Sep 13, 2016 - 1School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China. 5. 2 Guangdong Province Key Laboratory for Climate Change ...
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Feedback Attributions to the Dominant Modes of East Asian Winter Monsoon Variations YANA LI School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
SONG YANG School of Atmospheric Sciences, and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, and Institute of Earth Climate and Environment System, Sun Yat-sen University, Guangzhou, China (Manuscript received 4 April 2016, in final form 25 September 2016) ABSTRACT This study investigates the variations and feedback attributions of changes in surface temperature between strong and weak East Asian winter monsoons. The variations of winter-mean surface air temperature are dominated by two distinct principal modes that account for 70.9% of the total variance. The first mode features high correlation with the high-latitude atmospheric circulation, including a correlation coefficient of 20.53 with the Arctic Oscillation in January, and the second mode is significantly linked to El Niño– Southern Oscillation, with a correlation coefficient of 20.37. The surface temperature anomalies of each mode are decomposed into partial temperature anomalies resulting from radiative and nonradiative feedback processes by applying a coupled climate feedback–response analysis method to quantify contributions from thermodynamic and dynamic processes. Results indicate that the surface cooling associated with both modes is mainly attributed to the nonradiative feedback processes of atmospheric dynamics and surface sensible heating and to the radiative feedback processes of water vapor and clouds. The first mode exhibits a deep barotropic anomalous high that weakens the high-latitude westerly jet stream but strengthens the midlatitude westerly jet stream. This circulation feature traps cold and dry air over northern East Asia. For the second mode, the ocean and land heat storage processes induce a large thermal gradient over eastern China and the northwestern Pacific, resulting in a large pressure gradient. Northerly anomalies further reinforce the pressure gradient, which favors cold air intruding southward into the tropics.
1. Introduction The East Asian winter monsoon (EAWM) plays an important role in affecting the temperature and precipitation over East Asia. Previous studies have shown that the East Asian temperature and precipitation are highly correlated with high-latitude systems such as the Arctic Oscillation (AO) (Gong and Wang 2003; Park et al. 2014), the Siberia–Mongolia high (Wu and Wang 2002; Gong and Ho 2002), and the Ural blocking circulation (B. Wang et al. 2010; Cheung et al. 2012). These
Denotes Open Access content.
Corresponding author e-mail: Prof. Song Yang, yangsong3@mail. sysu.edu.cn DOI: 10.1175/JCLI-D-16-0275.1 Ó 2017 American Meteorological Society
systems influence East Asian climate through affecting the intensity and frequency of cold surges (Chang and Lau 1982; Jeong and Ho 2005). It has also been shown that the variations of EAWM are related to the atmospheric and oceanic conditions in the lower latitudes, for instance, El Niño–Southern Oscillation (ENSO) (Chang et al. 2004; He and Wang 2013). The EAWM becomes stronger when La Niña occurs (Li 1990). During strong EAWM years, the northerly wind strengthens along the east coastline, intrude toward the equator, and intensify deep convection and precipitation over the southern South China Sea and the Maritime Continent (Chang et al. 2004). The latent heat released by the enhanced convection in turn affects the upper branch of local meridional circulation, accelerating the uppertropospheric East Asian westerly jet stream (Yang and Webster 1990; Yang et al. 2002). These climatic
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fluctuations affect EAWM through the atmospheric bridge (Lau and Nath 1996), which redistributes heat, water vapor, and energy in the atmosphere. To understand the detailed features of the EAWM, previous studies have analyzed different monsoon modes. For example, Wu et al. (2006) revealed two modes in 850-hPa wind. The first mode shows an anomalous meridional wind pattern and the second mode presents an anomalous zonal wind pattern. Wang and Feng (2011) presented two EAWM modes in precipitation and depicted the variability of regional precipitation in strong and weak EAWM years. B. Wang et al. (2010) further revealed two prominent monsoon modes based on near-surface temperature. The first mode, called the northern mode, shows a cold core in the high latitudes, while the second one, the southern mode, displays a large cold hub over eastern China. Previous analyses have mostly focused on the atmospheric dynamic process of the EAWM (Zhou 2011; Pak et al. 2014). As B. Wang et al. (2010) argued, the strength and extension of EAWM are influenced by the snow cover in Siberia and Mongolia and modulated by ENSO. Also, the sea ice