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This version has been superseded by a newer version.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 Users should only access this superseded version for special cases, such as reproducing studies.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 derived from two independent analyses: 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, updated monthly, in gridded (5 degree x 5 degree) and time series formats. This data set is used in climate monitoring assessments of near-surface temperatures on a global scale. The changes from version 3.5.4 to version 4.0.0 include an update to the primary input dataset (ERSST) now at version 4.0.0 and GHCN-Monthly now at version 3.3.0. This dataset is formerly known as Merged Land-Ocean Surface Temperature (MLOST).

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

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.

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 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.

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.

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

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.

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 State is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 645675 rows across 5 variables.

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.

Climate change is one of the most pressing issues facing our planet today. In order to understand and address this issue, it is essential to have access to accurate and comprehensive data on global temperatures and other climate-related factors. This is where the Berkeley Earth dataset comes in. Berkeley Earth is an organization that has been collecting and analyzing temperature data from around the world for over a decade. They have compiled an extensive dataset that includes temperature records from over 39,000 weather stations in more than 180 countries. This dataset is widely considered to be one of the most accurate and comprehensive sources of climate data available. This is why I decided to generate a dataset with all countries merging the .txt files contained in their data section.

Dataset provides monthly surface temperature anomalies from 01/1900 to 12/2023

HadCRUT4 time series: ensemble medians and uncertainties The following files contain time series derived from the HadCRUT4 grids for selected regions. These 'best estimate' series are computed as the medians of regional time series computed for each of the 100 ensemble member realisations.

The dataset is presented as an ensemble of 100 dataset realisations that sample the distribution of uncertainty in the global temperature record given current understanding of non-climatic factors affecting near-surface temperature observations. This ensemble approach allows characterisation of spatially and temporally correlated uncertainty structure in the gridded data, for example arising from uncertainties in methods used to account for changes in SST measurement practices, homogenisation of land station records and the potential impacts of urbanisation.

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.

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.

This dataset captures average temperature data across different regions, providing insights into the climate variations by country, city, and state over the years. The dataset includes yearly records from 1995-2020, allowing for a comprehensive analysis of temperature trends and patterns.

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.

GLOBAL LAND-OCEAN TEMPERATURE INDEX

Earth’s surface continues to significantly warm, with recent global temperatures being the hottest in the past 2,000-plus years. Nineteen of the hottest years have occurred since 2000, with the exception of 1998, which was helped by a very strong El Niño.

Acknowledgements

https://climate.nasa.gov/

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