0 PSU for bottom salinity; and -40 pp for sea ice. These were also the chosen reference values for determining, via expert review, the sensitivity of ecosystem components to changes in these parameters (for further information refer to the Symphony method).

Notes on interpretation. This dataset does not highlight inter-annual or inter-decadal climate variability (e.g. extreme events) or changes in biochemical parameters (e.g. O2, chlorophyll, secchi depth etc) resulting from change in surface temperature. Areas of no-data inshore were filled using extrapolating from nearby cells (using similar depths for benthic data) so data near the coast and particularly within archipelagos, bays and estuaries is not robust. Users should refer to the associated climemarine uncertainty map for this parameter. The uncertainty map shows the interquatile range from the climate ensemble and the area of no-data as 'interpolated values'. For any application which requires more temporally or spatially explicit information (e.g. at sub/national decision making) it is highly recommended that the user contact SMHI for access to the latest climate model source data (in NetCDF format) which contains much more detail and a far wider selection of parameters. For regional applications (e.g. at the scale of the Baltic Sea) - it should be noted that these data will likely require normalisation to regional rather than national values and that sensitivity scores used may differ.

ClimeMarine was selective in its choice of pressure parameters. SMHI have additional data available for other parameters such as O2, secchi depth and nutrients which could be included in future. This is complicated because many parameters are influenced by riverine discharge and therefore by decisions related to watershed management - disentanglement of impacts from climate vs river basin management becomes a complication. In a similar way, data on sealevel rise is also available which could be used to estimate impacts on the coast but likewise complicating factors such as isostatic uplift and coastal defence and management policies would need to be considered.

For simplicity and to reduce the amount of datasets to a manageable level for this assessment the source data were further limited and summarised in several ways:

Only the monthly mean averages of seawater temperature, salinity and sea ice (i.e. key physical parameters) were utilized.
For seawater salinity and temperature, the depth dimension (i.e. the water column) was summarised from 56 depth levels to just two: the surface and the deepest (bottom) waters.
Only two of the three climate periods were selected: a historical reference period: 1976-2005 (to represent the current status) and the projected end of century period: 2070-2099. Only two of the three available emission scenarios were selected detailing the consequence of intermediate and very high climate gas emissions : Representative Concentration Pathway (RCP) 4.5 and 8.5 (see SEDAC 2021).

Each dataset included in the series comes with extensive metadata.

The data processing followed the following steps:

Extraction of data for each parameter from NetCDF to TIFF Rasters for each model, emission scenario, depth level (using scripts in NCO, CDO and R). Calculation of climate ensemble statistics - Minimum, Mean, Median and Maximum (using Arcpy and Numpy)
Reprojection and resampling from the 2nm NEMO-RCO from Lat/Long WGS84 grid to the 250m ETRS89 LAEA Symphony grid (using Arcpy)
Extrapolation to fill no-data cells based on proximity and similar depths (using Arcpy script and the ArcGIS spatial analyst extension) Calculation of change for each parameter as the end of century multi-annual mean minus the reference multi-annual mean (using an Arcpy script)
Inversion of if negative (i.e. decreases) to positive (i.e. magnitude of change)
Normalisation as a linear stretch from 0 to 100 where zero equates to no change and 100 equates to the maximum pixel value in Swedish waters from the RCP 8.5 ensemble mean dataset with any values over this pixel value also set to 100 (Arcpy script)

NetCDF source data used in this analysis can be requested from the Swedish Meteorological and Hydrological Institute - kundtjanst@smhi.se

Processing scripts (R and arcpy) and interim raster data can be requested from the Geological Survey of Sweden - kundtjanst@sgu.se

The version of the R-package esd that was used to produce results submitted to ERL: climate atlas for the Barents region for temperature, precipitation, and storm statistics. Frozen copy of https://github.com/metno/esd