Ocean mover’s distance: using optimal transport for analysing oceanographic data

Abstract

Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. The Wasserstein distance complements commonly used point-wise difference methods such as the root-mean-squared error, by quantifying differences in terms of spatial displacement in addition to magnitude. As a test case, we consider chlorophyll (a key indicator of phytoplankton biomass) in the northeast Pacific Ocean, obtained from model simulations, in situ measurements, and satellite observations. We focus on two main applications: (i) comparing model predictions with satellite observations, and (ii) temporal evolution of chlorophyll both seasonally and over longer time frames. The Wasserstein distance successfully isolates temporal and depth variability and quantifies shifts in biogeochemical province boundaries. It also exposes relevant temporal trends in satellite chlorophyll consistent with climate change predictions. Our study shows that optimal transport vectors underlying the Wasserstein distance provide a novel visualization tool for testing models and better understanding temporal dynamics in the ocean.

Towards Inverse Modeling of Landscapes Using the Wasserstein Distance

“…Originating from the work of Monge (1781) on Optimal Transport, calculation of Wasserstein distances have been developed by Kantorovich (1942) and Villani (2003). The Wasserstein distance has been applied to a range of problems in computing (Arjovsky et al., 2017), chemistry (Seifert et al., 2022), oceanography (Hyun et al., 2022; Nooteboom et al., 2020), climate science (Chang et al., 2015; Vissio et al., 2020), geophysics (Engquist & Froese, 2014; Métivier et al., 2016a, 2016b; Sambridge et al., 2022), sedimentology (A. Lipp & Vermeesch, 2023), and hydrology (Magyar & Sambridge, 2023). As far as we are aware it has not been used to invert landscapes for their properties (e.g., uplift histories, erosion rates, sedimentary fluxes).…”

Towards inverse modeling of landscapes using the Wasserstein distance

“…Originating from the work of Monge (1781) on Optimal Transport, calculation of Wasserstein distances have been developed by and Villani (2003). The Wasserstein distance has been applied to a range of problems in computing (Arjovsky et al, 2017), chemistry (Seifert et al, 2022), oceanography (Hyun et al, 2022;Nooteboom et al, 2020), climate science (Chang et al, 2015;Vissio et al, 2020), geophysics (Engquist & Froese, 2014;Métivier et al, 2016aMétivier et al, , 2016bSambridge et al, 2022), sedimentology (Lipp & Vermeesch, 2023), and hydrology (Magyar & Sambridge, 2022). As far as we are aware it has not been used to invert landscapes for their properties (e.g.…”