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.

A temperatura na África do Sul aumentou para 18,99 graus Celsius em 2024, em comparação com 18,58 graus Celsius em 2023. Esta página inclui um gráfico com dados históricos para a Temperatura Média da África do Sul.

Captured mean annual temperature for the years 1901-2021

In 2021, Africa recorded a temperature departure of 1.30 degrees Celsius above the 1910-2000 average. The temperature anomaly made 2021 the third-warmest year on the continent. According to the source, Africa's annual temperature has grown at an average rate of 0.13 degrees Celsius per decade since 1910.

Temperature in Central African Republic remained unchanged at 25.42 celsius in 2024 from 25.42 celsius in 2023. This dataset includes a chart with historical data for Central African Republic Average Temperature.

The mean surface temperature in North Africa increased by *** degrees Celsius in 2021, the largest variation in Africa. Western Africa followed, warming **** degrees Celsius. Among countries, Tunisia and Algeria recorded the largest increase in mean surface temperature on the African continent that year.

The CRU Time Series 4.05 dataset was developed and has been subsequently updated, improved and maintained with support from a number of funders, principally the UK's Natural Environment Research Council (NERC) and the US Department of Energy. Long-term support is currently provided by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre. Current gridded products (CRU TS) are presented either as ASCII grids, or in NetCDF format. The gridding process used in Brohan et al.. (2006) and earlier publications assigns each station to the 5 degree latitude/longitude box within which it is located. The gridding then simply averages all available station temperatures (as anomalies from 1961-90) within each grid box for each month from 1851. No account is taken of the station's elevation or location within the grid box (anomalies show little consistent dependence on altitude). A more up-to-date location for a station is not important for the gridding, unless a site change were to move the station to an adjacent grid box. In this instance, the data was derived as a subset of the original dataset. CRU publishes the data in NetCDF file format, however for data visualisation purposes the datasets was tranformed into tidy tables, represented in the South African Risk and Vulnerability Atlas (SARVA) by the South African Environmental Observation Network's uLwazi Node. Citation: University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D.; Osborn, T. (2021): CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020). NERC EDS Centre for Environmental Data Analysis, 2021. https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681

Global Temperature: Daily Average: Central African Republic: Bossangoa data was reported at 0.000 Degrees Celsius in 12 Mar 2024. Global Temperature: Daily Average: Central African Republic: Bossangoa data is updated daily, averaging 0.000 Degrees Celsius from Mar 2024 (Median) to 12 Mar 2024, with 1 observations. The data reached an all-time high of 0.000 Degrees Celsius in 12 Mar 2024 and a record low of 0.000 Degrees Celsius in 12 Mar 2024. Global Temperature: Daily Average: Central African Republic: Bossangoa data remains active status in CEIC and is reported by Climate Prediction Center. The data is categorized under Global Database’s Central African Republic – Table CF.CPC.GT: Environmental: Global Temperature: Daily Average.

Global Temperature: Daily Average: Central African Republic: Bossembele data was reported at 0.000 Degrees Celsius in 12 Mar 2024. Global Temperature: Daily Average: Central African Republic: Bossembele data is updated daily, averaging 0.000 Degrees Celsius from Mar 2024 (Median) to 12 Mar 2024, with 1 observations. The data reached an all-time high of 0.000 Degrees Celsius in 12 Mar 2024 and a record low of 0.000 Degrees Celsius in 12 Mar 2024. Global Temperature: Daily Average: Central African Republic: Bossembele data remains active status in CEIC and is reported by Climate Prediction Center. The data is categorized under Global Database’s Central African Republic – Table CF.CPC.GT: Environmental: Global Temperature: Daily Average.

Average daily maximum temperature. The baseline is calculated for 2001–2020, with projections for 2021–2040 and 2041–2060 under two climate scenarios: RCP 4.5 (moderate emissions) and RCP 8.5 (high emissions).

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.

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.

