Dynamic ice–ocean pathways along the Transpolar Drift amplify the dispersal of Siberian matter

Domain: earth_science
Published: 2025-04-14
Editorial outcome: major_revision

Abstract
The Transpolar Drift (TPD) plays a crucial role in regulating Arctic climate and ecosystems by transporting freshwater and key substances, such as terrestrial nutrients and pollutants, from the Siberian Shelf across the Arctic Ocean to the North Atlantic. However, year-round observations of the TPD remain scarce, creating significant knowledge gaps regarding the influence of sea ice drift and ocean surface circulation on the transport pathways of Siberian freshwater and associated matter. Using geochemical provenance tracer data collected over a complete seasonal cycle, our study reveals substantial spatiotemporal variability in the dispersal pathways of Siberian matter along the TPD. This variability reflects dynamic shifts in contributions of individual Siberian rivers as they integrate into a large-scale current system, followed by their rapid and extensive redistribution through a combination of seasonal ice–ocean exchanges and divergent ice drift. These findings emphasize the complexity of Arctic ice–ocean transport pathways and highlight the challenges of forecasting their dynamics in light of anticipated changes in sea ice extent, river discharge, and surface circulation patterns.

Introduction
The Transpolar Drift (TPD) is an important surface current in the Arctic Ocean, transporting freshwater and various types of matter from the Siberian Shelf to the Fram Strait and the Canadian Arctic Archipelago1,2. This transport, driven by sea ice drift and surface water advection, encompasses a wide range of dissolved and particulate substances originating from Siberian rivers, including nutrients, metals, gases, carbon, microplastics, and other pollutants3–8. The redistribution of this matter across the Arctic and into the North Atlantic impacts the region’s marine ecosystems, biogeochemical cycles, food security, and human health9–11, extending the TPD’s influence well beyond the effects of fresh water on large-scale oceanic and climate dynamics12.

Despite their importance, the mechanisms governing the transport of Siberian matter along the TPD remain poorly understood. A key challenge is distinguishing the respective contributions of sea ice drift and ocean surface circulation to matter transport. Accurate predictions of matter redistribution depend on a detailed understanding of these transport mechanisms alongside focused research into the physical properties and biogeochemical processes that independently influence matter movement in ice and water. Sea ice drift within the TPD is largely wind-driven, whereas surface circulation is shaped by a combination of ice–ocean drag and Ekman transport13. Additional circulation features—such as geostrophic currents, topographically constrained boundary currents and their detachments, upwelling between ice floes, and various scales of eddies—further modulate surface water transport and, in turn, ice motion through reverse ice–ocean drag14. Chemical exchanges between sea ice and the surface ocean during ice formation, drift, and melting add another layer of complexity, creating seasonally varying ice–ocean transport pathways. Current observational tools and models face limitations in resolving these pathways. Satellite data and drifting buoys provide valuable insights into sea ice origin and movement15 but offer only a partial understanding of surface hydrography. Numerical models hold promise for integrating sea ice and the surface ocean as interconnected matter reservoirs, but they require fine-scale resolution16 and precise representation of atmosphere–ice–ocean momentum transfer17,18. While some models isolate specific transport mechanisms effectively19,20, they often fall short in capturing the full extent of matter redistribution21. Ship-based observations, although invaluable for studying ice–ocean interactions and tracking matter transport, are limited by the logistical difficulties of accessing the central Arctic, particularly in winter. Overcoming these limitations calls for year-round monitoring with advanced observational tools capable of capturing the dynamic processes in and between sea ice and surface water that govern matter dispersal.

Source-sensitive geochemical tracers offer a promising approach for identifying large-scale matter dispersal trends by integrating circulation effects over time while smoothing out short-term, small-scale variability. Recent advancements in the combined use of stable oxygen isotopes (δ18O), radiogenic neodymium isotopes (εNd) and rare earth element (REE) concentrations in seawater have significantly improved our ability to trace fresh water and matter in the Arctic Ocean22–25. These tracers are particularly effective in the Eurasian Basin, where processes that could alter dissolved εNd and REE distributions—such as seawater–particle exchange26—can be accounted for or excluded. Studies employing these tracers have revealed substantial variability in contributions from individual Siberian rivers across the Siberian Shelf24 and the Eurasian Basin25. Fluctuations in micro- and macronutrients are linked to these contributions27,28, underscoring their ecological importance. The application of these provenance tracers to sea ice29,30 has further enhanced understanding of ice–ocean interactions and Arctic transport processes.

This study applies these tracers to explore the dispersal of Siberian matter by sea ice drift and ocean surface circulation along the TPD throughout the annual cycle. Building on previous Arctic provenance tracer studies, we quantitatively decompose water masses using seasonal water column and sea ice data from the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition conducted from October 2019 to September 2020 (‘Methods’). Our analysis focuses on polar winter, examining the signatures of sea ice and surface seawater over a ~200-day drift (Fig. 1). Observations from MOSAiC revealed considerable hydrographic variability beneath the sea ice, driven mainly by surface circulation and seasonal atmospheric forcing31–33. These findings are especially critical in light of the recent acceleration of sea ice drift34,35 and the sharp reductions in sea ice extent and thickness due to Arctic warming36. These shifts overlay interannual and fine-scale fluctuations in flow structure16 and broader variability tied to the Arctic Oscillation37. Understanding the mechanisms driving matter transport along and beyond the TPD is essential for assessing natural variability and predicting the future impacts of climate change.

Results
Context of the MOSAiC campaign: Sea ice drift and ocean surface hydrography
The MOSAiC drift campaign began in October 2019 when the RV Polarstern moored to an ice floe at 85°N and 137°E. This floe, formed in December 2018 near the New Siberian Islands38, served as the vessel’s drifting platform until it disintegrated in July 2020. The ship then relocated to the central Arctic Ocean, continuing its drift with a new floe through August and September 2020. The original floe, which survived the 2019 summer melt and was classified as second-year ice on January 1, 202039, was first sampled in October 2019. Shortly before, between September 1–13, 2019, first-year ice began forming in open waters or beneath the second-year ice as insulated first-year ice40. Figure 1 depicts the ice formation areas, floe drift paths, and sampling locations.

Throughout the campaign, dynamic oceanographic conditions were apparent. Surface current velocities averaged 3.3 km day–1 in winter 2019/2020, increasing to 4.3 km day–1 in August/September 2020 after RV Polarstern relocated to the central Arctic Ocean32. Despite these averages, fine-scale variations were significant, with peak speeds in the upper 50 m reaching 22 km day–1 during early spring 2020. Ice drift velocities frequently exceeded surface current velocities, with the predominant ice drift direction often diverging from the underlying water movement31,33.

Rabe, et al.31,32 and Schulz, et al.33 provide detailed analyses of hydrography during the MOSAiC drift campaign, summarized here with an emphasis on the upper water column. Throughout the drift, Arctic Atlantic Water (AAW)—a colder, less saline form of Atlantic Water—was observed at depths of 200–400 m. Above this layer, a stable halocline was present from October 2019 through March/April 2020, after which the mixed layer began to deepen. In the upper 40 m, near-freezing temperatures prevailed through winter 2019/2020 until May/June 2020. As the drift progressed into the southern Nansen Basin, mixed layer temperatures began to rise. Surface salinity had started to increase earlier, during the transition from the Amundsen Basin to the Nansen Basin, shifting from below 32 in early 2020 to above 34 by mid-year. Beyond early 2020, low surface salinities and temperatures were detected in the Fram Strait in late July 2020, during RV Polarstern’s transit to the second ice floe in August 2020, and near the North Pole during the second drift in late August and September 2020 (Fig. 2a). Examining the origins and characteristics of these low-salinity waters—defined here as having salinities ≤32—is a central focus of this study.

Origin, distribution and variability of freshwater along the Transpolar Drift
Freshwater in the surface layer of the Eurasian Basin primarily originates from the Siberian Shelf, with substantial contributions from river runoff7,8. After crossing the shelf and mixing to varying degrees with AAW, this water integrates into the TPD. The extensive δ18O datasets41,42 reveal a robust positive correlation between δ18O and salinity in the upper water column (<250 m, R2=0.85, p<0.05; δ18O and salinity sections shown in Supplementary Fig. S1), indicating that river water and AAW were the dominant contributors to the mixed layer and halocline during the MOSAiC drift. Using these datasets and a three-component water mass analysis based on δ18O and salinity (‘Methods’), we mapped the high-resolution distribution of river water content (fRIV) along the MOSAiC drift (Figs. 2b, 3a). Surface fRIV reached approximately 21% near the North Pole in late August and September 2020, confirming a significant riverine contribution to the low-salinity waters. Elevated fRIV values, peaking at 15%, were also observed nearby during January and February 2020, in Fram Strait by late July 2020, and between Fram Strait and the North Pole during RV Polarstern’s transit to the second ice floe in August 2020 (Fig. 2b). These findings, alongside persistently low sea surface salinities during the transit, suggest that the low-salinity waters between the Lomonosov Ridge and the western Fram Strait were interconnected components of a larger, continuous flow of river water across the Arctic. In contrast, in the Nansen Basin from April to June 2020, surface fRIV dropped to approximately 5%, reflecting a stronger influence of AAW on the mixed layer and halocline, consistent with the low stratification observed in this region32,33. Apart from thin surface meltwater lenses (<1 m) formed locally during summer 202043, contributions from sea ice meltwater (fSIM) were generally low throughout the campaign, with fractions below 2% beneath the halocline. However, brine contributions (negative fSIM) reached up to 10% in the mixed layer and halocline over extended periods. These brines likely originated partially from productive polynya regions on the Siberian Shelf, as sea ice formation along the TPD alone cannot fully account for their presence. Although a comprehensive analysis of the fSIM distribution is beyond the scope of this study, we observe that its patterns closely align with those of fRIV and river-borne nutrients, such as silicic acid44. This similarity underscores the strong compositional link between the Siberian Shelf and the Eurasian Basin throughout the MOSAiC drift.

Dissolved εNd compositions and REE concentrations offer deeper insights into the origin of river water and its interactions with AAW (Figs. 2c and 3b, and Supplementary Figs. S1 and S2). Neodymium isotopes are particularly effective tracers for ocean circulation due to the quasi-conservative behavior of Nd in the oceans and its long average residence time of several hundred years45. Even in surface waters with shorter residence times, Nd isotopes can effectively track short-term water mass advection and mixing. In the TPD, where ice and water transport occur on timescales shorter than a decade3,33,46, non-conservative processes, such as boundary exchange26, could alter dissolved εNd signatures over shorter periods. However, both our data and summer data from 201525 suggest that such processes have little influence on the dissolved εNd distribution in the upper water column of the Eurasian Basin (Supplementary Text 1). Instead, εNd signatures and REE concentrations primarily reflect mixing between AAW and waters from the Lena, Yenisei, and Ob rivers, aside from one outlier sample from the Fram Strait likely influenced by Greenland inputs. Compared to the 2015 dataset, which included the Makarov Basin, the MOSAiC dataset—more focused on the TPD—reveals an even stronger correlation between fRIV and [Nd] (R2=0.91, p<0.05, Fig. 4a). This relationship, along with progressive εNd shifts with increasing fRIV and decreasing depth (Fig. 4b), underscores the dominant role of these rivers and AAW in supplying dissolved REEs to the Eurasian Basin. The εNd shifts reflect gradual admixture of river water to AAW, initially from the Yenisei and Ob rivers (εNd = −6.1 to −5.2) and subsequently from the Lena river (εNd = −16.7 to −15). Each mixing step introduces a distinct εNd signature, while the similar REE concentrations among the rivers sustain a consistent increase in [Nd] irrespective of their relative contributions. This understanding supports an expanded four-component water mass model that integrates εNd with δ18O and salinity to distinguish contributions from the Lena (fLena) and Yenisei/Ob (fYenOb) rivers (‘Methods’). Adjusted REE concentrations for these endmembers account for estuarine processes that reduce riverine REE concentrations before entering the TPD (Supplementary Text 2.2). The analysis indicates that waters with the highest fRIV exhibited nearly equal contributions from the Lena and Yenisei/Ob rivers (Fig. 3c, d). However, their proportions varied considerably during the drift, with individual contributions ranging from 0% to 9–10%. Furthermore, elevated Lena contributions were consistently accompanied by at least ~6% Yenisei/Ob water. The excess of Yenisei/Ob water over Lena water (fYenOb_ex) was most pronounced at the surface during the early phase of the first drift (October/November 2019) and at subsurface depths (50–100 m) in early 2020 and September 2020. Nearly equal contributions were observed at the surface in January, late July, and September 2020, thus particularly in the presence of low-salinity waters (Figs. 2c and 3e). The admixture of significant AAW volumes to the mixed layer and halocline in the Nansen Basin, inferred from low surface fRIV values between April and June 2020, is reflected in shifts in εNd compositions and REEs towards AAW core values (εNd ~ −11, [Nd] ~ 16 pmol kg−1), consistent with previously reported AAW characteristics in the Eurasian Arctic Ocean22–25. While definitive tracers for Pacific water remain unavailable, our data and recent studies suggest negligible Pacific water influence in the Eurasian Basin (Supplementary Text 2.4), supporting earlier estimates25.

The predominance of Yenisei/Ob water over Lena water (fYenOb_ex > 0%) aligns with established shelf circulation patterns46–48 and previous tracer observations in the Eurasian Arctic Ocean22–25. The transformation of Atlantic Water into AAW, occurring through gradual mixing with Siberian runoff along the shelf and boundary currents, generates low salinity shelf waters in the Barents, Kara and Laptev seas, which subsequently disperse into the Eurasian Basin47. During the MOSAiC drift in October and November 2019, the mixed layer was primarily composed of AAW and freshwater from the Yenisei and Ob rivers, with surface fYenOb peaking at 8% (Fig. 3c). This two-component mixture likely originated in the Kara Sea, where AAW first interacts with substantial Arctic river runoff. Before joining the TPD, this Kara Sea water is likely advected into the Laptev Sea through the Vilkitsky Strait Current. Although the Yenisei/Ob plume dominates the central Kara Sea during summer and fall, it does not directly enter the Eurasian Basin. Instead, the plume migrates into the northwestern Laptev Sea during winter and largely vanishes from the Kara Sea by early spring49. By September, surface fYenOb can still reach up to 19% in the northwestern Laptev Sea50, highlighting this pathway’s significance even in summer. In the Laptev Sea, this water mixes variably with Lena water, forming high-fRIV surface waters that are exported north into the TPD. Denser Kara Sea waters, characterized by lower fRIV due to stronger mixing with AAW, submerge beneath these high-fRIV waters whenever the latter are present. This dynamic creates a distinct shelf-derived stratification feature within the TPD, observed in 201525 and during the MOSAiC drift in early 2020 and again in September of the same year (Fig. 3e). Direct mixing between AAW and Lena water is unlikely along this route, as AAW first interacts with Yenisei/Ob waters before reaching the Laptev Sea. This explains the absence of pure AAW-Lena mixtures during the MOSAiC drift. Nevertheless, Lena river water distinctly influences the water composition, evidenced not only by shifting εNd signatures but also flatter REE patterns (Supplementary Fig. 2, normalized to PAAS, Post-Archaean Australian Shale51) and thus lower PAAS-normalized heavy-to-light REE ratios (HREE/LREE) observed in the low-salinity waters with the highest fLena. Such REE characteristics stem from interactions with weathering inputs either in the river catchments or at the land-ocean interface24,52. Rapid transport of Lena water into the Eurasian Basin partly preserves these patterns within the TPD, counteracting oceanic REE fractionation. This fractionation occurs during estuarine REE removal through preferential scavenging of LREEs upon mixing with seawater53, gradually increasing HREE/LREE ratios to levels typical of AAW24.

The river water distribution along the TPD during the MOSAiC expedition reveals notable spatiotemporal variability in advection. Variations in ice drift direction caused by inertial oscillations54—the circular movement of sea ice driven by the Coriolis effect—appear to have no impact on tracer-derived fraction distributions when plotted over time and thus following ice motion, likely due to the low spatial resolution of the tracer data. Even in the well-resolved river water distribution, only broad advection and mixing patterns are visible, while smaller features fall below data resolution (Fig. 3a). These patterns also remained unaffected by local ice–water interactions during the drift, as tracer-derived fractions are consistent throughout the upper water column, extending to depths of up to 100 meters—well beyond the direct influence of ice–ocean drag and Ekman transport. Throughout the campaign, sea ice drifted up to six times faster than surface waters33, moving over high-fRIV water toward the Fram Strait until mid-March 2020, when the drift shifted towards the Nansen Basin. Therefore, tracer changes observed until spring 2020 mainly reflect variability in river water advection along the freshwater-rich TPD, along with the high-fRIV waters found later that year near the North Pole and at Fram Strait. The nearly equal contributions from the Lena and Yenisei/Ob rivers at these two sites, as well as during winter 2020, suggest a relatively stable river water composition over a distance of approximately 1500 km, spanning from the Lomonosov Ridge to western Fram Strait (Fig. 2a–c). However, sustaining such a stable river water composition along the TPD faces two significant challenges. First, strong variability in river water composition is evident before the appearance of the first low-salinity water in early 2020. The MOSAiC floe likely drifted through a Lena-Yenisei/Ob mix for about a month before the RV Polarstern arrived in October 2019, as indicated by first-year ice data (see next section). Over the seven-month drift spanning 600 km, river water composition thus fluctuated from a Lena-Yenisei/Ob mixture to predominantly Yenisei/Ob waters and back to a mixed composition (Fig. 2c). Second, strong seasonal and interannual hydrographic shifts on the Siberian Shelf, particularly in the Laptev Sea, make long-term export of a constant river water composition highly unlikely. A consistent composition between the Lomonosov Ridge and western Fram Strait would require continuous export of constant Lena-Yenisei/Ob mixtures over one to four years, assuming surface water velocities between 1 and 5 km day–17,32,55. This scenario is unlikely, as nearly half of Siberian river runoff occurs within a two-month period around the spring freshet, producing river-influenced regions with notable seasonal and interannual variability49,50. Wind patterns further influence the Lena plume’s direction, pushing it north into the TPD or eastward into the East Siberian Sea. Yenisei/Ob waters, variably advected from the Kara Sea49, occupy the central shelf when the Lena plume is deflected eastward24,50, likely experiencing similar export mechanisms into the TPD. The variability observed during the first half of the MOSAiC drift suggests that distinct river water mixtures exited the shelf within 40 to 200 days (based on above noted surface water velocities), aligning with surface water residence times of weeks to months in the Laptev Sea56. This suggests that hydrographic shifts on the shelf precondition the TPD, driving temporal variability in surface water composition across the entire freshwater-rich TPD, not just the 600 km segment where riverine contributions are distinguishable.

