AMERICAN METEOROLOGICAL SOCIETY Journal of Climate
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Impact of Eurasian spring snow decrement on East Asian summer precipitation
Ruonan Zhang Chinese Academy of Meteorological Sciences, Beijing, China National Climate Center, China Meteorological Administration, Beijing, China
Renhe Zhang* Chinese Academy of Meteorological Sciences, Beijing, China CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China
Zhiyan Zuo Chinese Academy of Meteorological Sciences, Beijing, China
Submitted to the Journal of Climate on March 12, 2016 Revised on January 6, 2017
*
Corresponding author:
Dr. Renhe Zhang No. 46, Zhong-Guan-Cun South Avenue, Beijing 100081, China E-mail:
[email protected]
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ABSTRACT: In this study, the relationship between Eurasian spring snow decrement (SSD) and
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East Asian summer precipitation and related mechanisms were investigated using observational
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data and the Community Atmospheric Model version 3.1 (CAM3.1). The results show that a
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west-east dipole pattern in Eurasian SSD anomalies, with a negative center located in the region
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between eastern Europe and the West Siberia Plain (EEWSP) and a positive center located around
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Baikal Lake (BL), is significantly associated with East Asian summer precipitation via triggering
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an anomalous mid-latitude Eurasian wave train. Reduced SSD over EEWSP corresponds to
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anomalously dry local soil conditions from spring to the following summer, thereby increasing
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surface heat flux and near-surface temperatures. Similarly, the increase in SSD over BL is
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accompanied by anomalously low near-surface temperatures. The near-surface thermal anomalies
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cause an anomalous meridional temperature gradient, which intensifies the lower-level
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baroclinicity and causes an acceleration of the subtropical westerly jet stream, leading to an
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enhanced and maintained Eurasian wave train. Additionally, the atmospheric response to changed
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surface thermal conditions tends to simultaneously increase the local 1000–500 hPa thickness,
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which further enhances the Eurasian wave train. Consequently, significant wave activity flux
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anomalies spread from eastern Europe eastward to East Asia and significantly influence the
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summer precipitation over China, with more rainfall over northeastern China and the Yellow
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River valley and less rainfall over Inner Mongolia and southern China.
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KEYWORDS: spring
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activity flux
snow decrement, East Asian summer precipitation, Eurasian wave train, wave
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2
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1. Introduction
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Variations in Eurasian snow cover in winter and spring play a major role in the East Asian climate
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and precipitation during the following seasons (Gong et al., 2003, 2004; Wu and Kirtman, 2007;
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Zhang et al., 2008; Wu et al., 2009a; Zuo et al., 2011, 2012; Mu and Zhou, 2012, 2015; Zhang et
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al., 2013; and many others). The associated mechanisms can be attributed to albedo and
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hydrological effects, with the latter considered the more important (Barnett et al., 1989; Cohen
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and Rind, 1991; Gong et al., 2004). Previous studies have used various snow parameters and
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representations to indicate the influence of Eurasian snow cover on circulation anomalies in the
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Northern Hemisphere and associated dynamic mechanisms. For example, Yasunari et al. (1991)
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found that excessive Eurasian snow mass during March may trigger the spreading of Rossby
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waves from the Eurasian continent to North America, leading to the positive phase in the Pacific
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North America (PNA) teleconnection. Chen and Song (2000a, b) found that the early spring
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retreat of snow cover extent in Eurasia absorbs large amounts of heat from the atmosphere,
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leading to thermal anomalies in the troposphere, which ultimately induce anomalous circulation
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patterns over the mid-high latitudes of the Eurasian continent.
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Several land factors, such as soil moisture, soil temperature, and frozen soil have been
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considered to be the links with the long memories of land surface hydrological and thermal
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conditions and related feedback to climate (Shinoda, 2001). Some studies have investigated the
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role of spring soil moisture (Matsuyama et al., 1998; Zuo and Zhang, 2007, 2016; Zhao et al.,
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2007; Zhang and Zuo, 2011) or frozen soil (Li et al., 2009) in the subsequent summer
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precipitation over East Asia and provide a theoretical basis for constructing the linkages. Chen
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and Song (2000a, b) suggested that the persistent soil moisture anomaly produced by early snow
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cover retreat from spring to summer appears to decrease the geopotential height field, which is 3
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not favorable for the development of blocking activity. Meanwhile, the soil moisture anomaly
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could also excite anomalous wave trains propagating from western Europe to East Asia through
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the geopotential height field, with opposite signs between northern and southern China. Using
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snow water equivalent (SWE) data, Wu et al. (2009a) examined the wave train response to
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reduced Eurasian spring SWE. Kripalani et al. (2002) and Wu et al. (2014) found a west-east
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contrast pattern of the snow cover over Eurasian continent is closely connected to summer
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precipitation anomalies over East Asia. Furthermore, Mu and Zhou (2012, 2015) demonstrated
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that the soil temperature and frozen soil anomalies associated with the extent of fresh snow in
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Eurasia might increase the northerly wind over the Baikal Lake (BL) region, leading to relatively
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colder conditions in northern East Asia, a strengthened subtropical upper-level jet stream, and a
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weaker Asian summer monsoon (Bamzai and Marx, 2000; Dash et al., 2005).
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The aforementioned studies are mainly based on snow cover variability itself. As a matter of
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fact, spring is a transition season from winter to summer. Spring snow decrement (SSD) is closely
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related with snowmelt and latent heat sink and simultaneously reflects changes in soil moisture
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and the hydrological cycle, which influences the local thermal conditions and ultimately leads to
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climate anomalies both locally and remotely (Walsh et al., 1985; Groisman et al., 1994). Although
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these possible physical processes are identified, the detailed and quantitative climate impacts of
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Eurasian SSD on the East Asian climate have not been convincingly and deeply understood. Little
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attention has been paid to variations in the SSD and its relationship with the East Asian summer
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climate.
