Data on climate change coverage on broadcast news programs in the United States revealed that in 2024, the combined number of minutes dedicated to the topic amounted to 771 minutes, or just under 13 hours. This marked a decrease from the 1,032 minutes (over 17 hours) of coverage recorded in 2023. ABC, CBS, and NBC each have their own initiatives when it comes to covering climate change, and overall growth in the amount of coverage is clear compared to 2020 when coverage was largely focused on COVID-19. However, the source noted that the amount of coverage in 2021 accounted for just over one percent of all broadcast news programming that year.

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

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

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

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

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

The raw data comes from the Berkeley Earth data page.

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.

This dataset includes processed climate change datasets related to climatology, hydrology, and water operations. The climatological data provided are change factors for precipitation and reference evapotranspiration gridded over the entire State. The hydrological data provided are projected stream inflows for major streams in the Central Valley, and streamflow change factors for areas outside of the Central Valley and smaller ungaged watersheds within the Central Valley. The water operations data provided are Central Valley reservoir outflows, diversions, and State Water Project (SWP) and Central Valley Project (CVP) water deliveries and select streamflow data. Most of the Central Valley inflows and all of the water operations data were simulated using the CalSim II model and produced for all projections.

These data were originally developed for the California Water Commission’s Water Storage Investment Program (WSIP). The WSIP data used as the basis for these climate change resources along with the technical reference document are located here: https://data.cnra.ca.gov/dataset/climate-change-projections-wsip-2030-2070. Additional processing steps were performed to improve user experience, ease of use for GSP development, and for Sustainable Groundwater Management Act (SGMA) implementation. Furthermore, the data, tools, and guidance may be useful for purposes other than sustainable groundwater management under SGMA.

Data are provided for projected climate conditions centered around 2030 and 2070. The climate projections are provided for these two future climate periods, and include one scenario for 2030 and three scenarios for 2070: a 2030 central tendency, a 2070 central tendency, and two 2070 extreme scenarios (i.e., one drier with extreme warming and one wetter with moderate warming). The climate scenario development process represents a climate period analysis where historical interannual variability from January 1915 through December 2011 is preserved while the magnitude of events may be increased or decreased based on projected changes in precipitation and air temperature from general circulation models.

2070 Extreme Scenarios Update, September 2020

DWR has collaborated with Lawrence Berkeley National Laboratory to improve the quality of the 2070 extreme scenarios. The 2070 extreme scenario update utilizes an improved climate period analysis method known as "quantile delta mapping" to better capture the GCM-projected change in temperature and precipitation. A technical note on the background and results of this process is provided here: https://data.cnra.ca.gov/dataset/extreme-climate-change-scenarios-for-water-supply-planning/resource/f2e1c61a-4946-4863-825f-e6d516b433ed.

Note: the original version of the 2070 extreme scenarios can be accessed in the archive posted here: https://data.cnra.ca.gov/dataset/sgma-climate-change-resources/resource/51b6ee27-4f78-4226-8429-86c3a85046f4

Data on climate change news in the United States revealed that in 2024, a total of 295 guest appearances were made during climate coverage on broadcast TV climate segments, the majority of whom were white. The source noted that despite climate change disproportionately affecting minority communities, the majority of people appearing on broadcast television to discuss the topic were non-Hispanic white men.

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 statistic represents the concerns about harm caused by climate change among adults in the United States as of April 2020. During the survey, over half of the respondents said that they believed global warming will cause a great deal of harm to plant and animal species.

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

This statistic represents the frequency that adults in the United States hear people that they know speak about global warming as of April 2020. During the survey, approximately ** percent of the respondents said that they heard people in their circle talking about global warming several times a year.

This v2.2 SST_cci Climatology Data Record (CDR) consists of daily climatological mean sea surface temperature on a global 0.05 degree latitude-longitude grid, derived from the SST CCI analysis data for the period 1982 to 2010 (29 years). This climatology includes the post-hoc dust corrections from Merchant and Embury (2020) https://doi.org/10.3390/rs12162554.

