Data from World Development Indicators and Climate Change Knowledge Portal on climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use.

In addition to the data available here and through the Climate Data API, the Climate Change Knowledge Portal has a web interface to a collection of water indicators that may be used to assess the impact of climate change across over 8,000 water basins worldwide. You may use the web interface to download the data for any of these basins.

Here is how to navigate to the water data:

• Go to the Climate Change Knowledge Portal home page (http://climateknowledgeportal.worldbank.org/)
• Click any region on the map Click a country In the navigation menu
• Click "Impacts" and then "Water" Click the map to select a specific water basin
• Click "Click here to get access to data and indicators" Please be sure to observe the disclaimers on the website regarding uncertainties and use of the water data.

Attribution: Climate Change Data, World Bank Group.

World Bank Data Catalog Terms of Use

Source: http://data.worldbank.org/data-catalog/climate-change

This dataset includes processed climate change datasets related to climatology, hydrology, and water operations. The climatological data provided are change factors for precipitation and reference evapotranspiration gridded over the entire State. The hydrological data provided are projected stream inflows for major streams in the Central Valley, and streamflow change factors for areas outside of the Central Valley and smaller ungaged watersheds within the Central Valley. The water operations data provided are Central Valley reservoir outflows, diversions, and State Water Project (SWP) and Central Valley Project (CVP) water deliveries and select streamflow data. Most of the Central Valley inflows and all of the water operations data were simulated using the CalSim II model and produced for all projections.

These data were originally developed for the California Water Commission’s Water Storage Investment Program (WSIP). The WSIP data used as the basis for these climate change resources along with the technical reference document are located here: https://data.cnra.ca.gov/dataset/climate-change-projections-wsip-2030-2070. Additional processing steps were performed to improve user experience, ease of use for GSP development, and for Sustainable Groundwater Management Act (SGMA) implementation. Furthermore, the data, tools, and guidance may be useful for purposes other than sustainable groundwater management under SGMA.

Data are provided for projected climate conditions centered around 2030 and 2070. The climate projections are provided for these two future climate periods, and include one scenario for 2030 and three scenarios for 2070: a 2030 central tendency, a 2070 central tendency, and two 2070 extreme scenarios (i.e., one drier with extreme warming and one wetter with moderate warming). The climate scenario development process represents a climate period analysis where historical interannual variability from January 1915 through December 2011 is preserved while the magnitude of events may be increased or decreased based on projected changes in precipitation and air temperature from general circulation models.

2070 Extreme Scenarios Update, September 2020

DWR has collaborated with Lawrence Berkeley National Laboratory to improve the quality of the 2070 extreme scenarios. The 2070 extreme scenario update utilizes an improved climate period analysis method known as "quantile delta mapping" to better capture the GCM-projected change in temperature and precipitation. A technical note on the background and results of this process is provided here: https://data.cnra.ca.gov/dataset/extreme-climate-change-scenarios-for-water-supply-planning/resource/f2e1c61a-4946-4863-825f-e6d516b433ed.

Note: the original version of the 2070 extreme scenarios can be accessed in the archive posted here: https://data.cnra.ca.gov/dataset/sgma-climate-change-resources/resource/51b6ee27-4f78-4226-8429-86c3a85046f4

We have completed an array of high-resolution simulations of present and future climate over Western North America (WNA) and Eastern North America (ENA) by dynamically downscaling global climate simulations using a regional climate model, RegCM3. The simulations are intended to provide long time series of internally consistent surface and atmospheric variables for use in climate-related research. In addition to providing high-resolution weather and climate data for the past, present, and future, we have developed an integrated data flow and methodology for processing, summarizing, viewing, and delivering the climate datasets to a wide range of potential users. Our simulations were run over 50- and 15-kilometer model grids in an attempt to capture more of the climatic detail associated with processes such as topographic forcing than can be captured by general circulation models (GCMs). The simulations were run using output from four GCMs. All simulations span the present (for example, 1968 to 1999), common periods of the future (2040 to 2069), and two simulations continuously cover 2010 to 2099. The trace gas concentrations in our simulations were the same as those of the GCMs: the IPCC 20th century time series for 1968 to 1999 and the A2 time series for simulations of the future. We demonstrate that RegCM3 is capable of producing present day annual and seasonal climatologies of air temperature and precipitation that are in good agreement with observations. Important features of the high-resolution climatology of temperature, precipitation, snow water equivalent (SWE), and soil moisture are consistently reproduced in all model runs over WNA and ENA. The simulations provide a potential range of future climate change for selected decades and display common patterns of the direction and magnitude of changes. As expected, there are some model to model differences that limit interpretability and give rise to uncertainties. Here, we provide background information about the GCMs and the RegCM3, a basic evaluation of the model output and examples of simulated future climate. We also provide information needed to access the web applications for visualizing and downloading the data, and give complete metadata that describe the variables in the datasets.

NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).

Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.

Terrestrial CDRs are composed of sensor data that have been improved and quality controlled over time, together with ancillary calibration data.

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.

description: This dataset replaces the previous Time Bias Corrected Divisional Temperature-Precipitation Drought Index. The new divisional data set (nCLIMDIV) is based on the Global Historical Climatological Network-Daily (GHCN-D) and makes use of several improvements to the previous data set. For the input data, improvements include additional station networks, quality assurance reviews and temperature bias adjustments. Perhaps the most extensive improvement is to the computational approach, which now employs climatologically aided interpolation. This 5km grid based calculation nCLIMGRID helps to address topographic and network variability. This data set is primarily used by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) to issue State of the Climate Reports on a monthly basis. These reports summarize recent temperature and precipitation conditions and long-term trends at a variety of spatial scales, the smallest being the climate division level. Data at the climate division level are aggregated to compute statewide, regional and national snapshots of climate conditions. For CONUS, the period of record is from 1895-present. Derived quantities such as Standardized precipitation Index (SPI), Palmer Drought Indices (PDSI, PHDI, PMDI, and ZNDX) and degree days are also available for the CONUS sites. In March 2015, data for thirteen Alaskan climate divisions were added to the nCLIMDIV data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all nCLIMDIV monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska nCLIMDIV data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the nCLIMGRID data set.; abstract: This dataset replaces the previous Time Bias Corrected Divisional Temperature-Precipitation Drought Index. The new divisional data set (nCLIMDIV) is based on the Global Historical Climatological Network-Daily (GHCN-D) and makes use of several improvements to the previous data set. For the input data, improvements include additional station networks, quality assurance reviews and temperature bias adjustments. Perhaps the most extensive improvement is to the computational approach, which now employs climatologically aided interpolation. This 5km grid based calculation nCLIMGRID helps to address topographic and network variability. This data set is primarily used by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) to issue State of the Climate Reports on a monthly basis. These reports summarize recent temperature and precipitation conditions and long-term trends at a variety of spatial scales, the smallest being the climate division level. Data at the climate division level are aggregated to compute statewide, regional and national snapshots of climate conditions. For CONUS, the period of record is from 1895-present. Derived quantities such as Standardized precipitation Index (SPI), Palmer Drought Indices (PDSI, PHDI, PMDI, and ZNDX) and degree days are also available for the CONUS sites. In March 2015, data for thirteen Alaskan climate divisions were added to the nCLIMDIV data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all nCLIMDIV monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska nCLIMDIV data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the nCLIMGRID data set.

The PRISM Climate Group gathers climate observations from a wide range of monitoring networks, applies sophisticated quality control measures, and develops spatial climate datasets to reveal short- and long-term climate patterns. The resulting datasets incorporate a variety of modeling techniques and are available at multiple spatial/temporal resolutions, covering the period from 1895 to the present.

