Pervasive glacier retreats across Svalbard from 1985 to 2023

Domain: earth_science
Published: 2025-01-15
Editorial outcome: minor_revision

Abstract
A major uncertainty in predicting the behaviour of marine-terminating glaciers is ice dynamics driven by non-linear calving front retreat, which is poorly understood and modelled. Using 124,919 calving front positions for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, generated with deep learning, we identify pervasive calving front retreats for non-surging glaciers over the past 38 years. We observe widespread seasonal cycles in calving front position for over half of the glaciers. At the seasonal timescale, peak retreat rates exhibit a several-month phase lag, with changes on the west coast occurring before those on the east coast, coincident with regional ocean warming. This spatial variability in seasonal patterns is linked to different timings of warm ocean water inflow from the West Spitsbergen Current, demonstrating the dominant role of ice-ocean interaction in seasonal front changes. The interannual variability of calving front retreat shows a strong sensitivity to both atmospheric and oceanic warming, with immediate responses to large air and ocean temperature anomalies in 2016 and 2019, likely driven by atmospheric blocking that can influence extreme temperature variability. With more frequent blocking occurring and continued regional warming, future calving front retreats will likely intensify, leading to more significant glacier mass loss.

Introduction
Svalbard is located in the most rapidly warming region of the Arctic and experiences frequent climate extremes 1–4. It is adjacent to the main pathway of the warm Atlantic Water towards the Arctic Ocean carried by the West Spitsbergen Current 5 and in the path of atmospheric rivers in the North Atlantic sector 3. ~57% of the Svalbard area is covered by glaciers 6,7 which are at relatively low elevation with flat interior accumulation areas, making mass loss of these glaciers strongly sensitive to climate warming 8–12. Glaciers in Svalbard have been losing mass at accelerated rates during the past several decades 13–16, their mass loss is expected to double by 2100 but with significant uncertainties in the predicted rates 11, partly due to a lack of consideration of retreats of marine-terminating glaciers 7,17 which drain ~69% of the glacierized area 18. Therefore, it is crucial to examine how glacier retreat at the ice-ocean boundary responds to different climate drivers across Svalbard.

Significant calving front retreats of marine-terminating glaciers have been identified for ice masses across the Arctic, which feature diverse atmospheric, oceanic and glaciological settings 19–29. However, detailed analyses of calving front change mechanisms have primarily focused on the Greenland Ice Sheet 19–22 and its periphery glaciers 23–25. Sofar, only two studies have quantified the frontal ablation (including solid ice calving and frontal melting at the glacier calving front) of all Svalbard glaciers, either over a short period from 2000 to 2006 30 or with a low temporal resolution on a decadal scale 31. There is a very limited understanding of the long-term and seasonal calving front changes of marine-terminating glaciers across Svalbard since the satellite era, due to a lack of a high spatiotemporal-resolution calving front dataset with a large spatial scale 32. In addition, detailed air-ice-ocean interactions have only been documented for a few marine-terminating glaciers in this region 33–36. As a result, the climate drivers and the governing processes for calving front dynamics remain largely unknown. Here we use a calving front data product over large spatial scales and at a high temporal resolution for 149 marine-terminating glaciers across Svalbard between 1985 and 2023, mapped from a deep learning model, to investigate calving front change dynamics and its relation to air-ice-ocean interactions at different timescales over the past four decades. We first assess seasonal front retreat rates to climate changes across different parts of the archipelago. We then link the interannual calving front variability to climate extremes in recent years and investigate the dynamic mass loss likely associated with calving front retreats of marine-terminating glaciers.

Results
Widespread seasonal calving front changes
With the dense calving front observations generated with deep learning, we identify widespread seasonal cycles in calving front changes for marine-terminating glaciers across entire Svalbard over the period of 2014–2023 (Fig. 1a), a phenomenon that was only documented for a handful of glaciers in this region 33–39. 86 out of the 138 (62%) non-surging marine-terminating glaciers have seasonal cycles (annual periodicity autocorrelation ≥0.08, see Methods). These glaciers are mainly located on the western side of Svalbard, including sectors Northwest and South Spitsbergen that are in close contact with warm ocean currents (Fig. 1a), as well as the northeastern part of Svalbard, such as Austfonna (Fig. 1a) that is featured by complex oceanography settings with a mix of warm Atlantic Water and cold water from the Arctic Ocean and the Barents Sea 40,41. The mean seasonal range along the glacier centerline for these 86 glaciers is 102 ± 6 m. At the regional scale, South Spitsbergen has the highest mean seasonal range of 141 ± 3 m, followed by Northwest and Vestfonna (Figs. 1a and 2a). In addition, glaciers Olsokbreen (RGI60-07.00265, Figure S3) and Paierlbreen (RGI60-07.00244, Figure S4) with the highest seasonal ranges of 411 m and 385 m respectively are located in South Spitsbergen. We consider calving front advance as when ice discharge exceeds frontal ablation and retreat as when frontal ablation exceeds ice discharge. We are not able to quantify ice discharge, and therefore frontal ablation, due to a lack of ice velocity dataset of sufficient spatial coverage and temporal resolution for the entire Svalbard over a long period that is required for this study. However, calving front retreat rate has proved to be a good indicator for the variability of frontal ablation in Svalbard 35,36. In these studies where ice discharge has been taken into account, it is shown that frontal ablation in winter almost shuts down, allowing most of the ice discharge to go into the seasonal readvance of the calving fronts.

Across Svalbard, glaciers generally start retreating from May to July, reach the highest retreat rates in August and September, and then start to re-advance in November and December (Fig. 1b–c). Examples of the seasonal calving front change timeseries of several non-surging glaciers are shown in Fig. 2a–d. The calving front locations display regular seasonal cycles with the peak retreat rates occurring around the same time of the year. The onset of calving front retreat coincides with the increased surface runoff and rising subsurface ocean temperature, while the peak retreat rates occur during the months of highest ocean temperatures (Fig. 1d). This is in agreement with a recent study focusing on outlet glaciers of Austfonna 36 which discovered synchronised seasonal cycles in calving front changes in phase with nearby ocean temperature measurements in 2019–2022. We identified eight glaciers across Svalbard having seasonal cycles during a glacier surge (Table S2 and Figure S9a). The seasonal calving front changes of surging glaciers follow similar patterns to non-surging glaciers (Figure S9b), although their average seasonal range is significantly higher, reaching 192 ± 54 m. Figure 2e, f show the seasonal calving front time series of two surging glaciers Vestre Osbornebreen (RGI60-07.00468) and Storisstraumen (RGI60-07.00027), respectively, where calving fronts usually retreat from July until December with a regular cycle. The similarity in seasonal cycles between surging and non-surging glaciers provides further evidence that dynamic regimes have minimal influence on the calving front cyclicity 35.

Ocean temperature variability drives divergent seasonal cycles
The coincidence between peak seasonal retreat rates and peak subsurface ocean temperatures indicates the impact of ocean heat content on seasonal calving front retreat cycles. Correlation analysis between seasonal calving front retreat rates with four different time-evolving environmental variables including sea ice concentration, surface runoff, 3 m air temperature, and 20–100 m depth-averaged subsurface ocean temperature, shows that ocean temperature is the strongest predictor for seasonal calving front rates with an R2 value of 0.97 (Figure S7). Similar relationships are also found for surging glaciers (Figure S10). Northwest and South Spitsbergen have the highest correlations (Table S1), where the 20–100 m depth-averaged subsurface ocean temperature is a mixture of surface water and Atlantic Intermediate Water 5. The spatial distributions of the fjord points used to extract ocean temperatures from ORAS5 ocean reanalysis data are shown in Figure S6. Although some fjord points are far from glacier terminus and sample ocean temperatures outside fjords due to limited spatial resolution and coverage of ocean reanalysis data near the coast, this does not significantly affect the relationship between ocean warming and seasonal calving front retreats. The fjord points in the open ocean can still be representative of the water structures within the fjord, as they are predominantly located in regions of deep bathymetry (Figure S6), indicating that these fjord points lie along the pathway where warm Atlantic water can enter the fjords. Furthermore, in-situ measurements in west Spitsbergen show that ocean temperatures in water depths less than 100 m are often similar both inside and outside the fjords 5,42.

From western to eastern Svalbard, there is a several-month delay in the timings of peak retreat rates and peak subsurface ocean temperatures (Fig. 3). On the west coast, the peak retreat rates typically occur in August and September, such as in the regions of Northwest (Figs. 2b and 3a) and South Spitsbergen (Figs. 2a and 3b). Meanwhile, along the east coast, peak retreat rates normally occur from September to November, such as Vestfonna (Figs. 2d and 3e) and Austfonna (Figs. 2c and 3f). This regional variation in peak retreat rates closely aligns with the seasonal subsurface ocean temperature patterns across different regions, suggesting that ocean temperature exerts an immediate impact on calving front retreat, as oceanic forcing can manifest the submarine melting of glaciers and increase glacier retreat rates by combined undercutting and calving 36,43–46. In contrast, the peak air temperature and surface runoff consistently occur in July, irrespective of spatial location. The seasonal ocean warming is likely caused by the seasonal inflow of warm Atlantic ocean water, which normally enters fjords in an annual cycle during the shift from winter to summer and can also be present during winter due to the increased winter cyclones passing through the Fram Strait 5. Peak retreat rates and peak ocean temperature are delayed by one-two months in Vestfonna and Austfonna compared to western Svalbard, matching the delayed ocean heat transport of the warm West Spitsbergen Current (WSC) along the northeastern coast (Fig. 1a). In addition, the earlier onset of seasonal ocean warming and higher subsurface ocean temperatures in Northwest compared to South Spitsbergen are consistent with in-situ ocean measurements 5, which indicate that the intensity of ocean water exchange in the Northwest fjord is nearly twice that of the South Spitsbergen fjord, highlighting the significant role of Atlantic water transport in driving this difference.

Sea ice concentration (R2 = 0.83) is the second strongest predictor for seasonal front retreat rates. Landfast sea ice (hereafter referred to as fast ice) can exert a buttressing effect on glacier calving fronts, stabilising the ice and reducing calving rates 47. However, in Svalbard, there is a large spatial variability in seasonal sea ice concentration (Figure S8), and glaciers in Northwest and South Spitsbergen are largely sea ice free in winter over the 2014–2023 period (Fig. 2a, b, e). Eastern Svalbard including Austfonna, Vestfonna, Barentsøya and Edgeøya, feature higher sea ice concentration. The average sea ice concentrations in early Spring around Austfonna and Vestfonna are around 70%, while it can exceed 80% around Barentsøya and Edgeøya (Figure S8), which is the region that has the highest correlation with sea ice (Table S1). This spatial pattern matches the fast ice distribution in the past two decades which is primarily concentrated along the coast of eastern Svalbard 48. We also found positive correlations for seasonal retreat rates with air temperature (R2 = 0.63) and surface runoff (R2 = 0.3) (Figure S7), indicating that they are likely contributing factors to seasonal variations in the glacier front. Surface meltwater caused by atmospheric warming can increase subglacial discharge which can lead to increased frontal melting, as it can create buoyant plumes that enhance the turbulent transfer of oceanic heat to glacier calving fronts 21,29,45,46,49, while accumulation of meltwater on surface contributes to crevasse hydrofracturing 50–52. The surface melt season in Svalbard usually lasts about six months between May and October (Fig. 1d) and the onset of surface runoff in May coincides with the most frequent starting month for frontal retreat (Fig. 1b–d).

