This tool produces maps of seasonal temperature forecasts from the International Research Institute for Climate and Society. In areas of Africa where temperature drives mosquito vector development and parasite development, skillful seasonal temperature forecasts may provide early warning of risk of an epidemic.
Seasonal Temperature Forecast Legend
Research has shown that sea surface temperatures in the Indian Ocean influence temperature and precipitation patterns in Africa.
One of the main advantages of using sea surface temperatures in the Indian Ocean to predict temperature in Africa is the "teleconnection" effect.
Using teleconnections, or lagged effects of sea surface temperatures on temperature elsewhere, we can make forecasts with 1-6 month lead times.
This map uses that relationship to produce temperature forecasts for specific seasons.
Barnston, A., Mason, S., Goddard, L., DeWitt, D., Zebiak, S.(2003). Multimodel Ensembling in Seasonal Climate Forecasting at IRI. Amer. Met. Soc. 84 (12): 1783-1796.
Goddard, L., Graham, NE.(1999). Importance of the Indian Ocean for simulating rainfall anomalies over eastern and southern Africa. J. Geo. Res., 104, 19099-19116.
IRI Seasonal Temperature Forecasts are produced monthly on a 1.0 degree spatial resolution basis.
Contact help@iri.columbia.edu with any technical questions or problems with this Map Room.
The IRI Seasonal Forecast interface allows for graphs to be made easily by pointing and clicking on a location anywhere in the world.
The graph shows the probability, or chance, that the target date being forecast will fall into certain categories relative to "normal" or average conditions.
The three categories are below normal, normal and above normal.
"Normal" conditions are defined as the average temperature for the forecast month(s) at the selected location since 1980. An above (below) normal forecast indicates there is confidence that temperature for that season will fall within the top (bottom) 33% of the observed average temperature.
The interface also allows for the month in which the forecast was issued to change. This may be useful to determine how a forecast for a specific area at a fixed target date has changed from older forecast to newer forecast.