Sensitivity of the simulated Oxygen Minimum Zone to biogeochemical processes at an oligotrophic site in the Arabian Sea

Development of standards for assessing water quality in marine coastal waters of Bahrain

Marine coastal waters of Bahrain are under pressure due to human activities and climate change. We used marine monitoring data (2005-2020) from 27 sites to establish baseline conditions and develop standards for assessments of water quality. Five hydrodynamic regions were identified: Oyster Beds, North, West, East, East (Coastal). Data from Oyster Beds sites, likely to be less impacted by human activities, were used to determine baseline conditions. For most parameters, candidate thresholds were based on 50 % and 100 % variation from baseline and 95th percentiles. Comparisons of data against different thresholds showed different outcomes. Overall, results indicate good water quality, with potential concerns in East (Coastal). Trend analyses showed some significant trends in all regions: downward (favourable) for some parameters (e.g. turbidity: North) and upward for others (e.g. nitrate: Oyster Beds, East and East (Coastal)). Future work requires greater understanding around optimum guidelines that protect and mitigate any adverse ecological impacts.

Upwelling and nutrient dynamics in the Arabian Gulf and sea of Oman

This study demonstrates the vertical and horizontal distribution of nutrients and the seasonal response of nutrients to upwelling in the Arabian Gulf and the Sea of Oman. Thus, monthly data on nitrate, phosphate, and silicate are obtained from the World Ocean Atlas 2018 (WOA), as well as estimates of coastal and curl driven upwelling in both regions. The results of the study indicate that the Sea of Oman's surface and deep waters contained higher concentrations of nutrients than the Arabian Gulf by 80%. In addition, both regions have exhibited a general increase in the vertical distribution of nutrients as the depth increases. Among the aforementioned nutrients, nitrate is found to be a more limiting nutrient for phytoplankton growth than phosphate as the nitrate-to-phosphate ratios (N:P) in surface waters are lower (≤ 4.6:1) than the Redfield ratio (16:1). As for the upwelling, curl-driven upwelling accounts for more than half of the total upwelling in both regions, and both play an important role in nutrient transport. Thus, nutrients are upwelled from the subsurface to the mixed layer at a rate of 50% in the Oman Sea from 140 m to 20 m during the summer and to 40 m during the winter. Similarly, the Arabian Gulf shows 50% transport for nitrates, but 32% for phosphates, from 20 m to 5-10 m. However, due to the abundance of diatoms at the surface of the Arabian Gulf, the surface silicate content is 30% higher than that of the deeper waters.

Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea

Marine biogeochemical models have been widely used to understand ecosystem dynamics and biogeochemical cycles. To resolve more processes, models typically increase in complexity, and require optimization of more parameters. Data assimilation is an essential tool for parameter optimization, which can reduce model uncertainty and improve model predictability. At present, model parameters are often adjusted using sporadic in-situ measurements or satellite-derived total chlorophyll-a concentration at sea surface. However, new ocean datasets and satellite products have become available, providing a unique opportunity to further constrain ecosystem models. Biogeochemical-Argo (BGC-Argo) floats are able to observe the ocean interior continuously and satellite phytoplankton functional type (PFT) data has the potential to optimize biogeochemical models with multiple phytoplankton species. In this study, we assess the value of assimilating BGC-Argo measurements and satellite-derived PFT data in a biogeochemical model in the northern South China Sea (SCS) by using a genetic algorithm. The assimilation of the satellite-derived PFT data was found to improve not only the modeled total chlorophyll-a concentration, but also the individual phytoplankton groups at surface. The improvement of simulated surface diatom provided a better representation of subsurface particulate organic carbon (POC). However, using satellite data alone did not improve vertical distributions of chlorophyll-a and POC. Instead, these distributions were improved by combining the satellite data with BGC-Argo data. As the dominant variability of phytoplankton in the northern SCS is at the seasonal timescale, we find that utilizing monthly-averaged BGC-Argo profiles provides an optimal fit between model outputs and measurements in the region, better than using high-frequency measurements.