The introduction looks good. It provides a comprehensive overview of the current state of the Antarctic Peninsula (AP) ice sheet, including the effects of climate change on temperature, ice shelf loss, and ice mass loss. The introduction also highlights the limitations of current methods used to estimate ice mass loss on the AP and identifies the need for further research, such as geodetic mass balance estimation. Overall, the introduction provides a clear and concise background to the study area and sets the stage for the research that follows.

The data used for the study appears to be scientifically sound and coherent. The authors used bistatic Synthetic Aperture Radar (SAR). They used two coverages of the AP with TDX data acquisitions from austral-winter 2013 and 2017 for their analysis, and a reference DEM (refDEM) based on the global TanDEM-X DEM at 12 m spatial resolution.

To obtain information on the CMB, the authors used output from the regional climate model MAR.

The authors computed the average CMB for the period July 2013 until June 2017 and the absolute and relative difference (dCMB) in respect to whole temporal coverage of the MAR data (2008-2022) is computed to obtain information CMB anomalies during the study period. They also defined the mass balance ratio (MBR) by dividing the CMB anomalies (dCMB) by the total mass change (ΔM/Δt) to indicate the contribution of CMB changes on the mass change.

Overall, the data used in the study appears to be based on established and widely accepted scientific methods and models, and the authors have taken care to provide appropriate references and explanations for their methods.

The method seems comprehensive and well thought out.  The differential interferometric SAR processing approach used to derive DEMs from TDX data is well explained, along with the advantages of this approach. The iterative coregistration procedure used to generate smooth DEM mosaics for each time step is also explained in detail. The text describes how the coregistration procedure had to be adapted for the study area due to the limited availability of ice-free areas and complex topography. The methods used to compute ice mass change rates are also described, including how different basin definitions and sub-region definitions were used, and how voids in the elevation change field were filled.  The text explains how the results were converted to ice mass changes using a volume-to-mass conversion factor. The method seems well developed and comprehensive.

TThe article discusses the variation in ice surface elevation in the northern part of Antarctica and its possible causes. The paper presents an analysis of changes in the surface elevation of glaciers and ice basins over an area of 770,000 km², covering 96.4% of the glaciated area of northern Antarctica. The results indicate a significant loss of ice mass, with loss rates of up to -8 m/yr on some glaciers and a total mass loss of -24.3 ± 5.8 Gt/yr in the study region. Possible causes for this ice mass loss are discussed based on previous studies, including warming ocean waters and changes in snow accumulation. However, the study highlights that additional analyses of ice dynamics are needed to confirm the conclusions drawn from variations in ice surface elevation. A limitation of the study is that it focuses only on the selected study region and does not consider the long-term temporal variation in Antarctic ice changes.

One potential critique is that the study relies solely on bi-static SAR data, which may have limitations in terms of accuracy and resolution compared to other measurement methods. The authors acknowledge that there is a need for improved ice thickness data towards the grounding line, which is a dominant error source in ice discharge estimates on the AP.

Additionally, while the study provides a detailed assessment of glacier mass balances at unprecedented spatially detailed scales and with high precision, there may be other factors that are not accounted for in the analysis. For example, the study identifies that most of the mass losses are caused by ice dynamic changes, but it may be difficult to distinguish between changes in ice dynamics caused by climate variability versus other factors such as ocean currents or internal ice sheet processes.

The study provides important new insights into glacier mass balance in the northern AP and is siginificant for cryosphere science, I advise that it be accepted for publication.

This study describes the derivation of elevation and mass changes for the entire northern Antarctic Peninsula between 2013 and 2017 based on bistatic interferometric SAR data from the TanDEM-X mission. Using a regional climate model, information on climate mass balance have been related to derived geodetic mass balance estimates in order to identify potential drivers of melt.

In general, this study is of interest, since information on elevation and mass change for the Antarctic Peninsula at such high spatial resolution could contribute to a better understanding of the interactions between climate warming and the dynamics of the Antarctic Ice Sheet.

