Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions.

Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20-year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K-means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a Quasi-Sverdrupian regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual dominantly nonlinear regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries

Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces.

An unsupervised learning method is presented for determining global marine ecological provinces (eco-provinces) from plankton community structure and nutrient flux data. The systematic aggregated eco-province (SAGE) method identifies eco-provinces within a highly nonlinear ecosystem model. To accommodate the non-Gaussian covariance of the data, SAGE uses t-stochastic neighbor embedding (t-SNE) to reduce dimensionality. Over a hundred eco-provinces are identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Using a connectivity graph with ecological dissimilarity as the distance metric, robust aggregated eco-provinces (AEPs) are objectively defined by nesting the eco-provinces. Using the AEPs, the control of nutrient supply rates on community structure is explored. Eco-provinces and AEPs are unique and aid model interpretation. They could facilitate model intercomparison and potentially improve understanding and monitoring of marine ecosystems

A barotropic vorticity budget for the subtropical North Atlantic based on observations

Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 49(11), (2019): 2781-2797, doi: 10.1175/JPO-D-19-0111.1.To ground truth the large-scale dynamical balance of the North Atlantic subtropical gyre with observations, a barotropic vorticity budget is constructed in the ECCO state estimate and compared with hydrographic observations and wind stress data products. The hydrographic dataset at the center of this work is the A22 WOCE section, which lies along 66°W and creates a closed volume with the North and South American coasts to its west. The planetary vorticity flux across A22 is quantified, providing a metric for the net meridional flow in the western subtropical gyre. The wind stress forcing over the subtropical gyre to the west and east of the A22 section is calculated from several wind stress data products. These observational budget terms are found to be consistent with an approximate barotropic Sverdrup balance in the eastern subtropical gyre and are on the same order as budget terms in the ECCO state estimate. The ECCO vorticity budget is closed by bottom pressure torques in the western subtropical gyre, which is consistent with previous studies. In sum, the analysis provides observational ground truth for the North Atlantic subtropical vorticity balance and explores the seasonal variability of this balance for the first time using the ECCO state estimate. This balance is found to hold on monthly time scales in ECCO, suggesting that the integrated subtropical gyre responds to forcing through fast barotropic adjustment.We thank Alonso Hernández-Guerra, M. Dolores Pérez-Hernández, and María Casanova-Masjoan for providing the inverse model results from Casanova-Masjoan et al. (2018). The A22 section is part of the WOCE/CLIVAR observing effort, with all data available at http://cchdo.ucsd.edu/. We thank Carl Wunsch, Patrick Heimbach, Chris Hill, and Diana Lees Spiegel for their assistance with the ECCO fields. The state estimates were provided by the ECCO Consortium for Estimating the Circulation and Climate of the Ocean funded by the National Oceanographic Partnership Program (NOPP) and can be downloaded at http://www.ecco-group.org/products.htm. The citable URL for the ECCO version 4 release 2 product is http://hdl.handle.net/1721.1/102062. We are grateful to Joseph Pedlosky and Glenn Flierl for their comments on an earlier version of this work. IALB and JMT were supported financially by U.S. NSF Grants OCE-0726720, 1332667, and 1332834. MS was supported by the U.S. NASA Sea Level Change Team (Contract NNX14AJ51G) and through the ECCO Consortium funding via the Jet Propulsion Laboratory. We thank two anonymous reviewers, whose thoughtful comments led to improvements.2020-04-1

Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning

Complex ocean systems such as the Antarctic Circumpolar Current play key roles in the climate, and current models predict shifts in their strength and area under climate change. However, the physical processes underlying these changes are not well understood, in part due to the difficulty of characterizing and tracking changes in ocean physics in complex models. Using the Antarctic Circumpolar Current as a case study, we extend the method Tracking global Heating with Ocean Regimes (THOR) to a mesoscale eddy permitting climate model and identify regions of the ocean characterized by similar physics, called dynamical regimes, using readily accessible fields from climate models. To this end, we cluster grid cells into dynamical regimes and train an ensemble of neural networks, allowing uncertainty quantification, to predict these regimes and track them under climate change. Finally, we leverage this new knowledge to elucidate the dynamical drivers of the identified regime shifts as noted by the neural network using the 'explainability' methods SHAP and Layer-wise Relevance Propagation. A region undergoing a profound shift is where the Antarctic Circumpolar Current intersects the Pacific-Antarctic Ridge, an area important for carbon draw-down and fisheries. In this region, THOR specifically reveals a shift in dynamical regime under climate change driven by changes in wind stress and interactions with bathymetry. Using this knowledge to guide further exploration, we find that as the Antarctic Circumpolar Current shifts north under intensifying wind stress, the dominant dynamical role of bathymetry weakens and the flow intensifies.Comment: 14 pages, 11 figures, NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learnin

Seasonal variability of sea surface height in the coastal waters and deep basins of the Nordic Seas

Sea surface height measured by the Envisat radar altimeter over open ocean and from leads in sea ice are combined to generate a complete view of variability in the Nordic Seas, geographically and seasonally. The observed seasonal variability is decomposed using empirical orthogonal functions, and is consistent with seasonal variations in steric and dynamic forcing. Wintertime increase in sea surface height on the east Greenland shelf is hypothesised to be caused by wind-forced downwelling, which provides direct evidence for the regional play of coastal dynamics. High levels of eddy kinetic energy around the sea ice edge in Fram Strait, and off east Greenland and Svalbard are consistent with the interaction of the wind with the ice edge

Bridging observations, theory and numerical simulation of the ocean using machine learning

Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades. Important timescales span seconds to millennia, with strong scale interactions and numerical modelling efforts complicated by details such as coastlines. This review covers the current scientific insight offered by applying ML and points to where there is imminent potential. We cover the main three branches of the field: observations, theory, and numerical modelling. Highlighting both challenges and opportunities, we discuss both the historical context and salient ML tools. We focus on the use of ML in situ sampling and satellite observations, and the extent to which ML applications can advance theoretical oceanographic exploration, as well as aid numerical simulations. Applications that are also covered include model error and bias correction and current and potential use within data assimilation. While not without risk, there is great interest in the potential benefits of oceanographic ML applications; this review caters to this interest within the research community

Unsupervised classification identifies coherent thermohaline structures in the Weddell Gyre region

The Weddell Gyre is a major feature of the Southern Ocean and an important component of the planetary climate system; it regulates air–sea exchanges, controls the formation of deep and bottom waters, and hosts upwelling of relatively warm subsurface waters. It is characterised by low sea surface temperatures, ubiquitous sea ice formation, and widespread salt stratification that stabilises the water column. Observing the Weddell Gyre is challenging, as it is extremely remote and largely covered with sea ice. At present, it is one of the most poorly sampled regions of the global ocean, highlighting the need to extract as much value as possible from existing observations. Here, we apply a profile classification model (PCM), which is an unsupervised classification technique, to a Weddell Gyre profile dataset to identify coherent regimes in temperature and salinity. We find that, despite not being given any positional information, the PCM identifies four spatially coherent thermohaline domains that can be described as follows: (1) a circumpolar class, (2) a transition region between the circumpolar waters and the Weddell Gyre, (3) a gyre edge class with northern and southern branches, and (4) a gyre core class. PCM highlights, in an objective and interpretable way, both expected and underappreciated structures in the Weddell Gyre dataset. For instance, PCM identifies the inflow of Circumpolar Deep Water (CDW) across the eastern boundary, the presence of the Weddell–Scotia Confluence waters, and structured spatial variability in mixing between Winter Water and CDW. PCM offers a useful complement to existing expertise-driven approaches for characterising the physical configuration and variability of oceanographic regions, helping to identify coherent thermohaline structures and the boundaries between them

The lipoxygenase-dependent oxygenation of lipid body membranes is promoted by a patatin-type phospholipase in cucumber cotyledons