The simultaneous emergence and advection of pure Yenisei/Ob-AAW mixtures from the northwestern Laptev Sea likely explain the low fYenOb values of up to 5% observed in the Nansen Basin between April and June 2020 (Figs. 2c and 3c). However, this advection appears confined to the Nansen Basin and is unlikely linked to the variability seen in the freshwater-rich segment in the Amundsen Basin. Strong compositional fluctuations along the 600 km segment—occurring without significant changes in ice drift direction—suggest that temporally variable export is the driving factor of these compositional changes, rather than the floe crossing distinct but relatively stable contributions from the western and eastern Laptev Sea. Basin circulation also seem to have had minimal influence on this distribution, as no cross-currents or large-scale features were detected33. Moreover, the low occurrence of eddies observed during MOSAiC33 suggests limited impact of mid-scale phenomena on large-scale water mass distribution. Instead, river water transport along the TPD is primarily governed by the mean geostrophic flow, which is influenced by wind- and ice-induced surface stress affecting sea surface height. Although short-term variability in all these components can affect matter transport, the mean transport aligns with this large-scale flow, which is fed by headwaters from the eastern Laptev Sea with shifting river water content and composition. This evolving understanding of ocean surface variability along the TPD emphasizes the critical role of shelf hydrography and seasonality in shaping freshwater transport, with important implications for the composition of sea ice formed along the drift route and the associated redistribution of Siberian matter.

Uptake of Siberian matter by sea ice through ice growth and drift
A discernible and consistent pattern emerges in the profiles of all tracer parameters and bulk salinity within first-year ice, indicating that surface water incorporation is the primary mechanism influencing their distributions (Fig. 5). We identified four depth intervals based on these variations: i) 0–30 cm, ii) 30–70 cm, iii) 70–120 cm, and iv) 120–150 cm (Fig. 5b). The δ18O profile best illustrates these intervals, with values as low as −0.95‰ in intervals i) and iii), contrasting with higher values of up to +0.89‰ in intervals ii) and iv) (Fig. 5). Bulk salinity, [Nd], εNd and the HREE/LREE ratio also exhibit these intervals, though shifted downwards by about 10 cm. This shift likely results from the sensitivity of these parameters to permeability changes during sea ice maturation (Supplementary Text 3). While δ18O integrates largely into the ice crystal lattice, major and trace elements, including salts and REEs, are excluded and accumulate in interstitial brine channels and pockets57. This brine rejection lowers salt and REE concentrations relative to parental seawater and, along with small-scale brine heterogeneity, results in a more varied distribution within the sea ice (Supplementary Text 3). By aggregating corresponding ice core depth segments across multiple cores (‘Methods’), we minimized the impacts of brine heterogeneity and brine spillage during sampling, isolating the effects of permeability changes and brine rejection. Notably, these factors do not appear to alter the influence of parental seawater composition, even for steady-state salinity, despite internal temperature- and salinity-induced changes affecting ice porosity and microstructure57. Typical C-shaped profiles of salinity were observed during the initial growth phase of the first-year ice but were replaced by less variable profiles later in the winter39. These profiles provide a timeline based on numerous individual ice cores, although none captured the finer fluctuations in salinity distribution reported here for the same period. Future studies exploring the influence of surface seawater composition on salinity and brine-associated substances in sea ice may find value in a sampling strategy like ours, which effectively reduces the influence of small-scale variability caused by changes in porosity.

A significant outcome of the MOSAiC drift campaign is the direct comparison of tracer signatures in the first-year ice with those of parental surface seawater from which the ice formed. Continuous sea ice thickness measurements and regular ocean surface sampling enable a direct linkage of surface seawater composition to specific sea ice depth horizons (‘Methods’). Notably, there is excellent agreement between first-year ice and surface seawater values for salinity, δ18O, and εNd, despite differences in absolute salinities and δ18O values (Fig. 5). As discussed previously, brine rejection influences salt content, leading to lower bulk salinity in sea ice. The observed offset of ~2 ‰ in δ18O between seawater and sea ice matches previous estimates of oxygen isotope fractionation during sea ice growth8, suggesting a consistent fractionation process during first-year ice formation (‘Methods’). Minor discrepancies in tracer profiles between ice and water likely stem from data resolution differences. Sea ice compositions, recorded at segments of 10 cm for bulk salinity, δ18O, and [Nd], and 20 cm for εNd, represent averages over approximately 14 to 28 days of ice growth and drift. In contrast, surface seawater samples were collected according to scheduling availability. Adjusting for permeability effects (Supplementary Text 3) could improve salinity alignment. However, better seawater–sea ice correspondence for εNd may be less visible due to limited vertical resolution in sea ice, sparse surface seawater data, and higher measurement uncertainties. Nonetheless, εNd signatures in sea ice correlate directly with surface seawater composition, as εNd remains unaffected by brine rejection or other sea ice processes. Intervals i) and iii) exhibit the least radiogenic εNd signatures, as low as −10.8 ± 0.5, while intervals ii) and iv) show more radiogenic values of up to −9.3 ± 0.5, reflecting variable mixing between Lena and Yenisei/Ob waters with AAW. The seawater εNd signature of −10.3 ± 0.1 at 108 cm ice core depth (surface sample from station PS122/2_22-71) can be extrapolated to an approximate depth of 72 cm, corresponding to the sea ice thickness on December 12, 2019, when the low-salinity waters (S<32) were first observed in winter (Fig. 3a). Given the high probability of these waters originating from a uniform shelf reservoir, their composition likely remained constant until February 10, 2020, when surface salinities rose above 32 (corresponding to about 114 cm ice core depth, Fig. 5a, c). The agreement within error between the extrapolated seawater εNd value and the sea ice signature establishes the link between the sea ice and surface waters signatures for interval iii), along with the alignment across other intervals reinforcing that distinct sea ice intervals defined by δ18O and salinity distributions reflect variations in Siberian river water content and composition at the ice–ocean interface during sea ice drift.

To reconstruct the history of parental seawater through the first-year ice profile, we conducted a quantitative analysis of water mass endmember contributions to the sea ice (‘Methods’). Intervals i) and iii) show nearly equal contributions from Lena and Yenisei/Ob waters, while intervals ii) and iv) are primarily dominated by Yenisei/Ob waters (Fig. 6a). The total river water volume in the ice (fLena + fYenOb) aligns closely with the fRIV calculated from δ18O and salinity for surface seawater, confirming the reliability of our methodology and the consistent incorporation of Siberian matter by the sea ice. Moreover, the dominance of Yenisei/Ob water over Lena water observed in the surface layer is also reflected in the sea ice, resulting in a combined ice–ocean compositional field within fLena–fYenOb space (Fig. 6b). Since seawater samples were not collected until after October 2019, before the MOSAiC ice floe had reached a thickness of about 40 cm, direct comparison between surface seawater and sea ice signals is only feasible for the younger ice intervals ii)–iv). However, the εNd signature of interval i) (−10.8 ± 0.5) is, within uncertainty, identical to that of surface seawater collected near the North Pole in September 2020 (−10.7 ± 0.2, station PS122_5_62-38). This similarity, also evident in δ18O and salinity (Fig. 5a, b), suggests that the parental seawater for interval i) possessed characteristics akin to waters sampled near the North Pole. These waters, with elevated fRIV values, likely originated from the eastern Laptev Sea and passed through the region to its north, where first-year ice formation began. Their presence near the North Pole confirm the potential circulation of such waters in this area (Fig. 1). Snow infiltration can be eliminated as a major contributor to the tracer signatures in interval i), as the δ18O (−6.59‰), εNd (−8.0 ± 0.2), and distinct REE patterns (Supplementary Fig. S3) in our snow sample do not match the sea ice compositions. Other snow samples from the MOSAiC campaign had even lower δ18O values, averaging −16.3‰58. While spatially variable snow infiltration associated with early winter thaw-freeze events could explain δ18O values as low as −6‰ in the upper 20 cm of some first-year ice cores from the δ18O time series58, the δ18O values of interval i) (−1‰) and the opposing εNd shift indicate that our cores were unaffected by substantial snow melt. Spring melt can also be ruled out, as the melt season had not begun by April 8, 2020, when the sea ice was recovered.

While sea ice values align closely with salinity and isotopic tracer signatures in surface water, differences are evident in the distribution of [Nd] and the PAAS-normalized HREE/LREE ratio (Fig. 5d, e). The [Nd] in sea ice constitutes approximately 5–7% of the corresponding seawater [Nd] in intervals i) and iii), and 9–10% in intervals ii) and iv), indicating variable REE incorporation in sea ice brine depending on the composition of the parental seawater. These variations seem linked to the presence (intervals i) and iii)) or absence (intervals ii) and iv)) of Lena water, highlighting the influence of the distinct river chemistries59, which likely involve variations in REE complexation by inorganic and organic ligands53. The speciation of LREEs and HREEs in seawater and sea ice brine remain poorly constrained but is suspected to cause stronger HREE rejection during sea ice formation, resulting in a lower HREE/LREE ratio in sea ice compared to seawater29,30 (Fig. 5e, Supplementary Fig. S3). However, despite this preferential rejection, the differences in HREE/LREE ratios between the parental source waters persist. The lower ratio in interval i) relative to other intervals aligns with a lower ratio in the surface water (Fig. 5e), likely due to a stronger contribution of Lena water (see previous section). These findings emphasize the importance of parental seawater composition in shaping the often highly heterogeneous distribution of substances in sea ice, such as microplastics5, alongside the unique physicochemical processes that influence their incorporation and retention60.

Implications for dispersal of Siberian matter
Our research highlights the critical role of ice and water dynamics in redistributing Siberian matter across and beyond the TPD. The hydrographic variability of the Siberian Shelf regulates the export of fresh water and associated matter into the TPD, where limited mixing preserves distinct riverine contributions. This leads to spatial and temporal variability in downstream matter transport, driven by a near-surface geostrophic current primarily confined to the Amundsen Basin but potentially extending southward to the North Atlantic via the East Greenland Current. Atmospheric changes can modify this current’s flow and trajectory14, affecting dispersal. However, divergent sea ice drift along the TPD exerts a much broader influence, extending matter dispersal far beyond the freshwater-dominated region over an annual cycle. Rapid exchanges at the ice–water interface throughout winter, driven by shifts in mixed-layer composition, highlight the efficiency of this redistribution process. Over a 200-day ice growth period, tracer distributions in sea ice and surface waters showed strong alignment, observed at 10 cm segments in the ice—representing approximately 14 days of ice growth and drift. This alignment emphasizes the rapid integration of river water and associated matter into sea ice within similar or shorter timescales. The REE data from the MOSAiC expedition exemplify this efficiency, revealing the storage of REEs from multiple Siberian rivers in sea ice within the Amundsen Basin during winter 2019/2020 and their subsequent release into the Atlantic Water-dominated Nansen Basin north of Svalbard by late April 2020. While dissolved REEs are present in trace amounts and pose minimal ecological risk, their long-range transport via ice drift and surface circulation illustrates the critical role of these processes in redistributing matter. These mechanisms collectively govern the movement of various forms of matter, ranging from dissolved substances to suspended particles. Notably, even suspended riverine particles, despite their size and density, have been detected far from their original sources61,62, showcasing the extensive reach and efficiency of these processes. However, variability in source inputs—particularly for anthropogenic pollutants—and differing behaviors among substances complicate the quantification and prediction of dispersion pathways based solely on observed distributions, such as those of REEs. Recognizing these challenges, our study highlights the importance of understanding ice drift and circulation dynamics to evaluate the TPD’s role in matter redistribution more effectively.

The TPD has traditionally been viewed as a straightforward conduit for transporting matter—via water or ice—from Siberian coasts to the Fram Strait (Fig. 7a). This perception has largely been based on limited winter observations and a tendency to study sea ice3–6 and ocean7,8 reservoirs separately. However, our findings reveal a more intricate and dynamic transport network (Fig. 7b). Variations in the velocity and direction of ice and ocean flows, combined with fluctuating freshwater inputs from the Siberian Shelf and seasonal ice–ocean interactions, redistribute matter far beyond its original terrestrial and marine sources. Sea ice formed along the TPD plays a pivotal role in this system, integrating contributions from multiple sources during extended drifts and significantly enhancing matter dispersal. Conversely, sea ice originating from the Siberian Shelf often carries matter from a single terrestrial source—dependent on whether contributions from that source were present in surface waters on the shelf during the autumn and winter ice formation periods. This evolving understanding expands the TPD’s role to encompass not only Siberian inputs but also other terrestrial contributions. For instance, meltwater from Greenland has been detected in sea ice floes sampled across the Fram Strait, even when reconstructed drift trajectories indicate ice origin and transport far from Greenland’s coast29. This suggests that Greenland-derived matter can be advected deep into the TPD’s influence zone before being incorporated into the ice, further underscoring the critical role of the complex and interconnected ice–ocean pathways within the TPD system.

As Arctic sea ice transitions from multi-year ice to predominantly seasonal ice, observational methods and modeling strategies must adapt. Reduced sea ice extent on the Siberian Shelf and along the TPD will create more variable liquid freshwater pathways, increasingly governed by atmospheric conditions18. A more fragmented ice pack will also heighten the responsiveness of sea ice drift to atmospheric forcing18,63, intensifying ice-driven dispersal of Siberian matter. With the decline of long-range transport of ice-rafted matter from the Siberian coast to the Fram Strait35, seasonal ice-driven redistribution along the TPD will become increasingly important (Fig. 7b). Climate-driven increases in river runoff64,65 and shifts in river water chemistry10,66 will further modify the transport of matter through these evolving pathways. Improving our understanding of long-range ice–ocean connectivity and water column variability is therefore crucial for refining predictions of the transport of natural substances and anthropogenic pollutants within the rapidly changing Arctic system.

Methods
Sampling and pre-treatment
Seawater and sea ice samples were collected from the MOSAiC Central Observatory, which features sampling facilities positioned on the main sea ice floe. These included ocean sampling sites, the RV Polarstern vessel, and an ice hole located several hundred meters away, referred to as Ocean City32. Over five research legs from October 2019 to September 2020, a total of 41 large-volume seawater samples (10 L; hereafter referred to as isotope samples) were collected using a Conductivity-Temperature-Depth (CTD) rosette fitted with 24 OTE (RV Polarstern samples) and 12 (Ocean City samples) Niskin bottles. The corresponding CTD bottle data have been published by Tippenhauer et al.67,68. At each station, seawater samples were obtained from the surface (0 to 5 m) and depths of 50, 100, and 200 or 250 m. The samples were collected in acid-cleaned LDPE-cubitainers and transported to the clean room laboratory at GEOMAR (MK-Versuchsanlagen, class 100 hoods). In the laboratory, filtration through 0.45 Merck Millipore® cellulose acetate filters was carried out using a peristaltic pump. After filtration, samples were acidified to pH ~2.2 with ultra-pure concentrated hydrochloric acid. Small aliquots (0.1 L) for δ18O analysis were obtained before filtration and acidification but after homogenization and stored in glass bottles at 4 °C until analysis. A set of seawater samples for δ18O analysis with a higher spatial resolution was collected separately in 0.2 L glass bottles from the Central Observatory using the same sampling facilities.