38 years of pervasive glacier retreats
After removing seasonal variability from the 1985–2023 calving front change time series (Figure S1), we found pervasive glacier retreats over the past 38 years across all six sectors (Fig. 4a), and for 91% of the 138 non-surging marine-terminating glaciers considered in this study (Fig. 4b). All non-surging glaciers lost a total area of 810 ± 26 km2 at the calving front over the past four decades, equivalent to an annual areal loss rate of 23.78 km2 yr−1 (Fig. 4c). The area change is calculated by multiplying the calving front length change by the glacier width, defined as the mean length of all terminus traces (see Methods). A comparison of the area loss of these glaciers with another calving front change dataset 53 in two different periods 2000–2010 and 2010–2020 is shown in Figure S11 (negative values denote area loss). The two datasets show good agreement, particularly during the period from 2010 to 2020 (R2 = 0.83). The total area loss estimated by Kochtitzky and Copland (2022) 53 in 2000–2020 was 456 km2, while the net area loss is 634 km2 in our study. This difference is mainly caused by a discrepancy in the temporal resolution of calving front mapping in 2000–2010 for five large glaciers including Braasvellbreen, Polakkbreen, Ordonnansbreen, Stonebreen, and Hochstetterbreen (Figure S12). A slight difference in their calving front location can lead to significant area changes due to the glacier’s large width. Especially, the calving front mapping frequency in Kochtitzky and Copland (2022) 53 is only 10 years which can be influenced by seasonal calving cycles compared to our long-term trend. After removing these five glaciers in the comparison, the area change difference in 2000–2020 is 49 km2.

The range of seasonal calving front variability correlates with the decadal retreat rates with a correlation coefficient R value of 0.6 (P < 0.05, Figure S13a), suggesting that glaciers experience a larger amplitude in the seasonal cycle also experience a greater amplitude of secular trend. For example, glaciers Olsokbreen (Figure S3) and Paierlbreen (Figure S4) have high seasonal ranges of 411 m and 385 m, respectively, and experience high decadal retreat rates of 205 m yr−1 and 155 m yr−1. This finding agrees with a recent study of glacier retreats in the Greenland Ice Sheet 19, which identified seasonal length variability as the sole significant predictor of long-term calving front change (R = 0.47, Extended Data Fig. 4 in 19) among various candidate predictors, including bed slope, surface slope, bed elevation, glacier width, terminus thickness, surface runoff, and ocean temperature. Glacier geometry is highly relevant to calving front retreats 54, especially the bed slope, as glacier can retreat faster over the over deepened bed 55–57. We found a correlation of R = −0.53 (P < 0.05) between decadal retreat rates and bed slope at calving fronts (Figure S13b), suggesting that a retrograde bed slope is associated with more significant long-term retreats. In comparison, bed slope shows no correlation with glacier retreat in the Greenland Ice Sheet 19. The links between seasonal front range, bed slope, and decadal retreat rates indicate a glacier-specific internal dynamics are in play in controlling the rates and magnitude of calving front changes, likely to be changes in tensile stresses controlled by bed topography 19,58. This is likely the reason that an increase in air and ocean temperature does not necessarily result in higher rates of change as the amplitude of glacier-specific front changes is influenced by complex factors, including bed topography and environmental conditions.

Rapid glacier response to climate extremes
Positive correlations exist between interannual variability in calving front positions and interannual changes in environmental forcings (Figs. 5 and S14). Similar to seasonal front changes, the highest correlation is found for subsurface ocean temperature (R2 = 0.5), followed by air temperature (R2 = 0.39) and sea ice concentration (R2 = 0.34), suggesting that ocean forcing is the most prominent factor in controlling the interannual calving front variability. However, air temperature also shows a comparable pattern and correlation, as well as the decreased sea ice cover and increased occurrences of sea-ice-free months around Svalbard (Figure S14). Over the past four decades, the Barents Sea has experienced a significant decline in sea ice cover, accompanied by rising ocean temperatures attributed to enhanced ocean heat transport from the West Spitsbergen Current, and strong increases in surface air temperatures over the Northern Barents Sea 41. In response to this warming trend, the marine-terminating glaciers have been continuously retreating with positive area loss rates over the entire study period (Fig. 5). Especially between 1985 and 2016, the interannual calving front area loss rates increased in parallel with trends in air temperature and subsurface ocean temperature anomalies. Glaciers with the most significant calving front retreat rates are located along the west and southeast coast (Fig. 4a), featured by the highest increases in ocean temperature 59,60. The effects of atmospheric and oceanic warming on long-term front retreat rates cannot be fully partitioned as they are climatically coupled 60, as noted by previous studies that emphasise the role of both factors in controlling the calving dynamics of marine-terminating glaciers in Greenland and Svalbard 21,34,39,61.

The interannual area loss rates from 1985 to 2023 capture two shifts in climate forcings in recent years (Fig. 5): 1) 2016–2018, here we refer to it as the warming phase; 2) 2019–2021, here we refer to it as the cooling phase. During the warming phase, the area loss rates across Svalbard almost doubled compared to 2015. This significant area losses are coincident with peak air and ocean temperatures in the entire 38-year record. Svalbard in 2016 is featured by extreme precipitation and air temperature and it has the wettest summer since 1955 3. Compared to the 2010–2015 mean, air temperature in 2016 was up to 4 °C higher across Svalbard, accompanied by an up to 2 °C ocean warming (Fig. 6a). This significant temperature increase is found to be linked with a strong flux of warm Atlantic water and associated warm air driven by the Scandinavian blocking pattern 3,4. During the cooling phase in 2019, the area loss rates decreased to the pre-2016 level (Fig. 5), accompanied by an ~2 °C cooling of air temperature and up to 1 °C decrease in subsurface ocean temperature (Fig. 6b). A recent study has identified this cooling to be associated with the slowdown of a large-scale ocean circulation in the Nordic Seas due to atmospheric blocking 2. Geodetic records of the annual mass loss for non-surging glaciers considered in this study across Svalbard shows a similar pattern over the last two decades (Figure S15a) 13. It has been demonstrated that atmospheric blocking can significantly modify atmospheric and oceanic circulation patterns in the northern Atlantic Ocean, also impacting submarine melting of glaciers in Greenland 2. Compared to Greenland, Svalbard is in closer contact with the Atlantic intermediate warm water and may be impacted more directly by any slight changes in the atmospheric blocking patterns. Over the past 40 years, atmospheric blocking events have occurred more frequently and with greater intensity, primarily in response to amplified Arctic warming 4.

Dynamic mass loss and future impact
Past studies have linked calving front retreat with increased ice dynamics in Greenland 62 and Antarctica 63. Here we investigate the relationship between glacier front changes and mass balance components extracted from other available data products. We aggregate cumulative mass balance estimates, excluding submarine changes, from GRACE/GRACE-FO satellite gravimetry measurements using JPL Mass Concentration blocks (mascons) from Svalbard since 2002 (Figure S15b). According to these data, Svalbard has lost ~368 ± 9 Gt of ice (1.02 mm sea level equivalent) over the past two decades. The rate of mass loss increased since 2012, in part caused by the massive surging of Storisstraumen in Austfonna (RGI60-07.00027) 64. To determine the proportion of mass loss due to surface and ice dynamical processes, we calculated the cumulative surface mass balance for Svalbard land ice using the high-resolution (2.5 km) surface mass balance data generated from the CryoGrid model forced with CARRA (Copernicus Arctic Regional Reanalysis) data 65 (red line in Figure S15b). The mass change due to ice dynamical processes (orange line in Figure S15b) was then calculated as the difference between the total mass balance and the surface mass balance (see Methods). The results indicate that during 2002–2022, Svalbard glaciers discharged about 210 ± 13 Gt of ice into the ocean, accounting for 57% of the total mass imbalance. In comparison, the ice discharge in Svalbard calculated by Kochtitzky et al. 31 during 2000–2020 was 193 ± 22 Gt. The acceleration of the total mass loss since 2012 is caused by both surface mass balance and ice discharge, but dominated by discharge.

The sustained increase in ice discharge is likely related to our observed glacier retreat of non-surging marine-terminating glaciers, as a result of both atmospheric and oceanic warming around Svalbard. The spatial distribution of long-term calving front area loss matches the observed long-term thinning of Svalbard glaciers in 1936–2010 11. The current interpretation of Svalbard glacier mass loss and its future sea-level rise contribution throughout this century has focused on the role of the atmosphere (air temperature increases) and precipitation on ice mass changes 11,12,66. Ice-ocean interaction of marine-terminating glaciers is yet to be adequately incorporated into mass loss projections of Arctic glaciers 17. Hereby, regional climate models in recent studies have not been coupled to a dynamic ocean model 12. As marine-terminating glaciers become exposed to warmer ocean water through Atlantification, ocean-induced undercutting in combination with surface melting is likely to increase frontal ablation, in turn leading to increased ice discharge and mass loss from Svalbard. This will at some point be counterbalanced by glaciers retreating into shallower waters or on land, but our results give no indication of that occurring yet, and about 13% of the glacier area on Svalbard is still grounded below sea level 18. As a region highly sensitive to climate change, the behavior of Svalbard marine-terminating glaciers offers an opportunity to explore the controls on calving front changes for marine-terminating glaciers in general, which are pervasive across the Arctic archipelagos and around Greenland.

Methods
Glacier calving front mapping
The calving fronts of marine-terminating glaciers in Svalbard between 1985 and 2023 were mapped from optical and SAR satellite images available from Landsat, Terra-ASTER, Sentinel-1 and Sentinel-2 satellites, using a deep learning model Charting Outlines by Recurrent Adaption (COBRA) 67. This model combines convolutional neural networks (CNN) for feature extraction and active contour models. It outperforms the traditional image segmentation model such as U-Net in mapping glacier calving fronts. After applying the post processing steps developed in Li et al. 68, the final calving front dataset contains 124,919 calving front traces for 149 marine-terminating glaciers in Svalbard during the past 38 years 68. The overall calving front mapping uncertainty across Svalbard of this data product is 31 m, by measuring the mean distance error in different calving front locations mapped on the same day for a given glacier. In addition to the long timespan, this calving front dataset offers unprecedented temporal density. After 2014, since the launch of the Sentinel-1 and Sentinel-2 satellites, the average temporal resolution is 4 days for all glaciers. For each glacier, the calving front change time series is generated using a centreline approach 68, by measuring the advance or retreat of the calving front in relation to their earliest position along a glacier centreline generated from the OGGM global glacier model 69.

Timeseries decomposition
The detailed workflow of decomposing the seasonal calving front change cycles and long-term calving front trend from the calving front time series is shown in Figure S1, with intermediate step-by-step examples in Figures S2, S3 and S4 for three glaciers: Vestre Osbornebreen, Olsokbreen and Paierlbreen. To derive the seasonal calving front change cycle, we used the calving front time series after 2014 due to its high temporal resolution of Sentinel-1 and Sentinel-2 satellite images. We first used a 30-day median filter to remove outliers from the time series after 2014. Then we used a low-pass filter with a cutoff frequency of 180 days to detrend the filtered calving front change times series. After deriving the detrended calving front time series, the calving front seasonality was calculated as the monthly mean detrended calving front retreat by grouping the detrended time series into bins of months of observation (Figures S2, S3 and S4), the standard deviation is calculated as the uncertainty. To assess the annual self-similarity of detrended calving front change time series of each glacier after 2014, we adopted a similar approach as Wallis et al. 70 by calculating the autocorrelation at lags of one to eight years. The mean of these yearly autocorrelation values was then assigned as a single statistic over the entire nine-year time period between 2014 and 2022 for each glacier. We define an autocorrelation value ≥0.08 as strong annual periodicity, meaning this glacier displays a seasonal cycle in calving front changes.

