Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, 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
• 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

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

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.

This dataset contains Russian Historical Soil Temperature Data. This data set is a collection of monthly and annual average soil temperatures measured at Russian meteorological stations. Data were recovered from many sources and compiled by staff at the University of Colorado, USA, and the Russian Academy of Sciences in Puschino, Russia. Soil temperatures were measured at depths of 0.02 to 3.2 m using bent stem thermometers, extraction thermometers, and electrical resistance thermistors. Data coverage extends from the 1800s through 1990, but is not continuous. Data are not available for all stations for the entire period of coverage. For example, data collection began at many stations in the 1930s and 1950s, and not all stations continued taking measurements through 1990. This research was supported by the National Science Foundation (NSF) Office of Polar Programs (OPP) awards OPP-9614557, OPP-9907541, and OPP-0229766. Data are available as tar.gz files.

This dataset is made up of raw temperature data collected at various sites in Alaska and for various studies from 1995 through 2001. Many of the data sets were collected along the Dalton Highway from Prudhoe Bay to Fairbanks or in other sites like Council and Barrow to characterize overwinter soil surface temperatures as part of our winter CO2 flux measurements. Other measurements were taken at Toolik Lake in experimental climate change plots (e.g., increased winter snow accumulation, elevated summer temperature plots, increased litter plots). All the data were collected using HOBO dataloggers. Unless otherwise indicated, air temperatures were collected at 1.6 m and soil temperatures were collected at 1cm below the soil surface. Measurements made at other soil depths or snow depths are indicated. All data is given in degrees C. A more complete description of each data set is provided in the readme.

The datasets are in a gridded filed format at 0.25-degree spatial resolution where the location of the grid is in the filename itself. Three columns in a file are daily observed precipitation (mm), maximum temperature (degree C), and minimum temperature (degree C) datasets. IMD datasets are available from 1951-2021.

The U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies (CREST) project (http://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. Coral reef organisms are very sensitive to high and low water-temperature extremes. It is critical to precisely know water temperatures experienced by corals and associated plants and animals that live in the dynamic nearshore environment to document thresholds in temperature tolerance. This dataset provides underwater temperature data recorded every fifteen minutes from 2009 to 2015 at five off-shore coral reefs in the Florida Keys, USA. From northeast to southwest, these sites are Fowey Rocks (Biscayne National Park), Molasses Reef (Florida Keys National Marine Sanctuary, FKNMS), Crocker Reef (FKNMS), Sombrero Reef (FKNMS), and Pulaski Shoal (Dry Tortugas National Park). A portion of the dataset included here was interpreted in conjunction with coral and algal calcification rates in Kuffner et al. (2013).

This data set includes estimates of aquatic chlorophyll a concentration and reservoir temperature for Blue Mesa Reservoir, CO. A Random Forest modeling approach was trained to model near-surface aquatic chlorophyll a using near-coincident Sentinel-2 satellite imagery and water samples analyzed for chlorophyll a concentration. The trained chlorophyll a model was applied to Sentinel-2 imagery to produce maps of modeled chlorophyll a concentrations at 10 m spatial resolution for May through October for 2016 through 2023. Chlorophyll a concentrations for three sections (basins) of Blue Mesa Reservoir were extracted from the raster data to produce time-series of modeled chlorophyll a concentration summary statistics (e.g., median, standard deviation, 90th percentile, etc). Water temperatures were approximated using the provisional Landsat surface temperature (PST) product collected with sensors on board Landsat 5, 7, 8, and 9 for May through October between 2000 and 2023. PST values for Landsat 8 and Landsat 9 were scaled to match in-situ water temperature observations in the top 1 m of the water column using a multivariate linear regression model. A harmonized water temperature record was produced by adjusting Landsat 7 PST values to align with the adjusted Landsat 8 values for near-coincident image dates. Similarly, Landsat 5 PST values were adjusted to match the adjusted Landsat 7 values. The modeled chlorophyll a and temperatures had root mean square errors of 1.9 micrograms per liter and 0.7 degrees Celsius, respectively. This data release includes three components with tabular and raster data: 1) Tabular .csv format in-situ and remotely sensed chlorophyll a data from Blue Mesa Reservoir, Colorado May through October 2016 - 2023: Data used to train the chlorophyll a model (chl_model_training.csv), ) and modeled chlorophyll a time series (chl_rs_values.csv) 2) Raster format remotely sensed aquatic chlorophyll a for Blue Mesa Reservoir, Colorado May through October 2016 - 2023: Raster data include 167 geotiffs of modeled chlorophyll a concentrations in zipped directory (chl_conc_ug_L.zip) 3) Tabular in-situ and remotely sensed temperature data from Blue Mesa Reservoir, Colorado May through October 2000 - 2023: Data used to train the temperature model (temp_model_training.csv) and modeled temperature timeseries (temp_rs_values.csv)

The BOREAS RSS-17 team collected several data sets in support of its research in monitoring and analyzing environmental and phenological states using radar data. This data set consists of tree bole and soil temperature measurements from various BOREAS flux tower sites. Temperatures were measured with thermistors implanted in the hydroconductive tissue of the trunks of several trees at each site and at various depths in the soil. Data were stored on a data logger at intervals of either 1 or 2 hours. The majority of the data were acquired between early 1994 and early 1995. The primary product of this data set is the diurnal stem temperature measurements acquired for selected trees at five BOREAS tower sites.

