High Salinity Shelf Water production rates in Terra Nova Bay, Ross Sea from high-resolution salinity observations

Domain: earth_science
Published: 2024-01-16
Editorial outcome: major_revision

Abstract
High Salinity Shelf Water (HSSW) formed in the Ross Sea of Antarctica is a precursor to Antarctic Bottom Water (AABW), a water mass that constitutes the bottom limb of the global overturning circulation. HSSW production rates are poorly constrained, as in-situ observations are scarce. Here, we present high-vertical-and-temporal-resolution salinity time series collected in austral winter 2017 from a mooring in Terra Nova Bay (TNB), one of two major sites of HSSW production in the Ross Sea. We calculate an annual-average HSSW production rate of ~0.4 Sv (10^6 m^3 s^−1), which we use to ground truth additional estimates across 2012–2021 made from parametrized net surface heat fluxes. We find sub-seasonal and interannual variability on the order of 0.1 Sv, with a strong dependence on variability in open-water area that suggests a sensitivity of TNB HSSW production rates to changes in the local wind regime and off-shore sea ice pack.

Introduction
High Salinity Shelf Water (HSSW) is a key component of the ~5.4 Sverdrups (Sv; 10^6 m^3 s^−1) of dense shelf water (DSW) that flow off the continental shelves of Antarctica to form Antarctic Bottom Water (AABW), a water mass that constitutes the bottom limb of the Meridional Overturning Circulation (MOC) and ventilates the deep ocean1–3. HSSW is produced in coastal polynyas, which are formed when intense katabatic winds, originating from the Antarctic Ice Sheet and channeled seaward through coastal valleys, continually push newly formed sea ice offshore. Polynyas serve as windows through which a wintertime ocean, otherwise sealed off by ice, exchanges gas, heat, and momentum with the atmosphere. Intensive heat loss to the atmosphere from these polynyas make them highly productive sea ice factories4 and the resulting brine plumes feed reservoirs of HSSW at depth. HSSW in the Ross Sea is of particular interest because substantial changes in its salinity observed over the past six decades have directly influenced the properties of AABW in the Pacific and Indian sectors of the Southern Ocean. From the 1960’s onward, Ross Sea HSSW has freshened at a rate of ∼0.03 decade−1 5,6, resulting in the freshening of newly formed AABW sourced from DSW exiting the western Ross Sea 5,7,8 and contributing to a weakening of the bottom limb of the MOC 2,9,10. This freshening is associated with the impacts of climate change on the broader Western Antarctic region, wherein a warming ocean has increased the amount of glacial meltwater advected into the Ross Sea from the nearby Amundsen and Bellingshausen Seas 6. Shorter term variability in Ross Sea salinity has been tied to climate anomalies, such as the co-occurrence of a positive Southern Annular Mode (SAM) with extreme El Niño conditions from 2015-2018 that resulted in a sudden reversal of the decades-long Ross Sea freshening trend 11,12. This anomaly induced weaker easterly winds in the Amundsen Sea, reducing sea ice import into the Ross Sea and allowing for an increase in local ice production, and thus brine rejection and salinity, across the Ross Sea continental shelf 13. A subsequent recovery of AABW salinity downstream of the western Ross Sea outflow followed 13,14. However, the long-term freshening trend suggests that if such reversals are not persistent, Ross Sea HSSW and the AABW it feed will drop below their defining densities by mid-century 6. As the MOC is thought to regulate Earth’s climate on centennial to glacial-interglacial timescales 9,15, variability in AABW has implications for our presently warming climate system and places great importance on our understanding of HSSW formation.

HSSW in the Ross Sea is formed primarily within the Terra Nova Bay (TNB) and Ross Sea Polynyas 16, which occur regularly every austral winter. It is broadly defined by temperatures near freezing and salinities greater than 34.62, though HSSW formed in TNB, bounded by Nansen Ice Shelf to the west, Cape Washington to the north, and Drygalski Ice Tongue to the south 17,18 (Fig. 1), is uniquely salty and dense, with salinities exceeding 34.8 16,19. Therefore, despite a smaller total volume contribution (33%) 20, TNB has an outsized impact on the density of overall Ross Sea HSSW. Furthermore, it directly supplies much of the estimated 0.2–0.54 Sv of HSSW exported from the western Ross Sea via the Drygalski Trough 20–23. HSSW is produced only during austral winter, once salinification of surface waters via brine rejection has broken down lingering stratification from summer ice melt and solar radiation, allowing full water column convection to occur and brine plumes to reach depth 24–26. While some of the HSSW produced in TNB is converted to Ice Shelf Water (ISW) through its interaction with the local glaciers 23 or exported southward under Drygalski Ice Tongue 12,20, most of it spreads northward through the Drygalski Basin, taking about 8 months to travel through the Drygalski Trough and reach the continental shelf break 27. It ultimately exits the continental shelf as dense, tidally-modulated gravity plumes 21,22,27–29. Though the circulation of HSSW within TNB and its export via the Drygalski Trough are somewhat understood 12,23, its production rate is poorly constrained; previous estimates using parameterized net surface heat fluxes 30 and simulated passive tracer experiments in a high-resolution regional model of the Ross Sea 20 differ by ~0.9 Sv, a discrepancy on the same order of magnitude as the estimated transport of TNB HSSW northward to the shelf break. More tightly constrained estimates of HSSW production rates in TNB are needed to better understand its variability and contribution to DSW exported from the western Ross Sea.

In this study, we present estimates of HSSW production rates made using moored high-spatial-and-temporal-resolution salinity measurements collected during austral winter 2017 within the TNB polynya. The mooring was deployed from February 2017 through February 2018 at 74.97°S, 163.96°E, approximately 7 km east of the Nansen Ice Shelf in a region of TNB that best exemplifies the response of the polynya to the regional katabatic regime, dominated by drainage through Reeves Glacier 31 (Fig. 1). The number of sensors and the mooring’s relatively shallow location on a local bathymetric high in 390 m water depth allowed for an unusually high density of measurements across the full water column in a region of TNB central to polynya growth, ice formation, and HSSW production. Moored instrumentation utilized in this study include 7 temperature-salinity sensors at depths ranging from 47 m to 360 m and an Acoustic Wave and Current Profiler (AWAC) with Acoustic Surface Tracking (AST) capabilities deployed at 37 m facing upward at the sea surface (Methods). The primary goal of this paper is to utilize data from a uniquely positioned and densely instrumented mooring to calculate HSSW production rates in TNB from continuous, in-situ observations. We use these estimates to explore several aspects of HSSW production, including the conversion of HSSW into Ice Shelf Water (ISW) and its variability in relation to the katabatic wind regime, as well as to ground-truth a method for estimating production rates from parameterized net surface heat fluxes in order to examine potential interannual variability in the context of broader Ross Sea salinity trends.

Results
HSSW production rates from moored salinity timeseries
Production of HSSW begins in early July, once turbulent mixing induced by katabatic winds breaks down stratification and allows brine rejection at the surface to salinize subsurface waters 24. We define this break down of stratification and the start of the HSSW production season to begin once the water column at the mooring site is mixed, i.e., potential density at the deepest instrument (360 m) is within 0.03 kg m−3 of that at the shallowest sensor 32. HSSW production events were systematically identified (Methods) where increases in salinity resulted in potential densities at the 47 m instrument of or exceeding 1028 kg m−3, the defining density threshold of TNB HSSW 12,20 (Fig. 2). Events were discarded if salinity increases appeared to be associated with processes other than in-situ ice production, determined by manually checking that each event was part of an expected chain of events associated with the opening of the polynya: first, an increase in wind speeds measured by Automatic Weather Station (AWS) Manuela to excess of 25 m s−1, marking the start of a katabatic wind event (Fig. 3a), followed by AST returns showing a transition from hard reflectors (>~70 dB; thick sea ice) at the sea surface to soft reflectors (<~60 dB; air bubbles, sea spray, and frazil ice) scattered above and below the surface (Fig. 3b), indicating the movement of sea ice offshore and an opening of the polynya, and finally, an average current velocity in the offshore direction (Fig. 3d) (Methods). In most all events, average current velocities measured by the upward-facing AWAC were uniformly eastward in the direction of the winds and showed little variability in speed, with standard deviations of order 0.01 m s−1. However, some events were discarded because of strong shoreward currents or abrupt changes in current direction that were clearly decoupled from the westerly katabatic winds and possibly associated with eddy activity previously observed in TNB 33,34. HSSW may have still been produced during these discarded events, but the brine rejection signal was obscured and thus the events were unsuitable for our analysis.

We identified 27 HSSW production events over the course of austral winter of 2017, with production beginning in July and continuing through October. The integrated increase in salinity across all 7 instruments at the start of each event was used to calculate rates of brine rejection (Methods). We note that the relative changes in salinity at each depth tracked one another and indicated that brine rejected at the surface was mixed to depth, as in Fig. 3c. The resulting brine rejection rates, also referred to as salt fluxes, at the mooring site were extrapolated across the full area of the polynya, derived from a daily 3.125 km-resolution sea ice concentration (SIC) product, to estimate HSSW production rates (Methods). The average production rate across the 27 events is 3.76 Sv, with a 95% bootstrapping confidence interval of [2.89, 4.75] (Methods; Supplementary Table 1). Conversion to an annual average yields 0.43 Sv [0.34, 0.55] (Methods).

The event-averaged ice production rate calculated from the measured salt fluxes is 67 cm day−1 (Methods, Supplementary Table 1), a value consistent with estimates of ice production rates in TNB from observations collected on a field campaign that occurred in May 2017 35,36, several months into our mooring deployment.

Sub-seasonal variability in HSSW production tied to the katabatic wind regime
The major components of the HSSW production rate calculation, polynya area and salt flux, are shown in Fig. 4b, and the resulting HSSW production rates are shown duplicated in Fig. 4c–e, colored by the average strength, average duration, and frequency of katabatic wind events, respectively, in the week preceding each production event. The same katabatic wind characteristics calculated across the 2 and 3 weeks prior to each production event exhibit similar patterns and are not shown here. Katabatic wind events were identified as periods of time during which westerly (from 225–315°) winds measured at AWS Manuela exceeded 25 m s−1 for more than 1 hour 24. Katabatic wind event frequency in units of week−1 was calculated as a cumulative count of wind events over the 1-week period. Variability in the calculation of HSSW production (Fig. 4c–e) is clearly driven by that of its polynya area component (Fig. 4b; Eq. 3), with pronounced peaks in polynya area, and therefore HSSW production rates, occurring on August 08 and September 18. These peaks appear associated with high frequencies (Fig. 4e), strengths (Fig. 4d), and durations (Fig. 4c) of preceding katabatic winds events. This is consistent with previous mooring-based studies that have shown interannual variability in the salinity of HSSW at depth 12,24 and polynya area 37 in TNB to be linked to katabatic wind event frequency and duration. An additional mechanic may be in effect, however: it has been previously suggested that the thickness, and therefore ease-of-advection, of ice at the periphery of a polynya can modulate the response of its growth to winds 31,38,39. We speculate that this is why the peak in polynya area and HSSW production on August 08 corresponds to anomalously frequent, but neither especially strong nor long-lasting, katabatic wind events in the week prior. Frequent clearing of sea ice would have resulted in a prevalence of newly formed, and more easily advected, thin ice, allowing for greater polynya area expansion in response to ensuing katabatic winds of even relatively mild strength and duration. Likewise, the final, late-season peak on October 30 may have resulted despite relatively low katabatic wind event strengths, durations, and frequencies in the week prior because months of polynya openings acted to diminish the overall presence of thick ice. This mechanic would also explain why relatively high strengths and durations of katabatic wind events preceding the production event on July 18 did not result in a peak in polynya area and HSSW production; the low frequency of katabatic wind events may have allowed pack ice to consolidate in the week prior, lowering the responsiveness of polynya growth to the wind regime.

