This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The table Global Temperatures by Country is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://redivis.com/datasets/1e0a-f4931vvyg. It contains 577462 rows across 4 variables.

This statistic shows a ranking of the estimated worldwide average temperature in 2020, differentiated by country. The figure refers to the projected annual average temperature for the period 2020-2039 as modelled by the GISS-E2-R model in the RCP 4.5 scenario (Medium-low emission).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

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.

Yearly Average Surface Temperature (ºC)

This dataset provides values for TEMPERATURE; reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in 1895. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

Temperatures have risen in the last 100 years around the world. In the 1910s, North America had an average temperature some 0.54 degrees Celsius lower than average temperatures between 1910 and 2000. In the most recent decade, this region experienced temperatures 1.19 degrees Celsius over the average.

All global regions (excluding Oceania) experienced an increased temperature over one degree Celsius in the 2010s, compared to the average between 1910 and 2000.

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Cumulative agriculture and land use greenhouse gas emissions from 1851 to 2021 caused global mean surface temperatures to rise of 0.59 degrees Celsius. The two biggest contributors to global mean surface temperature rise from agriculture and land use are Brazil and the United States, at 0.07 and 0.6 degrees Celsius respectively. Combined, the 10 biggest contributors account for almost 60 percent of global mean surface temperature change as a result of cumulative agriculture and land use emissions.

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.

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.

A record number of high temperature records, 24, were recorded across the world in 2019. The hottest record to be broken was in Hasakah, Syria which witnessed temperatures reach 50 degrees Celsius on August 13. That year was the second hottest year globally on record. Monthly temperature records were also broken on a large scale.

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

(From http://www.worldclim.org/methods) - For a complete description, see:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as 1 km2 resolution). Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables.

The WorldClim interpolated climate layers were made using: * Major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. * The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km) * The ANUSPLIN software. ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing splines. We used latitude, longitude, and elevation as independent variables.

In 2022, Bosnia and Herzegovina ranked first among the countries in Central and Eastern Europe (CEE) by mean temperature anomalies compared to the average from 1991 to 2021, which stood at 1.23 degrees Celsius. Serbia and Croatia followed with 1.21 and 1.19 degrees Celsius above the baseline, respectively. The lowest anomaly was recorded in Belarus, where the temperature departed from the average norm by about 0.5 degrees Celsius.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

Temperature at 30 sites around the country from at least 1972 to 2022. We provide data on average, minimum, and maximum for daily temperatures.

Variables: site: NIWA monitoring site. date: Date (day-month-year) statistic: Statistic (Average, Max, Min). temperature: Temperature in degrees Celsius.

Temperature at 30 sites around the country from at least 1972 to 2022. We report annual and seasonal trends for the period 1972 to 2022 as well as rate of temperature change per decade. We provide data on average, minimum, and maximum for daily, annual, and seasonal temperatures. Trends are reported for annual and seasonal statistics. Temperature change can have a significant effect on agriculture, energy demand, ecosystems, and recreation.Climate change projections for New Zealand suggest the greatest warming will be in summer/autumn and the least in winter and spring (MfE, 2018). Variables: site: NIWA monitoring site statistic: Statistic: (mean of Average, Minimum or Maximum daily temperature) season: Spring, Summer, Autumn, Winter, or Annual p_value: P value slope, conf_low, conf_high: Rate of change per year and their lower and upper confidence intervals conf_level: confidence level (66% or 90% to match IPCC likelihood levels) intercept, r_squared, sigma: Linear model statistics trend_method: Whether the information in this row correspond to the Linear model slope or the Mann-Kendall test n: number of observations used to calculate the trend note: analysis note s, var_s, tau: Mann-Kendall trend statistics z: Z score alternative: the alternative hypothesis used for the Mann-Kendall test trend_likelihood: Likelihood categories adapted from IPCC. Indicates the likelihood that a trend is increasing, decreasing, or indeterminate period_start: Start of the period for which the trend was assessed period_end: End of the period for which the trend was assessed lat :Latitude lon: Longitude Ministry for the Environment. (2018). Climate Change Projections for New Zealand: Atmosphere Projections Based on Simulations from the IPCC Fifth Assessment, 2nd Edition (Publication No. ME 1385). https://www.mfe.govt.nz/publications/climate-change/climate-change-projections-new-zealand