Interannual variability in Transpolar Drift ice thickness and potential impact of Atlantiﬁcation

. Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exits the Arctic Ocean through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic inﬂuences along the pathways of Arctic sea ice. We present an updated time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20% thinning of modal ice thickness 5 since 2001. A comparison with ﬁrst preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC ﬂoe and its wider vicinity are consistent with measurements from previous years. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age and 10 number of freezing degree days. We also show that the increased

. a) Map showing all EM-based summer (July/August/September) SIT measurements obtained between 2001 and 2020, as well as July/August mean sea ice concentration  for the period from 2000 to 2019 (Lavergne et al., 2019). Enclosed area (red, 80.5-86 • N and 30 • W-20 • E) indicates the selected area of interest (AOI, see Table 1 for an overview of the corresponding expeditions and basic SIT statistics for the selected AOI). The red line (86 -90 • N, 50 • E) shows the transect of Russian sea ice observations. b) Summer (July/August) mean (red) and modal (blue) SIT based on EM measurements conducted in the AOI (circles). Diamond shows modal EM SIT measured on the MOSAiC floe in July 2020 and filled circles indicate mean and modal EM SIT values obtained during the IceBird MOSAiC campaign in September 2020. c) shows the fractional occurrence of first-year (white), second-year (grey) and multi-year ice (black, ice older than two years) for the individual years. The age classification is based on ICETrack calculations of the number of days the ice particles travelled along their trajectories.
For the current study we focus on the SIT measurements from a selected area of interest (AOI, enclosed area in Fig. 1 a)) just north of Fram Strait. Sea ice reaching Fram Strait originates from multiple regions of the Arctic, which means long-term observations of SIT in its vicinity provide insight into integrated Arctic-wide thermodynamic and dynamic changes in the sea ice cover (Hansen et al., 2013). While previous studies recorded substantial thinning and across Fram Strait (79 • N) SIT 55 gradients during the first decade of the 21st century (Hansen et al., 2013;Renner et al., 2014), we focus on the evolution of summer (July/August) SIT further upstream of the Transpolar Drift. With the AEM being towed by a fixed-wing aircraft longer transects and ultimately a greater areal distribution of the measurements are achieved as compared to other in situ observations. The objectives of this study are to extend the summer SIT time series (from 2012 to 2020), first published by Krumpen et al. (2016), at the end of the Transpolar Drift and investigate the interannual variability in SIT in the selected AOI close to the 60 export gate of Arctic sea ice. We will use the Lagrangian sea ice tracking tool, ICETrack  to determine the source regions and drift trajectories of the sea ice sampled in the AOI. In order to provide insight into the driving mechanisms of the observed SIT variability a thermodynamic model is applied along the determined sea ice trajectories to reconstruct the AOI-sampled SIT. In addition we will compare the SIT changes in the AOI to long-term observations gathered during regular Russian cruises from Franz Josef Land to the North Pole. This additional comparison is conducted to discuss whether the observed changes are limited to the AOI or induced during ice formation and transit through the Arctic Ocean. Finally, we will use the unique opportunity to compare the long-term SIT time series to IceBird and ground-based EM (GEM) measurements conducted within the framework of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). At 85 • N and 136 • E the German icebreaker RV Polarstern (Alfred Wegener Institute, 2017) moored to a 2.8 × 3.8 km sized ice floe in October 2019 (Krumpen et al., 2020). After about 9 months of drifting through the Arctic Ocean, RV Polarstern and 70 the MOSAiC floe reached the selected AOI in summer 2020. This allows us to consider the MOSAiC floe in the context of the long-term time series, and determine whether the SIT of the MOSAiC floe in 2020 was exceptional or in agreement with historical observations.
2 Data and methods 2.1 EM sea ice thickness measurements 75 Electromagnetic induction (EM) SIT measurement systems take advantage of contrasting electrical conductivities between sea ice and sea water to determine the distance between the ice-water interface and the EM device (Kovacs and Morey, 1991;Haas et al., 1997). In 2001, measurements were conducted using a Geonics EM31 ground-based EM instrument (GEM). The GEM was pulled over the ice on a sledge and obtained the distance to the ice-water interface (Haas, 2004). GEM measurements included SIT values over melt ponds and pressure ridges, however, open water and thin ice were not adequately represented 80 in the data sets due to the practical limitations of sampling those areas on foot (Haas, 2004). The 2020 GEM measurements on the MOSAiC floe were taken with the Geophex GEM-2, a broadband EM sensor used for advanced thickness observations (Hunkeler et al., 2016). After 2001, SIT was obtained using the airborne EM system (AEM), EM Bird, that was towed by a helicopter (in 2004) and the research aircrafts Polar 5 and 6 (from 2010 onwards). The EM Bird was operated between 12 and 20 m above the ice surface (Krumpen et al., 2016). Following Pfaffling et al. (2007), SIT was calculated as the difference 85 between EM-derived distance to the ice-water interface and laser altimeter-recorded distance between EM device and the airsnow interface. EM measurement accuracy is within 0.1 m to drill-hole measurements over level sea ice, while water inclusions within pressure ridges lead to a general underestimation of ridges by as much as 40 to 50% (Pfaffling et al., 2007;Haas et al., 2009).
Thickness measurements using the ground-based and airborne EM methods always represent the total combined sea ice and 90 snow thickness (Haas et al., 1997). Given the study period from mid-July to mid-August and following climatological values of snow depth (Warren et al., 1999;Renner et al., 2014;Krumpen et al., 2016), we assume a 0.1 m snow or weathered layer thickness, which is negligible for the EM measurements. More snow may still have been present during episodic precipitation events, but likely melted within a few days.
In order to ensure comparability of the available EM-based measurements from 2001 to 2020 only data taken between 80.5 95 to 86 • N and 30 • W to 20 • E (AOI, Fig. 1 a)) were selected for the analysis. Following Krumpen et al. (2016), the AOI was also selected to be north of Fram Strait to concentrate the analysis on sea ice that was shaped along its pathways through the Arctic rather than by local melt phenomena in Fram Strait. Finally the selected AOI allows for a more reliable analysis of the trajectories of the sampled sea ice since low resolution sea ice motion products used for Lagrangian tracking are highly uncertain in Fram Strait (Krumpen et al., 2019). Expedition logistics and the prevailing weather conditions prevented us from 100 acquiring continuous and overlapping measurements over the full AOI each year. However, following Rabenstein et al. (2010) the lengths of the conducted EM profiles were adequate to consider the data to be sufficiently homogeneous in time and space and representative for the sampled region and time of year. Table 1 provides an overview of all relevant field campaigns, duration, profile lengths, basic statistics and references for the measurements from within the selected AOI.
The analysis of trends and interannual variability of summer SIT in the AOI is based on temporal and spatial averages 105 and the most frequently occurring EM SIT -the mode of the distribution. Modal SIT is a representation for the thickness of thermodynamically grown level ice, while mean SIT includes thermodynamically and dynamically grown sea ice and therefore is an indication for the general variability of SIT (Haas, 2017). Prior to the calculation of summer mean and modal SIT values from all available data points within the predefined AOI ( Fig. 1)

