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

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

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.

The table Global Temperatures by City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://redivis.com/datasets/1e0a-f4931vvyg. It contains 8599212 rows across 7 variables.

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).

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 1850 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. Changes to the data in version 5.1 included: removing the EOT filtering; filling in data gaps over the polar regions; and extending the beginning data coverage from 1880 to 1850.

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.

Abstract

Compilation of Earth Surface temperatures historical. Source: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Documentation

Data compiled by the Berkeley Earth project, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include 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

%3C!-- --%3E

• LandAverageTemperature: global average land temperature in celsius

%3C!-- --%3E

• LandAverageTemperatureUncertainty: the 95% confidence interval around the average

%3C!-- --%3E

• LandMaxTemperature: global average maximum land temperature in celsius

%3C!-- --%3E

• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature

%3C!-- --%3E

• LandMinTemperature: global average minimum land temperature in celsius

%3C!-- --%3E

• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature

%3C!-- --%3E

• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius

%3C!-- --%3E

• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

%3C!-- --%3E

**Other files include: **

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)

%3C!-- --%3E

• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)

%3C!-- --%3E

• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)

%3C!-- --%3E

• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

%3C!-- --%3E

The raw data comes from the Berkeley Earth data page.

The problem being investigated involves analyzing global land temperature changes for major cities as well as globally. This topic was chosen as it is representative of the international issue of global warming. The severity of the issue is reflected in The Paris Agreement, which is a legally binding treaty with the goal of limiting global warming to the target rate of 1.5°C (degrees Celsius), or at least 2°C within this century (United Nations, 2021). Global warming has been linked to intensification of extreme weather phenomena, which lead to fatalities, environmental damage, community devastation, and financial costs. For example, extreme heat can lead to drought, wildfires, and create the urban heat island effect (Center for Climate and Energy Solutions, 2018). The temperature of the ocean will be explored as well as this factor is significant since theoretically, changes in the ocean would take longer – which in turn provides clarity on the severity of climate change.

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

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. Each annual update is available around the 15th of the following January (e.g., 2020 is available Jan 15th, 2021). 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.

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.

Temperature, Emissions & Environmental Trends (2000-2024)

This dataset provides a comprehensive overview of key environmental indicators collected over a span of 24 years (2000–2024) across multiple countries. It is designed to support analyses that explore the interplay between climate variables, human activities, and environmental changes. The dataset is particularly useful for researchers, data scientists, and policy analysts interested in climate change, sustainability, and environmental impact studies.

Key Features:

• Year: Indicates the calendar year of each record.
• Country: The country from which the data was recorded.
• Avg_Temperature_degC: Average annual temperature in degrees Celsius, offering insights into regional and global warming trends.
• CO2_Emissions_tons_per_capita: Annual per capita carbon dioxide emissions, a critical metric for assessing environmental impact and sustainability.
• Sea_Level_Rise_mm: Measured sea level rise in millimeters, useful for understanding coastal and climate-related risks.
• Rainfall_mm: Total annual rainfall in millimeters, which can be analyzed in relation to climate variability and water resource management.
• Population: The total population of the country, providing context for per capita analyses.
• Renewable_Energy_pct: The percentage of energy derived from renewable sources, highlighting progress towards sustainable energy transitions.
• Extreme_Weather_Events: The number of recorded extreme weather events, enabling studies of climate variability and disaster management.
• Forest_Area_pct: The percentage of land covered by forests, an important indicator of ecological health and carbon sequestration potential.

Applications:

• Climate Change Analysis: Explore trends in temperature, sea level rise, and extreme weather events to better understand global warming and its impacts.
• Environmental Policy: Assess the effectiveness of policies aimed at reducing CO2 emissions and increasing renewable energy usage.
• Sustainability Studies: Investigate the relationships between population growth, environmental degradation, and forest cover changes.
• Time Series Forecasting: Use historical data to predict future environmental conditions and inform mitigation strategies.

