Skill assessment of Saudi-KAU and C3S models in prediction of spring season rainfall over the Arabian Peninsula

Almazroui, Mansour; Khalid, Salman; Kamil, Shahzad; Islam, M. Nazrul; Saeed, Sajjad; Abid, Muhammad Adnan; Ehsan, Muhammad Azhar; Hantoush, Ahmed S.

A skillful prediction of precipitation has great value, particularly for regions that suffer from water stress. In this study, we assess the potential predictability and skill of the Copernicus Climate Change Service (C3S) and SaudiKAU models in their simulation of precipitation over the Arabian Peninsula during spring (March–May) for the period 1993–2016. For this purpose, data from individual models as well as the multi-model ensemble (MME) is used. The prediction data for MAM precipitation initialized at Feb (Lead 1), Jan (Lead 2), and Dec (Lead 3), were obtained from the 5 C3S and Saudi-KAU coupled global climate model. The potential predictability was computed by evaluating the signal to noise ratio and the theoretical limit of correlation skill, while the prediction skill was estimated from the temporal anomaly correlation co-efficient. The results show that the Saudi-KAU, CMCC, and UKMO models have slightly higher potential predictability of about 0.25, 0.35, and 0.25 respec­ tively, as compared to other models. It is also observed that individual models as well as their MME show a high (low) potential predictability over southwestern (northern) regions of the Peninsula. Moreover, the Saudi-KAU, CMCC, and MME show a reasonably good correlation skill (0.68, 0.59, and 0.57) while the SEAS model displays lower skill (0.14) for spring precipitation. All model simulations reveal a decrease in prediction skill for longer lead times. On the other hand, the individual models and their MME successfully reproduced the Pacific (i.e. ENSO) teleconnection patterns while displaying lower skill over the tropical Atlantic Ocean. The results indicate that the model biases have negative impacts on potential predictability and prediction skill over the Arabian Peninsula during the spring season.


  • thumnail for 1-s2.0-S0169809522004471-main.pdf 1-s2.0-S0169809522004471-main.pdf application/pdf 6.67 MB Download File

Also Published In

Atmospheric Research
Elsevier BV

More About This Work

Academic Units
International Research Institute for Climate and Society
Published Here
March 13, 2024

Related Items