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The purpose of the present study is to investigate the effect of Eurasian SSD on East Asian
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summer precipitation and to explore the associated dynamic and thermodynamic processes. The
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remainder of this paper is organized as follows. In section 2, we describe the data and methods 4
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used in the present study. Section 3 presents the relationship between Eurasian SSD and East
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Asian summer rainfall. Section 4 explores the associated dynamic and thermodynamic
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mechanisms. Section 5 discusses the role of El Niño and Arctic sea ice in the relationship between
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Eurasian SSD and East Asia summer rainfall, and conclusions are reported in section 6.
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2. Data, methods and model
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The datasets used in this study include (1) the monthly mean atmospheric data from the National
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Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP/NCAR)
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Reanalysis I with a horizontal resolution of 2.5º×2.5º available after 1948 (Kalnay et al., 1996), (2)
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the monthly mean sea ice concentration (SIC) and sea surface temperature (SST) from the Met
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Office Hadley Centre with a horizontal resolution of 1º×1º available since 1870 (Rayner et al.,
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2003), (3) the monthly mean soil moisture from the ERA-Interim reanalysis with a horizontal
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resolution of 0.75º×0.75º and four layers of 0-7, 7-28, 28-100, 100-289 cm in depth for the period
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1979–2013 (Balsamo et al., 2009), which can well represent the features of observed soil moisture
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over East Asia (Zuo and Zhang, 2009; Liu et al., 2014), and (4) the monthly mean snow water
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equivalent (SWE) data of GlobSnow V2 from the Finnish Meteorological Institute for the period
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1979–2013 (http://www.globsnow.info/swe/), which are obtained using a combination of
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ground-based data and satellite microwave radiometer-based measurements and each monthly
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matrix having 721×721 grid (Solberg et al., 2009). We have converted the raw GlobSnow V2
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SWE data to regular 1º×1º grid for our analysis. For the majority of summer (June–September),
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measurable SWE is absent; thus, the data in summer are not credible and not utilized. In addition,
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SWE data are lacking south of 35ºN; thus, the climate impact of SWE over the Tibetan Plateau is
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not discussed in our analysis. 5
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In this study the time period is taken from 1979 to 2013. The methods used in the present study
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include the empirical orthogonal function (EOF) as well as correlation and regression analyses.
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The Student’s t-test is used to check the statistical significance for the correlation and regression
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analyses.
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The monthly snow decrement can be considered as the difference in SWE between two
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successive months. Positive and negative snow decrements represent increase and decrease SWE
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in succeeding month with respect to the previous one. Figure 1 shows the climatological annual
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cycle of SWE and snow decrement over the Eurasian continent (35º–76ºN, 0º–150ºE) during the
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period 1980–2013, with the absence of June–September SWE data. The monthly SWE rises
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considerably from October to the peak in February−March (approximately 61 mm) and then
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declines sharply in the following months to approximately 18 mm in May. Corresponding to the
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annual cycle of SWE, snow enhancement can be observed in cold seasons starting in October to
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the following February, followed by a hiatus in March; finally, the most significant snow
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reduction can be observed in April (-21 mm) and May (-24 mm). Therefore, the most severe snow
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reduction occurs mainly in the warming seasons in spring due to the gradually increasing
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temperature and decreasing snowfall. Therefore, in order to quantitatively depict the variations in
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snow decrement during the spring, in the present study, the Eurasian spring snow decrement (SSD)
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is defined as the SWE difference between early spring (February−March) and late spring (May).
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In this study, the NCAR Community Atmospheric Model version 3.1 (CAM3.1), with a
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horizontal resolution of T42 (approximately 2.8º×2.8º) and 26 vertical levels (up to 3.7 hPa)
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(Collins et al., 2006), was employed to better understand the dynamic and thermodynamic
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influences of Eurasian SSD on circulation anomalies over Eurasia, particularly East Asia and
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China. As shown in Collins et al. (2006) and Hack et al. (2006), this model is able to reproduce 6
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remarkably realistic atmospheric circulation and broad climatological characteristics of East Asia
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summer climate.
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3. Results
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3.1 Eurasian SSD and its relationship with East Asian summer precipitation
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An empirical orthogonal function (EOF) analysis was applied to the Eurasian SSD. The first three
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EOF modes of the SSD (see Supplementary Fig. S1) account for 22.2%, 14.3% and 7.4% of the
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total variance, respectively. According to North et al. (1982), all these three modes are well
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separated with each other. Figure 2 shows the spatiotemporal features of the second EOF mode
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(EOF2) of Eurasian SSD. The spatial pattern of EOF2 is characterized by a west-east dipole
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pattern, with a negative center located in the region between eastern Europe and the West Siberia
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Plain (EEWSP) and a positive center in the vicinity of Baikal Lake (BL). Additionally, two other
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positive centers over eastern Europe and the North Siberia Plain are also identified (Figure 2a).
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This dipole pattern somewhat resembles those of previous studies that are based on spring snow
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cover fraction (Yim et al., 2010; Wu et al., 2014). Wu and Kirtman (2007) also considered the West
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Siberia snow cover anomaly during spring to be a notable precursor for China seasonal
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precipitation. As shown by the principal component of EOF2 (PC2) (Figure 2b), this pattern
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exhibits significant interannual and interdecadal variations. To quantify the Eurasian SSD
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variability and its association with the East Asian summer climate, we defined a SSD index (SSDI)
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as the difference in area-averaged SSD between the domains with positive values (46º–58ºN, 96º–
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138ºE) and negative values (54º–68ºN, 48º–84ºE). The interannual and interdecadal variations of
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the SSDI are highly consistent with those of the PC2, and their correlation coefficient is as high as
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0.91, indicating that the SSDI index can provide a feasible representation of the spatiotemporal 7
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variations in Eurasian SSD during 1980–2013.