In addition to seawater, ten sea ice cores were drilled in proximity to each other with a Kovacs 9 cm diameter corer (Kovacs Enterprise, Roseburg, USA) on April 8, 2020, from the main coring site (MCS) for first-year ice and second-year ice (station PS122/3_35-80). These cores were promptly placed into plastic bags (LDPE tube films by Rische and Herfurth) and stored at −20 °C along with a snow sample, collected in a clean LDPE bag prior to core extraction. Upon arrival at the clean room laboratory at GEOMAR, the ice cores underwent a rinsing process with deionized water (Milli-Q, 18.2 MΩ cm) at a temperature of approximately 20 °C for around 15 seconds to remove contaminants from the core surface. Similar rinsing procedures are common practice in the preparation of glacial ice cores for tracer analysis69 and serve to eliminate loose surface contaminants that may not be completely removed by physical scraping alone. The rinse does not affect the brine content of the ice core, as only enclosed brines remain post-sampling, which are not altered by brief exposure to deionized water. After the rinse, careful scraping was conducted using a custom-made titanium grade 1 chisel to remove the wet top surface and any remaining contaminants. The cores were then sectioned into 10 cm pieces using a custom-made titanium grade 1 handsaw on a clean plastic table lined with acid-cleaned PFA foil. The resulting segments from nine ice cores, corresponding to specific depths, were combined in acid-cleaned LDPE buckets with sealed plastic lids for melting. The combination of core segments of equal depth was implemented to increase the sample volume needed for Nd isotope analysis while also mitigating the impact of sea ice heterogeneity and brine spillage during sampling (Supplementary Text 3). Although this approach cannot eliminate the effects of brine spillage during field core retrieval—a process known for its potential to cause significant brine loss—it reduces them by increasing the proportion of trapped brine relative to spilled brine. This is evidenced by the consistency of our sea ice tracer profiles derived from the combined cores. To further evaluate the effects of heterogeneity and brine loss, samples from one core were processed separately from the other nine. These individual 10 cm samples were melted in acid-cleaned LDPE bottles, which were cut open using an acid-cleaned ceramic knife to insert the ice core samples, and subsequently sealed with tight-fitting acid-cleaned LDPE covers. No leakage of brine was observed during the entire sample processing in the laboratory. Following the complete melting of the final piece of ice after approximately 12 h, the meltwater was filtrated through 0.45 µm Merck Millipore® cellulose acetate filters and directly transferred into acid-cleaned LDPE cubitainers (for pooled samples from nine ice cores) and acid-cleaned LDPE bottles (for individual samples from one ice core). After homogenization and sub-sampling for salinity and δ18O analysis, the filtered samples were acidified to pH ≈ 2.2 using ultra-pure concentrated hydrochloric acid. A minimum equilibration period of 48 h was allowed before another aliquot was separated into an acid-cleaned LDPE bottle for REE and Nd concentration analyses. The snow sample underwent a similar treatment, excluding the deionized water rinse and sectioning steps.

Neodymium isotopes of seawater, sea ice, and snow
The preparation of samples for εNd and REE analyses at the GEOMAR laboratory adhered strictly to the established GEOTRACES protocols and underwent validation through participation in the international GEOTRACES intercalibration study70. The pre-concentration of Nd involved Fe co-precipitation: A trace metal clean FeCl3 solution (~200 mg Fe ml–1) was added to each seawater, sea ice, and snow sample. Following a 24 h equilibration period, trace metal clean ammonia solution (25%, Merck Suprapur®) was added to raise the pH to 7.5–8.0. After 48 h, the precipitated trace elements, together with FeOOH, settled to the cubitainers bottom, and the supernatant water was suctioned off. The precipitates were centrifuged and rinsed three times with deionized water (MilliQ, 18.2 MΩ cm) to eliminate major ions. They were then transferred to PFA vials with 6 M HCl and evaporated to dryness. To eliminate organic components, the samples underwent treatment with aqua regia at 120 °C for 24 h. After evaporation to dryness, the samples were dissolved in 6 M HCl. They were then washed twice with pre-cleaned diethyl ether to remove approximately 99% of the Fe, which forms an ethereal complex that can be separated from the acidic phase containing the trace metals. Following separation, the samples were dried down again and dissolved in 1 M HCl for column chemistry. REEs were separated from matrix elements through cation exchange chromatography (BIORAD®, AG50W-X8 resin, 200–400 µm mesh-size, 1.4 ml resin bed) with a modified separation scheme based on Laukert et al.22. Further purification of Nd from other REEs for isotope measurements was accomplished using Eichrom® LN-Spec resin (2 ml, 50–100 µm) following established procedures22. To eliminate residual traces of the resin and organic compounds, the samples underwent a final treatment with concentrated quartz-distilled HNO3 before εNd measurements.

The 143Nd/144Nd ratios were measured on a Neptune Plus MC-ICP-MS at GEOMAR for 40 cycles at 4 s and concentrations between 10 and 30 ppb Nd for seawater, 2 to 4 ppb Nd for sea ice (1012 Ω resistors assigned to masses 143 and 146), and 7 ppb for the snow sample in 380 µL solution. Since all sea ice samples prepared for the εNd analysis had too low concentrations (<0.7 ng) despite the combination of 10 cm depth segments from nine ice cores, samples from two (0–100 cm and 130–150 cm) or even three (100–13 cm) depth segments had to be combined to achieve concentrations of at least 1 ng and external uncertainties below 0.5 ε units (2 s.d., see below). Instrumental mass bias was double-corrected with 146Nd/144Nd=0.7219 and 142Nd/144Nd=1.141876, with the condition that the 142Ce beam intensity was sufficiently low71. This was monitored by ensuring a raw measured 140Ce/144Nd < 1 (i.e. Ce/Nd < 0.3) for each sample and standard solution. Isobaric interferences between 144Sm and 144Nd were corrected by measuring the abundance of the interference-free isotope 147Sm and by calculating the potential 144Sm contribution on mass 144 from the natural abundance of Sm. The 143Nd/144Nd ratios of all samples were normalized to the accepted jNdi-1 standard value of 0.51211572. The external reproducibility of the Nd isotope measurements as estimated by repeated measurements of an in-house laboratory standard with concentrations matching those of the measured samples ranged between 0.15 and 0.3 ε units (2 s.d.) for seawater and 0.37 and 0.5 for sea ice. The external reproducibility of the snow sample was 0.23 ε units (2.s.d.). Blanks (n=6) were processed identically to the samples and had <5 pg Nd, which corresponds to <0.7% (sea ice/snow) and <0.4% (seawater) of the lowest Nd concentration used for the isotope measurements. Blank corrections were therefore not applied. The εNd is defined by Eq. (1) as follows:
(143Nd/144Nd)sample / (143Nd/144Nd)CHUR − 1 × 104 = εNd (1)
where the 143Nd/144Nd of CHUR (‘CHondritic Uniform Reservoir’) is 0.51263873.

Neodymium concentrations of seawater (isotope dilution method)
0.5 L aliquots of the acidified and filtered seawater samples were spiked with a pre-weighed 150Nd spike, preconcentrated using Fe co-precipitation, and purified by column chromatography using the same method as for the εNd measurements, with the difference that only cation exchange chromatography was applied. The isotope dilution measurement of the Nd concentration based on 150Nd/144Nd ratio was carried out on Nu Plasma II MC-ICP-MS at GEOMAR. External reproducibility (2 s.d.) was better than 0.8% according to repeated treatment and measurement of the same sample (n=3). Blanks (n=2) exhibited Nd concentrations <4 pg, obviating the need for correction.

Rare earth element concentrations of seawater, sea ice, and snow
REEs were pre-concentrated at GEOMAR offline using a SeaFAST system (model M5 from Elemental Scientific), following a method refined from Hathorne et al.74. Using this updated method, 12 ml (seawater) or 24 ml (sea ice segments, snow) of the acidified sample was introduced to the column via a fifth syringe pump. After matrix removal, the REEs were eluted twice with 200 µL of 1.5 M HNO3. Before pre-concentration, each blank, reference material, and sample (pH~2) underwent spiking with 12 µL of thulium solution (10 ng g−1) for yield monitoring, typically achieving for seawater 97 ± 4.6% yields (±1 s.d., n=141) and for sea ice 92 ± 3.7% yields (±1 s.d., n=92). Before analysis on a Thermo Element XR ICP-MS coupled with a CETAC “Aridus” desolvating nebulizer, all samples were diluted with 200 µL of 0.1% HNO3 containing 10 ng g−1 Re as an internal standard during measurement and to account for any sample evaporation post pre-concentration. The desolvating nebulizer enhanced sensitivity and reduced oxide formation, monitored with Ba, Ce, Pr+Nd, and Sm+Eu+Gd+Tb element solutions at the beginning of each analytical session. Oxide formation remained generally <0.01 (n=3) for Ba, <0.05 (n=3) for Ce, <0.04 (n=3) for Pr+Nd, and <0.04 (n=3) for the MREE. GEOTRACES inter-calibration sample BATS 15 m and in-house reference seawater underwent the same pre-concentration as seawater, sea ice and snow samples to monitor external reproducibility and accuracy. Additionally, certified natural river water reference material SLRS–675 was diluted ~500 times as a standard for sea ice and snow measurements at similarly low REE concentrations, and an in-house reference seawater served as an additional standard for seawater samples. The measured REE values of BATS 15 m and SLRS–6 align well with literature values. External reproducibility for seawater data based on BATS 15 m (n=17) was better than ~6% for the LREEs (from La up to and including Eu), and better than ~5% for MREEs (from Gd up to and including Dy) and HREEs (from Ho up to and including Lu). For sea ice and snow, external reproducibility based on diluted SLRS–6 (n=6) was better than ~11%, ~13%, and ~30% for LREEs, MREEs, and HREEs, respectively. Procedural laboratory blanks for seawater (n=4) had REE concentrations, on average, corresponding to ~3% of samples analyzed, except for Eu, which averaged ~8%. For sea ice and snow, the blanks (n=4) had concentrations on average corresponding to ~9% of the samples, except for Eu (~19%).

Stable oxygen isotopes of seawater, sea ice, and snow; sea ice bulk salinity
Oxygen isotope compositions in the seawater aliquots obtained from the isotope samples and in sea ice (pooled samples from nine ice cores and individual samples from one ice core) as well as the snow sample were analyzed at the Stable Isotope Laboratory of the College of Earth, Ocean, and Atmospheric Sciences at Oregon State University (Corvallis, USA) by applying the CO2-water isotope equilibration technique76 on at least 2 sub-samples on a Finnigan gas bench II unit coupled to a Finnigan DeltaPlus XL. Oxygen isotope compositions in the more extensive set of seawater samples41 were mainly analyzed at the Leibniz Laboratory (Kiel, Germany), applying the same technique. The external reproducibility for the δ18O measurements was ± 0.05‰ or better for sea ice, snow and aliquots from the isotope samples and ± 0.04‰ or better for the more extensive seawater dataset generated at the Leibniz Laboratory. A small subset of the more extensive seawater δ18O dataset was analyzed at the AWI with external reproducibility of ± 0.07‰. The measured 18O/16O ratio is provided as a deviation from V-SMOW (‘Vienna Standard Mean Ocean Water’) in the δ-notation following Craig77 with Eq. (2):
(18O/16O)sample / (18O/16O)V-SMOW − 1 × 103 = δ18O (2)
Where (18O/16O)sample and (18O/16O)V-SMOW are the 18O/16O ratios of the sample and the V-SMOW, respectively.

The bulk salinity of the sea ice and snow was determined with an AutoSal 8400A salinometer at GEOMAR with a precision of ± 0.003 and an accuracy of better than ± 0.005.

Water mass analysis
We assume that the waters encountered during the MOSAiC drift campaign are different mixtures of Atlantic water (AW), Lena river water (Lena), and a mix of Yenisei and Ob river water in discharge-weighted proportions (YenOb). In addition, we account for sea ice meltwater (SIM) or brine (negative SIM) contributions to the water column. The rationale for the selection of these endmembers and the potential contribution of other endmembers, such as other Siberian or Canadian rivers, glacial meltwater and Pacific water, is discussed in Supplementary Text 2. To determine the proportions of the selected endmembers, we employ mass balance calculations solved through matrix inversion, combining salinity, δ18O and εNd signatures, as outlined in Eqs. (3) to (6):
fAW + fLena + fYenOb + fSIM = 1 (3)
fAW × SAW + fLena × SLena + fYenOb × SYenOb + fSIM × SSIM = Ssample (4)
fAW × δ18OAW + fLena × δ18OLena + fYenOb × δ18OYenOb + fSIM × δ18OSIM = δ18Osample (5)
(fAW × [Nd]AW × (143Nd/144Nd)AW + fLena × [Nd]Lena × (143Nd/144Nd)Lena + fYenOb × [Nd]YenOb × (143Nd/144Nd)YenOb + fSIM × [Nd]SIM × (143Nd/144Nd)SIM) / (fAW × [Nd]AW + fLena × [Nd]Lena + fYenOb × [Nd]YenOb + fSIM × [Nd]SIM) = (143Nd/144Nd)sample (6)
Here, f represents the fraction of the respective endmember in the sample, while Tracer represents their respective endmember tracer values. The measured values for each sample are represented by Tracer sample. Our four-component analysis differs from the Nd-based approach of Paffrath et al.25, which relied on an Nd concentration balance based on sample [Nd] to distinguish between four water masses (AW, Pacific water, Lena and Yenisei/Ob). This method introduced sensitivity issues due to small variations in riverine [Nd], which significantly affected the calculated fractions, making attempts to quantitatively differentiate these water masses unreliable. Instead, we only use the endmember [Nd] to account for the average-weighted isotopic composition in Eq. (6), while excluding sample [Nd] from our analysis. This is possible because we were able to exclude Pacific water in our study region (see Supplementary Text 2.4). This improves the robustness of our method and allows for a clearer assessment of non-conservative processes influencing εNd and [Nd] beyond estuarine REE removal (see Supplementary Text 1). We have also refined the endmember definitions established by Paffrath et al.25, adjusting for scatter caused by natural (seasonal) variability, estuarine Nd removal, and incorporating analytical uncertainties where direct data on variability were unavailable (Supplementary Table S1). To account for these uncertainties, we applied a Monte Carlo simulation, generating 100,000 sampled values within the defined parameter space for the system of Eqs. (3–6). This provides a more statistically sound evaluation of water mass contributions and tracer behavior. The errors in Figs. 4–6 reflect the uncertainties derived from the Monte Carlo simulation. Slightly negative fLena values (<1%) cannot be fully attributed to these uncertainties and are likely linked to an overestimation of [Nd]AW, even after considering Nd removal processes during the transit of AW through the relatively productive Barents Sea (Supplementary Text 2.1). Adjusting [Nd]AW to values below 11 pmol kg−1 would slightly alter the relative proportions of the water mass fractions but would not impact the overall conclusions of this study.

The total river water fraction (fRIV, here: representing the combined contribution of the Lena and YenOb endmembers) can also be determined using salinity and δ18O alone8. This three-component analysis (i.e. RIV, AW, SIM) is accomplished by adjusting Eqs. (3–5) to account for fLena + fYenOb = fRIV, and by combining the parameter values for both rivers. This enables the determination of fRIV, for example, using the more extensive δ18O datasets41,42, for which no εNd data are available, offering insights into river water distribution (e.g., Fig. 3a) that cannot be obtained from the 4-component analysis above due to the lower resolution of εNd samples. The fRIV derived from this approach are within error identical to the combined fractions of Lena and YenOb from the εNd-based analysis, as εNd primarily serves to differentiate between the two river endmembers. Likewise, the fSIM values from both analyses agree within error. We assume negligible in situ precipitation in the central Arctic Ocean compared to river discharge volume7, which is supported by the strong correlation between fRIV and dissolved [Nd] (Fig. 4a).

Alignment of sea ice and ocean surface signatures; endmember contributions to sea ice
Accurate assignment of surface seawater signatures to the sea ice profile requires an age model for the sea ice, which we determine by a linear regression that correlates ice thickness with measurement date (R2=0.97, p-value < 0.05, n=56). This correlation is based on data from regular sampling at the Central Observatory between November 7, 2019, and May 9, 2020, during which a consistent linear growth rate of ca. 0.3 mm h−1 was observed for first-year ice39. During the initial growth phase in October and early November 2019, the sea ice either formed very slowly or did not form at all, maintaining a consistent first-year ice thickness of 40 ± 2.2 cm (±2 s.d., n=14), based on measurements at the MCS during this period. This lack of significant growth is not captured by the age model, which would otherwise predict unrealistic thicknesses below 40 cm for this period. To account for this, we assigned the average thickness of 40 cm for the first seawater sampling on October 4, 2019, and a thickness of 42 cm for the seawater sampling on October 24, 2019, measured at the MCS site on the same day. As we lack surface seawater data for the upper 40 cm of the sea ice profile, variations in ice growth rate within this depth are considered irrelevant for our study. The accuracy of the ice core depth is assessed by an analysis of multiple depth measurements from different ice cores taken on the same day39, resulting in an estimated uncertainty of 10 cm (2 s.d.), which is indicated in all relevant figures and factored into our interpretation of the data.

