Autumn Eurasian snow depth, autumn Arctic sea ice cover and East Asian winter monsoon

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: (2014) Published online 12 February 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.3936 Autumn Eurasian snow depth, autumn Arctic sea ice cover and East Asian winter monsoon Fei Li a,b,c * and Huijun Wang a,b a Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China b Climate Change Research Center, Chinese Academy of Sciences, Beijing, China c University of Chinese Academy of Sciences, Beijing, China ABSTRACT: We present observational evidence and model simulation demonstrating a significant change in the early 1990s in the relationships of the autumn Eurasian snow depth (AESD) and the autumn Arctic sea ice cover (ASIC) with the East Asian winter monsoon (EAWM). A weakening of the AESD EAWM relationship occurs over a period when the ASIC EAWM relationship strengthens. The possible physical process can be described as follows: before the early 1990s, the positive AESD anomaly tends to persist into winter. In contrast, after the early 1990s, the negative ASIC anomaly tends to excite a stationary Rossby wave across the North America Atlantic sector in autumn. This wave train propagates eastward in winter and favours the winter Eurasian snow depth (WESD) increase. The WESD anomaly is associated with the strengthening and expansions of the Siberian high west and north across the pole and the zonally elongated cold anomalies from Europe to the Far East. It has a stable connection with the EAWM throughout the period observed ( ) and may serve as a bridge linking AESD or ASIC with the EAWM. KEY WORDS Eurasian snow depth; Arctic sea ice cover; East Asian winter monsoon; Inter-annual variability Received 24 April 2013; Revised 1 January 2014; Accepted 4 January Introduction One of the most active atmospheric circulation systems in boreal winter, the East Asian winter monsoon (EAWM), is characterized by frequent cold surges and associated closely with the Siberian high, East Asian trough and high-level westerly jet stream. Previous studies have shown that decadal variations have been observed in the EAWM over the past century (Webster et al., 1998; Wang, 2001; Wang and Sun, 2009; Li and Wang, 2012a; He, 2013). Associated with the inter-decadal climate shift since 1976, the relationship between EAWM and El Niño Southern Oscillation (ENSO) also shows interdecadal change (Wang and He, 2012; He and Wang, 2013; He et al., 2013; Wang et al., 2013). It means that we should pay more attention to the extratropical signals that could influence the EAWM. Eurasian snow cover is the most variable land surface condition in both time and space (Gutzler and Rosen, 1992; Cohen, 1994). It has experienced similar decadal variations to the EAWM, peaking in the late 1970s and followed by a general decrease in the late-1980s and early 1990s (Robinson, 1996). Previous studies have linked autumn Eurasian snow depth (AESD) or cover with winter atmospheric circulation (Wagner, 1973; Cohen * Correspondence to: F. Li. Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing , China. lifei-715@163.com and Entekhabi, 2001; Orsolini et al., 2012). Cohen and Entekhabi (1999) postulated that cooling caused by early season (October December) snow cover is a dominant force in Northern Hemisphere climate variability. Saito et al. (2004) described the phase shift in the sub-decadal covariability between the Arctic Oscillation (AO) and the Eurasian snow cover due to the loss of autumn winter Eurasian snow cover autocorrelation. The Arctic climate is rapidly shifting as documented by the unprecedented warming. Such changes have been in turn accompanied by various environmental modifications including Eurasian snow and Northern Hemisphere atmospheric circulation (Budikova, 2009; Bader et al., 2011; Li and Wang, 2013a, 2013b; Li et al., 2013). Liu et al. (2012) detected substantial impact of the autumn Arctic sea ice cover (ASIC) on the EAWM, winter temperature and snowstorm activity. Li and Wang (2012b) documented the impacts of ASIC on the winter Northern Hemisphere Annular Mode and winter precipitation in Eurasia. Given the fact that Eurasian snow depth and Arctic sea ice cover are two important factors for the predictability of the East Asian climate (e.g. Fan, 2007, 2009, 2011), their impacts on the EAWM are complicated and may not be stable in the inter-decadal scales. The purpose of this contribution is to explore the complex relationships of AESD and ASIC with the EAWM, and the possible role played by the WESD Royal Meteorological Society

