Mazen E. Assiri

Saudi-KAU Coupled Global Climate Model: Description and Performance

BackgroundA new coupled global climate model (CGCM) has been developed at the Center of Excellence for Climate Change Research (CECCR), King Abdulaziz University (KAU), known as Saudi-KAU CGCM.PurposeThe main aim of the model development is to generate seasonal to subseasonal forecasting and long-term climate simulations.MethodsThe Saudi-KAU CGCM currently includes two atmospheric dynamical cores, two land components, three ocean components, and multiple physical parameterization options. The component modules and parameterization schemes have been adopted from different sources, and some have undergone modifications at CECCR. The model is characterized by its versatility, ease of use, and the physical fidelity of its climate simulations, in both idealized and realistic configurations. A description of the model, its component packages, and parameterizations is provided.ResultsResults from selected configurations demonstrate the model’s ability to reasonably simulate the climate on different time scales. The coupled model simulates El Niño-Southern Oscillation (ENSO) variability, which is fundamental for seasonal forecasting. It also simulates Madden-Julian Oscillation (MJO)-like disturbances with features similar to observations, although slightly weaker.ConclusionsThe Saudi-KAU CGCM ability to simulate the ENSO and the MJO suggests that it is capable of making useful predictions on subseasonal to seasonal timescales.

An improvement in mass flux convective parameterizations and its impact on seasonal simulations using a coupled model

A new closure and a modified detrainment for the simplified Arakawa–Schubert (SAS) cumulus parameterization scheme are proposed. In the modified convective scheme which is named as King Abdulaziz University (KAU) scheme, the closure depends on both the buoyancy force and the environment mean relative humidity. A lateral entrainment rate varying with environment relative humidity is proposed and tends to suppress convection in a dry atmosphere. The detrainment rate also varies with environment relative humidity. The KAU scheme has been tested in a single column model (SCM) and implemented in a coupled global climate model (CGCM). Increased coupling between environment and clouds in the KAU scheme results in improved sensitivity of the depth and strength of convection to environmental humidity compared to the original SAS scheme. The new scheme improves precipitation simulation with better representations of moisture and temperature especially during suppressed convection periods. The KAU scheme implemented in the Seoul National University (SNU) CGCM shows improved precipitation over the tropics. The simulated precipitation pattern over the Arabian Peninsula and Northeast African region is also improved.

Assessment of AOD variability over Saudi Arabia using MODIS Deep Blue products

The aim of this study is to investigate the variability of aerosol over The Kingdom of Saudi Arabia. For this analysis, Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Aerosol Optical Depth (AOD) product from Terra and Aqua satellites for the years 2000-2013 is used. The product is validated using AERONET data from ground stations, which are situated at Solar Village Riyadh and King Abdullah University of Science and Technology (KAUST) Jeddah. The results show that both Terra and Aqua satellites exhibit a tendency to show the spatial variation of AOD with Aqua being better than Terra to represent the ground based AOD measurements over the study region. The results also show that the eastern, central, and southern regions of the country have a high concentration of AOD during the study period. The validation results show the highest correlation coefficient between Aqua and KAUST data with a value of 0.79, whilst the Aqua and Solar Village based AOD indicates the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values which are, 0.17 and 0.12 respectively. Furthermore, the Relative Mean Bias (RMB) based analysis show that the DB algorithm overestimates the AOD when using Terra and Solar Village data, while it underestimates the AOD when using Aqua with Solar Village and KAUST data. The RMB value for Aqua and Solar Village data indicates that the DB algorithm is close to normal in the study region.