The interannual trend of calving front change throughout the entire study period was calculated by adding the calving front time series without the seasonal cycles after 2014 to the initial calving front time series before 2014 (Figure S1). We first calculated the annual mean calving front retreat distances along the glacier centerline by aggregating all the long-term calving front observations in bins of individual years across the observation period of a glacier. For years within this observation period that do not have any observations, we linearly interpolated the calving front change distances between neighbouring years. The calving front retreats were then converted into areal change rates by multiplying the glacier front width, which was calculated as the mean length of all terminus traces of each glacier. The uncertainty in glacier width is estimated as the standard deviation of terminus trace lengths. We assign 31 m 68 uncertainty to the annual calving front distance. The combined uncertainty in calving front area change is calculated as the product of the relative uncertainties of these two components. Using the mean length of terminus traces as glacier width may underestimate the area change in cases where the front traces are cut short due to restrictive front masks defined in Li et al. 68 based on Water Body Mask (WBM) and the Copernicus GLO-90 digital elevation model data, or overestimate the area change in cases where retreat along the centreline is larger than at the margins due to the typical transition from deep to shallow ocean water. However, the purpose of the area change estimation is to scale up the retreat rates by glacier size for larger-scale regional assessments, rather than providing exact measures of area change.

To evaluate the calving dynamics in different regions around Svalbard, we divided Svalbard into six different sectors based on Fig. 1 in Noël et al. 12 including Northwest Spitsbergen (NW), Northeast Spitsbergen (NE), Vestfonna (VF), Austfonna (AF), Barentsøya and Edgeøya (BE), and South Spitsbergen (SS). For seasonal calving front changes, all non-surging marine-terminating glaciers located in the same sector with a seasonal cycle (autocorrelation ≥0.08) were aggregated by months to calculate the mean of seasonal calving front change rates. For the sector-scale interannual calving front changes, the areal loss rates were aggregated by bins of year by calculating the average calving front area losses for all non-surging glaciers in each sector. We estimate the uncertainty of regional retreat rates as the root mean square of annual or monthly uncertainties of all glaciers. Surging glaciers (Figure S9) were excluded from both seasonal and interannual analyses of calving front changes because surging events are not well understood and are likely driven by internal glacier dynamics 71. The long-term cumulative calving front area losses at the sector scale were calculated as the cumulative sum of the annual front area losses. The excluded surging glaciers (Figure S9) were identified from the glacier calving front change time series visually if the calving front underwent significant advances >500 m over two years period 68. These glaciers are listed in Table S2, including the Storisstraumen on Austfonna (RGI60-07.00027) which started surging in 2012 64 and still has a highly dynamic calving front.

Bed topography and ice thickness
We derived the bed topography and ice thickness at the glacier calving front from SVIFT v1.1 - The Svalbard ice-free topography dataset 18. It uses a mass conservation approach to reconstruct ice thickness. To calculate the mean bed topography and ice thickness for each glacier, we sampled bed topography and ice thickness within 500 m of the ice margins in SVIFT dataset along the glacier centerline generated in Li et al. 68 and calculated the mean values. The bed slope is calculated by fitting a trend line to topography sampled along this 500 m length centerline segment.

MAR and ORAS5 data analysis
We derived daily surface runoff and 3-m air temperature from 1985 to 2023 from a Regional Climate Model (RCM) Modèle Atmosphérique Régional (MAR) 72,73 with 6 km spatial resolution. Data for our calving front analysis were extracted from a location upstream of the calving front traces time series of each studied glacier. The glacier point was firstly converted from the centre point of each glacier domain polygon defined in Li et al. 68, it was then manually adjusted based on the MAR ice mask file and the final glacier calving front traces time series. The climate variables from MAR daily files were interpolated at each glacier point using the nearest neighbour interpolation. The spatial distributions of the glacier points are shown in Figure S5.

Subsurface ocean temperatures were derived from the ECMWF ORAS5 (Ocean Reanalysis System 5) global ocean reanalysis monthly dataset from 1985 to 2023 74. The horizontal resolution of the reanalysis data is ~0.15° in Svalbard and we gridded data into 0.15° regular grids. This spatial resolution is enough to capture the spatiotemporal variability of ocean temperature outside the fjords as the width of the fjord mouth is normally larger than the grid size of ORAS5 data. The subsurface ocean temperature calculated as the mean temperature across the 20–100 m water depth, was derived at the fjord point of each studied glacier across Svalbard. The fjord point was derived by finding the closest non-empty grid point in the ORAS5 sea surface temperature grid to the corresponding glacier point of each glacier derived in the MAR data processing. The spatial distribution of the fjord points is shown in Figure S6.

Sea ice concentration
Sea ice concentration represents the areal fractional coverage of sea ice. To investigate the relationship between sea ice concentration and glacier calving front changes, we generated the sea ice concentration time series over the time period of 1985–2023 for every glacier studied. We use three different data sources including: 1) sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data at a spatial resolution of 25 km with a temporal coverage from 1985–2002 75; 2) sea ice concentrations from AMSR-E (Advanced Microwave Scanning Radiometer for EOS) at a spatial resolution of 3.125 km during 2002–2011 76; 3) sea ice concentrations from AMSR-2 from 2012 to 2023 at a spatial resolution of 3.125 km 77. The daily sea ice concentration was derived at the fjord point of each studied glacier across Svalbard. When summarising the sea-ice-free years in each month in the seasonality analysis (Figures S7 and S8), the sea-ice-free year is defined as when the mean sea ice concentration is less than 20%.

To determine the relationship between glacier calving front changes and time-evolving environmental variables including surface runoff, air temperature, subsurface ocean temperature, and sea ice concentration, linear regressions between parameters are applied in the correlation analysis at both seasonal and interannual timescales. In the sector-scale long-term calving front changes and their responses to climate forcings, we calculated the long-term climate anomalies by using the 1985–2022 mean as the baseline.

Mass balance data processing
We used the GRACE JPL Mass Concentration blocks (mascons) over Svalbard to estimate the cumulative mass balance from 2002 to 2022 at monthly temporal resolution 78,79. The JPL mascons fields are based on the Level-1B GRACE/GRACE-FO data, its C20 and C30 coefficients are replaced with solutions from satellite laser ranging in Technical Note TN-1466 and the degree-1 coefficients (trend) are estimated using methods from Sun et al. 80. In addition, the JPL mascons have been corrected for the glacial isostatic adjustment based on the ICE6G-D model 81. We fit a 12-month low-pass filter to the monthly GRACE mass change time series to derive the interannual trend, and the root mean square error (RMSE) between this filtered long-term trend and the original mass change time series is estimated to be the uncertainty of total mass change. For the surface mass balance over the same period as the JPL mascons, we used the monthly high-resolution (2.5 km) Svalbard glacier mass balance simulations generated from the CryoGrid community model forced by Copernicus Arctic Regional ReAnalysis (CARRA) data 65. The same low-pass filter was applied to the monthly SMB time series for the interannual trend, and its uncertainty was also calculated as the RMSE between the interannual SMB trend and the monthly time series. The mass change due to ice discharge, ignoring calving front position changes, was then calculated as the difference between the total mass balance and the surface mass balance (Figure S15b), and its uncertainty was estimated as the root mean square of the uncertainties from these two different components.

