Components of 21 years (1995-2015) of Absolute Sea Level Trends in the Arctic

The Arctic Ocean is at the frontier of the fast changing climate in the northern latitudes and sea level trends is a bulk measure of ongoing processes related climate change. Observations of sea level in the Arctic Ocean are nonetheless difficult to validate with independent measurements and is globally the region where the sea level trend (SLT) is most uncertain. The aim of this study is to create an satellite-independent reconstruction of Arctic SLT, as it is observed by altimetry and tide gauges (TG). Previous studies uses the Gravity Recovery and Climate Experiment (GRACE) observations to estimate the 5 manometric (mass component of) SLT. GRACE estimates are however challenged by large mass changes on land, which are difficult to sperate :::::: seperate : from much smaller ocean mass changes. Furthermore is GRACE not available before 2003 and thus ::::: which : significantly limits the period of which the trend can be estimated ::: and :::::: makes ::: the ::::: trend :::: more :::::::::: vulnerable :: to ::::::::: short-term :::::: changes. As an alternative approach, this study estimates the climate change driven Arctic manometric SLT from the Arctic sea level fingerprints of glaciers, Greenland, Antarctica and Glacial Isostatic Adjustment (GIA) and adding the long-term Inverse 10 Barometer (IB) effect. The haloand thermosteric components completes the reconstructed Arctic SLT and are estimated by interpolating 300,000 temperature (T) and salinity (S) in-situ observations. The SLT from 1995-2015 is compared to the observed SLT from altimetry and 12 selected tide gauges (TG) corrected for vertical land movement (VLM). The reconstructed estimate manifests that the salinity-driven halosteric component is dominating the spatial SLT-pattern with variations between -7 and 10 mm y−1. The manometric SLT is in comparison estimated 15 to 1-2 mm y−1 for most of the Arctic Ocean. The reconstructed SLT shows a larger sea level rise in the Beaufort Sea compared to altimetry. An issue that is also identified by previous studies. A TG-observed sea level rise in the Siberian Arctic is in contrast to the sea level fall from the reconstructed and altimetric estimate. From 1995-2015 does the reconstructed SLT agree within the 68% confidence interval with the SLT from observed altimetry in 87% of the Arctic between 65N and 82N (R=0.50) and with 5 of 12 TG-derived (VLM corrected) SLT estimates. The resid20 uals are seemingly smaller than results from previous studies using GRACE-estimates and modelled T/S-data. The correlation in the :::::: spatial ::::::::: correlation :: of :::: the ::::::::::: reconstructed :::: SLT :: to ::::::::: altimetric :::: SLT :::::: during ::: the : GRACE-period (2003-2015) is R=0.38 . If ::: and ::::::::::::: R=0.34/R=0.37 :: if GRACE-estimates are used instead of the constructed manometric component, similar correlations are reached (R=0.34/R=0.37). Thus is the reconstructed manometric component suggested as an legitimate alternative to GRACE, that can be projected into the past and future. 25

, are reduced and (iii) the mentioned problem of leakage from effects caused by the low spatial resolution (300-500 km (Tapley et al., 2004)) are avoided.
Combining the manometric 1995-2015 SLT estimates with satellite-independent steric SLT estimates (Ludwigsen and Andersen, 2020) aims to reconstruct the absolute SLT as it is observed by altimetry. Besides consolidating observed sea level 60 change, the sea level budget decomposition permits analysis of the sources of contemporary long-term Arctic sea level change, which also aids predictions of future change.