Climate change, that is a threat to ecosystems and the livelihoods of those that depend on them, is increasingly manifesting as an increased frequency and intensity of severe weather events such as droughts and floods (Déqué et al., 2017). Climate change has created an urgent need for early warning aids or models to enhance the sub-Saharan African health systems ability to prepare for, and cope with escalations in treatment needs of climate sensitive diseases (Nhamo & Muchuru, 2019). This dataset was created from the health and weather data of nine purposively selected study districts in Uganda, whose health and weather data were available for the development of an early warning health model (https://github.com/CHAIUGA/chasa-model) and an accompanying prediction web app (https://github.com/CHAIUGA/chasa-webapp). The districts were selected based on the following criteria: (a) were experiencing climate change and variability, (b) represented different climatologic, and agro-ecological zones, (c) availability of climate information and health information from a health facility within a 40 kilometres radius of a functional weather station. Historical weather data was retrieved from the Uganda National Meteorological Association databases, as monthly averages. The weather variables in this data included: atmospheric pressure, rainfall, solar radiation, humidity, temperature (maximum, minimum and mean), and wind (gusts and average wind speed). The monthly health aggregated data for the period starting September 2018 to December 2019, was retrieved from the National Health Repository (DHIS2) for referral hospitals within the selected districts. Only data for a selection of climate-sensitive disease aggregates was obtained. The dataset contains 436 complete matched disease and weather records. Ethical issues: Both the de-identified aggregate monthly disease diagnosis count data and weather data in this dataset are from national data available to the public on request.

Additional file 5. Annual incidence of malaria (per 1,000 population at risk) with data from World Health Organization (WHO) (available at: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/malaria-incidence-(per-1-000-population-at-risk ) and average annual mean temperature with data from Climate Change Knowledge Portal (CCKP) (available at: https://climateknowledgeportal.worldbank.org/ ) for selected countries in study sample: Nigeria, Ethiopia, South Africa, Kenya, Uganda, Ghana, Mozambique, Zambia and Zimbabwe.

Global Temperature: Daily Average: Central African Republic: Yalinga data was reported at 0.000 Degrees Celsius in 12 Mar 2024. Global Temperature: Daily Average: Central African Republic: Yalinga data is updated daily, averaging 0.000 Degrees Celsius from Mar 2024 (Median) to 12 Mar 2024, with 1 observations. The data reached an all-time high of 0.000 Degrees Celsius in 12 Mar 2024 and a record low of 0.000 Degrees Celsius in 12 Mar 2024. Global Temperature: Daily Average: Central African Republic: Yalinga data remains active status in CEIC and is reported by Climate Prediction Center. The data is categorized under Global Database’s Central African Republic – Table CF.CPC.GT: Environmental: Global Temperature: Daily Average.

This statistic shows a ranking of the estimated average temperature in 2020 in the Middle East and North Africa (MENA), 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).

Predicted mean monthly air temperature (annual). Predictions based on estimates by the Center for resource and Enviornmental Studies (CRES) which can be interpreted as estimates of standard means for the period of 1920 to 1980. The AIRTMP_MN grid data layer is comprised of 1450x1380 derivative raster air temperature features derived based on 0.05 degrees resolution data originally from CRES / FAO. The layer provides nominal analytical/mapping at 1:220 000 000. Madagascar not included. Annual Total Air Temperature, Average Monthly Air Temperature (Annual) and the Monthly Air Temperature from January to December are also available for download. Acronyms and Abbreviations: CRES - Centre for Resource and Environmental Studies, The Australian National University (ANU); FAO - Food and Agriculture Organization of the United Nations.

Additional file 6. Average annual mean temperature for Nigeria downloaded from Climate Change Knowledge Portal (CCKP) (Available at: https://climateknowledgeportal.worldbank.org/country/nigeria/climate-data-historical ).

Additional file 2. Average annual mean temperature for Ghana downloaded from Climate Change Knowledge Portal (CCKP) (Available at: https://climateknowledgeportal.worldbank.org/country/ghana/climate-data-historical ).

Additional file 7. Average annual mean temperature for Uganda downloaded from Climate Change Knowledge Portal (CCKP) (Available at: https://climateknowledgeportal.worldbank.org/country/uganda/climate-data-historical ).