Increased Impact of the Tibetan Plateau Spring Snow Cover to the Mei-yu Rainfall over the Yangtze River Valley after the 1990s

Chao Zhang aKey Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Zhejiang, China
bDepartment of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, China

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XiaoJing Jia aKey Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Zhejiang, China

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Zhiping Wen bDepartment of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, China
cJiangsu Collaborative Innovation Center for Climate Change, Nanjing, China

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Abstract

This study investigated the increased impact of the spring (March–May) snow-cover extent (SCE) over the western Tibetan Plateau (TP) (SSTP) to the mei-yu rainfall [June–July (JJ)] over the Yangtze River valley (YRV) (MRYRV) after the 1990s. The correlation between the MRYRV and SSTP is significantly increased from the period of 1970–92 (P1) to 1993–2015 (P2). In P1, the MRYRV-related SSTP anomalies are located over the southwest TP, which causes a perturbation near the subtropical westerly jet (SWJ) core and favors an eastward propagation in the form of a wave train. The wave train results in a southward shift of the SWJ over the ocean south of Japan in JJ and exerts a limited effect on the MRYRV. Differently, in P2, the MRYRV-related anomalous SSTP causes an anomalous cooling temperature and upper-level cyclonic system centered over the northwestern TP. The cyclonic system develops and extends eastward to the downstream region with time and reaches coastal East Asia in JJ. The anomalous westerly winds along its south flank cause an enhanced SWJ, which is accompanied by an anomalous lower-level air convergence and ascent motion near the YRV region, favoring enhanced MRYRV. In addition, the forecast experiments performed with empirical regression models illustrate that the prediction skill of the MRYRV variation is clearly increased in P2 with the additional forecast factor of the SSTP.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiaojing Jia, jiaxiaojing@zju.edu.cn

Abstract

This study investigated the increased impact of the spring (March–May) snow-cover extent (SCE) over the western Tibetan Plateau (TP) (SSTP) to the mei-yu rainfall [June–July (JJ)] over the Yangtze River valley (YRV) (MRYRV) after the 1990s. The correlation between the MRYRV and SSTP is significantly increased from the period of 1970–92 (P1) to 1993–2015 (P2). In P1, the MRYRV-related SSTP anomalies are located over the southwest TP, which causes a perturbation near the subtropical westerly jet (SWJ) core and favors an eastward propagation in the form of a wave train. The wave train results in a southward shift of the SWJ over the ocean south of Japan in JJ and exerts a limited effect on the MRYRV. Differently, in P2, the MRYRV-related anomalous SSTP causes an anomalous cooling temperature and upper-level cyclonic system centered over the northwestern TP. The cyclonic system develops and extends eastward to the downstream region with time and reaches coastal East Asia in JJ. The anomalous westerly winds along its south flank cause an enhanced SWJ, which is accompanied by an anomalous lower-level air convergence and ascent motion near the YRV region, favoring enhanced MRYRV. In addition, the forecast experiments performed with empirical regression models illustrate that the prediction skill of the MRYRV variation is clearly increased in P2 with the additional forecast factor of the SSTP.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiaojing Jia, jiaxiaojing@zju.edu.cn

1. Introduction

Over the East Asia (EA) subtropical regions [e.g., the Yangtze River valley (YRV) of China, central Japan], the rainy season, also named mei-yu, is closely related to the East Asian summer monsoon (EASM) (e.g., Tao and Chen 1987; Ding 1992; Ding et al. 2020). The vast interannual variability in mei-yu rainfall over the YRV (MRYRV) for both torrential rainfall days and total amount rainfall leads to floods or droughts in the YRV (e.g., Gao et al. 2016; Tung et al. 2020; Yao et al. 2020; Zhang et al. 2020), bringing about wide-ranging natural disasters, loss of life, and injuries (Gao et al. 2016). For instance, the 1998 summer devastating flood in the YRV directly caused 3004 deaths and economic losses of nearly $26 billion (U.S. dollars; Wang et al. 2012; Dou et al. 2020) and an approximately $10 billion loss in the 2013 summer drought (Sun et al. 2014; Yuan et al. 2016).

As the primary component of the EASM, the MRYRV is influenced by the combined effects of tropical and mid–high-latitude systems (e.g., Lau et al. 2000; Ding et al. 2020) and displays some special features due to global warming and urbanization (Kimoto 2005; Ma and Zhang 2015; Lau 2016; Liang et al. 2018). For example, Webster (2006) proposed that the strong cross-equatorial pressure gradient is the key physical factor that can modulate the EASM rainfall by cross-equatorial flow. Land conditions, for example, snow cover and soil moisture, can exert effects on the EASM rainfall by changing the large-scale energy and water budget (e.g., Yasunari 2006). The role of large-scale terrain forcing, such as the thermal and dynamic impacts of the Tibetan Plateau (TP) on the EASM, has also been extensively investigated in previous works (Yanai and Wu 2006; Wu et al. 2007, 2012, 2015; Wang et al. 2018). Yanai and Wu (2006) indicated that abnormal TP diabatic heating forcing can strengthen the YRV frontal rainfall by modulating the Rossby wave trains. The TP heating-associated deformed western Pacific subtropical high facilitates the transport of more moisture to the mei-yu regions. Wu et al. (2007, 2012, 2015) demonstrated that the TP–sensible heat air pump–related potential vorticity forcing favors stimulating a large-scale cyclonic system surrounding the TP in the lower troposphere, providing favorable moisture conditions for the YRV summer rainfall.