The changes from climatology v2.1 are: * Inclusion of post-hoc dust corrections from Merchant and Embury (2020) reduces biases in affected regions (tropical Atlantic Ocean and the Mediterranean, Red, and Arabian Seas). * Improved compliance with CF Conventions.

Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .

When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. (2019) Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223. http://doi.org/10.1038/s41597-019-0236-x

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.09°C.]What does the data show? This dataset shows the change in summer average temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, summer is defined as June-July-August. Note, as the values in this dataset are averaged over a season they do not represent possible extreme conditions.The dataset uses projections of daily average air temperature from UKCP18 which are averaged over the summer period to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a change (in °C) relative to the 1981-2000 value. This enables users to compare summer average temperature trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.PeriodDescription1981-2000 baselineAverage temperature (°C) for the period2001-2020 (recent past)Average temperature (°C) for the period2001-2020 (recent past) changeTemperature change (°C) relative to 1981-20001.5°C global warming level changeTemperature change (°C) relative to 1981-20002°C global warming level changeTemperature change (°C) relative to 1981-20002.5°C global warming level changeTemperature change (°C) relative to 1981-20003°C global warming level changeTemperature change (°C) relative to 1981-20004°C global warming level changeTemperature change (°C) relative to 1981-2000What is a global warming level?The Summer Average Temperature Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Summer Average Temperature Change, an average is taken across the 21 year period.We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?These data contain a field for each warming level and the 1981-2000 baseline. They are named 'tas summer change' (change in air 'temperature at surface'), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'tas summer change 2.0 median' is the median value for summer for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tas summer change 2.0 median' is named 'tas_summer_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tas summer change 2.0°C median’ values.What do the 'median', 'upper', and 'lower' values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Summer Average Temperature Change was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.The ‘lower’ fields are the second lowest ranked ensemble member. The ‘higher’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and higher fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksFor further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

The model used here is a coupled ocean-atmosphere model that consists of the CCSR/NIES atmospheric GCM, the CCSR ocean GCM, a thermodynamic sea-ice model, and a river routing model (Abe-Ouchi et al., 1996). The spatial resolution is T21 spectral truncation (roughly 5.6 degrees latitude/longitude) and 20 vertical levels for the atmospheric part, and roughly 2.8 degrees horizontal grid and 17 vertical levels for the oceanic part. Flux adjustment for atmosphere-ocean heat and water exchange is applied to prevent a drift of the modelled climate. The atmospheric model adopts a radiation scheme based on the k-distribution, two-stream discrete ordinate method (DOM) (Nakajima and Tanaka, 1986). This scheme can deal with absorption, emission and scattering by gases, clouds and aerosol particles in a consistent manner. In the calculation of sulphate aerosol optical properties, the volumetric mode radius of the sulphate particle in dry environment is assumed to be 0.2 micron. The hygroscopic growth of the sulphate is considered by an empirical fit of d'Almeida et al. (1991). The vertical distribution of the sulphate aerosol is assumed to be constant in the lowest 2 km of the atmosphere. The concentrations of greenhouse gases are represented by equivalent-CO2. Three integrations are made for 200 model years (1890-2090). In the control experiment (CTL), the globally uniform concentration of greenhouse gases is kept constant at 345 ppmv CO2-equivalent and the concentration of sulphate is set to zero. In the experiment GG, the concentration of greenhouse gases is gradually increased, while that of sulphate is set to zero. In the experiments GS, the increase in anthropogenic sulphate as well as that in greenhouse gases is given and the aerosol scattering (the direct effect of aerosol) is explicitly represented in the way described above. The indirect effect of aerosol is not included in any experiment. The scenario of atmospheric concentrations of greenhouse gases and sulphate aerosols is given in accordance with Mitchell and Johns (1997). The increase in greenhouse gases is based on the historical record from 1890 to 1990 and is increased by 1 percent / yr (compound) after 1990. For sulphate aerosols, geographical distributions of sulphate loading for 1986 and 2050, which are estimated by a sulphur cycle model (Langer and Rodhe, 1991), are used as basic patterns. Based on global and annual mean sulphur emission rates, the 1986 pattern is scaled for years before 1990; the 2050 pattern is scaled for years after 2050; and the pattern is interpolated from the two basic ones for intermediate years to give the time series of the distribution. The sulphur emission rate in the future is based on the IPCC IS92a scenario. The sulphate concentration is offset in our run so that it starts from zero at 1890. The seasonal variation of sulphate concentration is ignored. Discussion on the results of the experiments will be found in Emori et al. (1999). Climate sensitivity of the CCSR/NIES model derived by equilibrium runs is estimated to be 3.5 degrees Celsius. Global-Mean Temperature, Precipitation and CO2 Changes (w.r.t. 1961-90) for the CCSR/NIES model. For the A2 emissions scenario the main emphasis is on a strengthening of regional and local culture, with a "return to family values" in many regions. The A2 world "consolidates" into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attention on contributing to the local community. Elsewhere, the trend is towards increased investment in education and science and growth in economic productivity. Social and political structures diversify, with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities. The A2 world sees more international tensions and less cooperation than in A1 or B1. People, ideas and capital are less mobile so that technology diffuses slowly. International disparities in productivity, and hence income per capita, are maintained or increased. With the emphasis on family and community life, fertility rates decline only slowly, although they vary among regions. Hence, this scenario family has high population growth (to 15 billion by 2100) with comparatively low incomes per capita relative to the A1 and B1 worlds, at US$7,200 in 2050 and US$16,000 in 2100. Technological change is rapid in some regions and slow in others as industry adjusts to local resource endowments, culture, and education levels. Regions with abundant energy and mineral resources evolve more resource intensive e... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.329.1 for complete metadata about this dataset.