Conversion of HSSW into TISW
Most of the annual-average ∼0.4 Sv of HSSW that we estimate to be formed in TNB are likely transported northward towards the continental slope 20,21,27,40, with some unknown portion of the remainder converted into Ice Shelf Water (ISW). ISW is a water mass that forms when HSSW is circulated beneath an ice shelf, and due to the depth-dependence of freezing point, melts the basal ice 41. Mixing of HSSW with the resulting meltwater produces ISW that either sinks along the continental shelf to directly contribute to the exported DSW 21,42 or rises along the front of the ice shelf as a buoyant plume 43,44. In TNB, HSSW is cooled and diluted through interactions with Nansen Ice Shelf and Drygalski Ice Tongue, forming a distinct water mass known as TNB ISW (TISW) 12,23,35,45, defined by temperatures below surface freezing point (~−1.95°C) 16 and potential density below that of the defining threshold of HSSW, 1028 kg m−3 12. TISW serves as a cold end-member for DSW exported off of Cape Adare and while its volumetric contribution is thought to be small, it may contribute significantly to the sinking of DSW off the continental shelf due to the thermobaric effect 21. We can attempt a simple budget analysis to constrain its volume rate of production: Gordon et al. 21 estimated that an annual average of ~0.2 Sv of HSSW exit the western Ross Sea, based on hydrographic surveys collected during the Anslope-2 program in February-April 2004, which showed 0.8 Sv of benthic-layer water transported past Cape Adare, with approximately 25% (~0.2 Sv) consisting of HSSW with salinity >34.70. If approximately 0.3–0.5 Sv of HSSW are formed in TNB, based on the 95% confidence interval calculated for the average of our mooring-based estimates, and approximately 0.2 Sv exit the western Ross Sea 21, then assuming minimal transport south of Drygalski Ice Tongue 20, we can speculate that up to O(0.1 Sv) may be converted to TISW. This residual represents an upper bound on TISW, as it includes all possible pathways for HSSW other than direct transport northward to the continental shelf, which could include conversion to a lower salinity HSSW through mixing with surrounding water masses in and out of TNB, or even non-negligible transport southward. There is also considerable uncertainty in this budget analysis that arises from the assumption that the export of HSSW past Cape Adare, estimated based on data from 2004 21, is constant at 0.2 Sv. As we explore in a later section, HSSW production rates in TNB may vary on the order of 0.1 Sv year to year. An alternate calculation of TISW production rate from satellite-based estimates of basal meltwater flux from Nansen Ice Shelf and Drygalski Ice Tongue suggests TISW to be an order of magnitudes smaller: total basal meltwater flux is reported to be between 3.5 and 8.7 Gt year−1 46,47 in the recent decade, which, assuming a meltwater density of 1000 kg m−3, is approximately equivalent to 0.0001–0.0002 Sv. Further assumption that TISW volume is composed of approximately 0.3% glacial meltwater yields an TISW production rate O(0.01 Sv) (Methods).

Revisiting prior estimates of HSSW production rate
Previously reported estimates of HSSW production rate in TNB range from 0.28 Sv 20 to 1.2 Sv 30, a discrepancy of ∼0.9 Sv and source of error that is on par with the annual-average ∼0.8 Sv of DSW reported exiting the Western Ross Sea past Cape Adare 21. The rate of 0.28 Sv was reported by Jendersie et al. 20, based on simulated passive tracer experiments in a high-resolution coupled ocean-ice shelf model of the Ross Sea. It falls only slightly lower than the 95% confidence interval of our 2017 estimate (Table 1) and sources of uncertainty, such as the use of Special Sensor Microwave/Imager (SSM/I)-derived surface heat and freshwater fluxes in lieu of explicit ice production, would likely produce confidence intervals that overlap significantly with our own. The latter study, Fusco et al. 30, estimated production rates from surface heat fluxes parameterizations calculated using reanalysis over the period of 1990-2006. This calculation, first published by Van Woert et al. 48, assumes a balance between net surface heat loss from TNB and latent heat gain from the production of sea ice, allowing production rates of HSSW to be inferred from the resulting estimates of sea ice production rates. A constant polynya area of 1300 km2, based on satellite imagery from 1979 17, is assumed. The resulting average of 1.2 Sv is significantly higher than the our average value of ~0.4 Sv, as well as that of Jendersie et al. 20. Interannual variability in HSSW production may contribute substantially to this discrepancy with our 2017 value: a calculation of HSSW production rate anomalies following the methods described in Fusco et al. 30 and using European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5) from 1990–2021 suggests the average difference between the 1990–2006 period and 2017 to be ∼0.5 Sv. Uncertainties inherent to the parameterization of heat fluxes and other key components of the calculation likely also contribute, however. As in-situ wind data from AWS Manuela did not become consistently available until 2012, surface heat fluxes in Fusco et al. 30 were parameterized using wind speeds from ERA 40-Year (ERA40), which, like many reanalysis products, underestimates wind speed magnitudes and dynamics in Antarctica 49–55. To account for this, Fusco et al. 30 applied a heuristic correction to their calculation of HSSW production rates based on a comparison of heat flux-based estimates calculated using available AWS wind speeds against those using reanalysis wind speeds in a single year 50. Also notable is the sensitivity of the calculation of turbulent heat fluxes to the choice of heat transfer coefficients, for which a scarcity of in-situ data has limited the development and validation of in polar environments 56. Fusco et al. 30 follows the heat flux parameterizations of Budillon et al. 56, using coefficient values of CE = CH = 1.75 × 10−3 derived from eddy correlation measurements of heat fluxes made from masts mounted onto thick sea ice cover in the Arctic 57. However, the suitability of a range of coefficients, from values closer to those typically used in open-ocean settings (e.g. 1.1 × 10−3 58) to as high as 2.2 × 10−3 36, can also be argued (Methods). Lastly, our examination of sub-seasonal variability in HSSW production rates showed a strong dependence on polynya area; the application by Fusco et al. 30 of the same polynya area across all 16 years of the study period would not capture year-to-year variability in average polynya size, which satellite observations show to be on the order of hundreds of square kilometers for the TNB Polynya 31,37.

Interannual variability in HSSW production from parameterized heat fluxes
Despite the uncertainties associated with the use of parametrized heat fluxes to estimate HSSW production rates, it remains a necessary approach in a region wherein in-situ data collection is sparse. We use this approach to estimate HSSW production rates across 2012-2021, allowing us to place our mooring-based estimate from austral winter 2017 into a broader context. We make several changes to the calculation implemented by Fusco et al. 30, discussed in the previous section and further detailed in Methods, which include: (1) Use of in-situ wind speed measurements from AWS Manuela in place of those from reanalysis, (2) calculation of average polynya areas specific to each year of the study using an Advanced Microwave Scanning Radiometer 2 (AMSR-2) SIC product ground-truthed against visible satellite imagery, (3) calculation of net surface heat fluxes only across the months of active HSSW formation (July through October 24) rather than the entire year, and (4) use of low and high heat transfer coefficients of CE = CH = 1.1 × 10−3 and 2.2 × 10−3, respectively, to bound uncertainty due to the choice of coefficient. We find that this modified calculation yields annual-average HSSW production rate values for 2017 bounded by 0.38 Sv (using the lower transfer coefficient) and 0.74 Sv (higher coefficient) in austral winter 2017, overlapping with the confidence interval of our mooring-based rate and suggesting that this approach may be used to estimate HSSW production rate with some accuracy. Coincidentally, these values are the same as the averages of values calculated across the full 2012-2021 period, which we report in Table 1. Regardless of the absolute values of the estimated production rates, however, we believe this method to be a useful tool in assessing potential relative changes in production rate over time.

HSSW production rates calculated from 2012–2021 are shown in Fig. 5, which indicates that our mooring-based estimate in 2017 may be part of a broader trend of increasing production rates in the latter half of the decade. The primary components of this calculation are net surface heat flux out of TNB and polynya area (Eqs. 12, 13). As with the sub-seasonal mooring-based estimates, variability is largely driven by polynya area, with the increase in production rates from 2015 onward clearly reflecting an increase in year-to-year average polynya area (Supplemental Figure 1). Net surface heat flux actually decreases during this period due to a decreasing sensible heat component, suggesting that increases in ice production rates did not contribute to the apparent increase in HSSW production rates. Based on the connection we found between polynya area and katabatic wind strength, duration, and frequency on a sub-seasonal scale, we might expect the same mechanics to explain the increase in polynya area, and therefore HSSW production rates, from 2015 onward. However, while a peak in average wintertime katabatic wind speeds and total hours of katabatic winds in 2017 is reflected in a slightly higher average production rate in the same year, there is no clear interannual trend in katabatic wind conditions concurrent with the increase in polynya area. Instead, we speculate that the increasing polynya area in TNB was enabled by the same process that Silvano et al. 13 found to facilitate the recovery of western Ross Sea HSSW salinity: a decrease in sea ice import from the Amundsen Sea to the Ross Sea beginning in 2014 that resulted in lower sea ice concentrations across the Ross Sea continental shelf, allowing for greater sea ice divergence and open-water areas (and therefore, in-situ sea ice production). These same conditions may have made it easier for the TNB Polynya to expand to increasingly larger average areas, despite little interannual change in the katabatic wind regime.

Implications
Variability in the properties and production rate of AABW impacts the strength of MOC, and thus the capacity of the ocean to sequester atmospheric heat and carbon and replenish abyssal oxygen 9,59,60. Though the recently observed recovery of the salinity, density, and thickness of AABW downstream of the western Ross Sea was driven by conditions favoring increased sea ice production across the broader Ross Sea continental shelf rather than local processes within TNB 13, this does not preclude the latter from contributing to AABW variability. As TNB HSSW is the densest component of DSW exported from the Ross Sea, substantial changes in its rate of production could feasibly alter the proportion of HSSW within, and thereby the density of, downstream AABW. Our estimates of annual-average HSSW production rates show variability of O(0.1) Sv sub-seasonally as well as interannually. On both timescales, this variability is driven by changes in polynya area of O(100) km2. Across austral winter 2017, we found changes in polynya area to be tied to katabatic wind speeds, frequency, and duration, but speculate it to be additionally modulated by the thickness of offshore ice. This same influence of offshore ice (or relative lack thereof) is possibly why the multi-year increase in polynya area, and thus estimated HSSW production rates, from 2015 to 2021 occurred despite no concurrent trends in the katabatic wind regime. Therefore, the dominance of polynya area variability in the HSSW production rate calculation suggests that processes affecting either the katabatic wind regime or sea ice divergence in the TNB region have the potential to greatly impact local production of HSSW. The proportionality of increases in HSSW production rates to increases in the open-water area of the polynya must be further explored, however. Our calculations necessarily assume a constant brine rejection rate across the entire open-water area of the polynya (Eqs. 3, 13), but a katabatic wind parcel will both lose momentum and gain heat as it travels across water, reducing the magnitude of ocean-to-atmosphere heat loss at longer fetches. Reduced HSSW production rates offshore would result in a lower sensitivity to changes in polynya area than our calculations imply. We expect our conservative definition of polynya area as the open-water area defined by SIC less than 30% (Methods) to limit the significance of spatial variability in our calculations, but further mooring-based measurements of brine rejection rates across the open-water area of TNB are needed to confirm this. Additional mooring deployments would also help to address a key question that arises from our work: how does the variability we have observed in TNB HSSW production rates manifests downstream in DSW and ultimately, AABW? Specifically, co-deployed moorings in TNB and the Drygalski Trough would allow for direct observation of the magnitude and timescale at which local variability in HSSW production propagates. In light of the changes recently observed in AABW, ongoing, in-situ monitoring of its critical source regions is necessary to constrain the future of MOC.

Methods
Dataset
Moored instrumentation utilized by this study consisted of 7 SeaBird Electronics conductivity and temperature (SBE37-SM/SMP MicroCAT) sensors at depths of 47, 95, 119, 144, 168, 219, and 360 m and an upward-facing 600 kHz Nortek Acoustic Wave and Current (AWAC) profiler at 37 m depth, collecting both current velocity profiles in the upper ~35 m of the water column as well as Acoustic Surface Tracking (AST) measurements. The 47 and 95 m MicroCATs were pumped and sampled at a rate of once every 2 min while the remaining MicroCATs were unpumped and sampled once per minute. Temperature and salinity measurements were validated against a shipboard conductivity-temperature-depth (CTD) cast performed upon deployment of the mooring. Temperature and salinity at 47 m and 95 m were additionally validated against a CTD cast performed upon recovery of the mooring; deeper MicroCats had ceased sampling in the weeks prior. The AWAC profiler head has four beams, three at an angle that collect velocity profiles and a fourth vertical beam in the center, on which Acoustic Surface Tracking (AST) relies. AST ensonifies the surface of the ocean and records the distance from the instrument head that corresponds to the strongest decibel return. It is often used to calculate wave statistics and under certain conditions may be used to measure the thickness of ice pack 61, but here we use it to more generally assess the presence of ice at the surface. The AST footprint at the surface depends on its depth of deployment, with a 40 m depth corresponding to a surface footprint 1.2 m in diameter 62.

Measurements of air temperature, humidity, wind speed and direction, and atmospheric pressure collected by Automatic Weather Station (AWS) Manuela (74.97°S, 163.93°E) are made available by the University of Wisconsin-Madison AWS Program. Data were downloaded at 10-minute resolution for the years of 2012-2021, with 2012 being the first full year of data available. Additionally, shortwave and longwave radiation measurements collected at the nearby AWS Rita (74.72°S, 164.03°E) in 2009 were obtained for the purpose of ground truthing the parameterizations of shortwave and longwave heat fluxes.