2 AUTUMN EURASIAN SNOW DEPTH, AUTUMN ARCTIC SEA ICE COVER AND EAWM Data Four data sets are used: (1) The National Centers for Environmental Prediction (NCEP) atmospheric resolution reanalysis for (Kalnay et al., 1996). (2) The Hadley Centre sea ice and sea surface temperature (SST) data set version 1 (HadISST1) with 1 1 resolution for (Rayner et al., 2003). (3) The Northern Hemisphere 25-km Equal Area Earth Grids (EASE-Grids) snow water equivalent climatology for (Armstrong et al., 2007). (4) K. Matsuura and C. J. Willmott s gridded Arctic Land-Surface Air Temperature (LSAT) data set for (available at geog.udel.edu/ climate/html_pages/archive.html). The common time period is set to The winter of 1979 corresponds to the 1979/1980 winter. The months of September to November and December to February are used to calculate the autumn and winter mean. The AESD and WESD indices are computed as the area-averaged continental snow depth over Eurasia (50 N 75 N, 45 E 135 E). The ASIC index is computed as the area-averaged sea ice cover in the region of 65 N 82 N, 105 E 135 W. We use the EAWM index defined as the mean 500 hpa geopotential height (Z500) in the region of (35 N 50 N, 110 E 130 E) to describe the East Asian trough. All indices were standardized and detrended prior to all analyses. 3. Relationships between AESD, ASIC and WESD First, we plotted the temporal variations of AESD (blue solid line) and WESD (dashed line with closed circle) indices for (Figure 1(a)). As described in Section 1, the intensity of AESD and WESD has exhibited decadal variations, collapsing to minimum values in the late-1980s and early 1990s (Robinson, 1996). Meanwhile, a weaker AESD WESD correlation is identified after the early 1990s (R = 0.27) as compared with that before (R = 0.71, significant at 99% confidence interval). The temporal variations of the negative ASIC ( ASIC; solid line) and WESD (dashed line with closed circle) indices are presented in Figure 1(b). Comparatively, the inter-annual variability of ASIC is enlarged after the early 1990s. We noted a strong ASIC WESD correlation after the early 1990s (R = 0.59, significant at 95% confidence interval) as compared with that before (R = 0.05). We also carried out the 15-year-sliding correlation coefficients between AESD and WESD (dashed line) and between ASIC and WESD (solid line) in Figure 1(c). It becomes clear that the AESD WESD in-phase correlations are quite significant before the early 1990s, but become insignificant thereafter. This result is similar to Saito et al. (2004), although they used snow cover as a measurement. While the correlations between ASIC and WESD experience a reverse process, significant correlations exist only after the early 1990s. Based on the short periods, the Northern Hemisphere snow Figure 1. (a) Time series of the autumn Eurasian snow depth (AESD; solid line) and the winter Eurasian snow depth (WESD; dashed line with closed circle) for (b) Time series of the negative autumn Arctic sea ice cover ( ASIC; solid line) and WESD (dashed line with closed circle). (c) The 15-year-sliding correlation coefficients between AESD and WESD (dashed line) and between ASIC and WESD (solid line). The dashed line denotes correlations required for 95% confidence interval, as estimated using a Student s t-test. depth have been extensively monitored and the identified change in the early 1990s in the AESD WESD and the ASIC WESD relationships, we select two periods, (P1) and (P2) (in sea ice cases ), for further analysis. In order to enhance the knowledge of the weakened AESD WESD connection delineated by the early 1990s, the correlations of the grid-point WESD and winter Arctic land-surface air temperature (WLSAT) with the AESD index during the two periods are compared in Figure 2. In the earlier period, a large AESD WESD positive correlation area appears over most parts of the northern Eurasia. Meanwhile, there is a coherent AESD WLSAT negative correlation area in Eastern Europe, Scandinavia and eastern Russia. In the latter period, the AESD WESD positive correlations are sporadic and the AESD WLSAT negative correlation area is limited in Eastern Europe. Figure 3 shows similar correlations as presented in Figure 2, however, for the ASIC index. During P1, there are sporadic ASIC WESD positive correlations and ASIC WLSAT negative correlations. During P2, a large ASIC WESD positive correlation area are detected over southwestern Russia and the coherent ASIC WLSAT negative correlation area in Scandinavia and southwestern Russia. The results presented here (Figures 1 3) indicate an obvious change in the early 1990s in the relationships of

3 3618 F. LI AND H. WANG Figure 2. Correlations between the grid-point WESD and the AESD index during (a) and (b) Correlations between the grid-point winter Arctic land-surface air temperature (WLSAT) and the AESD index during (c) and (d) Shaded regions represent correlations required for 90%, 95% and 99% confidence intervals, as estimated using a Student s t-test. Figure 3. Correlations between the grid-point WESD and the ASIC index during (a) and (b) Correlations between the grid-point winter Arctic land-surface air temperature (WLSAT) and the ASIC index during (c) and (d) Shaded regions represent correlations required for 90%, 95% and 99% confidence intervals, as estimated using a Student s t-test.