MW-LST is a data record of land surface temperature (LST) derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020).

The data record has been produced by the company Estellus working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci). Compared with the remaining infrared LST data records of the LST_cci, the spatial resolution of the MW-LST is coarser, and the associated retrieval errors are larger. However, it offers LST estimates for clear-sky and cloudy conditions, therefore complementing the IR LST data records, which can only provide LST for clear skies. The data record is temporally and spatially complete, although in rare occasions some data can be missing due to missing observations, e.g., due to satellite maintenance operations or anomalous behavior. The data record is provided on a regular grid of 0.25x0.25 degrees, saved as daily, monthly, and yearly netcdf files. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.

This version of the data is v2.33. It fixes an issue that was found with the variable 'lst_unc_time_correction' in the previous v2.23, but is otherwise identical.

This dataset contains v2.0 of the Daily Snow Water Equivalent (SWE) product from the European Space Administration (ESA) Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution. Snow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces, in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979/01 to 2020/05. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. The dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modeling, and aspects of hydrology and meteorology. The Finnish Meteorological Institute is responsible for the SWE product development and generation. This version 2 dataset has some notable differences compared to the v1 data. In v2, passive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data-sets/), the grid spacing is reduced from 25 kilometers (km) to 12.5 km, and spatially and temporally varying snow density fields are used to adjust SWE retrievals in post processing. The output grid spacing is reduced from 0.25-degree to 0.10-degree WGS84 latitude / longitude to be compatible with other Snow_cci products. The time series has been extended by two years with data from 2018 to 2020 added. The ESA CCI phased product development framework allowed for a systematic analysis of these changes to the input data and snow density parameterization that occurred between v1 and v2 using a series of step-wise developmental datasets. In comparison with in-situ snow courses, the correlation and Root Mean Squared Error (RMSE) of v2 improved 18 percent (%) (0.1) and 12% (5 millimeters (mm)), respectively, relative to v1. The timing of peak snow mass is shifted two weeks later and a temporal discontinuity in the monthly northern hemisphere snow mass time series associated with the shift from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS) in 2009 is removed in v2. Luojus, K.; Moisander, M.; Pulliainen, J.; Takala, M.; Lemmetyinen, J.; Derksen, C.; Mortimer, C.; Schwaizer, G.; Nagler, T.; Venäläinen, P. (2022): ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2020), version 2.0. NERC EDS Centre for Environmental Data Analysis, 17 March 2022. doi:10.5285/4647cc9ad3c044439d6c643208d3c494. https://dx.doi.org/10.5285/4647cc9ad3c044439d6c643208d3c494