To separate mass changes from surging and non-surging glaciers, we used the per-glacier annual mass change time series from Hugonnet et al. 13 derived from differencing of ASTER DEMs from 2000 to 2020. Based on the marine-terminating glacier calving front data product generated in our study, we updated the RGI 6.0 glacier database to address the mismatch in glacier types between Hugonnet et al. 13 dataset and our glacier calving font product 68,82. We then generated an annual time series of mass loss for non-surging glaciers (Figure S15a). Excluded surging glaciers are listed in Table S2.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The paper “Atmospheric and oceanic warming drives pervasive retreat of marine terminating glaciers in Svalbard” deals with the delineation of glacier front positions of 149 marine-terminating glaciers in Svalbard. They use these front positions to describe decadal and seasonal variations and find a correlation with atmospheric and oceanic forcings, a correlation of decadal and seasonal variations and highlight the importance of ice-ocean processes at the calving front. The 124919 glacier front positions, that are created by the authors represent a novel, extensive and interesting dataset. However, we highly question the novelty of their findings regarding the analysis for reasons explained below.\n\nThe fact that the glaciers in Svalbard are governed by atmospheric and oceanic forcing is not a novelty, as the authors themselves show in the introduction (lines 32-42), where they provide several examples of studies showing that. Also, the importance of incorporating ice-ocean interactions has been well-established (Straneo and Heimbach, 2013; Cowton et al., 2018; Catania et al., 2020). Therefore, in our opinion the findings do not add new insights to the existing literature.\nFurthermore, a main finding of the paper is the “significant” positive correlation between seasonal and decadal front positions (lines 77-83). However, we question the significance of this finding, which is specified with an R2 of 0.35. The authors compare this number to a study from Greenland (Greene et al. 2024) where an R2 of 0.71 was found. Instead of drawing the same conclusions as for Greenland, the authors should investigate why it is more challenging to observe a positive correlation for Svalbard glaciers compared to Greenland. Factors such as glacier size, velocity, and fjord depth could be explored.\n\nOther major comments:\n• Calving front positions are interpreted without taking flow velocities into account, which could change the observed pattern of calving immensely.\n• Line 17-19: It states that glaciers with seasonal calving cycles are more sensitive to long-term climate change. This requires further investigation into glacier front geometry for a representative sample of glaciers with large and small seasonality. They should explore why some glaciers exhibit seasonal cycles while others do not, and whether this could be related to glacier geometry and potential changes as glaciers may retreat to new pinning points.\n• Line 65: The uncertainty of the annual areal change rate of 0.47 ±1.01 km²/yr, seems very high to us, being more than twice as large as the amount of the actual value mentioned. Also, the mean seasonal rate of 91.3±75 m (line 75) has very large uncertainties, making it difficult to rely on these results.\n• Line 75: There are inconsistencies between the text and figures. For instance, line 75 refers to Olsokbreen showing a 411 m seasonal front range, which is double the range shown in figure 2a.\n• Line 79-80: The wording of the two examples given can be misleading. Potentially focusing on one example or breaking the sentence apart leads to more clarity.\n• Line 105, 153: On several occasions the authors treat calving as equivalent to front position changes, therefore neglecting submarine melt and changes in ice flow velocity.\n• Line 119-120: The argument is invalid. Holland et al. (2008) suggested that changes in Jakobshavn after 1997 were triggered by ocean warming but did not state that the peak of 15 m/yr was a result of a 2-degree ocean warming. This needs to be corrected as well as their concluding remarks based on this.\n• Line 20 states: \" There is also a strong correlation between grounded ice mass loss and front retreat, indicating that calving can be used as a proxy for the long-term mass loss under climate change.\" but, the manuscript does not explain grounded ice mass loss or discuss the use of calving as a proxy for long-term mass loss, and also the role of submarine melt is missing.\n• Line 82: The argument for extrapolating retreat rates to longer time scales is not convincing, as retreat rates also depend on the glacier front geometry. As glaciers retreat, they may reach new pinning points that could reduce seasonal sensitivity. This needs a more detailed explanation, especially considering the R2 value of 0.35.\n• Line 142: “In addition, the peaks in annual cumulative surface runoff anomaly coincide with the peaks in mean interannual areal loss rates (Figure 4a), indicating surface runoff has an instant effect on calving front retreat rates.”. Figure 4, there are other anomaly peaks coinciding with the peak in areal loss (panel b & c). The text should clearly identify why they only use panel a to explain what affects calving front retreat rates.\n• Line 172-175: The logic in this part is unclear and needs to be elaborated.\n• Line 195-200: The differences between NW and NE Svalbard and the influence of atmospheric and oceanic temperatures on each other are not clearly explained and need further detail.\n• Line 201-202: “In addition, our study finds that the drastic ice speed change ranging from surging events does not impact the seasonal calving cycles (Figure S2).” this part does not seem to be linked to the rest of the manuscript.\n• Surging glaciers were mentioned a few times in the manuscript, but it does not explain the differences between surging and non-surging glaciers, or why the distinction is important. This needs to be clarified.\n• Line 238-242: Again, this part does not seem to be linked to the rest of the manuscript. It should be integrated better or clarified.\n• Figure captions in the supplementary materials are incomplete, making it difficult to follow their arguments. The figures themselves are not self-explanatory and would benefit from more detailed descriptions. For example, figure 1c doesn’t show errors, figure 2a doesn’t say what the size of squares means, figures S2,S3 in panel f what is dotted line?", "reviewer_2": "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.", "reviewer_3": "The paper assesses controls on glacier retreat in Svalbard. Thus, it tackles an interesting topic and has a wealth of data, which can be used to investigate some really important questions. Overall, the paper presents a number of novel and interesting results, e.g. seasonal patterns on Svalbard, the correlation between season changes and inter annual and that surge type glaciers seem to have a similar pattern as non-surge type (although the final point is noted but not properly explored). However, the paper feels like it needs a bit more thought and precision, to ensure it real does disentangle these important questions. There are several examples of this, like the surge seasonality point, whether surface elevation / SMB changes are caused by retreat or cause it (i.e. the direction of the relationship, whether the relationship between total mass loss and area change is really pervasive given the apparent impact of two data points on the relationship, whether sea ice plays any role (it is mentioned as an explanation a few times, but never really explored properly). There are also small scale examples of this, e.g. melt rates are taken from Jakobshavn Isbrae but there isn't much consideration of how appropriate these might be, using frontal ablation and calving interchangeably, adding / removing the surge type glaciers in different parts, making it confusing about when they are in/out. I know that Nature Comms is short format, which makes exploring ideas difficult, but unfortunately some of the arguments did not feel fully pinned down and explored, which amkes the narrative less convincing. I think you have a really solid dataset here and some really interesting findings, so regardless of what is decided by Nature Comms, I strongly encourage submission elsewhere and this might give you the space to fix some of the issues noted above. Please see my annotated pdf for detailed comments.", "reviewer_4": "Review of Atmospheric and oceanic warming drives pervasive retreat of marine- terminating glaciers in Svalbard by Li et al.\nGeneral Comments\nThis is an interesting article which relates environmental variables to glacier retreat on a pan-Svalbard scale. Overall, I think the manuscript is well written and scientifically sound. However, I have one general comment related to the location of the fjord points used for extracting the oceanographic data. In the supplementary material, Fig S5 shows the fjord points used. Many of this fjord points are a considerable distance from the glacier fronts, and even outside of the fjord mouth. Previous research has shown that the water masses present in the inner fjords can be quite different to those present outside of the fjord mouth (see e.g. Fig. 4 in Prominska et al, https://doi.org/10.1016/j.oceano.2017.07.003 and/or Divya et al https://doi.org/10.1016/j.polar.2021.100735). I understand that the location of these fjord points may be an unavoidable consequence of the pan-Svalbard scale of the study / the datasets used, but feel that this needs to be addressed in the main text. For example, a short paragraph could be added to the discussion.\n\nIn addition, I have several minor comments which are listed below:\nMinor comments\nL38: I would appreciate a little more context here: is frontal ablation not considered in other studies, or assumed to be included in some other metric etc?\nL60: ‘Area at the calving front’ – I see this explained in the methods, but was not fully sure what this meant until I found this explanation. Maybe a short explanation can be included in the text here\nL64: I appreciate the comparison with Kochitsky et al, as well as the possible explanation for the difference between their results and yours. I wonder If you would get a more similar result if you used lower (temporal) resolution data / have you tried this and was this the outcome? Then it would be clear if this was the reason for the difference in results, or suggest that something else is at play\nL78: It is not clear to me whether your description of the R2 values as significant means you think this shows a strong relationship, or whether you just mean the result is statistically significant. Related to this, it would be useful if you state what you consider a ‘significant’ R2 value to be, and also what the p values are for each R2 value. A value of 0.37 is perhaps quite good for a natural system, but is much lower than any of your R2 values linking retreat rates to environmental values, so some discussion of how you interpret the R2 values would be beneficial.\nL83: Do you have any suggestions for why the seasonal range/long-term range R2 value is twice as high for Greenland?\nL113: Interesting that the r2 value for subsurface ocean temperatures is very similar to that for one month lagged air temperatures – do the time series for these two variables look similar?\nL120: Please state whether this is basal melt under grounded or floating ice, and also specify if this is an additional 15 m/yr of basal melting or an absolute value.\nL124: You could link this to e.g. Slater and Straneo (https://www.nature.com/articles/s41561-022-01035-9), where atmospheric temps was found to exert a first order control on submarine melt\nFig. 3: Please add p values to the annontation on the plots (the same for other figures)\nL175: Could it also be some internal dynamic feedbacks? E.g. climate drivers cause front retreat, which cause acceleration and then thinning etc?\nL204: I think this should be ‘mirroring a recent finding FROM/FOR THE Greenland Ice Sheet’\nL205: Reference should be in brackets\nL318: Can you add a comment on the impact of the resolution of the ocean data set, given the known importance of fjord circulation for submarine melt rates"}, "author_response": "Responses to Reviewers’ Comments\nTian Li on behalf of other co-authors\nReviewer 1\nThepaper“Atmosphericandoceanicwarmingdrivespervasiveretreatofmarinetermi-nating glaciers in Svalbard” deals with the delineation of glacier front positions of 149 marine-terminating glaciers in Svalbard. They use these front positions to describe decadal and seasonal variations and find a correlation with atmospheric and oceanic forcings, acorrelationofdecadalandseasonalvariationsandhighlighttheimportanceof ice-ocean processes at the calving front. The 124919 glacier front positions, that are created by the authors represent a novel, extensive and interesting dataset. How-ever, we highly question the novelty of their findings regarding the analysis for reasons explained below.\nThe fact that the glaciers in Svalbard are governed by atmospheric and oceanic forc-ing is not a novelty, as the authors themselves show in the introduction (lines 32-42), where they provide several examples of studies showing that. Also, the importance of incorporating ice-ocean interactions has been well-established (Straneo and Heim-bach, 2013; Cowton et al., 2018; Catania et al., 2020). Therefore, in our opinion the findings do not add new insights to the existing literature.\nThank you very much for your constructive and detailed comments, they have signifi-cantly improved the quality of this research.\nFirst, in the initial submission, we claim that the influence of atmospheric and oceanic forcing on the retreats of marine-terminating glaciers is well established in the Green-land Ice Sheet, not in Svalbard, due to a lack of dense calving front measurements that cover a long time span and large spatial coverage. Since the Arctic has diverse oceanic, atmospheric and glaciological settings (please see comments from Review-ers 2&3), what has been found for Greenland may not apply to Svalbard. So far, the air-ice-ocean interactions in Svalbard have only been investigated for a few marine-terminating glaciers and over a short time period. Therefore, climate drivers and the governing processes for calving front dynamics remain largely unknown across Sval-bard. Ourresearchnowfillsthisknowledgegapbyconductingthefirstcomprehensive analysis of seasonal and long-term calving front changes and their relationships with different environmental variables across the entire Svalbard over the past 38 years. This is a novelty.