Sea ice pathways and source regions
In order to determine the pathways and source regions of the ice that was sampled in the selected AOI we utilized the Lagrangian ice tracking tool, ICETrack . The starting points for the backward tracking of AOI-sampled sea ice were derived based on the positions of the EM measurements. EM SIT data were gridded to a 25×25 km Equal-Area Scalable Ice parcels were tracked backward in time on a daily basis. Termination criteria for the tracking were either met when the ice 120 reached a coastline or when SIC dropped to 25% or less. When SIC reaches 25% or less ICETrack assumes that ice is formed.
The applied SIC product is provided by the Center for Satellite Exploitation and Research (CERSAT) and is based on 85 GHz SSM/I brightness temperatures, using the ARTIST Sea Ice algorithm (Ezraty et al., 2007). The number of days from the first day of tracking until ice formation provided the age of the sea ice sampled in the AOI. The tracking was based on a weighted approach to determine the most appropriate of the three available low resolution sea ice motion products (Krumpen et al.,125 2019): (i) motion estimates from scatterometer and radiometer data from CERSAT (Girard-Ardhuin and Ezraty, 2012), (ii) the OSI-405-c motion product produced by the Ocean and Sea Ice Satellite Application Facility (OSISAF) (Lavergne, 2016) and (iii) Polar Pathfinder Daily Motion Vectors from the National Snow and Ice Data Center (NSIDC) (Tschudi et al., 2019).
CERSAT was prioritized as it provides the most consistent time series of motion vectors (from 1991 onwards). However, when CERSAT data were missing (especially during summer months), OSISAF data were used. Prior to 2012 or when OSISAF data 130 were not available NSIDC data were utilized (Krumpen et al., 2019). A detailed description of the three motion products is given by Sumata et al. (2014). Beside sea ice trajectories, ICETrack provided information about satellite-derived SIT and sea ice concentration (SIC) as well as atmospheric parameters, like surface air temperature, 10 m wind speed and surface pressure in daily increments along the trajectories. Due to this comprehensive approach to analyse sea ice along its trajectories through the Arctic ICETrack has been widely used in previous studies, e.g. Damm et al. (2018); Peeken et al. (2018); Krumpen et al. 135 (2019, 2020).

