Coastal environmental and atmospheric data reduction in the Southern North Sea supporting ecological impact studies - Mészáros, Lőrinc; van der Meulen, Frank; Jongbloed, Geurt; El Serafy, Ghada (Frontiers in Marine Science, 2022)
Coastal climate impact studies make increasing use of multi-source and multi-dimensional simulated atmospheric and environmental datasets to investigate causal relationships between climate signals and the ecological response. The large quantity of numerically simulated data may, however, include redundancy, multi-colinearity and excess information not relevant to the studied processes. In such cases techniques for feature extraction and identification of latent processes prove useful. Using dimensionality reduction techniques this research provides a statistical underpinning of variable selection to study the impacts of atmospheric processes on coastal chlorophyll-a concentrations, taking the Dutch Wadden Sea as case study. We apply dimension reduction techniques to environmental data simulated by the Delft3D coastal water quality model, the HIRLAM numerical weather prediction model and the Euro-CORDEX climate modelling experiment. The dimension reduction techniques were selected for their ability to incorporate (1) spatial correlation via multi-way methods, (2) temporal correlation through Dynamic Factor Analysis, and (3) functional variability using Functional Data Analysis. The data reduction potentials and explanatory value of these methods are showcased and important atmospheric variables affecting the chlorophyll-a concentration are identified. Our results indicate room for dimensionality reduction, and that solar radiation followed by air temperature are the most important atmospheric variables related to coastal chlorophyll-a concentrations. Common trends and different regional system characteristics have been identified through dynamic factor analysis between the deeper channels and the shallower intertidal zones, where the onset of spring blooms occur earlier. The functional analysis of climate data revealed clusters of atmospheric variables with similar functional features. Moreover, functional components of Euro-CORDEX climate scenarios have been identified for radiation and temperature variables, which provide information on the mean functional shape and its uncertainties. The findings suggest that radiation and temperature projections of different Euro-CORDEX scenarios mainly differ in their amplitudes and seasonal shifts, offering opportunities to borrow statistical strength if their characteristics are pooled together in statistical models. The presented results were used in follow up studies to construct a Bayesian stochastic generator to complement existing Euro-CORDEX climate change scenarios and to quantify climate change induced trends and uncertainties in phytoplankton spring bloom dynamics in the Dutch Wadden Sea.