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 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

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.

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.

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).

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.

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.

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).

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.

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).

Captured mean annual temperature for the years 1901-2021

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.

Water is an essential ingredient to life on Earth. In its three phases (solid, liquid, and gas), water continuously cycles within the Earth and atmosphere to create significant parts of our planet’s climate system, such as clouds, rivers, vegetation, oceans, and glaciers. Precipitation is a part of the water cycle, where water particles fall from clouds in the form of rain, sleet, snow, ice crystals, or hail. So how does precipitation form? As water on Earth’s surface evaporates it changes from liquid to gas and rises into the atmosphere. Because air cools as altitude increases, the vapor rises to a point in the atmosphere where it cools enough to condense into liquid water or freeze into ice, which forms a cloud. Water vapor continues to condense and stick to other water droplets in the cloud until the weight of the accumulated water becomes too heavy for the cloud to hold. If the air in the cloud is above freezing (0 degrees Celsius or 32 degrees Fahrenheit), the water falls to the Earth as rain. If the air in the cloud is below freezing, ice crystals form and it snows if the air between the cloud and the ground stays below 0 degrees Celsius (32 degrees Fahrenheit). If a snowflake falls through a warmer part of a cloud, it can get coated in water, then refrozen multiple times as it circulates around the cloud. This forms heavy pellets of ice, called hail, that can fall from the sky at speeds estimated between 14 and 116 kmph (9 and 72 mph) depending on its size. A hailstone can range from the size of a pea (approximately 0.6 cm or 0.25 inches) to a golf ball (approximately 4.5 cm or 1.75 inches), and sometimes even reach the size of a softball (approximately 10 cm or 4 inches).Precipitation doesn’t fall in the same amounts throughout the world. The presence of mountains, global winds, and the unequal distribution of land and sea cause some parts of the world to receive greater amounts of precipitation compared with others. Areas with rising moist air generally indicate regions with high precipitation. According to the Köppen Climate Classification System, tropical wet and tropical monsoon climates receive annual precipitation of 150 cm (59 inches) or greater. Tropical wet regions, where rain occurs year-round, are found near the equator in central Africa, the Amazon rainforest, and southern India. Monsoons are storms with large patterns of wind and heavy rain that can span over a continent. Tropical monsoon climates are located mainly in Southeast Asia and areas around the Pacific Ocean, where annual rainfall is equal to or greater than areas with a tropical wet climate. Here, intense monsoon rains fall during the three hottest months of the year, which are usually between June and October. Snow and ice, which are most common in high altitudes and latitudes, cover most of the Earth’s polar regions. High altitude regions of the Andes, Tibetan Plateau, and the Rocky Mountains maintain some amount of snow cover year-round.Over the next century, it is predicted warming global temperatures will increase the temperature of the ocean and increase the speed of the water cycle. With a quicker rate of evaporation, there will be more water in the atmosphere, allowing clouds to produce heavier precipitation and more intense storms. Although storms would be more intense in wetter regions, increased evaporation could also lead to extreme drought in drier areas of the world. This would greatly affect farmers who grow crops in dry locations like Southern California or Kansas.This map layer shows Earth's mean precipitation (measured in centimeters per month) averaged from 1981 to 2012 as calculated but the Copernicus Climate Change Service. The data was collected from the Copernicus satellite and validated with precipitation measurements from weather stations. Scientists averaged all of the amounts (originally collected in meters) occurring each month together, and they calculated the average of each month over 30 years to create this map.

A set of 13 raster layers representing the monthly and annual, average precipitation for the Okavango Rive Basin. Source: Africa Water Resources Database (FAO). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project†document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

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.