The observed consistent deviation of about 2‰ in δ18O between the values for seawater at the surface and sea ice within the first-year ice core depth of 40−150 cm can be explained by δ18O fractionation during sea ice growth8. The equilibrium fraction value for δ18O in sea ice exhibits variability ranging from <0.1‰ for fast-growing (>10 mm h–1) frazil ice to >2.5‰ for slow-growing (<0.01 mm h–1) column ice78. Considering the linear growth rate of about 0.3 mm h–1 for the first-year ice, the estimated equilibrium fractionation value of −2‰ agrees well with other field observations when the ice growth rate is considered79. The partition coefficient for salinity between surface seawater and sea ice is determined to be 0.13 ± 0.1 (2 s.d., n=28) and is calculated for the ice core depth at which steady-state salinity is reached (i.e. excluding the lowermost 10 cm), based on a regression line derived from the average salinities of seawater and sea ice at depths of 40–70 cm and 90–100 cm within the ice core. The salinity representing the surface seawater salinity during sea ice formation reconstructed from the regression line lies between 30.56 and 32.91, which agrees well with the salinity of the surface seawater sampled during the corresponding drift interval. Using this salinity, we calculate the expected salinity without the effects of sea ice formation or melting following Rosén et al.80, and by adjusting the 18O/16O ratio of the sea ice sample by −2‰ for equilibrium fractionation. The corrected salinity is then used, along with εNd and δ18O, to determine the contribution of the three endmembers to sea ice, applying Eqs. (3–6). Here, (143Nd/144Nd)sample and δ18O represent the measured 143Nd/144Nd ratio in the sea ice and the fractionation-corrected δ18O. The calculated fSIM, ideally expected to be zero, is effectively zero within error, validating the approach. Here, too, a Monte Carlo simulation with 100,000 sampled values was used to determine the fraction errors associated with endmember variability and measurement uncertainty. The differences in sea ice sampling resolution between salinity, δ18O and [Nd] (10 cm) compared to εNd signatures (20 cm) lead to deviations in their trends at the 30–40 cm depth horizon in the ice profile. While the δ18O, [Nd] and HREE/LREE values at this depth align more closely with other values of interval ii), the εNd signature is more consistent with the uppermost signature of interval i) (Fig. 5). From this comparison, we infer that the εNd signature of the 40–60 cm ice core depth better represent the 30–40 cm depth horizon in the water mass analysis than the signature reported for the 20–40 cm ice core depth. Therefore, we adjust the εNd signature for the 30–40 cm depth horizon to match that of the 40–60 cm ice core depth in our analysis. The fLena and fYenOb values derived from this sea ice sample, as shown in Fig. 6, are based on this corrected εNd signature. After this correction, the sample falls within the data envelope in fLena–fYenOb space in Fig. 6b, supporting the validity of our approach. The excellent agreement between the calculated fRIV in sea ice and seawater (Fig. 6a) confirms the suitability of our water component analysis in sea ice.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of \"Arctic Transpolar Drift Amplifies Siberian Matter Dispersal through Decoupled Sea Ice and Ocean Surface Pathways\" by Laukert et al., submitted to Nature Communications\nIn this ms., the authors present a suite of novel neodymium and oxygen isotope, rare earth element and salinity data from a unique expedition sampling throughout a winter season in the Arctic ice. Their data are interpreted in terms of input of freshwater from the Siberian rivers to the Transpolar Drift (TPD), an important surface current that crosses the Arctic Ocean, into the highest North Atlantic. Their conclusions are fine, but rather ambiguous – statements that tend to amount to “there is complexity,” but not exactly what this means.\nI fear that much of the perceived impact of this paper lies in the unique nature of the sampling campaign, which is an impressive feat. But the data generated and conclusions reached are almost identical to their prior publication in 2021 (Paffrath et al., Nature Scientific Reports). I was hoping that this more recent ms. would have had further, more definitive conclusions on the nature of distribution of riverine sourced tracers, and how they might impact the North Atlantic. Particularly because these data are interesting and I think that they should be published. However, as is, this ms. reads the same as their last (GEOTRACES) work. – perhaps a longer-format journal would be more appropriate for more detailed and deeper discussion of both these data sets?\nRegardless of decision, I might offer the authors the following concerns to consider:\n1. River water input is highly variable over the year, particularly in the Arctic. The winter has near-negligable discharge, compared to massive spring flows. There in no mention of this in the ms., but I would imagine it is important. Similarly, what is the residence time of river water in the shelf area, or in the TPD?\n2. The systematics of eNd are oversimplified. This is not an open-ocean setting, and the residence time of Nd is not the same as deep, open ocean (it cannot be). The “quasi-conservate” nature of eNd in this setting cannot be assumed.\n3. I would not be shocked to see 75% Pacific water at these depths. Saying it is “improbable” seems suspect. Pacific water penetrates very far into the Arctic, particularly in the winter when river flows are very low. Again, see point 1.\n4. I do not trust any of the [Nd] arguments. There is nothing conservative about [Nd] to suggest it can be used in these ways – particularly when the “mixing” plot already assumes a 75% loss in the measured riverine end member (i.e., most of the Nd is lost in the estuary, but how consistently?). That they found a linear mixing curve is interesting, to be sure (dependent on some assumptions). But it makes little sense with respect to the isotopes; how can the concentration continue to increase with apparent equal sensitivity/change in the isotopic fingerprint? The downstream isotopes should not change as readily, unless there is an effective scavenging, which calls into question the [Nd] patterns.\n5. I want to see the pacific treated quantitatively in their ms., and shown to be invalid, in figures and calculations. They did this in their 2021 publication, but did not here. In neither publication is a 1/[Nd] vs. eNd plot shown, which should, if conservative, show clear mixing patterns.\n6. The boundary exchange problem for eNd must be addressed. Boundary exchange has been shown to not impact [Nd] from original observations. The authors dismiss this citing lack of evidence in overlying water, but this is weak, and was indeed weak in the original boundary exchange publications.\n7. Brine spillage – I appreciate the difficulties of sample collection, and this should not be held against the authors, but should be treated as honestly as possible. The fact that brine has far greater [Nd] concentrations than non-brine water makes a linear mixing plot even more confusing. This should be treated with greater discussion.\n8. L68-69 needs references? This seems like a bold statement, given Ekman transport is one of the original “oceanographic” theories, based on years of observation and is a cornerstone of physical oceanography. (The story of Nansen is the oceanography equivalent to Newton’s apple…). Throughout the ms., bold statements confronting established physical oceanographic principles should be considered deeper.\nIn summary, I feel that there are a few critical points that should be addressed regardless of where this ms. is published. I do hope these data are published, and it would be great if published with Nature Communications - but I think this would take a significant re-write.", "reviewer_2": "Comments to the authors:\nLaukert et al. present a well-written manuscript presenting geochemical provenance tracer data collected from the MOSAiC campaign in the Eurasian Arctic Ocean. This exciting dataset is potentially significant because it fills the knowledge gap of sea ice and ocean surface pathways in the polar winter. The manuscript is clear, thorough, and nearly free of typos. The authors have done a good job combining multiple chemical tracers and disentangling complex endmembers. However, I have some substantive comments, as listed below. One of my main concerns is that the authors may have mistakenly omitted the sea ice melt fraction in their mass balance Equation 3, which could generate quite different results from the current version, at least during part of the cruise. Overall, I recommend publication with major revisions.\n%\nMajor interpretation points\nThis reviewer has some major questions about Equations 3 to 5. First, the authors should add fice in Equation 3 if I correctly understand Rosen et al. (2015)'s Equation 3. Without fice, the sum of fAW, fLena, and fYenOb wouldn’t be 1. Secondly, the authors correct for salt fluxes related to sea ice formation and melting for salinity in Equation 4, but no such correction exists for Equation 5. The authors could argue that neodymium isotopes remain unfractionated during sea ice processes, but sea ice processes do contribute to Nd concentrations, as seen in Figure 5d. This would change the denominator of Equation 5. Thirdly, if my previous two points are correct, I am curious how these fractions would change after taking into account the fraction of sea ice melt in Equations 3 and 5. I don’t think the general story would change, but there could be some changes to some interpretations, at least during part of the cruise.\n%\nInterpretation points, by line #\nLines 31–32: The decoupling between sea ice and ocean surface transport doesn't seem to only occur in the winter.\nLines 50–51: The Fram Strait is not the only outflow of the TPD. What about the outflow into the Canadian Arctic Archipelago (CAA)? For example, the authors can check out their own schematic Figure S3 or Carmack et al. (2016) (10.1002/2015JG003140).\nLines 206–208: The authors should consider plotting d18O and Nd section plots in the supplemental. It's not immediately obvious if the Pacific water has some influence on the subsurface water mass, especially for those close to the Makarov Basin, which can be identified by Paffrath et al. (2021). Additionally, how would the Greenland glacier runoff influence (εNd<-17; Figure S3) the end-member mixing model close to the Fram Strait?\nLines 335–337: It's hard to reach this conclusion with such a limited number of Nd or REE measurements as compared to salinity and d18O. I am also not sure why the authors have different conclusions between the pattern of εNd (e.g., Lines 292–295) and Nd or HREE/LREE. All of those parameters only have 5 points in Figure 5. Are there any statistical reasons?\nLine 344: Why does this have implications for particles? All parameters discussed in the manuscript are dissolved. Particles tend to sink, so they cannot be transported as long distance as the dissolved phase. Please clarify.\nLines 397–402: Concentrations of certain elements may be high in sea ice, but what about their relative mass compared to the surface ocean? In other words, how much mass is incorporated into the sea ice in a budget perspective? The volume of sea ice seems to be much smaller than that of the polar mixed layer.\nLines 592–594: The constants α and a did get mentioned in the reference cited here. However, the authors still need to do a better job explaining these two terms, at least briefly. What do they mean? Just showing their values is not enough.\nLines 603–608: Are there any figures? If using d18O alone generates similar results, what new information do we get by adding an extra Nd tracer in the end-member mixing model?\nLines 780–795: Why are the white arrows just one way? What about sea ice algae falling, dirty sea ice exchange, or ice melting along the way?\n%\nEditorial remarks (by line #):\nLine 1: Is “material” a better word than \"matter\"? I found the phrase “Siberian matter” a bit strange.\nLine 69: Ditto. It's a bit weird to use \"matter fluxes\" here. Do the authors think other phrases such as \"material fluxes\" or \"elemental fluxes\" work better? If so, the authors should consider changing this word/phrase throughout the manuscript.\nLine 90: I don't think \"the reasons mentioned above\" are immediately before this sentence or even in this paragraph. If that's the case, the authors should consider summarizing the reasons briefly and adding them here.\nLine 614: I may have missed it, but what is “CO”? Please clarify.\nLines 671–673: The end of the first drift is not labeled by a dashed black circle.\nLines 675–678: What are those dashed arrows? Please clarify.", "reviewer_3": "This study uses “provenance” tracers (salinity, d18O and eps_Nd) to identify the origin of surface waters and sea ice. They use standard linear deconvolution techniques for surface seawater (<200 m), and also apply the technique to sea ice samples, which is novel. Critically, their sampling campaign (from the MOSAiC project) followed an ice flow through an entire annual cycle. There have been a couple of prior floating platform expeditions to the Arctic, but they came before our capacity to collect and perform isotopic analyses on rare earth and element (REE) samples. Thus, the authors’ dataset is truly unique and represents a major leap forward for provenance studies of Arctic waters. Their results use a recently described river tracer, neodymium isotopes, to assess which rivers are contributing to the riverine fraction in their samples. The results indicate an admixture of Lena and Yenisey/Ob waters in the surface ocean and sea ice; suggesting that sea ice incorporates dissolved rare earth constituents in the same abundances that it is in the surface ocean. Further, they show that the balance of Laptev Sea (Lena) vs Kara Sea (Ob/Yenisey) waters shifts in major ways along the route of the platform’s drift. And finally, they demonstrate that these shifts are visible in the ice, so that a vertical core through the ice can be viewed as an archive of surface water chemistry. These results have implications for the transport of constituents by sea ice to locations distal from the source.\nThe submission represents the latest in a series of excellent studies from this group. This latest effort includes a superb dataset and some exciting results. However, we have several serious problems with the presentation that need to be addressed before publication. We recommend accepting the paper, pending major revisions.\nGeneral Comments\nEarly in the text, the authors indicate that prior work mainly views the near-surface waters as traveling in the same direction as, and at about the same speed as, the sea ice. And at several points in the text, the authors indicate that an important part of their contribution is to show that the surface waters travel differentially from the ice. But this has been well known for many years. In fact, later in the text, and in their Figure 2, they indicate that the pre-existing consensus model is that the near-surface waters travel to the right of, and significantly slower than, the sea ice. Thus, the initial characterization of prior work feels to us like a “straw man” argument: setting up an inaccurate version of their colleagues’ previous work in order to stand out in comparison.\nPrevious work has documented an Ekman spiral in Arctic waters, localized areas of intense upwelling between ice floes, eddies of various sizes, geostrophic currents based on horizontal density gradients, topographically trapped boundary currents, and horizontal detrainment from boundary flows, in addition to ice-ocean surface stresses as drivers of near-surface currents. If we take this diversity of dynamics as the actual prior state of knowledge, then the authors’ unique contribution is not the fact that surface water and sea-ice motions diverge, but something specific about how they diverge.\nRegarding the mechanism of divergence, we had difficulty understanding with some precision what the authors are suggesting. On the one hand, they refer to hydrodynamics on the Siberian shelves as the origin of distinct assemblages of water masses crossed by their floe. On the other hand, they suggest that a variety of short-term, mid-scale phenomena might be responsible, such as inertial motions and eddies. The former hypothesis suggests a relatively stable TPD into which a varying set of “head waters” are inserted. The latter is a different narrative, in which time-varying motions in the pelagic ocean might be responsible for shifting TPD content. Perhaps they envision both: varying sources where the TPD detrains from the shelf, followed by insertions and/or detrainments along its route. Whatever the narrative, it needs to be clearer.\nWe note as well that dissolved tracers have historically been applied to problems that require a long-term integrative metric. This is because they disperse over their residence times, smoothing out short-term, small-scale flows and structures. Current measurements and purposeful tracer injections have been the tools of choice for evaluating dynamics with shorter time scales. The MOSAIC program included current profilers, and we found it curious that the manuscript made no mention of that data, which should have a lot to say about the divergence of sea ice and water velocities along the track.\nA second major outcome of the work is that sea ice transport of marine constituents is an important process. The study identifies that riverine constituents are a component of sea ice; and suggests that melting of the ice transports riverine signals to the central Arctic and beyond. They state that this paradigm is more important than previously thought, but they do not quantify the relative import of this signal (melting sea ice) to the surface ocean for the various constituents. The reader cannot know how important the mechanism is. Could it still be negligible relative to the role of advected waters? Is the bulk of material transported from the ice “factories” of the Siberian shelf to the Greenland and Canadian shelves? Some sort of scale should be set to support the claim of importance. Furthermore, they imply that sea ice as a modality of transport of materials from Siberian shelves has so far been neglected, and do not site studies that show that sea ice entrains and redistributes materials. We agree that quantification of this process is an ongoing area of study, and this study offers a lens into sea ice formation endmembers that permit a better quantification than previous studies. However, the role of entrainment of geochemical parameters and other material has not previously been neglected. Entrainment of geochemical parameters in proportions present in the surface seawater is consistent with our understanding of sea ice formation (e.g., major geochemical constituents are driven by brine/salinity dynamics). To acknowledge some of the previous work addressing the transport of sea ice incorporated constituents we advise the authors look at: https://doi.org/10.1016/0048-9697(95)04174-Y, https://doi.org/10.1029/2022GB007320, https://doi.org/10.1038/srep16179,\nThe authors assume that the samples are composed of Yenisey/Ob water, Lena water, sea-ice melt, and Arctic Atlantic water. In the Supplementary Material, they make the case that Pacific inflow is not present in the samples. But what about other freshwater sources, such as Kolyma River water and other Eurasian rivers, groundwater seep, and Mackenzie River water? It seems to us that something needs to be said about why these sources can be ignored.\nThe only error mentioned in the manuscript is the analytic error on neodymium measurements. However, in our experience, by far the largest source of error in water mass analysis is the scatter in the end member observations. This is a serious gap, and really needs to be addressed.\nWe have a question about the water mass analysis: To estimate their four unknowns, the authors have four parameters: mass, salinity, oxygen isotopes, and neodymium isotopes. Why not set up a 4-by-4 linear problem, and invert a single matrix for each sample? Rather, they take two steps, first identifying the sea-ice fraction, and then identifying the other three components. What is gained by splitting the process in two?\nWe are attaching a marked-up pdf file of the manuscript with a number of more specific comments and questions.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "RESPONSES TO REVIEWER COMMENTS\nLine numbers correspond to the finalized, clean version of the document without marked edits.\nReviewer #1:\nIn this ms., the authors present a suite of novel neodymium and oxygen isotope, rare earth element and salinity data from a\nunique expedition sampling throughout a winter season in the Arctic ice. Their data are interpreted in terms of input of\nfreshwater from the Siberian rivers to the Transpolar Drift (TPD), an important surface current that crosses the Arctic Ocean,\ninto the highest North Atlantic. Their conclusions are fine, but rather ambiguous – statements that tend to amount to “there is\ncomplexity,” but not exactly what this means.