Annual and monthly global mean surface temperature anomalies in degrees Celsius, from NASA GISTEMP and UK Met Office HadCRUT5, covering 1850–present. Anomalies are relative to source-specific base periods.

This version has been superseded by a newer version.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 Users should only access this superseded version for special cases, such as reproducing studies.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).

Since 1979, NOAA satellites have been carrying instruments which measure the natural microwave thermal emissions from oxygen in the atmosphere. The intensity of the signals these microwave radiometers measure at different microwave frequencies is directly proportional to the temperature of different, deep layers of the atmosphere. Every month, John Christy and Dr. Roy Spencer update global temperature datasets that represent the piecing together of the temperature data from a total of fifteen instruments flying on different satellites over the years.

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The table Global Temperatures is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 3192 rows across 9 variables.

Since the 1980s, the annual temperature departure from the average has been consistently positive. In 2025, the global land and ocean surface temperature anomaly stood at 1.13 degrees Celsius above the 20th-century average, the largest recorded across the displayed period. What are temperature anomalies? Temperature anomalies are differences from a baseline temperature. Positive anomalies mean warmer than average, while negative anomalies mean cooler. Land areas usually show higher anomalies than oceans, though the exact reasons are still debated. They are useful because they reduce the impact of location factors like elevation. A warming planet The past decade includes the warmest years on record, with a peak in 2024. This warming is mainly caused by greenhouse gas emissions. It is also visible in the declining extent of sea ice in the Northern Hemisphere. The strongest warming and ice loss occur in the Arctic, though regional patterns vary.

Track the global surface temperature anomaly against the 1850–1900 baseline, including the latest rolling 12-month estimate and 2,000 years of context.

Global warming datasets converted to the uniform baseline. NASA, NOAA and Berkeley Earth datasets of global surface temperature changes in the period 1850-2021 for land+ocean, 1750-2021 for land only and 1880-2021 for ocean only, converted to the 1850-1900 baseline.

The online application is available at (no login required):
https://nowagreen.ct.ws/globalwarming/2

The average surface temperature of Earth is now above 15°C (59°F). Explore yearly global data over 2,000 years and the rapid warming trend since 1850.

Based on policies and actions in place as of November 2025, the global temperature increase is estimated to reach a median of 2.6 degrees Celsius in 2100. In the best-case scenario, where all announced net-zero targets, long-term targets, and Nationally Determined Contributions (NDCs) are fully implemented, the global temperature is still expected to rise by 1.9 degrees Celsius, when compared to the pre-industrial average. In 2015, Paris Agreement parties pledged to limit global warming to well below two degrees Celsius above pre-industrial levels, with the aim of reaching a maximum of 1.5 degrees. As of 2025, a warming of 1.3 degrees above the pre-industrial average was recorded.

This is the official UK temperature record for the entire planet going back 170+ years. Instead of reporting actual temperatures, it shows how much warmer or cooler each month was compared to the average from 1961 to 1990. That baseline is just a reference point, so you can see the warming trend clearly. The data includes uncertainty ranges because measurements aren't perfect, especially from the 1800s. Scientists use this alongside similar records from the US and other organizations to confirm what's happening with global warming.

Some argue that climate change is the greatest threat of our time, while others argue that it is fiction based on flawed research. We're giving you some of the info so you can create your own opinion. A long-term study of climate patterns requires a significant amount of data cleaning and preparation, much more than previous data sets published on Kaggle. Technicians used mercury thermometers to collect early data, and any fluctuation in visit time affected readings. Many weather stations were relocated throughout the 1940s due to airport building. In the 1980s, there was a shift toward electronic thermometers with a cooling bias. Given the complexities of climate patterns, a variety of groups collect data on them. NOAA's MLOST, NASA's GISTEMP, and the UK's HadCrut are the three most often quoted land and ocean temperature data sets. We repackaged the data from a recent collection prepared by Berkeley Earth, which is linked with the Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study brings together 1.6 billion temperature reports from 16 existing archives. It comes in a neat bundle and can be sliced into interesting subgroups (for example by country). They make available the underlying data as well as the code for the modifications they used. They also employ approaches that allow weather data from shorter time periods to be included, resulting in fewer observations being discarded. In this dataset, we have included several files: Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv): Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures LandAverageTemperature: global average land temperature in celsius LandAverageTemperatureUncertainty: the 95% confidence interval around the average LandMaxTemperature: global average maximum land temperature in celsius LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature LandMinTemperature: global average minimum land temperature in celsius LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature LandAndOceanAverageTemperature: global average land and ocean temperature in celsius LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

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.

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a monthly global land-ocean surface temperature product. It combines water surface temperatures over oceans (excluding the Arctic Ocean), seas, and large lakes with near-surface air temperatures over land and the Arctic Ocean to produce a globally complete surface temperature dataset. The input data sources include the Extended Reconstructed Sea Surface Temperature (ERSST), the Global Historical Climatology Network - Monthly (GHCN-M), the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), and the International Arctic Buoy Program (IABP) dataset. NOAAGlobalTemp is analyzed on 5°×5° spatial grids, spans the period from 1850 to present, and is updated monthly. The product is used for climate monitoring and climate assessments of near-surface temperatures on a global scale. This version, v6.1, differs from the previous version, v6.0, in that it utilizes the ERSST version 6, which employs artificial neural netword for its SST reconstruction, and also it uses the current WMO climatological standard normals period 1991–2020 as the reference period for temperature anomalies.

