Northward flow of Antarctic Bottom Water (AABW) across the Southern Ocean comprises a key component of the global overturning circulation. Yet AABW transport remains poorly constrained by observations and state estimates, and there is presently no means of directly monitoring any component of the Southern Ocean overturning. However, AABW flow is dynamically linked to Southern Ocean surface circulation via the zonal momentum balance, offering potential routes to indirect monitoring of the transport. Exploiting this dynamical link, this study shows that wind stress (WS) fluctuations drive large AABW transport fluctuations on time scales shorter than (Formula presented.) 2 years, which comprise almost all of the transport variance. This connection occurs due to differing time scales on which topographic and interfacial form stresses respond to wind variability, likely associated with differences in barotropic versus baroclinic Rossby wave propagation. These findings imply that AABW transport variability can largely be reconstructed from the surface WS alone.

The oceanic Meridional Overturning Circulation (MOC) plays a key role in the climate system, and monitoring its evolution is a scientific priority. Monitoring arrays have been established at several latitudes in the Atlantic Ocean, but other latitudes and oceans remain unmonitored for logistical reasons. This study explores the possibility of inferring the MOC from globally-available satellite measurements via machine learning (ML) techniques, using the ECCOV4 state estimate as a test bed. The methodological advantages of the present approach include the use purely of available satellite data, its applicability to multiple basins within a single ML framework, and the ML model simplicity (a feed-forward fully connected neural network (NN) with small number of neurons). The ML model exhibits high skill in MOC reconstruction in the Atlantic, Indo-Pacific, and Southern Oceans. The approach achieves a higher skill in predicting the model Southern Ocean abyssal MOC than has previously been achieved via a dynamically-based approach. The skill of the model is quantified as a function of latitude in each ocean basin, and of the time scale of MOC variability. We find that ocean bottom pressure generally has the highest reconstruction skill potential, followed by zonal wind stress. We additionally test which combinations of variables are optimal. Furthermore, ML interpretability techniques are used to show that high reconstruction skill in the Southern Ocean is mainly due to (NN processing of) bottom pressure variability at a few prominent bathymetric ridges. Finally, the potential for reconstructing MOC strength estimates from real satellite measurements is discussed.

On behalf of the journal, AGU, and the scientific community, the Editors would like to sincerely thank those who reviewed manuscripts for Geophysical Research Letters in 2018. The hours reading and commenting on manuscripts not only improves the manuscripts but also increases the scientific rigor of future research in the field. We particularly appreciate the timely reviews, in light of the demands imposed by the rapid review process at Geophysical Research Letters. With the revival of the “major revisions” decisions, we appreciate the reviewers' efforts on multiple versions of some manuscripts. Many of those listed below went beyond and reviewed three or more manuscripts for our journal, and those are indicated in italics. In total, 4,484 referees contributed to 7,557 individual reviews in journal. Thank you again. We look forward to the coming year of exciting advances in the field and communicating those advances to our community and to the broader public.