Ensemble average time series of NPI (thick black solid line) with its long-term linear trend (thick red dashed line). b) NPI time series for the three reanalysis datasets (red: 20CR, blue: HadISLP and green: ERA20C). We project changes to the Aleutian Low (AL) due to global warming using ensemble simulations with a fully coupled ocean–atmosphere–sea ice model, the Kiel Climate Model (KCM). In particular, sea ice loss in the Sea of Okhotsk promotes SLP change around the Aleutian Islands and contributes to AL strengthening.
KCM을 활용한 지구온난화에 따른 알류샨 겨울 저기압의 변화 예측에 관한 연구. 핵심 단어: 알류샨 저기압, 지구 온난화, 킬 기후 모델, 해빙 변화.
Introduction
They suggested that the increase in eastern equatorial Pacific SST and the resulting Pacific–North American (PNA) teleconnection is the main reason for the strengthening of the AL. 2016) used a hierarchy of climate model simulations to investigate the winter response of the Northern Hemisphere extratropical atmosphere to projected Arctic sea ice loss. They found that the westerly jet weakens along the poleward side and slightly strengthens along the equatorward side, which can lead to AL deepening in a fully coupled system. 2017) used several models, the results of which can be affected by model uncertainty and internal variability in an individual climate model. In fact, there may be shortcomings in accurately estimating global warming signals or natural variability based on the output of a multimodel ensemble (e.g., CMIP5 Gan et al. 2017) because the multimodel ensemble includes model uncertainty as well as internal variability in each climate model of the CMIP5 ensemble members. .
We estimate the change in strength and location of the AL in response to global warming. One of the methods to obtain ensemble spread is to calculate the standard deviation for each ensemble model. 2017) considered the internal variability as the ensemble spread of multiple models, which aims to calculate the standard deviation of the means of model years for each grid point.
Results
Ensemble spreads of NPI changes and related indexes
Next, following Gan et al. 2017), we consider three factors that influence the intensity of the Aleutian Low: land–ocean thermal contrast (ocean minus land) change (∆LOTC), tropical Pacific SST warming (∆SST) and sea ice loss in the marginal ocean of the North Pacific Ocean (∆SIC). In other words, the increase in air temperature due to global warming is relatively greater on the continent than the ocean and thereby LOTC is reduced. This suggests that the weakened LOTC in response to global warming is accompanied by a weakening of the AL, which is consistent with the results of multi-model ensembles by the Gan et al.
However, in the KCM the correlation coefficient between ∆LOTC and ∆NPI is relatively less significant (r = ‒0.26) than Gan et al. Difference between the mean SST increase of the two regions for each ensemble and the ∆NPI coefficient is ‒0.29, with a statistical significance of 93%. In the KCM, all ensembles reproduce the El Niño-like SST warming, while in Gan et al.
Their results are based on multi-models, with each model showing a different tropical Pacific response to global warming, while our results here are derived from a single model, and the KCM's response is El Niño-like SST warming. It is noteworthy that even a single model shows a large spread in the magnitude of the east–west difference in SST, from 0.17 to 0.62°C. Contrary to their result, the reduction of sea ice in the marginal seas of the North Pacific appears to be an important factor in driving the change of NPI in the KCM.
In fact, the reduction in sea ice due to global warming is evident in other areas, including the Barents Sea, the Chukchi Sea, and the Canadian Basin (not shown). However, the correlation coefficient between ∆SIC and ∆NPI is very low and insignificant: 0.05 (statistical significance: 25.2%) in the Laptev and Kara Seas and 0.21 with a statistical significance of 81.6% in the Chukchi and Beaufort Seas. 8 Scatter plot of the changes in the NPI and (a) winter Land Ocean Thermal Contrast (LOTC), (b) ∆(EEP SST–WEP SST), and (c) SIC reduction in the Pacific Ocean for 40 ensemble members.
Inter-ensemble regression patterns
Compared to Gan et al. 2017) (see their Figure 10a), the center of maximum negative correlation is shifted to the southeast of the Aleutian Islands, outward from the Bering Sea. In the KCM, as we confirmed in the correlation (see Fig. 8 ), the sea ice loss in marginal seas of the North Pacific is the most important factor in changing the AL strength. Correlation coefficient between ∆NPI and ∆SIC of the Okhotsk Sea is 0.42 and ∆SIC of the Bering Sea is 0.47; both correlation coefficients have a statistical significance of more than 99%.