Method
Sea level observations from satellite altimetry are measured relative to a terrestrial reference frame and is referred to as geocentric or absolute sea level (ASL) observations. Tide gauges (TG) measures the sea level while being grounded to the coast, and is affected by vertical deformations of the solid earth, called vertical land movement (VLM). When VLM is defined with respect to the same reference frame as altimetry and added to TG-measured relative sea level (RSL) the ASL is restored: Changes of ASL (Ȧ SL) originates either from changed ocean density (steric,η) due to changes in salinity (halosteric) or temperature (thermosteric) or from changes in ocean mass, denoted as manometric sea level change,Ṁ (Gregory et al., 2019)). 70 According to (Gregory et al., 2019), manometric sea level change can be referred to as the 'non-steric' sea level change and is assumed indifferent to the commonly used Ocean Bottom Pressure (OBP). In this study, the manometric component is both reconstructed  and retrieved from GRACE observations (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015).
ASL =η +Ṁ As already mentioned, the steric sea level change is composed of halosteric (η S ) and thermosteric (η T ) sea level change: The manometric component is further divided into contributions from changes in the gravitational field, G that together with a spatial uniform constant, c, composes the gravitational sea level fingerprint (N ) due to different land-to-ocean mass changes, i, which in this study originates from either different sources of land ice (Greenland (GRE), Northern Hemisphere (NH) Glaciers and Antarctica (Ant) + Southern Hemisphere (SH) glaciers) or GIA. Change in atmospheric pressure (Inverse Barometer, IB) 80 is added to the sea level fingerprints to create the total manometric sea level change,Ṁ.
By substituting eq. 4 and eq. 3 into eq. 2, we achieve the reconstruction of absolute sea level, ASL r , that is comparable with the altimetry observed ASL (denoted as ASL A ): 85 VLM is split into the viscoelastic solid earth deformation caused from past millennial ice (un-)loading, GIA, and the elastic adjustment from contemporary  change in ice loading, VLMe, which, as G, is a composite of the elastic response from different origins of land ice (i).
Possible local VLMs not associated with glacial mass redistribution (i.e. non-glacial land water change, tectonics or oil 90 depletion) is not accounted for since little knowledge on their VLM-contribution exist. Frederikse et al. (2019) estimated the non-glacial VLM from GRACE-observations to vary between -0.5 mm y −1 in North America and +0.2 mm y −1 in the Barents/Kara sea region.

Data
This study combines various in-situ data (temperature and salinity (T/S) profiles, tide gauges and ocean bottom recorders), satellite altimetry, GRACE-observations and model data (ECCOv4r4, VLM and geoid change) to reconstruct the Arctic sea level change. In this section follows a description of the different datasets and how they are obtained.

100
The DTU/TUM Arctic Ocean sea level anomaly (SLA) record (Rose et al., 2019) provides an independent estimate of ASL change (ȦSL A ). The altimetric time series is covering the whole altimetric era given as monthly grids from September 1991 to September 2018, covering 65 • N to 81.5 • N and 180 • W-179.5 • E.
Geophysical corrections such as tides and atmospheric delays is applied to the altimetric sea level estimate. Leads (cracks in the sea ice cover) and open ocean are located and separated according to the different classification of their surfaces. The 105 detection of leads is not flawless, and their sparse distribution in the sea ice cover, and the uncertainty of the the applied geophysical corrections in the Arctic (Stammer et al., 2014;Ricker et al., 2016) makes the sea level estimates more uncertain in the sea ice covered region. The altimetric record includes data from four ESA satellites: ERS-1 (1991ERS-1 ( -1995, ERS-2 (1995ERS-2 ( -2003, Envisat (2002Envisat ( -2010 and CryoSat-2 (2010CryoSat-2 ( -2018. It combines results of different retrackers as well as conventional and SAR-altimetry (Rose et al., 2019). In particular ERS-1/2 has a relatively low spatial resolution and measurements from leads in 110 sea ice are limited. Observations are in particular sparse and uncertain in sea ice regions from ERS-1 (Rose et al., 2019), which is why the altimetry record used for this study begins in 1995. The SAR altimeter on CryoSat-2 is designed to measure over the sea ice cover, which increases the observations from leads and decreases the uncertainty (Rose et al., 2019). The applied version of the DTU/TUM altimetry product is not corrected for GIA or atmosphere pressure loading.

