Synergistic use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea with models Intercomparison

Wenzhao Li1, Surya Prakash PRAKASH Tiwari2, Hesham Mohamed El-Askary3,4, Mohamed Qurban5, Vassilis Amiridis6, K.P ManiKandan7, Michael J Garay8, Olga V. Kalashnikova9, Thomas Piechota1 and Daniele Struppa1, (1)Chapman University, Schmid College of Science and Technology, Orange, CA, United States, (2)KFUPM, Center for Environment and Water, Dhahran, Saudi Arabia, (3)Alexandria University, Department of Environmental Science, Faculty of Science, Alexandria, Egypt, (4)Chapman University, Center of Excellence in Earth Systems Modeling & Observations, Orange, United States, (5)King Fahd University of Petroleum and Minerals, Geosciences Department, the college of Petroleum Engineering & Geosciences, Dhahran, Saudi Arabia, (6)National Observatory of Athens, Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, Athens, Greece, (7)King Fahd University of Petroleum and Minerals, Center for Environment and Water, The Research Institute, Dhahran, Saudi Arabia, (8)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (9)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States
Primary Productivity (PP) has been recently investigated using remote sensing based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. Oursualization and moving averagesour study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models—the VGPM (Vertically Generalized Production Model), the Eppley-VGPM and the CbPM (Carbon-based Production Model) – produced over the Red Sea domain for the 1998–2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization and moving averages correlative analysis to uncover the models’ responses to various climate factors. Here we use the models’ 8-day composite and monthly averages compared with satellite-based variables including chlorophyll-a (Chl-a), mixed layer depth (MLD) and sea surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the Multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO) and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models’ connection to oceanographic and atmospheric parameters, as well as the trends in the southern Red Sea, thus calling for a further validation efforts.