\nSecond, the current mass balance measurements for Svalbard have not incorporated the frontal ablation of marine-terminating glaciers [1, 2] and the mass loss projections of most glacier models did not include this component [3], therefore we demonstrate that it is crucial to examine the mechanism of marine-terminating glacier retreats, es-pecially in Svalbard, a region located in the most rapidly warming region of the Arctic.\nThird, in revision, we have implemented additional analyses based on the review com-ments to investigate spatiotemporal variabilities in calving front changes at different timescales and have significantly restructured the paper. The key messages and re-sults have been greatly improved compared to the initial submission.\nHere we briefly outline the main novelties in the revision:\n• We find that that 91% of the non-surge marine-terminating glaciers have been retreating since 1985 and 62% of them have seasonal cycles across Svalbard.\nNo study before has done such a detailed analysis over a long period of almost 40 years. In addition, previous studies on seasonal calving front change cycles have only focused on specific glaciers or small regions. This conclusion has provided new understanding into the historical changes and current status of marine-terminating glaciers in Svalbard and allows us to compare with other ice masses in the Arctic such as the Greenland Ice Sheet.\n• From western to eastern Svalbard, there are several months’ time delays in sea-sonal calving front retreat rates, coincident with the regional variability in ocean warming, demonstrating the dominant role of ocean forcing on seasonal calving front cycles. We are the first to directly observe such a spatial pattern in calv-ing fronts across Svalbard, and demonstrate the clear link between warm water inflow and the timing in seasonal calving front changes.\n• We find the interannual variability in calving front rates is sensitive to both at-mospheric and ocean warming. Previous studies on frontal ablation of marine-terminating glaciers in Svalbard mainly focused on ocean processes, instead of both. This further supports the conclusion by [4] on the Greenland Ice Sheet, highlighting that long-term dynamic mass loss cannot be adequately addressed by focusing solely on ice-ocean interactions without also considering the effects of atmospheric warming.\n• We find immediate responses of glacier calving front changes to large air and ocean temperature anomalies in 2016 and 2019. The large temperature anoma-liesduring2016-2020havebeenflaggedasclimateextremesbytworecentstud-ies [5, 6], likely driven by atmospheric blockings in the Arctic. The possible link between atmospheric blockings and changes in marine-terminating glaciers has never been discovered before for Svalbard.\nIn summary, our study provides significant advances in understanding the seasonal cycles and long-term retreat of calving fronts in Svalbard, through a high-resolution calving front dataset with exceptional spatial and temporal coverage. These findings enhance our knowledge of calving processes in Svalbard and can contribute to im-proving the parameterization of calving in glacier models.\nFurthermore, a main finding of the paper is the “significant” positive correlation be-tween seasonal and decadal front positions (lines 77-83). However, we question the significanceofthisfinding, whichisspecifiedwithanR2of0.35. Theauthorscompare this number to a study from Greenland (Greene et al. 2024) where an R2 of 0.71 was found. Instead of drawing the same conclusions as for Greenland, the authors should investigate why it is more challenging to observe a positive correlation for Svalbard glacierscomparedtoGreenland. Factorssuchasglaciersize, velocity, andfjorddepth could be explored.\nThank you for this suggestion which is very helpful.\nWe would like to admit that our previous conclusion regarding the correlation between these two variables in Svalbard being lower than in Greenland Ice Sheet was incor-rect. In fact, the correlations are very similar. The R2 value of 0.71 for the Greenland Ice Sheet in [7] was calculated between the long-term mass loss and seasonal mass loss, not the effective length changes that we measured in Svalbard. In the Extended Data Fig. 4 in [7], they normalized the terminus mass change values by terminus area and calculated the correlation between seasonal range and long-term changes in length variability, the R-value is only 0.47, which is slightly lower than the R-value of 0.6 (P<0.05) for Svalbard (Figure S13a). Therefore, we confirm that the relationship is nearly identical between Svalbard and Greenland. We have corrected this statement in Line 169 as ’This finding agrees with a recent study of glacier retreats in the Green-land Ice Sheet’.\nIn addition, we also found a negative correlation (R=-0.53, P<0.05) between bed slope and decadal retreat rates in Svalbard (Figure S13b), indicating that glaciers retreat fasteralongaretrogradebedslope,atheorythathasbeenwellestablishedformarine-terminatingglaciersandiceshelves[8,9,10,11]. Incomparison, [7]didnotobservea similar relationship, it is currently unknown why marine-terminating glaciers in Green-land do not display similar changes along a retrograde bed slope in [7]. Nonetheless, our main focus is Svalbard, any investigation on the Greenland Ice Sheet is out of the research scope of this study.\nCalvingfrontpositionsareinterpretedwithouttakingflowvelocitiesintoaccount,which could change the observed pattern of calving immensely.\nThank you for pointing this out. First, here we are observing calving front changes, we consider calving front advance as when ice discharge exceeds frontal ablation and retreat as when frontal ablation exceeds ice discharge. We are not able to quantify ice discharge, and therefore frontal ablation, due to a lack of ice velocity dataset of sufficient spatial coverage and temporal resolution for the entire Svalbard over a long period that is required for this study. However, calving front retreat rate has proved to be a good indicator for the variability of frontal ablation on Svalbard [12, 13]. In these studies where ice discharge has been taken into account, it is shown that frontal ablation in winter almost shut down, allowing most of the ice discharge to go into the seasonal readvance of the calving fronts. This has now been clarified in Line 70-77.\nLine 17-19: It states that glaciers with seasonal calving cycles are more sensitive to long-term climate change. This requires further investigation into glacier front geom-etry for a representative sample of glaciers with large and small seasonality. They should explore why some glaciers exhibit seasonal cycles while others do not, and whether this could be related to glacier geometry and potential changes as glaciers may retreat to new pinning points.\nThank you for this comment. We have now removed the statement on correlation be-tween seasonal front changes and long-term front change rates from the abstract and no longer listed this discovery as a main finding. In the revision, we set a threshold of calving front length change annual periodicity autocorrelation value ≥ 0.08 to define a glacier having seasonal cycles, instead of using ≥ 0.1 in the initial submission to clas-sify strong and weak seasonal cycles. The reason for this change is becausefor some glaciers, a low autocorrelation value does not necessarily indicate weak seasonal cy-cles, instead, it reflects a high noise-to-signal ratio in the detrended time series, which makes it difficult to detect any meaningful seasonal changes. To make it simple, we set a uniform threshold of 0.08, this is based on the visual checks of the detrended calving front change time series in 2014-2023 of all studied glaciers.\nWe investigated the correlations between seasonal front range and six different poten-tial predictors for seasonality shown in Figure R1 below, including bed slope, bed ele-vation, ice thickness, glacier width, air temperature seasonality and subsurface ocean temperature seasonality - calculated as the standard deviation of monthly mean tem-peratures. Wefoundnoclearcorrelationswithallofthesevariables, indicatingthatthe amplitudes of seasonal changes are likely caused by glacier-specific internal dynam-ics.\nLine 65: The uncertainty of the annual areal change rate of 0.47 ±1.01 km²/yr, seems very high to us, being more than twice as large as the amount of the actual value mentioned. Also, the mean seasonal rate of 91.3±75 m (line 75) has very large uncer-tainties, making it difficult to rely on these results.\nThank you for pointing this out. We made a mistake in calculating the errors for both interannual area change rates and seasonal front retreat rates in each region by cal-culating the standard deviation of these two metrics among all different glaciers. Now we have corrected this by using the error propagation principles (please see Online Methods section ’Time series decomposition’), and all the related figures and tables havebeenupdatedtoreflectthenewlycalculateduncertainties. AttheSvalbardscale, the updated mean annual areal change rate in 2016 is now 0.47 ± 0.04 km²/yr (Figure 5), and the mean seasonal range for 86 non-surge marine-terminating glaciers is 102 ± 6 m.\nLine 75: There are inconsistencies between the text and figures. For instance, line 75 refers to Olsokbreen showing a 411 m seasonal front range, which is double the range shown in figure 2a.\nThank you for the comment. The figure in the initial submission was plotted correctly; however, the mismatch arose from the arbitrary thresholds we manually set for the minimum and maximum values of the error bar to enhance visualization. We have now updated this in Figure 1a.\nLine 79-80: The wording of the two examples given can be misleading. Potentially focusing on one example or breaking the sentence apart leads to more clarity.\nThank you and agree, we have now changed the wording to ’Glaciers Olsokbreen (Figure S3) and Paierlbreen (Figure S4) have high seasonal ranges of 411 m and 385 m, respectively, and experience high decadal retreat rates of 205 m/yr and 155 m/yr.’ in Line 167-169.\nLine 105, 153: On several occasions the authors treat calving as equivalent to front positionchanges,thereforeneglectingsubmarinemeltandchangesiniceflowvelocity.\nThank you for this comment, we have now made it clear that we observe ’calving front change’ in this study throughout the manuscript, please also see our previous response.\nLine 119-120: The argument is invalid. Holland et al. (2008) suggested that changes in Jakobshavn after 1997 were triggered by ocean warming but did not state that the peakof15m/yrwasaresultofa2-degreeoceanwarming. Thisneedstobecorrected as well as their concluding remarks based on this.\nAgree, we have now removed this statement from the manuscript.\nLine 20 states: \" There is also a strong correlation between grounded ice mass loss and front retreat, indicating that calving can be used as a proxy for the long-term mass loss under climate change.\" but, the manuscript does not explain grounded ice mass loss or discuss the use of calving as a proxy for long-term mass loss, and also the role of submarine melt is missing.\nThank you for this comment and agree. We have removed the correlation analysis between grounded ice mass loss rates with front retreat rates from the paper, as Re-viewers 2 and 3 pointed out that this correlation is severely skewed by two data points which makes this interpretation less reliable.\nLine 82: The argument for extrapolating retreat rates to longer time scales is not con-vincing, as retreat rates also depend on the glacier front geometry. As glaciers retreat, they may reach new pinning points that could reduce seasonal sensitivity. This needs a more detailed explanation, especially considering the R2 value of 0.35.\nThis is a very good point, especially considering that our latest analysis on possible predictors for the long-term calving front retreat rates in Figure S13 shows that there is a negative correlation between decadal retreat rates and bed slope at calving fronts (R=-0.53, P<0.05), indicatingthataretrogradebedslopeislikelycontributingtohigher long-term retreat rates. We have now removed the statement on extrapolating retreat rates to longer time scales.\nLine 142: “In addition, the peaks in annual cumulative surface runoff anomaly coincide with the peaks in mean interannual areal loss rates (Figure 4a), indicating surface runoff has an instant effect on calving front retreat rates.”. Figure 4, there are other anomaly peaks coinciding with the peak in areal loss (panel b and c). The text should clearly identify why they only use panel a to explain what affects calving front retreat rates.\nThank you for this suggestion. We agree and have significantly changed the inter-pretation of results related to the interannual variability in calving front change rates, now in Section ’Rapid disintegration in response to climate extremes’. We now explain the air-ice-ocean interactions mainly based on correlation analysis in Figure S14 and Table S4. In addition, we found that there is an immediate response of glacier front changes to high air and ocean temperature anomalies in 2016 and 2019, which are possibly caused by atmospheric blockings according to [6, 5].\nLine 172-175: The logic in this part is unclear and needs to be elaborated.\nThank you for this comment, we have now removed the entire paragraph from the manuscript as we no longer correlate calving front area loss rates with Hugonnet et al. (2022) mass loss rates. Please also see our responses to Reviewer 3.\nLine 195-200: The differences between NW and NE Svalbard and the influence of atmospheric and oceanic temperatures on each other are not clearly explained and need further detail.