Single-level European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis 5 (ERA5) parameters across the years of 2012 through 2021 were obtained from the Climate Data Store at a 6-hour timestep and 0.25° × 0.25° spatial grid. ERA5 proceeded ERA-interim in 2017, and features higher spatial and temporal resolution, among other improvements 63. Variables selected were 2-meter air temperature, 2-meter dew point, sea surface temperature, land-sea-mask, mean sea level pressure, and total cloud cover. Data were subset to an area representative of Terra Nova Bay (TNB), a box bounded by 75.3°S, 74.66°S, 163.5°E, and 167.0°E. Any non-ocean data points within this geographic subset were masked by removing data where land-sea-mask had a value greater than 0.5.

A daily 3.125 km-resolution sea ice concentration (SIC) product is made available by the University of Bremen, generated from Advanced Microwave Scanning Radiometer 2 (AMSR-2) imagery using the Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) algorithm. The ASI algorithm exploits the difference in horizontally and vertically polarized brightness temperatures at high frequencies (86.5 GHz) between open water and sea ice 64 to assign concentration percentages ranging from 0–100 for each pixel. SIC maps were obtained for the year of 2012-2021. Visible imagery collected by the National Aeronautics and Space Administration (NASA) Aqua, Terra, and Suomi satellites were obtained across the year 2017 from NASA Worldview for the purpose of ground-truthing SIC concentration maps.

Calculation of polynya area
Polynya area was calculated as the sum of SIC pixels representing open water multiplied by the area of each pixel (3.125^2 km^2). The value of SIC that delineates the open water and thin ice of the polynya from surrounding sea ice cover is called a threshold value and is heuristically 31 assigned values ranging from 15% 65,66 to upwards of 70% 67–70, depending on the product and its spatial resolution. To determine a suitable threshold value for the ASI SIC product used in this study, we compared visual satellite imagery of the open polynya in March and April 2017 with corresponding SIC scenes from the same days. The Suomi, Aqua, and Terra satellites pass over TNB at approximately 04:00, 05:00 and 20:00. Because the polynya is known to vary in size on scales as short as hours 71,72, only days in which the polynya area remained the same across all three flyover periods were used to ground truth the SIC product, which represents a daily-averaged view. Two distinct regions of the open polynya are apparent in the visible imagery: a dark blue open-water area marked by grease or shuga ice streaks aligned with the offshore katabatic winds and an apparent thin sea ice area bordered by a ridge of thicker accreted ice (Supplemental Fig. 2). The open-water area defined by wind streaks is almost certainly actively forming new ice, and previous studies have used these streaks as a visual check on the extent of the TNB polynya 37,68. It is unclear what stage of ice formation the thin ice area represents, as it may be actively forming new ice with HSSW production rates comparable to those of the open-water area, or it may be a conglomerated mass of ice such as pancake ice, where air-sea interaction and therefore in-situ ice and HSSW formation would be reduced. These regions correspond to SIC threshold values of roughly 30% and 60%, respectively. We select a threshold of 30%, which is consistent with the wind-streak definition of prior studies and close to the 20% threshold used by a recent study using the same SIC product in TNB 12.

The University of Bremen SIC product uses an outdated land mask that does not account for the recent calving of the Nansen Ice Shelf in April of 2016. To correct for this, SIC maps in 2017 were overlaid on the visual satellite imagery showing the present boundary of the Nansen Ice Shelf and open-water pixels were manually added in to fill the gap wherever SIC was less than 30%. The average number of pixels added across July-October 2017 was 25. This average of 25 pixels was added to calculations of polynya area in all years beginning in 2016.

Calculation of HSSW production rate from in-situ salinity measurements
High Salinity Shelf Water (HSSW) production rates were calculated from individual production events, which were identified from the moored timeseries as follows:
1. First, periods of time were flagged during which the potential density timeseries, calculated from salinity and temperature at the 47 m instrument, exceeded the defining threshold of HSSW (1028 kg m−3) 12.
2. These time periods were divided into distinct HSSW production events, defined to start/end when potential density at the instrument first increased above/decreased below 1028 kg m−3, with the interval between start and end being at least 1 hour.
3. To filter out increases in salinity due to processes other than in-situ brine rejection (e.g., tidal or eddy-related advection), events were manually checked for association with active katabatic winds, as determined by wind speeds at AWS Manuela, an opening of the polynya as determined by scattered AST returns at the surface, and currents in the direction of the wind in the upper 35 m as measured by the AWAC.

Katabatic winds here are defined as nominal wind speeds at AWS Manuela greater than 25 m s−1 24 and coming from a direction of 225–315°, as in Yoon et al. 12. The defining magnitude of katabatic winds is somewhat subjective, with some studies defining katabatic winds less strictly, using lower magnitude threshold values (e.g., 17 m s−1 35) or wind speeds referenced to 10 m rather than nominal wind speeds. However, all of the identified HSSW production rate events either directly followed or were concurrent with katabatic winds as defined by wind speeds greater than 25 m s−1, thus no new were discarded as a result of this criterion.

The above steps to identify and filter HSSW production events yield 27 distinct events occurring from early July through October, each of which is used to calculate a HSSW production rate. First, the mass of salt in units of g m−2 at the mooring site is calculated as
m_s(t) = ∫_0^H ρS dz (1)

H is the depth to which salinity changes can be attributed to in-situ brine rejection 73, which at the wintertime-mixed mooring site is designated as the depth of the deepest sensor, 360 m. The potential density of seawater (ρ) in kg m−3 and practical salinity, S, assigned units of g kg−1, are calculated using the Gibbs Seawater Toolbox 74.

The rate of brine rejection in units of g m−2 s−1 at the mooring site is then calculated as
dm_s/dt = (m_s(t_1) - m_s(t_0)) / Δt (2)

across an interval of time defined by an increase in salinity due to brine rejection that begins at time t_0 and ends at time t_1 (e.g. gray-shaded region in Fig. 3c). Alternatively, dm_s/dt is taken to be the slope of the least-squares linear fit of the data between t_0 and t_1. We opt for the latter, as it is less sensitive to high-frequency variation in the rate of brine rejection as well as to the somewhat subjective choice of t_0 and t_1. For two separate events, density remained elevated above 1028 kg m−3 through the following day, suggesting a steady-state balance between brine rejection and advection offshore. An additional production rate was calculated for the following day using the initial brine rejection rate with the second-day polynya area.

To estimate the rate of brine rejection across the entire polynya, P_s, in units of kg s−1, Eq. 2 is multiplied by the area of the polynya, A_p, in units of m2.
P_s = (dm_s/dt) * A_p (3)

Finally, the rate of transformation of ambient water into HSSW, or the production of HSSW, is given by
P_HSSW = (P_s / (ρ * (S_HSSW - S_LSSW))) * 10−6 (4)

in units of Sv. Prior studies 30,48,50 have assigned fixed, historical values to the density of HSSW (ρ_HSSW), salinity of HSSW (S_HSSW), and the salinity of the ambient water, or low salinity shelf water (S_LSSW) 29,47,49 converted into HSSW. The value of S_HSSW - S_LSSW can have a large impact on the final value of P_HSSW while being relatively subjective to varying salinity definitions of the two water masses. This is avoided in our event-wise calculations, as our in-situ salinity measurements allow us to define ρ_HSSW and S_HSSW as the density and salinity of the water at the end of the HSSW production event and S_LSSW as the salinity at the start of the event.

Values are converted to an annual average by multiplication of the ratio of cumulative days of katabatic winds from July through October, 42.12 days, to the total days in a year, 365 days. This is based on an assumption, supported by measured wind speeds, AST returns, and salinity, that whenever katabatic winds blow during the winter HSSW production season, the polynya will open and the ocean will lose heat and form new ice, producing HSSW.

Ice production rate calculation
Ice production rates (P_i) in m s−1 are calculated from measured brine rejection rates (Eq. 2) as:
P_i = (dm_s/dt) * (ρ_i * s_i)−1 (5)

where s_i is the salinity of frazil ice in g kg−1, approximated as 0.31 multiplied by the salinity of seawater 75, and ρ_i is the density of ice (0.95 × 103 kg m−3).

ISW meltwater fraction estimate
Given that Ice Shelf Water (ISW) is the product of mixing between HSSW and glacial meltwater, the fraction of meltwater can be calculated as follows:
Meltwater Fraction_temperature = (T_HSSW - T_ISW) / (T_HSSW - T_meltwater) (6)
Meltwater Fraction_salinity = (S_HSSW - S_ISW) / (S_HSSW - S_meltwater) (7)

With assigned values of 34.85, -1.91, 34.73, and -2.1 for S_HSSW, T_HSSW, S_ISW, and T_ISW, respectively 12 and 0 and -90.75 for S_meltwater and T_meltwater, respectively 76, Eq. 5 yields 2.1 permille and Eq. 6 yields 3.4 permille. An average of the two values suggests that the meltwater fraction of ISW is ~3 permille, or 0.003.

Calculation of surface heat fluxes
Total surface heat flux, Q_net, is calculated as Q_net = Q_s + Q_b + Q_H + Q_E, with fluxes out of the ocean defined as negative by convention. Each component of the net surface heat flux is defined below.

Incoming shortwave radiation. Incoming solar radiation is estimated as
Q_s = (1 - α) * C_c * T_r * S_0 * cosη (8a)

where α is the albedo of the water surface, 0.08 56,77, for conditions of no ice cover, C_c is a cloud cover correction, T_r is the transmittance ratio of a clear sky atmosphere, S_0 is the solar constant, 1353 W m−2, and cosη is the cosine of the zenith angle of the Sun. The cloud cover correction is calculated as
C_c = α_1 + α_2 * C^α_3 (8b)

where α_1 = 1, α_2 = 0.6, α_3 = 3 and C is total cloud cover from ERA5 reanalysis 78. Shine 79 introduced a Zillman-type 80 model for the transmittance ratio of a clear sky atmosphere modified for polar regions,
T_r = (β_1 * cosη + β_2) / (cosη + β_3) * exp(-T_d * (β_4 + β_5))

where β_1 = 1, β_2 = 1, β_3 = 10−5, β_4 = 0.046 and T_d is the water vapor pressure in Pascals calculated from ERA5 2-meter dew point temperature, T_d, in Kelvin 81:
e_T = 611 * 10^(7.5 * (T_d - 273.16) / (T_d - 35.86)) (8d)

The cosine of the zenith angle of the sun is calculated following Marcus et al. 52:
cosη = sinφ * sinδ + cosφ * cosδ * cosh (8e)

Values of cosη < 0 (indicating the sun is below the horizon) are set to 0. Latitude, φ, is 75°S for TNB. The solar declination angle (δ) is given by
δ = 23.44° * cos(172 - d) (8f)

where d is the day of year. The solar hour (h) is
h = (12 - t_s) * 15 (8g)

where t_s is local solar time, which is a corrected local time given by
t_s = local time + TC/60 (8h)

TC is a correction factor:
TC = 4 * (longitude - LSTM) + EoT (8i)

In TNB, the longitude is 165°E and the local standard time meridian (LSTM) is 195°E. The Equation of Time (EoT) is an empirical equation that corrects for the eccentricity of the Earth’s orbit and the Earth’s axial tilt 82.
EoT = 9.87 * sin(2B) - 7.53 * cos(B) - 1.5 * sin(B) (8j)

where B = 360/365 * (d - 81).

Net longwave radiation. Net longwave radiation flux is calculated as
Q_b = 4 * ε * σ * T_A^3 * (T_A - SST) + ε * σ * T_A^4 * (0.39 - 0.05 * sqrt(e_T)) * (1 - χ * C^2) (9)

Here, ε is open ocean emissivity, σ is the Stephan-Boltzman constant (5.67 × 10−8 W m−2 K−4), T_A is ERA5 2-meter air temperature in Kelvin, SST is ERA5 sea surface temperature in Kelvin, and χ accounts for changes in cloud type with latitude, ranging from 0.5 at the equator and 1 at the poles. We select a value of 0.82 for Terra Nova Bay based on Table 9 in Budyko 83 for the latitude of 75°S. Shortwave and longwave radiation as calculated from Eqs. 5 and 6 for the year 2009 show good agreement with concurrent in-situ measurements by AWS Rita (Supplemental Fig. 3).