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Corresponding to the variations in Eurasian SSD, the related East Asian summer precipitation
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anomaly is investigated by correlation analysis with the SSDI index. As shown in Figure 3a, the
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correlation coefficient pattern reveals a meridional quadrupole structure prevailing in the regions
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between northern and southern East Asia, with excessive precipitation over regions west of BL,
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northeastern China and the Yellow River valley and deficient precipitation over Inner Mongolia
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and southern China. We checked the first three EOF modes of the East Asian summer
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precipitation (Supplementary Fig. S2). The first three modes account for 12.9%, 11.1% and 8.0%
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of the total variance, respectively, and they are well separated with each other according to North
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et al. (1982). The meridional quadrupole pattern strongly resembles that of the spatial distribution
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of the third EOF mode (EOF3) of East Asian summer precipitation (Figure 3b), and their spatial
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correlation coefficient is as high as 0.67. Furthermore, the variations in PC3 of the East Asian
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summer precipitation is similar to that of the SSDI index during 1980–2013 (Figure 3c), with
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correlation coefficient being 0.45, which exceeds the 95% confidence level. The aforementioned
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indicates the significant relationship between the variations in Eurasian SSD and the East Asian
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summer precipitation.
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However, there are also some discrepancies between the two precipitation patterns. The
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negative regions over Inner Mongolia in the EOF3 pattern appear to extend more northward
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toward BL and are more significant than those of the correlation pattern (Figure 3b). These
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discrepancies illustrate that other factors may also have certain contributions, or their linkage may
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derive from the combined effects of sea surface temperature (Zhang et al., 1999) and Arctic sea ice
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(Wu et al., 2009a). For instance, in 1998, the East Asian summer precipitation has negative
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anomalies larger than two standard deviations, whereas the SSD anomaly is insignificant (Figure 8
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3c), which may be related to Tibetan Plateau thermal forcing and East Pacific sea surface
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temperature anomalies (Chinese National Climate Center, 1998; Chen, 2001; Lau and Weng,
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2001).
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The above analysis reveals a statistical linkage between the Eurasian SSD and East Asian
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summer precipitation. In the next section, we will illustrate the physical mechanisms responsible
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for this linkage and associated causal relationship through exploring the possible dynamic and
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thermodynamic mechanisms associated with Eurasian SSD and East Asian summer precipitation,
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with a focus on surface thermal forcings.
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3.2 Possible physical mechanisms
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Previous studies suggest that Eurasian snowmelt may directly increase the soil moisture and affect
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soil temperature over some regions of Siberia during warming seasons, thereby leading to the
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reduction of surface air temperature and tropospheric diabatic heating (Cohen and Rind, 1991;
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Matsuyama et al., 1998; Saito and Cohen, 2003; Zuo et al., 2011). Figure 4 displays the regressed
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100-289 cm soil moisture anomalies during late spring (AMJ) and summer (JJA) onto the SSDI
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index. Figure 4a illustrates that the soil moisture anomalies feature an out-of-phase structure over
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the middle and high latitudes of the Eurasian continent during the late spring. In comparison, the
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western SSD anomalies are accompanied by stronger negative in situ moisture anomalies over
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EEWSP while the eastern SSD anomalies by positive soil moisture anomalies over regions around
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BL. The east-west contrast in soil moisture anomalies during late spring is consistent with the
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east-west contrast in the SSD anomaly pattern; thus, the soil moisture anomalies may be attributed
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to the effect of local spring SSD. The colocation of SSD and soil moisture anomalies supports the
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clear snow hydrological effect during the period 1980–2013, with excessive (less) SSD leading to
9
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more (less) snowmelt and thus wetter (drier) soil. Notably, the regions north of 70ºN feature
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notable negative soil moisture anomalies, which is not in accordance with the excessive SSD
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during spring. In fact, these regions have the latest melt season due to their higher latitudes and
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the longest snowy seasons. Correspondingly, the soil moisture anomalies may not correspond well
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to the snow decrement during spring. In addition to the mid-high latitudes, the majority of regions
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south of 35ºN show coherent soil moisture anomalies. Because of the long memory of surface
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hydrologic conditions in Eurasia (Shinoda, 2001), the Eurasian soil moisture structure persists to
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the subsequent summer. As shown in Figure 4b, the pattern of the soil moisture anomalies in
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summer is quite similar to that in spring. In this study, we focused on the 100-289 cm layer soil
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moisture; other shallower soil levels display the coherent features but with relatively weaker
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signals (figures not shown), which may be attributed to the longer memory of soil moisture
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anomalies in deeper layer.
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The soil moisture in summer (Figure 4b) can affect the surface thermal balance. Zhang and Zuo
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(2011) examined the impact of soil moisture over East Asia on the surface heat balance and
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pointed out that a wetter (drier) soil corresponds to a lower (higher) surface air temperature. To
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analyze the effect of soil moisture on surface thermal condition in summer, we investigate the
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surface solar radiation flux, longwave radiation flux, sensible heat flux, latent heat flux, and net
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heat flux in regression onto the SSDI index, and the results are shown in Figure 5. The solar
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radiation at the surface is enhanced over EEWSP and weakened in the vicinity of BL (Figure 5a).
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Meanwhile, the longwave radiation varies oppositely (Figure 5b). These may result from locally
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changed cloud cover in the two regions (Zampieri et al. 2009). The changed surface energy
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further results in more sensible heat but less latent heat in EEWSP, and less sensible heat and
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latent heat in the surrounding of BL (Figures 5c and 5d). A clear picture comes out in the net heat 10
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flux (Figure 5e), which illustrates a similar anomalous pattern to that of the solar and sensible heat
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flux. The similarity of the anomalous net heat flux pattern to the distribution of the Eurasian SSD
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in spring as well as the soil moisture anomalies in spring and summer suggests a change of the
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local energy balance by the Eurasian SSD anomalies.
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In Figure 6 we show the atmospheric thermal features over Eurasian continent associated with
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the SSD. From Figure 6a it can be seen that in summer over the dryer area in EEWSP significant
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higher surface temperatures is observed, whereas over the wetter area around BL significant lower
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temperatures appears. These anomalous surface thermal forcings can warm or cool the lower
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troposphere. As shown in Figure 6b, the near-surface thermal conditions associated with SSD
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substantially change the local atmospheric thickness of the lower-middle troposphere between
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500 hPa and 1000 hPa (Z500-Z1000), with a notably thickened atmosphere over EEWSP and
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thinner atmosphere over regions west of BL. The west-east dipole pattern in atmospheric
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thickness is quite similar to those in surface temperature and soil moisture. The meridional heat
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flux anomalies over East Asia shown in Figure 5e form the north-south dipole structure in
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temperature anomalies over East Asia. This dipole further strengthens the meridional temperature
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gradient and atmospheric baroclinicity over the mid-latitude of East Asia at 700 hPa and
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simultaneously decreases them to the north and south (Figure 6c). These results reflect the
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thermal processes associated with anomalous Eurasian SSD, and illustrate the possible
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thermodynamic influences on atmospheric circulation over East Asia in summer.
