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.

La temperatura in Sudafrica è aumentata a 18,58 gradi Celsius nel 2023 rispetto ai 18,41 gradi Celsius del 2022. Questa pagina include un grafico con dati storici sulla temperatura media del Sud Africa.

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 decreased to 25.39 celsius in 2023 from 25.56 celsius in 2022. This dataset includes a chart with historical data for Central African Republic Average Temperature.

The mean surface temperature in North Africa increased by 1.8 degrees Celsius in 2021, the largest variation in Africa. Western Africa followed, warming 1.72 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

As part of the the World Bank's review of its rural development strategy, the Bank sought the assistance of the Food and Agriculture Organization of the United Nations (FAO) in evaluating how farming systems might change and adapt over the next thirty years. Amongst other objectives, the World Bank asked FAO to provide guidance on priorities for investment in food security, poverty reduction, and economic growth, and in particular to identify promising approaches and technologies that will contribute to these goals. The results of the study are summarized in a set of seven documents, comprising six regional reports and the global overview contained in this volume. This document, which synthesises the results of the six regional analyses as well as discussing global trends, cross-cutting issues and possible implementation modalities, presents an overview of the complete study. This document is supplemented by two case study reports of development issues of importance to farming systems globally.

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).

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.

Average daily minimum 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).

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).

The daily average temperature in the United Kingdom (UK) has remained relatively stable since 2001, with temperatures rarely straying below 10 degrees Celsius. In 2024, the UK had an average daily temperature of 11.9 degrees Celsius. This was the highest average daily temperature recorded since the turn of the century. British summertime Britain is not known for its blisteringly hot summer months, with the average temperatures in this season varying greatly since 1990. In 1993, the average summer temperature was as low as 13.39 degrees Celsius, whilst 2018 saw a peak of 15.8 degrees Celsius. In that same year, the highest mean temperature occurred in July at 17.2 degrees Celsius. Variable weather Due to its location and the fact that it is an island, the United Kingdom experiences a diverse range of weather, sometimes in the same day. It is in an area where five air masses meet, creating a weather front. Each brings different weather conditions, such as hot, dry air from North Africa and wet and cold air from the Arctic. Temperatures across the UK tend to be warmest in England.

This dataset contains the best estimates for Mean Annual Temperature Reconstructions from marine core MD96-2048.

Under Other version, the xlsx file contains the 1) best estimates for Mean Annual Temperature Reconstructions, 2) posterior distribution of probabilities for each sample, 3) conversion table of plant species to pollen taxa, 4) percentage data used in the reconstruction, 5) list of selected and excluded pollen taxa.

Gridded values of mean monthly daily minimum and maximum air temperatures. Data obtained from the Centre for Resource and Environmental Studies (CRES) at the Australian National University. Mean annual wind velocity was obtained from UNEP/DEIA/GRID-Geneva. Resolution 5 km x 5 km.

This map features the GLDAS total monthly precipitation modeled globally by NASA. The map shows the monthly precipitation for the period of May 2016 to May 2018, focused on Africa. You can click the Play button on the time slider to see precipitation over time.Great parts of Northern Africa and Southern Africa, as well as the whole Horn of Africa, mainly have a hot desert climate, or a hot semi-arid climate for the wetter locations. The equatorial region near the Intertropical Convergence Zone is the wettest portion of the continent. Annually, the rain belt across the country marches northward into Sub-Saharan Africa by August, then moves back southward into south-central Africa by March.Precipitation is water released from clouds in the form of rain, sleet, snow, or hail. It is the primary source of recharge to the planet's fresh water supplies. This map contains a historical record showing the volume of precipitation that fell during each month from March 2000 to the present. Snow and hail are reported in terms of snow water equivalent - the amount of water that will be produced when they melt. Dataset SummaryThe GLDAS Precipitation layer is a time-enabled image service that shows average monthly precipitation from 2000 to the present, measured in millimeters. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. A complete list of the model inputs can be seen here, and the output data (in GRIB format) is available here.Phenomenon Mapped: PrecipitationUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean, instead of total evapotranspiration. Mean evapotranspiration for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is eight years.Variables: This layer has two variables: rainfall and snowfall. By default the two are summed, but you can view either by itself using the multidimensional filter, or by applying the relevant raster function. You must disable time animation on the layer before using its multidimensional filter.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

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.

In 2021, Africa registered its fourth-warmest year on record, with a temperature departure of 1.30 degrees Celsius above the 1910-2000 average. The land temperature anomaly was slightly lower than that from 2019. Since 1910, 2016 has been the hottest year on the continent, with a deviation temperature of 1.44 degrees Celsius. Additionally, Africa's ten-warmest years have occurred since 2005.

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.

Climate change and weather variability pose serious threats to food and nutrition security as well as ecosystems, especially when livelihoods depend heavily on natural resources. This study examines the effect of weather variability (shock) occurring up to three planting and growing season prior on per capita monthly household expenditure in rural Tanzania, Uganda, and Ghana. The analyses combine monthly temperature (1950–2013) and precipitation (1981–2013) data with data from several rounds of household surveys conducted between 1998 and 2013. Substantial spatial and temporal heterogeneity is documented in the incidence of shocks, with effects dependent on both the study and lag period considered. Analysis of short panel data shows the cumulative effect of above-average precipitation on expenditure to be negative in Uganda -while positive in Tanzania-, but the relationship does not persist when pooling survey data spanning over a decade. The evidence from pooled data suggests a positive association between above-average temperature (heat wave) and expenditure in (historically cooler) Uganda, with the opposite effect observed in (the relatively warmer) Tanzania. For Ghana, the association between heat wave and expenditure is positive. There is no evidence of heterogeneous effects along several dimensions, except by agro-ecological condition. Further research into the effects of shocks on more direct outcomes–such as agricultural practices, yields, and dietary intake–is therefore recommended to shed light on possible impact pathways and appropriate localized adaptation strategies.

Daily mean temperatures are calculated by finding out the average of maximum and minimum temperature of a day.