over the polar region exerts a great impact on the strength of EAWM, especially on the northern mode (Chen et al. 2014a) and its relationship with AO (Wu et al. 2011; Li et al. 2014; Sun et al. 2016). Moreover, Wang and Chen (2014) indicated that the large heat contrast between land and oceans was the primary energy source of strong EAWM. Chen et al. (2015) argued that the SST anomalies in the Yellow Sea and the Sea of Japan were closely associated with the northern component of the EAWM, whereas the southern component had a large impact on the change in subtropical western North Pacific SST. Comparatively, many fewer studies have addressed the contributions to monsoon variations from different physical processes such as radiative and nonradiative processes. One of the main purposes of this study is to understand the key contributors among various feedback processes for the changes in surface temperature over the EAWM domain. We take the advantage of an offline diagnostic method named the climate feedback–response analysis method (CFRAM) (Lu and Cai 2009; Cai and Lu 2009) and separate the total temperature change into several partial temperature changes resulting from individual feedback processes. The CFRAM has been used to quantify the contributions of atmospheric dynamic process to polar warming amplification (Lu and Cai 2010) and identify the response of climate to external forcings such as increases in carbon dioxide concentration and solar radiation (Cai and Tung 2012). It has also been applied to diagnose the climate feedback attributions to the seasonal variation of global zonal mean
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surface warming (Sejas et al. 2014) and examine model bias attributions (Song et al. 2013; Yang et al. 2014). In this study, we first apply the CFRAM as a diagnostic method to quantify the feedback attributions to the variation of EAWM. Here, we use surface temperature to measure the strength of the EAWM because of its larger spatial homogeneity compared to precipitation and winds. Section 2 describes the data and analysis methods applied in this study including a brief introduction of the CFRAM. In section 3, we discuss the dominant modes of EAWM, focusing on their unique circulation structure. Section 4 presents the change in surface temperature between strong and weak EAWMs attributed to individual feedback processes with use of the CFRAM. Section 5 addresses the physical mechanisms for the features revealed. The last section concludes with the major findings of this study.
2. Data and analysis methods All input variables used in this study are obtained from ERA-Interim (Dee et al. 2011), which is the latest European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric reanalysis ranging from 1979 to the present. ERA-Interim has 37 unevenly divided pressure levels from 1000 to 1 hPa and a horizontal resolution of 18 3 18 in latitude and longitude. Variables for a complete feedback attribution analysis in this study include temperature, specific humidity, surface albedo, surface latent and sensible heat fluxes, solar insolation at the top of the atmosphere, cloud amount, cloud liquid and ice water content, and ozone mixing ratio. We focus on the seasons of boreal winter [December– February (DJF)] from 1979 to 2013 (January and February for 1979). The dominate modes of EAWM are derived by the empirical orthogonal function (EOF) analysis. Strong and weak EAWM years are determined by the values of principal components (PCs) above and below one standard deviation, respectively. For EOF1, the strong EAWM years include 1985, 2001, 2005, 2006, 2010, 2011, 2012, and 2013, referred to as strong EAWMEOF1 years, and the weak ones include 1989, 1992, 1993, 1995, 1999, 2002, 2004, and 2007, referred to as weak EAWM-EOF1 years. For EOF2, strong EAWM years are 1981, 1984, 1986, 1996, 2008, and 2012, referred to as strong EAWM-EOF2 years, and the weak ones comprise 1979, 1987, 1998, 1999, 2001, 2007, and 2009, referred to as weak EAWM-EOF2 years. We use the two-tailed Student’s t test to assess the statistical significance of correlation or regression. The CFRAM is a method based on the total energy balance within an atmosphere–surface column to evaluate
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climate feedbacks, complementing the traditional feedback approach that treats temperature change as a response to the energy change (radiative) at the top of the atmosphere (TOA) (Wetherald and Manabe 1988). The CFRAM provides a direct estimate of linearly addable contributions to observed temperature change from external forcing and feedback processes, including nonradiative feedback processes such as the oceanic heat storage and surface latent and sensible heat fluxes. It is different from and more revealing than the TOA approach (Hu et al. 2016). We first apply the CFRAM to attribute the change in the surface temperature between weak and strong EAWM years to each thermal and thermodynamic process to identify the main contributor for each mode. However, as CFRAM is an offline method, there should be an offline error, which is very small in this study, compared with online methods. Following Deng et al. (2012) and Hu et al. (2016), we take into account the difference in energy balance equation between two equilibrium states: D