The data this week comes from the NASA GISS Surface Temperature Analysis (GISTEMP v4). This datasets are tables of global and hemispheric monthly means and zonal annual means. They combine land-surface, air and sea-surface water temperature anomalies (Land-Ocean Temperature Index, L-OTI). The values in the tables are deviations from the corresponding 1951-1980 means.

The GISS Surface Temperature Analysis version 4 (GISTEMP v4) is an estimate of global surface temperature change. Graphs and tables are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas), combined as described in their publications Hansen et al. (2010) and Lenssen et al. (2019). These updated files incorporate reports for the previous month and also late reports and corrections for earlier months.

When comparing seasonal temperatures, it is convenient to use “meteorological seasons” based on temperature and defined as groupings of whole months. Thus, Dec-Jan-Feb (DJF) is the Northern Hemisphere meteorological winter, Mar-Apr-May (MAM) is N.H. meteorological spring, Jun-Jul-Aug (JJA) is N.H. meteorological summer and Sep-Oct-Nov (SON) is N.H. meteorological autumn. String these four seasons together and you have the meteorological year that begins on Dec. 1 and ends on Nov. 30 (D-N). The full year is Jan to Dec (J-D). Brian Bartling

Data Dictionary

global_temps.csv

variable	class	description
Year	double	Year
Jan	double	January
Feb	double	February
Mar	double	March
Apr	double	April
May	double	May
Jun	double	June
Jul	double	July
Aug	double	August
Sep	double	September
Oct	double	October
Nov	double	November
Dec	double	December
J-D	double	January-December
D-N	double	Decemeber-November
DJF	double	December-January-February
MAM	double	March-April-May
JJA	double	June-July-August
SON	double	September-October-November

nh_temps.csv

variable	class	description
Year	double	Year
Jan	double	January
Feb	double	February
Mar	double	March
Apr	double	April
May	double	May
Jun	double	June
Jul	double	July
Aug	double	August
Sep	double	September
Oct	double	October
Nov	double	November
Dec	double	December
J-D	double	January-December
D-N	double	Decemeber-November
DJF	double	December-January-February
MAM	double	March-April-May
JJA	double	June-July-August
SON	double	September-October-November

sh_temps.csv

variable	class	description
Year	double	Year
Jan	double	January
Feb	double	February
Mar	double	March
Apr	double	April
May	double	May
Jun	double	June
Jul	double	July
Aug	double	August
Sep	double	September
Oct	double	October
Nov	double	November
Dec	double	December
J-D	double	January-December
D-N	double	Decemeber-November
DJF	double	December-January-February
MAM	double	March-April-May
JJA	double	June-July-August
SON	double	September-October-November

zonann_temps.csv

variable	class	description
Year	double	Year
Glob	double	Global
NHem	double	Northern Hemisphere
SHem	double	Southern Hemisphere
24N-90N	double	24N-90N lattitude
24S-24N	double	24S-24N lattitude
90S-24S	double	90S-24S lattitude
64N-90N	double	64N-90N lattitude
44N-64N	double	44N-64N lattitude
24N-44N	double	24N-44N lattitude
EQU-24N	double	EQU-24N lattitude
24S-EQU	double	24S-EQU lattitude
44S-24S	double	44S-24S lattitude
64S-44S	double	64S-44S lattitude
90S-64S	double	90S-64S lattitude

Want to know if the planet is actually warming? This is the data that settles the argument. NASA combines temperature readings from weather stations around the world and ocean buoys to calculate how much hotter or colder each month was compared to a standard baseline from the 1950s. If the number is positive, that month was warmer than average. If negative, it was cooler. It goes all the way back to 1880, so you can see the long-term trend. This is one of the main datasets climate scientists point to when they talk about global warming.

Global average surface temperature anomaly over time — how much warmer Earth is than the 20th-century baseline. Live chart from HadCRUT, free.

Temperatures have risen in the last 100 years around the world. In the 1910s, global average temperatures were some 0.38 degrees Celsius lower than the average temperatures between 1910 and 2000. In the most recent decade, the world experienced temperatures that were 1.21 degrees Celsius over the average.

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.

To map out temperature of a region in a given month compared to the norm for that same month in the same region.

These maps do not depict absolute temperature but instead show temperature anomalies, or how much it has changed.

Data contains: shades of red and orange indicating areas where the average monthly temperatures are warmer than they were in that area during the base period from 1951-1980 and shades of blue show cooling compared to the base period.

Data is based on monthly mean station measurements of the Global Historical Climatology Network (GHCN) version 2, which is available monthly from the U.S. National Climatic Data Center.

This statistic presents the perceived changes in annual global temperatures in the last 18 years, in selected European Countries in 2018. According to data published by Ipsos, the average guess among respondents in these countries was between 7 to 13 years, compared to the actual figure of **.