The table Global Temperatures by Country is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 577462 rows across 4 variables.

The table Global Temperatures by Major City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 239177 rows across 7 variables.

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.

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 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.

This dataset contains temperature exposure statistics for Europe (e.g. percentiles) derived from the daily 2 metre mean, minimum and maximum air temperature for the entire year, winter (DJF: December-January-February) and summer (JJA: June-July-August). These statistics were derived within the C3S European Health service and are available for different future time periods and using different climate change scenarios. Temperature percentiles are typically used in epidemiology and public health when defining health risk estimates and when looking at current and future health impacts, and they allow to identify a common threshold and comparison between different cities/areas. The temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. The statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided.

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 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.

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.

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.

The synthetic sensor dataset contains more than 3000 samples, each representing a set of sensor readings. It consists of six columns: Temperature, Sensor1, Sensor2, Sensor3, Sensor4, and Sensor5.

The dataset is designed to mimic a scenario where temperature readings are influenced by multiple independent sensor measurements. The values of the independent variables and the added noise introduce variability in the temperature readings.

In 2024, the global ocean surface temperature was 0.97 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.

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.

Temperature in Iraq decreased to 23.85 celsius in 2024 from 24.06 celsius in 2023. This dataset includes a chart with historical data for Iraq Average Temperature.

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.

This data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011. At its maximum, the network consisted of over 200 wireless sensor installations (nodes), with a range of 6 to 27 nodes per site. The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) were installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors were installed at 5 cm depth at six of the sites. Data collection started in August 2011 and continues at eight sites through the present. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional (airborne, e.g. NASA's Airborne Microwave Observation of Subcanopy and Subsurface Mission - AirMOSS) and national (spaceborne, e.g. NASA's Soil Moisture Active Passive - SMAP) scales.

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in mean temperature (°C) based on an ensemble of twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1901-2100. Projected change in mean temperature (°C) is with respect to the reference period of 1986-2005. The 5th, 25th, 50th, 75th and 95th percentiles of the ensembles of projected change in mean temperature change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in mean temperature (°C) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.