›E 5 DS 2 DR 1 DQ , ›t
(1)
where D means the difference between strong and weak EAWM years. On the left-hand side of (1), D›E/›t is the difference in energy storage between the two equilibrium states. On the right-hand side, DS (DR) indicates the difference in the vertical profiles of convergence (divergence) of shortwave (longwave) radiation flux within individual layers, and DQ refers to the difference in the vertical profiles of energy flux convergence due to nonradiative dynamic processes. According to the linear approximation of the CFRAM, the differences in longwave radiation flux divergence DR and shortwave flux convergence DS can be separated into partial differences resulting from individual radiative feedback processes as shown below: DS ’ DSwv 1 DSc 1 DSa 1 DSO3
and
›R DR ’ DRwv 1 DRc 1 DRO3 1 DT , ›T
changes in longwave radiative energy flux resulting from 1-K warming in the jth layer alone. We show the linear approximations adopted in (2) and (3) by calculating each term of both sides separately by taking the advantage of the Fu–Liou radiative transfer model (Fu and Liou 1992; Fu and Liou 1993). Substituting (2) and (3) into (1) yields DT 5
21 ›R (DSwv 2 DRwv ) 1 (DSc 2 DRc ) ›T ›E . 1 (DSO3 2 DRO3 ) 1 (DSa ) 1 DQ 2 D ›t (4)
As shown in (4), we can calculate the changes in partial temperature resulting from certain feedback processes separately. The first four terms of (4) can be obtained easily since (›R/›T)21 and the difference in partial radiative heating or cooling rate have been readily obtained from the reanalysis data for the two states of each EAWM mode. We further separate the term (DQ 2 D›E/›t) into surface and atmospheric components. At the surface, the nonradiative heating rate perturbation DQ includes the changes in surface latent heat DQLH and sensible heat DQSH fluxes over both land and oceans. Both DQSH and DQLH can be obtained directly from the composite features of weak and strong EAWM years. Over oceans, DQ also contains nonradiative heating perturbation due to oceanic energy transport convergence. Since both the energy storage term D›E/›t at the surface and the nonradiative heating perturbation due to oceanic energy transport convergence are not available from the ERAInterim product, we have to calculate the sum of them as the residual term of the surface energy balance equation, which responds to the oceanic dynamics and the ocean and land heat storage term and is represented as DQocean:
(2)
DQocean 5 2(DS 2 DR)surf 2 DQSH 2 DQLH .
(3)
In the same way, the sum of the total nonradiative heating perturbations and the heat storage term in the atmospheric column can also be calculated as the residual of the atmospheric portion of (1), that is,
where the terms with superscripts wv, c, a, and O3 refer to the differences in partial radiative heating or cooling rate due to the differences in water vapor, clouds, albedo, and ozone between the two states, respectively. The last term in (3) corresponds to the difference in partial radiative cooling rate due to the temperature difference between the two climate states at each atmospheric layer and at the surface (denoted as DT). The matrix ›R/›T is called the Planck feedback matrix in which the jth column represents the vertical profile of
DQatmos 5 2(DS 2 DR)atmos ,
(5)
(6)
where (DS 2 DR)atmos is identical to (DS 2 DR) in the atmosphere. However, this is not the case for the surface component, which is set to zero. The term DQatmos includes all forms of nonradiative heating perturbations in the atmosphere such as the energy transport convergence
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by atmospheric motions, the energy entering the atmosphere from the surface via surface sensible and latent heat fluxes, and the atmospheric energy storage. As the energy storage term is relatively very small compared with the others after applying a long-term average, we simply refer to DQatmos as the atmospheric dynamic term. The term DT in the vertical profile can be decomposed into radiative terms and nonradiative terms, based on the discussion above. The temperature changes resulting from the radiative terms can be written as DTx 5
21 ›R (DSx 2 DRx ) , ›T
(7)
whereas those resulting from the nonradiative processes can be obtained by 21 ›R DQx , DT 5 ›T
x
(8)
where the superscript x on both sides of (7) and (8) can be substituted with SH, LH, ocean, and atmos, respectively. Note that DQSH, DQLH, and DQocean are zero in all atmospheric layers. Solving (7) and (8) grid by grid enables us to obtain these partial temperature perturbations as a function of pressure, longitude, and latitude. More about the CFRAM can be found in Lu and Cai (2009) and Cai and Lu (2009).
