Forced and internal components of observed Arctic sea-ice changes
Abstract. The Arctic sea ice cover is strongly influenced by internal variability on decadal time scales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea ice variability and trends, and thus help to disentangle the role of forced and internal sea ice changes over the satellite record. In particular, we identify a mode of decadal ocean-atmosphere-sea ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea, but has contributed less to trends in the Barents and Kara Seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments.
Jakob Simon Dörr et al.
Status: open (until 20 Apr 2023)
- RC1: 'Comment on tc-2023-29', Qinghua Ding, 27 Mar 2023 reply
Jakob Simon Dörr et al.
Jakob Simon Dörr et al.
Viewed (geographical distribution)
How much of Arctic sea ice melt over the past decades was due to impacts of natural processes remains very uncertain, although many studies have been devoted to addressing it by utilizing different approaches. This study represents one of these recent attempts aiming to tease apart the forced component of Arctic sea ice melt in summer and winter from those unforced parts over the past 43 years using a statistical tool known to be effective in disentangling complex variability with different origins. The method was used twice in this study on sea ice field alone and joint fields including sea ice, SST, and circulation. The identified leading modes were then carefully assessed and further linked to possible forcing mechanisms and impacts on other different local and remote fields. The main finding is that internal variability, most of which is captured by high-order modes, may explain 10% and 40% of the sea ice decline over the entire period and the 13 years (2000-2012), respectively. This finding supports some of the early studies on the same topic from the observation point of view, which is the most novel contribution of this work. Thus, this study makes some new scientific contribution to our physical understanding of the issue. Regarding the quality of its reasoning and presentation, I feel that it is a well-prepared and well-written paper with clear and well-designed figures. Taken together, my recommendation is the acceptance of the paper with minor revision. The remaining concerns I still have are that LFP 1 (Fig. 6) may still contain some signals belonging to natural variability. At least, the null-hypothesis of this argument (the leading mode has nothing to do with anthropogenic forcing) should be tested or discussed somewhere in the paper.
The Z500 pattern associated with LFP1 exhibits a strong regionality in the Arctic with a high-pressure center over Greenland, which is similar to that in LPF2 and also at odds with those forced patterns generated by models. The authors can plot the same field associated with the leading LPF in supplementary Fig. 3a. I am prtetty sure that a very uniform Z500 change will be seen. This leads me to doubt that some signals in this LPF may own a strong origin from regional circulation variations, which are usually not favored by anthropogenic forcing to the first-order approximation.
In addition, the time series of this mode (LPF1) shows a surge in 1990. I don’t think this is due to a change in CO2 concentration or aerosol forcing around the same time (aerosol forcing should favor a drop in sea ice). How to explain this feature?
A new test could be done to test the aforementioned null hypothesis. In the CESM LEN Pre-industrial simulation, a 43-year period with a monotonic decline or increase of Pan-Arctic sea ice can be selected, although these trends could be much weaker than the observed one over the past 43 years. The LPF method can be further used on this period to see what the leading LPF mode is. Since, by design, these sea ice trends are 100% internally driven, we would like to see how LFP can disentangle these variability when anthropogenic forcing is completely absent, but variability may also show some long-term trend signals.
It is nice to see that the authors examined lead-lag connections of each LPF mode with pre-season variability. I am wondering how these leading modes in summer connect with those LPF modes in winter directly.
In Fig. 4, the contour interval should be smaller for the summer case. 30m may be better to clearly illustrate the circulation pattern.