[논문]Variation of the Relationship Between Arctic Oscillation and East Asian Winter Monsoon in CCSM3 Simulation

Wie, Jieun; Moon, Byung-Kwon; Lee, Hyomee

doi:10.5467/jkess.2018.40.1.1

Variation of the Relationship Between Arctic Oscillation and East Asian Winter Monsoon in CCSM3 Simulation 원문보기

한국지구과학회지 = Journal of the Korean Earth Science Society, v.40 no.1, 2019년, pp.1 - 8

Wie, Jieun (Division of Science Education) , Moon, Byung-Kwon (Division of Science Education) , Lee, Hyomee (Division of Science Education)

Abstract ▼ AI-Helper

Although recent reports suggest that the negative correlation between the Arctic Oscillation (AO) and the East Asian winter monsoon (EAWM) has been strengthened, it is not clear whether this intermittent relationship is an intrinsic oscillation in the climate system. We investigate the oscillating behavior of the AO-EAWM relationship at decadal time scales using the long-term (500-yr) climate model simulation. The results show that ice cover over the East Siberian Seas is responsible for the change in the coupling strength between AO and EAWM. We found that increased ice cover over these seas strengthens the AO-EAWM linkage, subsequently enhancing cold advection over the East Asia due to anomalous northerly flow via a weakened jet stream. Thus, this strengthened relationship favors more frequent occurrences of cold surges in the EAWM region. Results also indicate that the oscillating relationship between AO and EAWM is a natural variability without anthropogenic drivers, which may help us understand the AO-EAWM linkage under climate change.

주제어

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문제 정의

This study examined the natural variability of the AO-EAWM linkage using a long-term (500-yr) unforced free-run simulation of the CCSM3 climate model, and it identified the role of Arctic sea ice on this linkage. The model simulation produced alternating epochs of strengthening and weakening relationships between the AO and EAWM.

제안 방법

Here, the sign of the AOI was reversed to use nAOI, so that the higher correlation represents the fact that the EAWM is more strongly tied to the AO. As the three EAWMIs exhibited a similar temporal variation in the AO-EAWM relationship (Fig. 3a, 3b, and 3c), we defined the correlation coefficient between EAWM (4) and nAOI (Fig. 3d) as the Arctic Oscillation and EAWM relation index (AERI), and used it to examine possible reasons for the strengthening and weakening of the AO-EAWM linkage.
In addition, as the observation data contained information on anthropogenic factors affecting climate change, including the recent increase in temperature and the extent of sea ice reduction in the Arctic, the natural variability of the AO-EAWM relationship could be more accurately understood using a longterm climate model simulation. Thus, we identified and analyzed the natural variability of the AO-EAWM relationship in a climate model run without anthropogenic forcing and explored the related mechanism.
This study examined the natural variability of the AO-EAWM linkage using a long-term (500-yr) unforced free-run simulation of the CCSM3 climate model, and it identified the role of Arctic sea ice on this linkage. The model simulation produced alternating epochs of strengthening and weakening relationships between the AO and EAWM. This indicates that the AO-EAWM relationship is basically unstable over time, a fact that has not been clarified by previous analyses of short-term observational data.
In addition, as the observation data contained information on anthropogenic factors affecting climate change, including the recent increase in temperature and the extent of sea ice reduction in the Arctic, the natural variability of the AO-EAWM relationship could be more accurately understood using a longterm climate model simulation. Thus, we identified and analyzed the natural variability of the AO-EAWM relationship in a climate model run without anthropogenic forcing and explored the related mechanism. Detailed descriptions of the model and methodology are provided in section 2.
The AO index (AOI) representing the variation of AO is defined as the principal component (PC) time series of the leading empirical orthogonal functions (EOF) of PSL anomalies in the Northern Hemisphere poleward of 20ºN (Thompson and Wallace, 1998). To examine the AO-EAWM linkage, a new EAWM index was obtained by averaging three conventional EAWM indices. The first EAWM index (EAWMI (1)) captured the difference of area-averaged zonal wind at 300 hPa in winter between a particular region (27.
To explore the atmospheric circulation associated with the coupling strength of the AO-EAWM linkage, we applied a regression analysis of the AERI on atmospheric variables at the surface and 200 hPa levels (Fig. 4). The Arctic region showed a positive surface pressure anomaly (Fig.
To identify the cause of the decadal variability in the AO-EAWM relationship, the regressed winter sea ice thickness on the AERI was analyzed (Fig. 5). It appears that the East Siberian Seas have statistically significant positive anomalies of sea ice.

데이터처리

Hatched areas denote statistical significance at the 95% confidence level using the Student’s t-test.

이론/모형

This study analyzed long-term integrated data of the Community Climate System Model Version 3 (CCSM3), which is a fully coupled climate system model including the atmosphere (Community Atmosphere Model version 3; CAM3), land (Community Land Model version 3; CLM3), ocean (Parallel Ocean Program; POP), and sea ice (Community Sea Ice Model; CSIM) models. The atmosphere model has a finite volume dynamic core with a horizontal resolution of 2º×2.

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