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According to the thermal wind adjustment theorem, the intensified atmospheric baroclinicity
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can accelerate the 200 hPa westerly winds over the mid-latitudes of the Eurasian continent,
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leading to a strengthened subtropical jet stream over northern China and easterly wind anomalies
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north and south of the subtropical jet stream (Figure 7a). The strengthened subtropical jet tends to 11
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act as a connection between near-surface thermal forcings and the development of meridional
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circulation anomalies, as suggested by Mu and Zhou (2015). Figure 7b illustrates that at the 500
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hPa geopotential heights, an anomalous cyclonic pattern and anomalous anticyclonic pattern
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emerge to the north and south, respectively, of the exit region of the East Asian subtropical jet
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stream. In addition to this dipole pattern, an evident mid-latitude Eurasian wave train also prevails
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over the regions from the West Siberia Plain to the Northwest Pacific, with a negative center
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located over Mongolia and positive centers over EEWSP and the Northwest Pacific, respectively.
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This Eurasian wave train can be interpreted as a manifestation of a single stationary Rossby wave
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train propagation. Meanwhile, coexisted with the mid-latitude wave train, in the higher latitudes
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there is a relatively weaker polar wave train with weakened heights emerged over regions around
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western Siberian coast and strengthened heights over eastern Siberian coast. These results imply
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that the anomalous SSD-induced upper-level westerly anomalies can provide favorable dynamic
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conditions for the development of a north-south dipole pattern and Eurasian wave train pattern
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over the lower-middle and higher latitudes of East Asia, which tend to act as the atmospheric
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bridge linking the surface thermal forcings and East Asian summer climate.
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In addition to the influences of the wave trains discussed above, the SSD and/or soil moisture
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anomalies may also directly trigger the wave trains during late spring (AMJ) and summer. The
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origins and development of the Eurasian wave trains are demonstrated in Figure 8, which presents
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the 500 hPa stream function anomalies and associated wave activity flux (WAF, Takaya and
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Nakamura, 2001) during late spring and summer. Over EEWSP, significant positive stream
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function anomalies associated with reduced SSD and strengthened thermal heating occur in situ
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during late spring. Meanwhile, there are relatively weak WAFs spreading from extratropical North
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Atlantic northeastward to eastern Europe along the sub-polar belt. This wave train is then 12
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markedly reinforced over eastern Europe, propagating northeastward to the West Siberia Plain,
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and then splitting into two branches, with one turning to the higher latitudes and another
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southeastward to East Asian (Figure 8a). This reinforced propagation pattern can be attributed to
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the thermal forcing associated with SSD anomalies. In summer, similar and significant
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mid-latitude WAFs emanated from eastern Europe and propagated directly eastward to BL and
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further to East Asia, exhibiting predominantly zonally oriented. The horizontal propagation of
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quasi-stationary wave train in summer (Figure 8b) resemble the Silk-Road and polar wave trains
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as suggested by Orsolini et al. (2015) that have substantial impacts on precipitation over northeast
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China. Compared with those during late spring, in summer the upper-stream wave train across the
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extratropical North Atlantic is much weaker and the wave propagation is more zonally oriented.
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This implies that the Eurasian wave trains are essentially triggered by SSD anomaly over EEWSP,
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and the persistence of the wave propagation is mainly related to the consecutive surface thermal
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forcings.
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Some recent studies have emphasized the interference between the forced and climatological
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wave trains (Smith et al. 2011, 2012; Peings and Magnusdottir 2014; Orsolini et al. 2015). These
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studies suggest the importance of both forced and climatological wave trains in affecting the
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atmospheric circulation. For instance, the atmospheric circulation is stronger when the forced
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wave train is in phase with the climatological one, and vice versa. Figure 9 plots the forced
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anomalous wave train regressed on the SSDI index along with the climatological wave train,
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which are represented by 500 hPa geopotential heights. The Fourier decomposition is used to
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decompose the stationary waves from wave 1 to wave 4 according to zonal wavenumber. In the
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original field (Figure 9a), the climatological wave trains in summer exhibit the co-existing of the
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polar and mid-latitude wave trains. The forced and climatological wave trains are in phase over 13
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EEWSP and regions south of BL, and out of phase over regions north of BL and around Okhotsk
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Sea, with a spatial correlation coefficient of 0.17. Over EEWSP the spatial correlation coefficient
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is as high as 0.90, which denotes a constructive linear interference between the forced and
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climatological wave train. Concerning wave 1 and wave 3 (Figures 9b and 9d), the forced wave
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train is out of phase with the climatological one with the spatial correlation coefficients being
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-0.53 and -0.44 for waves 1 and 3, respectively. These destructive interferences indicate that the
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atmospheric circulation modified by wave 1 and wave 3 is opposite to the climatological wave
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train. As for the wave 2 and wave 4 (Figures 9c and 9e), their spatial correlation coefficients with
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the climatological wave train are 0.36 and 0.41, respectively, suggesting that the anomalous
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forced waves should be linearly interfered with and enhance the climatological wave train. Thus,
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the constructive linear interferences exhibited in wave 2 and 4 would reinforce the climatological
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wave train and then prompt the influence on East Asian summer climate.
287
These anomalous circulation patterns provide favorable conditions for summer precipitation
288
over East Asia, particularly over China, with excessive precipitation over regions west of BL,
289
northeastern China and the Yellow River valley, and deficient precipitation over Inner Mongolia
290
and southern China (Figure 3a). These results indicate that the Eurasian SSD-induced diabatic
291
heating forcings may make prominent contributions to East Asian summer precipitation through
292
the formation and development of the Eurasian wave trains. However, these results depend largely
293
on observation analyses and cannot guarantee a causal relationship. Considering that the
294
numerical model simulations are essential to confirming the diagnostic results, the simulations are
295
addressed in the next section.
