The Red Sea holds one of the most diverse marine ecosystems, primarily due to coral reefs. However, knowledge on large-scale phytoplankton dynamics is limited. Analysis of a 10-year high resolution Chlorophyll-a (Chl-a) dataset, along with remotely-sensed sea surface temperature and wind, provided a detailed description of the spatiotemporal seasonal succession of phytoplankton biomass in the Red Sea. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) data, four distinct Red Sea provinces and seasons are suggested, covering the major patterns of surface phytoplankton production. The Red Sea Chl-a depicts a distinct seasonality with maximum concentrations seen during the winter time (attributed to vertical mixing in the north and wind-induced horizontal intrusion of nutrient-rich water in the south), and minimum concentrations during the summer (associated with strong seasonal stratification). The initiation of the seasonal succession occurs in autumn and lasts until early spring. However, weekly Chl-a seasonal succession data revealed that during the month of June, consistent anti-cyclonic eddies transfer nutrients and/or Chl-a to the open waters of the central Red Sea. This phenomenon occurs during the stratified nutrient depleted season, and thus could provide an important source of nutrients to the open waters. Remotely-sensed synoptic observations highlight that Chl-a does not increase regularly from north to south as previously thought. The Northern part of the Central Red Sea province appears to be the most oligotrophic area (opposed to southern and northern domains). This is likely due to the absence of strong mixing, which is apparent at the northern end of the Red Sea, and low nutrient intrusion in comparison with the southern end. Although the Red Sea is considered an oligotrophic sea, sporadic blooms occur that reach mesotrophic levels. The water temperature and the prevailing winds control the nutrient concentrations within the euphotic zone and enable the horizontal transportation of nutrients.

[1] Coral reef ecosystems, often referred to as “marine rainforests,” concentrate the most diverse life in the oceans. Red Sea reef dwellers are adapted in a very warm environment, fact that makes them vulnerable to further and rapid warming. The detection and understanding of abrupt temperature changes is an important task, as ecosystems have more chances to adapt in a slowly rather than in a rapid changing environment. Using satellite derived sea surface and ground based air temperatures, it is shown that the Red Sea is going through an intense warming initiated in the mid‐90s, with evidence for an abrupt increase after 1994 (0.7°C difference pre and post the shift). The air temperature is found to be a key parameter that influences the Red Sea marine temperature. The comparisons with Northern Hemisphere temperatures revealed that the observed warming is part of global climate change trends. The hitherto results also raise additional questions regarding other broader climatic impacts over the area.

The overturning circulation in the Red Sea exhibits a distinct seasonally reversing pattern and is studied using high-resolution MIT general circulation model simulations. In the first part of this study, the vertical and horizontal structure of the summer overturning circulation and its dynamical mechanisms are presented from the model results. The seasonal water exchange in the Strait of Bab el Mandeb is successfully simulated, and the structures of the intruding subsurface Gulf of Aden intermediate water are in good agreement with summer observations in 2011. The model results suggest that the summer overturning circulation is driven by the combined effect of the shoaling of the thermocline in the Gulf of Aden resulting from remote winds in the Arabian Sea and an upward surface slope from the Red Sea to the Gulf of Aden set up by local surface winds in the Red Sea. In addition, during late summer two processes associated, respectively, with latitudinally differential heating and increased salinity in the southern Red Sea act together to cause the reversal of the contrast of the vertical density structure and the cessation of the summer overturning circulation. Dynamically, the subsurface northward pressure gradient force is mainly balanced by vertical viscosity resulting from the vertical shear and boundary friction in the Strait of Bab el Mandeb. Unlike some previous studies, the three-layer summer exchange flows in the Strait of Bab el Mandeb do not appear to be hydraulically controlled.

This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

In this paper, an extended SEIR model with a vaccination compartment is proposed to simulate the novel coronavirus disease (COVID-19) spread in Saudi Arabia. The model considers seven stages of infection: susceptible (S), exposed (E), infectious (I), quarantined (Q), recovered (R), deaths (D), and vaccinated (V). Initially, a mathematical analysis is carried out to illustrate the non-negativity, boundedness, epidemic equilibrium, existence, and uniqueness of the endemic equilibrium, and the basic reproduction number of the proposed model. Such numerical models can be, however, subject to various sources of uncertainties, due to an imperfect description of the biological processes governing the disease spread, which may strongly limit their forecasting skills. A data assimilation method, mainly, the ensemble Kalman filter (EnKF), is then used to constrain the model outputs and its parameters with available data. We conduct joint state-parameters estimation experiments assimilating daily data into the proposed model using the EnKF in order to enhance the model’s forecasting skills. Starting from the estimated set of model parameters, we then conduct short-term predictions in order to assess the predicability range of the model. We apply the proposed assimilation system on real data sets from Saudi Arabia. The numerical results demonstrate the capability of the proposed model in achieving accurate prediction of the epidemic development up to two-week time scales. Finally, we investigate the effect of vaccination on the spread of the pandemic.