Decadal variability of extreme precipitation in northern of Oman

Climate variability and climate change are major drivers for extreme precipitation patterns on a global scale. However, under the application of weather engineering techniques such as cloud seeding, the climate variability signals must be analysed before concluding the benefits of these seeding operations in arid and semi-arid regions. The primary aim of this study is to gain insights on the drivers that contribute to extreme precipitation variability in northern Oman. In this research, the monthly high-resolution precipitation data from Climate Research Unit (CRU) Time Series (TS) version 4.05 dataset for northern Oman from 1950-2019 are analysed. The quantile perturbation method and the non-parametric Monte Carlo simulations are employed to compute high decadal and seasonal anomalies, and their statistical significance respectively. The teleconnections of Optimum Interpolation Sea Surface Temperature (OISST), Mean Sea Level Pressure (MSLP) and decadal variability patterns represented by the North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), and El Niño-Southern Oscillation (ENSO) with extreme precipitation anomalies are conducted. The severity and spatial and temporal variability of precipitation and deep convection are investigated using outgoing longwave radiation (OLR) as a proxy for extreme precipitation. These findings address the role of internal forcing on precipitation variability in the Al Hajar Mountains, an area characterised by natural convective precipitation. The extreme precipitation variability analysis is conducted to understand better whether cloud seeding operations induce the occurrence of extreme precipitation in the Al Hajar Mountains.