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 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 highest average temperature recorded in 2024 until November was in August, at 16.8 degrees Celsius. Since 2015, the highest average daily temperature in the UK was registered in July 2018, at 18.7 degrees Celsius. The summer of 2018 was the joint hottest since institutions began recording temperatures in 1910. One noticeable anomaly during this period was in December 2015, when the average daily temperature reached 9.5 degrees Celsius. This month also experienced the highest monthly rainfall in the UK since before 2014, with England, Wales, and Scotland suffering widespread flooding. Daily hours of sunshine Unsurprisingly, the heat wave that spread across the British Isles in 2018 was the result of particularly sunny weather. July 2018 saw an average of 8.7 daily sun hours in the United Kingdom. This was more hours of sun than was recorded in July 2024, which only saw 5.8 hours of sun. Temperatures are on the rise Since the 1960s, there has been an increase in regional temperatures across the UK. Between 1961 and 1990, temperatures in England averaged nine degrees Celsius, and from 2013 to 2022, average temperatures in the country had increased to 10.3 degrees Celsius. Due to its relatively southern location, England continues to rank as the warmest country in the UK.

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.

Maximum air temperature at two stations in or near the LEF have been compiled by the National Climate Data Center. Here we present monthly averages of the maximum air temperatures. Data are averaged per month through July 1992. Station general details are as follows:StationLongitude LatitudeElevation(in meters)PeriodCoveredType of record (Precipitation or Temperature)Pico del Este65 45'18 15'10511969-1992P,TFajardo65 39'18 20'121931-1992P,T

Based on current monthly figures, on average, German climate has gotten a bit warmer. The average temperature for January 2025 was recorded at around 2 degrees Celsius, compared to 1.5 degrees a year before. In the broader context of climate change, average monthly temperatures are indicative of where the national climate is headed and whether attempts to control global warming are successful. Summer and winter Average summer temperature in Germany fluctuated in recent years, generally between 18 to 19 degrees Celsius. The season remains generally warm, and while there may not be as many hot and sunny days as in other parts of Europe, heat waves have occurred. In fact, 2023 saw 11.5 days with a temperature of at least 30 degrees, though this was a decrease compared to the year before. Meanwhile, average winter temperatures also fluctuated, but were higher in recent years, rising over four degrees on average in 2024. Figures remained in the above zero range since 2011. Numbers therefore suggest that German winters are becoming warmer, even if individual regions experiencing colder sub-zero snaps or even more snowfall may disagree. Rain, rain, go away Average monthly precipitation varied depending on the season, though sometimes figures from different times of the year were comparable. In 2024, the average monthly precipitation was highest in May and September, although rainfalls might increase in October and November with the beginning of the cold season. In the past, torrential rains have led to catastrophic flooding in Germany, with one of the most devastating being the flood of July 2021. Germany is not immune to the weather changing between two extremes, e.g. very warm spring months mostly without rain, when rain might be wished for, and then increased precipitation in other months where dry weather might be better, for example during planting and harvest seasons. Climate change remains on the agenda in all its far-reaching ways.

A long-term timeseries of monthly averaged weather at Palmer Station, Antarctic, was created by combining calculated averages of daily weather from 1989-present with additional historical temperature measurements made between 1974-1989. The selected variables in this dataset include temperature, air pressure, precipitation, sea surface temperature, and wind speed. Monthly averages (means) are made for each calendar month, and dated with the month's start date. Historical monthly average temperatures (through March 1989) are from "Baker, K.S. (1996), Palmer LTER: Palmer Station air temperature 1974 to 1996." Monthly averages from April 1989 onwards are computed from the daily weather averages calculated at Palmer Station and made available by the Antarctic Meteorological Research Center (AMRC) archive at https://amrdcdata.ssec.wisc.edu/group/palmer-station/ The daily averages are available in aggregate form as PAL dataset #28 (knb-lter-pal.28.10), from which this dataset was generated.

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.

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: What was the average temperature for the month? A: Colors show the average monthly temperature across the contiguous United States. White and very light areas had average temperatures near 50°F. Blue areas on the map were cooler than 50°F; the darker the blue, the cooler the average temperature. Orange to red areas were warmer than 50°F; the darker the shade, the warmer the monthly average temperature. Q: Where do these measurements come from? A: Daily temperature readings come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments collect the highest and lowest temperature of the day at each station over the entire month, and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly average of daily mean temperatures, then plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). Q: What do the colors mean? A: Shades of blue show areas that had monthly average temperatures below 50°F. The darker the shade of blue, the lower the average temperature. Areas shown in shades of orange and red had average temperatures above 50°F. The darker the shade of orange or red, the higher the average temperature. White or very light colors show areas where the average temperature was near 50°F. Q: Why do these data matter? A: The 5x5km NClimGrid data allow scientists to report on recent temperature conditions and track long-term trends at a variety of spatial scales. The gridded cells are used to create statewide, regional and national snapshots of climate conditions. Energy companies use this information to estimate demand for heating and air conditioning. Agricultural businesses also use these data to optimize timing of planting, harvesting, and putting livestock to pasture. 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. This set of snapshots is based on NClimGrid climate data produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Average Temperature References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions) NCEI Monthly National Analysis) Climate at a Glance - Data Information) NCEI Climate Monitoring - All Products Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-us-monthly-averageThis upload includes two additional files:* Temperature - US Monthly Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots.* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