3. Two EAWM modes Figure 1 shows that in winter near-surface temperature (Ts) decreases rapidly northward and there exit deep westerlies in the midlatitudes, accompanied with a major trough over eastern East Asia. Meridionally, the EAWM exhibits distinct regional features. Considering the homogeneity and consistency of data analyzed, we choose surface air temperature to measure the intensity of the EAWM. Here, we focus on the monsoon domain over 08–608N, 1008–1408E, just as in B. Wang et al. (2010). An EOF analysis indicates that in the first mode (EOF1; Fig. 2a), accounting for 52.2% of the total variance, negative anomalies are located to the north of 408N with a cold core over the Mongolia–Siberia region. The second mode (EOF2; Fig. 2b), which accounts for 18.7% of the total variance, illustrates that temperature decrease over most of southeastern China and the cold area extends to the equatorial region in strong EAWMEOF2 years. In addition to the obvious interannual variations, both modes exhibit interdecadal variability. The EOF1 exhibits strong EAWM phases in early 1980s and late 2010s and weak phases during the 1990s and the 2000s (Fig. 2c), whereas EOF2 shows weak phases in the
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late 1980s and late 1990s and during the 2000s and a strong phase in early 1980s (Fig. 2d) that is well consistent with the result of Ding et al. (2014). Nevertheless, because of the length of the data record analyzed in this study, this study is focused mainly on the interannual features rather than the interdecadal variations of the EAWM. How well do the two modes depict the circulation features of EAWM? Here, we analyze seven indices that measure the strength of EAWM from the four categories given by Wang and Chen (2010). As shown by correlation coefficients given in Table 1, both PCs can well represent EAWM circulation. PC2 is significantly correlated with all seven indices at the 95% confidence level, while PC1 is only significantly correlated with those indices defined in relatively higher latitudes, for instance, Islp-Gong [sea level pressure (SLP) over 408– 608N, 708–1208E; Gong et al. 2001], IH500-Sun [500-hPa geopotential height F over 308–458N, 1258–1458E; Sun and Li 1997), Iu300-Jhun (300-hPa zonal wind u over 27.58– 37.58N, 1108–1708E; Jhun and Lee 2004), and Iu200-LiY (200-hPa u; Li and Yang 2010). The indices Islp-Chl (SLP gradient for 308–558N, 1008–1208E minus 308–558N, 1508–1708E; Chan and Li 2004), Iv850-Yang (850-hPa meridional wind y over 208–408N, 1008–1408E; Yang et al. 2002), and Iv1000-JiLR (1000-hPa y over 108–308N, 1158–1308E; Ji et al. 1997) are better correlated with PC2 than with PC1 because their definition regions are mainly south of 508N. EOF1 is coherent with the mid-to-high-latitude circulation (Figs. 3a,b). During strong EAWM-EOF1 years, cold air extends southeastward from the polar region to the northwestern Pacific Ocean. A large barotropic anomalous center is located over northern Siberia, accompanied by strong anomalous westerlies to the south, and it extends from the surface to the midtroposphere. The major East Asian trough (EAT) strengthens and moves westward, and the anomalous westerly wind to the south of the large anomalous cyclone strengthen the westerly jet stream in the lower latitudes (Fig. 3b). EOF2 is strongly correlated with lower-latitude atmospheric circulation. As shown in Fig. 3c, negative SLP anomalies cover East Asia and the northwestern Pacific with a center over eastern China, associated with strong northerly anomalies and low temperature over the region. Several anomalous temperature centers appear, with a positive center over north of 608N and a negative center over eastern China extending to the Maritime Continent. In the meantime, the EAT strengthens. Different from EOF1, a deep baroclinic anomaly center is seen over Siberia for EOF2. Overall, the atmospheric circulation features are very different between the two
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FIG. 1. Climatological winter (DJF) mean (a) Ts (shading, K), SLP (contours, Pa), and 850-hPa wind (vectors, m s21), and (b) precipitation (shading, mm day21), 200-hPa wind (vectors, m s21), and 500-hPa geopotential height (contours with interval of 10 gpm).
modes, and we further depict and explain this difference from a dynamic diagnostic analysis in the next section.