4 AUTUMN EURASIAN SNOW DEPTH, AUTUMN ARCTIC SEA ICE COVER AND EAWM 3619 AESD and ASIC with the WESD. A pertinent question is whether the complex relationships of AESD and ASIC with the EAWM also change in the early 1990s. Figure 4. (a) Time series of AESD (solid line) and EAWM (dashed line with closed circle) for (b) Time series of WESD (solid line) and EAWM (dashed line with closed circle). (c) The 15-yearsliding correlation coefficients between AESD and EAWM (solid line) and between WESD and EAWM (dashed line). The dashed line denotes correlations required for 95% confidence interval, as estimated using a Student s t-test. 4. Relationships between AESD, ASIC and EAWM The temporal variations of AESD (blue solid line) and EAWM (brown dashed line with closed circle) indices are presented in Figure 4(a). The intensity of EAWM weakens in the late-1980s and early 1990s. Consistent with the AESD WESD correlation (Figure 1(a)), a weaker AESD EAWM correlation occurs after the early 1990s (R = 0.09) as compared with that before (R = 0.47, significant at 90% confidence interval). Figure 4(b) indicates a stable WESD EAWM correlation over the whole period (R = 0.62, significant at 95% confidence interval). Figure 4(c) shows the 15-year-sliding correlation coefficients between AESD and EAWM (solid line) and between WESD and EAWM (dashed line). It is clear that the WESD EAWM in-phase correlations are quite significant nearly throughout the whole period ( ). However, the AESD EAWM in-phase correlations become insignificant after the early 1990s. The correlations of the grid-point WESD and WLSAT with the EAWM index during the two periods are compared in Figure 5. For each period, there is a large EAWM WESD positive correlation area over most parts of Russia. The EAWM WLSAT negative correlation area appears in southern Russia. Although the regional Figure 5. Correlations between the grid-point WESD and the EAWM index during (a) and (b) Correlations between the grid-point winter Arctic land-surface air temperature (WLSAT) and the EAWM index during (c) and (d) Shaded regions represent correlations required for 90%, 95% and 99% confidence intervals, as estimated using a Student s t-test.

5 3620 F. LI AND H. WANG Figure 6. Linear regressions of the grid-point winter 1000-hPa air temperature (T1000; unit: C) and sea-level pressure (SLP; unit: hpa) upon the WESD index during (a, c) and (b, d) Shaded regions represent correlations required for 90% confidence intervals and gridding (dotted) regions represent negative (positive) correlations required for 95% confidence intrevals, as estimated using a Student s t-test. details differ somewhat, there are EAWM WLSAT negative correlations in Eastern Europe and Scandinavia in P1, which do not exist in P2. Next, we discuss the linear regressions of the gridpoint winter 1000-hPa air temperature (T1000) and sealevel pressure (SLP) upon the WESD index during the two periods (Figure 6). During P1, the positive WESD anomaly is associated with negative phase of the North Atlantic Oscillation (NAO) and the strengthening and expansions of the Siberian high west and north across the pole. The most remarkable feature of T1000 is zonally elongated cold anomalies from Europe to the Far East. During P2, the significant SLP anomalies located in Europe and Arctic shrank, so did the T1000 anomalies. However, it should be noted that the spatial distribution of the T1000 anomalies over East Asia in P2 is much similar to that in P1. This implies that the linkage between WESD and EAWM is much robust. Figure 7 shows similar linear regressions as presented in Figure 6, but for the AESD index. During P1, the negative phase of the NAO, the northwestward expansions of the Siberian high and the zonally elongated cold anomalies despite small amplitude are reproduced. This result is consistent with Cohen and Entekhabi (1999) who found that Eurasian snow cover exhibits a stronger relationship with the North Atlantic sector, which is upstream. During P2, the circulation and temperature anomalies are sporadic and barely significant over East Asia. As described in Figure 2, much of this change can be attributed to the loss of memory in WESD. Moreover, the wavelet coherence analysis (Grinsted et al., 2004) is another useful tool to measure how coherent the cross wavelet transforms of the ASIC and EAWM are in time frequency space (Figure 8). There is a larger significant section with an in-phase behaviour between ASIC and EAWM, at a period of 3 6 years after the early 1990s. The results presented here and in Figure 3 indicate the strengthening of the ASIC WESD and the ASIC EAWM relationships after the early 1990s. The WESD may serve as a bridge linking ASIC with the EAWM and the physical process for this linkage will be described below. Figure 9 shows the linear regressions of the gridpoint ASIC, autumn and winter Z500/wave-activity flux (WF) [formulated by Plumb (1985)] upon the ASIC index during the two periods. In the earlier period, the negative ASIC anomaly in the region of 65 N 82 N, 105 E 135 W is associated with sea ice loss in the Barents Kara Seas (Figure 9(a)). Near-surface anomalous diabatic heating related to the sea ice loss tends to excite stationary Rossby waves over the North Atlantic and North Pacific in autumn, as indicated by the arrows. These wave trains propagate eastward in winter and forms two anticyclonic anomalies, one over the Barents Sea

