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.

WATCH Forcing Data 20th Century. A meteorological forcing dataset (based on ERA-40) for land surface and hydrological models (1901-2001). Data generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001. Five variables are at 6 hourly resolution and five variables are at 3 hourly resolution: Tair_WFD_ - 2m Air temperature (K) Tmin_WFD_ - 2m Minimum air temperature (K) Tmax_WFD_ - 2m Maximum air temperature (K) PSurf_WFD_ - 10m Surface pressure (Pa) Qair_WFD_ - 2m Specific umidity (kg/kg) Wind_WFD_ - 10m Wind speed (m/s) LWdown_WFD_ - Downwards long-wave radiation flux (W/m-2) SWdown_WFD_ - Downwards short-wave radiation flux (W/m-2) Rainf_WFD_GPCC_ - Rainfall rate GPCC bias corrected and undercatch corrected Snowf_WFD_GPCC_ - Snowfall rate GPCC bias corrected and undercatch corrected (kg/m-2/s) Rainf_WFD_CRU_ - Rainfall rate CRU bias corrected and undercatch corrected (kg/m-2/s) Snowf_WFD_CRU_ - Snowfall rate CRU bias corrected and undercatch corrected (kg/m-2/s). This data set has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 1 (WP1). WP1 (Past climate variability) aimed to provide consolidated data to other WPs in ClimAfrica, and to analyze the interactions between climate variability, water availability and ecosystem productivity of Sub-Saharan Africa. Various data streams that diagnose the variability of the climate, in particular the water cycle, and the productivity of ecosystems in the past decades, have been collected, analyzed and synthesized. The data streams range from ground-based observations and satellite remote sensing to model simulations. More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Important data from the African Centre of Meteorological Applications for Development (ACMAD) collection have been recently rescued from unstable fiche media and scanned to digital images by the EU funded Copernicus Climate Change Service and the Royal Meteorological Institute (RMI) of Belgium. The team at the C3S2-311 Lot 1 Collection and Processing of In Situ Observations service led by the Irish Climate Analysis and Research UnitS (ICARUS) at Maynooth University, Ireland enrolled the help of 2nd year university undergraduate students to transcribe quickly and effectively some of these important ACMAD meteorological surface observations. New and unique datasets for Macenta, Guinea (1947-1953) and Andapa, Madagascar (1949-1957) were digitised with each station consisting of sub-daily observations for: cloud, temperature, humidity, evaporation, pressure and wind as well as daily observations for: evaporation, precipitation and temperature. The newly digitised Sub-Saharan African data will increase the temporal and spatial coverage of data in this important data-sparse region where climate change impact studies are crucial., Students gained new skills and a deep appreciation of historical climatology while helping the global scientific community unearth new insights into past sub-Saharan African climate. The Climate Data Rescue Africa project (CliDaR-Africa project) model has the potential for a broader roll-out to other educational contexts and there is certainly no shortage of data to be rescued with millions of images remaining untouched. Therefore, this paper provides details of the project, and all supporting information such as project guidelines and templates to enable other organisations to instigate similar programs in future.

Additional file 9. Average annual mean temperature for Zimbabwe downloaded from Climate Change Knowledge Portal (CCKP) (Available at: https://climateknowledgeportal.worldbank.org/country/zimbabwe/climate-data-historical ).

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

Bias Correction of ERA-interim meteorological forcing dataset for Africa, based on Piani et al. (2010) for the period 1979 - 2011. Variables description Tair: air temperature (K) Tmin: minimum air temperature (K) Tmax: maximum air temperature (K) PSurf: surface pressure (Pa) Qmean: specific humidity (kg/kg) Wind: wind speed (m/s) Precip: precipitation (mm) LWdown: Downwards long-wave radiation flux (W/m-2) SWdown: Downwards short-wave radiation flux (W/m-2). This data set has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 1 (WP1). WP1 (Past climate variability) aimed to provide consolidated data to other WPs in ClimAfrica, and to analyze the interactions between climate variability, water availability and ecosystem productivity of Sub-Saharan Africa. Various data streams that diagnose the variability of the climate, in particular the water cycle, and the productivity of ecosystems in the past decades, have been collected, analyzed and synthesized. The data streams range from ground-based observations and satellite remote sensing to model simulations. More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

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

These digitized observations were taken 3 times daily.

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 ).

This Research Unit (RU) addresses the growing public health concern of accelerated disease burden as a consequence of climate change. So far, there have been very limited concerted efforts by public health scientists, climate change researchers, and social scientists to quantify the climate change impacts on human health, and to design appropriate adaptation strategies. This is particularly true for vulnerable populations in sub-Saharan Africa, despite the facts that rural populations in Africa are strongly affected by climate change and exhibit the lowest adaptive capacity. Indeed, this sub-continent faces an unfinished agenda of combatting undernutrition and infectious diseases with all the negative societal and economic consequences. At the same time, non-communicable conditions have been rapidly emerging in sub-Saharan Africa over the past decades, and their management now competes with the limited resources of the local health systems. To date, the additional impacts of climate change on three of these major health problems in the region, namely childhood undernutrition, malaria and cardio-vascular dysfunction have been insufficiently defined. This project was funded by the German Research Foundation (DFG).

