This version has been superseded by a newer version. It is highly recommended for users to access the current version. Users should only access this superseded version for special cases, such as reproducing studies. If necessary, this version can be accessed by contacting NCEI. 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 1880 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. The changes from version 4 to version 5 include an update to the primary input datasets: ERSST version 5 (updated from v4), and GHCN-M version 4 (updated from v3.3.3). Version 5 updates also include a new netCDF file format with CF conventions. This dataset is formerly known as Merged Land-Ocean Surface Temperature (MLOST).

Website link to get more datasets: https://power.larc.nasa.gov/

The NASA POWER Project provides a wealth of data to support various applications related to energy, climate, and agriculture. One of the key datasets provided by the project is temperature data, which offers valuable insights into regional and global temperature patterns and trends. The temperature datasets are generated using advanced satellite remote sensing technologies and cover a wide range of spatial and temporal scales, from daily to monthly, and from local to global.

The temperature data sets provided by the POWER Project have a number of uses. For example, they can be used to monitor and analyze the impacts of climate change on the planet, and to understand how changes in temperature are affecting ecosystems and the distribution of plant and animal species. They can also be used to inform energy planning and management decisions, such as the design and operation of renewable energy systems and building energy efficiency measures. The temperature data sets are also useful for agricultural planning and management, providing critical information on crop growth, water usage, and other factors that impact food production and food security.

The temperature datasets from the NASA POWER Project are freely available to researchers, policymakers, and the general public, making them an important resource for anyone interested in the impacts of climate change and the use of renewable energy. Whether you're looking to understand the changing climate of our planet, plan and manage sustainable energy systems, or to ensure food security, the temperature datasets from the POWER Project are a valuable resource that can help you make informed decisions.

The monthly mean temperature data presented in this dataset was obtained from the Climate Prediction Center (CPC) Global Land Surface Air Temperature Analysis, which was loaded into Python using xarray. The data was then filtered to include only the latitude and longitude coordinates corresponding to each city in the dataset. In order to select the nearest location to each city, the 'select' method with the nearest point was used, resulting in temperature data that may not be exactly at the city location. The data is presented on a 0.5x0.5 degree grid across the globe.

The temperature data provides a valuable resource for time series analysis, and if you are interested in obtaining temperature data for additional cities, please let me know. I will also be sharing the source code on GitHub for anyone who would like to reproduce the data or analysis.

Since 1880, the annual global land temperature anomaly has fluctuated, showing an overall upward tendency. In 2024, the global land surface temperature stood at 1.98 degrees Celsius above the global average between 1901 to 2000. This was the highest annual temperature anomaly recorded during the period in consideration. Anomalies in global ocean surface temperature followed a similar trend over the same period of time. Man-made change The Earth's temperature increases naturally over time as the planet goes through cyclic changes. However, the scientific community has concluded that human interference, particularly deforestation and the consumption of fossil fuels, has acted as a catalyst in recent centuries. Increases in the unprecedented number of natural disasters in the past few decades, such as tropical cyclones, wildfires and heatwaves, have been attributed to this slight man-made increase in the Earth's surface temperature. End of an ice age? Although a one- or two-degree anomaly may not seem like a large difference, changes in the ocean and land temperatures have significant consequences for the entire planet. A five-degree drop triggered the last major ice age – the Quaternary Glaciation – over 20,000 years ago, which technically is still continuing today. This ice age is in its final interglacial period, and it will not officially end until the remnants of the final ice sheets melt, of which there are only two left today, in Antarctica and Greenland.

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).

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a monthly global merged land-ocean surface temperature analysis product that is derived from two independent analyses. The first is the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the second is a land surface air temperature (LSAT) analysis that uses the Global Historical Climatology Network - Monthly (GHCN-M) temperature database. The NOAAGlobalTemp data set contains global surface temperatures in gridded (5° × 5°) and monthly resolution time series (from 1850 to present time) data files. The product is used in climate monitoring assessments of near-surface temperatures on a global scale. This version, v6.0, an updated version to the current operational release v5.1, is implemented by an Artificial Neural Network method to improve the surface temperature reconstruction over the land.

The dataset provides official temperature data measured from 513 weather stations in Germany from 1990 to 2021.

The original data are provided by the German Meteorological Service (DWD, Deutscher Wetterdienst) via the OpenData area of the Climate Data Center (CDC). These data are provided in 1611 files, resulting in > 500 million rows of measurement information (or missing values), a format that is poorly suited for further analysis.

Therefore, the data are converted from "long format" to "wide format". The result is a time series with 10 minute frequency containing one column per weather station. The exact columns in the file are: - MESS_DATUM: the datetime values of the time series, representing the index of the time series - list of weather station ids: one column per weather station, represented by the weather station id

From the five numerical measurement values of the original data, only "air temperature at 2m height in °C" was kept.

In addition to the extracted temperature data, a notebook is provided which can be used to extract the other four types of measurements in the same format.

