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Arctic Dipole Anomaly and Its Contribution to Sea Ice Export from the Arctic Ocean in the 20Th Century

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Arctic Dipole Anomaly and Its Contribution to Sea Ice Export from the Arctic Ocean in the 20Th Century GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L23703, doi:10.1029/2006GL028112, 2006 Click Here for Full Article Arctic dipole anomaly and its contribution to sea ice export from the Arctic Ocean in the 20th century Eiji Watanabe,1 Jia Wang,2 Akimasa Sumi,1 and Hiroyasu Hasumi1 Received 7 September 2006; accepted 26 October 2006; published 5 December 2006. [1] The winter dipole anomaly (DA) in the Arctic dependence of the correlation on data period. Local atmo- atmosphere and its contribution to sea ice export are spheric forcing is also important for sea ice export. A sea- investigated by using a high-resolution coupled global level pressure gradient across the Fram Strait explains the general circulation model. The spatial distributions of the most variance in the sea ice export [Kwok and Rothrock, first two leading EOF modes of winter mean sea level 1999]. Based on previous studies that indicate a possible pressure (SLP) and geopotential height at 500 hPa north of important impact of the second-leading empirical orthogo- 70°N obtained by the long-term simulation (1900–2010) nal function (EOF) mode of Arctic sea level pressure (SLP) are highly similar to those derived from the National Center anomalies on sea ice [Wang et al., 1995; Skeie, 2000], Wu et for Environmental Prediction and the National Center for al. [2006] suggest the winter dipole anomaly (DA) structure the Atmospheric Research (NCEP/NCAR) reanalysis in the Arctic atmosphere. The DA also appears to have a datasets (1948–2004). The first-leading mode corresponds close relationship with sea ice motion based on the Inter- to the Arctic Oscillation (AO). The DA is defined as the national Arctic Buoy Programme (IABP) dataset (1979– second-leading mode. The AO and DA account for 59% 1998) and the National Center for Environmental Prediction and 19% of the total variance, respectively. Composite and the National Center for the Atmospheric Research spatial patterns of SLP, sea ice thickness and velocity in the (NCEP/NCAR) reanalysis data (1960–2002 [Kalnay et extreme years when both the absolute values of principal al., 1996]). The DA is defined as the second EOF mode component (PC1 and PC2) exceed 1.0 standard deviation of monthly mean SLP north of 70°N during the winter indicate that the DA plays a great important role in sea ice season (Oct.–Mar.). It is expected that the DA has a great export from the Arctic Ocean to the Greenland Sea due to its importance to sea ice export due to its strong meridionality, strong meridionality. Sea ice export is highly promoted although it accounts for only 13% of the total variance. (restricted) in the positive (negative) DA phase. The Available observational data are limited to only the recent dependence of sea ice export on the DA is comparable to few decades. It is difficult to estimate contribution of the or rather larger than that on the AO. Citation: Watanabe, E., DA to sea ice export quantitatively and to discuss signifi- J. Wang, A. Sumi, and H. Hasumi (2006), Arctic dipole anomaly cance of the correlation without inspection using long-term and its contribution to sea ice export from the Arctic Ocean in the coupled climate simulations. In this study, characteristics of 20th century, Geophys. Res. Lett., 33, L23703, doi:10.1029/ the DA and dependence of sea ice export on the DA are 2006GL028112. investigated using the long-term simulation results (1900– 2010) reproduced by a high-resolution coupled general circulation model (CGCM). 1. Introduction [2] Observational and modeling studies have indicated a 2. The 20th Century Climate Experiment recent rapid decrease of sea ice in the Arctic Ocean, and its close relationship with the Arctic Oscillation (AO [Thompson [3] The 20th century climate experiment was conducted and Wallace, 1998; Wang and Ikeda, 2000, 2001]) and the by the Model for Interdisciplinary Research on Climate North Atlantic Oscillation (NAO [Hurrell, 1995]) [Rigor (MIROC) version 3.2 [K-1 Model Developers, 2004], which et al., 2002; Zhang et al., 2003; Wang et al., 2005]. was developed by the Center for Climate System Research, However, the AO does not always explain sea ice varia- University of Tokyo (CCSR/UT), the National Institute for tions, because sea ice response lags behind atmospheric Environmental Studies (NIES), and the Frontier Research variations [Watanabe and Hasumi, 2005] and is affected by Center for Global Change (FRCGC) as subject No. 1 of some components other than the AO. While high correlation Kyousei project (K1). The horizontal resolution is T106 between sea ice export through the Fram Strait and the AO/ (1.1°) spectral resolution with 56 vertical levels in the NAO indices appears after the late 