\nWe thank Reviewer 1 for this helpful review and comments and address each of their points below. Following the detailed\nconstructive feedback from Reviewers 3 and 4, we now articulate our conclusions more clearly.\nI fear that much of the perceived impact of this paper lies in the unique nature of the sampling campaign, which is an\nimpressive feat. But the data generated and conclusions reached are almost identical to their prior publication in 2021\n(Paffrath et al., Nature Scientific Reports).\nWe respectfully disagree with the reviewer on this important point. While the earlier study made notable progress in developing\nan oxygen and neodymium isotope-based method for quantifying riverine contributions in the Eurasian Basin, our manuscript\nexplores a distinctly different aspect: the redistribution of Siberian matter driven by ocean surface variability and sea ice–ocean\ninteractions. Our research, through the first-ever comparison of sea ice and seawater isotope signatures over a 200-day\nperiod, provides critical new insights into the seasonal dynamics of matter transport, a topic that has not been addressed in\nsuch detail in previous research, either by Paffrath et al. or by anyone else.\nI was hoping that this more recent ms. would have had further, more definitive conclusions on the nature of distribution of\nriverine sourced tracers, and how they might impact the North Atlantic. Particularly because these data are interesting and I\nthink that they should be published. However, as is, this ms. reads the same as their last (GEOTRACES) work. – perhaps a\nlonger-format journal would be more appropriate for more detailed and deeper discussion of both these data sets?\nWe appreciate the reviewer’s comments but would like to clarify that our study builds upon, rather than replicates, the work of\nPaffrath et al. (2021). While their study established a robust framework for tracing river water in the Eurasian Basin using O\nand Nd isotopes, our research extends this foundation by conducting a quantitative water mass analysis based on these\ntracers. Specifically, we examine matter dispersal mechanisms through a more comprehensive dataset that encompasses\nboth seasonal water column and sea ice data. This allows us to investigate key processes in sea ice and the surface ocean.\nWe recognize that the original manuscript may not have sufficiently emphasized these advancements. To address this, we\nhave revised the text to better articulate how our work builds upon and expands the scope of earlier studies (e.g., L94-97).\nWhile we believe that Paffrath et al. have in large parts already addressed the reviewer’s concerns regarding river discharge\nvariability, the influence of Pacific water, and non-conservative processes (points 1-6 raised below), we acknowledge the\nimportance of revisiting these issues in light of our recent observations and studies published after Paffrath et al. Accordingly,\nwe have thoroughly revised and expanded our discussion to explore these issues in greater depth, as outlined in our individual\nresponses below. River discharge variability is now emphasized in the main text, as it plays a crucial role in understanding\nmatter variability. However, since non-conservative processes and Pacific water do not impact our conclusions, we have opted\nto address them in the supplement. We believe this approach aligns with the journal’s format, as including these topics in the\nmain text would not add significant insights or affect the overall conclusions and their significance.\nRegardless of decision, I might offer the authors the following concerns to consider:\n1. River water input is highly variable over the year, particularly in the Arctic. The winter has near-negligable discharge,\ncompared to massive spring flows. There in no mention of this in the ms., but I would imagine it is important. Similarly, what is\nthe residence time of river water in the shelf area, or in the TPD?\nWe recognize that readers unfamiliar with previous studies (e.g., Paffrath et al., 2021, Sci. Rep.; Janout et al., 2020, Frontiers;\nLaukert et al., 2017, EPSL) may require more background regarding river discharge variability and its impact on the TPD.\nAs we mentioned in the original manuscript, hydrographic variability in the source regions is a key factor contributing to the\nfreshwater distribution encountered along the TPD. To clarify this, we have extended our original discussion of the processes\ndriving this shelf variability by including the influence of highly variable river discharge across seasons, L254-267. We have\nalso refined our water mass analysis by providing an uncertainty estimate for the variability in end-member signatures, L620-\n623. This estimate, based on a Monte Carlo simulation, now accounts for the slight seasonal differences in tracer signatures,\ne.g., between winter and summer months for the Lena River (endmember ranges are now provided in Suppl. Table S1).\nAs for the residence and advection times, Paffrath et al. (2021) compared their TPD data from 2015 with data from\nearlier years (2013, 2014) from the Laptev Sea (Laukert et al., 2017, EPSL) and found that the distribution of tracers in both\nthe TPD and its main source region, the Laptev Sea, aligns well with known residence and advection times of surface waters\nin this area. For clarity, we now provide relevant residence and advection times in the manuscript, L254-267.\n1\n2. The systematics of eNd are oversimplified. This is not an open-ocean setting, and the residence time of Nd is not the same\nas deep, open ocean (it cannot be). The “quasi-conservate” nature of eNd in this setting cannot be assumed.\nWe agree that the residence time of Nd in the surface and coastal ocean is shorter than in the deep ocean; however, this\ndifference, which is poorly defined, does not affect our analysis or conclusions. The transport of ice and water along the TPD\noccurs over relatively short timescales, estimated at less than 10 years (Pfirman et al., 1997, Sci. Total. Environ.; Anderson et\nal., 1994, JGR Oceans; Schulz et al., 2024, Elementa). Thus, a residence time of hundreds of years, as in the deep open\nocean, is not necessary to explain the observed εNd behavior. This point is now acknowledged and discussed in the main text,\nL166-173, and Suppl. Text 1.\nFurthermore, Laukert et al. (2017, EPSL) demonstrate that REE removal, which reduces Nd residence time, is\nconfined to the shelf, a finding supported by our water mass analysis, which shows close alignment between measured and\nexpected [Nd] following removal rates of 70-80% (see our reply to point 4 and new Suppl. Fig. S5). Moreover, invariant vertical\nREE profiles in the central Arctic Ocean indicate the absence of (reversible) scavenging. This stability has been attributed to\nsuppressed biological productivity and limited particle flux, which restrict REE cycling (Paffrath et al., 2021, JGR Oceans) and\nassociated non-conservative changes in dissolved εNd (see our reply to point 6). This is now also mentioned in Suppl. Text 1.\n3. I would not be shocked to see 75% Pacific water at these depths. Saying it is “improbable” seems suspect. Pacific water\npenetrates very far into the Arctic, particularly in the winter when river flows are very low. Again, see point 1.\nWe regret our initial wording was strong and appreciate the opportunity to clarify. However, to the best of our knowledge, no\nstudy convincingly demonstrates significant penetration of Pacific water into the deep Eurasian Basin. The reviewer's assertion\nis thus not supported by literature. The perception of extensive Pacific water distribution in the Eurasian Arctic Ocean may\narise from older studies based on nutrient relationships, which have since been shown to be unreliable for tracing Pacific water\n(see summary in Suppl. Text 2.4). In fact, recent studies based on standard hydrographic data demonstrated that only minor\nfractions of Pacific water (<30%, Lin et al., 2021, GRL) are present in the form of summer Pacific water in the uppermost\nlayers (<100 m) of the Canada Basin and that this water is separated from the Eurasian Basin by a front along the northern\nside of the Mendeleev and Alpha ridges (Planat et al., in review at JGR Oceans). Even if we assume that some Pacific water\nescapes this circulation regime and enters the Eurasian Basin, it would undergo significant dilution during transport, likely\nreducing fractions to well below 30% in the upper 100 m. However, our data suggest that even this scenario is unlikely for our\nsites (see response to point 5). We have now expanded our rationale for excluding Pacific water from our water mass analysis\nin the supplement (Suppl. Text 2.4), incorporating the above arguments and references.\n4. I do not trust any of the [Nd] arguments. There is nothing conservative about [Nd] to suggest it can be used in these ways –\nparticularly when the “mixing” plot already assumes a 75% loss in the measured riverine end member (i.e., most of the Nd is\nlost in the estuary, but how consistently?). That they found a linear mixing curve is interesting, to be sure (dependent on some\nassumptions). But it makes little sense with respect to the isotopes; how can the concentration continue to increase with\napparent equal sensitivity/change in the isotopic fingerprint? The downstream isotopes should not change as readily, unless\nthere is an effective scavenging, which calls into question the [Nd] patterns.\nThe reviewer raises concerns regarding an apparent inconsistency between the strong correlation observed between [Nd] and\nriver water fraction (R² = 0.9) and the simultaneous change in εNd in our data (Figs 4a and b). As argued before, the εNd shift\ncan be explained by gradual admixture of river water to AAW, initially from the Yenisei/Ob rivers and subsequently from the\nLena river. The persistence of increasing [Nd] despite the changing εNd can be attributed to the similar [Nd] concentrations but\ndiffering εNd of the two riverine endmembers (Suppl. Table S1), combined with differences in their densities (and thus river\nwater fractions) due to their respective trajectories across the continental shelves. We have incorporated this information to the\nmain text, L176-184 and L216-221. Paffrath et al. (2021, Sci. Rep.) provide compelling evidence for this interpretation,\ndemonstrating, through the consideration of advection times, near-identical tracer properties between the source regions of the\nwaters and the corresponding TPD river water domains, confirming that a change in river water origin drives the εNd shift.\nFurthermore, we do not include [Nd] in our water mass analysis, which allows us to compare the calculated [Nd]\nvalues resulting from the conservative mixing of the selected endmembers with the measured [Nd] values. These data closely\nalign (new Suppl. Fig. S5), supporting a mixing relationship. We have detailed this comparison now in Suppl. Text 1 and\nreferenced it in the ‘Methods’ section. The Nd removal process necessary for this mixing has been well characterized in the\nLaptev Sea (Laukert et al., 2017, EPSL), where removal rates exceed 70% for surface waters exported from the Laptev Sea\n(S>25). We now acknowledge the limited variability in estuarine Nd removal (70-80%) and have incorporated an uncertainty\nestimate for this process in our water mass analysis through a Monte Carlo simulation, which is mentioned in the main text\n(L186-188), the ‘Methods’ section (L618-620) and Suppl. texts 1 and 2.2.\nThe scavenging process proposed by the reviewer appears less plausible than our interpretation for explaining the\nobserved patterns due to several key inconsistencies. First, it does not account for the systematic shift to less radiogenic εNd\nvalues at fRIV > 10% while maintaining a consistent and robust [Nd]-fRIV relationship. Second, it fails to address the origin of the\nmore radiogenic signatures observed at intermediate fRIV reaching -9, especially given the dominance of AAW in this region,\nwhich has εNd values no more positive than -10. Lastly, under the scavenging scenario, a shift to less radiogenic values at fRIV\n> 10% would correspond with decreasing [Nd] (AW < 16 pmol/kg), rather than the observed steady increase in concentrations.\n2\n5. I want to see the pacific treated quantitatively in their ms., and shown to be invalid, in figures and calculations. They did this\nin their 2021 publication, but did not here. In neither publication is a 1/[Nd] vs. eNd plot shown, which should, if conservative,\nshow clear mixing patterns.\nPaffrath et al. did include a 1/[Nd] vs. εNd plot, albeit in their supplement to illustrate mixing patterns (their Fig. S1). However,\nwe opted not to include this type of plot in the present study because the observed mixing patterns remain ambiguous. The\nnearly identical εNd signatures of Pacific water and Yenisei/Ob water render these patterns inconclusive (as discussed by\nPaffrath et al.). We also recognize the importance of quantitative analysis in assessing Pacific water influence. However, the 4-\ncomponent mixing calculations presented by Paffrath et al. are inherently sensitive to small variations in the [Nd] of riverine\nendmembers. Our sensitivity tests confirm that even minor changes in riverine Nd concentrations significantly impact the\ncalculated fractions, primarily through the Nd concentration balance. This limitation, which Paffrath et al. also acknowledged\n(Supplementary Text: “Limitations of the water mass inventory based on the Nd-method”), makes 4-component assessments\nwith Pacific water as one of the endmembers unreliable.\nWe have therefore adopted a different approach, now refined further. We first incorporated recent findings from other\nstudies on the spatial Pacific water distribution in the Arctic Ocean (see response to point 3). We then replaced the Yenisei/Ob\ncomponent in our analysis with Pacific water to determine the volume required to explain the more radiogenic εNd signatures\nobserved in the surface to ~100 m depth. This analysis indicated that an unrealistically large volume of Pacific water—\ncontradicting recent studies—would be required to account for the observed signal, thereby supporting its exclusion from our\nstudy site. Additionally, even if Pacific water were hypothetically present below 60 m depth (a highly unlikely scenario based\non current data), it would not alter our conclusions about sea ice–ocean interactions and surface εNd variability, given that the\nsurface εNd signal would still predominantly reflect Yenisei/Ob contributions. We added this expanded discussion, along with\nsupporting evidence against the presence of Pacific water, to a new section in the supplement (Suppl. Text 2.4).\n6. The boundary exchange problem for eNd must be addressed. Boundary exchange has been shown to not impact [Nd] from\noriginal observations. The authors dismiss this citing lack of evidence in overlying water, but this is weak, and was indeed\nweak in the original boundary exchange publications.\nThank you for this suggestion. We acknowledge the need for a more robust discussion on this topic. In our initial submission,\nwe had briefly addressed the influence of seawater-particle interactions on εNd in a paragraph within Supplementary Text 1,\nmainly referring to earlier studies. However, we have now expanded this discussion into a dedicated section (new Suppl. Text\n1). In this new section, we present several arguments to illustrate why boundary exchange or REE release from particles is\nunlikely to occur in our study region. These arguments include: the absence of observed processes to date (supported by five\nindependent studies: Laukert et al., 2017, GCA; Laukert et al., 2017, EPSL; Laukert et al., 2019, Chem. Geol.; Paffrath et al.,\n2021, Sci. Rep.; Paffrath et al., 2021, JGR Oceans), limited biological activity due to perennial ice cover, minimal riverine\nparticle flux, and high ratio of organic to inorganic nanoparticles and colloids in Siberian rivers. Only one sample—which we\nhave excluded from our source apportionment analysis—could potentially be influenced by such processes. We have also\nrevised the introduction and results sections to acknowledge potential non-conservative influence, see L86-88 and L169-171.\n7. Brine spillage – I appreciate the difficulties of sample collection, and this should not be held against the authors, but should\nbe treated as honestly as possible. The fact that brine has far greater [Nd] concentrations than non-brine water makes a linear\nmixing plot even more confusing. This should be treated with greater discussion.\nWe appreciate the reviewer’s concern, but we are unsure how brine spillage specifically during ice core retrieval would affect\nthe [Nd]-fRIV mixing plot. If the reviewer’s comment refers instead to brine rejection—that is, the influence of sea ice formation\non water column [Nd]—we acknowledge that this was not fully addressed in our initial water mass analysis. We have now\nupdated the analysis to account for sea ice processes, see L599-608. This adjustment slightly changed fractions but did not\nalter our conclusions. Please also see our extended response to Reviewer 2 for additional context.\n8. L68-69 needs references? This seems like a bold statement, given Ekman transport is one of the original “oceanographic”\ntheories, based on years of observation and is a cornerstone of physical oceanography. (The story of Nansen is the\noceanography equivalent to Newton’s apple…). Throughout the ms., bold statements confronting established physical\noceanographic principles should be considered deeper.\nOur intention was not to question basic principles of physical oceanography. Instead, we focused on assessing the combined\nimpact of Ekman transport and other processes on the dispersion of Siberian matter, which is a more nuanced issue. We have\nnow revised the manuscript following the detailed constructive feedback from Reviewers 3 and 4 on this topic.\nIn summary, I feel that there are a few critical points that should be addressed regardless of where this ms. is published. I do\nhope these data are published, and it would be great if published with Nature Communications - but I think this would take a\nsignificant re-write.\nWe appreciate the reviewer’s support for publishing our data and the encouragement for eventual publication in Nature\nCommunications. In response to this and the other reviews, we have made substantial revisions to the manuscript.\nLaukert et al. present a well-written manuscript presenting geochemical provenance tracer data collected from the MOSAiC\ncampaign in the Eurasian Arctic Ocean. This exciting dataset is potentially significant because it fills the knowledge gap of\nsea ice and ocean surface pathways in the polar winter. The manuscript is clear, thorough, and nearly free of typos. The\nauthors have done a good job combining multiple chemical tracers and disentangling complex endmembers. However, I\nhave some substantive comments, as listed below. One of my main concerns is that the authors may have mistakenly\nomitted the sea ice melt fraction in their mass balance Equation 3, which could generate quite different results from the\ncurrent version, at least during part of the cruise. Overall, I recommend publication with major revisions.\nWe would like to thank the reviewer for this positive assessment and the constructive review. The main issue raised by the\nreviewer regarding the sea ice fraction, as well as all other issues, have been thoroughly addressed as described below.\nMajor interpretation points\nThis reviewer has some major questions about Equations 3 to 5. First, the authors should add fice in Equation 3 if I correctly\nunderstand Rosen et al. (2015)'s Equation 3. Without fice, the sum of fAW, fLena, and fYenOb wouldn’t be 1. Secondly, the\nauthors correct for salt fluxes related to sea ice formation and melting for salinity in Equation 4, but no such correction exists\nfor Equation 5. The authors could argue that neodymium isotopes remain unfractionated during sea ice processes, but sea\nice processes do contribute to Nd concentrations, as seen in Figure 5d. This would change the denominator of Equation 5.\nThirdly, if my previous two points are correct, I am curious how these fractions would change after taking into account the\nfraction of sea ice melt in Equations 3 and 5. I don’t think the general story would change, but there could be some changes\nto some interpretations, at least during part of the cruise.\nWe appreciate the reviewer’s thorough analysis of our water mass calculations. Initially, we chose to exclude the sea ice\nfraction (fSIM) from our analysis to focus solely on water mass origin, without considering sea-ice influence. However, we agree\nwith the reviewer that including fSIM, as well as correcting for [Nd] in relation to sea ice processes, is indeed relevant and\nvaluable. Consequently, we have revised our water mass analysis to include the sea ice fraction and δ18O balance in the\nmatrix inversion, thereby also addressing suggestions from Reviewers 3 and 4. To account for uncertainties in endmember\nvalues, the matrix inversion was performed using a Monte Carlo simulation with 100,000 iterations. See ‘Methods’, L599-632.