4. Individual feedback attributions to the two monsoon modes We further decompose the change in surface temperature between the weak and strong EAWM
years into partial temperature changes resulting from several individual feedback processes to reveal their relative contributions, as shown in (4) for CFRAM in section 2. The spatial pattern of the sum of partial temperature changes (Fig. 4h) resembles the pattern of observed change in surface temperature (Fig. 4i), which adds confidence to our attribution analysis.
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FIG. 2. Spatial patterns of the (a) first and (b) second EOF modes of boreal winter (DJF) mean 2-m air temperature, and PCs of the (c) first and (d) second EOF modes. The horizontal black solid lines in (c) and (d) indicate the values of corresponding one standard deviation.
Given that the contribution due to the albedo feedback process is much smaller than the contributions by other terms, we will not discuss the albedo feedback process. For the cold core region of EOF1, it is the feedback processes of water vapor, clouds, atmospheric dynamics, and surface sensible heat flux that make positive contributions to the formation of the cold hub, while land heat storage and surface latent heat flux processes cause warming (i.e., a negative contribution). On the other hand, oceanic dynamics and ocean and land heat storage (OCH) play a key role in the spatial pattern over the entire EAWM
domain of EOF1. The ocean and land attributions (oceanic dynamics and ocean and land heat storage) warm the oceans and cool the land in the midlatitudes, leading to a larger thermal gradient between land and oceans. In the lower latitudes, however, they cool the oceans more than the adjacent land, leading to a smaller thermal contrast between land and oceans, unfavorable for strong EAWM. The contributions of individual processes and key factors vary in different regions. For a clear explanation, we focus on the key areas of each mode. Because the cold and dry air from high latitudes contains less water vapor in strong
TABLE 1. Correlation coefficients of seven EAWM indices with PC1 and PC2. The significant value of 95% confidence level is 60.334 and is identified with boldface.
PC1 PC2
Islp-Gong
Islp-Chl
Iv850-Yang
Iv1000-JiLR
IH500-Sun
Iu300-Jhun
Iu200-LiY
0.440 0.597
0.095 0.672
20.118 20.736
20.07 20.60
20.396 20.49
0.321 0.546
0.423 0.353
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FIG. 3. Regressions of PC1 against (a) Ts (shading), SLP (contours with interval of 0.3 hPa K21), and 850-hPa winds (vectors, m s21), and (b) precipitation (shading), 200-hPa wind (vectors, m s21), and 500-hPa geopotential height (contours with interval of 0.3 gpm K21). (c),(d) As in (a),(b), but for PC2. The confidence level is 95%.
EAWM-EOF1 years, which weakens the greenhouse gas warming effect caused by water vapor, the water vapor feedback process makes the cold core of EOF1 even colder (Fig. 4b). The cloud feedback process also cools the surface, because less cloud amount in the strong EAWM-EOF1 years results in less longwave radiation trapped in the lower troposphere. The sensible heat flux strengthens in the strong EAWMEOF1 years, resulting in colder surface, whereas the latent heat flux weakens, leading to warming in the region. Compared with the thermodynamic terms, the dynamic term of the atmospheric dynamics feedback process is more complicated and will be discussed later. For EOF2, the southern EAWM mode, the change in surface temperature caused by oceanic feedback term is the largest (Fig. 5e). Moreover, the change in surface temperature over oceans is much larger than that over the adjacent land, eastern China, which strengthens the thermal contrast and facilitates a strong EAWM. However, latent heat flux reduces the land–ocean thermal gradient by heating the land and cooling the oceans. Water vapor decreases and sensible heat flux that transports heat form the surface to the atmosphere
increases during the strong EAWM-EOF2 years, both of which cool the surface. The feedback process of clouds exerts a large impact on tropical regions than on the cold core. Among these individual feedback processes, the change in partial temperature due to water vapor feedback is the smallest, except that due to albedo feedback, which is not considered. However, the pattern of their sum resembles that of water vapor, implying that the large partial temperature changes resulting from certain feedback processes counteract each other. Therefore, the change in water vapor could be very important for the strength of EAWM. To quantify the relative contributions of various individual feedback processes to the mean amplitude of surface temperature anomalies of a given region, we further apply the pattern-amplitude projection (PAP) method (Deng et al. 2012; Park et al. 2012) to analyze the two EAWM modes. The PAP coefficient is calculated as follows: ð A21 a2 DTi DT cosu dl du A , PAPi 5 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð 21 A a2 (DT)2 cosu dl du A
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FIG. 4. For EOF1, the partial temperature changes near surface resulting from the changes in (a) other feedback processes, including albedo and ozone, (b) water vapor, (c) cloud, (d) atmospheric dynamics, (e) OCH, (f) surface sensible heat flux, and (g) surface latent heat flux. (h) The sum of (a) through (h) and (i) the observation is shown, and the area with dots is significant at the 95% confidence level.