Armed conflict within nations has had disastrous humanitarian consequences throughout much of the world. Here we undertake the first comprehensive examination of the potential impact of global climate change on armed conflict in sub-Saharan Africa. We find strong historical linkages between civil war and temperature in Africa, with warmer years leading to significant increases in the likelihood of war. When combined with climate model projections of future temperature trends, this historical response to temperature suggests a roughly 54% increase in armed conflict incidence by 2030, or an additional 393,000 battle deaths if future wars are as deadly as recent wars. Our results suggest an urgent need to reform African governments' and foreign aid donors' policies to deal with rising temperatures.

The dataset described includes second bias corrected daily weather data using ground-based weather data and simulated crop growth data under potential and water-limited potential conditions for 109 sites across ten countries in SSA including Burkina Faso, Ghana, Mali, Niger, Nigeria, Ethiopia, Kenya, Tanzania, Uganda, and Zambia. Whereas the weather data covers all sites, the simulated growth data for the four crops (maize, millet, sorghum, wheat) covers subsets of the sites depending on the geographic distribution of cultivation areas for the given crop. The stations can represent 65% of the harvested area for maize, 90% for millet, 83% for sorghum, and 59% for wheat the four crops together can represent 72% of the total harvested area in those countries. The data covers three time periods: historical (1995-2014), 2030 (2020-2039), and 2050 (2040-2059).

Daily Evaporation is the total of two recorded values (one in the morning and one in the evening) in a 24 hour period.

The dataset covers Land Surface Temperature (Day and Night) based on the MOD11C2 climate modelling grid (CMG). The resulting product is the average of all 0.05 degree grid cells, within a 0.5 degree grid cell. This data set has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 1 (WP1). WP1 (Past climate variability) aimed to provide consolidated data to other WPs in ClimAfrica, and to analyze the interactions between climate variability, water availability and ecosystem productivity of Sub-Saharan Africa. Various data streams that diagnose the variability of the climate, in particular the water cycle, and the productivity of ecosystems in the past decades, have been collected, analyzed and synthesized. The data streams range from ground-based observations and satellite remote sensing to model simulations. More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Additional file 1. Average annual mean temperature for Ethiopia downloaded from Climate Change Knowledge Portal (Available at: https://climateknowledgeportal.worldbank.org/country/ethiopia/climate-data-historical ).

Additional file 4. Average annual mean temperature for Mozambique downloaded from Climate Change Knowledge Portal (CCKP) (Available at: https://climateknowledgeportal.worldbank.org/country/mozambique/climate-data-historical ).

Additional file 3. Average annual mean temperature for Kenya downloaded from Climate Change Knowledge Portal (CCKP) (Available at: https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-historical ).

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.

A consistent and harmonized product of satellite observed fraction of absorbed photosynthetically active radiation (FAPAR). FAPAR is a biophysical variable that indicates the state and health of the vegetation. This data set provides monthly FAPAR from 1982 until 2010 at global scale and for the African continent. The construction of the data set made use of existing products from AVHRR, SeaWiFS and MERIS satellite sensors.

This data set has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 1 (WP1). WP1 (Past climate variability) aims to provide consolidated data to other WPs in ClimAfrica, and to analyze the interactions between climate variability, water availability and ecosystem productivity of Sub-Saharan Africa. Various data streams that diagnose the variability of the climate, in particular the water cycle, and the productivity of ecosystems in the past decades, have been collected, analyzed and synthesized. The data streams range from ground-based observations and satellite remote sensing to model simulations. More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-05-15

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Martin Jung

Resource constraints:

Please contact the data originator to get info on data access and use

Online resources:

Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

Bias-corrected and downscaled future climate meterological forcing data for Africa, for the period 1948-2099. This dataset is derived from the Global Meteorological Forcing Dataset for Land Surface Modeling, produced by the Princeton University [Department of Civil and Environmental Engineering]. The source data is a 150-yr (1948-2099) dataset of meteorological forcings for driving land surface models and other land modeling schemes. It is derived by bias correcting and downscaling WCRP CMIP3 climate model data for the 20th century and 21st century future climate projections. The dataset is bias-corrected and downscaled using the newly developed equidistant quantile matching method (Li et al., 2010) which better represents changes in the full distribution (not just the mean change). In addition to precipitation and temperature, radiation, humidity, pressure and windspeed are also downscaled. The downsclaing is based on the observational based global forcing dataset of Sheffield et al. (2006) also available from this website. The dataset is currently available at 1.0 degree, 3-hourly resolution globally for 1948-2008. Experimental versions include a 1901-2008 version, real-time updates, higher resolution versions at 0.25deg and 0.5deg and future climate projections based on bias-corrected climate model output. The data are currently available for one climate model (NCAR-PCM1) for the 20th century historical forcing (20C3M; 1948-2000) and one future climate scenario (SRES A2; 2001-2099). This work was supported by NSF Project 0629471 "Collaborative research: Understanding change in the climate and hydrology of the Arctic land region: Synthesizing the results of the ARCSS Fresh Water Initiative Projects". Variables description Tair: 2 m air temperature (K) PSurf: 2 m surface pressure (Pa) Qair: specific humidity (kg/kg) Wind: 10m wind speed (m/s) Precip: precipitation (mm) LWdown: Downwards long-wave radiation flux (W/m-2) LWnet: Net short-wave radiation flux (W/m-2) SWdown: Downwards short-wave radiation flux (average) (W/m-2) This data set has been produced for driving land surface models and other land modeling schemes, in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 1 (WP1).