\nImportantly, as expected by the Reviewer, the overall fractions and patterns did not change significantly from our\noriginal results. This is largely because the key impact of sea ice—its effect on salinity—was already accounted for in our initial\ncalculations. Furthermore, the influence of the [Nd] correction on the fractions appears to be limited, presumably because of\nthe very high endmember [Nd] of the rivers. However, the river fraction has increased slightly due to refinements in\nendmember values and updates to the δ18O dataset, which is now complete and has been quality-checked. The calculated fSIM\nvalues align with previous studies, indicating significant brine addition to the upper 100 m and small amounts of melt beneath\n100 m. We have added this information to the revised manuscript, L154-163. Please note that a detailed discussion of the fSIM\ndistribution is outside the scope of this study, though we have plans to address this topic in a follow-up separate publication.\nInterpretation points, by line #\nLines 31–32: The decoupling between sea ice and ocean surface transport doesn't seem to only occur in the winter.\nThis part of the introduction has been completely revised and no longer contains this statement.\nLines 50–51: The Fram Strait is not the only outflow of the TPD. What about the outflow into the Canadian Arctic\nArchipelago (CAA)? For example, the authors can check out their own schematic Figure S3 or Carmack et al. (2016)\n(10.1002/2015JG003140).\nWe agree and included the CAA as a potential exit region of TPD waters and sea ice, L50-52. We also added the reference\nprovided by the reviewer to the manuscript, making it the first reference in the main text.\nLines 206–208: The authors should consider plotting d18O and Nd section plots in the supplemental. It's not immediately\nobvious if the Pacific water has some influence on the subsurface water mass, especially for those close to the Makarov\nBasin, which can be identified by Paffrath et al. (2021). Additionally, how would the Greenland glacier runoff influence\n((cid:1)Nd<-17; Figure S3) the end-member mixing model close to the Fram Strait?\nWe have added these plots to the supplement (Suppl. Fig. S1), but unfortunately, they do not provide new insights into the\ninfluence of Pacific water, as the underlying issue with tracer sensitivity to Pacific water remains unchanged (see Paffrath et\nal., 2021, Sci. Rep., and our reply to point 5 of Reviewer 1). However, we note that our data do not exhibit the deviations in the\n[Nd]-fRIV relationship observed by Paffrath et al. in the Makarov Basin. This is shown in our new Fig. 4 (with recalculated fRIV\nvalues for the Paffrath dataset) and discussed in the main text (L176–180). These deviations may reflect Pacific water input,\nbut this is irrelevant to our study. We added this point to a new, extended discussion on Pacific water influence in the\nsupplement (new Suppl. Text 2.4), which also includes new references, as detailed in our response to point 3 of Reviewer 1.\nIndeed, we observed some influence of Greenland meltwater in one sample from Fram Strait. This sample was\nexcluded from our water mass analysis, as detailed in the original manuscript (L720-L723) and now mentioned in L173-176.\nWe have included a deeper discussion about the potential contribution of this endmember in the supplement (Suppl. Text 2.2).\nsalinity and d18O. I am also not sure why the authors have different conclusions between the pattern of (cid:1)Nd (e.g., Lines\n292–295) and Nd or HREE/LREE. All of those parameters only have 5 points in Figure 5. Are there any statistical reasons?\nWe acknowledge that five data points are generally not considered statistically significant. The low number of samples results\nfrom the more challenging sampling and processing requirements for large-volume εNd samples, as well as the limited\nsampling capacities during MOSAiC. Nevertheless, the observed changes in the surface water and their relationship with sea\nice data are clear and distinguishable from measurement uncertainties. Additionally, these trends align with existing\nknowledge about REE systematics in sea ice (Laukert et al., 2022, GRL), supporting the validity of our interpretation.\nDespite having the same quantity of seawater data, our conclusions regarding the comparability of ice and water\ndiffer due to the lower resolution of εNd measurements in sea ice (20 cm intervals) compared to Nd, HREE/LREE, salinity,\nand δ18O (10 cm intervals). We want to emphasize that the original text (L292–295) discusses the impact of an adjustment of εNd\ndue to permeability effects, which we conclude would be less evident than in salinity/δ18O due to the 20 cm resolution. We\nhave now refined this part of the manuscript (L324-331) and hope that this adequately addresses the reviewer's comment.\nLine 344: Why does this have implications for particles? All parameters discussed in the manuscript are dissolved. Particles\ntend to sink, so they cannot be transported as long distance as the dissolved phase. Please clarify.\nThank you for bringing attention to this important point and the opportunity to clarify. Broadly speaking, the transition from\ndissolved to particulate phases is gradual rather than discrete; the spectrum spans from ionic solutions to nanoparticulates,\nligand-associated compounds, colloidal matter, and small particles. Many of these size classes, including smaller particles, are\nneutrally buoyant and can therefore be transported over considerable distances as suspended particulate matter (SPM). For\nexample, natural SPM linked to river input has shown to travel hundreds of kilometers away from the river mouth (e.g.,\nWegner et al., 2013, BG; Yu et al., 2023, Mar. Environ. Res.; Brown et al., 2014, JGR Oceans). This transport can be amplified\nsimilarly to the dissolved load when sea ice is involved through multiple freezing/thawing cycles. In addition, some particles\nalways stay at the ocean surface (so called floaters, e.g., certain plastic particles). Therefore, our assessment of potential\ntransport routes considers the longest possible distances that both dissolved substances and particles can cover. We have\nnow clarified this in the manuscript, albeit in a very condensed form due to the length restriction, L407-410.\nLines 397–402: Concentrations of certain elements may be high in sea ice, but what about their relative mass compared to the\nsurface ocean? In other words, how much mass is incorporated into the sea ice in a budget perspective? The volume of\nsea ice seems to be much smaller than that of the polar mixed layer.\nWe appreciate the reviewer’s point regarding the relative mass of elements incorporated into sea ice compared to the surface\nocean. Indeed, our objective was not to suggest that this process significantly influences the overall Nd or REE mass balance\nin the Arctic. Instead, our study aims to demonstrate that Nd and its isotopes can serve as a valuable tools for examining\nredistribution processes and identifying potential transport pathways. While the impact of sea ice melt on REE levels in the\nmixed layer is likely minor, it is important to note that this primarily applies to elements present naturally in trace amounts.\nApart from that, the redistribution behavior varies significantly among substances due to their distinct physical and\nchemical properties, which is now acknowledged early on (L60–63). For example, substances such as floaters often remain\nconfined to the surface rather than mixing throughout the mixed layer, suggesting they may be fully redistributed by sea ice.\nAdditionally, variability in source inputs, particularly for anthropogenic pollutants, complicates precise flux estimations,\nsee e.g. microplastics, which exhibit extremely high source variability (e.g., Peeken et al., 2018, Nat. Commun.). These\ncomplexities underscore the challenges of deriving accurate budgets based on observed distributions, such as those of REEs.\nTherefore, we rather emphasize the importance of the underlying transport mechanisms, which we now clearly articulated,\nL410–415.\nLines 592–594: The constants (cid:2) and a did get mentioned in the reference cited here. However, the authors still need to do a\nbetter job explaining these two terms, at least briefly. What do they mean? Just showing their values is not enough.\nThis passage has been revised and for the calculation of S0 we now refer entirely to Rosen et al. 2015, L676-678.\nLines 603–608: Are there any figures? If using d18O alone generates similar results, what new information do we get by\nadding an extra Nd tracer in the end-member mixing model?\nThank you for pointing this out; we agree that clarification was needed. Incorporating εNd allows us to differentiate between\nLena and Yenisei/Ob waters, which is not possible with δ18O alone. While δ18O enables calculation of the combined river\ncontributions (fRIV), allowing us to examine these contributions at a higher spatial resolution (Fig. 3a), εNd provides a coarser\nbut more distinct separation (Fig. 3c-e). This complementary approach enhances our understanding of riverine inputs and their\ndistribution. We have revised the main text (L184-186) and the methods section (L633-639) to clarify this distinction.\nLines 780–795: Why are the white arrows just one way? What about sea ice algae falling, dirty sea ice exchange, or ice\nmelting along the way?\nWe wanted to emphasize the dominant redistribution mechanisms for river-borne substances, which we now clarified in the\nfigure caption, L839-840.\nThis study uses “provenance” tracers (salinity, d18O and eps_Nd) to identify the origin of surface waters and sea ice. They\nuse standard linear deconvolution techniques for surface seawater (<200 m), and also apply the technique to sea ice\nsamples, which is novel. Critically, their sampling campaign (from the MOSAiC project) followed an ice flow through an\nentire annual cycle. There have been a couple of prior floating platform expeditions to the Arctic, but they came before our\ncapacity to collect and perform isotopic analyses on rare earth and element (REE) samples. Thus, the authors’ dataset is\ntruly unique and represents a major leap forward for provenance studies of Arctic waters. Their results use a recently\ndescribed river tracer, neodymium isotopes, to assess which rivers are contributing to the riverine fraction in their samples.\nThe results indicate an admixture of Lena and Yenisey/Ob waters in the surface ocean and sea ice; suggesting that sea ice\nincorporates dissolved rare earth constituents in the same abundances that it is in the surface ocean. Further, they show that\nthe balance of Laptev Sea (Lena) vs Kara Sea (Ob/Yenisey) waters shifts in major ways along the route of the platform’s\ndrift. And finally, they demonstrate that these shifts are visible in the ice, so that a vertical core through the ice can be viewed\nas an archive of surface water chemistry. These results have implications for the transport of constituents by sea ice to\nlocations distal from the source.\nThe submission represents the latest in a series of excellent studies from this group. This latest effort includes a superb\ndataset and some exciting results. However, we have several serious problems with the presentation that need to be\naddressed before publication. We recommend accepting the paper, pending major revisions.\nGeneral Comments\nEarly in the text, the authors indicate that prior work mainly views the near-surface waters as traveling in the same direction\nas, and at about the same speed as, the sea ice. And at several points in the text, the authors indicate that an important part of\ntheir contribution is to show that the surface waters travel differentially from the ice. But this has been well known for many\nyears. In fact, later in the text, and in their Figure 2, they indicate that the pre-existing consensus model is that the near-\nsurface waters travel to the right of, and significantly slower than, the sea ice. Thus, the initial characterization of prior work\nfeels to us like a “straw man” argument: setting up an inaccurate version of their colleagues’ previous work in order to stand\nout in comparison.\nPrevious work has documented an Ekman spiral in Arctic waters, localized areas of intense upwelling between ice floes,\neddies of various sizes, geostrophic currents based on horizontal density gradients, topographically trapped boundary\ncurrents, and horizontal detrainment from boundary flows, in addition to ice-ocean surface stresses as drivers of near-surface\ncurrents. If we take this diversity of dynamics as the actual prior state of knowledge, then the authors’ unique contribution is\nnot the fact that surface water and sea-ice motions diverge, but something specific about how they diverge.\nRegarding the mechanism of divergence, we had difficulty understanding with some precision what the authors are\nsuggesting. On the one hand, they refer to hydrodynamics on the Siberian shelves as the origin of distinct assemblages of\nwater masses crossed by their floe. On the other hand, they suggest that a variety of short-term, mid-scale phenomena might\nbe responsible, such as inertial motions and eddies. The former hypothesis suggests a relatively stable TPD into which a\nvarying set of “head waters” are inserted. The latter is a different narrative, in which time-varying motions in the pelagic\nocean might be responsible for shifting TPD content. Perhaps they envision both: varying sources where the TPD detrains\nfrom the shelf, followed by insertions and/or detrainments along its route. Whatever the narrative, it needs to be clearer.\nWe appreciate the reviewers’ insights and acknowledge that our initial discussion of previous work may have unintentionally\nappeared as a \"straw man\" argument. It was not our intent to misrepresent earlier studies, and we regret that our focus on\ncertain analytical and technical aspects led to an incomplete presentation of our key findings.\nWe now emphasize prominently that the existing literature already documents a variety of dynamic processes\ninfluencing ice and ocean transport, L63-67. As the reviewers correctly pointed out, the novelty of our study lies not merely in\nidentifying the divergence between sea ice and surface water motions, but in detailing how this divergence occurs and,\nspecifically, how it affects Siberian matter dispersal over long distances—an aspect that has not yet been explored in\nsuch detail. We propose that shelf hydrodynamics drive the observed TPD variability, while the influence of short-term,\nintermediate-scale phenomena is limited, L234-288 and L388-393.\nAlthough this point is more conceptual in nature, we believe the substantially revised manuscript now conveys this\nnarrative more effectively across all sections, which should become evident upon reading.\nThis is because they disperse over their residence times, smoothing out short-term, small-scale flows and structures.\nCurrent measurements and purposeful tracer injections have been the tools of choice for evaluating dynamics with shorter time\nscales. The MOSAIC program included current profilers, and we found it curious that the manuscript made no mention of that\ndata, which should have a lot to say about the divergence of sea ice and water velocities along the track.\nThank you for bringing up this important distinction between the use of tracers and other tools for studying circulation changes.\nWe have now clarified the role of tracers in resolving long-range transport in the introduction (L81–83). In our initial\nsubmission, we referred to sea ice and surface current velocities, as well as their predominant directions, in the ‘Context of the\nMOSAiC Campaign’ section. However, we chose not to explicitly mention the current profiler data from which these velocities\nwere derived. We have since refined this passage to state: “Ice drift velocities frequently exceeded surface current velocities,\nwith the predominant ice drift direction often diverging from the underlying water movement” (L120-121). While the divergence\ncaptured in these data is valuable, our primary focus is on understanding how this divergence impacts large-scale matter\ndispersal, where tracers provide more suitable insights than current measurements, as also noted by the reviewers. For a\nmore detailed analysis of the variations in ice drift, surface current velocities, and their predominant directions, we now refer\nreaders to Rabe et al. (2024, Elementa) and Schulz et al. (2024, Elementa) (e.g., Figs. 15a,d and 16a,b in Rabe et al., 2024;\nFig. 5a in Schulz et al., 2024).\nA second major outcome of the work is that sea ice transport of marine constituents is an important process. The study\nidentifies that riverine constituents are a component of sea ice; and suggests that melting of the ice transports riverine signals\nto the central Arctic and beyond. They state that this paradigm is more important than previously thought, but they do not\nquantify the relative import of this signal (melting sea ice) to the surface ocean for the various constituents. The reader cannot\nknow how important the mechanism is. Could it still be negligible relative to the role of advected waters? Is the bulk of material\ntransported from the ice “factories” of the Siberian shelf to the Greenland and Canadian shelves? Some sort of scale should\nbe set to support the claim of importance.\nThank you for raising this important point. As we noted in our response to Reviewer 2, our focus is not on asserting that sea\nice redistribution significantly impacts the overall Nd/REE mass balance in the Arctic. Rather, our goal is to illustrate how Nd\nisotopes can serve as a useful tracer for studying redistribution processes and identifying potential transport pathways, rather\nthan for providing precise budgets of each substance. Contaminants such as microplastics show highly variable\nconcentrations, which complicates reliable predictions regarding their redistribution. Even if quantitative budgets for certain\nsubstances could be determined, they would still fall short of capturing the shifts in transport dynamics that might arise from\nchanging source inputs. The significance of this mechanism also varies based on the unique physical and chemical properties\nof the transported substances. To address this point, we made it clearer that our aim is not to provide exact quantitative\nbudgets—which would be challenging and potentially impractical—but to shed light on the redistribution processes and their\nsignificance within the broader context. We have clarified and expanded on this point in the revised manuscript (L401-415).\nFurthermore, they imply that sea ice as a modality of transport of materials from Siberian shelves has so far been neglected,\nand do not site studies that show that sea ice entrains and redistributes materials. We agree that quantification of this process\nis an ongoing area of study, and this study offers a lens into sea ice formation endmembers that permit a better quantification\nthan previous studies. However, the role of entrainment of geochemical parameters and other material has not previously\nbeen neglected. Entrainment of geochemical parameters in proportions present in the surface seawater is consistent with our\nunderstanding of sea ice formation (e.g., major geochemical constituents are driven by brine/salinity dynamics). To\nacknowledge some of the previous work addressing the transport of sea ice incorporated constituents we advise the authors\nlook at: https://doi.org/10.1016/0048-9697(95)04174-Y, https://doi.org/10.1029/2022GB007320,\nhttps://doi.org/10.1038/srep16179,\nWe appreciate the reviewer’s suggestion and fully acknowledge the well-established role of sea ice in transporting constituents\nfrom the water column. This is now reflected in the introduction, which early on emphasizes sea ice as a transport mechanism\nand incorporates one of these references along with similar ones (L52-54). However, the point we wanted to make was that\nmost prior studies have primarily focused on the transport of matter from coastal or shelf regions into the deep Arctic Ocean or\nthe North Atlantic. This has contributed to the prevailing view of the TPD as a largely direct transport pathway from the\nSiberian shelves to Fram Strait, with minimal redistribution or interaction en route (e.g., Krumpen et al., 2019, Sci. Rep.). Our\nstudy seeks to extend this understanding by offering new insights into the provenance and processes affecting sea ice-borne\nmatter, specifically highlighting how ice-ocean exchanges and surface layer variability facilitate further redistribution along the\nTPD pathway. We have now clarified this distinction in the revised ‘Implications’ section of the manuscript, L410-433. We\nbelieve this redistribution mechanism will likely intensify as the Arctic transitions toward a seasonally ice-free state, L438-440.\nThe authors assume that the samples are composed of Yenisey/Ob water, Lena water, sea-ice melt, and Arctic Atlantic water.\nIn the Supplementary Material, they make the case that Pacific inflow is not present in the samples. But what about other\nfreshwater sources, such as Kolyma River water and other Eurasian rivers, groundwater seep, and Mackenzie River water?\nIt seems to us that something needs to be said about why these sources can be ignored.