14
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3.3 Model simulations
297
To elucidate the influence of anomalous Eurasian SSD on East Asian summer climate, we
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performed a set of control experiments with CAM3.1 to derive different initial fields. The model
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is spun up for 20 years and then integrates forward for 50 years. The data for December 1 of the
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last 50 years are used as initial fields, and the 50 initial conditions are used for ensemble
301
simulation. Then, the control and sensitivity experiments, each with 50 members, are integrated
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from the beginning of December to the end of August in the following year. In the sensitivity
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experiments, we refer to Barnett et al. (1989) for the experimental design. To quantitatively mimic
304
the observed Eurasian SSD anomalies (as shown in Figure 2), we reduce the snowfall rate by 50%
305
over EEWSP and simultaneously increase the snowfall rate by 50% over regions around eastern
306
Europe, North Siberia Plain, and BL. Such modification is applied to the entire period of the
307
sensitivity simulations. Finally, the simulation results are obtained as the ensemble difference
308
between the sensitivity and the control experiments. To increase the robustness and reliability of
309
the numerical experiments, a 50-member ensemble mean is conducted to reduce the uncertainties
310
arising from differing initial conditions. Corresponding to the change in snowfall rate, other
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hydrological and energy variables in CAM3.1 simulations change accordingly (see
312
Supplementary Fig. S3). In both late spring and summer, the deficient snowfall is accompanied by
313
negative anomalies of 100-289 cm layer soil moisture (Fig. S3a) and higher temperature over
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EEWSP (Fig. S3c), while more snowfall by positive soil moisture anomalies (Fig. S3a) and lower
315
temperature (Fig. S3c) over regions around BL. These anomalous patterns are similar to the
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Eurasian SWE and SSD anomalies over Eurasia (Figure 10).
317
To substantiate the conjecture of our designed experimental schemes, we primarily examined
318
the model-simulated response of spring SWE and SSD to the anomalous snowfall rate. As shown 15
319
in Figure 10a, two regions of large and significant negative anomalies are induced in the surface
320
SWE field during spring, one located over EEWSP and the other over the Tibetan Plateau, with
321
the maximum value occurring in the latter region and exceeding -8 mm. This reflects the in-phase
322
variation in SWE over the two regions and offsets the absence of observed lower-latitude SWE.
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Similarly, three positive SWE anomalous regions are also triggered, with centers emerging over
324
eastern Europe, North Siberia Plain and BL. The maximum positive SWE center occurs in regions
325
west of BL, with the center value exceeding 8 mm. Corresponding to the simulated anomalous
326
SWE, the SSD reveals a similar pattern, namely, more SSD over eastern Europe and BL and less
327
SSD over EEWSP and the northern Tibetan Plateau (Figure 10b). However, there are some
328
differences between SWE and SSD. One is that the largest negative SSD center occurs over
329
EEWSP at approximately 60ºN, which differs from that of the SWE at approximately 70ºN.
330
Another is that the negative Tibetan Plateau SSD center is much weaker than that of the SWE and
331
even positive center appears, implying weak snowmelt and snow enhancement over the northern
332
and southern Tibetan Plateau, respectively, possibly due to the lower temperature over the Plateau.
333
Moreover, an insignificant negative SSD anomaly is simulated over the North Siberia Plain
334
maybe due to the persistent snowfall, even during early summer. These simulations largely
335
reproduce the observed anomalous SSD patterns (Figure 2a), indicating that our numerical
336
schemes of the snowfall rate changes are representative of the SSD anomalies in the CAM3.1.
337
To verify whether the anomalous SSD can induce the Eurasian wave train, we simulated the
338
physical processes that determine the circulation in response to snowfall rate changes. Figure 11
339
presents the simulated composite differences between sensitivity and control experiments in those
340
fields parallel to reanalysis (Figure 6-8). The surface air temperature field (Figure 11a) shows a
341
positive difference over eastern Europe and a negative difference over BL, which reproduce the 16
342
west-east dipole pattern in reanalysis. In the atmospheric thickness field (Figure 11b), two
343
enhanced anomalous thickness centers appear over eastern Europe and central China, whereas a
344
decreased thickness center emerges over regions north of BL. Meanwhile, in the 700 hPa
345
meridional temperature gradient field (Figure 11c), an anomalous baroclinicity center appear to
346
the south of BL. These patterns respond directly to the thermal forcing of Eurasian SSD. Then, a
347
strengthened subtropical jet stream (Figure 11d), an Eurasian mid-latitude wave train and a
348
north-south dipole structure over East Asia with a decreased height over BL and an enhanced
349
height over southern China (Figure 11e) are subsequently simulated. Figure 11f shows weak
350
WAFs propagating from extratropical North Atlantic eastward to East Asia, which is markedly
351
reinforced in eastern Europe, indicating the active role of SSD anomaly over EEWSP in triggering
352
a strong Rossby wave train.
353
The linear interference between the model forced and climatological wave trains are further
354
examined in Figure 12. In the total wave (Figure 12a), the forced and climatological waves are in
355
phase over EEWSP with the spatial correlation coefficient being 0.83 and out of phase over
356
regions north of BL. Concerning the decomposed waves, the forced wave is out of phase with the
357
climatological wave for the wave 1 and wave 3 with spatial correlation coefficients being -0.23
358
and -0.36, respectively, and in phase for the wave 2 and wave 4 with spatial correlation
359
coefficients being 0.23 and 0.39, respectively. These responses further confirm that the linear
360
interference between the forced and climatological waves in wave 2 and wave 4 intensifies the
361
influence on East Asian summer climate.
362
In general, these simulations reproduce the thermodynamic and dynamic responses of
363
atmospheric circulation to anomalous Eurasian SSD as identified in the analysis using observation
364
data, and demonstrate the previous argument that the anomalous Eurasian SSD can excite a 17
365
Eurasian wave train propagating from eastern Europe eastward to East Asia. However, there are
366
still some discrepancies in the simulated geopotential height response for the polar wave train
367
compared to that in reanalysis. For example, the total wave response (Figure 12a) is further north
368
than in the reanalysis (Fig 9a). Examining Figure 12d, it seems that the model has a stronger
369
forced response at high latitudes than the reanalysis (Fig 9d). In Fig 12e, the response is slightly
370
shifted north compared to reanalysis (Fig 9e). These discrepancies may be due to the weak SSD
371
forcing over high-latitudes in CAM3.1 or contributions from other factors such as North Atlantic
372
SST (Wu et al., 2011) and Arctic sea ice (Wu et al., 2013).