Q: How warm do afternoons usually get during this month? A: Based on daily observations from 1991-2020, colors on the map show the long-term average maximum temperature, sometimes referred to as the daytime or afternoon high, in 5x5 km grid cells for the month displayed. The map reveals the average of daytime high temperatures during the month over the previous three decades. Q: Where do these measurements come from? A: Daily temperature readings come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers and automated instruments collected the highest temperature at each station every day from 1991 to 2020, and sent them to the National Centers for Environmental Information (NCEI). After scientists checked the quality of the data to omit any systematic errors, they calculated each station’s average monthly maximum temperature by taking the sum of all the daily maximum temperatures for a month (for example, all Junes in the 1991-2020 period) and dividing it by the total number of daily measurements (the number of days in the month times 30 years). NCEI scientists then plotted the values on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolated (or estimated) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). Q: What do the colors mean? A: The color in each 5x5 km grid cell shows the average of the highest temperature recorded every day of the month for the 30 years from 1991 to 2020. Shades of blue show where the highest daily temperatures measured from 1991 to 2020 averaged below 50°F for the month. The darker the shade of blue, the lower the temperature. Areas shown in shades of orange and red have long-term average maximum temperatures above 50°F. The darker the shade of orange or red, the higher the temperature. White or very light colors show areas where the average maximum temperature is near 50°F. Q: Why do these data matter? A: Understanding these values provides insight into the “normal” conditions for a month. This type of information is widely used across an array of planning activities, from designing energy distribution networks, to the timing of crop and plant emergence, to choosing the right place and time for recreational activities. 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. This set of snapshots is based on NClimGrid climate data produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used this specific data source: NClimGrid Temperature Normals References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions) NCEI Monthly National Analysis) Climate at a Glance - Data Information) NCEI Climate Monitoring - All Products Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-maximum-1991-2020-monthly-a...This upload includes two additional files:* Temperature - Maximum, 1991-2020 Monthly 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-maximum-1991-2020-monthly-a

This file is based on the new high-resolution Berkeley Earth global temperature data set. It expands upon the previous Berkeley Earth temperature data set by including predictive structures based on historical weather patterns and increasing the underlying resolution to 0.25° x 0.25° latitude-longitude.

Files based on this new data set are being provided as part of an early preview to aid in the identification of any remaining bugs or errors. While, we believe the current data set to be accurate and useful, it is still in development and substantial revisions remain possible if significant issues are identified.

This file contains a detailed summary of the changes in Earth's global average surface temperature estimated by combining the new high-resolution Berkeley Earth land-surface temperature field with a reinterpolated version of the HadSST4 ocean temperature field.

As a preliminary data product, no citation for this work currently exists.

This global data product merges land-surface air temperatures with ocean sea surface water temperatures. For most of the ocean, sea surface temperatures are similar to near-surface air temperatures; however, air temperatures above sea ice can differ substantially from the water below the sea ice. In sea ice regions, temperature anomalies are extrapolated from the land-surface air temperatures when ice is present, and from the ocean temperatures when ice is absent.

The percent coverage of sea ice was taken from the HadISST v2 dataset and varies by month and location. In the typical month, between 3.5% and 5.5% of the Earth's surface is covered with sea ice. For more information on the processing and use of HadISST and HadSST refer to the description file for the combined gridded data product.

Temperature data contributing to this analysis include (but are not limited to):

GHCN-Monhtly v4, Menne et al. 2018, https://doi.org/10.1175/JCLI-D-18-0094.1

Global Summary of the Day, https://www.ncei.noaa.gov/products/global-summary-day MET-Reader, Scientific Committee for Antaractic Research, British Antarctic Survey, https://legacy.bas.ac.uk/met/READER/ HADSST4, Kennedy et al. 2019, https://doi.org/10.1029/2018JD029867

Ice mask data comes from:

HadISST2, Titcher and Rayner 2014, https://doi.org/10.1002/2013JD020316 Sea Ice Index, NSIDC, https://nsidc.org/data/g02135/versions/3

High-resolution downscaling algorithms were trained using high-resolution data, though none of this data is used directly in the reconstruction. High-resolution datasets used in training include:

ERA5 from the Copernicus Climate Change Service, Hersbach et al. (2018), http://doi.org/10.24381/cds.adbb2d47 https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels

The above list of data sources is only a partial list. For a more complete set of references please refer to Berkeley Earth's previous description papers.