Oilseed germination is characterized by the mobilization of storage lipids as a carbon and energy source for embryonic growth. In addition to storage lipid degradation in germinating oilseeds via the direct action of a triacylglycerol lipase (TGL) on the storage lipids, a second degradation pathway that is dependent on a specific lipid body trilinoleate 13-lipoxygenase (13-LOX) has been proposed in several plant species. The activity of this specific 13-LOX leads first to the formation of ester lipid hydroperoxides. These hydroperoxy fatty acids are then preferentially cleaved off by a TGL and serve as a substrate for glyoxysomal β-oxidation. As a prerequisite for triacylglycerol (TAG) mobilization, a partial degradation of the phospholipid monolayer and/or membrane proteins of the oil body has been discussed. Evidence has now been found for both processes: partial degradation of the proteins caleosin and oleosin was observed and simultaneously a patatin-like protein together with transient phospholipase (PLase) activity could be detected at the oil body membranes during germination. Moreover, in vitro experiments with isolated oil bodies from mature seeds revealed that the formation of 13-LOX-derived lipid peroxides in lipid body membranes is increased after incubation with the purified recombinant patatin-like protein. These experiments suggest that in vivo the degradation of storage lipids in cucumber cotyledons is promoted by the activity of a specific oil body PLase, which leads to an increased decomposition of the oil body membrane by the 13-LOX and thereby TAGs may be better accessible to LOX and TGL

Ocean model utility dependence on horizontal resolution

This thesis examines the change in ocean model utility with changing horizontal resolution. Oceans are a crucial part of the climate system, with numerical models offering important insights into our mechanistic understanding. We use a 30 year integration (1978 to 2007) of the NEMO model at 1º, 1/4º and 1/12º to investigate the impact of modelling choices associated with horizontal resolution changes. Changes in degrees of freedom associated with the increasing resolution allow alternative energy dissipation pathways, with potential impact on model accuracy. We develop a measure of utility based on an estimate of the accuracy, as well as a penalisation which scales with resolution. Overall, accuracy is thought to increase with resolution, and we examine the associated change in utility on a range of model fields. The exploration of the NEMO model assesses the surface mixed layer, deep (&gt;2000m) to surface (&lt;2000m) communication through the ocean interior and the changes in the meridional overturning with topographic interactions. Assessing these areas, we illustrate potential changes in the energy pathways in the system. We investigate the surface in terms of the mixed layer depth globally, but also investigating a case study in the Southern Ocean. We find that the mixed layer does not change significantly with resolution, and that NEMO compares well with observations. Minor changes with resolution are attributed to increased numbers of fronts with increasing resolution. When the mixed layer is assessed, we see no significant change with resolution, and so find that 1º has the highest utility. For our case study, we investigate the zonally asymmetric deepening of the mixed layer in the Southern Ocean. We find that the stratification set by the advection is key, and confirm this using the 1D Price-Weller-Pinkel model.The communication between the surface and the deep ocean is assessed by looking at the steric height variability, and specifically its covariance between the surface and the deep. We find that there are large changes with resolution, and attribute these to the higher resolutions' ability to include eddy effects. This suggests that the Gent-McWilliams scheme that is active at low resolution fails to capture this. We look at the low and high frequency parts of the variance, finding that strongly eddying regions dominate the high frequency steric height covariance, confirming the importance of eddies. The ratio between the surface and the deep steric height shows poor utility in both ORCA1 and ORCA025, while we find seasonal leakage obscuring our accuracy measure for the steric height.The overturning is assessed in density space, and we notice a strengthening of the anti-clockwise component in the Southern Ocean. Decomposing the transport into its baroclinic and barotropic components, we find that changes in the baroclinic overturning can account for this. The lack of western boundaries in the Southern Ocean suggests that eddies, as well as interaction with topography, are especially important here, and we investigate the change in the balance of forcing in terms of the associated vortex stretching. We assess this in terms of the bottom pressure torque, but find the major changes in the baroclinic component of the bottom pressure torque. We find that increasing the resolution still leads to increased utility, particularly in the barotropic and baroclinic density space overturning case.The major implications of our results are that low resolution is appropriate for fields such as the mixed layer depth, but increasing the resolution is seen to improve the mean overturning through allowing eddy activity.<br/