Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of hydrometeorological extremes over East Africa
The skill of North American multimodel ensemble (NMME) seasonal forecasts in East Africa (EA), which encompasses one of the most food and water insecure areas of the world, is evaluated using deterministic, categorical, and probabilistic evaluation methods. The skill is estimated for all three primary growing seasons: March–May (MAM), July–September (JAS), and October–December (OND). It is found that the precipitation forecast skill in this region is generally limited and statistically significant over only a small part of the domain. In the case of MAM (JAS) [OND] season it exceeds the skill of climatological forecasts in parts of equatorial EA (Northern Ethiopia) [equatorial EA] for up to 2 (5)  months lead. Temperature forecast skill is generally much higher than precipitation forecast skill (in terms of deterministic and probabilistic skill scores) and statistically significant over a majority of the region. Over the region as a whole, temperature forecasts also exhibit greater reliability than the precipitation forecasts. The NMME ensemble forecasts are found to be more skillful and reliable than the forecast from any individual model. The results also demonstrate that for some seasons (e.g. JAS), the predictability of precipitation signals varies and is higher during certain climate events (e.g. ENSO). Finally, potential room for improvement in forecast skill is identified in some models by comparing homogeneous predictability in individual NMME models with their respective forecast skill.
|Publication Subtype||Journal Article|
|Title||Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of hydrometeorological extremes over East Africa|
|Series title||Climate Dynamics|
|Contributing office(s)||Earth Resources Observation and Science (EROS) Center|
|Other Geospatial||East Africa|
|Google Analytic Metrics||Metrics page|