373
The associated summer precipitation anomalies are also shown in Figure 13. Positive
374
precipitation anomalies are identified over regions from BL eastward to northeastern China and
375
the lower Yellow River valley, and negative anomalies over western Inner Mongolia and most
376
regions of southern China. When compared with the observed anomalous precipitation patterns in
377
Figures 3a and 3b, opposite signs appear in regions from northern China to the middle Yellow
378
River valley. These discrepancies may arise from the deficiency of CAM3.1 in simulating
379
variations in East Asia summer precipitation, as described in Hack et al. (2006) and Wei et al.
380
(2011). Nevertheless, the numerical simulations demonstrate the significant role of Eurasian SSD
381
in influencing the East Asian summer precipitation through modulating atmospheric circulation
382
pattern.
383 384
4. Discussion on other factors’ effect
385
Although the close connection between Eurasian SSD and East Asian summer climate has been
386
examined with both observations and numerical model simulations in the above sections, other
387
atmospheric factors and lower boundary forcings, such as the ENSO (Zhang et al., 1996, 1999; 18
388
Ferranti and Molteni, 1999; Shaman and Tziperman, 2005) and Arctic sea ice (Wu et al., 2009b,
389
2009c; Zhang and Wu, 2011; Bader et al., 2011; Liu et al., 2012), can act as important drivers of
390
variations in both Eurasian snow cover and East Asian summer climate. In this section, we will
391
employ partial correlation analyses (Spiegel, 1988) to examine if the relationship between
392
Eurasian SSD and East Asian summer climate revealed in our present study is affected by the
393
ENSO and Arctic sea ice.
394
Figure 14 shows the partial correlation coefficient patterns between the SSDI index and East
395
Asian summer precipitation after the linear parts related to Nino3.4 index and SIC index are
396
removed, respectively. The Nino3.4 index is defined as the average of eastern equatorial Pacific
397
sea surface temperature anomalies in the area -5ºS–5ºN, 120º–170ºW. The SIC index refers to the
398
regional averaged SIC north of 60ºN. The partial correlation patterns feature similar structures as
399
that of the EOF3 pattern of East Asian summer precipitation (Figure 3a) and the correlation
400
coefficient pattern (Figure 3b), with anomalous meridional quadrupole precipitation centers
401
extending from northern to southern East Asia. To further examine their resemblance, we
402
perform a spatial correlation analysis to confirm the resemblances between the partial correlation
403
coefficient patterns and EOF3 pattern of East Asian summer precipitation. The spatial correlation
404
coefficients between the EOF3 pattern of East Asian summer precipitation and the partial
405
correlation coefficient patterns with ENSO and SIC removed are 0.65 and 0.68, respectively,
406
which are close to 0.67 between the correlation pattern (Figure 3a) and the EOF3 pattern of East
407
Asian summer precipitation (Figure 3b). Therefore, the relationship between Eurasian SSD and
408
East Asian summer precipitation reveal in our present study should be independent of ENSO and
409
Arctic sea ice concentration.
410
5. Conclusions
411
In the present study, we investigated the relationship between Eurasian spring snow decrement
412
(SSD) and East Asian summer precipitation and the related thermodynamic and dynamic 19
413
mechanisms using both observational data and the CAM3.1 model. The results show that the
414
second EOF mode of Eurasian SSD exhibits a west-east dipole pattern, with a negative center
415
located over eastern Europe and the West Siberia Plain (EEWSP) and a positive center over the
416
area around Baikal Lake (BL). This anomalous SSD pattern is significantly associated with the
417
third EOF mode of East Asian summer rainfall through triggering an anomalous mid-latitude
418
Eurasian wave train. The reduced SSD over EEWSP tends to decrease the local soil moisture from
419
spring to the following summer, thereby increase the surface heat flux and near-surface
420
temperatures. Similarly, the increase in SSD over BL is accompanied by anomalously low
421
near-surface temperatures. Changes in near-surface temperatures further intensify the meridional
422
temperature gradient and lower-level baroclinicity, leading to the acceleration of the upper-level
423
subtropical westerly jet stream. At the 500 hPa geopotential heights, an anomalous cyclone and
424
anomalous anticyclone emerge to the north and south, respectively, of the exit region of East
425
Asian subtropical jet stream. Meanwhile, the changed surface thermal conditions enhance the
426
local 1000–500 hPa thickness over EEWSP while decreasing it over BL. These factors both create
427
favorable physical conditions for the maintenance and enhancement of the anomalous
428
mid-latitude Eurasian wave train prevailing over the regions from eastern Europe eastward to the
429
Northwest Pacific. We further explored the origin of the Eurasian wave train and found that there
430
are zonally oriented WAFs spreading from eastern Europe eastward to East Asia. This finding
431
demonstrates the role of anomalous SSD in triggering the Eurasian wave train. These circulation
432
patterns ultimately significantly influence the precipitation over East Asia, especially over China,
433
with excessive precipitation over regions west of BL, northeastern China and the Yellow River
434
valley and deficient precipitation over Inner Mongolia and southern China. Therefore, Our study
435
confirms the significant role of Eurasian SSD in influencing East Asian summer precipitation. 20
436
Our model results demonstrate that the CAM3.1 can reproduce the positive summer
437
precipitation anomalies over most of northern East Asia and negative anomalies over southern
438
China. Corresponding to the imposed anomalous Eurasian SSD forcings in CAM3.1, positive
439
surface air temperature and atmospheric thickness responses over EEWSP and negative responses
440
over BL are primarily simulated. In the simulation the subtropical jet stream over East Asia is
441
strengthened, the Eurasian mid-latitude wave train is formed, and reinforced WAFs propagate
442
from eastern Europe to East Asia. These simulated dynamic and thermodynamic processes result
443
in according precipitation anomalies to occur over East Asia.