In addition, the TP snow can also impact the EASM and circulations on multiple time scales (Zhang et al. 2004; Zhao et al. 2007; Wu et al. 2012a,b; Si and Ding 2013; Fan et al. 2014; Liu et al. 2014; Xiao and Duan 2016; W. K. Li et al. 2018). Xiao and Duan (2016) revealed that the preceding winter TP snow-cover extent (SCE) anomalies over the Himalayas can prolong the signature to the following summer and can impact the summer interannual rainfall variability along the YRV through eastward-propagating water vapor and synoptic disturbances. W. K. Li et al. (2018) argued that on a medium time scale, the SCE anomalies over the TP in winter are closely related to the subsequent atmospheric circulation over EA. Wang et al. (2018) illustrated that the western and southern TP SCE anomalies in summer can modulate the EASM through different pathways. Si and Ding (2013) advocated that the change in the winter TP snow depth after the late 1990s is responsible for the decadal northward advanced YRV summer rainfall. The remarkable increased sensible heat and longwave radiation associated with the decadal reduction in the winter TP snow depth intensify the land–sea thermal contrast in the ensuing spring–summer, thereby causing the northward migration of the EASM rainfall. The possible influence of TP snow on the YRV summer rainfall variability at the decadal time scale has been investigated (e.g., Zhang et al. 2004; Xu et al. 2012; Si and Ding 2013; Liu et al. 2014). These researchers found that changes in the preceding winter–spring TP snow are responsible for the northward advanced YRV summer rainfall.

Many possible reasons have been proposed to explain how TP snow can impact the climate over the YRV. First, the snow hydrology effect (Zhang et al. 2004; Xiao and Duan 2016) occurs, in which excessive snow can melt into water vapor. Accompanied by westerly or eastward-propagating synoptic disturbances, the moisture generated over the TP can be brought to the YRV. The diabatic cooling effect of the TP snow (Barnett et al. 1988, 1989; Wu et al. 2012a,b; Jia et al. 2020) associated with upper troposphere low pressure response and then the TP snow-associated alternatively positive–negative wave trains are beneficial for energy dispersion eastward to the downstream YRV. Moreover, the albedo effect ensues (Si and Ding 2013; Liu et al. 2014), excessive TP snow can enlarge the land–ocean thermal contrast by reflecting more shortwave radiation over the TP and its surroundings. Accordingly, the expanded land–ocean thermal contrast can further modulate the YRV rainfall by means of the EASM.

The YRV summer rainfall exhibited a pronounced regime shift in the 1990s (Zhang et al. 2004; Si and Ding 2013; Fan et al. 2014). Meanwhile, the interdecadal changes in TP snow were also found in the 1990s, with snow over the eastern TP switching to a positive phase after the 1990s, contrary to the western TP (Wu et al. 2012a,b; Qian et al. 2020; Jia et al. 2021). However, it is unclear whether and how the relationship between the MRYRV and the SCE over the TP changes. If so, what are the dynamic mechanisms accounting for the changed relationship? Another issue we want to know is to what extent could the TP snow be used as a predictor to improve the seasonal forecast of MRYRV. The present study attempts to address these questions. Following the above introduction in the first section, section 2 introduces the dataset and methods. The interdecadal variations in the snow–rainfall relationship are provided in section 3. Section 4 explores the possible mechanism for the interdecadal changed TP snow–MRYRV relationship. Section 5 describes seasonal forecast experiments performed for the MRYRV variation. The last section presents the summary and discussion.

2. Data and methodology

a. Datasets

The datasets applied in the present study are presented in the following. 1) The observed daily precipitation data across China are available from the China Meteorological Administration (http://data.cma.cn/en/?r=data/index&cid=6d1b5efbdcbf9a58). The daily continuous records based on 824 gauge stations were interpolated into a horizontal 1° × 1° resolution and then were converted into monthly mean precipitation data covering 1970–2015. 2) The monthly snow-cover dataset was collected from the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (Robinson et al. 2012). The original Northern Hemisphere weekly snow cover with a horizontal 25-km resolution spanning from 1970 to the present was obtained from the Rutgers University Snow Laboratory (http://climate.rutgers.edu/snowcover/). The monthly mean data were converted to a resolution of 2° × 2° grid by those original data. 3) The monthly mean atmospheric radiation variables with T62 Gaussian grids including the upward solar radiation, upward longwave radiation, sensible heat flux, latent heat flux, and circulation variables with a spatial resolution of 2.5° × 2.5° containing the geopotential height, air temperature, winds, specific humidity, vertical velocity, surface pressure, and precipitation rate acquired from the National Centers for Environmental Prediction reanalysis from 1970 to the present (Kalnay et al. 1996; http://www.esrl.noaa.gov/psd/).

To describe the El Niño–Southern Oscillation (ENSO) variation, the boreal winter to spring (December–May) Niño-3.4 index is used in the current work with the time span from 1970 to the present, which is collected from the Earth System Research Laboratory of NOAA (https://www.esrl.noaa.gov/psd/data/climateindices/list).

b. Methods

In the present study, the mei-yu areas refer to the YRV (28°–34°N, 110°–122°E) in China, which is followed by Ding et al. (2020). The mei-yu season, followed by previous work (e.g., Yao et al. 2020), refers to June–July (JJ). The dataset is divided into two subperiods, based on the correlation between the spring (March–May) SCE over the western TP (SSTP) and the MRYRV, which is insignificant during 1970–92 (P1), while it becomes pronounced during 1993–2015 (P2). Because we focus on the interannual relationship between snow and rainfall, accordingly, a Fourier harmonic bandpass filter is applied to the dataset in the current work, and only the variation, with time scales shorter than 8 years, is retained. Partial regression analysis is utilized to remove the possible impact of the ENSO signals to the TP snow–MRYRV relationship. Other regular statistical tools, including linear regression analysis and Student’s t test, are also used in this work.

