Congratulations to Vimal Koul, who defended his PhD thesis last week at the University of Hamburg. Vimals thesis was awarded the grade „with distinction/mit Auszeichnung.“
Vimal is working in our department Matter Transport and Ecosystem Dynamic. We asked him to tell us about his awarded thesis:
Decadal prediction of shelf sea marine ecosystems in the eastern North Atlantic: The role of the subpolar gyre
In climate science, prediction in general is a challenging affair. It becomes more challenging when one wishes to predict a physical property, or an event, or the likely evolution of a process a decade ahead. Complexity and challenges increase further when one wishes to make such decade long predictions for marine ecosystems. Prediction of marine ecosystems has deep historical roots and prediction of fish stocks lies at its core. Using earth system models, climate scientists had shown that environmental variables, such as ocean temperature, are predictable a decade ahead. However, what had remained a challenge was to leverage such environmental predictability for prediction of fish stocks.
Last week I defended my doctoral thesis wherein I tackled this problem on decadal prediction of marine ecosystems, the Barents Sea cod stock biomass in particular. The starting point of my research was the anticlockwise rotating band of swift currents in the subpolar North Atlantic—the subpolar gyre (SPG). I resolved an inconsistency in our understanding of how variability in the size and strength of SPG circulation can change water properties in the eastern North Atlantic. These altered water properties, or oceanic anomalies as they are called, then flow along the mean current pathways towards the shelf seas in the far eastern parts of the subpolar North Atlantic, such as the Barents Sea, where they influence fish abundance.
By establishing an oceanic link between the dynamic of the SPG circulation and Barents Sea cod biomass, I could show that the total biomass of Barents Sea cod can be predicted a decade in advance. Such an extended prediction horizon makes use of a dynamical prediction system for predicting temperature combined with a statistical model for transforming temperature predictions into cod biomass. (by Vimal Koul)