Turbulent heat fluxes. Sensible heat flux is given by
Q_H = ρ_a * c_P * C_H * |V_a| * (T_A - SST) (10)

where ρ_a is the density of dry air, c_P is the specific heat of dry air (1004 J kg−1 Huschke 84), C_H is the transfer coefficient for sensible heat, discussed below, |V_a| is the magnitude of the winds measured by AWS Manuela. Wind speeds are divided by 1.5, a correction factor based on measurements along the katabatic wind pathway in TNB 85 that aims to account for the attenuation that occurs across the distance between the weather station and the open water of the polynya.

Latent heat flux is given by
Q_E = ρ_a * L_E * C_E * |V_a| * (q_a - q_s) (11a)

where L_E is the latent heat of vaporization (2.5 × 106 J kg−1; Haltiner & Martin 86), C_E is the transfer coefficient for latent heat, discussed below, which is considered to be roughly the same as C_H 58,87,88, and q_a and q_s are the water vapor mixing ratios of the air at 10 m and at the sea surface, respectively:
q_a = 0.622 * e_a / (p_s - 0.378 * e_a) (11b)
q_s = 0.622 * e_s / (p_s - 0.378 * e_s) (11c)

Here, e_a is vapor pressure and e_s is saturation vapor pressure in Pascals. The surface pressure, p_s, is taken to be the ERA5 mean sea level pressure in Pascals.
e = 611 * 10^(a * (T_d - 273.16) / (T_d - b)) (11d)

where (a, b) = (9.5, 7.66) if an ice cover exists and (a, b) = (7.5, 35.86) if it does not 81. No ice cover is assumed for the open-water area of the polynya. For the calculation of e_s, SST is substituted for T_d.

The choice of heat transfer coefficients is non-trivial, with a difference of 1 × 10−3 resulting in differences in net heat flux of hundreds of W m−2. Though functions of wind speed and atmospheric stability 89, the coefficients are commonly assigned constant values, a practice supported by early measurements in open-ocean settings that showed little variation across wind speeds 58,90,91. Maximum wind speeds assessed in these studies were 18–25 m s−1, with C_E ≈ C_H ≈ 1.1 × 10−3 in unstable atmospheric conditions. One study, utilizing a large set of flux measurements across wind speeds ranging from 5–20 m s−1, found C_E (with C_H approximately equivalent to and calculated as a function thereof) to increase steadily from 1.08 × 10−3 at wind speeds of 5 m s−1 to 1.2 × 10−3 at 18 m s−1, suggesting a slight dependence on wind speed 89. Model runs of Coupled Ocean–Atmosphere Response Experiment (COARE) Algorithm 89,92, a widely-used bulk flux algorithm that computes the coefficients as functions of atmospheric stability, extrapolated this result to ∼1.3 × 10−3 at 25 m s−1 89. However, little observational data exist in the kinds of extreme conditions occurring in TNB, where katabatic winds regularly reach strengths upwards of 40 m s−1. Field and lab studies in hurricane conditions show the drag coefficient, C_D, to level off at wind speeds between 30–40 m s−1, likely due to the effects of wave breaking and the generation of a sea foam layer at the ocean surface 93,94. Laboratory experiments show the enthalpy flux coefficient, related to C_E, to level off and even decrease at speeds greater than 20 m s−1 95. Based on the observed and inferred behavior of the heat transfer coefficients at winds ranging from 5-40 m s−1, it would not be unreasonable to apply a transfer coefficient close to the typical open-ocean value of 1.1 × 10−3 in a polynya setting. This would be consistent with coefficients obtained using an exponential, fetch-dependent transfer coefficient formula developed from flux measurements near arctic leads 96, which yields ~1.1 × 10−3 at longer fetches on the order of hundreds of meters (such as across a polynya) 97. However, a recently published study of shipboard bulk-flux and integral-heat-flux measurements in katabatic wind conditions in TNB suggests sea spray to greatly enhance sensible heat fluxes, resulting in a transfer coefficient of ∼2.2 × 10−3. This higher value is closer to the value used by Fusco et al. 30 and others 56 of 1.75 × 10−3, which was originally derived from eddy correlation measurements of heat fluxes made from masts mounted onto thick sea ice cover in the Arctic 57, as well as with the value of 2.0 × 10−3 used in a widely cited polynya modeling paper 98. We assign C_E = C_H, opting to use low and high values of 1.1 × 10−3 and 2.2 × 10−3, respectively, as a means of bounding our heat flux estimates.

Calculation of HSSW production rate from net heat fluxes
The rate of ice production, P_i, is defined 98 as
P_i = Q_net / (L_f * ρ_i) (12)

where L_f is the latent heat of fusion (3.34 × 105 J kg−1) and ρ_i is the density of ice (0.95 × 103 kg m−3). We calculate P_i from Q_net parameterized across the years 2012-2021 (Equations 8-11) during the months of active HSSW production, July through October. The rate of brine rejection across the polynya (corresponding to Eq. 3 in the mooring-based calculation) is calculated as
P_s = ρ_i * P_i * A_p * (S_i - S_w) (13)