115
Observations from tide gauges (TG) is obtained from the Permanent Service of Sea Level (PSMSL)-database (Holgate et al., 2012) given as monthly SLA. TGs with a consistent time series are few and unevenly distributed in the Arctic (Henry et al., 2012;Limkilde Svendsen et al., 2016). Usually, TG-observed RSL is aligned to ASL by utilizing vertical velocities from a nearby Global Navigation Satellite System (GNSS) receiver. However, only few reliable GNSS-data at the Arctic coast are spanning the time period of this study (Wöppelmann and Marcos, 2016;Ludwigsen et al., 2020a) and restricting TGs 120 to locations with usable GNSS significantly limits the selection of TGs further. Therefore, an Arctic-wide VLM-model with annual VLM-rates from 1995-2015 (Ludwigsen et al., 2020a) is used as a substitute for GNSS (figure 1). A detailed comparison between vertical rates from the used VLM-model and GNSS-measurements (from URL6B (Santamaría-Gómez et al., 2017)) showed good agreement, in particular along the Norwegian Coast (Ludwigsen et al., 2020a).
The region around the Ny-Ålesund TG and Reykjavik TG experiences extraordinary VLM that is caused by substantial 125 deglaciation during the Little Ice Age (LIA) (Svalbard) and low mantle viscosities in Iceland and Greenland. This is not captured in the spatially uniform REF6371 earth model (Kustowski et al., 2007) used in the VLM-model. Therefore, the two sites are corrected with nearby GNSS instead of the VLM-model. Large residual trends between the VLM-model (-1.4mm y −1 ) and GNSS (-3.2 mm y −1 ) was also found at Prudhoe Bay. This additional subsidence is likely caused by near-by construction or oil depletion sites. However, the tide gauge is located on a peninsula reaching into the Beaufort Sea 10 km away from the The VLM-model is composed from eq. 6. The GIA-component is based on the Caron2018 GIA-model (Caron et al., 2018), which includes an uncertainty estimate. Reported discrepancies from other GIA-models in central North America and Greenland (Caron et al., 2018;Ludwigsen et al., 2020a) has little affect at the locations of TGs of this study. Annual rates of VLMe is estimated from the 1995-2015 annual change of land ice using the Regional Elastic Rebound Calculator (REAR) (Melini 135 et al., 2015). REAR also provides the gravitational response G to land ice change used for estimating the manometric sea level.
Uncertainties of the elastic VLM-estimates are mainly due to uncertainties of the applied land ice change. An additional 10% of the VLM-signal (after Wang et al. (2012)) is added to represent uncertainties associated with the REF6371 earth model (Kustowski et al., 2007)

Steric sea level 155
The steric estimate is derived from the DTU Steric product (Ludwigsen and Andersen, 2020). The steric heights are calculated from a three dimensional T/S-grid that is interpolated from more than 300,000 T/S profiles and thus not constrained by any satellite observations. This approach is different to Morison et al. (2012) and Armitage et al. (2016), that use a difference between altimetry and GRACE to estimate steric heights and Henry et al.  spatial grid on 41 depth levels. If values are more than 3σ away from the mean of neighbouring grid cells, values from the same month in adjacent years is used.
Following the notion of Gill and Niller (1973); Stammer (1997); Calafat et al. (2012); Ludwigsen and Andersen (2020), the change in steric sea level is calculated as the sum of halosteric sea level, η S and thermosteric sea level, η T (equation 3). From the depth profiles of the T/S grid, η S and η T are calculated: where H denotes the minimum height (maximum depth (z)). The maximum integration depth is as in Ludwigsen and Andersen (2020) 2000 meters. S and T are defining salinity and temperature anomalies, with reference values 0 C°and 35psu, respectively. β is the saline contraction coefficient and α is the thermal expansion coefficient. The opposite sign of η S is needed since 175 β represents a contraction (opposite to thermal expansion). α and β are functions of absolute salinity, conservative temperature and pressure, and is determined with the freely available TEOS-10 software (Roquet et al., 2015). Sea level trends of η S and η T from 1995-2015 are shown in figure 4.  (Spada, 2017). Following Spada (2017), c is defined as GIA is assumed to be unaffected by contemporary ice changes. This means that the GIA contribution to global mean sea level, c, is defined from the right part of equation 10, which is estimated to 0.3 mm y −1 consistent with other studies (Peltier,195 2009; Spada, 2017). The gravitational sea level change of RF and NOL is less than 0.05 mm y −1 , and are included in the Northern Hemisphere glacial contribution to G.