To inspect the atmospheric wave train propagation dynamics, a phase-independent wave activity flux (WAF) is calculated via stationary Rossby waves based on the anomalies and the corresponding climatological mean. The horizontal component of the WAF was given by Takaya and Nakamura (2001), who expressed the following formulation:
W=12|U|[U(φx2φφxx)+V(φxφyφφxy)U(φxφyφφxy)+V(φy2φφyy)],
where U = (U, V) and φ denote the geostrophic winds and streamfunction, respectively. The prime symbol refers to deviations from the time mean, and the subscripts indicate partial derivatives.
The vorticity forcing formulation is applied to verify the Rossby wave source (RWS). Following Sardeshmukh and Hoskins (1988), the linearized RWS formulation is expressed as follows:
S=H[uχ(f+ζ¯)]H[u¯χζ],
where uχ = (uχ, υχ) refers to the zonal and meridional divergent wind, ∇H indicates the horizonal gradient, and subscript χ denotes the component of the divergence. The prime symbol indicates the variable anomalies, and the overbar represents its corresponding climatological mean.

3. Decadal changes in the TP snow–MRYRV relationship

The climatological mean JJ rainfall in China and the vertically integrated moisture are depicted in Fig. 1a. The JJ rainfall in China is mainly concentrated over Southwest China, with a sharp reduction in inland China. The airflow that can transport the water vapor from the JJ rainfall mainly contains two branches. The first is the southerly along the west flank of the western Pacific subtropical high (WPSH). Another is the southwesterly associated with the EASM, which prevails over the Bay of Bengal and South China Sea and advances northward to Southwest China and the YRV.

Fig. 1.
Fig. 1.

(a) The spatial distribution of the climatological JJ rainfall over China (shading; mm day−1) and the vertically integrated (from the surface to 300 hPa) moisture (vector; kg m−1 s−1) for 1970–2015. (b) The normalized MRI (solid color bars), which is defined by averaging the JJ rainfall over the mei-yu region (110°–122°E, 28°–34°N). The high-frequency component of the MRI is represented by transparent bars. (c) The regression map of the JJ rainfall (mm day−1) against MRI for 1970–2015. The dotted areas represent the significant anomalies at the 95% confidence level. The purple rectangles in (a) and (c) denote the mei-yu region in the Yangtze River valley.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

To represent the variation in the MRYRV, an index is constructed by averaging the precipitation over the YRV region (110°–122°E, 28°–34°N; purple box in Fig. 1a) for epoch 1970–2015 and is shown as solid color bars in Fig. 1b. To focus on the interannual variability of the MRYRV, the index is highly filtered and is referred to as the mei-yu rainfall index (MRI) (transport bars in Fig. 1b). The anomalous rainfall related to the MRI is obtained by regression and is presented in Fig. 1c. Significant positive rainfall appears over the YRV region, and a negative rainfall anomaly can be observed over South China and Southwest China. This result indicates that the MRI can well represent the MRYRV variability and is used in the following analysis.

The spatial distributions of the climatological mean SCE over the TP in winter and spring are depicted in Figs. 2a and 2b (contour), and the standard deviations are overlaid as shadings. Apart from the fact that the magnitude of the winter SCE is larger than those in the spring, the spatial patterns of the climatological mean SCE in winter and spring show many similarities with two maximum SCE centers located over the western and central-eastern TP (represented by purple and blue rectangles in Figs. 2a,b), consistent with previous work (e.g., Wang et al. 2019). The climatological mean snow cover in these regions account for more than 90% of the snow cover during winter and spring. The large variability in the SCE matches the large climatological mean SCE over the TP. Accordingly, area-averaged western and eastern TP snow indices are constructed for winter and spring by averaging the SCE over the key areas (represented by the boxes in Figs. 2a,b) and are presented in Figs. 2c and 2d.

Fig. 2.
Fig. 2.

The spatial distributions of the standard deviation (shading; %) and climatological mean SCE (contour; %) for (a) winter and (b) spring during 1970–2015. The purple (69°–83°E, 29.5°–41°N) and blue (88°–102°E, 26°–35°N) rectangles in (a) and (b) indicate the key regions that have been used to construct the western and eastern TP snow indices. The high-filtered western (transparent bars) and eastern (solid bars) TP snow indices for (c) winter and (d) spring.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

To examine whether the relationship between the MRYRV and TP snow experiences interdecadal changes, the sliding correlation between the MRI and the TP snow indices with a 21-yr window is calculated and displayed in Fig. 3. The results are not sensitive to the length of the sliding correlation coefficient window. The temporal correlation coefficient (TCC) between the MRI and the TP snow indices in winter and spring varies with time. Generally, the TCCs are insignificant before 1993 but gradually increase with time. After 1993, the TCC of the MRI and spring SCE over the western TP index (SSTPI) is statistically significant. It suggests that the MRYRV is only closely correlated with the variation in the spring SCE over the western TP and that the correlation is only significant after 1993. Accordingly, the dataset is divided into two periods, 1970–92 (P1) and 1993–2015 (P2), and the focus is investigating the changed relationship between the MRYRV variation and western TP snow.

Fig. 3.
Fig. 3.

The sliding correlation coefficients, with a 21-yr window, between the TP snow indices and the MRI. The curves with open dots denote the sliding correlation between the MRI and the western TP snow index in winter (blue) and spring (red). The curves with filled dots denote the sliding correlation between the MRI and the eastern TP snow index in winter (blue) and spring (red), respectively. The horizontal dashed lines are significant correlation coefficients at the 90% and 95% levels.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

The anomalies of the JJ rainfall associated with the SSTPI for P1 and P2 are depicted in Figs. 4a and 4b, respectively. The SSTPI-associated JJ rainfall anomalies are basically insignificant over the YRV during P1 (Fig. 4a), while a prominent positive anomaly appears over the YRV during P2 (Fig. 4b), consistent with Fig. 3. In consideration of the possible impact of the ENSO on the TP snow–MRYRV relationship, Figs. 4c and 4d presents the partial regression results after the winter–spring (December–May) ENSO signals are removed. The results are quite similar to Figs. 4a and 4b, suggesting that the relationship between the spring western TP snow and the MRYRV is ENSO independent. We also examined the spring western TP snow related anomalous MRYRV while removing the June–July ENSO signal and Indian Ocean dipole signal (not shown), the results are quite similar to Figs. 4c and 4d, indicating that the relationship between the western TP and MRYRV is robust.