where A_p is calculated using the ASI SIC product from July-October of each year, S_w is water salinity (set to the same value as S_LSSW), and S_i is the salinity of frazil ice (0.31 * S_w). P_HSSW is then calculated as in Eq. 4, where values of ρ_HSSW, S_HSSW and S_LSSW are set to 1028 g kg−1, 34.81, and 34.79, respectively. The latter two values are the average S_LSSW and S_HSSW values across the 27 production events observed in the 2017 mooring timeseries. We make the assumption that the magnitude of (S_HSSW - S_LSSW), 0.03, in Eq. 4 stays constant across the 10 years over which we use parameterized heat fluxes to infer interannual HSSW production rate variability, but acknowledge that this introduces further uncertainty to these estimates; changes to (S_HSSW - S_LSSW) on the order of 0.01 result in changes to P_HSSW on the order of 0.01 Sv. Production rates are converted to an annual average via multiplication by the number of months over which net heat fluxes were calculated, 4, and the total months in a year, 12.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This work focus on High Salinity Shelf Water producfion in Terra Nova Bay using moored, near-surface salinity (47 m), current velocifies profiles (35 m to surface), and acousfic surface tracking (AST) measurements collected during austral winter 2017.\nAlthough the main topic is very interesfing, the paper does not provide important advances of significance adequate to the high-quality standards required for a publicafion in Nature Communicafions.\nMoreover, the paper has also some unclear aspects (somefimes caused by incomplete reasoning), lacks of care in presentafion and takes advantage of very short fime series (one year).\nAfter a careful reading of the paper, my impression is that of a potenfially valuable methodological work that could be befter presented in a different, more technical journal after the necessary improvement.\nFor these reasons I suggest rejecfing the paper and encourage resubmission to a different journal.", "reviewer_2": "The paper esfimates the HSSW producfion and its 10-year variability from the atmospheric measurements. The esfimafion allows us to infer the oceanic condifions from relafively inexpensive atmospheric measurements, which are more available than direct ocean measurements. This paper evaluate the wind derived esfimate with the near-surface ocean mooring measurements, which is not an easy measurement to make, in 2017. The method verified in 2017 is extrapolated to derive the HSSW producfion rate in 2012-2021. I am generally in favour of publicafion with a few minor issues.\nAs the goal is to infer the HSSW producfion rate from the wind speed data, it is important to describe the correlafion of the HSSW producfion rate from the mooring data to the wind speed. The correlafion coefficient is presented; however, the uncertanfies in the correlafion coefficient is not well described. Although the p-value is quoted, the authors do not clarify and provide number of degree of freedom (dof) and its jusfificafion. I wonder if the dof considers the autocorrelafion of the wind and mooring fimeseries. It is also informafive to present the percentage of the segments that yields the significant correlafion.\nThe second point is in the HSSW pathways. The authors esfimate 0.6 sv of the HSSW producfion and 0.4 sv exit the Ross Sea. The 0.2 sv loss is compared to the TISW formafion from the satellite-based esfimates of basal meltwater flux (line 147-151). I am not sure if this is relevant: 1) it’s unclear to me that the basal meltwater ends at the surface from the Nansen Ice Shelf. With a relafively small basal melfing, does the meltwater have enough buoyancy to rise to the surface and able to mix with the HSSW? Does the HSSW driven convecfion reach the depth where the basal meltwater penetrates?; and 2) it seems more appropriate to consider the mixing of HSSW with the surface runoff from the Nansen Ice Shelf, which has been documented e.g.\nBell, R., Chu, W., Kingslake, J. et al. Antarcfic ice shelf potenfially stabilized by export of meltwater in surface river. Nature 544, 344–348 (2017). hftps://doi.org/10.1038/nature22048\n“Supplemental Methods” seems redundant as the content is described in “Method Secfion” of the paper. There are no equafion labels in “Supplemental Methods”, yet the document refers to Equafion 19 (line 100 of 396077_0_supp_354074_rj1gdp.docx).\nLine 32: sea-ice formafion\nLine 131: mechanism?\nLine 134 – 135: it is unclear how the frequency of the katabafic wind event is calculated. Any spectrograms to confirms 6.8 per week and 5.4 per week cycles?\nLine 423 and 425: missing a space between 25 and 40, and m, should be “25 m s-1” and “40 m s-1”.", "reviewer_3": "Review of High Salinity Shelf Water producfion in Terra Nova Bay, Ross Sea from high-resolufion near-surface salinity observafions\nBy Una Kim Miller, Christopher J. Zappa, Arnold L. Gordon, Seung Tae Yoon, Craig Stevens, Won Sang Lee\nReviewer: Keith Nicholls\nThe authors use a shallow mooring in Terra Nova Bay (TNB) to calculate total brine rejecfion in the TNB polynya through the austral winter of 2017 for all katabafic wind events. Events were idenfified using data from an automafic weather stafion located on the nearby Nansen Ice Shelf and data from the moored sensors themselves. Satellite data were used to calculate the area of the polynya. They then then develop a parameterizafion from a combinafion of weather stafion wind data, reanalysis data, and satellite derived polynya area data to calculate a high salinity shelf water (HSSW) producfion fime series for TNB over an extended period in order to place the results from the mooring in a temporal context.\nThe authors show that the variability in wind speed prior to the HSSW producfion events plays a significant role in HSSW producfion, and that the role changes in different parts of the winter. They also find their parameterized result for 2017 compares well with the result from the mooring data, lending credibility to the mulfiyear fime series. Their derived fime series finds an increasing TNB HSSW producfion rate since 2015.\nThe ambifion of improving our ability to obtain accurate esfimates of HSSW producfion without the need for moored instruments is, I believe, a worthy one. In the main, my comments cover some areas where the choice of methodology needs more evidence in its support.\nThe text is nicely wriften, with few typographical errors that I spofted. There is an occasional curious word choice. I have made some minor textual suggesfions in the aftached Word document, in tracked changes. A general language point is that the word following a colon should not start with a capital lefter, and for some unknown (to me) reason, ice features do not carry a definite arficle, so “the Nansen Ice Shelf” for example should be simply “Nansen Ice Shelf”.\nI do have some more significant concerns that I would like to see addressed before this manuscript was published. I outline those below, and they are also noted in brief in the Word document.\nThe study has two quite separate parts: analysis of the moored sensor data, and the calculafion of the HSSW producfion rate fime series using AWS, remotely sensed, and model data. The principal connecfion is that the result of the mooring analysis is used to provide a degree of validafion for the parameterizafion. It seems a shame that one of the key results from the mooring analysis (for me at least), that the HSSW producfivity of a katabafic event has a significant dependence on the preceding wind variability, is not used in the parameterizafion. It might be that for some reason this is too difficult to implement, but it seems like a missed opportunity. It would be useful to menfion that this is not being aftempted here, and why that is the case. Perhaps the authors plan to implement such a scheme in the future.\nThe salinity and temperature data in Figure 2 (and also the example event in Figure 3) raise some important quesfions. Had the authors not indicated that the sensors were carefully calibrated, they would have appeared to be a bit suspect. The temperature of the majority of what is classed as HSSW is, in fact, below the surface freezing point and therefore formally ice shelf water (ISW). I also wonder if the authors can check the locafion of the freezing point line in Figure 2 – it looks a shade warm to me, possibly by about 0.02 degrees C? That would help bring more of the high salinity water close to the freezing point, or at it. Even then, much of the high salinity water is below even the in situ freezing point (ie taking into account the pressure at 47 m depth), which is odd and needs further comment. Having in situ supercooled water (if that’s what it is), masquerading as HSSW, is worth a menfion.\nWhen HSSW is formed at the surface, it will be at the surface freezing point, and so can only be cooled further by mixing with ISW, or by coming into contact with deep ice. I assume the authors are assuming that the former case applies here, which implies quite a lot of recycling of ISW within the polynya. If much of that ISW is recycled into HSSW the calculafion of the fracfion of HSSW converted to ISW (line 139 onwards) is likely to be less relevant. I think the manuscript would be improved if the authors discussed this issue. The fact that the majority of the HSSW detected is formally ISW needs a menfion at least. It would be useful to include the T-S from a CTD profile, to give the data in Figure 2 some context. I recognize that they would be a summer snapshot only, but it would help the reader get their T-S bearings.\nI have a difficulty with the calculafion of the HSSW producfion. First, equafion (1) is expressed as an integral over the upper 47 m (zero to H) of the water column, except that the authors only have data from 47-m depth. It would be less confusing to express it as H*S*rho. Eqn (1) gives the mass of salt between the surface and 47 m, and (2) shows how that salt mass is increasing during the event. Eqn (3) then integrates across the area of the polynya, and (4) converts this salt gain to an HSSW producfion rate. What causes me concern is that I don’t understand the basis for the assumpfion that the increase in salinity happens only in the water column between the instrument and the surface. Is there any evidence to suggest that it doesn’t descend to the sea floor? This is what I would have expected, but it’s difficult to judge without knowledge of the strafificafion. Overall, my suspicion is that the answers from this calculafion might well form a good indicator of HSSW producfion and its relafive intensity during the events, but not an absolute value.\nThe authors use the assumpfion that the HSSW is produced only during katabafic events, and the definifion of those events is highly prescripfive. I haven’t tried to calculate the fracfion of the period between July and October when katabafic events were acfive, but unless that is a very large fracfion, some sort of argument is needed to convince the reader that HSSW being produced between katabafic events is insignificant. For example, if events occupied only 10% of the fime then the polynya during non-katabafic condifions would need to be a very weak produced of HSSW to be making an insignificant contribufion to the total.\nISW meltwater fracfion esfimate in line 373-380. This calculafion needs to be re-wriften. T_meltwater and S_meltwater have been assigned the same values as T_ISW and S_ISW. Presumably a typo. S_meltwater should be zero, and T_meltwater should be roughly T_HSSW-L/c, where L is the latent heat of fusion of ice, and c is the specific heat capacity of water. This is about 86 degrees C. This then gives about the correct answers.\nLines 167+ This doesn’t seem right. They are each expressed as an average for an enfire year. The authors’ value is also for an enfire year and should not be reduced in this way. The adjustment would have been correct had they been expressed in terms of producfion rate per month. That would place the observed value in the middle of the published values.\nLine 196 Can the authors check this. I’m not sure the correcfion can have been mulfiplicative – the units wouldn’t work out. (Or am I missing something?)\nLines 348-350 I think this is the first menfion of ither sensors, so they come as a surprise at this stage. Might be worth giving a fuller descripfion of the mooring earlier in the manuscript, and just saying that only the uppermost instruments will be used here (and perhaps say why).\nThere are several other comments in the aftached marked-up version for the authors to review. I have not aftempted to review the calculafion of the surface heat fluxes – I leave that to a reviewer who knows more about that then me (I’ve also run out of fime – apologies)."}, "author_response": "We sincerely thank anonymous Reviewers #1, #2 and Dr. Nicholls for their feedback. Please find our responses (yellow text) to individual comments (black text) below, and note that any line numbers are in reference to the “No/Simple Markup” version of the tracked changes manuscript document. We would like to make the reviewers aware that as part of Nature Communications software availability policy, the code showing the calculation of HSSW production rates from both moored salinity and from parameterized heat fluxes is now available at: https://github.com/unamiller/TNB-HSSW-Production\nFirst, however, we would like to explain significant changes made to Figure 5, which shows the 2012-2021 time series of HSSW production rates estimated using parametrized net heat fluxes.\nDuring the process of revising the manuscript, a colleague of ours found an error in our heat flux calculation. We miscalculated the saturation vapor pressure ( ) used to calculate the water vapor mixing ratio at the sea surface ( ) and ultimately, latent heat flux ( ). Specifically, should be calculated using the “surface temperature”, and we mistaken𝑒ly 𝑠 assigned this as ERA5 air temperature at 2 meters height ( ) instead of s𝑞e 𝑠 a surface temperature. 𝑄𝐸 𝑒𝑠\n(Eq. 10d)\n𝑇𝐴\n𝑎(𝑇𝐴−273.16)/(𝑇𝐴−𝑏)\n𝑒𝑠 = 611×10 (Eq. 10c)\n0.622𝑒𝑠\n𝑞𝑠 = 𝑝𝑠−0.378𝑒𝑠 (Eq. 10a)\nThis corr𝑄e𝐸ct=ion𝜌 c𝑎h𝐿a𝐸n𝐶g𝐸e|s𝑉̅ t|h(𝑞e 𝐴ca−lc𝑞u𝑠la)ted latent heat fluxes, and therefore net heat fluxes, by about -100 W/m2 and increases the resulting HSSW production rates. Please see the “Original” (top right) and “Fixed latent heat flux calculation” (top left) versions of Figure 5 below.\nBecause the resulting HSSW production rates in the “Fixed latent heat flux calculation” version extend to values significantly outside the error bars of our mooring-based calculation, we checked each aspect of the equations used to calculate production rate from net heat fluxes. These equations are:\n(Eq. 11)\n𝑃𝑖 = 𝑄𝑛𝑒𝑡/𝐿𝑓𝜌𝑖 (Eq. 12)\n𝑃𝑠 = 𝜌𝑖𝑃𝑖𝐴𝑃(𝑠𝑤 −𝑠𝑖) (Eq. 4)\n𝑃𝑠 −6\nThe 𝑃𝐻𝑆𝑆𝑊 = 𝜌𝐻𝑆)𝑆 𝑊te(r𝑆m𝐻𝑆 𝑆in𝑊 E−q𝑆𝐿u𝑆a𝑆t𝑊io)n× 41 h0as a large impact on the final value of HSSW production rate\n( , while being relatively subjective. In the original text (Van Woert, 1999), this term refers to the salin(it𝑆y 𝐻 c 𝑆 h 𝑆𝑊 an−ge𝑆 a 𝐿 s 𝑆 s 𝑆 o 𝑊 ciated with the conversion of “Low Salinity Shelf Water” to HSSW, where is\na𝑃s 𝐻 si 𝑆 g 𝑆 n 𝑊 e)d 34.8 and is assigned 34.5. In our calculation of from moored salinity, these values\n𝑆𝐻𝑆𝑆𝑊\n𝑆𝐿𝑆𝑆𝑊 𝑃𝐻𝑆𝑆𝑊\n1\nare much less subjective, as they set to the starting and ending salinity of each production event. In our calculation of using heat fluxes, these values were set to the average of the aforenoted starting and ending salinities, 34.79 and 34.82, respectively. We find that performing linear regression (see below figure) o𝑃f 𝐻 t 𝑆 h 𝑆 e 𝑊 salinity time series over the same four months used to calculate net heat flux (July through October) and using the beginning and ending values of the regression (34.74 and 34.8) in Equation 4 lowers estimated using net heat fluxes closer to the range of the mooring-based estimate. We acknowledge that the magnitude of heat-flux-based estimates of is highly sensitive to both the choice 𝑃o 𝐻 f 𝑆 h 𝑆 e 𝑊 at transfer coefficient (as discussed in Lines 193 - 198) and to the value of\n) (now discussed in Lines 398-403), but maintain that it provide𝑃s 𝐻 v 𝑆 a 𝑆 l 𝑊 uable insight on relative changes to production rates across the decade. This\n(𝑆𝐻𝑆𝑆𝑊−𝑆𝐿𝑆𝑆𝑊\npoint is now emphasized in Lines 222-224.\nAn unrelated, but equally impactful change, is our\nchanging of the integration depth in Equation 1 from to\nin response to Dr. Nicholl’s questioning of the appropriate\nintegration depth (see comment and response on Pag4e7 9 𝑚 of this\n3do0c 𝑚um ent). This lowers the HSSW production rate from an\naverage of to , resulting in the “Final” (bottom\nright) version of Figure 5 shown above.