We’re grateful for the referee’s positive outlook and constructive criticism of our work. We’ve strived to answer the referee’s comments, which we think will improve the clarity of our manuscript. While we’ll update our manuscript to fully address the referee’s comments at a later date, we’re already summarising below some of the changes we intend to make:

L. 27-28: ArcAmp is defined as the lower tropospheric warming over ALL regions poleward of 55N. The definition we originally provided in lines 27-28 was incorrect, and we apologise for this error.

Lines 60-61: We agree that our original statement did not adequately reflect the degree of (un)certainty in the proposed causation mechanisms between AA and midlatitude weather extremes. We will modify line 60-61 to highlight that this is still a topic of active research: “The changes to the Arctic climate system have also been suggested to have caused in increase in the frequency and intensity of certain extreme weather over the Northern Hemisphere mid-latitudes (Cohen et al., 2014), even if the mechanism of action and broader importance of such polar-to-midlatitude teleconnection remains controversial (Vavrus, 2018)”. [Ref: Vavrus S.J., 2018: The influence of Arctic amplification on mid-latitude weather and climate. Current Climate Change Reports, 4, 238-249.]

Table 1: We thank the reviewer for this helpful suggestion. While point-to-point correlation as shown on Table 1 may be the most stringent criterion for evaluating the efficacy of our MLR model, we also agree that it may be overly influenced by spatial noise or slight difference in patterns. We will provide at a later date a table similar to that in Mindlin et al. 2020, which we will post to this comment section.

Figure 2: Indeed, a positive BKWarm index appears to be associated with a greater sea-ice fraction over some areas of the Barents Sea. While we do not have any hypothesis to propose to explain this counter-intuitive result, we note that the significance of this result is low, as very little ice is found over the Barents sea in summer in most models. We will add a statement acknowledging this counter-intuitive aspect of the climate response to the manuscript.

Figures 3-6: We fully agree with the reviewer’s assertion that the total response (MMM + anomalies) is what is most relevant to the end-users. Nevertheless, showing the total response makes it harder to distinguish what differentiate storylines, because storylines’ patterns are strongly influenced by the common MMM component. Since this study is focused on describing and explaining what differentiates storylines (rather than assessing their final response and impacts on the surface climate), we think that showing anomalies with respect to the MMM still remains the clearest way to visualize the differences between storylines. We will add a statement in the result section to make this point clear. For optimal clarity, we will add the total response (MMM + anomalies) in a new appendix section (Appendix B) .

p.15, final paragraph: In the last paragraph of Section 4, we provide a qualitative assessment of our storylines on various risks. While it is primarily based on the 2-m temperature response, which is the target variable best explained by our MLR model, we also mention the mitigating / modulating effect of our other target variable on those climate risks. We are not able to provide more than a brief qualitative assessment of climate risks for each storyline due to the complexity of modeling climate risk from physical changes. However, our storylines will be communicated to and used by climate services experts to model those climate risks within the scope of PolarRES (our funding source project).

Minor comments:

Line 63/333: We will correct these typos.

Line 340 and elsewhere: We also agree with the referee’s suggestion that adaptation may be a better choice of word in this context, although mitigation would also be applicable if narrowly defined (e.g. one could say that building resilient infrastructures in the Arctic may be a form of adaptation to, as much as mitigation against, climate change). For clarity we will change Line 340: “Criterion (i) is critical to the viewpoint of the end-users who need a plausible range of 340 climate change scenarios, for instance to develop adaptation strategies (...)”.

We thank the referee for the thoughtful and constructive review of our work, which has helped improve the quality and readability of our manuscript. While we’ll update our manuscript to fully address the referee’s comments at a later date, we’re already summarizing below some of the changes we intend to make. The referee will find the cited figures and tables in the pdf supplement attached to our response. 

Major comments:

L182-183: We agree with the reviewer that Arctic atmospheric warming shows strong seasonal and vertical contrast, and this is already noticeable in the climate change response observed in the past decades (Graversen et al., 2008). As shown in past studies (e.g., Fig. 1 in Graversen et al., 2008), warming rates generally peak in the mid-troposphere (~600 hPa) in summer, but in the boundary layer (<800 hPa) in winter. Likewise, we find that tropospheric warming is nearly uniform across levels in summer (Fig. 1a), unlike winter when it peaks in the boundary layer (Fig. 1b). In summer, we expect the marine boundary layer to be sensitive primarily to changes in ocean currents and state, while the terrestrial boundary layer to respond primarily to atmospheric processes. This is due to the fact that the troposphere is often found to be stably stratified over the Arctic ocean in summer, consistently with the Arctic ocean acting as a heat sink for the boundary layer (Tjernström and Graversen, 2009). The effect of this stable stratification on the climate response is made readily apparent when comparing separately over ocean and land regions how surface temperature changes co-vary with the Arctic temperature profile changes. Specifically, we find that the mid-tropospheric warming (between 850 and 500 hPa) covaries much more strongly with land surface warming than ocean surface warming (Fig. 2a,b), as would be expected from a thermal decoupling of the marine boundary layer from the free troposphere.