Fig. 4.
Fig. 4.

The anomalies of JJ rainfall (shadings; mm day−1) regressed against the SSTP index for (a) P1 and (b) P2. (c),(d) As in (a) and (b), but for the partial regression maps after removing the ENSO signals. Open and dotted areas indicate the significant anomalies at the 90% and 95% levels, respectively.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

To better understand the TP snow–MRYRV linkage, the low-level atmospheric circulation anomalies associated with the SSTPI in JJ are depicted in Fig. 5. Obvious differences of the MRYRV related circulation anomalies can be observed between P1 and P2. During P1, a cyclonic anomaly prevails over coastal EA centered over the ocean south of Japan (Fig. 5a). To the west side of this cyclone, northeasterly winds and moisture divergence appear over the YRV regions (Fig. 5c). In contrast, in P2, responding to positive SSTPI, a cyclonic system can be seen lying over Northeast China (Fig. 5b). Anomalous northerly winds along the west flank of this cyclone prevail in northern and eastern China, which meet the anomalous southwesterly winds associated with the western North Pacific anticyclone (WNPAC), causing pronounced moisture convergence over the YRV region and favoring a greater-than-normal MRYRV (Fig. 5d).

Fig. 5.
Fig. 5.

The partial regression maps of (a),(b) JJ 850-hPa wind [vector; scale at the top-right corner of (b); m s−1] and (c),(d) vertically integrated (from the surface to 300 hPa) moisture [vector; scale at the top-right corner of (d); kg m−1 s−1] and divergence (shadings; 10−6 kg−2 s−1) upon the SSTPI after removing ENSO signals for P1 in (a) and (c) and P2 in (b) and (d). The gray and dark vectors indicate the significant anomalies at the 90% and 95% levels, respectively. The dotted areas in (c) and (d) denote the significant anomalies at the 95% confidence level.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

4. Possible mechanisms of the changed TP snow–MRYRV relationship

In the last section, we demonstrate that the relationship between the spring TP snow and the MRYRV experienced interdecadal changes in the early 1990s. In this section, we will further explore the possible reasons for the changed TP snow–MRYRV relationship.

First, to shed light on the persistence of the spring SCE over the TP, the time evolution of the anomalous SCE over the TP from spring to the following summer for both epochs is obtained by regressing the SCE onto the SSTPI, and the results are depicted in Fig. 6. During P1, significant positive SCE anomalies are observed and limited to the western and southwestern TP and continue to the following May–June (MJ). Negative SCE anomalies can also be noticed over the eastern TP during this period (Figs. 6a,c). In JJ, the SSTPI-related SCE anomalies are weak with significant regions mainly over southwestern TP (Fig. 6e). In contrast, during P2, the SSTPI-related positive SCE anomalies are more significant than those in P1 and prevail over the whole TP (Fig. 6b). The anomalous SCE can also persist to the following MJ (Fig. 6d), while in JJ, only the western and northwestern TP has a significant anomalous SCE, which is mainly located north of 37°N (Fig. 6f). In summary, the MRYRV related positive SCE anomalies over the TP can persist from spring to the following JJ in both epochs, implying a persistent impact over the TP spring snow forcing to the following season. However, the differences in the SSTP anomaly patterns also suggest possible different climate impacts to the local climate between P1 and P2.

Fig. 6.
Fig. 6.

Anomalous SSTP-related TP SCE (shadings; %) in (a),(b) spring (March–May), (c),(d) MJ, and (e),(f) JJ obtained by regression for P1 in (a), (c), and (e) and P2 in (b), (d), and (f). Open and dotted areas indicate the significant anomalies at the 90% and 95% levels, respectively.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

The effects of the SSTP anomalies on the local climate in spring for P1 and P2 are examined by analyzing the surface heat fluxes over the TP and are depicted in Figs. 7 and 8, respectively. During P1, corresponding to positive anomalous SSTP, pronounced negative surface temperature can be observed dominating the southwestern TP (Fig. 7a), and SSTP-induced negative temperature anomalies prevail over the whole troposphere (Fig. 7b). The heat flux analysis shows that more snow cover at the surface causes increased upward shortwave radiation (USWR) over the western TP owing to the increased snow-related albedo effect (Fig. 7c). Increased USWR also means less downward shortwave radiation absorbed by the ground and causes anomalous cooling at the surface; therefore, the upward sensible heat flux and longwave radiation (ULWR) are negative (Figs. 7d,e). Moreover, the excessive snow cover can also intercept the air–land energy exchange, which results in a negative upward latent heat flux (Fig. 7f). The effects of the SSTP on the local climate in P2 are similar to those in P1 (Fig. 8); however, differences can also be noticed between them. Compared to P1, the SSTP-related local climate anomalies around the TP tend to be located more northward in P2, probably caused by a more northward location of the SSTP-related SCE anomalies in P2 than P1 (Fig. 6). The latitude–pressure cross section of anomalous air temperature averaged from 69° to 83°E (western TP) associated with the SSTPI are also examined (not shown). The significant negative air temperature associated with the TP snow centered at about 35°N in P1 while that centered at 45°N in P2.

Fig. 7.
Fig. 7.