Thermodynamic sea ice model
In order to investigate the driving mechanisms of interannual variability in modal SIT in the AOI, ICETrack was combined with a simple one-dimensional thermodynamic sea ice model developed by Thorndike (1992). Parallel to retrieving SIC, SIT, atmospheric parameters, and sea ice motion from ICETrack, the model calculated daily sea ice growth and melt along the 140 determined sea ice trajectories. Latent heat of melting/freezing, ocean heat flux, and conductive heat loss are balanced to model ice growth at the bottom, ∆H ∆t (Thorndike, 1992;Pfirman et al., 2004): The model used along-track snow depth, H snow , computed from the Warren climatology (Warren et al., 1999) as well as the NCEP re-analysis sea surface temperature (T surf ) data (Kanamitsu et al., 2002) that were extracted along the trajectories by

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ICETrack (Krumpen et al., 2019). T 0 is the temperature at the ice-water interface (-1.9 • C) and k is the thermal conductivity . Latent heat of fusion, L, was constant at 3 · 10 8 Jm −3 . ∆t equals 86400 s for daily increments of ice growth. Ocean heat flux, F , was assumed to be constant at 2 Wm −2 . Based on these input parameters the model computed daily changes in SIT along the trajectories. Summer melt at the surface was set to 0.005 md −1 (Thorndike, 1992 year. This modelled value provides SIT excluding the snow layer that is inherently included in the EM SIT values. We therefore added a 0.1 m snow layer to the final model SIT averages for the comparison to EM SIT. Like modal EM SIT, the modelled SIT is a representation of thermodynamically grown level sea ice. Snow depth is an important parameter in modelling sea ice growth, and due to the limitations of the Warren snow depth climatology (Warren et al., 1999), a major source for uncertainty in the modelled SIT values calculated for each trajectory 155 ending in the AOI. Following Laxon et al. (2013) and Ricker et al. (2014) we also reduced Warren snow depth by 50% over FYI. This step accounts for the fact that Warren et al. (1999) snow depth is based on observations during a period where Arctic sea ice was dominated by MYI with thicker snow.
Another major source of uncertainty of the modelled SIT is the selected ocean heat flux value. However, due to the simplicity of the selected sea ice model (Thorndike, 1992) for this current study the input of a constant ocean heat flux value was required. 160 We followed previous studies (Maykut and Untersteiner, 1971;Pfirman et al., 2004;Peeken et al., 2018;Krumpen et al., 2019) and selected a constant ocean heat flux value of 2 W m −2 . This value was applied to the sea ice growth model along each trajectory from ice formation to the AOI.

Shipborne sea ice thickness observations
In general, ship-based observations of SIT benefit from the increasing number of regular ship transits through the Arctic Ocean.