1970s [Kwok and atmospheric part, and is 1/4° zonally and 1/6° meridionally Rothrock, 1999], some studies show no significant correla- with the North Pole rotated to Greenland in sea ice and tion between them [Vinje, 2001]. With respect to this ocean parts. The ocean has 48 vertical levels. The sea ice contradiction, Hilmer and Jung [2000] investigate the model adopts 0-layer thermodynamics [Semtner, 1976] and elastic-plastic-viscous rheology [Hunke and Dukowicz, 1997]. The initial ocean temperature and salinity are given 1Center for Climate System Research, University of Tokyo, Chiba, by the climatology of Levitus and Boyer [1994] and Levitus Japan. et al. [1994]. The CGCM was spun up for 100 years with 2International Arctic Research Center, University of Alaska-Fairbanks, the climate forcing in 1900, then the model was integrated Fairbanks, Alaska, USA. from 1900 to 2100 with several external forcings (solar Copyright 2006 by the American Geophysical Union. irradiance, volcanic eruption, greenhouse gases, stratospher- 0094-8276/06/2006GL028112$05.00 ic and tropospheric ozone, carbonaceous and sulfate aero- L23703 1of4 L23703 WATANABE ET AL.: THE 20TH CENTURY ARCTIC DA AND ICE EXPORT L23703 sols) based on the historical (1900–2000) and B1 (2001– 2100) scenarios of the Intergovernmental Panel on Climate Change 4th Assessment Report (IPCC FAR). Note that neither restoring method nor flux adjustment is used throughout the spin up and simulation [Suzuki et al., 2005]. We analyze the simulation data for 110 years from 1900 to 2010 (K1, hereafter). 3. Simulating DA in the Arctic Atmosphere in the 20th Century 3.1. Winter Climatology [4] The winter climatology (Dec.–Feb. mean) of SLP and 500 hPa geopotential height is reproduced well by the long-term simulation. The spatial patterns are similar to that derived from the NCEP/NCAR reanalysis data, while the anti-cyclone over the Beaufort Sea seems to be weaker in the K1 than the reanalysis. Geopotential height is low over the entire Arctic Ocean, while the location of the low center is shifted to the Siberian side, compared to the reanalysis. Even by using the same period as the NCEP (i.e. 1948– Figure 1. Regressed winter mean SLP anomalies 2004 mean), the spatial patterns of SLP and geopotential (Dec.–Feb.) to (a) the first- and (b) the second-leading height are almost same as the 1900–2010 mean. The winter EOF modes derived from the K1 (1900–2010). (c) Same mean sea ice export through the Fram Strait is 0.071 ± 0.023 as Figure 1a except for the NCEP reanalysis (1948–2004). 6 3 À1 Sv (1 Sv = 10 m s ), which is smaller than the (d) Same as Figure 1b except for the NCEP reanalysis observational estimate [Vinje, 2001]. (1948–2004). Contour interval is 0.5 hPa. White solid and 3.2. EOF Analysis dash lines represent the 95% and 99% significance level of correlation coefficient between principal components and [5] Following Wu et al. [2006], the EOF analysis was winter mean SLP anomalies, respectively. applied to winter mean anomalies of SLP and 500 hPa geopotential height north of 70°N. The spatial distributions for the first two modes of SLP derived from the K1 and the when the PC1 of SLP is greater than 1.0 or smaller than NCEP/NCAR reanalysis data are shown in Figure 1. The À1.0 standard deviation, respectively. There are 18 (19) first-leading EOF mode has an annular structure, which years corresponding to the positive (negative) phase of the accounts for 59% (K1) and 63% (NCEP) of the total AO during the 110 simulation years (1900–2010). The variance, respectively. This annular structure corresponds composite AO fields are constructed by averaging the to the AO [Wang and Ikeda, 2000]. The second-leading SLP, geopotential height, sea ice thickness and velocity in mode, which accounts for 19% (K1) and 14% (NCEP) of these years (not shown). The SLP difference between in the the variance, displays an obvious seesaw pattern between positive and negative AO phases is statistically significant the Atlantic side (Greenland-Canadian Archipelago) and the based on the Student T-test, and is particularly, noticeable Siberian side of the Arctic region in both the K1 and the over the eastern Arctic. During the positive AO phase, SLP NCEP. There are two action centers with opposite signs - has a negative anomaly over the whole Arctic region, so that one center is located over the Greenland Sea, the other the Beaufort High is weakened. The corresponding cyclonic exists over the Laptev Sea. This structure is defined as the wind stress drives sea ice in the same manner and promotes DA [Wu et al., 2006]. Correlation coefficients between the sea ice export through the Fram Strait to the Greenland Sea principal components of the leading mode and the interann- (0.073 ± 0.024 Sv). In contrast, during the negative AO ually varying original anomaly are significant (more than phase, the dominant Beaufort High induces anti-cyclonic 99% confidence level) over each action center.
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