The following files are provided in this dataset: - german_temperature_data_1990_2021.csv, containing the extracted original data (download and transformation, see this notebook). - german_temperature_data_1996_2021_from_selected_weather_stations.csv, containing a selection of the original data from 55 weather stations that have continuously provided a high amount of measurements from 1996-2021 (and thus no change in distribution over time). For the selection process, see this notebook. - zehn_min_tu_Beschreibung_Stationen.txt, additional information about the weather stations. - DESCRIPTION_obsgermany_climate_10min_tu_historical_en.pdf, the official data set description.

The terms of use are described by https://opendata.dwd.de/climate_environment/CDC/Nutzungsbedingungen_German.pdf and https://gdz.bkg.bund.de.

Temperature in the United States increased to 10.73 celsius in 2024 from 10.25 celsius in 2023. This dataset includes a chart with historical data for the United States Average Temperature.

This Climate Data Record (CDR) provides Land Surface Temperature (LST) derived from the Meteosat Visible and InfraRed Imager (MVIRI) on board the Meteosat First Generation (MFG) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites. The covered time period ranges from January 1983 to December 2020. Original thermal radiances were inter-calibrated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The LST is derived from Meteosat by use of single-channel LST retrieval based on radiative transfer calculations. The LST is presented as hourly data and as monthly averaged diurnal cycle composites on a 0.05°x0.05° grid covering the Meteosat disk (Africa and Europe). A summary of the retrieval algorithms is provided by Duguay–Tetzlaff et al. 2015. This is a Thematic Climate Data Record (TCDR).

Rasters of air temperature, including both Alaskan and lower 48 datasets, with historical and projected data, and projected change.

The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation, following similar patterns. For instance, in August 2025, the average temperature across the North American country stood at 22.98 degrees Celsius. Rising temperatures Globally, 2016, 2019, 2021 and 2024 were some of the warmest years ever recorded since 1880. Overall, there has been a dramatic increase in the annual temperature since 1895. Within the U.S. annual temperatures show a great deal of variation depending on region. For instance, Florida tends to record the highest maximum temperatures across the North American country, while Wyoming recorded the lowest minimum average temperature in recent years. Carbon dioxide emissions Carbon dioxide is a known driver of climate change, which impacts average temperatures. Global historical carbon dioxide emissions from fossil fuels have been on the rise since the industrial revolution. In recent years, carbon dioxide emissions from fossil fuel combustion and industrial processes reached over 37 billion metric tons. Among all countries globally, China was the largest emitter of carbon dioxide in 2023.

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a merged land&ocean surface temperature analysis (formerly known as MLOST) It is a spatially gridded (5° - 5°) global surface temperature dataset, with monthly resolution from January 1880 to present. We combine a global sea surface (water) temperature (SST) dataset with a global land surface air temperature dataset into this merged dataset of both the Earth's and land's and ocean surface temperatures. The SST dataset is the Extended Reconstructed Sea Surface Temperature (ERSST) version 5. The land surface air temperature dataset is similar to ERSST but uses data from the Global Historical Climatology Network Monthly (GHCN-M) database, version 4.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

Q: Where was the monthly temperature warmer or cooler than usual? A: Colors show where average monthly temperature was above or below its 1991-2020 average. Blue areas experienced cooler-than-usual temperatures while areas shown in red were warmer than usual. The darker the color, the larger the difference from the long-term average temperature. Q: Where do these measurements come from? A: Weather stations on every continent record temperatures over land, and ocean surface temperatures come from measurements made by ships and buoys. NOAA scientists merge the readings from land and ocean into a single dataset. To calculate difference-from-average temperatures—also called temperature anomalies—scientists calculate the average monthly temperature across hundreds of small regions, and then subtract each region’s 1991-2020 average for the same month. If the result is a positive number, the region was warmer than the long-term average. A negative result from the subtraction means the region was cooler than usual. To generate the source images, visualizers apply a mathematical filter to the results to produce a map that has smooth color transitions and no gaps. Q: What do the colors mean? A: Shades of red show where average monthly temperature was warmer than the 1991-2020 average for the same month. Shades of blue show where the monthly average was cooler than the long-term average. The darker the color, the larger the difference from average temperature. White and very light areas were close to their long-term average temperature. Gray areas near the North and South Poles show where no data are available. Q: Why do these data matter? A: Over time, these data give us a planet-wide picture of how climate varies over months and years and changes over decades. Each month, some areas are cooler than the long-term average and some areas are warmer. Though we don’t see an increase in temperature at every location every month, the long-term trend shows a growing portion of Earth’s surface is warmer than it was during the base period. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's Environmental Visualization Laboratory (NNVL) produces the source images for the Difference from Average Temperature – Monthly maps. To produce our images, we run a set of scripts that access the source images, re-project them into desired projections at various sizes, and output them with a custom color bar. Additional information Source images available through NOAA's Environmental Visualization Lab (NNVL) are interpolated from data originally provided by the National Center for Environmental Information (NCEI) - Weather and Climate. NNVL images are based on NOAA Merged Land Ocean Global Surface Temperature Analysis data (NOAAGlobalTemp, formerly known as MLOST). References NCEI Monthly Global Analysis NOAA View Temperature Anomaly Merged Land Ocean Global Surface Temperature Analysis Global Surface Temperature Anomalies Climate at a Glance - Data Information Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-global-monthly-difference-a...This upload includes two additional files:* Temperature - Global Monthly, Difference from Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-global-monthly-difference-a...)* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

We provide temperature and anomaly data alongside Intergovernmental Panel on Climate Change (IPCC) global land temperature anomalies against the 1961 to 1990, and 1991 to 2020 baseline periods.