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SIT data used here were either observed visually by a group of Arctic and Antarctic Research Institute (AARI) sea ice scientists using the traditional unified methodological principles in accordance with the requirements of the regulatory guidance (AARI, 2011;Alekseeva et al., 2019), or by the so-called shipborne television complex (STK). The STK consists of a high resolution telecamera, a computer for camera control and processing, and a GPS recorder. The system records images of overturning sea ice floes in the vicinity of the moving ship as well as GPS time and coordinates. After manual selection of appropriate 170 images the software is able to measure the detailed geometry of single ice blocks from the ice camera feed and retrieve ice and snow thickness data. The purpose of this system is to provide navigation data for following ships and reliable SIT data  Table 1).
To some degree the interannual modal thickness variability can be explained by the variability in source regions of the ice (Fig. 2), its age and the number of freezing degree days (FDDs) the ice experienced during the transit through the Arctic 195 Ocean (Fig. 3 a)). FDDs are defined as days with surface air temperatures below 0 • C. Figure  It has to be noted that the varying number of EM surveys each year and the variation in areal coverage within the AOI of the different surveys makes the analysis of SIT trends challenging. However, large-scale and year-to-year overlapping surveys as well as sampling during the same season each year strengthen the assessment that sea ice sampled in the AOI is changing in thickness and age.

Reconstruction of observed SIT using a thermodynamic model
To further investigate the processes driving interannual variability of modal SIT we compare observed AOI values with modelled SIT values of thermodynamically grown sea ice. Sea ice growth along the ICETrack sea ice trajectories was calculated using the thermodynamic sea ice model by Thorndike (1992). The modelled SIT values at the end of the trajectories provide the AOI-mean modelled SIT for each year.  The general agreement between modal EM and modelled SIT supports the hypothesis that sea ice age and FDDs govern the modal SIT in the AOI. However, it is evident that the exceptional modal SIT observed in 2016 can not be explained by the model i.e. atmospheric processes alone, indicating that additional factors contributed to this minimum in modal SIT in the AOI.  Ocean heat flux is a widely debated and still investigated parameter that is the main source of sea ice bottom melting (Lin and Zhao, 2019). It is the sum of heat that enters the surface mixed layer from the deep ocean and heat that enters the surface 240 mixed layer through leads and openings in the ice cover (Zhang et al., 2000). Multiple existing studies have shown that Arctic ocean heat flux is highly variable in time and space (Maykut, 1982;Maykut and McPhee, 1995;Krishfield and Perovich, 2005;Lin and Zhao, 2019). Nevertheless, the assumption of a constant average ocean heat flux value seemed sufficient for thermodynamic sea ice modelling in the past (Peeken et al., 2018;Krumpen et al., 2020) and is confirmed in this study by the agreement between modelled and modal SIT in all years except 2016.

