Estimation of oceanographic profiles and climatological regions in the Barents Sea

A novel heuristic method for detecting overfit in unsupervised classification of climate model data

“…Unsupervised classification methods, that is, methods that do not know a priori what the properties of these groups might be, have proven adept at identifying coherent spatial structures within climate data, even when no spatial information is supplied to the algorithm. In studies of ocean and atmospheric data, two commonly used unsupervised classification methods are k-means (Solidoro et al, 2007; Hjelmervik and Hjelmervik, 2013; 2014; Hjelmervik et al, 2015; Sonnewald et al, 2019; Houghton and Wilson, 2020; Yuchechen et al, 2020; Liu et al, 2021) and Gaussian mixture modeling (GMM) (Hannachi and O’Neill, 2001; Hannachi, 2007; Tandeo et al, 2014; Maze et al, 2017a; Jones et al, 2019; Crawford, 2020; Sugiura, 2021; Zhao et al, 2021; Fahrin et al, 2022). K-means attempts to find coherent groups by “cutting” the abstract feature space using hyperplanes, whereas GMM attempts to represent the underlying covariance structure in abstract feature space using a linear combination of multi-dimensional Gaussian functions.…”

“…The most commonly used statistical criterion is the minimum in the Bayesian information criterion (BIC; Schwarz, 1978), used in Fahrin et al (2022), Sugiura (2021), Zhao et al (2021), Jones et al (2019), Maze et al (2017b), Hjelmervik and Hjelmervik (2013, 2014), Hjelmervik et al (2015), and Sonnewald et al (2019). The BIC is comprised of two terms: a term that rewards the statistical likelihood of the model and a term that penalizes overfitting.…”