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FIG. 5. As in Fig. 4, but for EOF2.
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FIG. 6. PAPs of eight partial temperature anomalies onto the changes in temperature near surface at the (a) cold core of EOF1 (508–608N, 1008–1208E), and (b) cold core of EOF2 (258–458N, 1058–1208E). At left, ‘‘others’’ includes partial temperature anomalies due to changes in ozone and albedo, and, at right is the sum of the first seven terms. The remaining six terms refer to the atmospheric dynamic feedback process, OCH, surface latent heating, surface sensible heating, water vapor, and cloud.
where u and l refer to latitude and longitude, respectively. Also, a is the mean radius of the earth, and A is the area of the region under consideration; DTi and DT are two vectors whose elements are respectively observed total temperature anomalies and partial temperature changes associated with the ith feedback process at the surface and 37 pressure levels. The symbols of various processes are the same for both monsoon modes, and the difference is the amplitude. For EOF1 (508–608N, 1008–1208E; Fig. 6a), the most important positive factor is the atmospheric dynamic feedback process, followed by clouds, surface sensible heat flux, and water vapor. The negative feedback process is the oceanic dynamics and surface latent heat flux. For EOF2 (258–458N, 1058–1208E; Fig. 6b), surface sensible heat flux and atmospheric dynamics contribute mostly to cooling the surface, followed by water vapor. The cloud feedback is less obvious than water vapor feedback, different from EOF1 (Fig. 6a), indicating that water vapor feedback process plays a more important role in the formation of the cold core in EOF1.
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FIG. 7. As in Fig. 6, but for the 2–8-yr filtered interannual variation of two EAWM modes.
Therefore, the domains and strength of EAWM vary with radiative feedback processes such as water vapor and clouds, and with nonradiative feedback processes such as atmospheric dynamics and surface sensible heat flux. For both EAWM modes, nonradiative processes affect the domain of monsoon more significantly than the radiative processes. Since the EAWM is a system in which lower- and higher-latitude circulations interact actively and frequently (Liu et al. 2012), atmospheric dynamics may act as the root trigger, which will be discussed in the next section. The filtered interannual variation of two monsoon modes for time scales of 2–8 yr has also been analyzed. The anomalous cold cores are located at the nearly same regions for both modes. The pattern and the relative magnitude of each feedback process for filtered data also share large similarity with those shown Figs. 4 and 5. However, certain differences are also found. As the PAP result shows, the main contributors to the interannual variation of EOF1 (Fig. 7a) are still the nonradiative feedback processes (the atmospheric dynamics and the surface sensible heat flux) and radiative feedback processes (change in water vapor and cloud), but the relative magnitude of contributions from cloud and water vapor is different from that shown in Fig. 6a and the surface sensible heat flux has a larger impact on the
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TABLE 2. Correlation coefficients of AO, Niño-3.4 with PC1, from the previous September to the following May. Statistical significant values at 95% confidence level are in boldface. PC1
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
AO Niño-3.4
20.0846 20.1085
20.0928 20.0835
20.3326 20.1062
20.3832 20.1378
20.5265 20.1058
20.4817 20.0855
20.3122 20.0805
0.0855 20.0858
20.1466 20.0760
interannual variation of the first monsoon mode. For the interannual variation of EOF2 (Fig. 7b), the main contributors are the same as those in Fig. 6b, but the surface latent heat flux plays a less important role than the OCH does in the interannual variation of the second monsoon mode. The results from two filtered modes and two nonfiltered modes share large similarity, meaning that the analyses of nonfiltered results explain interannual variation of the monsoon and the small difference is due to the interdecadal variation of the monsoon. However, as the effective samples are only half of the nonfiltered ones and the time coverage of the date set (1979–2013) is not long enough for a convincing discussion of the interdecadal variation, only the results from nonfiltered modes is presented in this study.