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The studies by Polyakov et al. (2017Polyakov et al. ( , 2020 showed that the observed decline in sea ice extent and increased open water area enables increased ocean ventilation and weakening of the upper ocean stratification in the eastern marginal ice zones. The resulting change in stratification, warming of the upper pycnocline and shoaling of the Atlantic Water (AW) layer result in enhanced upward AW heat flux in winter, which leads to further thinning of the overlaying ice cover. This process of so-called Atlantification is considered to be a positive feedback mechanism (Polyakov et al., 2020) and was mainly observed at the 250 inflow gates of AW into the Arctic Ocean in the Barents Sea (Smedsrud et al., 2013) and north of Svalbard (Ivanov et al., 2012;Onarheim et al., 2014). However, based on mooring and buoy data, Polyakov et al. (2017Polyakov et al. ( , 2020 showed that Atlantification is progressing eastward, impacting ocean stratification and sea ice growth even in the main regions of Arctic sea ice formation in and around the Laptev Sea.  (Fig. 4 b)). This anomaly is likely a result of multiple factors, including increased upward ocean heat fluxes due to Atlantification, but also the 265 observed increase in ice export from the Russian shelves (Krumpen et al., 2013;Itkin and Krumpen, 2017).
In order to quantify the impact of increased upward ocean heat flux on sea ice in the Laptev Sea and in the AOI we adjusted the thermodynamic model to provide a constantly higher ocean heat flux value along the 2016 sea ice trajectories during the Atlantification event observed by Polyakov et al. (2017) in winter 2015 (Fig. 3 c)). Conservative estimates of ocean heat flux during the Atlantification event between January and May 2015 (shaded red area) vary between averages of 4 Wm −2 from 270 moorings closer to the Laptev Sea shelf (Polyakov et al., 2020) and averages of 8 Wm −2 estimated for the moorings further north (Polyakov et al., 2017). Based on these estimates we adjusted the model to provide both values along the red parts of the sea ice trajectories (Fig. 4 a)), which resulted in a mean reduction of SIT of 0.03 (4 Wm −2 run) and 0.15 m (8 Wm −2 run) at the end of the winter (May 2015, Fig. 4 c)). These values are significantly lower than the 0.4 m reduced sea ice growth suggested by Polyakov et al. (2017), which indicates that our adjusted heat fluxes might still be too low. However, the model 275 confirms that increased ocean heat flux reduces sea ice growth in the Laptev Sea. In the AOI, modelled SIT reduced by 0.04 (4 Wm −2 run) and 0.13 m (8 Wm −2 run) in July 2016 (Fig. 4 c)). Although the adjusted model runs show that SIT anomalies induced by increased ocean heat fluxes at the beginning of the drift trajectories persist, they are not able to fully overcome the overestimation of observed SIT in the AOI. The adjusted model assumptions about ocean heat fluxes are still too crude and it is clear that a more realistic representation of ocean heat fluxes along sea ice trajectories are required. Additionally, more data are 280 needed to determine the spatial extent on which Atlantification effects sea ice growth. Nevertheless, the presented downstream EM measurements of SIT and our model analyses suggest that the winter 2015 SIT anomaly in the Laptev Sea persisted into the central Arctic Ocean and was ultimately recorded in the AOI as late as summer 2016. In general, atmospheric influences are able to induce SIT anomalies similar to the one measured in the AOI in 2016. However, there is no indication that increased air temperatures at the sea surface between May to August 2016 resulted in the measured AOI modal SIT minimum (Fig. 4 d)). 285 We therefore consider the ice conditions measured in the AOI in 2016 to be the result of extreme events of intensified upward ocean heat fluxes that occurred between ice formation in autumn 2014 and sampling in July/August 2016. Our adjusted ocean heat fluxes were not able to explain the entire offset between modelled and measured modal SIT in the AOI, which is likely the result of conservative estimates and insufficient temporal and spatial representation of ocean heat fluxes as well as other influences that effected ice growth along the trajectories that remain unknown.

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We argue that the analysis of the 2016 IceBird SIT data allow for a first estimation of the impact of Atlantification on sea ice, its thickness and the time-scale on which these signals can persist. However, it has to be noted that the upward AW heat fluxes vary in strength from year to year, but Atlantification is a process that continuously increases in the eastern marginal ice zone. In fact, Polyakov et al. (2020) showed that the influence of AW heat flux on the ice in the Laptev Sea showed a dramatic increase during the last decade. The upward directed AW heat fluxes in the Laptev Sea showed an increase during 295 winter periods between 2007/2008 to 2018, that resulted in a more than two-fold reduction of winter ice growth in the last decade (Polyakov et al., 2020).
The example of the 2016 minimum in modal SIT in the AOI is a first indication that the increasing influence of Atlantification potentially persists far beyond the eastern Arctic shelf regions due to its preconditioning effect on SIT. However, to further confirm this discovery it is vital to build continuous long-term SIT time series in the Laptev Sea as well as in the vicinity of the of sea ice, as well as the persistence of SIT anomalies due to oceanic influences.