This dataset provides agroclimatic indicators used to characterise plant-climate interactions for global agriculture. Agroclimatic indicators are useful in conveying climate variability and change in the terms that are meaningful to the agricultural sector. The objective of this dataset is to provide these indicators at a global scale in an easily accessible and usable format for further downstream analysis and the forcing of agricultural impact models. ERA-interim reanalysis and bias-corrected climate datasets have been used to generate the agroclimatic indicators for historical and future time periods. The input data was provided through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), of which the ISIMIP Fast Track product was used. This product contains daily, biased-corrected, climate data from 5 CMIP5 General Circulation Models covering the period 1951-2099 (historical run up to 2005). The agroclimatic indicators were also generated using the WFDEI (Watch Forcing Data methodology applied to ERA-Interim) for the 1981-2010 climatological period. A total of 26 indicators are provided in this dataset at a spatial resolution of 0.5°x0.5° on a lat-lon grid. The temporal resolution of the variables differs depending on the indicator - they are available at 10 consecutive days (10-day), seasonal or annual resolution. Agroclimatic indicators are often used in species distribution modelling to study phenological developments of plants under varying climate conditions. For many users in the agricultural community, assessments of crop development for the current or future cropping seasons are particularly important. This is especially true for the agro-policy and the agro-business communities, as early indications of production anomalies are of paramount importance for tax/subsidies and price volatility. The provision of pre-computed agroclimatic indicators make them readily available to the user and will facilitate the use of climate data by the agricultural community. The data was produced on behalf of the Copernicus Climate Change Service.

Variables in the dataset/application are: Biologically effective degree days, Cold spell duration index, Frost days, Growing season length, Heavy precipitation days, Ice days, Maximum number of consecutive dry days, Maximum number of consecutive frost days, Maximum number of consecutive summer days, Maximum number of consecutive wet days, Maximum of daily maximum temperature, Maximum of daily minimum temperature, Mean of daily maximum temperature, Mean of daily mean temperature, Mean of daily minimum temperature, Mean of diurnal temperature range, Minimum of daily maximum temperature, Minimum of daily minimum temperature, Precipitation sum, Simple daily intensity index, Summer days, Tropical nights, Very heavy precipitation days, Warm and wet days, Warm spell duration index, Wet days

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset includes the following parameters clipped to BA_SYD extent.

1) Mean annual BAWAP (Bureau of Meteorology Australian Water Availability Project) rainfall of year 1981 - 2013

2) Mean annual penman PET (potential evapotranspiration) of year 1981 - 2013

3) Mean annual runoff using the 'Budyko-framework' implementation of Choudhury

Dataset History

Lineage is as per the BA All mean climate data for Australia except the national data has been clipped to BA SYD extent.

The mean annual rainfall data is created from monthly BAWAP grids which is created from daily BILO rainfall.

Jones, D. A., W. Wang and R. Fawcett (2009). "High-quality spatial climate data-sets for Australia." Australian Meteorological and Oceanographic Journal 58(4): 233-248.

The Mean annual penman PET is created as per the Donohue et al (2010) paper using the fully physically based Penman formulation of potential evapotranspiration, exept that daily wind speed grids used here were generated with a spline (i.e., ANUSPLIN) as per McVicar et al (2008), not the TIN as per Donohue et al (2010). For comprehensive details regarding the generation of some of these datasets (i.e., net radiation, Rn) see the details provided in Donohue et al (2009).

Donohue, R.J., McVicar, T.R. and Roderick, M.L. (2010) Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology. 386(1-4), 186-197. doi:10.1016/j.jhydrol.2010.03.020

Donohue, R.J., McVicar, T.R. and Roderick, M.L., (2009) Generating Australian potential evaporation data suitable for assessing the dynamics in evaporative demand within a changing climate. CSIRO: Water for a Healthy Country Flagship, pp 43. http://www.clw.csiro.au/publications/waterforahealthycountry/2009/wfhc-evaporative-demand-dynamics.pdf

McVicar, T.R., Van Niel, T.G., Li, L.T., Roderick, M.L., Rayner, D.P., Ricciardulli, L. and Donohue, R.J. (2008) Wind speed climatology and trends for Australia, 1975-2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophysical Research Letters. 35, L20403, doi:10.1029/2008GL035627

The Mean annual runoff was created as per the Donohue et al (2010) paper. The data represent the runoff expected from the steady-state 'Budyko curve' longterm mean annual water-energy limit approach using BAWAP precipitation and the Penman potential ET described above.

Choudhury BJ (1999) Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model. Journal of Hydrology 216, 99-110.