5. Difference in atmospheric dynamics between two monsoon modes Here we focus on the role of atmospheric dynamics feedback process in the variability of the two EAWM modes and its relationship with other feedback processes. We choose the AO and ENSO, which have dominant effects on the intensity of EAWM. PC1 is closely correlated with AO from November to February of the following year but poorly correlated with the Niño-3.4 index (Table 2). On the contrary, PC2 shows a close and persistent relationship with the Niño-3.4 index but not with the AO (Table 3). It should be pointed out that Wei et al. (2015) also revealed that the relationship between the first EAWM mode and AO was stable, while the relationship between the second EAWM mode and stratospheric polar vortex became stronger since late 1980s. Therefore, it can be concluded that EOF1 is more affected by high-latitude atmospheric circulations such as the ‘‘blocking type’’ cold surges in negative AO periods (Park et al. 2011), while lower-latitude circulations
especially those over the Pacific Ocean are more important for EOF2. Similar relationships have also been explained by Chen et al. (2014b) and Sun et al. (2016). We further examine the evolution of atmospheric dynamics from a comprehensive view. As seen from Fig. 8, EOF1 obviously features strong easterly anomalies at 200 hPa in the former autumn, leading to weakened high-latitude westerly jet stream that benefits southward extension of the cold air from the polar region and a barotropic anomalous high center that strengthens and expands with time (from autumn to winter). In winter, the westerly jet stream weakens significantly by the rapidly strengthened easterly anomalies in the high latitudes. The outbreak of cold and dry air from the Ural Mountains leads to less water vapor and low cloud in the midlatitudes, and so the surface becomes colder because less longwave radiation is trapped in the lower atmosphere. As shown in the 500-hPa geopotential height anomalies, the EAT becomes stronger and moves westward, accompanied by large 200-hPa westerly anomalies to the south. The process intensifies the midlatitude westerly jet stream. Therefore, cold air is trapped north of 408N and exerts only a small impact on the lower latitudes, where climate signals are insignificant in the following spring. For EOF2 (Fig. 9), no significant signals can be found in the former autumn, except an anomalous high geopotential height center over Siberia, with a baroclinic structure. In winter, there exists an anomalous convergence over the northwestern Pacific Ocean, together with broad anomalous divergence over East Asia, leading to strong northerly anomalies over eastern China. The anomalous high center in the former autumn is maintained into winter due to the reduced baroclinicity, and the EAT is enhanced in the process (Wang et al. 2009). There also exist intensive easterly anomalies in the context of the thermal wind theory, which reduce the intensity of the midlatitude westerly jet stream. The atmospheric circulation with reinforced pressure
TABLE 3. As in Table 2, but for PC2. PC2
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
AO Niño-3.4
0.1781 20.3702
0.1514 20.3732
20.0470 20.3709
20.0010 20.3291
0.2220 20.3028
0.0005 20.3092
0.0323 20.3146
20.0015 20.2722
20.0517 20.2052
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FIG. 8. Spatial patterns of the correlation of (a) Ts (shading), SLP (contours with interval of 0.15 hPa K21), and 850-hPa wind (vectors, m s21), and (d) precipitation (shading), 200-hPa wind (vectors, m s21), and 500-hPa geopotential height (contours with interval of 0.2 gpm K21) with PC1 in prior year autumn [SON(21)]. (b),(e) As in (a),(d), but for winter [DJF(0)]. (c),(f) As in (a),(d), but for the following year spring [MAM(11)]. The magnitude of vectors in different panels is different. The correlation coefficients in the areas with shadings or vectors are above the 95% confidence level.
gradient at the lower level and the weakened midlatitude westerly jet stream favor a southward extension of cold air, which reaches the Maritime Continent and enhances local convection. On the contrary, the southward extended cold air reduces atmospheric water vapor and cloud amount in the cold core region of EOF2 (Fig. 10). Moreover, surface sensible heating fluxes increase due to the intensified northerly flow. Therefore, the land surface loses more heat in the strong EAWMEOF2 years, and PC2 is closely related with the atmospheric circulation of the following spring. The adjacent
oceans cool uniformly over the northwestern Pacific and the Indian Ocean in spring. In addition, there is an anomalous barotropic high over the oceans to the north of Japan. Thus, it seems that EOF2 can be a good predictor for the climate features of the following spring.
