Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The mean of the 12 months each year is displayed here. Each annual update is available around the 15th of the following January (e.g., 2020 is available Jan 15th, 2021). The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report summary report by NOAA NCEI is available here. GHCN monthly mean station averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here.What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each year since 1880. Be sure to configure the time settings in your web map to view the timeseries correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. For a more detailed temporal analysis, a monthly mean is available here.

In 2023, the global ocean surface temperature was 0.91 degrees Celsius warmer than the 20th-century average. Oceans are responsible for absorbing over 90 percent of the Earth's excess heat from global warming. Departures from average conditions are called anomalies, and temperature anomalies result from recurring weather patterns or longer-term climate change. While the extent of these temperature anomalies fluctuates annually, an upward trend has been observed over the past several decades.

Effects of climate change

Since the 1980s, every region of the world has consistently recorded increases in average temperatures. These trends coincide with significant growth in the global carbon dioxide emissions, greenhouse gas, and a driver of climate change. As temperatures rise, notable decreases in the extent of arctic sea ice have been recorded.

Outlook

An increase in emissions from the use of fossil fuels is projected for the coming decades. Nevertheless, global investments in clean energy have increased dramatically since the early 2000s.

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.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.

Hear is the Temperature data for the year 2019.

link: https://data.world/indoagri/iot-temperature-dataset-of-2019

Temperature in Sudan decreased to 27.57 celsius in 2022 from 28.17 celsius in 2021. This dataset includes a chart with historical data for Sudan Average Temperature.

Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. This data set contains the following parameters: time, wtr_{z}, which are defined below.

Temperature in Trinidad and Tobago decreased to 26.46 celsius in 2022 from 26.58 celsius in 2021. This dataset includes a chart with historical data for Trinidad and Tobago Average Temperature.

Temperature in Russia increased to -2.87 celsius in 2022 from -3.65 celsius in 2021. This dataset includes a chart with historical data for Russia Average Temperature.

A gridded 1 km resolution global (50°S ~79°N) daily maximum and minimum near-surface air temperature dataset for 2013 that was generated using a seamless 1 km resolution land surface temperature dataset, a 30-arc second (~1 km) resolution digital elevation model (DEM) data, and air temperature observations at weather stations and a spatially varying coefficient model with sign preservation (SVCM-SP) algorithm. The gridded air temperature dataset is of great use in global studies of urban, climate, and hydrology. Documentation for this dataset can be found with the 2003 data. Data from other years is available at: https://doi.org/10.25380/iastate.c.6005185

The Met Office Hadley Centre's sea surface temperature data set, HadSST.4.0.1.0 is a monthly global field of SST on a 5° latitude by 5° longitude grid from 1850 to 2018. The data have been adjusted to minimise the effects of changes in instrumentation throughout the record. The data set is presented as a set of interchangeable realisations that capture the temporal and spatial characteristics of the estimated uncertainties in the biases. In addition there are files providing the measurement and sampling uncertainties which must be used in addition to the ensemble to obtain a comprehensive estimate of the uncertainty. The data are not interpolated.

MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). This map shows the temperature of Earth's lands during the nighttime.

Temperature in Georgia decreased to 9.92 celsius in 2022 from 10.05 celsius in 2021. This dataset includes a chart with historical data for Georgia Average Temperature.

Data on daily maximum, mean and minimum temperatures (Please visit the reference link for other climate information). The multiple file formats are available for datasets download in API.

The maximum and minimum temperatures are the highest and lowest temperatures (respectively) which occurred throughout the 24 hour period up to 9am. The observed minimum daily temperature is assigned to the date the observation was made, as the diurnal cycle typically reaches its minimum at approximately 5am. The observed maximum daily temperature is assigned to the day prior to the date the observation was made, as the diurnal cycle typically reaches its maximum at approximately 3pm. If the data are not recorded daily (for example, the instrument malfunctioned), the first observation following the no-report period is flagged as an accumulation.

(From http://www.worldclim.org/methods) - For a complete description, see:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as 1 km2 resolution). Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables.

The WorldClim interpolated climate layers were made using: * Major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. * The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km) * The ANUSPLIN software. ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing splines. We used latitude, longitude, and elevation as independent variables.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The mean annual temperature in North America stood at -4.5 degrees Celsius in 1995. It is expected that, 30 years later in 2025, the average temperature will increase by 1.6 degrees Celsius due to the effects of global warming, under a scenario where global temperatures increase by 1.5 degree Celsius.

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a blended product from two independent analysis products: the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the land surface temperature (LST) analysis using the Global Historical Climatology Network (GHCN) temperature database. The data is merged into a monthly global surface temperature dataset dating back from 1850 to the present. The monthly product output is in gridded (5 degree x 5 degree) and time series formats. The product is used in climate monitoring assessments of near-surface temperatures on a global scale. Changes to the data in version 5.1 included: removing the EOT filtering; filling in data gaps over the polar regions; and extending the beginning data coverage from 1880 to 1850.

This data set consists of daily air, water, and soil temperature, wind speed, vapor pressure, and the sum of global radiation and unfrozen precipitation data from the Toolik Lake area of Alaska between 1998 and 2002. The data includes readings from two sites: the Toolik Arctic Long Term Ecological Research (LTER) Tundra site and the nearby Toolik Tussock Experimental plots site that includes soil measurements from fertilized and unfertilized greenhouse and 'shadehouse' areas.

Data loggers recorded soil temperatures at various intervals down to 150 cm. Air temperatures were recorded between 1 and 5 meters. The data consist of 28 comma-delimited ASCII text files, and are available via ftp. Data files for each site contain slightly different meteorological parameters. This data set is a subset of the more comprehensive data set, Monitoring and Manipulation of Tundra Response to Climate Change, Arctic LTER, Toolik Lake, Alaska.

The research project was funded by the Arctic System Sciences (ARCSS) Program, grant number OPP-9810222.