Overall, we select the predictors of our Arctic storylines such as to best explain the warming of the marine and terrestrial boundary layers in the Arctic. Our profile analysis suggests that free tropospheric warming (above 850 hPa) may be well suited for explaining changes in the terrestrial warming, due to its strong correlation with land warming (Fig. 2b). In our study, we select the Arctic-mean 850-hPa temperature change as our predictor for the Arctic free tropospheric warming, due to its strong performance for explaining 2-m temperature changes in the Arctic in a multivariate linear regression (MLR) framework in conjunction with our other predictor, the Barents-Kara sea warming. Compared with other vertical levels, the 850 hPa level maximizes the variance explained by the MLR (Table 1, top row) while being only weakly correlated with the Barents-Kara sea warming (Table 1, bottom row). The relevance of ArcAmp to land temperatures is confirmed by our pattern response analysis (Fig. 3a). We select the Barents-Kara sea warming as a predictor for the marine boundary layer. We picked the Barents-Kara sea as our reference region specifically because of its role as a climate gateway between the North Atlantic and the Arctic ocean (e.g. Smedsrud et al., 2013). Our pattern response analysis confirms the strong covariability of the Barents-Kara seas warming with both the Central Arctic and North Atlantic sea warming (Fig. 3b). However, our analysis does not seek to establish a causation between North Atlantic, Barents-Kara or Central Arctic warming, but rather to leverage the strong co-variability across all three regions with global warming. Hence, Central Arctic or North Atlantic warming could have been chosen as predictors instead of the Barents-Kara sea warming, with little difference in the overall fitness of the model (see Table 2).

=> We will expand section 2.4 ("Choice of predictor indices”) to better explain those points, as follows: “In this study, we select Arctic atmospheric amplification and Barents-Kara Sea warming as our predictors (...). Both ‘ArcAmp’ and ‘BKWarm’ are defined over the extended summer season (May to October). We choose those two predictors owing to their ability to explain a large fraction of the inter-model variability in climate change projections in the Arctic, specifically the warming of the boundary layer over marine and terrestrial regions. Indeed, comparing the vertical profile of temperature against surface temperature in the Arctic regions shows a strong covariability over land but weak covariability over marine areas (see Fig 2a,b), consistent with the thermal decoupling of the marine boundary layer from the free troposphere in summer (e.g., Tjernström and Graversen, 2009). Over ocean regions, the warming of the marine boundary layer is found to warm coherently across the Central Arctic, Barents-Kara, and North Atlantic regions, in agreement with a coherent increase in sea surface temperature across those regions. Due to its role as a climate gateway between the North Atlantic and the Arctic ocean (e.g. Smedsrud et al., 2013), we select the Barents-Kara sea  as our reference region for defining our ocean warming predictor in the Arctic. Conversely, we select the 850 hPa Arctic mean temperature warming as our second predictor due to its high degree of covariability with the warming of the terrestrial boundary layer and low degree of covariability with the marine boundary layer warming (see Table 1). ” We will be adding an appendix B in the manuscript featuring our vertical profile analysis (Fig. 2, Tables 1 & 2). 

L185-193: We’ve addressed some of those points in our previous comment about the choice of predictors. To briefly summarize, we find a strong covariability between free tropospheric warming and land temperatures, while the marine boundary layer is found to warm coherently across the Arctic ocean and North Atlantic. We expect surface temperature over terrestrial areas to be more sensitive to atmospheric processes than ocean regions, due to the strong thermal inertia of the ocean and the stable thermal stratification of the troposphere in summer over the Arctic ocean. The processes tying temperature anomalies in the free troposphere to that of the surface over land likely involve multiple atmospheric feedbacks, such as cloud and clear sky radiative feedbacks and boundary layer mixing changes; those feedbacks are beyond the scope of this study, however, although we would like be analyze them in a future study. Regarding our selection of the Barents-Kara sea warming (BKWarm), we motivate it primarily based on its known importance as a mediator of climate change between the North Atlantic and Central Arctic. As mentioned in our previous comments, our study does not seek to outline a mechanism connecting those 3 regions, as it would require an in-depth analysis of changes in ocean current, upper-ocean mixing, and surface fluxes, which are beyond the scope of this study.