6. Summary and discussion In this study, we have analyzed the two dominant modes of EAWM and the main feedback processes attributed to the variability of the dominant monsoon
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FIG. 9. As in Fig. 8, but for PC2.
modes. The CFRAM is adopted to attribute the changes in surface temperature between weak and strong EAWM years to radiative and nonradiative feedback processes. We choose eight strong EAWM-EOF1 years and seven weak years for EOF1, and six strong years and seven weak years for EOF2. The change in near-surface temperature between weak and strong EAWM years is divided to radiative terms such as feedback processes of albedo, clouds, and water vapor, and nonradiative terms by using the CFRAM. The feedback processes of atmospheric dynamics, oceanic dynamics and ocean and land heat storage, surface sensible heat flux, and latent heat flux are analyzed. Radiative feedback processes of water vapor and clouds, as well as nonradiative feedback processes of
atmospheric dynamics and surface sensible heat flux, play key roles in the cold core of EOF1. The main processes for EOF2 are the same as those for EOF1, but their amplitude is different. Cooling is mostly contributed by the atmospheric dynamic term over the cold core region for EOF1, but by surface sensible heat flux for EOF2. The EAWM is a system with active atmospheric dynamics that affects radiative processes. Furthermore, atmospheric dynamics is modulated by heat gradient that is controlled by ocean and land heat storage. Therefore, we have focused on the terms of surface heat storage and atmospheric circulation. From the result from CFRAM reanalysis for EOF1 (i.e., the northern EAWM mode), the ocean and land heat
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FIG. 10. Differences in integrated water vapor content (kg kg21) in the atmosphere from 1000 to 700 hPa and 850hPa wind anomaly (m s21) between strong and weak EAWM years of (a) EOF1 and (b) EOF2.
storage process increases (decreases) the heat gradient in the midlatitudes (lower latitudes), which favors enhanced EAWM in the midlatitudes and weakened EAWM in the lower latitudes. There is a deep barotropic anomalous high over Siberia, weakening the extratropical westerly jet stream to its south. As the EAT strengthens and shifts westward, the westerly jet stream intensifies in the midlatitudes. These circulation features favor cold and dry air to reach northern East Asia. Less water vapor content and cloud lead to less longwave radiation trapped in the lower troposphere, and so the surface cools significantly. For EOF2 (the southern EAWM mode), the ocean and land heat storage process reinforces the thermal gradient along the East Asia coast regions, and so eastern China and the northwestern Pacific are featured by a larger pressure gradient and covered by strong northerly anomalies, enhancing surface sensible heat flux from the surface. A baroclinic anomalous pressure center appears over East Asia, with a high center at 500 hPa in the high latitudes. The westerly jet stream weakens in the midlatitudes, and cold air mass expands southward and reaches the tropics. The high-latitude dry air contains less water vapor, and cloud amount is deficient in eastern China but abundant in the Maritime Continent. These features contribute to the surface cooling in the cold core of EOF2. As discussed in section 4, the results of the interannual variation of two EAWM modes from filtered data
share large similarity with those from nonfiltered data, for both observed variations and CFRAM reanalysis. Therefore, the results shown in this study largely explain the interannual variation of the two dominant EAWM modes. However, certain differences between filtered and nonfiltered data also exist, attributed to the interdecadal variation in spite of short data record, which may be largely due to the variations of the nonradiative feedback process, the oceanic dynamics and land and ocean heat storage, and surface latent and sensible heat fluxes. Acknowledgments. We thank Prof. Ming Cai (Florida State University), Prof. Yi Deng (Georgia Institute of Technology), and Miss Xiaoming Hu (Sun Yat-sen University) for a number of helpful discussions. The comments and suggestions from the editor and three anonymous reviewers are helpful for improving the overall quality of the paper. The study was supported by the National Key Research Program of China (Grant 2014CB953900), the National Natural Science Foundation of China (Grants 41690123, 41375081, 41661144019, 91637208, and 41375081), the China Special Fund for Meteorological Research in the Public Interest (GYHY201406018), the Jiangsu Collaborative Innovation Center for Climate Change, and the Zhuhai Joint Innovative Center for Climate, Environment and Ecosystem. The ERA-Interim data used in this study
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