\nfindings on the dynamic nature of redistribution. While contributions from smaller Siberian rivers cannot be quantified in our\nanalysis, we exclude significant input from Canadian rivers, including the Mackenzie River, as their discharge is primarily\ndirected into the Canadian Arctic Archipelago. To address these considerations, we have added a new section to the\nendmember discussion in Suppl. Text 2.2.\nThe only error mentioned in the manuscript is the analytic error on neodymium measurements. However, in our experience,\nby far the largest source of error in water mass analysis is the scatter in the end member observations. This is a serious gap,\nand really needs to be addressed.\nWe appreciate this valuable observation and agree with the importance of addressing endmember variability. To address this\nissue, we have conducted a Monte Carlo simulation with 100,000 iterations to thoroughly account for uncertainties in\nendmember values (see ‘Methods’ section). Although these uncertainties are notable (e.g., see Fig. 6), they do not\nsignificantly affect the overall interpretations. We have included the ranges of endmember values used in the simulation in the\nSupplement (Suppl. Table S1).\nWe have a question about the water mass analysis: To estimate their four unknowns, the authors have four parameters:\nmass, salinity, oxygen isotopes, and neodymium isotopes. Why not set up a 4-by-4 linear problem, and invert a single matrix\nfor each sample? Rather, they take two steps, first identifying the sea-ice fraction, and then identifying the other three\ncomponents. What is gained by splitting the process in two?\nWe appreciate this insightful question. As noted in our response to Reviewer 2, our initial approach of separating the sea-ice\nfraction from the other components allowed us to address the unique influence of water mass origin independently. However,\nwe recognized the value in directly integrating the sea-ice fraction (fSIM) within the broader water mass analysis, consistent with\nprior studies. This updated approach incorporates both the δ18O mass balance and a correction for [Nd] redistribution\noriginating from sea ice, as emphasized by Reviewer 2.\nFollowing the reviewers’ suggestion, we revised our methodology to use a single matrix inversion approach for the\nfour parameters. This adjustment, along with a Monte Carlo simulation to account for endmember uncertainties, is detailed in\nthe methods section (L599-632). Despite these refinements, the primary water mass fractions and patterns remained similar to\nour original results, with only minor adjustments due to refined endmember values and updates to the δ18O dataset, now\nincluding the additional quality-checked Leg 5 data (September 2020). The river water fraction, now calculated using our\nupdated analysis rather than previously published fractions from Paffrath et al. (2021), showed a small change (Fig. 4).\nRemarks copied from the marked-up PDF (by line #):\nL54-56: This may overstate the case.\nWe have revised this passage to present a more moderate tone, now substantiated with more appropriate references. L54-57.\nL61: \"incorporates\" rather than absorbs?\nNo longer relevant here but changed everywhere else as suggested.\nL72-74: Is this true? May be for some ... but I'd be surprised to find that it's a generally true statement.\nWe have revised this paragraph and statement following the major comment. L72-76.\nL104: This is not intuitive and might require more explanation.\nWe rephrased this sentence, see L100-102.\nL118: This is the first time I've seen this usage--describing new growth on an old floe as FYI. I like it; just saying: has it\nbecome common? Or is this a novel usage?\nWe believe this term was introduced by Angelopoulos et al. (2022, Front. Earth Sci.).\nL135: citation needed for depth\nNo longer relevant as text was changed.\nL136-137: To what depth are we talking about here?\nWe meant the partial admixture of AAW to all upper layers. While no longer relevant here due to changes in the text, this is\nclarified elsewhere, see L152-154.\nL139: Was there a period when there was no \"relatively fresh lens\"? Maybe a sentence defining what this means in this\ncontext. Also, is this relative to a history of surface water data from the region? Or relative to neighboring sites?\nWe have adjusted the wording and now refer to low-salinity water, see L133-134.\nEkman spiral ... the surprising thing is when they do align. Maybe this is all explained in detail below.\nWe agree and adjusted the sentence accordingly. L120-121.\nL156-162: The authors seem to have written the manuscript as though it were clear that the river water on the Eurasian side of\nthe Arctic basin, including the area between the North Pole and the western Fram Strait gets no, or only negligible,\ncontributions from the Canadian side of the basin. In the supplementary text, they make a convincing case that there is little\nPacific water in their samples. But I'm not self-evident to me that absence of a Pacific layer means that the negative d18O\nsignal is entirely from Siberian runoff (and not from the Mackenzie, from Pacific inflows modified on the Chukchi shelf, or snow\nmelt). Mightn't the surface circulation drag Mackenzie water into the TPD?\nWe agree that more clarity was needed here. As mentioned in our reply to the corresponding major comment, Suppl. Text 2.2\nnow discusses the potential contributions of Canadian rivers and smaller Siberian rivers. There, we argue based on literature\nthat even major Canadian rivers like the Mackenzie River do not significantly contribute to the TPD.\nIn terms of modified Pacific water, in Suppl. Text 2.4, we tested for the presence of Pacific water by using the\nendmember εNd signature of the Pacific water emerging from the Chukchi Shelf (Suppl. Table S1), and thus account for this\nmodified version. The strong correlation between fRIV and dissolved [Nd], alongside the observed patterns in fRIV-εNd space,\nfurther rules out a significant contribution from snowmelt (precipitation). Snowmelt would introduce greater variability in these\nrelationships, as [Nd] and εNd are more heterogeneous in snow and differ from river water signatures. It would also lead to\nother REE patterns, as shown by the snow pattern in our study (Suppl. Fig. S2) and in Laukert et al., 2022 (GRL).\nL177: All of them? Isn't there a (varying degree of) particle scavenging, some mineralogic and some biogenic? Are you saying\nthat Nd and other REEs are conservative away from the shelf seas? Need some clarity here; the statement as it is is too\nbroad.\nWe refined this passage in response to the comments of Reviewer 1, see L166-176. As a result, we have also included a\ndedicated section in the supplement (Suppl. Text 1) discussing the influence of non-conservative processes on dissolved [Nd]\nand εNd. In brief, we demonstrate that only estuarine REE removal affects concentrations beyond conservative water mass\nadvection and mixing. However, this can be corrected for in the water mass analysis by adjusting the river endmember [Nd].\nL183-184: I had to read this a few times and I'm still not sure I understand the meaning. When you say continental input, what\nis the boundary of that (does continental slope sediments count? or just terrigenous runoff?). Is there a known residence time\nof Nd and the HREE/LREE components relative to a specific process that sets a timeline or age limit on these observations?\nHREE/LREE ratio is pretty niche and although the reader may be directed to the supplementary material, a sentence\nintroducing the parameter might help the reader digest this information.\nWe understand that this description may be confusing for readers not familiar with REE systematics in the oceans. To improve\nclarity, we have expanded this section to include more detailed information on the processes involved and where they occur.\nThe comparison with AAW makes clear that the river water carries a younger “continental signal” relative to ambient ocean\nwater. L228-233.\nL184: So: not entirely conservative?\nCorrect, they are not entirely conservative. Non-conservative influence is now mentioned in the main text (L86-88 and L168-\n171) and addressed in Suppl. Text 1.\nL186-187: Are the signals of the Lena and Yenisey/Ob indicative of rivers on east and west of some location (e.g., Severnaya\nZemlya) or are there other rivers (minor and major) from the Siberian sector that could contribute to this?\nThe endmember values of these rivers represent pure river water signals, which remain unchanged in the ocean beyond\nconservative dilution through water mass mixing (i.e. mixing with AAW). Therefore, they are unsuitable for distinguishing\nbetween water advected east or west of specific locations, such as Severnaya Zemlya. However, we have considered the\npotential contributions from other Siberian rivers, both major and minor, that may influence these signals. In the text passage\ncited by the reviewer (now L184-186), we refer to the methods section, where the potential contribution of other sources is\nmentioned, and where the reader is directed to Suppl. Text 2.2 for a more detailed discussion on this subject.\nL189-194: This makes it seem as though Paffrath's dataset is one example among many. Aren't there just two Nd datasets--\nyours and hers?\nNo longer relevant as text was changed.\nL195: Why 'preceded'? Is there some temporal ordering implied here?\nYes, there is a temporal ordering implied, as explained in the section following this statement (now L203-233). However, since\nthis sentence appears earlier and we want to avoid confusion, we have adjusted the wording to reflect a mixing relationship\nrather than a temporal component. L191-192.\npresent in your data?\nWe can confirm the presence of up to 2% of meltwater at these depths (100-200m). We have added this information to the\nmain text along with the observation of a brine signal in the upper 100 m (L154-159). We also adjusted the quoted sentence.\nL196-200.\nL212: I don't think absorption is an appropriate word to use here. \"Mixing\" maybe.\nThis terminology was adopted from Bert Rudels, but we have now changed it following the suggestion. L205.\nL214-221: I get it; but maybe you could explain this more simply. Or maybe start with the observation, and then the\nexplanation.\nWe have reformulated this passage for the sake of clarity. L207-219.\nL234: Figure 3 makes it look like there is no Nd data between the end of November and the end of January. If this is the case,\ncan this time period be described?\nWhile no εNd data are available for this period due to the extremely difficult winter sampling conditions, the consistency of the\nfRIV distribution derived from the extensive δ18O dataset (Fig. 3a) suggests minimal variability beyond what can be resolved by\nεNd data before and after this period. i.e. the freshwater lens observed during this period is unlikely derived from different\nsources. Furthermore, the sea ice εNd profile supports this conclusion, at least for the mixed surface layer, demonstrating the\ncontinuity of the εNd signatures during this period.\nL240: \"explained\" seems like the wrong word here.\nNo longer relevant as text was changed.\nL242-245: I don't understand the binary set up in this paragraph. It seems to me that the ice is obviously drifting over different\nsurface waters. This has been known for a very long time ... the speeds and directions have long been measured to be\ndifferent. I don't see how \"drift dynamics\" could influence the surface water mass decompositions. These must be water-mass\ncomposition changes in the surface ocean. If you're arguing that the shifts take place primarily on the shelves ... and not over\nthe pelagic central Arctic ... then I don't see that you've made your argument. In the study we have not seen data on either the\nSiberian wind shifts or open Arctic basin wind shifts (both of which we know can redirect the surface plumes.) Maybe I'm just\nmissing something. If so ... what?\nThis appears to be a misunderstanding due to the vague wording in our original manuscript. We referred specifically to the\nsampling setup during MOSAiC, where ice drift, unlike ship-based sampling, was non-linear and influenced by changing winds.\nThis movement made continuous, linear sampling impossible and led us to exclude inertial oscillation—the circular motion of\nsea ice due to the Coriolis effect—as a significant factor in water column variability. The ice floe's movement can create\npatterns in 2D section plots that may not accurately represent the water column, as it can cause repeated or displaced\nfeatures from crossing the same water mass multiple times. Thus, we are not suggesting that drift dynamics change surface\nwater composition (in fact, we show the opposite in our ms). Instead, we emphasize that the drift dynamics can lead to\napparent shifts in water mass composition in the plots, which do not reflect actual changes. We have clarified this in the\nrevised text, noting that ice drift had only a minor effect on observed tracer variability, given the observed sea ice dynamics\nand the low spatial and temporal resolution of our data. Even the high-resolution river water fraction distribution from the δ18O\ndataset (Fig. 3a) shows no small-scale variations beyond general water mass advection patterns. L235-240.\nL256-258: Mechanism for and cause of what?\nRephrased to improve clarity. L290-292.\nL258: Could rephrase for clarity. Were the samples collected in these intervals, or is the discussion herein using these\nintervals, and are they selected for a specific reason or are they arbitrary intervals?\nRephrased for clarity. The intervals were identified based on the specific trends observed in the sea ice profiles. L292-295.\nL260: the antecedent to \"this\" is unclear.\nChanged for clarity. L294.\nL262: Agreed, for Eps_Nd. But for bulk Nd, the deviations don't appear so minor to me.\nIn Suppl. Text 3, which we refer to in the main text, we mention that an upward shift of [Nd] by 10 cm would increase the\ncorrelation coefficient between [Nd] and δ18O from 0.4 to 0.8, which is comparable to the effect of the salinity correction.\nAlthough the [Nd] shift may appear larger, it is therefore of a similar order of magnitude to that of salinity or εNd.\nThe original text was misleading, as it implied a comparison between δ¹⁸O and other parameters concerning their sensitivity to\nchanges in permeability. It is evident that brine-associated parameters exhibit greater sensitivity, given that most of the δ¹⁸O\noriginates from the ice crystal lattice, as stated in the main text. While we could theoretically compare the sensitivities of\nsalinity and REEs, including Nd, the lack of relevant studies on REEs precludes us from doing so. Therefore, the revised text\nnow emphasizes the overall sensitivity of brine-associated parameters to variations in permeability. L297-302.\nL265: either use this consistently or just say 'water' here.\nNo longer relevant as text was changed.\nL263-267: It would help the reader parse this information if it were two separate sentences. As-is, the interjection about\ndel18O pulls the reader one way; then it is surprising to be talking about concentrations at the end and not isotopes.\nWe rephrased this passage for clarity. L297-302.\nL269: The authors do a great job mitigating brine spillage; especially in the processing phase. However, in the methods, there\nis no statement regarding spillage during core extraction; which unfortunately can't be quantified but is likely the moment with\nthe highest amount of exchange.\nWe have added a statement to the methods section acknowledging the unavoidable brine spillage that occurs during core\nextraction, L484-486. As stated in the main text and described further below, while our approach cannot eliminate the effects\nof spillage, it can minimize them. L302-305.\nL274-276: Fine to crow a bit ... but perhaps it's more important to hammer home a message about the small-scale (intra-flow)\nvariability in porosity changes, which researchers thinking about sea ice-coring need to be cognizant of.\nWe welcome this suggestion and have modified the revised version accordingly. L311-314.\nL276-278: Did they see a different pattern, did their study not discuss salinity? Did they mis-interpret the salinity profile?\nWe now briefly mention the results of this study concerning salinity distribution, which was essentially uniform at the time of\nour sampling and did not reflect the trends we observed. L309-311.\nL282: What parameters were continuously measured? It looks like the parameters (d18O and Nd/epsilon-Nd) were measured\nroutinely, but not continously.\nChanged to ‘regular sampling’ as εNd samples were taken every 1-2 months and δ18O samples almost every week. L317.\nL283-284: Yeah; this is really cool.\nWe wholeheartedly agree! The time and effort invested in obtaining the data for this figure were immense, but it was obviously\nworth it!\nL300-303: This sentence is hard to understand.\nWe have rephrased the passage to enhance clarity and included additional details to improve understanding. L335-341.\nL314: It's not obvious to me what this has to do with the continuity of the profile??\nWhat we meant was that constant sea ice profiles without changes would not have allowed such a conclusion. We have now\nremoved this point because the consistent incorporation from the surface water is obvious from the seawater-sea ice\ncomparison.\nL332: does this mean that your cores did not have a shift -- or that the shift, also observed in your cores, contradicts the\nepsilon,nd signature. Could this be floe-based variability? Or could both be true since the surface formed before sampling\nbegan?\nWe have clarified this point in the text. Our cores did not exhibit the observed shift. We now argue that this is floe-based\nvariability established during thaw-freeze cycles in early winter, as only a few winter ice cores in the δ18O timeseries (\nMellat et al., 2024) show depletion in the uppermost 20 cm. L361-368.\nL337: HREE/LREE - I didn't see these terms spelled out above.\nWe introduced the abbreviations and the ratio in the original manuscript at L182, and it is now used frequently in that section\n(L224-233), so it does not need to be reintroduced later.\nL342: This is an intriguing hypothesis. But it's just sort of hanging there. What complexes? And how would these impact\nfractionation between ice and water? And are there observations in the Laptev and Kara seas or the deltas that might\ncorroborate the suggestion. I would want something more ... or else clarify that this is just a guess.\nL343-344: This is a vague phrase; not sure what mean.\nNo longer relevant as text was changed.\nL346-347: \"disparities of the complexation behavior\": are you talking about links to the water molecules ... or to other dissolved\nthings? Or do you know?\nWe now clarified in the revised text that we referred to REE complexation by inorganic and organic ligands, which could not\nonly account for differential incorporation of Nd sourced from different rivers, but also lead to preferential HREE rejection\ncompared to LREEs (HREE are almost entirely bound in stable carbonate complexes). L377-381.\nL355: 'precision' seems odd here. Do you mean that the differences are small compared to the precision of your\nmeasurements? If not, then maybe clarify.\nThis is no longer relevant as we have reworded that section.\nL358: I think this overstates what's been accomplished, at least to this point in the manuscript.\nThis is no longer relevant as we have reworded that section.\nL362-366: The structure of the sentence doesn't make sense. The first half implies that there should be significant variation in\ndistributions ... the second half notes that it has been found--then why \"Despite\"... ? Do you mean something like, \"Despite ...\n... ... the variability of their distribution has been tracked to a relatively simple dynamical mechanism.\"\nWe apologize for this incorrect wording. This passage has been removed from the manuscript.\nL366-368: I really like this narrative; and I suspect you are spot on. But I don't think you've quite made the case above. You\nseem to assume a \"freight train\" model of the TPD, in which parcels are loaded on at the Siberian shelf break, and no one gets\non or off along the trek to Fram Straight. If that's your image, I think it needs to be more explicit, and perhaps an argument\nmade for it. Alternatively, if your view is that the Ob/Yenesey and Lena river plumes enter the TPD at different points, then\nwhat is the evidence for temporal variability? Couldn't this just be the floe crossing over distinct, but relatively stable,\nspreading plumes?\nWe now clarified our position in the last paragraph of the section “Origin, distribution and variability of Siberian fresh water\nalong the Transpolar Drift“. L270-288.\nL368: Does this mean: the heterogeneity in observations along the TPD? Why does it imply shelf hydrography processes\nrather than variability in basin circulation.\nYes, that is correct. We were referring to the heterogeneity in observations along the TPD. We have improved the wording for\nclarity and now also address the limited impact of basin circulation in the revised manuscript. L277-281.\nL377-378: A couple of thoughts:\n1) cool\n2) did you determine this in this study, is there a citation from another mosaic study?\n3) adapts is a bit of an odd word. is replaced by?