Manometric sea level contributions
The manometric SLTs is completed with the loading from atmospheric pressure, IB (figure 5e). IB is estimated by the simple relationship derived from the hydro-static equation (Naeije et al., 2000;Pugh and Woodworth, 2014). Monthly averaged pressure estimates from National Center for Environmental Prediction (NCEP) are used for surface pressure change ∆p: The total manometric SLTs (Ṁ, figure 5f) is reconstructed as: Figure 5g shows the OBP-trend from the ECCOv4r4-model (Estimating the Circulation and Climate of the Ocean (ECCO) version 4 release 4) (Forget et al., 2015;Fukumori et al., 2019), which is a model estimate ofṀ. The ECCO consortium 205 (ecco-group.org) combines ocean circulation models with observations to estimate different physical parameters of the ocean.
The model is among others constrained with observations from GRACE, satellite altimetry and in-situ T/S-profiles (Fukumori et al., 2019). The difference between ECCO OBP andṀ is displayed in figure 5h.

Results
The reconstructed SLT from 1995 to 2015 (Ȧ SL r ) is shown in figure 7 panel (i) together with the SLT derived from altimetry (Rose et al., 2019). The residual of the reconstructed SLT to altimetry is shown in figure 9. In large, the spatial variability and residual is dominated by the halosteric sea level rise in the Beaufort Sea (10-15 mm y −1 ), halosteric sea level fall in the 215 East Siberian Sea (5-8 mm y −1 ) and thermosteric sea level rise (2-5 mm y −1 ) in the Norwegian Sea, where thermal expansion has a relatively larger impact compared to the near-freezing temperatures in the interior of the Arctic Ocean. A similar pattern is observed by altimetry (figure 7 panel (ii)), albeit a smaller sea level change in the Beaufort Sea and East Siberian Sea is detected.
The right panel of figure 9 shows the correlation matrix betweenȦ SL A/TG andȦ SL r . The matrix shows thatȦ SL r andȦ SL A 220 are largely correlated (R=0.50). There is a large accumulation around 2 mm y −1 , with slightly higherȦ SL A thanȦ SL r . This In the two other selected regions (Beaufort and Norwegian Sea) is the correlation between ASL A and ASL r better for the whole time series  than for the GRACE-period (2003GRACE-period ( -2015. In particular in the Beaufort Sea, is the correlation between ASL A and ASL r better before 2010. The correlation with altimetry is not significantly approved when ASL is reconstructed using GRACE-estimates (ASL r/grace for the two regions (figure 7 panel (iii)).

Comparing observed and reconstructed manometric sea level change
The reconstructed manometric sea level trend (Ṁ , figure 5f) is varying between 0 and 2 mm y −1 , with small spatial variability.

250
The reconstructed manometric contribution is generally much smaller than the estimates from GSFC mascons (RL05) (Luthcke et al., 2013) used by Raj et al. (2020) and CSR RL05 (Save et al., 2016) preferred in Carret et al. (2017). The two RL06 solutions shown in figure 6, are more consistent than the RL05 solutions shown in Ludwigsen and Andersen (2020), but still show significant differences. The trends disagree in particular along the Eastern Arctic coastlines and the Beaufort Gyre. This is also where the largest residuals between the reconstructed SLT and altimetry is observed (figure 9), hence is no obvious 255 manometric SLT derived from GRACE that is able to explain the residuals betweenȦ SL A andȦ SL r .
The annual manometric sea level change of two GRACE solutions and the reconstructed estimate for three selected regions   GRACE has a more significant sea level fall in coastal regions with land-deglaciation. It is likely that the GRACE-estimates are affected by leakage from land mass that is falsely interpreted as an manometric sea level change.

Comparing reconstructed absolute sea level with altimetry
For 87% of the area of the Arctic between 65N • and 82N • is the reconstructed sea level pattern (ȦSL r ) in agreement with the observed sea level trend (ȦSL A ) within the 68% confidence interval (figure 9). The main difference betweenȦSL r andȦSL A 290 is the mentioned larger sea level rise (residual of + 5-10 mm y −1 ) in the Beaufort Sea and sea level fall (residual of -2-5 mm y −1 ) in the East-Siberian seas ofȦSL r . In the Norwegian Sea the residuals are in the order of +/-1.5 mm y −1 , which because of the low uncertainty in the area falls outside the 68% confidence interval in large areas. for GSFC and R=0.34 for JPL and thereby slightly lower than with the reconstructed manometric contribution. This reflects the fact that trend estimates are naturally more sensitive over shorter timeseries, and in particular when the sea level is as dynamic 300 as in the Arctic Ocean.