Spring (a) surface air temperature (shading; °C), (b) longitude–pressure cross sections of air temperature (shading; °C) averaged from 28° to 38°N, (c) USWR (shading; W m−2), (d) ULWR (shading; W m−2), (e) sensible heat (SH) (shading; W m−2), and (f) latent heat (LH) (shading; W m−2) anomalies regressed against the SSTPI for P1. The stippling areas refer to the significant anomalies at the 95% level.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for P2.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

In general, the heat flux analysis suggests that the anomalous SSTP can exert cooling effects on the overlying air by modulating the energy budget in both P1 and P2. The SSTP-related climate anomalies around the TP tend to be northward in P2 than in P1, which may cause different climate impacts on the downstream regions.

Figure 9 displays the time evolution of the SSTP-related large-scale atmospheric circulation anomalies in the upper troposphere from spring to the subsequent JJ for both epochs. The corresponding WAF anomalies are presented in Fig. 10. In P1, a significant anomalous cyclone prevails over the western TP in spring, centered 35°N, 60°E (Fig. 9a). This cyclonic system, consistent with the anomalous positive SSTP and anomalous cooling over the southwestern TP, displays the atmospheric response to the underlying snow forcing. In the following MJ, the cyclone persists and intensifies with a positive and a negative height anomalies developed in the downstream region, centered over central North China and coastal EA, forming a zonal-oriented wave pattern dominating the extratropical east Eurasian–western North Pacific regions (Fig. 9b). Associated with this wave pattern, significant WAF can be observed, originating from the western TP and flowing eastward to the downstream regions (Figs. 10a,b). In JJ, this wave pattern and the wave flux remain and propagate westward with a significant cyclone dominating coastal EA centered over south of Japan (Figs. 9c, 10c). During P2, SSTP anomalies are also associated with pronounced cyclonic anomalies over the western TP in spring (Fig. 9d). Compared to P1, this cyclone is more northward and centered at approximately 45°N in spring. Different from P1, instead of a wave pattern, the SSTP related cyclonic anomalies develop with time and extend to the downstream region in the following MJ (Fig. 9e). In JJ, it across Lake Baikal and centered over Northeast China (Fig. 9f).

Fig. 9.
Fig. 9.

The partial regression maps at a 200-hPa geopotential height (shading; gpm) and wind (vector; m s−1) in (a),(d) March–May, (b),(e) MJ, and (c),(f) JJ onto the SSTPI after removing the ENSO signals for P1 in (a), (c), and (e) and P2 in (b), (d), and (f). The dotted areas indicate the significant anomalies at the 95% level.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

Fig. 10.
Fig. 10.

As in Fig. 9, but for 200-hPa wave activity flux (vector; m2 s−2) and geopotential height (shading; gpm).

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

As we mentioned before, the key system that links spring TP snow and the MRYRV is the cyclone over coastal EA (Fig. 5). Figure 9 shows that the anomalous SSTP in both P1 and P2 can induce a cyclonic response in JJ over coastal EA. However, a comparison between Figs. 9c and 9f suggests that the cyclone over coastal EA in P2 tends to be more northward in position than P1, which is attributed to the differences in the locations of the SSTP-related anomalies in spring. To further understand the different developments of the atmospheric response to the anomalous SSTP in P1 and P2, the SSTP-related RWS in spring is calculated and displayed in Fig. 11 (shading). The climatological subtropical westerly jet (SWJ) is overlaid as contours in Fig. 11. During P1, large negative RWS anomalies are observed over the western TP, with positive and negative RWS anomalies in the downstream region along the SWJ (Fig. 11a). The results indicate that in P1, the SSTP-related climate anomalies generate perturbations near the SWJ that easily develop over time. As pointed out in previous work, the SWJ can confine meridional dissipation and favor disturbances propagating eastward to the downstream regions in the form of a wave pattern. In contrast, in P2, the SSTP-related RWS is relatively weak in magnitude and not well organized (Fig. 11b), which is not favorable for wave pattern development.

Fig. 11.
Fig. 11.

Anomalies of the spring western TP SCE-associated 200-hPa RWS (shading; 10−11 s−2) with the ENSO signals removed for (a) P1 and (b) P2. The black contours refer to the climatological zonal wind greater than 30 m s−1.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

To inspect how the cyclonic system over coastal EA impacts the MRYRV in JJ, the anomalous zonal winds (Figs. 12a,b) and the midtropospheric vertical velocity (Figs. 12c,d) associated with SSTP variations are calculated. The climatological zonal winds in JJ are overlaid as contours in Figs. 12a and 12b. During P1, significant positive anomalous zonal winds can be noticed south of the SWJ centered over the ocean south of Japan, suggesting a southward shift of the SWJ in this period (Fig. 12a). Associated with the changes in the SWJ, pronounced ascending airflows mainly prevail over the western North Pacific. The anomalous motion over the YRV region is weak (Fig. 12c). During P2, positive anomalous zonal winds overlap on the climatological SWJ, suggesting an accelerated SWJ (Fig. 12b). An anomalous belt of ascending motion prevails over the YRV region, highly favoring the MRYRV during this period.

Fig. 12.
Fig. 12.