6 AUTUMN EURASIAN SNOW DEPTH, AUTUMN ARCTIC SEA ICE COVER AND EAWM 3621 Figure 7. Linear regressions of the grid-point winter 1000-hPa air temperature (T1000; unit: C) and sea-level pressure (SLP; unit: hpa) upon the AESD index during (a, c) and (b, d) Shaded regions represent correlations required for 90% confidence intervals and gridding (dotted) regions represent negative (positive) correlations required for 95% confidence intervals, as estimated using a Student s t-test. Period / year Figure 8. Wavelet coherence between the ASIC and EAWM. The values exceeding the 95% significance interval are shown within the white thick contour. The relative phase relationship is shown as arrows (with in-phase pointing right and antiphase pointing left). The software is provided online ( and another over the Bering Sea. Obviously, the winter wave trains associated with the preceding autumn negative ASIC anomaly is mainly confined in high latitudes. This might partially account for the insignificant ASIC EAWM relationship in this period (Figure 8). In the latter period, the larger scale negative ASIC anomaly (Figure 9(b)) tends to excite a stationary Rossby wave across the North America Atlantic sector in autumn This wave train propagates eastward to East Asia in winter, which makes the transmission of ASIC s impact into the following on the EAWM possible. Figure 10 shows the linear regressions of the gridpoint T1000 and SLP upon the ASIC index during the two periods. During P1, when the ASIC is decreased, anticyclonic anomalies become apparent over the Arctic and the Bering Sea. The near-surface warm anomalies are sporadic and significant over Greenland and the North Pacific. During P2, the northwestward expansions of the Siberian high are detected. The main feature of T1000 is the zonally elongated cooler conditions from Caspian Sea to the Far East. These distinguished differences well support the results revealed by Figures 8 and The numerical simulation To further interpret the findings depicted above, we conduct simulations with the NCAR Community Atmospheric Model Version 3.1 (CAM3.1; Collins et al., 2006). The simulation configuration has a horizontal resolution of 2.8 and 26 vertical levels extending up to 3.5 hpa. In these simulations, SST and sea ice concentrations are specified as boundary conditions based on a merged product of the HadISST1 and

7 3622 F. LI AND H. WANG Figure 9. Linear regressions of the grid-point ASIC (unit: 10 2 km 2 ) upon the ASIC index during (a) and (b) Dotted regions represent correlations required for 95% confidence interval, as estimated using a Student s t-test. Linear regressions of the grid-point autumn and winter 500-hPa geopotential height (Z500; unit: gpm)/wave-activity flux (WF; unit: m 2 s 2 ) [formulated by Plumb (1985)] upon the ASIC index during (c, e) and (d, f) Shaded regions represent correlations required for 90% confidence intervals and gridding (dotted) regions represent negative (positive) correlations required for 95% confidence intervals, as estimated using a Student s t-test. the National Oceanic and Atmospheric Administration (NOAA) weekly optimum interpolation SST analysis (Hurrell et al., 2008). The experimental design is similar to that of Liu et al. (2012). The impact of the diminishing Arctic sea ice during the freeze up on atmospheric circulation is assessed by comparing two experiments with different seasonally varying sea ice distributions, while all other external variables remain fixed. The control experiment is run with seasonally varying Arctic sea ice based on the climatology of the Hadley Centre sea ice concentrations for The perturbed experiment is integrated with seasonally varying sea ice loss in the area (65 N 82 N, 105 E 135 W). Sea ice losses are calculated as the period minus the period Global SSTs for both experiments are set to their climatological monthly values, based on the merged SST data set for the same period as the sea ice climatology in the