Donohue, R.J., McVicar, T.R. and Roderick, M.L. (2010) Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology. 386(1-4), 186-197. doi:10.1016/j.jhydrol.2010.03.020

Donohue, R.J., McVicar, T.R. and Roderick, M.L., (2009) Generating Australian potential evaporation data suitable for assessing the dynamics in evaporative demand within a changing climate. CSIRO: Water for a Healthy Country Flagship, pp 43. http://www.clw.csiro.au/publications/waterforahealthycountry/2009/wfhc-evaporative-demand-dynamics.pdf

McVicar, T.R., Van Niel, T.G., Li, L.T., Roderick, M.L., Rayner, D.P., Ricciardulli, L. and Donohue, R.J. (2008) Wind speed climatology and trends for Australia, 1975-2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophysical Research Letters. 35, L20403, doi:10.1029/2008GL035627

Dataset Citation

Bioregional Assessment Programme (2014) Mean annual climate data clipped to BA_SYD extent. Bioregional Assessment Derived Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/a8393a45-5e86-431b-b504-c0b2953296f4.

Dataset Ancestors

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

This dataset contains modeled temperature, ozone, and PM2.5 data for the United States over the 21st century, using two global climate model scenarios and two emissions datasets.

English: Taking into account measurements made by Météo-France recording stations, a data set related to temperature and precipitation is worked out for a 30 years period (1971-2000), it includes 14 variables able to characterize the climates and their specific variability. An original method, so called local interpolation, allows the representation of each of the 14 variables as a continuous field and stores them in the form of GIS data layers. This data is then processed by Coupling Correspondence Analysis and Ascending Hierarchical Classification in order to obtain a typology where 8 climates are identified and mapped on the French continental territory.French: Partant des mesures stationnelles de précipitation et de température mises à disposition par Météo-France, un jeu de 14 variables intégrant une série temporelle de 30 ans (1971-2000) est défini pour caractériser les climats et leurs modalités distinctives de variation. Une méthode originale dite d’interpolation locale permet de reconstituer les champs spatiaux continus des variables en question et de les exprimer sous forme de couches d’information gérables par SIG. Ces données sont ensuite soumises à un traitement associant analyse factorielle des correspondances et classification ascendante hiérarchique pour en obtenir une typologie où huit climats sont identifiés et cartographiés sur le territoire de la France continentale.

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.

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)

Source: Kaggle

https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Raw data: Berkeley Earth data page http://berkeleyearth.org/data/

Data from World Development Indicators and Climate Change Knowledge Portal on climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use.

The dataset in this record is the output of the CCSM4 (Community Climate System Model version 4). Please see the collection reference below for outputs from the other models described here.These data are the outputs of three general circulation climate models (GCMs), CCSM4, MRI-CGCM3, and IPSL-CM5A-LR for the period 1950-2100. Runs of each GCM were carried out as part of the fifth phase of the Coupled Model Intercomparison Project. Future runs were forced with the RCP 8.5 emissions scenario. They were downscaled to a one km spatial resolution using a quantile matching approach. The three GCMs were chosen because they were shown to recreate climate well in Alaska during the last few decades and because they span the range of potential conditions during the 21st century as projected by all climate models included in the IPCC AR5. Variables include daily minimum and maximum Temperature (°C), daily sum of precipitation (mm), daily sum of shortwave radiation (Mj m-2), and mean VPD (kPa). This dataset includes the following files. The gridded netCDFs are provided as compressed .tar.gz files. Extensive metadata is embedded within each netCDF.CCSM4-hist-prcp.tar.gz : Daily precipitation (mm) for 1950-2005 from the CCSM4 GCM.

CCSM4-future-prcp.tar.gz : Daily precipitation (mm) for 2006-2100 from the CCSM4 GCM.CCSM4-hist-srad.tar.gz : Daily shortwave radiation (mJ m-2) for 1950-2005 from the CCSM4 GCM.

CCSM4-future-srad.tar.gz : Daily shortwave solar radiation (mJ m-2) for 2006-2100 from the CCSM4 GCM.CCSM4-hist-tmax.tar.gz : Daily maximum temperature (deg C) for 1950-2005 from the CCSM4 GCM.