Temperatures are in Celsius and reported as anomalies relative to the Jan 1951-Dec 1980 average. Uncertainties represent the 95% confidence interval for statistical and spatial undersampling effects as well as ocean biases.

The land analysis was run on 06-Mar-2023 02:09:12 The ocean analysis was published on 13-Mar-2023 02:52:51

The land component is based on 50498 time series with 21081445 monthly data points

The ocean component is based on 456950592 instantaneous water temperature observations

Estimated Jan 1951-Dec 1980 global mean temperature (°C): 14.148 +/- 0.019

As Earth's land is not distributed symmetrically about the equator, there exists a mean seasonality to the global average temperature.

Estimated Jan 1951-Dec 1980 monthly absolute temperature: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 12.31 12.52 13.15 14.07 15.01 15.68 15.91 15.72 15.17 14.30 13.33 12.59 +/- 0.03 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.03 0.02 0.03 0.03

For each month, we report the estimated global surface temperature anomaly for that month and its uncertainty. We also report the corresponding values for 12-month, five-year, ten-year, and twenty-year moving averages CENTERED about that month (rounding down if the center is in between months). For example, the annual average from January to December 1950 is reported at June 1950.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

Global Land and Surface Temperature Trends Analysis

Assessing climate change year by year

By IBM Watson AI XPRIZE - Environment [source]

About this dataset

This dataset from Kaggle contains global land and surface temperature data from major cities around the world. By relying on the raw temperature reports that form the foundation of their averaging system, researchers are able to accurately track climate change over time. With this dataset, we can observe monthly averages and create detailed gridded temperature fields to analyze localized data on a country-by-country basis. The information in this dataset has allowed us to gain a better understanding of our changing planet and how certain regions are being impacted more than others by climate change. With such insights, we can look towards developing better responses and strategies as our temperatures continue to increase over time

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How to use the dataset

Introduction

This guide will show you how to use this dataset to explore global climate change trends over time.

Exploring the Dataset

• Select one or more countries by using df[df['Country']=='countryname'] command in order to filter out any unnecessary information that is not related to those countries;

• Use df.groupby('City')['AverageTemperature'] command in order to group all cities together with their respective average temperatures;

• Compute basic summary statistics such as mean or median for each group with df['AverageTemperature'].{mean(),median()}, where {} can be replaced with mean or median according various statistic requirements;

4 .Plot a graph comparing these results from line plots or bar charts with pandas plot function such as df[column].plot(kind='line'/'bar'), etc., which can help visualize certain trends associated form these groups

You can also use latitude/longitude coordinates provided alongwith every record further decompose records by location using folium library within python such as folium maps that provide visualization features & zoomable maps alongwith many other rendering options within them like mapping locations according different color shades & size based on different parameters given.. These are just some ways you could visualize your data! There are plenty more possibilities!

Research Ideas

• Analyzing temperature changes across different countries to identify regional climate trends and abnormalities.
• Investigating how global warming is affecting urban areas by looking at the average temperatures of major cities over time.
• Comparing historic average temperatures for a given region to current day average temperatures to quantify the magnitude of global warming in that region.

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: GlobalLandTemperaturesByCountry.csv | Column name | Description | |:----------------------------------|:--------------------------------------------------------------| | dt | Date of the temperature measurement. (Date) | | AverageTemperature | Average temperature for the given date. (Float) | | AverageTemperatureUncertainty | Uncertainty of the average temperature measurement. (Float) | | Country | Country where the temperature measurement was taken. (String) |

File: GlobalLandTemperaturesByMajorCity.csv | Column name | Description | |:----------------------------------|:-----------------------------------------------------------------------| | dt | Date...

