Is Computational Oceanography Coming of Age?

Abstract

Computational Oceanography is the study of ocean phenomena by numerical simulation, especially dynamical and physical phenomena. Progress in information technology has driven exponential growth in the number of global ocean observations and the fidelity of numerical simulations of the ocean in the past few decades. The growth has been exponentially faster for ocean simulations, however. We argue that this faster growth is shifting the importance of field measurements and numerical simulations for oceanographic research. It is leading to the maturation of Computational Oceanography as a branch of marine science on par with observational oceanography. One implication is that ultra-resolved ocean simulations are only loosely constrained by observations. Another implication is that barriers to analyzing the output of such simulations should be removed. Although some specific limits and challenges exist, many opportunities are identified for the future of Computational Oceanography. Most important is the prospect of hybrid computational and observational approaches to advance understanding of the ocean.

Interactions between the Arctic Mediterranean and the Atlantic Meridional Overturning Circulation: A Review

“…In the past decade, significant progress has been made in understanding Arctic-AMOC interactions. This understanding has been driven by observing and modeling advances such as the OSNAP array (Lozier et al, 2019), the Argo network (Jayne et al, 2017), new generations of coupled climate models (Fox-Kemper et al, 2019), very high-resolution ocean circulation models (Wang et al, 2018;Haine et al, 2021), and improved conceptual models that only include essential components of the Arctic and AMOC (Haine, 2021). The maturation of these capabilities and technologies are carrying us toward another phase of discovery.…”

“…For example, ocean models that are referred to as "eddy-resolving" (often using a spatial resolution of about 10 km) do not resolve the Arctic Ocean Rossby radius of deformation (1-15 km; Nurser and Bacon, 2014). Continuing improvements in computational capabilities (supercomputers), approaches (architectures), and algorithms (machine learning) are inevitably moving us toward ocean and climate models that will be able to resolve these critical scales in the next decade (Haine et al, 2021). This will allow us to resolve more of the smallscale processes critical for the large-scale Arctic Ocean and sea ice system and its connection to lower latitudes.…”

“…At the same time, the number of autonomous drifting Argo floats, which observe the temperature and salinity of the upper 2 km, has been increasing since 1999 (Jayne et al, 2017). Other important progress has been made in the development of better ocean models (Fox-Kemper et al, 2019) that can be quite realistic (Haine et al, 2021).…”

Cracking the code: An evidence-based approach to teaching Python in an undergraduate earth science setting

“…In addition to these observational programs, hard drives at institutions across the world are being filled with terabytes of data generated by numerical simulations. From highly resolved ocean general circulation models to the lower-resolution global climate models assessed in the Intergovernmental Panel on Climate Change (IPCC) reports, the natural ocean is being reproduced with ever-increasing fidelity (Haine et al, 2021). The resulting challenges in accessing and analyzing these data require new computational tools that enable truly open science, further motivated by the notion that "research conducted openly and transparently leads to better science" (National Academies of Sciences, Engineering, and Medicine, 2018).…”

A robust framework for comparing Lagrangian and gridded Eulerian velocity fields: an example application to surface drifters and altimetry-derived surface currents

“…with Lagrangian data arises quite often in oceanography. The proliferation of gridded satellite and reanalysis products, as well as numerical model output has produced a large number of gridded Eulerian data sets (e.g., Haine et al, 2021). These data sets are often compared to surface drifters from the Global Drifter Program (GDP; Lumpkin and Johnson, 2013) and profiling floats from the Argo network (Johnson et al, 2022) to yield a velocity field that utilizes the accuracy of the in situ Lagrangian data with the spatial perspective of the gridded Eulerian data.…”