the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Dec 2023
Storylines of Summer Arctic climate change constrained by Barents-Kara Sea and Arctic tropospheric warming for climate risks assessment
Abstract. While climate models broadly agree on the changes expected to occur over the Arctic with global warming on a pan-Arctic scale (i.e., polar amplification, sea-ice loss, increased precipitation), the magnitude and patterns of those changes at regional and local scales remain uncertain. This limits the usability of climate model projections for risk assessments and their impact on human activities or ecosystems (e.g., fires, permafrost thawing). Whereas any single or ensemble-mean projection may be of limited use to stakeholders, recent studies have shown the value of the storyline approach in providing a comprehensive and tractable set of climate projections that can be used to evaluate changes in environmental or societal risks associated with global warming.
Here, we apply the storyline approach to a large ensemble of CMIP6 models, with the aim of distilling the wide spread in model predictions into four physically plausible outcomes of Arctic summertime climate change. This is made possible by leveraging strong covariability in the climate system, associated with well-known but poorly constrained teleconnections and local processes: specifically, we find that differences in Barents-Kara Sea warming and lower tropospheric warming over polar land regions among CMIP6 models explain most of the inter-model variability in pan-Arctic surface summer climate response to global warming. Based on this novel finding, we compare regional disparities in climate change across the four storylines. Our storyline analysis highlights the fact that, for a given amount of global warming, certain climate risks can be intensified while others may be lessened, relative to a “middle-of-the-road” ensemble mean projection. We find this to be particularly relevant when comparing climate change over terrestrial and marine areas of the Arctic, which can show substantial differences in their sensitivity to global warming. We conclude by discussing potential implications of our findings for modelling climate change impacts on ecosystems and human activities.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2023-2741', Anonymous Referee #1, 19 Dec 2023
This paper applies the storyline approach developed by Zappa & Shepherd (2017) and Mindlin et al. (2020), which is based on multiple linear regression across CMIP models, to represent the uncertainty in the response of summertime Arctic climate to greenhouse warming in terms of two key drivers: BK SST warming, and pan-Arctic lower tropospheric warming. So far as I am aware this is the first time that the storyline approach has been applied to the Arctic. The key to the storyline approach is an appropriate identification of drivers, for the reasons explained in lines 335-341. The two drivers identified here are interesting is that they are surprisingly complementary. The analysis is competently performed and the results will be of value to the community. I am thus favourably disposed to publication but feel that just a little more effort would make the paper even more valuable. I also have a number of points which require clarification.
Major comments:
Lines 27-28: Here you describe the ArcAmp index as “lower tropospheric warming over polar land regions”, but in the body of the paper it is defined as everything poleward of 55N. Which is it?
Lines 60-61: Sandwiched between a number of incontrovertible statements comes this one about the links between AA and midlatitude weather extremes, using definitive language and with a reference to Cohen et al. (2014). The authors must surely know that this remains a highly contested topic of research. (Morever the statement here is to some extent contradicted by the text on lines 83-90.) I would suggest dropping the sentence (since you are anyway just giving examples here), but alternatively you should use more nuanced language and provide some more recent references to give a more balanced perspective.
Table 1: For 3 of your 4 fields, the fraction of variance explained by your storylines is not particularly impressive. But for spatially noisy fields, FVE at the gridpoint level is something of a misleading statistic. In Mindlin et al. (2020), which looked at precipitation changes, the FVE was also not very impressive, but when aggregated across a region the storylines did span the range of precipitation changes across the models (which is what matters) quite well. See e.g. Table 2 of Mindlin et al. (2020), where with the exception of one region, the range spanned by the storylines is much larger than the median absolute deviation (MAD) between the actual model response and the response predicted for the model by the storylines. I would encourage the authors to do something similar here, because the storylines may have more explanatory power for these 3 fields than the authors realize.
Figure 2: Storylines are easiest to interpret if they are causal, which in ZS17 and M20 was argued by identifying the statistical responses to drivers with relationships found in other studies, e.g. using model intervention. It would useful if the authors could clarify the extent to which Figure 2 aligns with expectations from other studies.
Figure 2: On this point, if I understand correctly, your BKWarm index is associated with increased rather than decreased sea-ice fraction in the Barents Sea. I didn’t see any comment about that. What is your interpretation of this curious relationship, which would seem to undermine the causality of your storylines?
Figures 3-6: Your usage of “storyline” is not consistent with the usage in ZS17 and M20. They used the term to describe physically plausible changes, the idea being that the future evolution could follow one of the storylines. You seem to be using it instead to describe deviations from the MMM. Hence, to obtain a plausible future change from one of your storylines, e.g. for climate impact studies, one would need to add the MMM to the storyline. Perhaps you have a reason for doing it the way you did, but it makes the interpretation of Figures 3-6 somewhat confusing. I would suggest following the usage in ZS17 and M20 and having your four storylines be the full changes, i.e. including the MMM.
p.15, final paragraph: You could do just a little more here. One of the benefits of storylines is that each storyline represents correlated aspects of climate change. So rather than describing the way different storylines affect the four different fields you analysed, you could describe the combined changes across those four fields for each storyline. Such a summary, in the spirit of compound events, would exploit the power of storylines.
