RT Journal Article
T1 Understanding rainfall prediction skill over the Sahel in NMME seasonal forecast
T2 Entendiendo la destreza de los modelos de predicción estacional del NMME para predecir la precipitación en el Sahel
A1 Martín Gómez, Verónica
A1 Mohino Harris, Elsa
A1 Rodríguez De Fonseca, María Belén
A1 Sánchez Gómez, Emilia
AB Sahelian rainfall presents large interannual variability which is partly controlled by the sea surface temperature anomalies (SSTa) over the eastern Mediterranean, equatorial Pacifc and Atlantic oceans, making seasonal prediction of rainfall changes in Sahel potentially possible. However, it is not clear whether seasonal forecast models present skill to predict the Sahelian rainfall anomalies. Here, we consider the set of models from the North American Multi-model ensemble (NMME) and analyze their skill in predicting the Sahelian precipitation and address the sources of this skill. Results show that though the skill in predicting the Sahelian rainfall is generally low, it can be mostly explained by a combination of how well models predictthe SSTa in the Mediterranean and in the equatorial Pacifc regions, and how well they simulate the teleconnections of these SSTa with Sahelian rainfall. Our results suggest that Sahelian rainfall skill is improved for those models in which the Pacifc SST—Sahel rainfall teleconnection is correctly simulated. On the other hand, models present a good ability to reproduce the sign of the Mediterranean SSTa—Sahel teleconnection, albeit with underestimated amplitude due to an underestimation of the variance of the SSTa over this oceanic region. However, they fail to correctly predict the SSTa over this basin, which is the main reason for the poor Sahel rainfall skill in models. Therefore, results suggest models need to improve their ability to reproduce the variability of the SSTa over the Mediterranean as well as the teleconnections of Sahelian rainfall with Pacifcand Mediterranean SSTa.
PB Springer
SN 0930-7575
YR 2022
FD 2022-04-05
LK https://hdl.handle.net/20.500.14352/71392
UL https://hdl.handle.net/20.500.14352/71392
LA eng
NO CRUE-CSIC (Acuerdos Transformativos 2022)
NO Unión Europea. Horizonte 2020
NO Ministerio de Ciencia e Innovación (MICINN)
DS Docta Complutense
RD 12 abr 2025