\nReviewer #1: 0.6 𝑆𝑣 0.4 𝑆𝑣\nThis work focus on High Salinity Shelf Water production in Terra Nova Bay using moored, near-surface\nsalinity (47 m), current velocities profiles (35 m to surface), and acoustic surface tracking (AST)\nmeasurements collected during austral winter 2017.\nAlthough the main topic is very interesting, the paper does not provide important advances of\nsignificance adequate to the high-quality standards required for a publication in Nature\nCommunications.\nMoreover, the paper has also some unclear aspects (sometimes caused by incomplete reasoning), lacks\nof care in presentation and takes advantage of very short time series (one year).\nAfter a careful reading of the paper, my impression is that of a potentially valuable methodological work\nthat could be better presented in a different, more technical journal after the necessary improvement.\nFor these reasons I suggest rejecting the paper and encourage resubmission to a different journal.\nWe understand your concern regarding clarity in our manuscript, as the other two reviewers\nraised issues with the presentation of several major components of our analysis. These included the\nstatistical relationship between High Salinity Shelf Water (HSSW) production rates and katabatic wind\ncharacteristics, the definition and calculation of HSSW production, the nature of Ice Shelf Water (ISW)\nformation and how it factors into our budget analysis for ISW production, as well as numerous other\nsmaller, but important, points. We have made extensive revisions to the manuscript and discussed these\nissues at length in the present document, and hope you will find the revised manuscript clearer and\nmore compelling.\nWe would like to respectfully dispute the notion that the use of a one-year timeseries in this\nregion is a detriment to our study. Observational data are sparse in Antarctica, especially data collected\nin the near-surface ocean during winter, when harsh conditions prohibit ship-based activity and the\nkeels of passing icebergs pose a real threat to upper-ocean moorings. As we assert in Lines 78-80 of the\nrevised manuscript, this mooring provides an unpresented look at active brine rejection (resulting in\nHSSW formation) in the upper ocean in a polynya across an entire winter season. This uniquely allows us\nto estimate production rates from in-situ salt fluxes that are directly tied to the opening of the polynya\nand subsequent ice formation. Previous estimates of HSSW production rate have relied ice production\n2\nrates calculated from heat fluxes parameterized using reanalysis, an approach with many inherent\nuncertainties (discussed in Lines 172 – 207). We argue that our ability to estimate HSSW production\nusing in-situ salt fluxes provides a valuable contribution to the Ross Sea research community’s\nunderstanding of sub-seasonal variability of HSSW production in Terra Nova Bay and how it is tied to the\nkatabatic wind regime, as well a valuable means for ground truthing the more accessible heat-flux\nmethodology for calculating production rates.\nReviewer #2: \nThe paper estimates the HSSW production and its 10-year variability from the atmospheric\nmeasurements. The estimation allows us to infer the oceanic conditions from relatively inexpensive\natmospheric measurements, which are more available than direct ocean measurements. This paper\nevaluates the wind derived estimate with the near-surface ocean mooring measurements, which is not\nan easy measurement to make, in 2017. The method verified in 2017 is extrapolated to derive the HSSW\nproduction rate in 2012-2021. I am generally in favour of publication with a few minor issues.\nAs the goal is to infer the HSSW production rate from the wind speed data, it is important to describe\nthe correlation of the HSSW production rate from the mooring data to the wind speed. The correlation\ncoefficient is presented; however, the uncertainties in the correlation coefficient is not well described.\nAlthough the p-value is quoted, the authors do not clarify and provide number of degrees of freedom\n(dof) and its justification. I wonder if the dof considers the autocorrelation of the wind and mooring\ntimeseries. It is also informative to present the percentage of the segments that yields the significant\ncorrelation.\nThank you very much for bringing the deficiencies in our statistical analysis to our attention.\nUpon meeting with a group of statistical consultants available through Columbia University, we have\nalso come to understand that our use of sliding-windowed data is not suitable for regression and that\nthe significance of our calculated regression coefficients is affected by autocorrelations in the time\nseries (as you have pointed out). Furthermore, both regression and correlation coefficients assume\nnormality in the data, which is not always true of our data, especially given the small (n=10) windowed\nsample sizes, and correlation assumes a lack of outliers, which is not always true for the windowed wind\ndata.\nWe have decided to forgo statistical analysis on sliding-windows of HSSW production rate and\nkatabatic wind, strength, and frequency in the one, two, and three weeks preceding each HSSW\nproduction event. Instead, we now simply plot the HSSW production rate time series colored by/labeled\nwith katabatic wind duration, strength, and frequency in the one week preceding each event. This is\nshown in the updated Figure 4 and discussed in Lines 108-135. We believe that forgoing the calculation\nof regression or correlation coefficients simplifies the analysis and its visual representation, and more\nclearly demonstrating the roles that different aspects of the katabatic wind regime play in modulating\nHSSW production. We hope you agree with this change and that our analysis is clearer for it.\nThe second point is in the HSSW pathways. The authors estimate 0.6 sv of the HSSW production and 0.4\nsv exit the Ross Sea. The 0.2 sv loss is compared to the TISW formation from the satellite-based\nestimates of basal meltwater flux (line 147-151). I am not sure if this is relevant: 1) it’s unclear to me\nthat the basal meltwater ends at the surface from the Nansen Ice Shelf. With a relatively small basal\nmelting, does the meltwater have enough buoyancy to rise to the surface and able to mix with the\nHSSW? Does the HSSW driven convection reach the depth where the basal meltwater penetrates?; and\n3\n2) it seems more appropriate to consider the mixing of HSSW with the surface runoff from the Nansen\nIce Shelf, which has been documented e.g.\nBell, R., Chu, W., Kingslake, J. et al. Antarctic ice shelf potentially stabilized by export of meltwater in\nsurface river. Nature 544, 344–348 (2017). https://doi.org/10.1038/nature22048\n1) Rusciano et al. (2013; https://doi.org/10.1016/j.csr.2013.04.002) gives the definition of ISW as the\nwater mass “formed by the interaction of salty HSSW with the base of the glacial ice”. ISW is\nformed when HSSW at the depth of the grounding line (~200m) is circulated beneath the ice\nshelf, rather than through basal meltwater rising and then mixing with HSSW near the surface.\nWhen HSSW comes into contact with the basal ice, its high salinity lowers the melting point of\nthe ice, causing it to melt. This is the source of meltwater that directly contributes to ISW\nformation, therefore we must consider the basal ice melt rate. Though we do not show data\nfrom deeper instruments (as they are used in the analysis of a second, forthcoming manuscript),\nyou’re correct that HSSW convection reaches the full depth of the water column (by late June,\nonce katabatic winds have fully broken down the lingering summertime stratification). We have\nadded text to Lines 137-148 to improve our description of ISW formation and make the\njustification for using basal melt rates clearer. We realize our prior description of ISW formation\nprocesses was insufficient and thank you for bringing this to our attention.\n2) Because ISW is defined to form directly from the interaction of HSSW with basal ice (which we\nhope is now made clearer in the manuscript), surface meltwater that is episodically discharged\nduring summer months, when HSSW production is not produced, would not be a direct\nconsideration in our budget. We do think summertime meltwater discharges of this magnitude\ncould have an interesting and potentially large impact on water column stratification and\nAntarctic Surface Water formation at our mooring site, however, and will consider it in the\nsecond manuscript currently underway with this mooring dataset.\n“Supplemental Methods” seems redundant as the content is described in “Method Section” of the\npaper. There are no equation labels in “Supplemental Methods”, yet the document refers to Equation\n19 (line 100 of 396077_0_supp_354074_rj1gdp.docx).\nThe supplemental methods section has more details (coefficient values, equations) on the heat\nflux calculations, but we agree that it is not necessary to reproduce these calculations in the\nsupplement. We have added the additional details to the original text within the manuscript and made\nsure all equations are numbered. The supplemental methods section has been deleted.\nLine 32: sea-ice formation\nThank you for catching this. A hyphen has been added.\nLine 131: mechanism?\nAgreed, though this sentence has since been deleted.\nLine 134 – 135: it is unclear how the frequency of the katabatic wind event is calculated. Any\nspectrograms to confirms 6.8 per week and 5.4 per week cycles?\nWe realize that was not made clear. Frequency was simply calculated as the number of events\nper week. When calculated over the two- and three-week periods, the number of events was\nnormalized to 1 week, resulting in non-integer values. For example, if we count 16 events over a three-\nweek period, we report it as 16/3 = 5.33 events . This is now clarified in Line 118. Please note\n−1\n𝑤𝑒𝑒𝑘 4\nthat in simplifying this analysis in response to your valid concerns over our use of regression, we are\nnow only considering the one-week period.\nLine 423 and 425: missing a space between 25 and 40, and m, should be “25 m s-1” and “40 m s-1”.\nThank you. This has been fixed.\nReviewer #3 (Keith Nicholls):\nThe authors use a shallow mooring in Terra Nova Bay (TNB) to calculate total brine rejection in the TNB\npolynya through the austral winter of 2017 for all katabatic wind events. Events were identified using\ndata from an automatic weather station located on the nearby Nansen Ice Shelf and data from the\nmoored sensors themselves. Satellite data were used to calculate the area of the polynya. They then\nthen develop a parameterization from a combination of weather station wind data, reanalysis data, and\nsatellite derived polynya area data to calculate a high salinity shelf water (HSSW) production time series\nfor TNB over an extended period in order to place the results from the mooring in a temporal context.\nThe authors show that the variability in wind speed prior to the HSSW production events plays a\nsignificant role in HSSW production, and that the role changes in different parts of the winter. They also\nfind their parameterized result for 2017 compares well with the result from the mooring data, lending\ncredibility to the multiyear time series. Their derived time series finds an increasing TNB HSSW\nproduction rate since 2015.\nThe ambition of improving our ability to obtain accurate estimates of HSSW production without the\nneed for moored instruments is, I believe, a worthy one. In the main, my comments cover some areas\nwhere the choice of methodology needs more evidence in its support.\nThe text is nicely written, with few typographical errors that I spotted. There is an occasional curious\nword choice. I have made some minor textual suggestions in the attached Word document, in tracked\nchanges. A general language point is that the word following a colon should not start with a capital\nletter, and for some unknown (to me) reason, ice features do not carry a definite article, so “the Nansen\nIce Shelf” for example should be simply “Nansen Ice Shelf”.\nI do have some more significant concerns that I would like to see addressed before this manuscript was\npublished. I outline those below, and they are also noted in brief in the Word document.\nThe study has two quite separate parts: analysis of the moored sensor data, and the calculation of the\nHSSW production rate time series using AWS, remotely sensed, and model data. The principal\nconnection is that the result of the mooring analysis is used to provide a degree of validation for the\nparameterization. It seems a shame that one of the key results from the mooring analysis (for me at\nleast), that the HSSW productivity of a katabatic event has a significant dependence on the preceding\nwind variability, is not used in the parameterization. It might be that for some reason this is too difficult\nto implement, but it seems like a missed opportunity. It would be useful to mention that this is not being\nattempted here, and why that is the case. Perhaps the authors plan to implement such a scheme in the\nfuture.\nWe agree that there are two somewhat distinct parts of the manuscript and thank you for\nraising the point that inclusion of katabatic wind characteristics in the heat flux parameterization is an\nimportant way to tie the two together. We argue that dependence on preceding katabatic wind\nconditions is actually already implicitly built into the existing parameterization, however, through its\ndependence on polynya area (Equation 12). As we discuss in Lines 124-135, polynya area is highly\ndependent on katabatic wind event frequency and duration. Prior studies have highlighted the close\nconnection between polynya area variability and the katabatic wind regime (e.g., Ciappa et al., 2012;\nhttps://doi.org/10.1016/j.rse.2011.12.017), with Aulicino et al. (2018; https://doi.org/10.3390/rs10030366)\n5\nspecifically finding interannual variability in polynya area to be dependent on katabatic wind frequency\nand duration. We have added text highlighting the connection between our initial analysis of\ndependence of HSSW production rate/polynya area on katabatic wind characteristics and the\nparameterization of HSSW production rate from heat flux to Lines 232-235. We believe you have raised\nan insightful point and hope that the manuscript now sufficiently addresses it.\nThe salinity and temperature data in Figure 2 (and also the example event in Figure 3) raise some\nimportant questions. Had the authors not indicated that the sensors were carefully calibrated, they\nwould have appeared to be a bit suspect. The temperature of the majority of what is classed as HSSW is,\nin fact, below the surface freezing point and therefore formally ice shelf water (ISW). I also wonder if the\nauthors can check the location of the freezing point line in Figure 2 – it looks a shade warm to me,\npossibly by about 0.02 degrees C? That would help bring more of the high salinity water close to the\nfreezing point, or at it. Even then, much of the high salinity water is below even the in situ freezing point\n(ie taking into account the pressure at 47 m depth), which is odd and needs further comment. Having in\nsitu supercooled water (if that’s what it is), masquerading as HSSW, is worth a mention.\nIt is not unusual for HSSW observed in Terra Nova Bay to fall partly or entirely below the surface\nfreezing point, as observed in previously published TS diagrams from Terra Nova Bay:\n- Figure 2 in Orsi and Wiederwohl (2009;DOI: 10.1016/j.dsr2.2008.10.033),\n- Figure 6 in Yoon et al. (2020; DOI:10.5194/os-16-373-2020)\n- Figure 2 in Le Bel et al. (2021; DOI:10.1017/S0954102021000146)\n- Figure 7 in Stevens et al. (2017; https://doi.org/10.1017/aog.2017.4)\nOshima et al. (2022l https://doi.org//10.1126/sciadv.adc9174) also notes the presence of supercooled HSSW in\nthe Cape Darnley Polynya.\nThe defining temperature of HSSW is\noften described as “near freezing point”, but\nnot necessarily above it. However, we agree\nthat its apparent supercooling, especially\nrelative to in-situ freezing point, requires some\nfurther thought. We believe there are two\nmechanisms that result in supercooled HSSW.\nThe first is supercooling of the surface water via\nintense winds, which has been documented in\nboth Arctic (Skogset et al., 2009; DOI:\n10.3189/002214309788608840) and Antarctic\npolynyas (Thompson et al., 2020; DOI: 10.5194/tc-\n14-3329-2020). In the case of the latter, CTD casts\nshow most if not all of the water column to be supercooled relative to the surface freezing point, and\nsupercooled water relative to in situ freezing point is evident as deep as 20m (resulting in frazil ice\nformation). The second mechanism is by mixing with ISW, which in TNB will have an endmember\ntemperature around freezing point at ~250m, the grounding line depth of Nansen Ice Shelf. It is possible\nthat ISW at depth is entrained by the deepening mixed layer in autumn months or injected into the fully\n6\nhomogenized water column in winter months via circulation under Nansen Ice Shelf around ~250m, and\nis then mixed up to the depth of our instrument at 47m. This would help to explain the presence of\nwater that is supercooled not only relative to surface freezing point, but to in situ (47m) freezing point\nas well.