International Journal of Marine Science, 2024, Vol.14, No.3, 182-192 http://www.aquapublisher.com/index.php/ijms 189 complexity of accurately modeling these systems. The use of unstructured-grid, finite-volume modeling approaches, such as the Finite-Volume Community Ocean Model (FVCOM), has shown promise in simulating coastal circulation in remote island settings like Vanuatu and New Caledonia. However, these models require extensive verification and calibration with limited observational data, which can be a significant hurdle (Lee et al., 2021). Additionally, the integration of various factors such as wind speed, direction, and sea level rise (SLR) into these models adds layers of complexity that must be meticulously managed to ensure accurate predictions (Lee et al., 2021). Figure 2 Map of CMEMS (GLO, IBI, and MED) and CMEMS-downstream (SOCIB WMOP and PdE SAMOA) operational ocean forecasts in the Western Mediterranean Waters of Spain (Adopted from Sotillo et al., 2021) Image caption: Links between forecast services are depicted: CMEMS regional IBI and MED systems nest into the GLO one. Coastal systems nested into CMEMS IBI and MED are depicted in red and blue, respectively (Adopted from Sotillo et al., 2021) 9.2 Knowledge gaps and research needs Despite advancements in modeling techniques, there remain substantial knowledge gaps in understanding how coastal circulation will respond to climate change. For instance, the non-linear relationships between SLR and maximum current speeds observed in some coastal reef platforms indicate that more research is needed to fully comprehend these dynamics 1. Furthermore, the influence of trade winds on coastal processes, and how changes in these winds due to climate change might further alter coastal circulation, is not yet fully understood (Lee et al., 2021). There is also a need for high-resolution projections of coastal wave climate and impacts, such as port operability and coastal flooding, which are currently based on statistical models that may not capture all local variations (Camus et al., 2017). 9.3 Emerging trends and innovations Emerging trends in the field include the development of more sophisticated statistical frameworks for projecting wave climate and coastal impacts. For example, the use of a semi-supervised weather-typing approach to train statistical models has proven flexible and effective in projecting wave climate at different spatial scales (Camus et al., 2017). This method allows for the integration of changes in storminess and SLR, providing a more comprehensive assessment of future coastal impacts (Camus et al., 2017). Additionally, innovations in high-resolution modeling and the use of large ensembles of global circulation models (GCMs) are enhancing the statistical confidence of expected changes in coastal systems (Camus et al., 2017). These advancements are crucial
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