This data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011. At its maximum, the network consisted of over 200 wireless sensor installations (nodes), with a range of 6 to 27 nodes per site. The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) were installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors were installed at 5 cm depth at six of the sites. Data collection started in August 2011 and continues at eight sites through the present. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional (airborne, e.g. NASA's Airborne Microwave Observation of Subcanopy and Subsurface Mission - AirMOSS) and national (spaceborne, e.g. NASA's Soil Moisture Active Passive - SMAP) scales.

Downhole temperature data for the three wells inside the West Flank FORGE footprint; 83-11, TCH 74-2 and TCH 48-11. TCH 74-2 and TCH 48-11 were both collected before 1990 and 83-11 was collected in 2009. The are compiled into one spreadsheet for ease of visualization. Plot of data included.

Temperature data-loggers market worth at USD 1383.4 Million in 2024, is expected to surpass USD 2358.02 Million by 2034, CAGR of 5.1% from 2025 to 2034.

The Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1 is a unique combination of monthly globally-complete fields of SST and sea ice concentration on a 1 degree latitude-longitude grid from 1870 to date.

This metadata record describes moored seawater temperature data collected at Santa Rosa Island, California, USA, by PISCO. Measurements were collected using a StowAway Tidbit Temperature Logger (Onset Computer Corp. TBIC32+4+27) beginning 2008-09-04. The instrument depth was 009 meters, in an overall water depth of 15 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.

This metadata record describes a mix of intertidal seawater and air temperature data collected at Waddell by PISCO. Measurements were collected using either Stowaway Tidbit Temperature Loggers (Onset Computer Corp TPIC-05+37) or HOBO Pendant Temperature/Light Logger (Onset Computer Corp UA-002-64)

This data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX15, the Soil Moisture Active Passive Validation Experiment 2015.

Water temperature data was compiled from data provided by different agencies around the Gulf of Mexico, research projects and cruises. In situ water temperature measured in degrees Celsius.

Data source: National Water Quality Monitoring Council (NWQMC), Environmental Protection Agency (EPA), United States Geological Survey (USGS), National Estuarine Research System (NERRS), Texas Commission on Environmental Quality (TCEQ), Florida Keys National Marine Sanctuary (FKNMS), National Park Water Services (NPWS), Louisiana Department of Environmental Quality (LDEQ), Louisiana Universities Marine Consortium (LUMCON), Mississippi Department of Environmental Quality (MDEQ), Alabama Department of Environmental Management (ADEM), Florida Department of Environmental Protection (FDEP) and Texas A&M University (TAMU).

This metadata record describes moored seawater temperature data collected at Santa Rosa Island, California, USA, by PISCO. Measurements were collected using a StowAway Tidbit Temperature Logger (Onset Computer Corp. TBIC32+4+27) beginning 2011-02-14. The instrument depth was 003 meters, in an overall water depth of 15 meters (both relative to Mean Sea Level, MSL). The sampling interval was 4.0 minutes.

The Special Sensor Microwave Imager/Sounder (SSMIS) is a series of passive microwave conically scanning imagers and sounders onboard the DMSP satellites beginning with F-16. SSMIS improves upon the surface and atmospheric retrievals of the previous Special Sensor Microwave Imager (SSM/I), and upon the atmospheric temperature and water vapor sounding capabilities of both the Special Sensor Microwave Temperature Sounder (SSM/T-1) and the Special Sensor Microwave Humidity Sounder (SSM/T-2). The SSMIS imaging and sounding sensors are able to estimate atmospheric temperature, moisture, and surface parameters. This temperature data record (TDR) contains earth-located sets of SSMIS antenna temperatures since November 2005 that have been surface tagged, calibrated, Doppler corrected, cross polarization and spill-over corrected or APC corrected according to Earth surface type, averaged along scan and along the ground track. The data have been converted to netCDF 3-hourly files.

This metadata record describes a mix of intertidal seawater and air temperature data collected at Cape Mendocino North, California, USA by PISCO. Measurements were collected using HOBO Temperature/Light Data Logger (Onset Computer Corp. UA-002-64) beginning 2011-05-19. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 15.0 minute intervals.

1D transient numerical simulations with a modified version of the SUTRA model (preliminary code) that accounts for variably-saturated freeze-thaw dynamics (e.g. McKenzie and Voss, 2013) to predict annual alluvial aquifer temperature dynamics using coupled fluid and heat transport physics. The model simulations were run with a modified version of SUTRA_ICE (unreleased) that accomadates a time-variable sinusiodal upper temperature boundary. This data release also includes the source code and Argus One GUI files used to build the models, though this proprietary software is not needed to run the models as described in the upper-level "readme" file.