\nMaybe: The sea ice drift encountered different water masses every 14 days\"\nAlso, do other mosaic studies comment on the encountering of multiple water masses (i.e., lagrangian sea ice drift, but not a\nlagrangian ocean drift) yet? Katrin Schmidts paper alludes to it. Did any of the overview papers call it out?\n1) Yes, it is!\n2) We determined this number based on our data, now also clarified in the main text. L398-400.\n3) No longer relevant as text was changed.\n4) Rabe et al. 2022 and Schulz et al. 2024 reported surface salinity changes and generally attributed those to ocean surface\nvariability and seasonal stratification changes without providing data that resolve source contributions.\nL378-380: In my experience, most researchers assume that there is significant divergence between the water and ice motions-\n-in part based on data from previous drift expeditions ... and more recently from profiling ice buoys. Where I've seen people\nassume coherence it has been for lack of detailed data on the surface water velocities.\nWe agree and have removed this narrative from our manuscript, see response to main comment on this issue.\nL382: rapid is a relative term; could you constrain this a bit? \"...the redistribution of sib fw over an annual cycle...\"\nAdjusted as suggested. L396.\n\"should\" (per our basic Ekman theory) travel both faster and at an angle to the underlying water.\nWe agree. We removed this argumentation from the manuscript, see response to main comment on this issue.\nL393: the release of ...material from sea ice...\nNo longer relevant as text was changed.\nL394: This is a bit confusing -- could you quantify how much the release of sea ice materials influenced the water column and\nhow much of the water column is influenced by advection of Atlantic-water?\nWe addressed this issue in our response to the related main comment. Please see above.\nL397-398: I'm not sure that \"neglected\" is a fair characterization to the growing body of literature working on this question.\nFor example, Damm et al, 2015 poses the notion that dissolved gasses in central Arctic Ocean might be transported from the\nsiberian margin and later (2018 I think) demonstrate it. In 1990s there was a paper regarding the question of \"does ice\ntransport pollutants. This question has been on the radar, and this study adds important information to that question.\nAs we clarified in our response to your general comment, the original text was misleading. Our intention was to highlight that\nthe specific role of sea ice formation along the entire TPD in the central Arctic Ocean has been largely overlooked, not the\nbroader research question itself.\nL461-463: It's unclear which part of the approach was adopted for these purposes.\nWe meant the combination of depth intervals from individual ice cores. We have adjusted the text accordingly. L481-483.\nA few thoughts/questions:\nbrine spillage during sampling was likely at a maximum when the core was pulled from the corer and placed in the bag.\nThis is correct. However, while the new approach cannot eliminate the effects of brine spillage during core retrieval, it\nminimizes them. Our data show that taking a larger number of ice cores increases the fraction of trapped brines compared to\nspilled brines, minimizing the impact on the tracers associated with the brine. We have added this information to the method\nsection and modified Suppl. Text 3 accordingly.\nIs the wash with DI water a method that's been used before for trace metals in sea ice? Could you provide a citation? Most\ntrace metal work with sea ice shaves the edges of the ice with a titanium blade. Has the water method been shown to reduce\ncontaminants from the coring process?\nWhile physically removing the outer ice layer is generally effective in eliminating surface contaminants, it can sometimes cause\ntheir redistribution if ice fragments are displaced during scraping. By rinsing with MilliQ water prior to scraping, we can remove\nmost surface contaminants, minimizing the risk of redistribution. This decontamination approach has been used in the\npreparation of glacial ice cores (e.g., Aarons et al., 2016, EPSL, https://doi.org/10.1016/j.epsl.2016.03.035) and is now applied\nby others without the ether step for trace metal and isotope analysis (Choi et al., in prep.). Notably, this rinse does not affect\nthe brine content of the ice core, as only enclosed brines remain after sampling, and these are not influenced by brief\nexposure to MilliQ water. We have included this clarification in the text, L472-476.\nDid your brine spillage control include the wash step, in the next step you state \"to serve as a reference for the latter two\nprocesses\" but the reader has to do a lot of re-reading to figure out what the latter two processes were.\nYes, the control included the wash step. We acknowledge the lack of clarity in the original text and have revised it accordingly.\nOur intent was to refer to sea ice heterogeneity and brine spillage during sampling, not the wash step. L487-488.\nL490: I don't know this method off the top of my head and would have to go to the citation, but just to double check: Is it\ncorrect that the solution was washed with di-ethyl ether after it was redissolved in 6M HCl? Does it reprecipitate?\nYes, your description is correct, but no reprecipitation is needed. After redissolving the sample in 6M HCl, the di-ethyl ether is\nadded to perform solvent extraction. The ether forms a complex with Fe, which is less dense and thus separates from the\nacidic phase. This process effectively removes Fe from the sample, while the trace metals remain in the acidic phase. The\nether extraction is repeated to ensure that most of the residual Fe is removed. We acknowledge that the text was unclear and\nhave now improved it. L513-515.\nL595-596: What about the uncertainties from scatter in the end-members? In my experience, those usually overwhelm the\nanalytic uncertainties. (As an example, the Rosen, et al., 2015 paper estimates a 10% error on sea-ice formation as a result of\nuncertainty on the end-member value of 18O in Siberian river waters.)\nWe have performed a Monte Carlo simulation to account for scatter in the endmembers. Please see our reply to your general\ncomment above.\nWe have clarified in the revised text that this uncertainty is reflected in all relevant figures and is factored into our interpretation\nof the data. L659-662.\nL623: Different citation format\nResolved.\nL634-639: Fair enough; your choice. But why did you use this 2-step method, rather than define a 4-equation/4-end-member\nproblem, and solve it in one matrix inversion?\nWe have adjusted the calculations as suggested. Please see our reply to your general comment above and to the general\ncomment of Reviewer 2.\nFigure 1: Flip the color bar, up/down.\nDone. We have also updated the color scheme for the timeline in Figs 1, 5 and 6 to ensure it is distinct from the scheme used\nfor tracer data in the other figures. This change is intended to avoid confusion and clearly differentiate metadata from the\nactual tracer data presented in the remaining figures.\nFigure 3: The figure labels are awfully small.\nWe agree and have enlarged the figure labels.\nL705-707: Should figs b-e be gridded? The data is well spaced out and, given the high variability in conditions that the authors\ndescribe, does the gridding permit over-interpretation?\nThe four sections illustrate the distribution of Nd isotopes and the parameters derived from them. Nd isotope signatures\noriginate from large-scale geological formations and broadly reflect water masses in the oceans on a basin or inter-basin\nscale, rather than capturing small-scale processes. Each water mass carries a distinct regional signature that remains stable\nover distances of several hundred kilometers. The variability we describe here pertains to differences in water mass\ndistribution, not fine-scale processes, which, although significant, are beyond the scope of this analysis. A higher resolution of\nthe water mass distribution will, however, unlikely be achieved by a higher number of Nd isotope data points. For instance, in\nthe Fram Strait in 2012 (Laukert et al., 2017, GCA), only five distinct water masses were identified despite high spatial\nresolution and over 100 Nd isotope data points. Similarly, in the Laptev Sea, only three water masses were detected with\ncomparable resolution (Laukert et al., 2017, EPSL). Neither study detected small-scale processes or circulation features using\nNd isotopes. Therefore, increasing Nd isotope sampling density in the TPD is unlikely to reveal additional distinct water\nmasses or finer-scale details, nor significantly alter the overall pattern in the gridded distribution.\nThe high resolution of the water mass distribution is best illustrated by the extensive O isotope dataset, as shown by\nthe river water fraction in Figure 3a. We assume that the neodymium isotope distribution at a finer resolution follows the\npatterns of the O isotope distribution, given that both parameters are strongly linked, allowing readers to infer the details of the\nneodymium distribution from the river water distribution derived from O isotopes. Consequently, we continue to use the\ngridded representation to emphasize the relative distribution of water masses. Individually colored data points would not\neffectively convey the broader patterns within the system.\nFigure 5: This chart was hard to read, would it help to ad a time component (z-axis) to the seawater data? It might help to\nremind the reader how the ice data was determined (i.e., is this just one core or a composite of several cores/dates).\nL739-740: It took way too long to understand this chart. Needs a better explanation. Imagine a reader that does not know\nwhat you've done here ... and explain it clearly.\nWe have added a time component to the seawater data in Figs 5 and 6 to enhance clarity. Additionally, we have refined the\nfigure captions to provide a clearer explanation of the plots. Please note that the distribution of all tracers has slightly changed\ndue to a refinement of the ice age model. Furthermore, the δ18O dataset has been updated and quality-checked based on\nduplicate samples, which has led to changes in the δ18O data and parameters derived from them in Figs 2-6.\nFigure 7/ L787-788: I really did not get this image from reading your text. I think you need to state this early and clearly in the\ntext. There's a lot of discussion of the basic fact that the ice and surface water diverge--\"are not aligned\"--which is not nearly\nclear enough to distinguish between your \"established\" and \"emerging\" views. Your take-home message in this chart has to\ndo with the impact of short-term variations (the 'eddy term' in classical phys oce texts) vs the long-term mean flow. But you\ndon't make that case clearly in the text.\nWe have removed the “decoupling” argument as a central point distinguishing between established and emerging views.\nInstead, we now focus on the role of shelf hydrography as the primary driver of TPD variability, and ice-ocean exchange along\nthe TPD, rather than basin circulation or short-term features.\nand \"b\" as well since those are the indications for the sub-figures.\nWe changed the letters and now explain lower case and capital letters in the figure caption.\nReviewer #4:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.\nWe are very grateful for the contribution of this reviewer and fully support the initiative to provide a direct opportunity for\nEarly Career Researchers for training in the peer review process.\nEditorial remarks (by line #):\nLine 1: Is “material” a better word than \"matter\"? I found the phrase “Siberian matter” a bit strange.\nWe appreciate the suggestion and gave this careful consideration. We prefer the term 'matter' for two main reasons. First,\n‘matter’ is a well-established term in oceanography, as seen in phrases like 'organic matter' and 'suspended particulate\nmatter,' and was similarly used in this context by Krumpen et al. (2019, Sci. Rep.) in discussing matter transport via sea ice\nalong the TPD. Second, ‘matter,’ by definition, encompasses anything with mass that occupies space, including atoms,\nmolecules, elements, and compounds, while ‘material’ more commonly refers to substances used to construct objects (e.g.,\nwood, metal, plastic). Thus, ‘matter’ better represents the broad range of physical substances involved in our study. For these\nreasons, we believe ‘matter’ is the most accurate and consistent choice for our study.\nLine 69: Ditto. It's a bit weird to use \"matter fluxes\" here. Do the authors think other phrases such as \"material fluxes\" or\n\"elemental fluxes\" work better? If so, the authors should consider changing this word/phrase throughout the manuscript.\nWhile we understand the concern, we believe that 'matter fluxes' is the most suitable term in this context. Unfortunately,\nalternatives like 'elemental fluxes' are too restrictive, as they would exclude the transport of non-elemental substances such as\nmicroplastics. Given the broad range of substances covered by our conclusions, including trace metals, particles, nutrients and\ncontaminants, 'matter fluxes' remains the most accurate term.\nLine 90: I don't think \"the reasons mentioned above\" are immediately before this sentence or even in this paragraph. If that's\nthe case, the authors should consider summarizing the reasons briefly and adding them here.\nThis is no longer relevant as we have reworded that section.\nLine 614: I may have missed it, but what is “CO”? Please clarify.\nWe meant “Central Observatory”, the abbreviation of which was introduced at the beginning of the method section. This\nabbreviation was only used twice, and we decided to remove it from the text for clarity.\nLines 671–673: The end of the first drift is not labeled by a dashed black circle.\nOur previous wording was unclear. We meant that only the FYI formation was marked with a circle. We have updated the\nfigure caption of Fig.1 to reflect this more accurately. We have also updated the figure by adding the new δ18O stations and\nusing a distinct color scheme for the timeline, different from the one used for the tracer data in the other figures. The latter\nchange is intended to avoid confusion and clearly differentiate metadata (shown in Fig. 1) from the actual tracer data\npresented in the remaining figures.\nLines 675–678: What are those dashed arrows? Please clarify.\nWe have clarified that these arrows indicate the approximate advection paths of the river inputs. L706-708.\nLine numbers correspond to the version with marked edits.\nReviewer #1:\nI appreciate the efforts taken by the authors to address the comments of their initial reviewers. I think the ms. has improved\nsignificantly from the first version. This new version reads well and is superbly illustrated. I still hold reservations on many\naspects of the work, but their arguments are generally all valid. While my original sentiment holds true – that these are\ninteresting data collected from a unique sampling campaign – I still have two reservations.\nWe thank the reviewer for their second review and thoughtful feedback. We are encouraged to hear that the re-write has\naddressed many of the initial concerns and that the manuscript is now perceived as significantly improved.\nThe first is a technical issue: The samples were collected in a lagrangian sense (drifting with the floes), but the data seems to\nbe interpreted in an eulerian sense. I worry about regional interpretations being made from locally collected data in this\ninstance – e.g., how much of their sampling could be simply caused by local ice floe-sea water interaction/evolution versus\nregional inputs/outputs?\nWe appreciate the reviewer’s insightful comment. However, based on the observations presented in our study and the\nunderlying physical processes, we are confident that the data reflect regional inputs and outputs rather than being dominated\nby local ice floe-sea water interaction or evolution. Below, we outline two key arguments supporting this interpretation:\n1. If the observed tracer signatures were solely a result of local ice floe-sea water interactions, they would remain\nlargely unchanged during drift, as the water column would drift alongside the ice. However, our study reveals highly variable\nsignatures of Siberian origin at the ice-water interface, which are inconsistent with a purely local interaction mechanism.\n2. Tracer signatures at the ice-water interface are consistent with those observed at depths of 50 to 100 meters (Fig.\n3a). This consistency indicates that the signatures extend throughout the mixed layer and into the halocline, where ice-ocean\ndrag and Ekman transport are negligible. As such, the observed patterns are not artifacts of the unique sampling setup but\ninstead reflect large-scale upper ocean circulation and its integration into sea ice, primarily influenced by the variability in\nsource regions of the water on the shelves.\nIn recognition of the need for clarification, we have added a sentence to the manuscript, L255-258:\nThe river water distribution along the TPD during the MOSAiC expedition reveals notable spatiotemporal variability in\nadvection. Variations in ice drift direction caused by inertial oscillations54—the circular movement of sea ice driven by\nthe Coriolis effect—appear to have no impact on tracer-derived fraction distributions when plotted over time and thus\nfollowing ice motion, likely due to the low spatial resolution of the tracer data. Even in the well-resolved river water\ndistribution, only broad advection and mixing patterns are visible, while smaller features fall below data resolution\n(Fig. 3a). These patterns also remained unaffected by local ice-water interactions during the drift, as tracer-derived\nfractions are consistent throughout the upper water column, extending to depths of up to 100 meters—well beyond\nthe direct influence of ice-ocean drag and Ekman transport. Throughout the campaign, sea ice drifted up to six times\nfaster than surface waters33, moving over high-fRIV waters toward the Fram Strait until mid-March 2020, when the\ndrift shifted towards the Nansen Basin. Therefore, tracer changes observed until spring 2020 mainly reflect variability\nin river water advection along the freshwater-rich TPD, along with the high-fRIV waters found later that year near the\nNorth Pole and at Fram Strait.\nThis clarification highlights how the river water distribution reflects broader regional processes, further supporting the validity of\nour interpretation. We trust that this addition addresses the reviewer’s concern effectively.\nMy second issue is simply that I was uninspired by their conclusions (a problem I had in the original version). This may not be\nan issue for publication, as the other reviewers seemed to consider this work important. I defer to the editors and other people\nmore interested in the themes addressed in this work; is this interesting work?\nConsidering the current challenges in Arctic research, we believe this work marks a significant advancement in understanding\nmatter dispersal in this rapidly evolving region. The geopolitical constraints on accessing Siberia underscore the importance of\ninnovative offshore, tracer-based methods like those utilized here to reveal dispersal pathways. These approaches are crucial\nfor evaluating the spread of hazardous substances, both now and in the future, with far-reaching implications for\nbiogeochemistry, ecosystems, and human health.\nIn summary, I think the re-write has helped tremendously, and I could envision this published in Nat. Comms.. Any perceived\nambivalence to publication should not be seen as a demerit, rather my own ignorance of the greater science this work\naddresses..\nWe value the reviewer's insights and the broader discussion they have prompted and hope that our response clarifies the\nstudy’s importance.\nThe authors have fully addressed my questions and done a good job responding to the points brought up by other reviewers. I\nam satisfied with the authors' explanations and edits in the revised manuscript. I recommend accepting the manuscript.\nWe thank the reviewer for their positive evaluation and recommendation to accept the manuscript.\nReviewer #3:\nThis is a resubmission of a manuscript that I reviewed previously.\nAll of my comments and criticisms have been fully addressed. In fact, the authors’ responses were more detailed and\nextensive than usual, and so have made interesting reading in themselves.\nThey have extensively reworked several parts of their argumentation; and they have added significantly to their\nsupplementary material. Changes to the charts and wordings of various sentences and paragraphs have made the\nmanuscript much clearer and easier to read.\nI recommend publication of the current manuscript without further revision.\nWe sincerely thank the reviewer for their feedback and kind words regarding our revisions. We are pleased that our efforts to\naddress the comments and enhance the manuscript were appreciated.\nReviewer #4:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.\nAgain, we are very grateful for the contribution of this reviewer and fully support the initiative to provide a direct opportunity for\nEarly Career Researchers for training in the peer review process.\n2"}]

Decision letter