The spatial correlation coefficient (R) betweenȦ SL r andȦ SL
Before the era of SAR altimetry (pre CryoSat-2, launched in October 2010), the ability to separate the leads and the sea ice was more difficult due to the larger footprint of the conventional satellites. Therefore, in areas with a dense sea ice cover (like the Beaufort Sea), more altimetric observations exist during the sea level high of the autumn and fewer during winter/spring where sea level is lower (e.g. Armitage et al. (2016)). The sampling of the seasonal signal (figure 7 panel (iv)) can create 305 a seasonal bias which was more pronounced before the CryoSat-2 era, because of the lower resolution in the pre-SAR era.
This bias can contribute to a flattening of the trend in the Beaufort Sea as seen from the time series in figure 7 panel (iii). In however less significant inȦSL r and a spatial bias in altimetry can therefore not be excluded. A thermosteric sea level rise that is countered by a halosteric sea level fall in the Norwegian Sea is also reported by the other studies (Henry et al., 2012;Carret et al., 2017;Raj et al., 2020). The residuals in the present study are however qualitatively smaller than the results of the mentioned studies, albeit they use different subsets of periods and for the case of Raj et al. (2020) only basin-wide averages are given.

Comparing ASL-trends in coastal regions
TGs are only able to observe coastal sea level change, which is often disturbed by the local environment that might be unknown (e.g. small river outflow, local construction, packing of sea ice etc.), which affects both sea level measurements from TGs and altimetry.
In figure 10 and table 1, the contributions toȦ SL r are quantified at the location at each of the 12 TGs by taking the mean 345 trend of a radius of 50 km (5 km for GIA and elastic VLM). This radius ensures, that Rorvik, Nome and Reykjavik overlaps the altimetric data, but the fewer number of data points might cause the altimetry estimates at these TGs to be more variable. The residuals between the TG-observed ASL-trend,ȦSL TG , andȦSL r are visible from figure 7.ȦSL TG is in agreement ofȦSL r at only 5 of the 12 TGs (8 of 12 forȦSL A /ȦSL TG ) are within the combined standard error, while 9 are within two standard errors (95 % confidence interval). Relative low standard errors ofȦSL TG contributes to the apparent low agreement.

350
The Norwegian tide gauges (Rorvik, Tromso, Vardo, Ny Ålesund) are together with Reykjavik the most consistent with the smallest errors. These are also the sites where ASL A and ASL r are most precise, due to little or no sea ice and high density of hydrographical data (figure 3). For Rorvik and Vardo, isȦ SL r more in alignment withȦ SL TG thanȦ SL A , whileȦSL TG of Tromso and Ny Ålesund is better aligned withȦ SL A . We see that for Vardo and Rorvik, theȦSL r is split between a steric and a mass contribution of roughly the same size, which is similar to the contributions share of the global sea level trend (Church 355 and White, 2011b; WCRP, 2018). At Tromso a local negative halosteric trend (more saline water) is loweringȦSL r , while for the area around Tromso (50-200 km),ȦSL r agrees well with the observedȦSL TG andȦSL A .
The Siberian coast has multiple river outlets that contributes with significant freshwater of the Arctic Ocean (Proshutinsky et al., 2004;Morison et al., 2012;Armitage et al., 2016). A positive halosteric sea level trend is visible at the coast of the Figure 9. Left map shows the difference betweenȦ SLr andȦ SLA/TG. The green contour shows the areas or tide gauges (green edge) where the absolute difference is larger than one standard error (68% confidence interval), but less than two standard errors (95% confidence interval) (combined error from figure 11). The magenta areas or tide gauges (magenta edge) where the absolute difference is larger than two standard errors. Right panel shows a correlation matrix betweenȦ SLr andȦ SLA/TG. The color indicates the number of data grid cells falling into bin size of 0.5 mm y −1 . 96% of the grid cells with data is covered within the bounds of the matrix (Ntotal=18150). The red line is whereȦ SLr is equal toȦ SLA/TG.
Bering and Kara Sea (figure 4), where the river OB has a major outflow. At Amderma TG, which is located on the coast 360 between the Barents and Kara Sea, an apparent large halosteric sea level fall is also recognized by the TG-measured sea level, despite rather large errorbars due to lack of in situ data (figure 4). Iceloss from Novaya Zemlya contributes with over 1 gigaton of freshwater to the Kara Sea every year and the ice loss has been accelerating (Melkonian et al., 2016), but the contribution is small compared to the +500 Gt coming from the rivers every year. The halosteric signal could (falsely) be extrapolated from the gulf of Ob which has major river outlets and the agreement withȦ SL TG is accidental. The halosteric sea level rise at Anderma 365 remains doubtful, sinceȦ SL A shows a negative ASL-trend in opposition toȦ SL TG andȦ SL r .
The four TGs along the eastern Siberian coast (Izvestia Tsik, Golomianyi, Kotelnyi, Kigiliah) all observe a rising sea levels, whileȦ SL A and in particularȦ SL r shows a negative trend in the region. Missing data in the end of the timeseries of Golomianyi  and Ny Ålesund, because significant local properties causes VLM that is not present in the VLM-model (Ludwigsen et al., 2020b). Glacier component of VLM includes the effect of rotational feedback, ocean loading, and Antarctica which is less than 0.5 mm y −1 combined.