The partial regression panels of JJ (a),(b) 200-hPa zonal wind (shading; m s−1) and (c),(d) 500-hPa vertical velocity (shading; ×10−3 Pa s−1) against the SSTPI after removing the ENSO signals for P1 in (a) and (c) and P2 in (b) and (d). The black contours in (a) and (b) refer to the climatological zonal wind greater than 25 m s−1. The stippling areas denote the significant anomalies at the 95% level.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

5. Seasonal prediction

In the previous section, we demonstrated that the anomalous SSTP can impact the MRYRV by modulating the SWJ after the early 1990s. Previous studies have revealed that the ENSO is a potential predictor of the MRYRV variation (Huang and Wu 1989; Wu et al. 2009; Xiao et al. 2015). To test to what extent SSTP could be used to perform seasonal forecasting for the MRYRV, we established a multiple linear regression model using Niño-3.4 and SSTPI as predictors to predict the observational MRI. The forecasting regression model can be expressed as
MRINiño3.4=α0+α1Niño3.4+ξ1,and
MRINiño3.4+SSTPI=β0+β1Niño3.4+β2SSTPI+ξ2,
where α0, β0, α1, β1, and β2 denote the regression coefficients, and ξ1 and ξ2 refer to the residuals. In the first forecasting model, only the Niño-3.4 index is used to establish the model, while in the second forecasting model, both Niño-3.4 and SSTPI are used to construct the forecasting. The differences in the forecasts between them implies a potential contribution of the SSTP to MRI forecasting.

Following Michaelsen (1987) and Lin and Wu (2011), to avoid data wasting or overfitting, test years accounting for 20%–30% of the total hindcast period are proposed, whereas the remaining years are used as the training set to construct the forecasting regression model. Thus, a “leaving 5 out” scheme is used in the forecasting experiments. Five years in each period account for approximately 22% of the whole hindcast epoch (23 years). Figure 13 displays the hindcast results of the MRI using the two forecasting models for both subperiods. During P1, the TCC of the observational MRI (blue line) and the hindcast with MRINiño-3.4 is 0.24, which is the same to the hindcast with MRINiño-3.4+SSTPI and does not exceed the confidence level. In contrast, during P2, the TCC between the observational MRI and the hindcast with MRINiño-3.4 is 0.28, while it reaches 0.61 with MRINiño-3.4+SSTPI, exceeding the 0.01 confidence level. These results indicate that the SSTPI is a useful predictor and can be used to improve the seasonal forecast skill of the MRI variation in P2, while it does not play an important role in P1.

Fig. 13.
Fig. 13.

(a) Time series of the observational MRI (blue curve), its hindcast with the Niño-3.4 index (red curve), and its hindcast with both the Niño-3.4 and SSTP indices (green curve) obtained by the empirical regression models during P1. (b) As in (a), but for P2.

Citation: Journal of Climate 34, 14; 10.1175/JCLI-D-21-0009.1

6. Conclusions and discussion

Previous studies have demonstrated that winter–spring TP snow is positively correlated with the MRYRV. In the current study, we found that the interannual variation of the MRYRV is only correlated with the SCE anomalies over spring and only limited to the western TP, and moreover this MRYRV–SSTP relationship is not steady. The positive correlation of the TP snow and MRYRV, independent of the ENSO, is insignificant during 1970–92 (P1), whereas it switches to being prominent during 1993–2015 (P2). The possible mechanisms responsible for the changed TP snow–MRYRV relationship are explored, and an empirical regression model is established to perform experiments to further test the importance of the TP snow forcing to the MRYRV.

Compared to P1, in P2, the MRYRV-related SSTP anomalies are more significant and have better persistence in the following early summer. In spring, the MRYRV-related SSTP anomalies cause anomalous cooling temperatures and upper-level low pressure systems centered over the northwestern TP at approximately 45°N. This low pressure system extends eastward and propagates to the downstream region with time. In JJ, the low pressure system reached coastal EA and centered Northeast China. The anomalous westerly winds along its south flank caused an enhanced SWJ. The changes in the SWJ are accompanied by an anomalous lower-level convergence and ascent motion near the YRV region, which favor an anomalous MRYRV. In contrast, in P1, compared to P2, the MRYRV-related SSTP anomalies is over southwest TP and can cause perturbations near the SWJ core, which favors eastward propagation in the form of a wave train. In the following JJ, the wave train causes a southward shift of the SWJ over the ocean south of Japan, resulting in pronounced ascent motion over the ocean, and accordingly exerting a limited effect on the MRYRV. In addition, regression models are constructed based on the Niño-3.4 index and the SSTPI to perform seasonal forecast experiments for the MRI. The hindcast with the TP snow precursor shows an obviously improved prediction skill after the early 1990s.

The current work explored the changed relationship between the MRYRV and the spring TP snow. However, the causes of the interdecadal changes in the TP snow are not clear. In addition, except for the TP snow, whether other external forcings contribute to the interdecadal changes in the MRYRV is unclear and beyond the scope of the current work. Future work should be done to further understand the MRYRV variability.

Acknowledgments

This research is funded by the National Natural Science Foundation of China (Grant 42075050).

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Save
  • Barnett, T. P., L. Dümenil, U. Schlese, and E. Roeckner, 1988: The effect of Eurasian snow cover on global climate. Science, 239, 504507, https://doi.org/10.1126/science.239.4839.504.

    • Search Google Scholar
    • Export Citation
  • Barnett, T. P., L. Dümenil, U. Schlese, E. Roeckner, and M. Latif, 1989: The effect of Eurasian snow cover on regional and global climate variations. J. Atmos. Sci., 46, 661686, https://doi.org/10.1175/1520-0469(1989)046<0661:TEOESC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ding, Y. H., 1992: Summer monsoon precipitations in China. J. Meteor. Soc. Japan, 70, 373396, https://doi.org/10.2151/jmsj1965.70.1B_373.

    • Search Google Scholar
    • Export Citation
  • Ding, Y. H., P. Liang, Y. Liu, and Y. Zhang, 2020: Multiscale variability of meiyu and its prediction: A new review. J. Geophys. Res. Atmos., 125, e2019JD031496, https://doi.org/10.1029/2019JD031496.

    • Search Google Scholar
    • Export Citation
  • Dou, J., Z. W. Wu, and J. P. Li, 2020: The strengthened relationship between the Yangtze River valley summer rainfall and the Southern Hemisphere annular mode in recent decades. Climate Dyn., 54, 16071624, https://doi.org/10.1007/s00382-019-05078-4.

