The manuscript by Bartsch et al. describes how pan-Arctic datasets of mean annual ground temperature at 2m depth (MAGT), snow water equivalent (SWE) and NDVI (as a proxy for plant growth) correlate with sea ice area (SIA). Sea ice loss is one of the main causes of the amplified warming of the Arctic, and together with changes in atmospheric humidity this influences MAGT, SWE and plant growth. Such links have been shown previously from observations, remote sensing and models (see e.g. Bhatt et al., 2010, 2014, 2017; Buchwal et al., 2020; Macias-Fauria et al., 2012, 2017; Parmentier et al., 2015; Rehder et al., 2020; Screen et al., 2012; Yu et al., 2021). This study aims to differentiate itself from this previous work by using satellite data where possible, and by focusing more on regional correlations rather than those made across the whole Arctic. 

While I appreciate the attempt by the authors to look further into this topic, I feel that the manuscript in its current form is a missed opportunity to learn something truly novel. In particular, I had hoped that this study would go beyond mere correlations by identifying causal links, and by showing more detail. More detailed regional analyses between sea ice and the terrestrial environment have been done for example by Parmentier et al. (2015) who performed a pan-Arctic pixel-wise correlation between local sea ice conditions and temperature and modeled methane emissions, and who argued a causal link in autumn but not in spring. Rehder et al. (2020) used causal-effect networks to identify temporal links to the land near the Laptev Sea, and showed that spring-time correlations in sea ice and atmospheric variables were both related to large scale atmospheric circulation, not to each other, although sea ice loss had a weak effect on the near coastal environment in summer. Regional links between NDVI and sea ice have also been shown before (see e.g. Yu et al. 2021 and the paper by one of the co-authors of this study, Macias-Fauria et al. 2017). In addition, see also chapter 10 of the 2017 AMAP report (the authors incorrectly state on line 45 that this report did not include vegetation trends). Btw, reverse links have also been argued, where terrestrial vegetation growth lowers surface albedo, affecting climate and subsequently sea ice loss (Zhang et al., 2020).

While many of these previous studies relied on models or reanalysis datasets, this study aims to use remote-sensing datasets as much as possible. However, the authors use the TTOP model to determine soil temperature at 2 m depth. While this model uses land surface temperature (LST) from MODIS as an input, it also uses reanalysis data when MODIS LST is unavailable. Moreover, it models the soil temperature depending on for example land cover and surface wetness. While the TTOP model is probably the best estimate we have for permafrost extent at the moment, it is still a (hybrid) model. If the authors wanted to compare to satellite data only, rather than reanalyses or models, it would have made more sense to compare to MODIS LST directly. Moreover, 2 m depth is rather deep in the Arctic, where the active layer is typically shallower than 1 m. Any warming signal would be strongly attenuated and lagged at 2 m depth, which makes it difficult to make instantaneous correlations.

The current study also shows correlations at short and long distances, but it is not clear whether these correlations have a common distant cause or whether they represent an internal dynamic in the Arctic. Are they due to large scale atmospheric circulation affecting both sea ice and the terrestrial variables? Or are they due to local feedbacks dominated by sea ice decline? Unfortunately, the answer to this question is left in the middle by the authors, who present the dataset as a baseline for further analyses of drivers and dependencies. The paper would have been much stronger if it included a proper discussion on the underlying causes for the apparent correlations.

While I generally appreciate the effort by the authors, they could have done a more detailed spatial analysis given the high spatial detail of the source data, and they should have provided better causal insights rather than showing correlations with little context. Unless the authors go beyond their basic presentation of the data, and given the fact that they urge others to take their dataset further, I feel like this manuscript in its current form would work better as a data paper (e.g. in ESSD) rather than as a research article. 

Some further comments:

-    Line 45: Vegetation is extensively discussed in the 2017 SWIPA report (see chapters 8 and 10).

-    Line 81: wouldn’t frequent cloud cover be a problem for MaxNDVI as well? Easy to miss the peak season in frequent cloudy parts of the Arctic, adding uncertainty to interannual variability in peak NDVI values.

-    Line 126-128: did this model result match the observations well?

-    Line 150: which parameters? Reference?

-    Line 157: what’s the pixel size?

-    Line: 164-165: This dataset appears to contain only the trend over the entire time series, not the original high temporal data used for the correlations, and there are no details on how the underlying dataset was processed. Is there a reference describing this?

-    Line 177-178: This assumption appears reasonable, but did you test whether it’s true?

-    Figure 1: could you give the area north of 60°N a different color from the one south of 60°N? Makes it easier to see the domain instead of just highlighting a latitudinal band.

-    Table 4: what’s the resolution of the source data for sea ice area? 

-    Line 201: entire northern hemisphere or only north of 60°N?

-    Line 255: I can imagine that correlations to more distant basins may show up but here we need better argumentation for why these correlations appear because at these large distances it may just as well be large scale atmospheric circulation affecting both (e.g. teleconnections related to Rossby wave propagation).

-    Line 269: a positive correlation may not be that surprising. For example, the Fram Strait is an area of sea ice export, which can actually be enhanced in warm summers because the strong sea ice melt makes the ice thinner and more mobile, subsequently leading to more export through this area. As such, a warmer Arctic leads to more sea ice in the Fram Strait, explaining positive correlations. These kinds of internal dynamics need to be considered when interpreting correlations to sea ice.

-    Line 275: do you mean figure A7 instead of A2?

-    Line 279-280: This is unclear. Relevant for TI-NDVI in what way?

-    Line 336: Please specify why solar absorption trends are increasing in this region. Is it less cloud cover or changes in surface albedo from e.g. earlier snow melt and/or shrubification?

-    Line 376-377: This sounds interesting, but what would be the reason for this temporal lag? 

-    Line 379-384: I’m not sure I’m following this. Why would warmer summers and increased absorption of radiation lead to regional cooling in the autumn?

-    Line 407: Unclear. Which “following ones”?

-    Line 433-435: I’m not sure how this agrees with Sasgen et al. (2024) since they explicitly state that they did not look at the influence of sea ice.

-    Line 449: why define the abbreviation FT for “freeze-thaw” if you only use it one more time?

-    Figure A2: please replace “source” in the caption with the actual reference.

-    Table A2: what does the “2000?” mean in the table caption?

References

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