We regressed ∆SAT and ∆SLP of individual groups on the average decrease in SIC (‒∆SIC) of the Sea of Okhotsk and the Bering Sea, separately (Fig. 11c-f). The regression pattern of SAT between ensembles on the ∆SIC of the Bering Sea shows greater warming over the northeastern Bering Sea, where SLP decreases ( Fig. 11e ). This may imply that the decrease in ice in the Bering Sea may be due to AL deepening, such that increased cyclonic circulation brings warm air into the Bering Sea.
Otherwise, the response of SAT to ∆SIC of the Sea of Okhotsk shows a large warming over the Sea of Okhotsk (Fig. 11c), although there is a similar deepening of the AL (Fig. 11d). AL strengthening due to the associated wind pattern should increase the inflow of cold air into the Sea of Okhotsk from the Arctic Sea, leading to an increase in sea ice there. However, the regression pattern shows that SIC in the Sea of Okhotsk decreases even though AL is enhanced, suggesting that the response of Okhotsk SIC may not be due to changes in AL.
On the other hand, Fig. 11d shows decreasing SLP from the Sea of Okhotsk to the southeastern part of the Aleutian Islands due to the decrease of SIC in the Sea of Okhotsk. 11 Regression pattern between ensembles of winter (DJF) SAT (a,c,e) on areal mean decrease in SIC (∆SIC). Therefore, we reaffirm that the contribution of ∆SIC to AL deepening is more critical than other factors.
Discussion
There are positive SLP anomalies in the Bering Sea and negative anomalies near the Okhotsk Sea to the North Pacific. In the case of Honda et al. 1996), there is a Rossby wave train pattern in changing SLP; anticyclonic SLP anomalies covered in the Sea of Okhotsk and cyclonic anomalies covered in the Bering Sea (See their Fig. 4a). However, in the KCM results, SLP anomalies are similar to the NPO model; positive anomalies covered the Bering Sea and negative anomalies broadly covered the Sea of Okhotsk and the central North Pacific (Fig. 14).
On the other hand, it can be assumed that there is a connection between the NPO and sea ice variation in the Bering Sea and Sea of Okhotsk. The NPO/WP represents a distinct mode of midlatitude winter variability in the North Pacific. In addition, the positive NPO/WP phase, which deepened the AL, results in marginal sea ice extent in the Arctic Sea, the western Bering Sea, and the Sea of Okhotsk.
In addition, the strengthening of westerly winds occurs in the mid-latitudes, especially in the North Pacific. The positive U700 anomalies in the North Pacific indicate the downstream strengthening of the jet core. The difference of 500 hPa geopotential height (Z3 500hPa) increased in L30 and relatively weak elevated pattern in the Bering Sea and the Aleutian Islands.
However, Z3 500hPa decreased in the southern Aleutian Islands in Deser et al. 2016), while the overall increase in KCM dominated. Both experiments showed a different pattern of geopotential height in the Bering Sea and the Aleutian Islands. The warming of SST is evident over the Northern Hemisphere in the Bering Sea and the eastern tropical Pacific in response to global warming.
Summary
We analyzed the effect on AL through atmospheric changes caused by the reduction of sea ice concentration in the Sea of Okhotsk and the loss of Arctic Sea ice from global warming. The SLP change due to sea ice reduction in the Sea of Okhotsk showed the NPO pattern, which has increased SLP over the Bering Sea and decreased SLP propagation in the Sea of Okhotsk in the North Pacific. In contrast, the SLP change due to Arctic sea ice loss showed a PNA pattern that is a negative anomaly in the Bering Sea and a positive anomaly in eastern Japan.
Through this changing pattern of SLP change, the loss of sea ice may be a clue that led to a change in SLP. The reduction of Arctic sea ice due to global warming causes the zonal wind to weaken at high latitudes and the zonal wind to strengthen at midlatitudes. Also, warmer SAT and SST and increased precipitation affect the interaction between the ocean and the atmosphere and change the climate of the North Pacific. 2016) examines only the sea ice loss-induced part of climate change, not a full response to global warming.
However, this study took into account all changes due to global warming, not only the sea ice loss effect, but also other factors. Therefore, further work is needed to evaluate the impact of sea ice loss in other areas and use only atmospheric model simulation to confirm the atmospheric dynamics. However, dynamical mechanisms driving the deepening of the AL were not investigated in this study and should be the subject of further studies with additional numerical experiments on atmospheric response.
The seasonal atmospheric response to projected Arctic sea ice loss in the late twenty-first century. Does ocean coupling matter for the northern extratropical response to projected Arctic sea ice loss? Trends in Northern Hemisphere winter atmospheric circulation during the latter half of the twentieth century.
