Interannual rainfall variability and ECMWF‐Sys4‐based predictability over the Arabian Peninsula winter monsoon region

Abstract

Interannual winter rainfall variability and its predictability are analysed over the Arabian Peninsula region by using observed and hindcast datasets from the state‐of‐the‐art European Centre for Medium‐Range Weather Forecasts (ECMWF) seasonal prediction System 4 for the period 1981–2010. An Arabian winter monsoon index (AWMI) is defined to highlight the Arabian Peninsula as the most representative region for the Northern Hemispheric winter dominating the summer rainfall. The observations show that the rainfall variability is relatively large over the northeast of the Arabian Peninsula. The correlation coefficient between the Niño3.4 index and rainfall in this region is 0.33, statistically significant at the 90% level, suggesting potentially some modest predictability, and indicating that El Niño increases and La Niña decreases the rainfall. Regression analysis shows that upper‐level cyclonic circulation anomalies that are forced by El Niño Southern Oscillation (ENSO) are responsible for the winter rainfall anomalies over the Arabian region. The stronger (weaker) mean transient‐eddy activity related to the upper‐level trough induced by the warm (cold) sea‐surface temperatures during El Niño (La Niña) tends to increase (decrease) the rainfall in the region. The model hindcast dataset reproduces the ENSO–rainfall connection. The seasonal mean predictability of the northeast Arabian rainfall index is 0.35, statistically significant at the 95% level. It is shown that the noise variance is larger than the signal over the Arabian Peninsula region, which tends to limit the prediction skill. The potential predictability is generally increased in ENSO years and is, in particular, larger during La Niña compared to El Niño years in the region. Furthermore, central Pacific ENSO events and ENSO events with weak signals in the Indian Ocean tend to increase predictability over the Arabian region.

ENSO relationship to summer rainfall variability and its potential predictability over Arabian Peninsula region

“…32 However, the perfect model potential predictability is assessed, as the square root of the signal to the total variance, which is also known as the theoretical limit of the correlation skill. 2 This shows that the model can achieve a maximum skill with perfect model and perfect boundary conditions. Further, we assessed the signal and noise for the El Niño and La Niña years.…”

“…The PP of the Saudi-KAU model is in good agreement with the ECMWF-SYS4, which is considered a state-of-the-art seasonal prediction system. 2 Although, the PP of the Saudi-KAU model is based on perfect boundary conditions, it is still worthwhile to compare it with ECMWF-SYS4 predictability. Both models show higher potential predictability in the southern AP region compared to other regions.…”

“…The PP is assessed by the signal to noise ratio as well as by the theoretical limit of the correlation skill 2 . Signal variance is defined as the variance of the ensemble mean, while noise variance is defined as the mean of the variance of ensemble members from the ensemble mean.…”

“…1,2 The Arabian Peninsula is a water scarce region, 3 and rainfall there is infrequent, in particular in the boreal summer season (June-August). Therefore, the summer rainfall predictability is of significant importance for the regional socio-economic sectors.…”

“…This period is the wet season for the peninsula, 4,5 during which ENSO modulates the rainfall. 2,6 On the other hand, during the boreal summer season the peninsula receives considerably less rainfall. 5 The summer rainfall over the peninsula is mainly an extension of the Sahel monsoon and is located over the mountain ranges along the Red Sea from Saudi Arabia to western Yemen.…”