Minor comments:
Line 63: “Shepard” -> “Shepherd”
Line 333: “ML20” -> “M20”
Line 340 and elsewhere: You refer to “mitigation strategies”, but don’t you actually mean “adaptation strategies”?
Citation: https://doi.org/10.5194/egusphere-2023-2741-RC1 -
AC1: 'Reply on RC1', Xavier Levine, 22 Jan 2024
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 (...)”.
Citation: https://doi.org/10.5194/egusphere-2023-2741-AC1
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AC1: 'Reply on RC1', Xavier Levine, 22 Jan 2024
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RC2: 'Comment on egusphere-2023-2741', Anonymous Referee #2, 20 Jan 2024
This paper uses a storyline approach to analyze CMIP6 projections of summertime Arctic climate change, which offers unique information for evaluating societal/environmental risk that goes beyond evaluating the multi-model mean. The authors effectively show that uncertainty in two parameters, Barents-Kara sea SST (BkWarm) and Arctic 850hPa temperature (ArcAmp), modulate the spatial pattern of near-surface temperature trends, particularly the relative warming over terrestrial and marine areas. The four storylines produced by these predictors have important implications for the risk of permafrost thawing, wildfire risk, and Arctic Ocean navigability.
As a reader who is familiar with Arctic amplification processes, but new to the storyline approach, I was impressed by the utility this methodology offers, and think these results will be useful to the polar climate science community. That said, I have two major concerns with the current manuscript. First, I found that there was insufficient consideration given to physical mechanisms connecting the predictors to the storylines. Second, it is not clear that the Arctic lower-tropospheric warming predictor (ArcAmp), is quantified in the optimal way, or how different definitions of this term might affect the robustness of the results. I hope my comments below will help the authors address these issues. I recommend major revisions.
Major Comments:
- L182-183: The definition of ArcAmp. How do we know that 850 hPa is the appropriate level to evaluate lower-tropospheric Arctic warming? Attention must be given to the distinct seasonality and vertical structure of Arctic-amplified warming, as well as the different warming mechanisms operating at different pressure levels (e.g., Graversen et al. 2008, Screen and Simmonds 2012, Feldl et al. 2020, Kaufman and Feldl 2022). Finally, it should be noted that the changing Arctic atmospheric energy budget is distinct over land and ocean (Deser et al. 2010). Beyond justifying their choice with prior research on these topics, the authors may want to test the robustness of their storyline results to different definitions of ArcAmp, using different pressure levels and horizontal domains for the spatial average.
- L185-193: The authors assert that their choice of predictor variables is justified due to “(i) their ability to explain a large fraction of the inter-model variability in climate change projections, and to (ii) their connection to a wide array of climatic phenomena in the Arctic and in midlatitude regions, especially near-surface warming.” While (i) is given sufficient evaluation in the subsequent analysis, there is far less work to justify (ii). I recognize that the importance of the Barents-Kara Sea and lower-tropospheric warming for surface climate has been demonstrated in previous studies, as the authors note in the introduction (L83-90). But the mechanisms underpinning these connections are not used to evaluate the storyline results. Why does lower-tropospheric warming influence terrestrial and marine areas differently (Fig. 2b)? Why does the Barents-Kara Sea have outsized influence relative to other areas of the Arctic Ocean?
- L336-343: Similar to my above comment, I was left wanting more discussion of the connection of the storyline predictor terms to physical processes (Criterion ii). The discussion section as written seems to focus almost exclusively to the implications of the different scenarios for climate risks (Criterion i).
Minor Comments:
- L60-61: This is still a heavily debated mechanism, so uncertainty in this consequence of AA should be made clear (e.g. Cohen et al. 2020)
- L120-122: Does having varying numbers of ensemble members included in each model average create any issues regarding inconsistent signal-to-noise ratios across them? Would using a single ensemble member for each model lead to a more consistent treatment?
- L155-158, L194-199: I am curious if the authors could elaborate on the condition of orthoganality amongst the predictors. They make a convincing case to me that the weak correlation between BK-Warm and ArcAmp (r^2 = .15) in this study is acceptable, but it would be useful to future users of this method to have an idea of what an unacceptably high correlation would be, and why.
- 3-6: Significance stippling is only shown for the multi-model mean. Why not show it for the storylines as well?
References:
Deser, Clara, et al. "The seasonal atmospheric response to projected Arctic sea ice loss in the late twenty-first century." Journal of Climate 23.2 (2010): 333-351.
Graversen, Rune G., et al. "Vertical structure of recent Arctic warming." Nature 451.7174 (2008): 53-56.
Screen, James A., Clara Deser, and Ian Simmonds. "Local and remote controls on observed Arctic warming." Geophysical Research Letters 39.10 (2012).
Feldl, Nicole, et al. "Sea ice and atmospheric circulation shape the high-latitude lapse rate feedback." NPJ climate and atmospheric science 3.1 (2020): 41.
Cohen, Judah, et al. "Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather." Nature Climate Change 10.1 (2020): 20-29.
Kaufman, Zachary S., and Nicole Feldl. "Causes of the Arctic’s Lower-Tropospheric Warming Structure." Journal of Climate 35.6 (2022): 1983-2002.
Citation: https://doi.org/10.5194/egusphere-2023-2741-RC2
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