CCSM4-future-tmax.tar.gz : Daily maximum temperature (deg C)for 2006-2100 from the CCSM4 GCM.CCSM4-hist-tmin.tar.gz : Daily minimum temperature (deg C) for 1950-2005 from the CCSM4 GCM.

CCSM4-future-tmin.tar.gz : Daily minimum temperature (deg C)for 2006-2100 from the CCSM4 GCM.CCSM4-hist-vap.tar.gz : Daily vapor pressure (kPa) for 1950-2005 from the CCSM4 GCM.

CCSM4-future-vap.tar.gz : Daily vapor pressure (kPa) for 2006-2100 from the CCSM4 GCM.

This dataset contains the tweet ids of 39,622,026 tweets related to climate change. They were collected between September 21, 2017 and May 17, 2019 from the Twitter API using Social Feed Manager. There is a gap in data collection between January 7, 2019 and April 17, 2019. Tweets were collected using the POST statuses/filter method of the Twitter Stream API, using the track parameter with the following keywords: #climatechange, #climatechangeisreal, #actonclimate, #globalwarming, #climatechangehoax, #climatedeniers, #climatechangeisfalse, #globalwarminghoax, #climatechangenotreal, climate change, global warming, climate hoax Because of the size of the collection, the list of identifiers is split into files of 10 million lines each, with a tweet identifier on each line. There is a README.txt file containing additional documentation on how the tweets were collected. The GET statuses/lookup method supports retrieving the complete tweet for a tweet id (known as hydrating). Tools such as Twarc or Hydrator can be used to hydrate tweets. Per Twitter’s Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Questions about this dataset can be sent to sfm@gwu.edu. George Washington University researchers should contact us for access to the tweets.

The dataset provides climate extreme indices related to temperature and precipitation as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), as well as selected heat stress indicators (HSI). The indices are provided for historical and future climate projections (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) included in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and used in the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This dataset provides a comprehensive source of pre-calculated and consistent ETCCDI and heat stress indicators commonly used by the climate science and impact communities. The majority of models used in this catalogue entry are now available in the Climate Data Store though the indices offered in this entry additionally include ensemble members obtained from the Earth System Grid Federation. The indices are calculated from CMIP6 models that have the necessary daily resolved data for both historical and at least two of the future projections. In addition, four of the chosen models contained a large number of ensemble members in order to enable the estimation of the associated uncertainty in the spread of model outcomes when calculating the ETCCDI indices (CanESM5, EC-Earth3, MIROC6 and MPI-ESM1-2-LR). All the ETCCDI indices in this dataset are calculated using the climdex.pcic R package, which was developed, evaluated and approved by the ETCCDI. To facilitate the usage of heat stress indicators in combination with thresholds on absolute values, this dataset additionally provides bias-adjusted heat stress indicators. Bias adjustment is carried out using the ISIMIP3b bias-adjustment method and employs the WATCH Forcing Data methodology applied to ERA5 (WFDE5) dataset as reference. Providing both pre-calculated bias-adjusted data and data without bias adjustment is of great value for climate and impact studies since the calculation of these datasets also are computationally expensive. The WFDE5 dataset is also available in the Climate Data Store. The heat stress indicators combine near-surface air temperature, near-surface specific humidity, and surface air pressure to give indications of adverse effects of heat on human health. Other variables like wind or solar radiation are not considered, and the selected heat stress indicators thus represent indoor conditions or calm conditions in the shade. This dataset was produced on behalf of the Copernicus Climate Change Service.

The maximum and minimum temperatures are the highest and lowest temperatures (respectively) which occurred throughout the 24 hour period up to 9am. The observed minimum daily temperature is assigned to the date the observation was made, as the diurnal cycle typically reaches its minimum at approximately 5am. The observed maximum daily temperature is assigned to the day prior to the date the observation was made, as the diurnal cycle typically reaches its maximum at approximately 3pm. If the data are not recorded daily (for example, the instrument malfunctioned), the first observation following the no-report period is flagged as an accumulation.

This dataset has climate change indicators for different countries with their associated codes(ISO2 AND ISO3). The measurement has been updated yearly till 2022 from 1961.

The 30 year average values of various climate variables. Determined per month, per season and per year. Organized by automatic weather stations (AWS).