However, the manuscript raises many questions about the data and methodology as well as considerable concerns about the validity of the results. One of the main problems of this study is the lack of quantitative validation of the derived elevation data. A detailed interpretation of the results, as well as the linkage with climate models (as in Section 4) only appears meaningful if the validity of the previously derived results on elevation change have been verified. Therefore, I cannot recommend this manuscript in its present form for publication in The Cryosphere. I suggest that the authors seriously address the following issues before re-submission:

1. In general, the input data and the methodology are described insufficiently in many parts of Section 2 and Section 3. There is no detailed information provided regarding the acquisition parameters (e.g. acquisition time, incidence angle, orbit, beam) of the TanDEM-X data. However, this information is of great interest for assessing the accuracy of the elevations represented by the mosaics. It would also be very helpful to provide an overview of the TanDEM-X footprints in Figure 2.
2. The description of the DEM generation (p.4, l120) is insufficient and far too short and the individual processing steps are not comprehensible for the reader. I suggest to include in a more detailed description of the interferometric SAR processing, possibly in combination with a flowchart.
3. Generation of such large mosaics covering the entire Antarctic Peninsula faces numerous challenges, as the authors confirm (lack of ice-free stable terrain for co-registration, mosaicking of multiple coverages in overlapping areas, treatment of height offsets between the individual DEMs, etc.). In this regard, it is indispensable to establish the accuracy of the derived elevation data through quantitative validation using independent height measurements and to address the horizontal and vertical accuracy of the whole mosaic and its limitations. However, a quantitative validation is completely missing. Consequently, the reliability of the results is in question. For validation purposes, time stamped REMA strips or IceBridge data might be a useful source of reference heights.
4. Another important point that receives far too little attention in the manuscript is the elevation bias due to signal penetration, which can have enormous effects on the accuracy of the elevation and mass changes. There are several studies demonstrating a significant bias in TanDEM-X elevation data due to signal penetration (Rizzoli et al., 2017; Abdullahi et al., 2019; Fischer et al., 2019, 2020; Rott et al., 2021; Wessel et al., 2021). Some studies suggest substantial seasonal variation in signal penetration from elevation, but also from year to year (e.g., from winter to winter, as in the present study). The assumption of a linear increase of the penetration bias above a certain altitude presumably cannot adequately reflect the complex interaction between (sub-)surface structures and continuously changing acquisition geometry. In this context, the validity of penetration correction model has to be demonstrated using independent reference data.
5. In addition, the authors have made some assumptions without demonstrating their validity:

• p.3, line 84f: The authors argue negligible signal penetration when comparing winter season data and refer to a study from Rott et al. (2018). However, this study points out that the negligibility of signal penetration is due to the same acquisition geometry (similar incidence angle, orbit and beam) of the TanDEM-X data, which is supported by the similarity of the backscatter coefficients of the individual scenes. It would be a possibility to analyze the backscatter coefficients of the used data to make a statement about the similarity of the backscatter behavior and thus the signal penetration.
• p.5, lines 145-148: Considering Figures 2a, c, and d, structures can certainly be guessed at, which are due to elevation differences caused by the different penetration biases in the individual DEMs. Integrating the footprints of the individual DEMs in Figure 2 would be important to support the authors' statement, rather than just saying that ‘the pattern does not match the outline of the individual DEMs’.
• p.6, lines 165ff: How do the authors conclude that signal penetration increases linearly between 1800 and 2400 m a.s.l.? What about the areas below and above this altitude level? Are these assumptions based on experience, analysis of data, other studies based on interferometric X-band SAR data conducted on the Antarctic Peninsula?

References

Abdullahi, S., Wessel, B., Huber, M., Wendleder, A., Roth, A., Kuenzer, C., 2019. Estimating Penetration-Related X-Band InSAR Elevation Bias: A Study over the Greenland Ice Sheet. Remote Sens., Remote Sensing 11, 2903.

Fischer, G., Jäger, M., Papathanassiou, K.P., Hajnsek, I., 2019. Modeling the Vertical Backscattering Distribution in the Percolation Zone of the Greenland Ice Sheet With SAR Tomography. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, 4389–4405.

Fischer, G., Papathanassiou, K.P., Hajnsek, I., 2020. Modeling and Compensation of the Penetration Bias in InSAR DEMs of Ice Sheets at Different Frequencies. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, 2698–2707.

Rizzoli, P., Martone, M., Rott, H., Moreira, A., 2017. Characterization of Snow Facies on the Greenland Ice Sheet Observed by TanDEM-X Interferometric SAR Data. Remote Sens., Remote Sensing 9, 315.

Rott, H., Scheiblauer, S., Wuite, J., Krieger, L., Floricioiu, D., Rizzoli, P., Libert, L., Nagler, T., 2021. Penetration of interferometric radar signals in Antarctic snow. The Cryosphere 15, 4399–4419.

Wessel, B., Huber, M., Wohlfart, C., Bertram, A., Osterkamp, N., Marschalk, U., Gruber, A., Reuß, F., Abdullahi, S., Georg, I., Roth, A., 2021. TanDEM-X PolarDEM 90 m of Antarctica: generation and error characterization. The Cryosphere 15, 5241–5260.