\nISW is defined by its temperature below surface freezing point and by its mechanism of formation via\nthe mixing of HSSW with meltwater. Because ISW is formed from HSSW, they are distinguished in\ntemperature-salinity space by salinity (or equivalently, because of the cold temperatures, by density),\neven in the case where HSSW is also below surface freezing point. For example, Yoon et al. (2000)\ndistinguishes between ISW and HSSW using a threshold of density = . Therefore, we\nrespectfully disagree that the HSSW observed in our TS diagram is technically ISW3 because it is below\n1028 𝑘𝑔/𝑚\nthe surface freezing point.\nWe have double checked our calculation of the freezing point in Figure 2 and believe it to be calculated\ncorrectly. It is calculated using the Gibbs SeaWater software package as:\ngsw.t_freezing(absolute_salinity,0,0)\nWhere absolute salinity is converted from practical salinity using the function\ngsw.SA_from_SP(salinity,0,lon,lat), the depth is set to 0, and the saturation fraction is set to 0 (setting\nthe saturation fraction to 1 makes virtually no difference).\nWhen HSSW is formed at the surface, it will be at the surface freezing point, and so can only be cooled\nfurther by mixing with ISW, or by coming into contact with deep ice. I assume the authors are assuming\nthat the former case applies here, which implies quite a lot of recycling of ISW within the polynya. If\nmuch of that ISW is recycled into HSSW the calculation of the fraction of HSSW converted to ISW (line\n139 onwards) is likely to be less relevant. I think the manuscript would be improved if the authors\ndiscussed this issue. The fact that the majority of the HSSW detected is formally ISW needs a mention at\nleast. It would be useful to include the T-S from a CTD profile, to give the data in Figure 2 some context. I\nrecognize that they would be a summer snapshot only, but it would help the reader get their T-S\nbearings.\nAs discussed above, supercooling of HSSW relative to the surface freezing point is likely caused\nby katabatic wind-induced heat loss rather than mixing with ISW, and given that ISW and HSSW are\ndistinguished by a density threshold of 1028 kg/m3 rather than temperature (Yoon et al,. 2020), we\ndisagree that the majority of HSSW observed here is ISW/mixed with ISW. However, we acknowledge\nthat the HSSW below in situ freezing point could be indicative of mixing with ISW, and that you raise a\npertinent point about ISW recycling. Though, we do not believe that ISW recycling would affect our\nbudget calculation for two reasons: The first is that whether or not ISW is recycled does not change the\n(was previously but has since been amended; Please see Page 1 of this document for\ndetails) production rate that we obtain from the moored salinity time series nor the export rate\n~0.48 𝑆𝑣 0.6 𝑆𝑣\nmeasured by Gordon et al. (2009). That is, still need to enter Terra Nova Bay and need to\n0.4 𝑆𝑣\nexit. What would change is the source of water that is salinified and converted into HSSW. For example,\n0.48 𝑆𝑣 0.4 𝑆𝑣\nif we assume ISW is not recycled, then of modified Circumpolar Deep Water (mCDW, the\ndominant ambient water mass in Terra Nova Bay) is salinifed by brine rejection and converted to HSSW.\n0.48 𝑆𝑣\nIf we assume ISW is recycled, then we might expect something like of mCDW and of\nISW converted into HSSW. Please see our rough box model diagram below detailing this thought\n0.44 𝑆𝑣 0.04 𝑆𝑣\n7\nprocess. Secondly, instances where the temperature measured by the 47m instrument drop below in\nsitu freezing point are episodic and relatively brief (see below figure), so while they may indicate mixing\nwith ISW, these occurrences are sparse and therefore arguably insignificant to our present analysis,\nthough a point of interest for future study.\nThough we believe our TS diagram (Figure 2) to be fairly typical compared to others in TNB (as\ndiscussed on Page 6 of this document), we understand that context for our TS diagram (Figure 2) would\nbe helpful. A copy of Figure 2 is shown below overlain with red dots corresponding to the downcast TS\ndata from the CTD cast taken upon deployment of the mooring. This is to illustrate the TS space\noccupied by the TS diagram and is not how we would intend to show the data in a final figure. Because\nof the restricted TS space of the summertime snapshot relative to the whole year of moored data, we\nare not sure it provides useful context either as part of Figure 2 or as a separate figure. If you disagree,\nwe will be happy to work further on a version of Figure 2 that incorporates the CTD data, or\nalternatively, could consider incorporating TS data from deeper instruments on the mooring, though we\nprefer to leave that data for a later publication.\nintegral over the upper 47 m (zero to H) of the water column, except that the authors only have data\nfrom 47-m depth. It would be less confusing to express it as H*S*rho. Eqn (1) gives the mass of salt\nbetween the surface and 47 m, and (2) shows how that salt mass is increasing during the event. Eqn (3)\nthen integrates across the area of the polynya, and (4) converts this salt gain to an HSSW production\nrate. What causes me concern is that I don’t understand the basis for the assumption that the increase\nin salinity happens only in the water column between the instrument and the surface. Is there any\nevidence to suggest that it doesn’t descend to the sea floor? This is what I would have expected, but it’s\ndifficult to judge without knowledge of the stratification. Overall, my suspicion is that the answers from\nthis calculation might well form a good indicator of HSSW production and its relative intensity during the\nevents, but not an absolute value.\nYour concern with this value is well taken, as the lead author debated over the appropriate\ndepth when first working through this calculation. We thank you for raising the question of proper\nintegration depth, as it has caused us to reconsider our choice of .\nYou are correct that the brine descends past the ins4tr7u 𝑚ment and that we see salinity\nincreases across the full water column, including the deepest instrument at . However, the\n47 𝑚\ncorrect depth of integration should be the depth of the in-situ frazil ice formation layer (the upper tens\n360 𝑚\nof meters of the water column), not the entire depth of the water column. This is because we are\ninterested in the salt flux directly caused by in-situ ice formation. If we were to integrate across the\nsalinities from instruments at greater depths, the calculated salt flux would represent both the flux due\ndirectly to frazil ice formation/brine rejection at the surface, as well as the flux of brine downward as it\nsinks/is mixed to depth. Put another way, the flux of salt entering the system via in-situ ice production,\nplus the flux of salt traveling through the water column. To estimate brine rejection, we should only\nconsider the flux due to in-situ ice formation; integrating across all available depths would include the\nflux due to mixing/sinking and would therefore result in an overestimation of HSSW production rate. To\nconfirm this, we recomputed the salt flux term ( ) using the full set of moored salinity\nmeasurements integrated across depths of 47m - 360m. This resulted in an average HSSW production\nd𝑚/𝑑𝑡\nrate of , which is unrealistically high compared to the measured exported from the region by\nGordon et al. (2009) and estimates of production rate using net heat fluxes, both our own and that of\n3 𝑆𝑣 0.4 𝑆𝑣\nFusco et al. (2009) (~1 Sv).\nThat said, while the depth of the “frazil ice formation layer” is the appropriate depth of\nintegration, we no longer believe 47m is the correct depth. For frazil ice formation to occur, the water\nmust be below in-situ freezing point. CTD casts in Thompson et al. (2020; DOI: 10.5194/tc-14-3329-2020)\nshow the presence of supercooled water relative to in-situ freezing point down to about water\ndepth, and a latent heat anomaly indicative of frazil ice formation above this depth. In their calculation\n20 𝑚\nof TNB ice production rates from salinity anomalies, they set their depth of integration to values around\nbased on the depth of in-situ freezing. Assuming the salinity of water is well mixed and uniform\nfrom the sensor at 47m to the surface, calculation of in situ freezing point at our mooring site matches\n20 𝑚,\nup with the measured in-situ temperature when a depth of is used, meaning the bottom of the\n“frazil ice formation layer” is . Therefore, we have redone our calculations using an integration\n30 𝑚\ndepth of instead of . This changes the average production rate from to .\n30 𝑚\n30 𝑚 47 𝑚 ~0.6 𝑆𝑣 ~0.4 𝑆𝑣\nThe authors use the assumption that the HSSW is produced only during katabatic events, and the\n9\ndefinition of those events is highly prescriptive. I haven’t tried to calculate the fraction of the period\nbetween July and October when katabatic events were active, but unless that is a very large fraction,\nsome sort of argument is needed to convince the reader that HSSW being produced between katabatic\nevents is insignificant. For example, if events occupied only 10% of the time then the polynya during\nnon-katabatic conditions would need to be a very weak produced of HSSW to be making an insignificant\ncontribution to the total.\nWe apologize that we are not confident we understand your concern about HSSW being\nproduced in between katabatic wind events. Please let us know if the following does not address your\nconcern.\nWe do not assume that HSSW production only occurs during katabatic wind events, rather, we:\n1) First, identify periods of time during which the density timeseries measured by the instrument\nexceeds 1028 kg/m3, i.e. that HSSW is present\n2) Divide these time periods into distinct “HSSW production events”, defined to start when density\nat the instrument first increases above 1028 kg/m3 and defined to end when density falls below\n1028 kg/m3 for more than an hour.\n3) Filter out events that are not definitively associated with the opening of the polynya, ie.,\nproceed/are concurrent to katabatic winds, proceed a transition in the acoustic surface tracking\nfrom hard to soft reflectors at the surface, and are concurrent to eastward flowing surface\ncurrents\n4) Use the increase in salinity (e.g. Figure 3c, copied below) associated with the start of each of the\n“verified” events to estimate brine rejection rates, from which we estimate HSSW production\nrates.\nBecause we are capturing every instance in the density time series where density increases from below\n1028 kg/m3 to above it, there should not be any HSSW production in between defined events, except\nfor those few we discard because they do not meet the AST and current velocity criteria. We realize that\nour description of how HSSW production events were detected and formally defined is not very clear in\nthe manuscript, and have updated Lines 342-370 to better describe the methodology.\nISW meltwater fraction estimate in line 373-380. This calculation needs to be re-written. T_meltwater\nand S_meltwater have been assigned the same values as T_ISW and S_ISW. Presumably a typo.\nS_meltwater should be zero, and T_meltwater should be roughly T_HSSW-L/c, where L is the latent heat\nof fusion of ice, and c is the specific heat capacity of water. This is about 86 degrees C. This then gives\nabout the correct answers.\nThank you for catching this. Please see the corrected values, now on Line 413.\nauthors’ value is also for an entire year and should not be reduced in this way. The adjustment would\nhave been correct had they been expressed in terms of production rate per month. That would place\nthe observed value in the middle of the published values.\nThis is something the we had debated on, and we appreciate that you took the time to address it. We\nthink you are correct.\nWe have removed this adjustment and updated the text in which we compare our average value to\npreviously published values accordingly (Line 172 onward).\nLine 196 Can the authors check this. I’m not sure the correction can have been multiplicative – the units\nwouldn’t work out. (Or am I missing something?)\nThank you very much for catching this and bringing it to our attention - you are correct that the units\nmean the correction cannot be multiplicative. We have also checked our conversion of the reported\ninto Sv and realized that we made a large error. The value is not 173 Sv, but rather:\n3\n2 𝑚\n0.15∗10 𝑑𝑎𝑦\n3\n2 𝑚 1 𝑑𝑎𝑦 1 𝑆𝑣 −9\n0.15∗10 ∗ ∗ 6 3 −1 = 1.74∗10 𝑆𝑣\nFusco et al (2009) states that the co𝑑r𝑎re𝑦ctio8n6 4is0 “0a 𝑠ppli1e0d”, 𝑚and𝑠 no further detail is given on how. Given the\nunits we would assume the correction is additive, but it is such a small value that it seems that it would\nbe insignificant. Because we are unsure of how it is applied, we have removed mention of the correction\nfrom our manuscript.\nLines 348-350 I think this is the first mention of ither sensors, so they come as a surprise at this stage.\nMight be worth giving a fuller description of the mooring earlier in the manuscript, and just saying that\nonly the uppermost instruments will be used here (and perhaps say why).\nWe have added text to Lines 275-279 that mentions the full mooring and explains why only the\nshallowest sensors are used in the present manuscript. We have also added more detail on the ADCP in\nresponse to your in-document comments (Lines 281-287).\nThere are several other comments in the attached marked-up version for the authors to review. I have\nnot attempted to review the calculation of the surface heat fluxes – I leave that to a reviewer who\nknows more about that then me (I’ve also run out of time – apologies).\n11"}, {"round": 2, "reviewer_comments": {"reviewer_3": "Second review of “High Salinity Shelf Water producfion in Terra Nova Bay, Ross Sea from high-resolufion near-surface salinity observafions”, by Miller et al.\nMany thanks to the authors for their detailed response to my comments. I’ve learnt a lot from them, not least that in situ supercooling due to heat loss to the atmosphere can be detected at many tens of metres depth. We don’t (yet) have similar observafions in the Weddell Sea, and my educafion was clearly lacking. I also now see the need for the definifion of Ice Shelf Water to be modified to take that into account.\nI clearly got the wrong end of the sfick on one or two points, but having given the study some more thought, I think there remain two areas that I would like to understand properly. Although I might not be the brightest, I’m probably not the dimmest of the likely readership; if I don’t understand the arguments, then I imagine others might struggle too. My revised opinion is that, while the calculafion of ice producfion from the AWS and polynya area data seems fine to me, the calculafion of HSSW producfion from the mooring data needs to be revised before publicafion.\nI have returned a marked-up pdf. This covers some (very) minor suggesfions for the text, and some more significant quesfions and comments. On the whole, I found the manuscript to be very well and clearly wriften.\nMy two major comments are:\n1. My understanding is that the rate of increase in salinity at 47 m depth is calculated for the period of the increase in salinity resulfing from a katabafic wind event. This is then converted into an HSSW producfion rate for the enfire polynya area for that interval in fime. That figure typically comes out as a few tenths of a Sv. The 28 producfion rates, one for each of the 28 events, are then averaged to get a mean HSSW producfion rate. But this is the rate of producfion during the individual events. The authors’ view is that no HSSW is being produced between these events. I think, therefore, that the mean producfion rate for the enfire year should be that mean value, mulfiplied by the total fime interval of producfion (eg 28 x mean HSSW producfion period) divided by one year. Put another way, HSSW producfion, integrated across all the producfion events, needs to be divided by one year in order to get an annual producfion rate. The authors’ approach seems to suggest that the producfion rate during an individual event is sustained over the enfire year. However, the rate of producfion (in Sv) must be averaged over a fime interval, and it would normally be one year. One could divide the integrated producfion by the interval between July and October, and say that X Sv is being generated during that winter producfion period. Either that or I’m missing something...