8 AUTUMN EURASIAN SNOW DEPTH, AUTUMN ARCTIC SEA ICE COVER AND EAWM 3623 Figure 10. Linear regressions of the grid-point winter 1000-hPa air temperature (T1000; unit: C) and sea-level pressure (SLP; unit: hpa) upon the ASIC index during (a, c) and (b, d) Shaded regions represent correlations required for 90% confidence intervals and gridding (dotted) regions represent negative (positive) correlations required for 95% confidence intrevals, as estimated using a Student s t-test. control experiment. In addition, for the perturbed experiment, in those areas where the sea ice is removed, SST is set to the freezing point of seawater, 1.8 C. To help gauge confidence in the model response to sea ice losses, each experiment consists of 20 ensemble members with slightly different initial conditions. The model response to the sea ice loss is examined by differentiating December, January and February Z500/WF, SLP and surface air temperature (SAT) between the ensemble mean of the perturbed and control experiments. As shown in Figure 11 (lower panels), strongly linked responses of the negative SAT anomalies are located in the northern Eurasia in December and January and zonally elongated from Caspian Sea to the Far East in February. Observational results (see Figure 10(d)) also suggest that the regions from Caspian Sea to the Far East were most strongly affected by sharp cooling. The SAT differences match well with the associated changes of Z500/WF and SLP. Figure 11 (upper panels) shows the strong stationary Rossby wave extends from the western Pacific in December through the eastern in January to the Far East in February. Positive Z500 and SLP anomalies (Figure 11, upper and mid-panels) dominate the Arctic Ocean in December and January and show a signal over Western Eurasia in February. Similarly, the spatial maps of February Z500/WF and SLP changes look rather similar to the observational results shown in Figures 9(d) and 10(b). These results are consistent with Honda et al. (2009), who suggested that the remote response to the anomalous ASIC tends to cause colder conditions over the Far East in February through the eastward propagation of the height anomalies in early winter. 6. Conclusions This study has investigated the inter-decadal change in the relationships of AESD and ASIC with the EAWM. A pronounced different correlation pattern is found. The AESD and EAWM show a significant positive correlation before the early 1990s (as high as 0.47, significant at 90% confidence interval), followed by an obvious weakening of correlation (0.09) in the early 1990s. By contrast, a larger significant sections with an in-phase behaviour between ASIC and EAWM, at a period of <6, are observed after the early 1990s. Besides, the temporal evolution of the correlation clearly indicated that the relationship between AESD and WESD has weakened in the early 1990s. On the contrary, the relationship between

9 3624 F. LI AND H. WANG Figure 11. Differences of December, January and February (upper panels) Z500 (unit: gpm)/wave-activity flux (WF; unit: m 2 s 2 ), (mid-panels) SLP (unit: hpa) and (lower panels) surface air temperature (SAT; unit: C) between the perturbed and control experiments. Shaded regions represent correlations required for 90% confidence intervals and gridding (dotted) regions represent negative (positive) correlations required for 95% confidence intervals, as estimated using a Student s t-test. ASIC and WESD strengthened in the early 1990s. Thus, the consistent high correlation between EAWM and WESD suggests that the WESD may serve as a bridge linking AESD or ASIC with the EAWM. The possible physical process behind the strengthened impact of ASIC on the EAWM can be described as follows. Before the early 1990s, the positive AESD anomaly is associated with the WESD increase. The near-surface anomalous diabatic heating related to the sea ice loss tends to excite stationary Rossby waves over the North Atlantic and North Pacific in autumn. This wave train can persist into the following winter and propagate eastward but is mainly confined in high latitudes. However, after the early 1990s, the winter stationary Rossby wave related to the preceding ASIC shifts southeastward to East Asia, which makes the transmission of ASIC s impact into the following winter on the EAWM possible and favours the WESD increase, the northwestward expansions of the Siberian high and the zonally elongated cooler conditions from Caspian Sea to the Far East. The possible role played by the ASIC in the change of the ASIC EAWM relationship has been verified by NCAE CAM3.1. The model responses of the February Z500/WF, SLP and SAT to the sea ice loss in the domain of (65 N 82 N and 105 E 135 W) between the ensemble mean of the perturbed and control experiments display

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