Thailand TH: Official Development Assistance: % of Total ODA: Climate Change Adaptation data was reported at 0.000 % in 2021. This stayed constant from the previous number of 0.000 % for 2020. Thailand TH: Official Development Assistance: % of Total ODA: Climate Change Adaptation data is updated yearly, averaging 0.000 % from Dec 2020 (Median) to 2021, with 2 observations. The data reached an all-time high of 0.000 % in 2021 and a record low of 0.000 % in 2021. Thailand TH: Official Development Assistance: % of Total ODA: Climate Change Adaptation data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Thailand – Table TH.OECD.GGI: Environmental: Environmental Policy, Taxes and Transfers: Non OECD Member: Annual.

This dataset contains v2.0 of the Daily Snow Water Equivalent (SWE) product from the European Space Administration (ESA) Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution. Snow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces, in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979/01 to 2020/05. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. The dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modeling, and aspects of hydrology and meteorology. The Finnish Meteorological Institute is responsible for the SWE product development and generation. This version 2 dataset has some notable differences compared to the v1 data. In v2, passive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data-sets/), the grid spacing is reduced from 25 kilometers (km) to 12.5 km, and spatially and temporally varying snow density fields are used to adjust SWE retrievals in post processing. The output grid spacing is reduced from 0.25-degree to 0.10-degree WGS84 latitude / longitude to be compatible with other Snow_cci products. The time series has been extended by two years with data from 2018 to 2020 added. The ESA CCI phased product development framework allowed for a systematic analysis of these changes to the input data and snow density parameterization that occurred between v1 and v2 using a series of step-wise developmental datasets. In comparison with in-situ snow courses, the correlation and Root Mean Squared Error (RMSE) of v2 improved 18 percent (%) (0.1) and 12% (5 millimeters (mm)), respectively, relative to v1. The timing of peak snow mass is shifted two weeks later and a temporal discontinuity in the monthly northern hemisphere snow mass time series associated with the shift from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS) in 2009 is removed in v2. Luojus, K.; Moisander, M.; Pulliainen, J.; Takala, M.; Lemmetyinen, J.; Derksen, C.; Mortimer, C.; Schwaizer, G.; Nagler, T.; Venäläinen, P. (2022): ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2020), version 2.0. NERC EDS Centre for Environmental Data Analysis, 17 March 2022. doi:10.5285/4647cc9ad3c044439d6c643208d3c494. https://dx.doi.org/10.5285/4647cc9ad3c044439d6c643208d3c494

The average temperature in December 2024 was 38.25 degrees Fahrenheit in the United States, the fourth-largest country in the world. The country has extremely diverse climates across its expansive landmass. Temperatures in the United States On the continental U.S., the southern regions face warm to extremely hot temperatures all year round, the Pacific Northwest tends to deal with rainy weather, the Mid-Atlantic sees all four seasons, and New England experiences the coldest winters in the country. The North American country has experienced an increase in the daily minimum temperatures since 1970. Consequently, the average annual temperature in the United States has seen a spike in recent years. Climate Change The entire world has seen changes in its average temperature as a result of climate change. Climate change occurs due to increased levels of greenhouse gases which act to trap heat in the atmosphere, preventing it from leaving the Earth. Greenhouse gases are emitted from various sectors but most prominently from burning fossil fuels. Climate change has significantly affected the average temperature across countries worldwide. In the United States, an increasing number of people have stated that they have personally experienced the effects of climate change. Not only are there environmental consequences due to climate change, but also economic ones. In 2022, for instance, extreme temperatures in the United States caused over 5.5 million U.S. dollars in economic damage. These economic ramifications occur for several reasons, which include higher temperatures, changes in regional precipitation, and rising sea levels.