444 445
Acknowledgments
446
The authors are very grateful for the constructive comments from anonymous reviewers. This
447
research was funded by the National Key Research and Development Program of China (Grant
448
2016YFA0601501, 2016YFA0600602), the National Basic Research Program of China (Grant
449
2013CB430203), the Basic Scientific Research and Operation Foundation of the CAMS (Grant
450
2015Z001), the National Natural Science Foundation of China (NSFC, Grants 41661144017,
451
41205059, and 41505053), and the Public Sector (Meteorology) Research and Special Project of
452
China (Grant GYHY20120617).
21
453
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601
Figure captions:
602
Figure 1. Climatological annual cycle of monthly snow water equivalent (SWE, light blue bars)
603
and snow decrement (SD, orange bars) over the Eurasian continent (35º–76ºN, 0º–
604
150ºE) during 1980–2013 (Units: mm). The data in June-September are absent.
605
Positive and negative SDs represent increase and decrease SWE in succeeding month
606
with respect to the previous one.
607
Figure 2. (a) Spatial pattern (shadings, units: mm) of the second EOF (EOF2) and (b) detrended
608
time series of the second principal component (PC2, black line) of SSD over the
609
Eurasian continent during 1980–2013. The blue line in (b) shows the SSDI index
610
defined as the difference of SSD between the rectangles with positive values (46º–
611
58ºN, 96º–138ºE) and negative values (54º–68ºN, 48º–84ºE) shown in (a). Purple and
612
black dotted areas in (a) denote values with statistical significance exceeding the 90%
613
and 95% confidence levels, respectively.
614
Figure 3. (a) Correlation coefficients between the SSDI index and the subsequent summer
615
precipitation, (b) spatial pattern of the third EOF mode of East Asian summer
616
precipitation, and (c) detrended time series of SSDI and summer precipitation (PC3).
617
R represents the correlation coefficient between the two indices. Purple and black
618
dotted areas denote values with statistical significance exceeding the 90% and 95%
619
confidence levels, respectively.
620
Figure 4. (a) Late spring (AMJ) and (b) summer (JJA) 100-289 cm soil moisture (SM) anomalies
621
(shadings; units: m3/m3) regressed onto the SSDI index. Purple and black dotted areas
622
denote values with statistical significance exceeding the 90% and 95% confidence
623
levels, respectively. The rectangles are same as those in Figure 2. 29
624
Figure 5. Summer (JJA) (a) surface solar radiation flux, (b) longwave radiation flux, (c) sensible
625
heat flux, (d) latent heat flux, and (e) net heat flux anomalies (shadings; units: W/m2)
626
regressed onto the SSD index. Purple and black dotted areas denote values with
627
statistical significance exceeding the 90% and 95% confidence levels, respectively.
628
The rectangles are same as those in Figure 2.
629
Figure 6. (a) Surface temperature (shadings; units: °C), (b) atmospheric thickness between 500
630
hPa and 1000 hPa (Z500-Z1000, units: gpm), and (c) 700 hPa meridional temperature
631
gradient (shadings; units: °C/grid) anomalies during summer regressed onto the SSDI
632
index. Purple and black dotted areas denote values with statistical significance
633
exceeding the 90% and 95% confidence levels, respectively. The rectangles are same
634
as those in Figure 2.
635
Figure 7. (a) 200 hPa zonal wind (shadings; units: m/s) and (b) 500 hPa geopotential height
636
(shadings; units: gpm) anomalies regressed onto the SSDI index. The climatological
637
zonal winds with speed greater than 20 m/s are enclosed by the black lines in (a).
638
Purple and black dotted areas denote values with statistical significance exceeding the
639
90% and 95% confidence levels, respectively. The rectangles are same as those in
640
Figure 2.
641
Figure 8. 500 hPa stream function (shading; units: m2/s) and the associated wave activity flux
642
(vector; units: m2/s2) anomalies regressed onto the SSDI index during (a) late spring
643
(AMJ) and (b) summer (JJA). The rectangles are same as those in Figure 2.
644
Figure 9. SSD forced (contours) and climatological (shading) stationary waves represented by
645
summer 500 hPa geopotential heights for (a) total wave and (b–e) zonal wavenumbers
646
1–4. The stationary waves are obtained by removing the zonal mean of the 30
647
geopotential heights. R denotes the spatial coefficients between the SSD forced and
648
climatological waves. Contour intervals are 10 and 4 gpm for the climatological and
649
SSD forced total waves, respectively, and 5 and 2 gpm for climatological and forced
650
waves of zonal wavenumbers 1–4, respectively. The rectangles are same as those in
651
Figure 2.
652
Figure 10. CAM3.1 simulations of (a) spring SWE and (b) SSD (shadings; units: mm) anomalies
653
in response to the anomalous snowfall rate over the Eurasian continent. The thin and
654
thick black lines denote values with statistical significance exceeding the 90% and 95%
655
confidence levels, respectively.
656
Figure 11. CAM3.1 simulations of summer (a) surface air temperature (shadings, units: °C), (b)
657
atmospheric thickness (Z500-Z1000), (c) 700 hPa meridional temperature gradient
658
(shadings; units: °C/grid), (d) 200 hPa zonal wind (shadings, units: m/s), (e) 500 hPa
659
geopotential height (shadings, units: gpm) anomalies and (f) 500 hPa stream function
660
(shadings, units: m2/s) and associated wave activity flux (vectors, units: m/s)
661
anomalies. The thin and thick black lines denote values with statistical significance
662
exceeding the 90% and 95% confidence levels, respectively. The rectangles are same
663
as those in Figure 2.
664
Figure 12. Same as Figure 9 but for CAM3.1 simulations.
665
Figure 13. CAM3.1 simulations of summer precipitation anomalies (shadings, units: mm). The
666
thin and thick black lines denote values with statistical significance exceeding the 90%
667
and 95% confidence levels, respectively.