385
The sea level drop in year 2000 observed by Altimetry in East Siberian Sea (figure 7 panel (iii)) is also to some extent seen by Kotelnyi and Kigiliah TGs (figure 2) that are located in the same region, which indicates that poor T/S measurements in the region has lead to a an false steric sea level high in the region from 1998-2002.
Nome and Prudhoe Bay in Alaska both show a positive steric TG-trend which is not reflected inȦ SL TG orȦ SL A , thus resulting in a rather large discrepancy betweenȦ SL r andȦ SL A/TG . The strong halosteric trend of the Beaufort Gyre, might 390 be extrapolated towards the Alaskan coastline and into the Bering Strait in the DTU steric model. There is no evidence in the literature for an extent of the Beaufort Gyre doming as shown from the halosteric trend, which indicates, that the weighted spatial interpolation in combination with higher hydrographic data density in the Beaufort Sea creates this widening of the Beaufort Gyre.
Ny-Ålesund on Svalbard is dominated by a large VLM caused by recent deglaciation. This uplift completely mitigates the 395 large sea level fall measured by the tide gauge and results in small rise ofȦ SL TG . In (Ludwigsen et al., 2020a) it is argued that the discrepancy between GNSS and the VLM-model in large originates from VLM because of post-LIA deglaciation on Svalbard (Rajner, 2018). This viscoelastic GIA-like LIA-effect will certainly also have a gravitational sea level fingerprint (Ṅ ) that should be added to the manometric SLTsṀ. This can explain some of the difference betweenȦ SL r andȦ SL A/TG . A possibly positive dynamicṀ-change (from the (ECCO OBP)−Ṁ difference in figure 5h) could further close the gap between 400Ȧ SL r andȦ SL A/TG .
None of the TG-sites in this study experience a net sea level fall due to contemporary deglaciation and GIA (Ṅ in table 1) and only Ny-Ålesund (-0.4 mm y −1 ) and Reykjavik (-0.2 mm y −1 ) will experience a small sea level fall from contemporary deglaciation alone. So even though the Arctic is heavily prone to ice mass loss and thus a weakened gravitational pull, the Arctic as a region is not experiencing an absolute sea level fall from comtemporary deglaciation. On the contrary, it causes the 405 sea level to rise with around 1 mm y −1 in most of the Arctic. However, by accounting for the deglaciation effect on VLM, contemporary deglaciation will contribute to an relative sea level fall in most areas of the Arctic.