\nThank you for this comment. We have plotted the regional seasonal front retreat rates and monthly mean air and subsurface ocean temperatures for six different sectors in Figure 3. Examples of detrended front change time series of typical glaciers with different environmental variables are plotted in Figure 2. The results and associated explanation are now in Section ’Ocean temperature variability drives divergent sea-sonal cycles’. From these two figures, we see the divergence in the timings between peakairtemperatureandpeaksubsurfaceoceantemperaturegrowslargerfromwest-ern to eastern Svalbard. While the peak air temperature remains fixed in July across different regions, the changes in the timing of peak seasonal retreat rates match the peak subsurface ocean temperature.\nLine 201-202: “In addition, our study finds that the drastic ice speed change ranging fromsurgingeventsdoesnotimpacttheseasonalcalvingcycles(FigureS2).”thispart does not seem to be linked to the rest of the manuscript. Surging glaciers were men-tioned a few times in the manuscript, but it does not explain the differences between surging and non-surging glaciers, or why the distinction is important. This needs to be clarified.\nGood point, we have now made it clear how we defined surging-type glaciers in the Online Methods Line 329-331: ’The excluded surging glaciers were identified from the glacier calving front change time series visually if the calving front underwent signif-icant advances > 500 m over two years period.’ In addition, we analyzed the sea-sonal cycles of surge-type glaciers and their relationship with different environmen-tal variables including sea ice concentration in Figures S9 and S10. We then com-pared the seasonality in the calving front changes between non-surging and surging glaciers. These contents can be found in the Section ’Widespread seasonal calving front changes across Svalbard’.\nLine238-242: Again,thispartdoesnotseemtobelinkedtotherestofthemanuscript. It should be integrated better or clarified.\nThankyouforthiscommentandagree. WehaveremovedLine238-239intherevision but still kept Line 240-242 as a concluding remark for the paper.\nFigure captions in the supplementary materials are incomplete, making it difficult to follow their arguments. The figures themselves are not self-explanatory and would benefit from more detailed descriptions. For example, figure 1c doesn’t show errors, figure 2a doesn’t say what the size of squares means, figures S2,S3 in panel f what is dotted line?\nThank you for this comment, we have now revised the supplementary figures and added detailed information in the captain for all the figures.\nReviewer 2\nIco-reviewedthismanuscriptwithoneofthereviewerswhoprovidedthelistedreports. 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.\nReviewer 3\nThe paper assesses controls on glacier retreat in Svalbard. Thus, it tackles an inter-esting topic and has a wealth of data, which can be used to investigate some really important questions. Overall, the paper presents a number of novel and interesting results, e.g. seasonal patterns on Svalbard, the correlation between season changes and inter annual and that surge type glaciers seem to have a similar pattern as non-surge type (although the final point is noted but not properly explored).\nThank you very much for your time in providing constructive and detailed comments on this study which have been very useful in improving the analysis and results in the revision.\nHowever, the paper feels like it needs a bit more thought and precision, to ensure it real does disentangle these important questions. There are several examples of this, like the surge seasonality point, whether surface elevation / SMB changes are caused by retreat or cause it (i.e. the direction of the relationship, whether the relationship betweentotalmasslossandareachangeisreallypervasivegiventheapparentimpact oftwodatapointsontherelationship,whetherseaiceplaysanyrole(itismentionedas an explanation a few times, but never really explored properly). There are also small scale examples of this, e.g. melt rates are taken from Jakobshavn Isbrae but there isn’t much consideration of how appropriate these might be, using frontal ablation and calving interchangeably, adding / removing the surge type glaciers in different parts, making it confusing about when they are in/out.\nThank you for the comments and agree. In the revision, we have added additional ex-periments based on the review comments and significantly changed the paper struc-ture and narration. Major changes are outlined below:\n• We added regional analysis for seasonal calving front change variability and its relationship with seasonal atmospheric and oceanic warmings.\n• We excluded all the surging glaciers when calculating the regional seasonal and interannual retreat rates.\n• Weanalyzedtheseasonalcalvingfrontcyclesforsurgingglaciersandcompared them with non-surging glaciers.\n• We processed the daily sea ice concentrations for all studied glaciers from 1985 to 2023 and discussed the role of sea ice in both seasonal and long-term calving front changes.\n• We investigated the large calving front change variability, as well as the high air and ocean temperature anomalies, in 2016 and 2019. We found they are likely driven by atmospheric blocking.\n• We processed the CryoSat-2 CryoTEMPO-ELIOS gridded datasets from 2010 and 2023 but found the spatial coverage is very poor for marine-terminating glaciers we are interested in (see Figure R2 below). Therefore we decided to abandon the correlation analysis between annual glacier mass loss rates and calving front changes, and only focused on analyzing the overall ice discharge in Svalbard. We are planning on a separate paper on this relationship by improving Svalbard mass balance estimates from multi-source satellite data.\n• Wehavemadesignificantchangestothepaperstructuretoaccommodatethese changes.\nI know that Nature Comms is short format, which makes exploring ideas difficult, but unfortunatelysomeoftheargumentsdidnotfeelfullypinneddownandexplored,which amkes the narrative less convincing. I think you have a really solid dataset here and some really interesting findings, so regardless of what is decided by Nature Comms, I strongly encourage submission elsewhere and this might give you the space to fix some of the issues noted above. Please see my annotated pdf for detailed comments.\nMajor comments\nLine 29-31: I’m not sure about this statement: the Arctic has diverse oceanic / at-mospheric / glaciological settings. I’d remove this and focus on what it tells us about glacier dynamics in an area with lots of ice that is highly responsive to climate change.\nThank you for this comment, agree and we have removed this statement.\nLine 35-36: I would take surging out here, as it complicates things - it can obviously occur independent of climate but can also be linked. Given Svalbard has so many surge type glaciers, I’d make it clear you exclude them and focus only on the climate signal.\nAgree and we have removed ’surging’ from this sentence. In addition, we have made it clear throughout the manuscript that we excluded surging glaciers in the regional analyses for seasonal and long-term calving front changes.\nLine 37-28: And to some extent melt at the calving front - need to state somewhere how you separate this out and/or demonstrate that almost all of the ice loss is from calving (as opposed to enhanced melting across the ice front, e.g. due to plumes).\nGood point, we made it clear throughout the manuscript that we measure calving front changes. In the same Section, we added a statement on why we use calving front changes instead of frontal ablation which requires ice velocity in Line 70-77 ’We con-sider calving front advance as when ice discharge exceeds frontal ablation and retreat as when frontal ablation exceeds ice discharge. We are not able to quantify ice dis-charge, and therefore frontal ablation, due to a lack of ice velocity dataset of sufficient spatialcoverageandtemporalresolutionfortheentireSvalbardoveralongperiodthat is required for this study. However, calving front retreat rate has proved to be a good indicator for the variability of frontal ablation on Svalbard [12, 13]. In these studies where ice discharge has been taken into account, it is shown that frontal ablation in winter almost shut down, allowing most of the ice discharge to go into the seasonal readvance of the calving fronts.’\nLine 40-42: So are you including this portion of ice loss? I.e. it’s net ablation from all processes? This is more sensible, but needs to be made clear in the abstract, which talks about calving specifically, i.e. not net ablation at the front.\nYes, the calving front changes we observed include both calving and frontal melting. In the abstract, we now made it clear we measure calving front retreats in this study.\nLine 46-47: There has been some recent work on this in NVZ. Please see (https://www.cambridge.org/core/journals/journal-of-glaciology/article/rapid-and-synchronous-response-of-outlet-glaciers-to-ocean-warming-on-the-barents-sea-coast-novaya-zemlya/B1B061E7867A7FCDC863EF4EE2D946C9) and Tepes references therein.\nThank you for the suggestion. It is a great piece of work on seasonal calving front changes on NVZ and we learned a lot from reading this paper. After checking the CryoSat-2 data, we decided to remove the relationship between dynamic mass loss andcalvingfrontareachanges. Therearetworeasonsforthischange: 1)asreviewers 2 and 3 pointed out already, the current correlation is severely skewed by two data points in the plot which makes the correlation with Hugonnet data less convincing; 2) we checked the CryoSat-2 CryoTEMPO gridded datasets over Svalbard and found that the spatial coverage on marine-terminating glaciers is very poor (Figure R2); in addition, the time series of CryoSat-2 is only 12 years while we have 38 years of calving front observation. Therefore we think it is better to focus on discussing the integrated ice discharge over Svalbard from GRACE and CARRA datasets.\nLine 48-49: Temporal or spatial? Please indicate the resolution here.\nGood point, we have changed the sentence to ’Here we use a new calving front data product over large spatial scales and at a high temporal resolution for 149 marine-terminating glaciers across Svalbard between 1985 and 2023’ in Line 50-51.\nLine 50-52: As noted above, to be calving is loss of ice through icebergs being re-moved from the front. It doesn’t include melting e.g. due to plumes. You need to be consistent about which term you mean - total frontal ablation (i.e. calving + melt) i s what I think you’re measuring and is easier, as you don’t have to separate out the two.\nAgree, we made it clear now that we used calving front change which includes both calving and frontal melting throughout the manuscript.\nResults Section: This seems to be a mixed results and discussion section. Either needs clearly separating or blending together fully. There are numerous points where the results move towards discussion.\nThank you for this suggestion, we have now merged the results with relevant discus-sions under different sections to better blend the whole story.\nLine 57-59: So are you including surge type glaciers in here?? I think you should remove them, because this complicates the signal, particularly because of the long duration of surge cycles e.g. in Austfonna. Or, put another way, if you have the surging glaciers in here, how to do you determine what is climate driven versus internal glacier dynamics?\nThis is a very good point and we agree. In the revision, we have removed the surging glaciers from long-term analysis and only focused on non-surging marine-terminating glaciers. All the relevant figures, statistical analysis and text have been updated.\nLine 63-64: Feels like this is drifting into discussion, unless they are meant to be combined?\nAgree, we have now fully blended results with relevant discussion under individual section.\nLine 68: This is nice novel work.\nThank you!\nLine 71-73: Useful information. For interest, this is much less consistent than what we foundonNVZ:thereretreatusuallybeganinJune/JulyandadvanceinNov/Decand was really quite consistent between glaciers year.\nThank you for this comment. In the revision, we find a spatial variability in the timing of seasonal calving front retreat rates between west and east Svalbard, coincident with the spatial variability in subsurface ocean temperatures, likely driven by the delayed ocean warming along the east coast. This is likely why the seasonal cycles show a higher degree of inconsistency than NVZ. However, if we look at the time series of detrended calving front changes of individual glaciers from 2014 to 2023 in Figure 2, the timings of the retreat and advance between different glacier years are very consistent.\nLine 73-75: Interesting\nThank you.\nLine 81: remove would\nAgree and done.\nLine 84-87: I think you need to make it really clear here that these are REGULAR seasonal variations, i.e. they happen every year and are imprinted on top of the surge, rather than e.g. short-term surge events. If they are regular and mirror the timings you see on other glaciers, this is an interesting finding, hence why I think it really needs to be properly pinned down.\nThank you for this comment. Yes, these are regular seasonal cycles that happened every year and on top of the surging events. Please see Figures 2e and 2f for exam-ples of two surging glaciers Vestre Ocbornebreen and Storisstraumen. We have also mentioned this in Line 87-93: \"The seasonal calving front changes of surging glaciers follow similar patterns to non-surging glaciers (Figure S9b), although their average seasonal range is significantly higher, reaching 192 ± 54 m. Figures 2e and 2f show the seasonal calving front time series of two surging glaciers Vestre Osbornebreen (RGI60-07.00468) and Storisstraumen (RGI60-07.00027), respectively, where calving fronts usually retreat from July until December with a regular cycle. The similarity in seasonal cycles between surging and non-surging glaciers provides further evidence that dynamic regimes have minimal influence on the calving front cyclicity [12].\"\nFigure 1a: I’m being picky but this legend doesn’t match the figure, i.e. the circles are red and blue, this has white circles. I’m assuming blue = advance and red = retreat, but should be in the legend.\nIn this figure, the size of the circle denotes the magnitude of the decadal front change rate, the blue circle denotes the advancing glacier and the red circle denotes the re-treating glacier. We have added a label for colors in Figure 4a and included this in-formation in the figure captain.\nFigure 1c: This has really dropped off in recent years - perhaps it is discussed later, but interesting to know why.