Interpretation of sea ice surveys from the MOSAiC year
The continuation of the IceBird SIT time series in the MOSAiC year 2020 was aggravated by the corona pandemic which only allowed for survey flights over the AOI from Longyearbyen (Svalbard) and after the usual sampling period from mid-July to mid-August (Table 1). Mean and modal SIT were obtained over the AOI in September 2020 and are shown in Fig. 1 b). The 315 pathway analysis (Fig. 2) confirms the trend, that ice reaching the AOI in summer is increasingly dominated by SYI (Fig. 1 c)).
Although the modal SIT is similar to the 2016 value, it has to be noted that measurements were conducted considerably later in the melt season, which makes a direct comparison difficult and shows that summer melt has a considerable impact on SIT in the AOI.
Due to the late IceBird MOSAiC campaign in 2020 and the ensuing limitations for the comparability to the existing IceBird  Fig. 3 a)) the DNR reached the southernmost border of our selected AOI already in the second half of July 2020 (Fig. 2). Along its trajectory through the Arctic Ocean DNR SIT was continuously measured using ground-based and airborne EM devices. Unfortunately, technical problems and unfavourable 335 weather conditions limited the availability of SIT measurements covering larger areas in the vicinity of the floe in the second half of July 2020. However, regular GEM measurements were conducted on the remainders of the CO. The GEM thickness results shown here are based on the rapid-release quickview thickness data, have undergone initial quality control, and have been calibrated against manual observations. In order to ensure the best possible comparability to the IceBird SIT time series, we only consider GEM measurements that were conducted while the floe was in the AOI and during the sampling period of 340 the previous measurements. The resulting preliminary AOI SIT values are based on a total of 4 surveys obtained between July 16 and July 21. Although GEM measurements were only conducted on the central, more stable part of the MOSAiC floe, large-scale, AEM measurements conducted over the DNR in April 2020 indicate that modal and mean SIT values measured on the extended MOSAiC floe were representative for the DNR (Fig. S1). Additional AEM measurements conducted in the beginning of July 2020 confirm that the modal SIT derived from the GEM surveys is in fact reliable and representative for the 345 wider area (Fig. S2).
On the basis of AEM surveys conducted over the DNR in April and early July 2020 and the already existing IceBird time series we argue that the modal SIT of 1.71 m measured on the MOSAiC floe is not just representative for the wider area around the floe but also in line with measurements from previous years (Fig. 1 b)). The modal thickness of the MOSAiC floe is within one standard deviation of the long-term average over all modal SIT values derived for the AOI.This agreement indicates that 350 the MOSAiC floe and its wider vicinity are not exceptional in terms of modal thickness compared to the long-term time series.
The comparison of the MOSAiC floe modal SIT with SIT values reconstructed by the thermodynamic model from Thorndike (1992) confirms that the MOSAiC floe was not exceptionally thin. In fact, Fig. 3 b)

Comparison to Russian shipborne SIT observations
Due to the position of the selected AOI just north of the Fram Strait the presented SIT time series provides the possibility to 360 investigate interannual variability of the time-integrated signal of Arctic-wide SIT changes. Nevertheless, the selected AOI is a highly variable, and small excerpt of the Arctic Ocean. Additionally, the presented time series is interrupted and still too short to provide insight into SIT changes on climatological scales. For example, the transition from a MYI-dominated towards a FYI/SYI-dominated Transpolar Drift (Kwok, 2018) that was accompanied by a drastic reduction in Arctic SIT and sea ice volume (Kwok, 2018) and accelerated drift speeds along the Transpolar Drift (Spreen et al., 2011;Krumpen et al., 2019)  However, we also attribute the lower estimates of SIT from the Russian observations to the inherent differences between the observation techniques. While visual and STK SIT observations are largely dependent on the ships route and the avoidance of thicker ice patches for faster navigation through the ice, AEM measurements provide SIT distributions on larger spatial scales. We would therefore assume a bias towards, on average, thinner sea ice for the Russian observations compared to AEM measurements. Nevertheless, the Russian observations provide a much longer time series than the IceBird measurements, 380 which allows us to confirm general changes in the overall SIT regime in the central Arctic (Kwok, 2018). While the AOI time series indicates further thinning of sea ice between 2010 and 2020, Russian observations show no trend at all.
The length of the Russian observational time series and its ability to show the previously observed regime shift in SIT indicates how valuable consistent long-term time series are. However, the deviations from the IceBird measurements also show its limitations and the importance of joint observations for a better understanding of differences and ultimately a better basis 385 for the interpretation of past, present and future ship-based observations from Russian sea ice experts.

