The Earth Institute's International Research Institute for Climate and Society (IRI) is pleased to present "Revisiting seasonal predictions of tropical rainfall using subseasonal scenario: a new approach to detect and extract predictable signals at regional scale" with Vincent Moron, Aix-Marseille Université, CEREGE UM 34 CNRS, Aix en Provence, France.
Open to the public.
Tropical rainfall are highly seasonal in relation with large-scale shift of the Inter Tropical Convergence Zone mostly driven by annual cycle of absorbed solar radiation coupled with sea-continent geography. The largest scale of motion tends to organize smaller scales toward the convective scale which is also strongly forced by the diurnal cycle. These scale interaction leads to a complex spatio-temporal variability.
Current seasonal prediction of rainfall typically focuses on 3-month rainfall totals at regional-scale. This temporal summation reduces the noise related to smaller-scale weather variability (from the convective to the meso scales), but also implicitly emphasizes the peak of the climatological seasonal cycle of rainfall, since it obviously conveys the largest variations at interannual time scale. This approach may hide potentially predictable signals when rainfall is lower, but more spatially-covariant, for example near the onset or cessation of the rainy season.
We illustrate such a case for the East African long rains (March-May) on a network of 36 stations in Kenya and North Tanzania from 1961 to 2001. Spatial coherence as well as potential predictability of seasonal rainfall anomalies associated with tropical sea surface temperature (SST) anomalies and also SST-forced variance in GCM experiments, clearly peak during the early stage of the rainy season (in March) while the largest rainfall (in April and May) is far less spatially-coherent; the latter is shown to contain a large noise component at the station scale that characterizes interannual variability of the March–May seasonal total amounts.
Combining empirical orthogonal function of both interannual and subseasonal variations with a fuzzy k-means clustering is shown to capture the most spatially-coherent subseasonal “scenarios” that tend to filter out the noisier variations of the rainfall field and emphasize the most consistent signals in both time and space. This approach is shown to provide insight into the seasonal predictability of long dry spells and heavy daily rainfall events at local scale, and their subseasonal modulation.