the climatefish database collates abundance data of 15 fish species proposed as candidate indicators of climate change in the mediterranean sea. an initial group of eight mediterranean indigenous species (epinephelus marginatus, thalassoma pavo, sparisoma cretense, coris julis, sarpa salpa, serranus scriba, serranus cabrilla and caranx crysos) with wide distribution, responsiveness to temperature conditions and easy identification were selected by a network of mediterranean scientists joined under the ciesm programme ‘tropical signals’ (https://www.ciesm.org/marine/programs/tropicalization.htm; azzurro et al. 2010). soon after, and thanks to the discussion with other expert groups and projects, c. crysos was no longer considered, and lessepsian fishes (red sea species entering the mediterranean through the suez canal) were included, namely: fistularia commersonii, siganus luridus, siganus rivulatus, pterois miles, stephanolopis diaspros, parupeneus forskali, pempheris rhomboidea and torquigener flavimaculosus. considering the trend of increase of these species in the mediterranean sea (golani et al. 2021) and their projected distribution according to climate change scenarios (d’amen and azzurro, 2020), more data on these tropical invaders are expected to come in the future implementation of the study.data were collected according to a simplified visual census methodology (garrabou et al. 2019) along standard transects of five minutes performed at a constant speed of 10m/min, corresponding approximately to an area of 50x5m. four different depth layers were surveyed: 0-3m, 5-10 m, 11-20 m, 21-30 m. so far, the climatefish database includes fish counts collected along 3142 transects carried out in seven mediterranean countries between 2009 and 2021, for a total number of 101'771 observed individuals belonging to the 15 fish species.data were collected by a large team of researchers which joined in a common monitoring strategy supported by different international projects, which are acknowledged below. this database, when associated with climate data, offers new opportunities to investigate spatio-temporal effects of climate change in the mediterranean sea and test the effectiveness of each species as a possible climate change indicator. contacts: ernesto.azzurro(at)cnr.it references:azzurro e., maynou f., moschella p. (2010). a simplified visual census methodology to detect variability trends of coastal mediterranean fishes under climate change scenarios. rapp. comm. int. mer médit., 39.d’amen, m. and azzurro, e. (2020). lessepsian fish invasion in mediterranean marine protected areas: a risk assessment under climate change scenarios. ices journal of marine science, 77(1), pp.388-397.garrabou, j., bensoussan, n., azzurro, e. (2019). monitoring climate-related responses in mediterranean marine protected areas and beyond: five standard protocols.golani d., azzurro e., dulčić j., massutí e., orsi-relini l. (2021). atlas of exotic fishes in the mediterranean sea. 2nd edition [f. briand, ed.] 365 pages. ciesm publishers, paris, monaco. isbn number 978-92-990003-5-9

The Climate Change Narratives Survey 2020 is a nationally-representative survey (n=1,518) conducted in November and December 2020 on public perceptions of coronavirus and climate change. The survey extended previous research by systematically comparing perceptions of (personal and government) responsibility, efficacy and trust, as well as support for policies to address the two issues. The survey also used a novel approach to understand the trade-offs between hazards reduction, economic impact and personal freedom people are willing to make. The survey further included two ‘test’ narratives: one exploring respondents’ sense of agency, the other exploring the potential for health messaging to connect experiences of Covid-19 and climate change (See questionnaire for the two narratives). Respondents were asked to highlight which phrases they most strongly liked and disliked, and to explain the reasons for their choices. Data were collected online from 19 November to 12 December 2020 by DJS Research, a market research company. The sample consisted of 48% male and 51% female respondents. 10% were 18-24, 42% were 25-49, 25% were 50-64, and 23% were 65 years of age or over. Fourteen percent (14%) of the sample was from a Black, Asian and minority ethnic (BAME) background.