    • Search Google Scholar
    • Export Citation
  • Fan, K., Z. Xu, and B. Tian, 2014: Has the intensity of the interannual variability in summer rainfall over South China remarkably increased? Meteor. Atmos. Phys., 124, 2332, https://doi.org/10.1007/s00703-013-0301-5.

    • Search Google Scholar
    • Export Citation
  • Gao, J., H. Lin, L. You, and S. Chen, 2016: Monitoring early-flood season intraseasonal oscillations and persistent heavy rainfall in South China. Climate Dyn., 47, 38453861, https://doi.org/10.1007/s00382-016-3045-3.

    • Search Google Scholar
    • Export Citation
  • Huang, R. H., and Y. F. Wu, 1989: The influence of ENSO on the summer climate change in China and its mechanism. Adv. Atmos. Sci., 6, 2132, https://doi.org/10.1007/BF02656915.

    • Search Google Scholar
    • Export Citation
  • Jia, X. J., C. Zhang, R. G. Wu, and Q. F. Qian, 2021: Influence of Tibetan Plateau autumn snow cover on interannual variations in spring precipitation over southern China. Climate Dyn., 56, 767782, https://doi.org/10.1007/S00382-020-05497-8.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
  • Lau, K. M., 2016: The aerosol-monsoon climate system of Asia: A new paradigm. Acta Meteor. Sin., 30, 111, https://doi.org/10.1007/s13351-015-5999-1.

    • Search Google Scholar
    • Export Citation
  • Lau, K. M., K. Kim, and D. Yang, 2000: Dynamical and boundary forcing characteristics of regional components of the Asian summer monsoon. J. Climate, 13, 24612482, https://doi.org/10.1175/1520-0442(2000)013<2461:DABFCO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Li, W. K., W. Guo, B. Qiu, Y. Xue, P. C. Hsu, and J. F. Wei, 2018: Influence of Tibetan Plateau snow cover on East Asian atmospheric circulation at medium-range time scales. Nat. Commun., 9, 4243, https://doi.org/10.1038/s41467-018-06762-5.

    • Search Google Scholar
    • Export Citation
  • Liang, P., L. J. Chen, Y. H. Ding, J. He, and B. Zhou, 2018: Relationship between long-term variability of meiyu over the Yangtze River and ocean and meiyu’s predictability study. Acta Meteor. Sin., 76, 379393.

    • Search Google Scholar
    • Export Citation
  • Lin, H., and Z. W. Wu, 2011: Contribution of the autumn Tibetan Plateau snow cover to seasonal prediction of North American winter temperature. J. Climate, 24, 28012813, https://doi.org/10.1175/2010JCLI3889.1.

    • Search Google Scholar
    • Export Citation
  • Liu, G., R. G. Wu, Y. Z. Zhang, and S. L. Nan, 2014: The summer snow cover anomaly over the Tibetan Plateau and its association with simultaneous precipitation over the mei-yu–baiu region. Adv. Atmos. Sci., 31, 755764, https://doi.org/10.1007/s00376-013-3183-z.

    • Search Google Scholar
    • Export Citation
  • Ma, Y. X., and Y. C. Zhang, 2015: Numerical study of the impacts of urban expansion on meiyu precipitation over eastern China. Acta Meteor. Sin., 29, 237256, https://doi.org/10.1007/s13351-015-4063-5.

    • Search Google Scholar
    • Export Citation
  • Michaelsen, J., 1987: Cross-validation in statistical climate forecast model. J. Climate Appl. Meteor., 26, 15891600, https://doi.org/10.1175/1520-0450(1987)026<1589:CVISCF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Qian, Q. F., X. J. Jia, and R. G. Wu, 2020: On the interdecadal change in the interannual variation in autumn snow cover over the central eastern Tibetan Plateau in the mid-1990s. J. Geophys. Res., 125, e2020JD032685, https://doi.org/10.1029/2020JD032685.

    • Search Google Scholar
    • Export Citation
  • Robinson, D. A., T. W. Estilow, and NOAA CDR Program, 2012: NOAA Climate Data Record (CDR) of Northern Hemisphere (NH) snow cover extent (SCE), version 1. Subset used: 1972–2009, NOAA/National Centers for Environmental Information, accessed 27 July 2020, https://doi.org/10.7289/V5N014G9.

  • Sardeshmukh, P. D., and B. J. Hoskins, 1988: The generation of global rotational flow by steady idealized tropical divergence. J. Atmos. Sci., 45, 12281251, https://doi.org/10.1175/1520-0469(1988)045<1228:TGOGRF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Si, D., and Y. H. Ding, 2013: Decadal change in the correlation pattern between the Tibetan Plateau winter snow and the East Asian summer precipitation during 1979–2011. J. Climate, 26, 76227634, https://doi.org/10.1175/JCLI-D-12-00587.1.

    • Search Google Scholar
    • Export Citation
  • Sun, Y., X. Zhang, F. W. Zwiers, L. Song, H. Wan, T. Hu, H. Yin, and G. Ren, 2014: Rapid increase in the risk of extreme summer heat in Eastern China. Nat. Climate Change, 4, 10821085, https://doi.org/10.1038/nclimate2410.

    • Search Google Scholar
    • Export Citation
  • Takaya, K., and H. Nakamura, 2001: A formulation of a phase-independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow. J. Atmos. Sci., 58, 608627, https://doi.org/10.1175/1520-0469(2001)058<0608:AFOAPI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tao, S. Y., and L. X. Chen, 1987: A review of recent research on the East Asia summer monsoon in China. Monsoon Meteorology, C.-P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 60–92.

