O'Beirne, Catherine (2024) Application of tailored decadal predictions for Eastern North Atlantic. PhD thesis, National University of Ireland Maynooth.

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Abstract

Predictability is the result of both externally forced and internally generated variability on time scales such as seasonal, annual, and decadal (Meehl et al., 2014). There have been improvements in the field of global decadal prediction. This is in terms of a better understanding of the interactions that occur in our world and in the improvement of the models that are being used. While the consensus is that the models are accurate on a global scale, there is limited confidence in prediction skill on a regional scale. There have been studies conducted on the model components for target areas with some success. This is one of the motivators for this thesis, which investigates the benefits of developing targeted decadal predictions for stakeholder needs. The second motivator is how these predictions can be tailored to stakeholder needs. It will explore the predictability of the North Atlantic Ocean on a decadal time scale for oceanographic properties like ocean temperature, sea salinity, the subpolar gyre (SPG), and Atlantic Multidecadal Variability (AMV). Making this information usable on a regional scale for Ireland would allow tailoring for different applications such as fisheries. The fishery sector is of vast importance to the Irish economy. The ability to predict changes in future stock will support adaptation and fish stock management. The different stages of fish development are dependent on oceanic variables like temperature and salinity so decadal prediction skill for those variables would allow us to make statements on potential changes in fish stock for a species such as Mackerel.

Item Type:	Thesis (PhD)
Keywords:	tailored decadal predictions; Eastern North Atlantic;
Academic Unit:	Faculty of Social Sciences > Geography Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
Item ID:	19019
Depositing User:	IR eTheses
Date Deposited:	14 Oct 2024 10:46
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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