Conclusions
This study provides an important extension of the first long-term EM-derived summer SIT time series at the end of the the sea ice covered Arctic. The presented model analyses also revealed that the assumption of a constant and also our adjusted ocean heat fluxes along the sea ice trajectories are insufficient to fully explain the observed modal SIT minimum in 2016.
However, it is evident that the influence of oceanic heat on sea ice is drastically increasing (Polyakov et al., 2020) and sea ice growth models require improved representations of spatial and temporal variability of ocean heat fluxes.
Further investigations and measurements are required to monitor the development of Atlantification in the eastern marginal 415 ice zones. But in order to strengthen our conclusion that Atlantification is able to precondition sea ice and that this preconditioning persists far beyond the eastern Arctic, additional uninterrupted SIT time series are vital along the pathways and at the exit gates of Arctic sea ice. The presented IceBird SIT time series at the end of the Transpolar Drift is an important effort to establish long-term measurements of SIT, especially during the melt season. Airborne EM measurements of SIT during IceBird campaigns provide the necessary accuracy and areal coverage that is unmatched by any other non-satellite SIT mea-420 surement approach. Russian shipborne SIT measurements show significant differences to EM-based measurements, but their regularity and spatial consistency enable the depiction of regime shifts in SIT that are hardly resolved by the presented IceBird SIT time series. Obtaining SIT distributions over large areas and developing and continuing long-term SIT time series will provide unique input data for modelling efforts, and ultimately will improve predictions of Arctic sea ice and its thickness in the future. Continuing regular IceBird measurement campaigns in the vicinity of Fram Strait and combining the results with 425 reliable models and ice tracking tools will prove indispensable for monitoring the complex and radical change of sea ice on an Arctic-wide scale.
All MOSAiC-related data are archived in the MOSAiC Central Storage (MCS) and will be available on PANGAEA after finalisation of the respective datasets according to the MOSAiC data policy.

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ESA Sea Ice Climate Change Initiative (Sea_ Ice_ cci): Northern hemisphere sea ice thickness from ENVISAT satellite (Hendricks et al., 2018b) and from CryoSat-2 satellite (Hendricks et al., 2018a) on a monthly grid (L3C), v2.0 are available from the Centre for Environmental Data Analysis data base.
Surface air temperature reanalysis data is available from www.esrl.noaa.gov/psd/product.
Author contributions. HJB and TK analysed the sea ice thickness data and conducted the backward-tracking and model simulations for the 435 sea ice sampled north of Fram Strait. HJB prepared the manuscript. All authors contributed to the discussion and provided input during the writing process. HJB, TK, and CH participated in the different expeditions during which the EM sea ice thickness data were gathered. AH was vital for the planning and organisation of multiple IceBird campaigns including the one in 2020. IP provided ocean heat flux estimates, calculations for the model analysis, and valuable input to the manuscript. LvA, IR, MW and SH planned and conducted the GEM surveys during Leg 4 of MOSAiC and processed the preliminary SIT data. RR provided preliminary airborne EM SIT data from Leg 3 of MOSAiC.

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TA, SF and SS gathered and analysed the observational data from the Russian cruises to the North Pole.
Competing interests. All authors declare that they have no conflict of interest.
Acknowledgements. This project was carried out in the framework of the BMBF-funded Russian-German cooperation QUARCCS (grant: 03F0777A).
2020 AEM and GEM data used in this manuscript were produced as part of the international Multidisciplinary drifting Observatory for the 445 Study of the Arctic Climate (MOSAiC20192020) during IceBird MOSAiC summer (P6-222_IceBird_MOSAiC_2020) and the RV Polarstern Legs 3 (AWI_PS122_03) and 4 (AWI_PS122_04).
The processing of visual and STK data by T. A, Alekseeva, S. V. Frolov and S.S. Serovetnikov was funded by the Russian Foundation for Basic Research (RFBR) according to the research project number 18-05-60048.
We want to thank the AWI logistics department, the crews of the research aircrafts Polar 5 and 6, the crews of Station North in Greenland,

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the pilots and crews on RV Polarstern (Alfred Wegener Institute, 2017) and the observers on the Russian icebreakers for their tireless efforts during the various expeditions. This unique collection of data from different expeditions would not exist without you! Table 1. Summary of used research platforms, sampling periods, profile lengths, and basic sea ice thickness statstics for the selected area of interest (see Fig.1 Figure S2. Comparison of MOSAiC floe sea ice thickness distribution gathered with the ground-based EM (GEM) between July 16 to July 21, 2020 (black) and airborne EM (AEM) derived sea ice thickness distribution (red) of the floe and wider area (radius about 5 km around the floe) from July 1 and July 7, 2020.