Global average temperatures have increased by around 1 degree Celsius in the last century, almost certainly a result of high levels of atmospheric greenhouse gases emitted from human activities. While this change may seem small, relatively small changes in our climate can have big effects on our environment (Ministry for the Environment [MfE] & Stats NZ, 2019).

Temperature change can have a significant effect on agriculture, energy demand, ecosystems, and recreation. Climate change projections for New Zealand suggest the greatest warming will be in summer/autumn and the least in winter and spring (MfE, 2018).

Temperature is also influenced by natural processes such as climate oscillations like the El Niño Southern Oscillation (ENSO). However, ENSO does not affect the long-term warming trend of the national temperature time series (World Meteorological Organization [WMO], 2014).

Variables: year: Year. temperature: Temperature in degrees Celsius. data_released: Year the data was released. source: Source of data. anomaly: Anomaly against the average temperature of a given reference period. reference_period: Reference period.

References: Ministry for the Environment. (2018). Climate Change Projections for New Zealand: Atmosphere Projections Based on Simulations from the IPCC Fifth Assessment, 2nd Edition (Publication No. ME 1385). https://www.mfe.govt.nz/publications/climate-change/climate-change-projections-new-zealand Ministry for the Environment & Stats NZ. (2019). New Zealand’s Environmental Reporting Series: Environment Aotearoa 2019 (Publication No. ME 1416). https://www.mfe.govt.nz/publications/environmental-reporting/environment-aotearoa-2019 World Meteorological Organization. (2014). El Niño/Southern Oscillation. WMO. (WMO-No. 1145). https://library.wmo.int/records/item/53800-el-nino-southern-oscillation

A station observation-based global land monthly mean surface air temperature dataset at 0.5 0.5 latitude-longitude resolution for the period from 1948 to the present . It uses a combination of two large individual data sets of station observations collected from the Global Historical Climatology Network version 2 and the Climate Anomaly Monitoring System (GHCN + CAMS), so it can be regularly updated in near real time with plenty of stations and (2) some unique interpolation methods, such as the anomaly interpolation approach with spatially-temporally varying temperature lapse rates derived from the observation-based Reanalysis for topographic adjustment.

This data set includes daily, population-weighted mean values of various heat metrics for every county in the contiguous United States from 2000-2020. The dataset methodology, usage notes, and additional citations are published in Scientific Data (see reference below for Spangler et al. [2022]). Minimum, maximum, and mean ambient temperature, dew-point temperature, humidex, heat index, net effective temperature, wet-bulb globe temperature, and Universal Thermal Climate Index are included. Note that Monroe County, Florida (FIPS: 12087) and Nantucket County, Massachusetts (FIPS 25019) are missing due to unavailability of ERA5-Land data for Key West, Florida and Nantucket, MA. To use these data, assign the data from the .Rds file to a new data frame in R using the readRDS() function. Please cite the use of this data set with the following reference. Note that additional citations for specific variables can be found in Table 2.

K.R. Spangler, S. Liang, and G.A. Wellenius. "Wet-Bulb Globe Temperature, Universal Thermal Climate Index, and Other Heat Metrics for US Counties, 2000-2020." Scientific Data (2022). doi: 10.1038/s41597-022-01405-3

This data set contains modified Copernicus Climate Change Service information (2022), as described and cited in the manuscript referenced above. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. This data set is provided “as is” with no warranty of any kind.

Since the 1980s, the annual temperature departure from the average has been consistently positive. In 2024, the global land and ocean surface temperature anomaly stood at 1.29 degrees Celsius above the 20th-century average, the largest recorded across the displayed period. What are temperature anomalies? Temperature anomalies represent the difference from an average or baseline temperature. Positive anomalies show that the observed temperature was warmer than the baseline, whereas a negative anomaly indicates that the observed temperature was lower than the baseline. Land surface temperature anomalies are generally higher than ocean anomalies, although the exact reasons behind this phenomenon are still under debate. Temperature anomalies are generally more important in the study of climate change than absolute temperature, as they are less affected by factors such as station location and elevation. A warming planet The warmest years have been recorded over the past decade, with the highest anomaly in 2024. Global warming has been greatly driven by increased emissions of carbon dioxide and other greenhouse gases into the atmosphere. Climate change is also evident in the declining extent of sea ice in the Northern Hemisphere. Weather dynamics can affect regional temperatures, and therefore, the level of warming can vary around the world. For instance, warming trends and ice loss are most obvious in the Arctic region compared to Antarctica.

Temperature in Iran decreased to 19.18 celsius in 2024 from 19.61 celsius in 2023. This dataset includes a chart with historical data for Iran Average Temperature.