=> We will expand section 2.4 ("Choice of predictor indices”) to explicitly state the limitations of our study.  “In this study, we select Arctic atmospheric amplification and Barents-Kara Sea warming as our predictors (...). Conversely, we select the 850 hPa Arctic mean temperature warming (...) marine boundary layer warming (see Table 1). The processes tying temperature anomalies in the free troposphere to that of the surface over land likely involve multiple atmospheric feedbacks, such as radiative or boundary layer mixing changes, which is beyond the scope of this study. Likewise, while our study leverages the connection between the North Atlantic, Barents-Kara and Central Arctic warming to produce a predictor for the marine boundary layer warming, it does not seek to outline a mechanism connecting those three regions as it would require an in-depth analysis of changes in ocean current, upper-ocean mixing, and surface fluxes, which is beyond the scope of this study.”

L336-343: Our study does not address the mechanism behind the links of our predictors with regional and global changes in the atmospheric circulation, which is beyond the scope of this study. Yet, our predictors are connected to changing surface climate on a global scale, tying our predictors to possible global circulation changes with global warming. For instance, the response of the 2-m temperature to changes in ArcAmp and BKWarm show distinctively different spatial patterns (Fig. 4). ArcAmp is associated with a bipolar climate change signal, with warming over the Northern Hemisphere land regions and over the Pacific ocean, but a cooling over the Southern ocean (Fig. 4a). This is likely linked to changes in sensible and latent poleward heat fluxes (Kauffman and Feldl, 2022), reaching far across hemispheres. BKWarm is associated with a strong warming of the northern hemisphere high latitude marine regions and slight cooling of the deep tropics (Fig. 4b). We intend to explore more in depth those teleconnections in a future study. 

Minor comments:

L60-61: We agree with the reviewer that our original statement did not adequately reflect the degree of (un)certainty in the proposed causation mechanisms between AA and mid-latitudes weather extremes. We will modify line 60-61 to highlight that this is still a topic of active research: “The changes to the Arctic climate system have also been suggested to have caused an increase in the frequency and intensity of certain extreme weather over the Northern Hemisphere mid-latitudes (Cohen et al., 2014), even if the mechanism of action and broader importance of such polar-to-midlatitude teleconnection remains controversial (Vavrus, 2018)”.

L120-122: This is a fair point made by the reviewer. Ideally, we would have liked to use multiple members for all models, and the same number of members for all models. Initially, we were concerned that having only one member for all members would make it challenging to disentangle natural variability from climate change in our storyline analysis. Fortunately, we show that individual members of the same model are more alike than the ensemble-mean of different models, as shown by our 2 predictors (Fig. 1b in the manuscript). This provides strong evidence for the climate change response to dominate over natural variability, and suggests that using only one member would provide similar storylines. We will add a point to this effect in section 2.1 (“Model data”): “Our analysis uses a set of 42 climate models from CMIP6 (...). As most models only have a few members, setting a maximum of 15 members seems a reasonable trade-off for reducing internal variability while including as many models as possible. We find little difference in using only a single member or an ensemble-mean of members, as the climate projections are dominated by the effect of the climate forcing with only a small contribution from natural variability (see Fig. 1b)”.

L155-158: Orthogonality is not a necessity for constructing storylines since one can build a meaningful MLR model with covarying predictors, yet orthogonality remains desirable for interpreting changes. However, we do not offer any specific guidance on what may constitute a ‘good enough’ orthogonality (i.e., what may be the maximum value of a correlation coefficient), as this may be dependent on the context and situation. It is also possible to use a modified linear regression analysis when predictors are more strongly correlated, which preserves some of the essential properties of interpretability for the storylines (see ‘sequential regression’ in Mindlin et al., 2020).

Fig 3-6: We will add significance stippling on the storyline plots (Figs. 3, 4, 5, 6), which will consist of all regions in which at least one of the response coefficients shows significance (as defined and shown in the normalized response coefficients, Fig. 2 ).

References:

Graversen, R.G., Mauritsen, T., Tjernström, M., Källén, E. and Svensson, G., 2008. Vertical structure of recent Arctic warming. Nature, 451, 53-56.

Tjernström, M. and Graversen, R.G., 2009. The vertical structure of the lower Arctic troposphere analysed from observations and the ERA‐40 reanalysis. Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 135, 431-443.

Smedsrud, L.H., Esau, I., Ingvaldsen, R.B., Eldevik, T., Haugan, P.M., Li, C., Lien, V.S., Olsen, A., Omar, A.M., Otterå, O.H. and Risebrobakken, B., 2013. The role of the Barents Sea in the Arctic climate system. Reviews of Geophysics, 51, 415-449.