Data Source:

The dataset is curated with high-quality environmental metrics and is referenced from OpenML, ensuring a robust foundation for academic research and policy analysis.

Global Temperature: Daily Average: United States: Sheppard Afb data was reported at 27.500 Degrees Celsius in 01 Oct 2024. This records a decrease from the previous number of 30.000 Degrees Celsius for 24 Sep 2024. Global Temperature: Daily Average: United States: Sheppard Afb data is updated daily, averaging 30.000 Degrees Celsius from Sep 2024 (Median) to 01 Oct 2024, with 5 observations. The data reached an all-time high of 35.500 Degrees Celsius in 19 Sep 2024 and a record low of 25.000 Degrees Celsius in 23 Sep 2024. Global Temperature: Daily Average: United States: Sheppard Afb data remains active status in CEIC and is reported by Climate Prediction Center. The data is categorized under Global Database’s United States – Table US.CPC.GT: Environmental: Global Temperature: Daily Average.

This file contains daily land-surface average temperature results produced using anomalies and average temperature by the Berkeley Earth averaging method.

I claim no ownership of this dataset, everything was done by the Berkeley Earth team, I have merely converted the text dataset into a CSV file.

Here's the description of Berkeley dataset: Temperatures are in Celsius and reported as anomalies relative to the Jan 1951-Dec 1980 average.

The current dataset presented here is described as: Berkeley Earth daily TAVG full dataset This current analysis product is preliminary and may be subject to significant future revisions.This analysis was run on 06-Sep-2022 15:50:29

Results are based on a 50461 monthly time series with 21047039 observations and 48263 daily time series with 512331899 observations Estimated Jan 1951-Dec 1980 land-average temperature (C): 8.59 +/- 0.05

Initially, the temperature dataset is provided with specific coordinates in terms of longitude and latitude. To tailor this data to each country, we utilize geographical boundaries as defined by the World Bank. The method involves trimming the global temperature dataset to match the exact geographical shape of each country. To correct for potential distortions caused by the Earth's curvature on a flat map, we apply a latitude-based weighting. This step is essential for maintaining accuracy, especially in high-latitude regions where distortion is more pronounced. The result of this process is a latitude-weighted average temperature for each nation.

It's important to note, however, that due to the resolution constraints of the Copernicus dataset, this methodology might not be as effective for countries with very small landmasses. In these cases, the process may not yield reliable data.

The derived 2-meter temperature readings for each country are calculated based on administrative borders, encompassing all land surface types within these defined areas. As a result, temperatures over oceans and seas are not included in these averages, focusing the data primarily on terrestrial environments.

Global temperature averages and anomalies are calculated over all land and ocean surfaces.

The temperature anomaly is calculated by comparing the average surface temperature of a specific time period (e.g., a particular year or month) to the mean surface temperature of the same period from 1991 to 2020.

When calculating anomalies for each country, the average surface temperature of a given year or month is compared to the 1991-2020 mean temperature for that specific country.

The reason for using the 1991-2020 period as the reference mean is that it is the standard reference period used by our data source, the Copernicus Climate Change Service. This period is also adopted by the UK Met Office. This approach ensures consistency in identifying climate variations over time.

This dataset offers a historical view on temperature anomalies (i.e. deviations from the corresponding 1951-1980 means) starting with January 1880 and until October 2019. The estimated values contained in this dataset are averages for every month of the year (at a global level and earth hemispheres), for the entire year and for every season of the year.

What does the data show?

This data shows the monthly averages of maximum 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 'tmax' (temperature maximum), the month and ‘upper’ ‘median’ or ‘lower’. E.g. ‘tmax Mar Lower’ is the average of the daily minimum 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 ‘tmax 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 maximum 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

This is an output from ccc-gistemp. It is a an estimate of temperature change over the global land surface (of Earth). The units are in centikelvin.