The Red Sea convergence zone (RSCZ) is formed by opposite surface winds blowing from northwest to southeast directions at around 18°-19°N between October and January. A reverse-oriented, low-level monsoon trough at 850hPa, known as the Red Sea trough (RST), transfers moisture from the southern Red Sea to RSCZ. The positions of the RSCZ and RST and the intensity of the RST have been identified as important factors in modulating weather and climatic conditions across the Middle East. Here, we investigate the influence of the El Niño southern oscillation (ENSO) on the interannual variability of RSCZ, RST, and regional rainfall during winter months. Our results indicate that El Niño (warm ENSO phase) favours a shift of the RSCZ to the north and a strengthening of the RST in the same direction. Conversely, during November and December of La Niña periods (cold ENSO phase), the RSCZ shift to the south and the RST strengthens in the same direction. During El Niño periods, southeasterly wind speeds increase (20-30%) over the southern Red Sea and northwesterly wind speeds decrease (10-15%) over the northern Red Sea. Noticeable increases in the number of rainy days and the intensity of rain events are observed during El Niño phases. These increases are associated with colder than normal air intrusion at lower levels from the north combined with warm air intrusion from the south over the RSCZ. Our analysis suggests that during El Niño winters, warmer sea surface temperatures and higher convective instability over the Red Sea favour local storms conditions and increase rainfall over the Red Sea and adjoining regions.
|Original language||English (US)|
|Number of pages||15|
|Journal||International Journal of Climatology|
|State||Published - Jul 18 2017|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research was funded by King Abdullah University of Science and Technology (KAUST) and the Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). The authors are thankful to Ute Langner (GIS expert) in preparation of background for the Figure 15. This research made use of the resources of the Supercomputing Laboratory and/or computer clusters at KAUST.