\nThanks for pointing this out. The drop in area loss rates in 2019 happened in different parts of Svalbard and in sync with drops in air and ocean temperatures (Figures 5 and 6b), likely driven by atmospheric blocking that slows down the large-scale ocean circulation in the Nordic Seas discovered by [5]. This has now been discussed in the Section ’Rapid glacierresponse to climate extremes’.\nFigure 2: I like this figure overall, but a few suggestions:\n• 1) The squares and colour range don’t work for me - I find it really hard to see the pale squares. Suggest either circles or + and a different set of colours that are more obvious at the lower end of the scale.\n• 2) Pale bars on the charts are hard to see. Suggest using a different colour, but a bolder one. Line 106-108: I’d add the spatial resolution here - e.g. do you have one grid square per glacier or 10 glaciers per grid square??\nThank you for the comments. We have now 1) changed squares into circles with a different set of colors; 2) changed the colors of histogram charts into bolder colors. This figure is now Figure 1.\nLine 108-109: Same for this. To what extent do we actually have different data for different glaciers, given the resolution of these forcing datasets?\nThank you for this comment. For extracting variables from MAR regional climate model, the glacier point is generated as the center point of each glacier domain in [14] which means the locations of the glacier points for different glaciers are very dif-ferent (Figure S5), this applies to the derived MAR variables. For ORAS5 data, the spatial resolution is 0.15◦ in Svalbard and the spatial coverage near the coast is low (Figures S6), this has resulted in some fjord points are located in the outer fjords, as pointed out by Reviewer 4. This, however, does not influence our conclusion on the relationship between ocean warming and glacier retreat rates at different timescales, because (also see our response to Reviewer 4) ’The fjord points in the open ocean are representative of the water structures within the fjord, as they are predominantly located in regions of deep bathymetry (Figure S6), indicating that these fjord points lie along the pathway where warm Atlantic water can enter the fjords. Furthermore, in-situmeasurementsinwestSpitsbergenshowthatoceantemperaturesinwaterdepths lessthan100maresimilarbothinsideandoutsidethefjords[15,16].’ inLine106-111.\nLine 117-119: What about sea ice? Do you think this may be important here? Or, put another way, do the switches from advance to retreat (and visa versa) coincide with sea ice formation / disintegration?\nGood point. We have now analyzed seasonal changes in sea ice concentration for 86 non-surging marine-terminating glaciers in Svalbard by using daily sea ice concentra-tions from AMSR-E/2 available from the University of Bremen and their relationship with seasonal calving front changes over the period of 2014-2023. We also plotted the monthly sea ice concentration changes for specific glaciers in Figure 2 and calculated the regional monthly sea ice concentration and sea-ice-free years shown in Figure S8. We find \"Sea ice concentration (R2=0.83) is the second strongest predictor for sea-sonal front retreat rates. Landfast sea ice (hereafter referred to as fast ice) can exert a buttressing effect on glacier calving fronts, stabilizing the ice and reducing calving rates [17]. However, in Svalbard, there is a large spatial variability in seasonal sea ice concentration (Figure S8), and glaciers in Northwest and South Spitsbergen are largely sea ice free in winter over the 2014-2023 period (Figures 2a,2b and 2e). East-ern Svalbard including Austfonna, Vestfonna, Barentsøya and Edgeøya, feature higher sea ice concentration. The average sea ice concentrations in early Spring in Aust-fonna and Vestfonna are around 70%, while it can exceed 80% in Barentsøya and Edgeøya (Figure S8), which has the highest correlation with sea ice (Table S1). This spatialpatternmatchesthefasticedistributioninthepasttwodecadeswhichprimarily concentrated along the coast of eastern Svalbard [18].\" now in Line 131-140.\nLine 119-120: First, this is really discussion. There are several points where this hap-pens - results should just state what you found. Second, how appropriate is this refer-ence to use - it’s for Jakobshavn Isbrae, which used to have a large floating tongue, so is perhaps not comparable to the glaciers you have here.\nThank you for the comment and agree. We have now removed this sentence.\nLine 123-125: How does this compare to when ocean temperatures increase? Are we sure this is just air temperatures that are changing??\nGood point, the onset of seasonal calving front retreats are coincident with both sur-face runoff and subsurface ocean temperature. We have rephrased this sentence as \"Theonsetofcalvingfrontretreatcoincideswiththeincreasedsurfacerunoffandrising subsurface ocean temperature, while the peak retreat rates occur during the months of highest ocean temperatures (Figure 1d).\" in Line 82-84.\nLine 130-131: What do you define as ’abnormal’? A certain standard deviation above the mean?\nThank you for the comment, this is a very good point. First, we have removed word ’abnormal’. Second, a recent study on climate extremes in Svalbard over the last two millenniaflaggedthehighairtemperaturein2016as’climateextreme’[6]. Asfor2019, we can see that both air and subsurface ocean temperatures significantly dropped around 6 ◦C and 3 ◦C compared to 2016 (Figure 6b). This significant temperature cooling has been discussed in a recent study [5] to be related to the slowdown of large-scale ocean circulation in the Nordic Seas driven by atmospheric blocking.\nLine 130-132: As above, what does the normal state mean? The long term average. Try to be precise in the definition of these terms.\nAgree, we have changed the wording from ’the normal state’ to ’pre-2016 level’ in Line 210.\nLine132: ‘Svalbard-widemeancumulativearealosses’,Includingthesurgeglaciers??\nIt did not include surging glaciers in the initial submission. In the revision, we have made it clear that for long-term glacier retreats we only consider non-surging marine-terminating glaciers.\nLine 135-137: But you haven’t analyzed sea ice data, so how do you know it’s impor-tant? Also, this is again moving towards discussion.\nThank you for the comment, we have now added sea ice concentration analysis and changed this statement to \"Similar to seasonal front changes, the highest correla-tion is found for subsurface ocean temperature (R2=0.5), followed by air temperature (R2=0.39) and sea ice concentration (R2=0.34), suggesting that ocean forcing is the most prominent factor in controlling the interannual calving front variability. However, air temperature also shows a comparable pattern and correlation, as well as the de-creasedseaicecoverandincreasedoccurrencesofsea-ice-freemonthsaroundSval-bard (Figure S14).\" in Line 185-189.\nLine 140-142: But you said above there was also a correlation with air temperatures with a month lag, so how does that fit with this statement? What about ocean temper-atures? Can we rule out their impact, e.g. are they low at this time of the year?\nThank you for this comment. We have now removed the analysis between one-month lagged air temperature and seasonal calving front changes as this information no longer fits into our storyline in the revision.\nLine 164-165: Useful figure\nThank you.\nLine 165-167: Drifting into discussion.\nWe have changed the paper structure to fully blend results and associated discussion in each section.\nLine 167: Give the % split as this is an important point. Please comment on the year to year variability, e.g. is this increase mainly due to the big spike in ice loss around 2018? Has the proportion reduced in later years? I think it is important to note this variability in contribution from glacier dynamics. ALSO, are surges remove from this, as they will complicate the picture, e.g. if you have a large surge event in one year and net advance of a large basin (e.g. Basin 3) then you could really effect the numbers for that year.\nThank you for this comment. We have decided not to present dynamic imbalance as a key conclusion in the paper anymore. However, we believe that the increased dis-charge over the past two decades remains crucial for providing context on the broader dynamicmasslossassociatedwithretreatsofmarine-terminatingglaciersinSvalbard. Weare notableto isolatethemass lossrelatedto surgeeventsfrom theicedischarge calculated from GRACE mascons and CARRA SMB data, as 1) it is difficult to identify all the surging events happening in Svalbard during our study periods; 2) therefore measuring the mass loss of surging events requires a significant amount of extra work which is more suitable for a separate paper on this topic. Given these two reasons, we decided not to investigate the interannual variability in the glacier percentage changes in this study.\nLine 168-170: I’d also use one of the many glacier inventories noting surging as you might not pick them all up from this, given Svalbard’s long surge times.\nThank you for this comment, this is a very good advice. However, we think the current anlaysis of Hugonnet mass loss rates by using the same non-surge type glaciers from our study is more appropriate compared to using a global surging glacier inventory such as RGI. The reason is surging events mostly happen in a very short time period that is difficult to capture, so there will be a mismatch between the surge glaciers identified by RGI and the surge events flagged by our study based on satellite image analysis.\nLine173-175: Doesn’tnecessarilyhavetobeviacommonclimatedrivers-therecould be a retreat due to ocean temperatures in one area and air temps in another, but still correlates. Also,pleasebrieflyoutlinetheevidenceforthedirectionofthisrelationship. i.e. what is the evidence that glacier retreat is causing negative surface mass balance or ice loss (presumably via dynamic thinning) versus negative SMB causing retreat (e.g. by reducing ice delivery and thinning near terminus) regions)\nThankyouforthiscomment. Wehaveremoved’viacommonclimatedrivers’. Asmen-tioned in our responses to previous comments that we have analyzed the 12 years of CryoSat-2 CryoTempo time series across Svalbard but we found that the spatial coverage is very poor for marine-terminating glaciers we are interested in (Figure R2). Therefore we think using CryoSat-2 data will introduce very high uncertainties. We think the way to move forward is to improve mass changes in Svalbard by combin-ing different satellite measurements including altimetry and gravimetry, a topic to be explored in our future study.\nFigure 5: I think this correlation is getting really skewed by the top two points, If you takethisout,Idon’tthinkyou’llseemuchofacorrelation. Unfortunatelythisiscommon with glacial datasets (i.e. the big glaciers dominate) but I don’t think you can say from thisplotthatforallglaciersthereisastrongcorrelationbetweenretreatandtotalmass loss, as those top points are strongly impacting the relationship.\nThank you for this comment. We agree that this correlation is skewed by the top two points and we decided to remove this correlation analysis from the manuscript.\nLine 186-188: I’m not sure it does tell you that this process is important. It might be, but other processes may also contribute, e.g. undercutting at the water line. Do you have additional evidence, e.g. of meltwater plumes that would support this idea?\nAgree, we have added new analyses to better prove the role of ocean warming in controlling seasonal calving front changes. We have modified this sentence into \"This regional variation in peak retreat rates closely aligns with the seasonal subsurface ocean temperature patterns across different regions, suggesting that ocean tempera-ture exerts an immediate impact on calving front retreat, as oceanic forcing can man-ifest the submarine melting of glaciers and increase glacier retreat rates by combined undercutting and calving [19, 20, 4, 21, 13].\" in Line 116-120.\nLine 190-192: How would this accumulated effect work? Are we saying that we need fractures to fill up enough or is it e.g. repeated opening and reclosing until a critical stress is reached?\nThank you for the comment, we have removed this sentence from the manuscript.\nLine 192-193: As above, is there evidence to support this here, e.g. plumes at the front?\nThank you for the comment, we have removed this sentence from the manuscript.\nLine 195-197: How are they preconditioned? This also brings me back to teh point above-isthesurfaceloweringwe’reseeingacauseofretreatordrivenbyit? Thisisn’t an easy question. You may find some useful thoughts on this in the following paper, which tries to untangle this relationship for NVZ. https://www.cambridge.org/core/journals/journal-of-glaciology/article/rapid-and-synchronous-response-of-outlet-glaciers-to-ocean-warming-on-the-barents-sea-coast-novaya-zemlya/B1B061E7867A7FCDC863EF4EE2D946C9\nThank you for this comment, and we agree this is not an easy question to solve which requires improving the current elevation change and mass balance measurements in Svalbard. We have decided to remove this correlation analysis from the revision.\nLine201-202: Thisfeelsoutofplacehere. Thepointaboutseasonalcyclesstillmatch-ing even between surge and non-surge type glaciers is useful finding, but feels lost here.\nAgree, we have now added new analyses on the seasonal calving front changes for eight surging glaciers that exhibit seasonal cycles in our study, this information is now included in Section ’Widespread seasonal calving front changes across Svalbard’.\nLine 206-207: In the discussion, I think you should note the massive spike in the late 2010s and explain it. You should also discuss the subsequent reduction in retreat, which is also really worth noting and goes against general trends.\nThankyouforthisvaluablecomment. Wehaveanalyzedthecalvingfrontareachanges in years 2016 and 2019 and their links to significant temperature changes in these two years, which is likely driven by atmospheric blocking. Relevant information are now in Section ’Rapid glacier response to climate extremes’.\nLine 211-213: Reference the figure or paper showing this.\nThank you for this comment, we have deleted this sentence from the paper.\nLine 216: Be consistent in reference style - should be numbered.\nAgree and done.