Q: Is annual sea surface temperature warmer or cooler than usual? A: Colors on this map show where and by how much annual sea surface temperature differed from a long-term average (1985-1993, details from Coral Reef Watch). Red and orange areas were warmer than average, and blue areas were cooler than average. The darker the color, the larger the difference from the long-term average. White and very light areas were near average. Q: Where do these measurements come from? A: Monthly measurements are made from NOAA's CoralTemp sea surface temperature (SST) data. Every day, instruments on eight satellites in two different orbits (geostationary and polar) measure sea surface temperature by checking how much energy is radiated by the ocean at different wavelengths. Computer programs plot these measurements on a gridded map and then merge and smooth the data into a gap-free product using mathematical filters. Each grid point covers an area approximately 5 x 5 km. Daily temperatures at each grid point are averaged together to calculate monthly average temperature. To calculate the difference-from-average temperatures shown here, a computer program takes the monthly average temperature at each grid point, and subtracts the long-term average for that month. Monthly measurements are averaged together to generate an annual image. If the result is a positive number, the sea surface was warmer than the long-term average. A negative result from the subtraction means the sea surface was cooler than usual. Q: What do the colors mean? A: Shades of blue show locations where sea surface temperature was cooler than its long-term average. Locations shown in shades of orange and red are where the sea’s surface was warmer than the long-term average. The darker the shade of red or blue, the larger the difference from the long-term average or “usual” sea surface temperature. Locations that are white or very light show where sea surface temperature was the same as or very close to its long-term average. Q: Why do these data matter? A: Water covers more than 70% of our planet's surface, so gathering data on ocean temperatures gives us a better picture of global temperatures. Tracking the temperature of the sea’s surface helps scientists understand how much heat energy is in the ocean and how it changes over time. Sea surface temperatures can have dramatic impacts on weather, including weather patterns such as El Niño-Southern Oscillation (ENSO) that travel hundreds of miles inland. Sea surface temperatures also play a significant role in the extent and thickness of Arctic and Antarctic sea ice, which serve as our planet’s built-in air-conditioning system. And sea surface temperatures have significant effects on marine life. The upwelling of cold water, for instance, provides nutrients to phytoplankton, the base of the marine food chain. In contrast, warm ocean surface waters deprive phytoplankton of nutrients, sometimes with devastating effects up the chain. 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 Sea Surface Temperature Anomaly files. To produce our images, we run a set of scripts that access these NNVL source files, re-project them into a Hammer-Aitoff globe, and output them in a range of sizes. References NOAA NNVL Sea Surface Temperature Anomaly (SSTA) NOAA NNVL SSTA FTP access NOAA Coral Reef Watch CoralTemp data CoralTemp climatology (long-term average) CoralTemp climatology methodology Source: https://www.climate.gov/maps-data/data-snapshots/data-source/sst-global-yearly-difference-average This upload includes two additional files:* SST - Global, Yearly Difference from Average _NOAA Climate.gov.pdf is a scre

This dataset replaces the previous Time Bias Corrected Divisional Temperature-Precipitation Drought Index. The new divisional data set (NClimDiv) is based on the Global Historical Climatological Network-Daily (GHCN-D) and makes use of several improvements to the previous data set. For the input data, improvements include additional station networks, quality assurance reviews and temperature bias adjustments. Perhaps the most extensive improvement is to the computational approach, which now employs climatologically aided interpolation. This 5km grid based calculation nCLIMGRID helps to address topographic and network variability. This data set is primarily used by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) to issue State of the Climate Reports on a monthly basis. These reports summarize recent temperature and precipitation conditions and long-term trends at a variety of spatial scales, the smallest being the climate division level. Data at the climate division level are aggregated to compute statewide, regional and national snapshots of climate conditions. For CONUS, the period of record is from 1895-present. Derived quantities such as Standardized precipitation Index (SPI), Palmer Drought Indices (PDSI, PHDI, PMDI, and ZNDX) and degree days are also available for the CONUS sites. In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set. As of November 2018, NClimDiv includes county data and additional inventory files.

The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. In March 2015, new Alaska data was included in the nClimDiv dataset. The Alaska nClimDiv data were created and updated using similar methodology as that for the CONUS. It includes maximum temperature, minimum temperature, average temperature and precipitation. In January 2025, the National Centers for Environmental Information (NCEI) began summarizing the State of the Climate for Hawaii. This was made possible through a collaboration between NCEI and the University of Hawaii/Hawaii Climate Data Portal and completes a long-standing gap in NCEI's ability to characterize the State of the Climate for all 50 states. NCEI maintains monthly statewide, divisional, and gridded average temperature, maximum temperatures (highs), minimum temperature (lows) and precipitation data for Hawaii over the period 1991-2025.

This dataset contains monthly averages of the QQ variable, representing cloud cover or a related atmospheric parameter, across Europe. It spans multiple years and regions, enabling analysis of spatial and temporal variability in cloud conditions. The data support climate monitoring, trend analysis, and research into links between cloud cover and broader environmental or climatic patterns. The dataset’s standardized format facilitates integration with other European climate datasets, including temperature, precipitation, and wind speed measurements.

This dataset provides monthly average values of the TG variable, representing mean air temperature across European regions. It spans multiple years, supporting analysis of seasonal and interannual temperature variability. The data are suitable for climate research, trend detection, modeling efforts, and understanding temperature-related environmental impacts across Europe. Structured for compatibility with other Copernicus climate datasets, it can be integrated with variables such as precipitation, cloud cover, and wind speed to examine broader climate patterns.