\nI recognise that this would dramafically reduce the calculated value, but see below.\n2. The authors responded to my quesfion about the depth over which the salinity increase needs to be integrated. They told me that they wished to determine the salinity increase due to producfion of frazil ice within the water column, and referred to the paper by Thompson et al. But note that Thompson et al were invesfigafing the interesfing process of frazil producfion in the upper few tens of metres of the water column; they were not looking at HSSW producfion per se. I have also always assumed that the vast majority of sea-ice producfion takes place at the sea surface, with brine rejecfion causing overturning. In the case of strong katabafics, wind-induced mixing is presumably the principal agent causing supercooled water to penetrate into the upper few tens of metres, assisted by a negafive buoyancy gradient.\nI sfill don’t understand how the authors’ approach can be correct for calculafing the total HSSW producfion during each producfion event using the salinity increase over the upper 30 m or so. Whether my assumpfions about where the majority of sea ice is formed are correct or not, the point is that the aim must be to calculate a salt budget, which must include the enfire water column not just the component due to frazil formafion in the upper few tens of metres. The authors seem to think that calculafing the salt budget over the enfire water column is in some way double-counfing, but I don’t think that’s correct. In the one-dimensional view enforced by having data from a single mooring, the change in verfically-integrated salt content must surely represent change due to sea-ice producfion, which is what is needed. As the authors point out in their response to this comment, this would give a value for HSSW producfion an order of magnitude greater than that observed. However, as pointed out above, this is for a short period only, namely, the durafion of the producfion event."}, "author_response": "We sincerely thank Dr. Nicholls for his continued time and effort in reviewing our manuscript. After considering his latest comments carefully, we agree that brine rejection should be computed from the integral of full water column salinity, rather than salinity only at the shallowest instrument, and that values should be converted to an annual average. Please find our response to Dr. Nicholls’ comments below, along with the following description of other notable changes made:\n• For clarity, parts of the final paragraph of the introductory text and the first paragraph of the “variability tied to katabatic winds” section were moved into a new section titled “HSSW production rates from moored salinity time series” (Lines 96-129)\n• Because it may be of interest to the Ross Sea community, we added an estimate of ice production rate based on the measured brine rejection rate from the mooring to Lines 126-129. Ice production rates for each HSSW production event are now given in Supplementary Table 1. Their calculation is described in the Methods section, Lines 431-435\n• In light of a recent study of shipboard bulk and integral heat flux measurements in TNB that describes a heat transfer coefficient of 2.2 (cid:1876) 10(cid:2879)(cid:2871) (Guest et al., 2022b; 10.1029/2021JD034904) we have expanded the coefficient range used to bound our heat flux calculations to include this higher coefficient. This is referenced in Line 222, discussed in Lines and 537-542, and reflected in an updated Figure 5.\n• Because the use of the 2.2 (cid:1876) 10(cid:2871) coefficient results in much larger heat fluxes, and therefore heat- flux-based HSSW rate estimates, we opt to return to our original assignment of (cid:1845) and (cid:3009)(cid:3020)(cid:3020)(cid:3024) (cid:1845) in Equation 4 to their respective average values observed across the 27 production events (cid:3013)(cid:3020)(cid:3020)(cid:3024) in 2017 in the moored salinity data (Lines 556 – 557), which brings the resulting production rates, both using the upper and lower bound heat flux coefficients, into a more reasonable range of our mooring-based estimate. Note these average values are slightly different from those reported in the original submission as we now utilize salinity across the full water column rather than just at the 47 (cid:1865) sensor. As Dr. Nicholls has rightly pointed out, the sensitivity of the heat- flux-based estimates of HSSW production rate to the choice of ((cid:1845) - (cid:1845) ) is a large source (cid:3009)(cid:3020)(cid:3020)(cid:3024) (cid:3013)(cid:3020)(cid:3020)(cid:3024) of uncertainty, which we now discuss in Lines 557 – 561.\n• In the first round of revisions, we removed the mention of Fusco et al. (2009)’s use of reanalysis wind speeds and associated application of a correction factor of 0.15×10(cid:2870) (cid:1865)(cid:2871) (cid:1856)(cid:1853)(cid:1877)(cid:2879)(cid:2869) because Dr. Nicholls pointed out that we had wrongly assumed this factor was multiplicative and the original source text does not describe how exactly this correction factor applied. We have added parts of this discussion back into the present manuscript (Lines 210-215) because the lack of katabatic wind dynamics in reanalysis is an important source of uncertainty to acknowledge, while being careful not the mischaracterize the correction factor.\n• Because we now use salinity across all 7 sensors instead of just the shallowest sensor, we updated the criteria for HSSW production events to include a formal metric for a mixed water column (lines 97-101). This resulted in the disqualification of one production event in June, bringing our count from 28 to 27 events."}, {"round": 3, "reviewer_comments": {"reviewer_3": "Second review of “High Salinity Shelf Water production in Terra Nova Bay, Ross Sea from high-resolution near-surface salinity observations”, by Miller et al.\nMany thanks to the authors for their detailed response to my comments. I’ve learnt a lot from them, not least that in situ supercooling due to heat loss to the atmosphere can be detected at many tens of metres depth. We don’t (yet) have similar observations in the Weddell Sea, and my education was clearly lacking. I also now see the need for the definition of Ice Shelf Water to be modified to take that into account.\nI clearly got the wrong end of the stick on one or two points, but having given the study some more thought, I think there remain two areas that I would like to understand properly. Although I might not be the brightest, I’m probably not the dimmest of the likely readership; if I don’t understand the arguments, then I imagine others might struggle too. My revised opinion is that, while the calculation of ice production from the AWS and polynya area data seems fine to me, the calculation of HSSW production from the mooring data needs to be revised before publication.\nI have returned a marked-up pdf. This covers some (very) minor suggestions for the text, and some more significant questions and comments. On the whole, I found the manuscript to be very well and clearly written.\nMy two major comments are:\n1. My understanding is that the rate of increase in salinity at 47 m depth is calculated for the period of the increase in salinity resulting from a katabatic wind event. This is then converted into an HSSW production rate for the entire polynya area for that interval in time. That figure typically comes out as a few tenths of a Sv. The 28 production rates, one for each of the 28 events, are then averaged to get a mean HSSW production rate. But this is the rate of production during the individual events. The authors’ view is that no HSSW is being produced between these events. I think, therefore, that the mean production rate for the entire year should be that mean value, multiplied by the total time interval of production (eg 28 x mean HSSW production period) divided by one year. Put another way, HSSW production, integrated across all the production events, needs to be divided by one year in order to get an annual production rate. The authors’ approach seems to suggest that the production rate during an individual event is sustained over the entire year. However, the rate of production (in Sv) must be averaged over a time interval, and it would normally be one year. One could divide the integrated production by the interval between July and October, and say that X Sv is being generated during that winter production period. Either that or I’m missing something...\nWe understand your point, as we had originally debated whether to convert our event-average estimate to an annual average by multiplying by the ratio of the number of months out of active HSSW production to the total number of months in a year (4/12). This was based on a similar rationale employed in Gordon et al. (2009), who used 6/12.\nA more refined conversion could be as you suggested: a ratio of the total time interval of our observed production events to a year. However, our criteria (Lines 105 – 116) resulted in our removal of a number of events that likely were representative of HSSW production but were obscured by advective anomalies (perhaps eddies). Therefore, the sum of time captured by our identified HSSW production events would be an underestimation of the total time of HSSW production. The alternative that we propose is the sum total of time across the months of active HSSW production, July-October, during which wind speeds at AWS Manuela exceed 25 (cid:1865) (cid:1871)(cid:2879)(cid:2869), our defining threshold for katabatic winds. This is 42.11 days, which results in a conversion factor of 42.11/365 = 0.115 (Lines 426-430). Our rationale is that during the HSSW production months of July-October, whenever katabatic winds blow, the polynya will open, the surface will freeze, new ice will form, and HSSW will be produced. An analysis of AST returns as a function of wind speeds during this period suggest the polynya to open whenever wind speeds exceed 25 m/s. Here, the standard deviation of AST pings is calculated over 10-minute bins, with higher standard deviations indicating the polynya is open (pings are highly scattered when the polynya is open due to reflection off of frazil ice, injected air bubbles, and sea spray, as discussed in Lines 108-111 of the manuscript and shown in Figure 4b) and low standard deviations indicating the polynya is closed.\nOne could argue for the conversion factor to be calculated based on a wind speed threshold of 20 (cid:1865) (cid:1871)(cid:2879)(cid:2869). This would be 68.56/365 = 0.188, resulting in an annual-average production rate of 0.69 Sv rather than the 0.43 Sv that we report. However, the above figure shows 25 (cid:1865) (cid:1871)(cid:2879)(cid:2869) to be more clear-cut wind speed threshold, with virtually all standard deviations of 10-minute AST bins exceeding 2 m. The 20 (cid:1865) (cid:1871)(cid:2879)(cid:2869) threshold would likely include periods of time when HSSW production is not or only partially occurring, such as when the polynya is first opening and as it is closing\nBecause we convert the mooring-based estimate into an annual average, we must do the same for the heat-flux-based estimates. This is done using the simple months-of-active-HSSW-formation ratio employed in Gordon et al. (2009) and described above, 4/12. (Lines 561 - 563).\nI recognise that this would dramatically reduce the calculated value, but see below.\n2. The authors responded to my question about the depth over which the salinity increase needs to be integrated. They told me that they wished to determine the salinity increase due to production of frazil ice within the water column, and referred to the paper by Thompson et al. But note that Thompson et al were investigating the interesting process of frazil production in the upper few tens of metres of the water column; they were not looking at HSSW production per se. I have also always assumed that the vast majority of sea-ice production takes place at the sea surface, with brine rejection causing overturning. In the case of strong katabatics, wind-induced mixing is presumably the principal agent causing supercooled water to penetrate into the upper few tens of metres, assisted by a negative buoyancy gradient.\nI still don’t understand how the authors’ approach can be correct for calculating the total HSSW production during each production event using the salinity increase over the upper 30 m or so. Whether my assumptions about where the majority of sea ice is formed are correct or not, the point is that the aim must be to calculate a salt budget, which must include the entire water column not just the component due to frazil formation in the upper few tens of metres. The authors seem to think that calculating the salt budget over the entire water column is in some way double-counting, but I don’t think that’s correct. In the one-dimensional view enforced by having data from a single mooring, the change in vertically-integrated salt content must surely represent change due to sea-ice production, which is what is needed. As the authors point out in their response to this comment, this would give a value for HSSW production an order of magnitude greater than that observed. However, as pointed out above, this is for a short period only, namely, the duration of the production event.\nWe have thought through this carefully and have come to agree that the salt content integrated across the full mooring line is necessary. We realize that our previous assertion that salinity at 30m (“frazil ice depth”) can be assumed identical at 47m relies on the assumption that the water column above 47m is well mixed. Well, full water column destratification is a requirement for HSSW production and if we assume the near surface is well mixed, then we must assume the whole water column is well mixed if HSSW is being produced. Therefore, rejected brine would be distributed throughout the water column and the full set of MicroCATs (which reach the seafloor) must therefore be used. We are grateful to you for continuing to question the depth of integration and feel our methods are now more rigorous and take fuller advantage of the mooring’s uniquely high density of salinity measurements in the upper ocean.\nYou are right that converting to an annual average brings the large HSSW production rates obtained from integrating across the full water column depth to a more typical range. In fact, the average value remains ~0.4 (cid:1845)(cid:1874), which is what we had reported in the last round of revisions."}, "author_response": "Our sincerest thanks to Dr. Nicholls for his time, effort, and interest in improving the quality of our manuscript.\nPlease find the reviewer comments in black and the authors’ responses in yellow, below.\nLine 31\nI suppose I'm a bit of a Wunsch-ist, and see the GTC driven by winds, and substantially modified by deep water formation. But bearing in mind Arnold G is on the author list, if he is happy with \"drives\" rather than \"contributes to\", for example, then I guess I should be too.\nWe appreciate that there are different ways to define GTC and its “driving” forces. We have changed our wording in the manuscript to:\n“… (AABW), a water mass that constitutes the bottom limb of meridional overturning circulation and directly impacts the heat, carbon, oxygen, and nutrient inventories of the deep ocean1–3.”\nLine 113\nCan we define this acronym here? Not used previously. I understand it's defined in Methods below.\nThe acronym is now defined.\nLine 268\nSimilar comment to earlier. A Wunsch-ist would probably claim that the strength is determined by global winds. But I'm not expert in these matters.\nA fair point – it would have perhaps been better for us to specify “…the strength of the bottom limb of the MOC”. We have changed this line to:\n“Variability in the properties and production rate of AABW directly impacts the capacity of the ocean to sequester atmospheric heat and carbon and replenish abyssal oxygen and nutrient inventories60,61.”\nLine 295\n\"key\"? Or is \"various\" what's meant? Perhaps \"a spread of locations\"?\nThe last few sentences of the implications section have been reworked and our call for additional moorings has been fleshed out. We now separately call for additional moorings to a) assess spatial variability in HSSW production rates caused by down-fetch heat loss within TNB and b) assess how variability in TNB HSSW production propagates downstream in the Drygalski Trough."}]

Decision letter