\nLine224-226: I’;mnotsureIagreewiththis. Asnotedabove,Ithinkyouaregettingthis correlation because of the two largest retreats. i.e. this isn’t a pervasive relationship across all of the glaciers.\nAgree, we have removed this part from the manuscript as mentioned in our previous responses.\nReviewer 4\nReview of Atmospheric and oceanic warming drives pervasive retreat of marine- ter-minating glaciers in Svalbard by Li et al.\nGeneral Comments\nThis is an interesting article which relates environmental variables to glacier retreat on a pan-Svalbard scale. Overall, I think the manuscript is well written and scientifically sound. However, Ihaveonegeneralcommentrelatedtothelocationofthefjordpoints used for extracting the oceanographic data. In the supplementary material, Fig S5 shows the fjord points used. Many of this fjord points are a considerable distance from the glacier fronts, and even outside of the fjord mouth. Previous research has shown that the water masses present in the inner fjords can be quite different to those present outside of the fjord mouth (see e.g. Fig. 4 in Prominska et al, https://doi.org/10.1016/j.oceano.2017.07.003 and/or Divya et al https://doi.org/10.1016/j.polar.2021.100735). I understand that the location of these fjord points may be an unavoidable consequence of the pan-Svalbard scale of the study / the datasets used, but feel that this needs to be addressed in the main text. For example, a short paragraph could be added to the discussion.\nWe would like to thank the reviewer for the positive and constructive comments, which have greatly improved this paper.\nAs the reviewer has noticed that some of the fjord points generated in our study are located a bit far away from the glaciers. The reason for this spatial deviation is caused by the limited spatial coverage of the ORAS5 data in the coast areas. We generated the fjord points for marine-terminating glaciers around Svalbard by finding the closest non-empty grid point in the ORAS5 grid to the corresponding glacier point of each glacier derived from MAR data (see Online Methods). Despite this, we argue that the fjord points in the outer fjords are still representative of the ocean water structures in-side the fjord. In Figure S6, we can see that the majority of fjord points are located in regions where the bathymetry is deep, indicating that they are in the pathway where the warm Atlantic water can enter the fjords. In addition, the CTD samplings in several fjordsinwestSpitsbergenin[15,16]showthattheoceantemperaturesinwaterdepths less than 100 m outside and inside the fjords are quite similar. A short discussion has been added to the Section ’Ocean temperature variability drives divergent seasonal cycles’ in Line 102-111:\n\"The spatial distributions of the fjord points used to extract ocean temperatures from ORAS5oceanreanalysisdataareshowninFigureS6. Althoughsomefjordpointsare far from glacier terminus and sample ocean temperatures outside fjords due to limited spatial resolution and coverage of ocean reanalysis data near the coast, this does not impact the relationship between ocean warming and seasonal calving front retreats. The fjord points in the open ocean can still be representative of the water structures within the fjord, as they are predominantly located in regions of deep bathymetry (Fig-ure S6), indicating that these fjord points lie along the pathway where warm Atlantic water can enter the fjords. Furthermore, in-situ measurements in west Spitsbergen show that ocean temperatures in water depths less than 100 m are often similar both inside and outside the fjords [15, 16].\"\nIn addition, I have several minor comments which are listed below:\nMinor comments\nL38: I would appreciate a little more context here: is frontal ablation not considered in other studies, or assumed to be included in some other metric etc?\nThank you for this comment. We have rephrased this sentence as \"Glaciers in Sval-bard have been losing mass at accelerated rates during the past several decades [22, 23, 24, 25], their mass loss is expected to double by 2100 but with significant un-certainties in the predicted rates [1], partly due to a lack of consideration of retreats of marine-terminating glaciers [2, 3] which drain ~69% of the glacierized area [26].\" in Line 34-37.\nL60: ‘Area at the calving front’ – I see this explained in the methods, but was not fully sure what this meant until I found this explanation. Maybe a short explanation can be included in the text here\nThank you for this comment. We have added a sentence to explain the area changes atglaciercalvingfronts: \"Theareachangeiscalculatedbymultiplyingthecalvingfront length change by the glacier width, defined as the mean length of all terminus traces (see Online Methods)\" in Line 153-154.\nL64: I appreciate the comparison with Kochitsky et al, as well as the possible expla-nation for the difference between their results and yours. I wonder If you would get a moresimilarresultifyouusedlower(temporal)resolutiondata/haveyoutriedthisand was this the outcome? Then it would be clear if this was the reason for the difference in results, or suggest that something else is at play\nThank you for this comment, this is a very good point. We have now compared the area changes between our study and the Kochtitzky 2022 dataset using a common set of glaciers over two periods: 2000-2010 and 2010-2020. The results are shown in Figures S11 and S12 and in Table S3. We find that the mismatch in area changes mainly comes from five large glaciers with large glacier widths in the period of 2000-2010 (Figure S12), so a slight mismatch in the calving front measurements will result in large differences in area changes. After removing these five large glaciers, the difference in area changes between these two datasets is only 49 km2 between 2000 and 2020. We have now added a discussion on area change comparison in Line 154-164.\nL78: It is not clear to me whether your description of the R2 values as significant means you think this shows a strong relationship, or whether you just mean the result is statistically significant. Related to this, it would be useful if you state what you consider a ‘significant’ R2 value to be, and also what the p values are for each R2 value. A value of 0.37 is perhaps quite good for a natural system, but is much lower than any of your R2 values linking retreat rates to environmental values, so some discussion of how you interpret the R2 values would be beneficial.\nThank you for this comment. In our initial submission, we mean ’significant’ as sta-tistically significant because of a low P value (<0.05). To avoid confusion, we have rephrased this sentence as \"The range of seasonal calving front variability correlates with the decadal retreat rates with a correlation coefficient R value of 0.6 (P<0.05, Figure S13a), suggesting that glaciers experience a larger amplitude in the seasonal cycle also experience a greater amplitude of secular trend.\" in Line 165-167.\nL83: Do you have any suggestions for why the seasonal range/long-term range R2 value is twice as high for Greenland?\nThank you for the commnet. Please see our response to the comment made by Re-viewer 1:\nWe would like to admit that our previous conclusion regarding the correlation between these two variables in Svalbard being lower than in Greenland Ice Sheet was incor-rect. In fact, the correlations are very similar. The R2 value of 0.71 for the Greenland Ice Sheet in [7] was calculated between the long-term mass loss and seasonal mass loss, not the effective length changes that we measured in Svalbard. In the Extended Data Fig. 4 in [7], they normalized the terminus mass change values by terminus area and calculated the correlation between seasonal range and long-term changes in length variability, the R-value is only 0.47, which is slightly lower than the R-value of 0.6 (P<0.05) for Svalbard (Figure S13a). Therefore, we confirm that the relationship is nearly identical between Svalbard and Greenland. We have corrected this state-ment in Line 169 as ’This finding agrees with a recent study of glacier retreats in the Greenland Ice Sheet’.\nL113: Interestingthatther2valueforsubsurfaceoceantemperaturesisverysimilarto that for one month lagged air temperatures – do the time series for these two variables look similar?\nThank you for the comment. The time series of mean air temperatures and ocean temperatures are similar on the Svalbard scale. However, we have now removed this statement from the manuscript, our new analysis on regional seasonal calving front retreats shows that the spatial variability of seasonal calving front retreats coincident with changes in ocean warming while the timings of peak air temperatures are sim-ilar across different regions. This directly demonstrates the dominant role of ocean warming in driving seasonal calving front changes.\nL120: Please state whether this is basal melt under grounded or floating ice, and also specify if this is an additional 15 m/yr of basal melting or an absolute value.\nThank you for the comment, we have now removed this sentence according to the comments made by Reviewers 1 and 3.\nL124: Youcouldlinkthistoe.g. SlaterandStraneo(https://www.nature.com/articles/ s41561-022-01035-9), where atmospheric temps was found to exert a first order con-trol on submarine melt\nThank you for this suggestion, we have added a statement on the role of atmospheric warming on seasonal calving front changes by linking the Slater and Straneo (2022) along with other relevant studies in Lines 136-145:\n\"Evidence suggests that both these factors can influence calving rates through en-hancedhydrofractureatthesurfaceandsubaqueousthermohalinecirculationbeneath the floating ice [4]. Surface meltwater caused by atmospheric warming can increase subglacial discharge which can lead to increased basal melting, as it can create buoy-ant plumes that enhance the turbulent transfer of oceanic heat to glacier calving fronts [27, 28, 29, 21], while the accumulation of surface meltwater contributes to crevasse hydrofracturing [30, 31, 32]. The summer melt season in Svalbard usually lasts about six months between May and October (Figure 1d) and the onset of surface runoff in May coincides with the most frequent starting month for frontal retreat (Figures 1b-d). Similar relationships are also found for surge-type glaciers (Figure S9).\"\nFig. 3: Pleaseaddpvaluestotheannontationontheplots(thesameforotherfigures)\nAgree and done for all the figures.\nL175: Could it also be some internal dynamic feedbacks? E.g. climate drivers cause front retreat, which cause acceleration and then thinning etc?\nYes it can be internal dynamic feedback between calving front retreat and glacier ac-celeration and thinning. We have removed this sentence from the manuscript, please also see our responses to Reviewer 3.\nL204: I think this should be ‘mirroring a recent finding FROM/FOR THE Greenland Ice Sheet’\nThank you for the suggestion, but we have rephrased this sentence in Line 169-171:\n\"This finding agrees with a recent study of glacier retreats in the Greenland Ice Sheet [7], which identified seasonal length variability as the sole significant predictor of long-term calving front change (R = 0.47, Extended Data Fig. 4 in [7]) among various candidate predictors\".\nL205: Reference should be in brackets\nAgree and revised.\nL318: Can you add a comment on the impact of the resolution of the ocean data set, given the known importance of fjord circulation for submarine melt rates\nThank you for this comment. We rechecked the spatial resolution of ORAS5 data and found the horizontal resolution is approximately 0.15◦ in irregular grids in Svalbard, we have updated this number instead of directly citing the value provided in [33]. We have regridded the data to regular grids of 0.15◦ , relevant information in Online Methods have been updated. We added a statement in the Online Methods: ’This spatial reso-lution is enough to capture the spatiotemporal variability of ocean temperature outside thefjordsasthewidthofthefjordmouthisnormallylargerthanthegridsizeofORAS5 data’ in Line 349-352."}, {"round": 2, "reviewer_comments": {"reviewer_3": "I feel that the authors have provided a comprehensive, effective and well-considered response to my comments. I am satisfied by their responses and think they have a really nice paper with an excellent dataset, that provides novel insights into Arctic glacier behaviour. They should be commended on a really nice piece of work and their throughout response to reviewer comments. I have no further comments and look forward to seeing the final version.", "reviewer_4": "Thank you to the authors for your comprehensive responses to all the reviews, and the associated changes in the manuscript. The edits have, in my opinion, led to significant improvements and I do not have any major concerns with the article in its current form.\n\nHowever, I would ideally like to see your statement regarding the ocean temperature dataset changed. I appreciate you adding in more information about the dataset but am not fully convinced by the statement that the distance of data points from the coast ‘does not impact the relationship between ocean warming and seasonal calving front retreats’. Data/figures from e.g. Promińska et al that show AW in your chosen depth range is present in the outer fjord but not near the glacier front in several years (e.g. 2002, 2007, 2013, 2014). Other previous research has showed that the relationships between ocean temps and frontal ablation is different when you take ocean data from closer to the calving front (Holmes et al.). Whilst I am onboard with your approach being appropriate given the scale of the study, the statement quoted above needs to be toned down.\n\nOne minor comment is that your units are sometimes italicized (e.g. L164) but sometimes not (e.g. L169) – this should be consistent throughout the manuscript."}, "author_response": "Thank you for taking the time to review our paper and for your positive comments on our revision.\n\nThank you for taking the time to review our paper and for your positive comments on our revision.\n\nThank you for this suggestion and we agree. We have changed this statement to ‘this does not significantly affect the relationship between ocean warming and seasonal calving front retreats’.\n\nThank you for the suggestions, we have changed the style of the unit to italicized throughout the manuscript."}]

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