Data are included from the GISS Surface Temperature (GISTEMP) analysis and the global component of Climate at a Glance (GCAG). Two datasets are provided: 1) global monthly mean and 2) annual mean temp...

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.

About Dataset

Global Temperature Anomalies (1880-2025) tracks how much hotter (or cooler) Earth has been compared to average temperatures from 1951-1980 🌡️.

Simple Dataset Breakdown

This beginner-friendly CSV has 1,751 rows of monthly data from April 1880 to August 2025. Three easy columns: 1. time ⏲️(like 1880.04 for April 1880).

1. station 🚉(global temperature change in °C)

3.**land** (just land areas in °C)

Great for Learning

• Plot simple line charts to see trends (use matplotlib in jupyter notebook).
• Practice time series basics or basic math like averages.
• Compare global vs. land temperatures side-by-side.

Give your reviews for my first dataset on this platform , I will take any advice from you guys on how to improve this dataset 😀.

Mature Support Notice: This item is in mature support as of April 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item. Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The mean of the 12 months each year is displayed here. The 2025 values are the last that will be included and no future updates are planned. The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report summary report by NOAA NCEI is available here. GHCN monthly mean station averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here. What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each year since 1880. Be sure to configure the time settings in your web map to view the timeseries correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. For a more detailed temporal analysis, a monthly mean is available here.

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

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.

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 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).

What does the data show?

This data shows the monthly averages of surface temperature (°C) for 1981-2010 from CRU TS (v. 4.06) dataset. It is provided on the WGS84 grid which measures approximately 60km x 60km (latitude x longitude) at the equator. This is the same as the 60km grid used by UKCP18 global datasets.

What are the naming conventions and how do I explore the data?

This data contains a field for each month’s average over the period. They are named 'tas' (temperature at surface) and the month. E.g. ‘tas March’ is the average of the daily average surface air temperatures in March throughout 1981-2010.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tas January’ values.

Data source

CRU TS v. 4.06 - (downloaded 12/07/22)

Useful links

Further information on CRU TS Further information on understanding climate data within the Met Office Climate Data Portal

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

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.

What does the data show?

This data shows the monthly averages of surface temperature (°C) for 2040-2069 using a combination of the CRU TS (v. 4.06) and UKCP18 global RCP2.6 datasets. The RCP2.6 scenario is an aggressive mitigation scenario where greenhouse gas emissions are strongly reduced.

The data combines a baseline (1981-2010) value from CRU TS (v. 4.06) with an anomaly from UKCP18 global. Where the anomaly is the change in temperature at 2040-2069 relative to 1981-2010.

The data is provided on the WGS84 grid which measures approximately 60km x 60km (latitude x longitude) at the equator.

Limitations of the data

We recommend the use of multiple grid cells or an average of grid cells around a point of interest to help users get a sense of the variability in the area. This will provide a more robust set of values for informing decisions based on the data.

What are the naming conventions and how do I explore the data?

This data contains a field for each month’s average over the period. They are named 'tas' (temperature at surface), the month and ‘upper’ ‘median’ or ‘lower’. E.g. ‘tas Mar Lower’ is the average of the daily average temperatures in March throughout 2040-2069, in the second lowest ensemble member.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tas Jan Median’ values.

What do the ‘median’, ‘upper’, and ‘lower’ values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

To select which ensemble members to use, the monthly averages of surface temperature for the period 2040-2069 were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.

The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

Data source

CRU TS v. 4.06 - (downloaded 12/07/22)

UKCP18 v.20200110 (downloaded 17/08/22)

Useful links

Further information on CRU TS Further information on the UK Climate Projections (UKCP) Further information on understanding climate data within the Met Office Climate Data Portal

Based on policies and actions in place as of November 2024, the global temperature increase is estimated to reach a median of 2.7 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 2024, a warming of 1.3 degrees above the pre-industrial average was recorded.

Mature Support Notice: This item is in mature support as of April 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item. Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service. The 2025 values are the last that will be included and no future updates are planned. The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report that summarizes the data is released each month (and end of the year) by NOAA NCEI is available here. GHCN monthly mean averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here. What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for the past month. Analysis: The full archive from 1880 – present is available here, and can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses.

HadCRUT5 is a gridded dataset of global historical surface temperature anomalies relative to a 1961-1990 reference period. Data are available for each month from January 1850 to December 2018 (updates will be available in time), on a 5 degree grid. The dataset is a collaborative product of the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia.

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.

Data are sourced from Carbon Dioxide Information Analysis Center (CDIAC). Four different series are provided: Global Annual Temperature Anomalies (Land) 1880-2014, Global Annual Temperature Anomalies (Land and Ocean) 1880-2014, Hemispheric Temperature Anomalies (Land+ Ocean) 1880-2014 and Annual Temperature anomalies (Land + Ocean) for three latitude bands that cover 30%, 40% and 30% of the global area, respectively, 1900-2014.

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

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a 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.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.