668
Figure 14. Partial correlation coefficients of SSDI index with East Asian summer precipitation
669
when the linear parts related to the (a) Nino3.4 and (b) Arctic sea ice concentration 31
670
(SIC) are removed. The Nino3.4 and Arctic SIC are detrended before calculating the
671
correlation coefficients. Purple and black dotted areas denote values with statistical
672
significance exceeding the 90% and 95% confidence levels, respectively.
32
673 674
Figure 1. Climatological annual cycle of monthly snow water equivalent (SWE, light blue bars)
675
and snow decrement (SD, orange bars) over the Eurasian continent (35º–76ºN, 0º–150ºE) during
676
1980–2013 (Units: mm). The data in June-September are absent. Positive and negative SDs
677
represent increase and decrease SWE in succeeding month with respect to the previous one.
678
33
679 680
Figure 2. (a) Spatial pattern (shadings, units: mm) of the second EOF (EOF2) and (b) detrended
681
time series of the second principal component (PC2, black line) of SSD over the Eurasian
682
continent during 1980–2013. The blue line in (b) shows the SSDI index defined as the difference
683
of SSD between the rectangles with positive values (46º–58ºN, 96º–138ºE) and negative values
684
(54º–68ºN, 48º–84ºE) shown in (a). Purple and black dotted areas in (a) denote values with
685
statistical significance exceeding the 90% and 95% confidence levels, respectively.
686
34
687 688
Figure 3. (a) Correlation coefficients between the SSDI index and the subsequent summer
689
precipitation, (b) spatial pattern of the third EOF mode of East Asian summer precipitation, and (c)
690
detrended time series of SSDI and summer precipitation (PC3). R represents the correlation
691
coefficient between the two indices. Purple and black dotted areas denote values with statistical
692
significance exceeding the 90% and 95% confidence levels, respectively.
693
35
694 695
Figure 4. (a) Late spring (AMJ) and (b) summer (JJA) 100-289 cm soil moisture (SM) anomalies
696
(shadings; units: m3/m3) regressed onto the SSDI index. Purple and black dotted areas denote
697
values with statistical significance exceeding the 90% and 95% confidence levels, respectively.
698
The rectangles are same as those in Figure 2.
699
36
700 701
Figure 5. Summer (JJA) (a) surface solar radiation flux, (b) longwave radiation flux, (c) sensible
702
heat flux, (d) latent heat flux, and (e) net heat flux anomalies (shadings; units: W/m2) regressed
703
onto the SSD index. Purple and black dotted areas denote values with statistical significance
704
exceeding the 90% and 95% confidence levels, respectively. The rectangles are same as those in
705
Figure 2.
706
37
707 708
Figure 6. (a) Surface temperature (shadings; units: °C), (b) atmospheric thickness between 500
709
hPa and 1000 hPa (Z500-Z1000, units: gpm), and (c) 700 hPa meridional temperature gradient
710
(shadings; units: °C/grid) anomalies during summer regressed onto the SSDI index. Purple and
711
black dotted areas denote values with statistical significance exceeding the 90% and 95%
712
confidence levels, respectively. The rectangles are same as those in Figure 2.
38
713 714
Figure 7. (a) 200 hPa zonal wind (shadings; units: m/s) and (b) 500 hPa geopotential height
715
(shadings; units: gpm) anomalies regressed onto the SSDI index. The climatological zonal winds
716
with speed greater than 20 m/s are enclosed by the black lines in (a). Purple and black dotted
717
areas denote values with statistical significance exceeding the 90% and 95% confidence levels,
718
respectively. The rectangles are same as those in Figure 2.
719
39
720 721
Figure 8. 500 hPa stream function (shading; units: m2/s) and the associated wave activity flux
722
(vector; units: m2/s2) anomalies regressed onto the SSDI index during (a) late spring (AMJ) and (b)
723
summer (JJA). The rectangles are same as those in Figure 2.
724
40
725 726
Figure 9. SSD forced (contours) and climatological (shading) stationary waves represented by
727
summer 500 hPa geopotential heights for (a) total wave and (b–e) zonal wavenumbers 1–4. The
728
stationary waves are obtained by removing the zonal mean of the geopotential heights. R denotes
729
the spatial coefficients between the SSD forced and climatological waves. Contour intervals are
730
10 and 4 gpm for the climatological and SSD forced total waves, respectively, and 5 and 2 gpm
731
for climatological and forced waves of zonal wavenumbers 1–4, respectively. The rectangles are
732
same as those in Figure 2.
733
41
734
735 736
Figure 10. CAM3.1 simulations of (a) spring SWE and (b) SSD (shadings; units: mm) anomalies
737
in response to the anomalous snowfall rate over the Eurasian continent. The thin and thick black
738
lines denote values with statistical significance exceeding the 90% and 95% confidence levels,
739
respectively.
740
42
741 742
Figure 11. CAM3.1 simulations of summer (a) surface air temperature (shadings, units: °C), (b)
743
atmospheric thickness (Z500-Z1000) (shadings, units: gpm), (c) 700 hPa meridional temperature
744
gradient (shadings; units: °C/grid), (d) 200 hPa zonal wind (shadings, units: m/s), (e) 500 hPa
745
geopotential height (shadings, units: gpm), and (f) 500 hPa stream function (shadings, units: m2/s)
746
and associated wave activity flux (vectors, units: m/s) anomalies. The thin and thick black lines
747
denote values with statistical significance exceeding the 90% and 95% confidence levels,
748
respectively. The rectangles are same as those in Figure 2.
749
43
750 751
Figure 12. Same as Figure 9 but for CAM3.1 simulations.
752
44
753 754
Figure 13. CAM3.1 simulations of summer precipitation anomalies (shadings, units: mm). The
755
thin and thick black lines denote values with statistical significance exceeding the 90% and 95%
756
confidence levels, respectively.
757
45
758 759
Figure 14. Partial correlation coefficients of SSDI index with East Asian summer precipitation
760
when the linear parts related to the (a) Nino3.4 and (b) Arctic sea ice concentration (SIC) are
761
removed. The Nino3.4 and Arctic SIC are detrended before calculating the correlation coefficients.
762
Purple and black dotted areas denote values with statistical significance exceeding the 90% and
763
95% confidence levels, respectively.
46