  • Tung, Y. S., S. Wang, J. Chu, C. Wu, Y. Chen, C. Cheng, and L. Lin, 2020: Projected increase of the East Asian summer monsoon (meiyu) in Taiwan by climate models with variable performance. Meteor. Appl., 27, e1886, https://doi.org/10.1002/met.1886.

    • Search Google Scholar
    • Export Citation
  • Wang, H. J., and Coauthors, 2012: Extreme climate in China: Facts, simulation and projection. Meteor. Z., 21, 279304, https://doi.org/10.1127/0941-2948/2012/0330.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., R. G. Wu, S. Chen, G. Huang, G. Liu, and L. Zhu, 2018: Influence of western Tibetan Plateau summer snow cover on East Asian summer rainfall. J. Geophys. Res. Atmos., 123, 23712386, https://doi.org/10.1002/2017JD028016.

    • Search Google Scholar
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  • Fig. 1.

    (a) The spatial distribution of the climatological JJ rainfall over China (shading; mm day−1) and the vertically integrated (from the surface to 300 hPa) moisture (vector; kg m−1 s−1) for 1970–2015. (b) The normalized MRI (solid color bars), which is defined by averaging the JJ rainfall over the mei-yu region (110°–122°E, 28°–34°N). The high-frequency component of the MRI is represented by transparent bars. (c) The regression map of the JJ rainfall (mm day−1) against MRI for 1970–2015. The dotted areas represent the significant anomalies at the 95% confidence level. The purple rectangles in (a) and (c) denote the mei-yu region in the Yangtze River valley.

  • Fig. 2.

    The spatial distributions of the standard deviation (shading; %) and climatological mean SCE (contour; %) for (a) winter and (b) spring during 1970–2015. The purple (69°–83°E, 29.5°–41°N) and blue (88°–102°E, 26°–35°N) rectangles in (a) and (b) indicate the key regions that have been used to construct the western and eastern TP snow indices. The high-filtered western (transparent bars) and eastern (solid bars) TP snow indices for (c) winter and (d) spring.

  • Fig. 3.

    The sliding correlation coefficients, with a 21-yr window, between the TP snow indices and the MRI. The curves with open dots denote the sliding correlation between the MRI and the western TP snow index in winter (blue) and spring (red). The curves with filled dots denote the sliding correlation between the MRI and the eastern TP snow index in winter (blue) and spring (red), respectively. The horizontal dashed lines are significant correlation coefficients at the 90% and 95% levels.

  • Fig. 4.

    The anomalies of JJ rainfall (shadings; mm day−1) regressed against the SSTP index for (a) P1 and (b) P2. (c),(d) As in (a) and (b), but for the partial regression maps after removing the ENSO signals. Open and dotted areas indicate the significant anomalies at the 90% and 95% levels, respectively.

  • Fig. 5.

    The partial regression maps of (a),(b) JJ 850-hPa wind [vector; scale at the top-right corner of (b); m s−1] and (c),(d) vertically integrated (from the surface to 300 hPa) moisture [vector; scale at the top-right corner of (d); kg m−1 s−1] and divergence (shadings; 10−6 kg−2 s−1) upon the SSTPI after removing ENSO signals for P1 in (a) and (c) and P2 in (b) and (d). The gray and dark vectors indicate the significant anomalies at the 90% and 95% levels, respectively. The dotted areas in (c) and (d) denote the significant anomalies at the 95% confidence level.

  • Fig. 6.

    Anomalous SSTP-related TP SCE (shadings; %) in (a),(b) spring (March–May), (c),(d) MJ, and (e),(f) JJ obtained by regression for P1 in (a), (c), and (e) and P2 in (b), (d), and (f). Open and dotted areas indicate the significant anomalies at the 90% and 95% levels, respectively.

  • Fig. 7.

    Spring (a) surface air temperature (shading; °C), (b) longitude–pressure cross sections of air temperature (shading; °C) averaged from 28° to 38°N, (c) USWR (shading; W m−2), (d) ULWR (shading; W m−2), (e) sensible heat (SH) (shading; W m−2), and (f) latent heat (LH) (shading; W m−2) anomalies regressed against the SSTPI for P1. The stippling areas refer to the significant anomalies at the 95% level.

  • Fig. 8.

    As in Fig. 7, but for P2.

  • Fig. 9.

    The partial regression maps at a 200-hPa geopotential height (shading; gpm) and wind (vector; m s−1) in (a),(d) March–May, (b),(e) MJ, and (c),(f) JJ onto the SSTPI after removing the ENSO signals for P1 in (a), (c), and (e) and P2 in (b), (d), and (f). The dotted areas indicate the significant anomalies at the 95% level.

  • Fig. 10.

    As in Fig. 9, but for 200-hPa wave activity flux (vector; m2 s−2) and geopotential height (shading; gpm).

  • Fig. 11.

    Anomalies of the spring western TP SCE-associated 200-hPa RWS (shading; 10−11 s−2) with the ENSO signals removed for (a) P1 and (b) P2. The black contours refer to the climatological zonal wind greater than 30 m s−1.

  • Fig. 12.

    The partial regression panels of JJ (a),(b) 200-hPa zonal wind (shading; m s−1) and (c),(d) 500-hPa vertical velocity (shading; ×10−3 Pa s−1) against the SSTPI after removing the ENSO signals for P1 in (a) and (c) and P2 in (b) and (d). The black contours in (a) and (b) refer to the climatological zonal wind greater than 25 m s−1. The stippling areas denote the significant anomalies at the 95% level.

  • Fig. 13.

    (a) Time series of the observational MRI (blue curve), its hindcast with the Niño-3.4 index (red curve), and its hindcast with both the Niño-3.4 and SSTP indices (green curve) obtained by the empirical regression models during P1. (b) As in (a), but for P2.

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