Machine Learning‐Derived Inference of the Meridional Overturning Circulation From Satellite‐Observable Variables in an Ocean State Estimate

Solodoch, Aviv; Stewart, Andrew L.; Hogg, Andrew McC.; Manucharyan, Georgy E.

doi:10.1029/2022ms003370

Cited by 4 publications

(1 citation statement)

References 76 publications

(148 reference statements)

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“…13) Machine learning can be used in several ways to help address AABW-related questions. For example, a possible means of circumventing the limitations of satellite-derived proxies of AABW circulation (SSH, SST, and OBP; see Section 4.2) is to leverage machine learning techniques, which have been used, e.g., to infer subsurface velocity fields (Chapman and Charantonis, 2017) and subsurface meridional heat transport/overturning from SSH (George et al, 2021;Solodoch et al, 2023).…”

Section: The Need For An Aabw Observing Systemmentioning

confidence: 99%

Observing Antarctic Bottom Water in the Southern Ocean

Silvano,

Purkey,

Gordon

et al. 2023

Front. Mar. Sci.

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Dense, cold waters formed on Antarctic continental shelves descend along the Antarctic continental margin, where they mix with other Southern Ocean waters to form Antarctic Bottom Water (AABW). AABW then spreads into the deepest parts of all major ocean basins, isolating heat and carbon from the atmosphere for centuries. Despite AABW’s key role in regulating Earth’s climate on long time scales and in recording Southern Ocean conditions, AABW remains poorly observed. This lack of observational data is mostly due to two factors. First, AABW originates on the Antarctic continental shelf and slope where in situ measurements are limited and ocean observations by satellites are hampered by persistent sea ice cover and long periods of darkness in winter. Second, north of the Antarctic continental slope, AABW is found below approximately 2 km depth, where in situ observations are also scarce and satellites cannot provide direct measurements. Here, we review progress made during the past decades in observing AABW. We describe 1) long-term monitoring obtained by moorings, by ship-based surveys, and beneath ice shelves through bore holes; 2) the recent development of autonomous observing tools in coastal Antarctic and deep ocean systems; and 3) alternative approaches including data assimilation models and satellite-derived proxies. The variety of approaches is beginning to transform our understanding of AABW, including its formation processes, temporal variability, and contribution to the lower limb of the global ocean meridional overturning circulation. In particular, these observations highlight the key role played by winds, sea ice, and the Antarctic Ice Sheet in AABW-related processes. We conclude by discussing future avenues for observing and understanding AABW, impressing the need for a sustained and coordinated observing system.

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Section: The Need For An Aabw Observing Systemmentioning

confidence: 99%

Observing Antarctic Bottom Water in the Southern Ocean

Silvano,

Purkey,

Gordon

et al. 2023

Front. Mar. Sci.

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Opportunities for Earth Observation to Inform Risk Management for Ocean Tipping Points

Wood,

Baker,

Beaugrand

et al. 2024

Surv Geophys

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As climate change continues, the likelihood of passing critical thresholds or tipping points increases. Hence, there is a need to advance the science for detecting such thresholds. In this paper, we assess the needs and opportunities for Earth Observation (EO, here understood to refer to satellite observations) to inform society in responding to the risks associated with ten potential large-scale ocean tipping elements: Atlantic Meridional Overturning Circulation; Atlantic Subpolar Gyre; Beaufort Gyre; Arctic halocline; Kuroshio Large Meander; deoxygenation; phytoplankton; zooplankton; higher level ecosystems (including fisheries); and marine biodiversity. We review current scientific understanding and identify specific EO and related modelling needs for each of these tipping elements. We draw out some generic points that apply across several of the elements. These common points include the importance of maintaining long-term, consistent time series; the need to combine EO data consistently with in situ data types (including subsurface), for example through data assimilation; and the need to reduce or work with current mismatches in resolution (in both directions) between climate models and EO datasets. Our analysis shows that developing EO, modelling and prediction systems together, with understanding of the strengths and limitations of each, provides many promising paths towards monitoring and early warning systems for tipping, and towards the development of the next generation of climate models.

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GRACE Satellite Observations of Antarctic Bottom Water Transport Variability

Jeffree,

Hogg,

Morrison

et al. 2024

JGR Oceans

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Antarctic Bottom Water (AABW) formation and transport constitute a key component of the global ocean circulation. Direct observations suggest that AABW volumes and transport rates may be decreasing, but these observations are too temporally or spatially sparse to determine the cause. To address this problem, we develop a new method to reconstruct AABW transport variability using data from the GRACE (Gravity Recovery and Climate Experiment) satellite mission. We use an ocean general circulation model to investigate the relationship between ocean bottom pressure and AABW: we calculate both of these quantities in the model, and link them using a regularized linear regression. Our reconstruction from modeled ocean bottom pressure can capture 65%–90% of modeled AABW transport variability, depending on the ocean basin. When realistic observational uncertainty values are added to the modeled ocean bottom pressure, the reconstruction can still capture 30%–80% of AABW transport variability. Using the same regression values, the reconstruction skill is within the same range in a second, independent, general circulation model. We conclude that our reconstruction method is not unique to the model in which it was developed and can be applied to GRACE satellite observations of ocean bottom pressure. These advances allow us to create the first global reconstruction of AABW transport variability over the satellite era. Our reconstruction provides information on the interannual variability of AABW transport, but more accurate observations are needed to discern AABW transport trends.

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Machine Learning‐Derived Inference of the Meridional Overturning Circulation From Satellite‐Observable Variables in an Ocean State Estimate

Cited by 4 publications

References 76 publications

Observing Antarctic Bottom Water in the Southern Ocean

Observing Antarctic Bottom Water in the Southern Ocean

Opportunities for Earth Observation to Inform Risk Management for Ocean Tipping Points

GRACE Satellite Observations of Antarctic Bottom Water Transport Variability

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