Uncertainty of the contributions
The uncertainties of the trend estimates for RSL TG , VLM, gravitational fingerprint (N ), steric (η) in table 1 and figure 10 are derived as the standard error of the detrended and deseasoned timeseries of the contributions. GIA (Caron et al., 2018) and 410 altimetry (Rose et al., 2019) has a associated uncertainty that is used. For the VLM-model a 10% error is added to account for uncertainties of the earth model (Wang et al., 2012).
The spatial distribution of the uncertainties are shown in figure 11. Generally, the largest uncertainties are found along the Siberian coast, with the steric uncertainty in most cases being the largest source of uncertainty (figure 10). The standard error naturally reflects if the steric heights are unstable and poorly constrained (if for example there are few hydro-graphic data 415 (figure 3)). In principle, this method requires temporal independence, which is not entirely true, since outliers are replaced with data from adjacent years. Furthermore, large influence by the non-periodic and non-linear Arctic Oscillation, would enhance the uncertainty, even though this is a real physical signal. Thereby is the estimated error a composite of uncertainties originating from the way the sea level component is constructed and from interannual variability.
The mass contribution and VLM has naturally the largest uncertainties close to glaciated areas. Glacial ice loss on Baffin is reflecting the data availability of areas with sea ice contrary the ice-free ocean, while the largest uncertainties of the TGs are those with largest interannual variability.

Conclusion
All significant contributions to the sea level change from 1995-2015 in the Arctic Ocean were mapped and assessed at 12 tide 425 gauges located throughout the Arctic Ocean. As the first study, the observed sea level was attempted reconstructed without the use of GRACE data or modeled steric data in a region where observations are sparse and very uncertain. Thus is it possible to attribute the observed sea level changes to their origin and understand the components of the altimetry and TG-observed sea level trend. Figure 7 shows that the spatial pattern of altimetry observed sea level trend (Ȧ SL A ) is generally restored from the re-430 constructed trend-estimate (Ȧ SL r ). The spatial correlation between the reconstructed trend map and altimetry derived trend (R=0.50) from 1995-2015 is outperforming a similar analysis for GRACE-based reconstructions from 2003-2015 (R=0.34-0.37), however, when using the reconstructed manometric sea level component instead of GRACE, a similar correlation is reached (R=0.38). Hence is the calculated manometric contribution an equal alternative to GRACE that should be considered for studying long-term past and future Arctic sea level change.
435 Figure 9 shows the residual between observed sea level (Ȧ SL A/TG ) and the reconstructed ASL estimate within the combined uncertainty. The reconstructed ASL-trend agrees with altimetry at 87% of the area within the 68% confidence level (96% of the area within the 95% confidence level). The residual map indicate an improvement over previous studies (Carret et al., 2017;Raj et al., 2020), however this assessment is only qualitatively since different subsets of periods are used. The two major residuals between altimetry and the reconstructed product are found in the Beaufort Sea and East Siberian Sea. In both regions, the 440 altimetry estimate by Armitage et al. (2016) has a better agreement than the used DTU/TUM-altimetry product. A dominant halosteric trend in the Beaufort Sea, that is larger than the altimetric trend is also observed by Carret et al. (2017) and Raj et al. (2020).
The sea level trend at 5 (9 using the 95% confidence interval) of the 12 VLM-corrected TGs agree with the reconstruction, while 8 of 12 TGs agree with altimetry. The relatively poor correlation at TG sites, can be attributed sparse T/S-data to compute 445 steric sea level along the coast of the Beaufort Sea and the Siberian Arctic and possible local unknown land subsidience/uplift affecting the tide gauge record.
From figure 10 and figure 11 it is evident that the steric estimate is the main source of uncertainty. Some areas, in particular, the Norwegian Sea, has more observations (from both altimetry and hydrographic data) and thus are the individual contributions estimated with lower uncertainty. The Siberian Seas are however poorly constrained with observations and both 450 the steric product, altimetry and tide gauges show large uncertainties. The manometric sea level change has a more uniform and smaller contribution to ASL with smaller associated uncertainties compared to the steric component. However, considering the difference to GRACE-estimates and modelled ECCO-estimate, is the uncertainty of the total manometric contribution also significant and its reasons are not yet resolved. Except for the central Arctic Ocean, where GRACE is less affected by leakage-related issues, is GRACE not able to explain the obtained residuals.

455
Generally, would the Arctic sea level reconstruction be improved, if steric estimate is further constrained, since it is the dominant feature of Arctic Ocean sea level change. Eventually integrating sea surface temperature and salinity from satellite observations could improve the estimates in areas with few in-situ data. Furthermore an independent estimate of the dynamic contribution to manometric sea level change is needed to include the significant wind-driven sea level changes in the Arctic.