1. Introduction
Stretched across the eastern Eurasian continent, the Tibetan Plateau (TP) features complex topography with an average elevation of more than 4000 m. As a vast terrain, it blocks the westerly airflows and forces them to split (Yeh 1950), exciting Rossby waves in the evolution of stationary waves (Held et al. 2002). As an uplifting heat source, it pumps moist and warm air from the oceans to the land, acting like a heat engine and affecting the generation, evolution, and intensity of the Asian summer monsoon (e.g., Wu and Zhang 1998; Hsu and Liu 2003; Wu et al. 2012; He et al. 2019; Duan et al. 2020). Its remarkable mechanical and thermal effects play critical roles in the climate changes of East Asia and even the globe (Wu et al. 2015; Liu et al. 2020).
During boreal spring, the surface sensible heating (SH) over the TP is rapidly intensified and becomes dominant, and thus, the air column over the TP turns from a heat sink to a heat source (Luo and Yanai 1984; Wu et al. 1997). The enhanced SH forces local air to ascend and induces strong updrafts that efficiently pump the lower-layer moist air from the surrounding oceans to the land (Wu et al. 2007). This mechanism is intuitively referred to as the SH-driven air pump (SHAP). Owing to this SHAP process, the TP thermal forcing in spring can significantly affect the onset of the Asian summer monsoon (Wu and Zhang 1998; Abe et al. 2013; Geen et al. 2018), the western Pacific subtropical high (Duan et al. 2017), and the variation in sea surface temperature in the North Pacific (Sun et al. 2019).
The TP is an active area of intraseasonal oscillation (ISO) in the midlatitudes, affecting the weather and climate in local and downstream regions. Compared with other seasons, relatively more studies have focused on the TP ISO in summer in terms of its characteristics, mechanism, and effects on climate. Long-term data reveal that the main oscillation period of different meteorological variables over the TP is quasi biweekly (Nitta 1983; Fujinami and Yasunari 2004, 2009; Wang and Duan 2015; Hu et al. 2016; Yang and Li 2017; Zhong et al. 2020). The quasi-biweekly oscillation exists almost every year, but the 30–60-day oscillation is not always significant (Yang et al. 2017). In the cyclonic phase of the TP quasi-biweekly oscillation, TP vortices occur more frequently in summer, which inevitably leads to persistent heavy precipitation events in the downstream region during the process of a TP vortex’s eastward migration (Zhang et al. 2014; Li et al. 2018).
In addition to the summer monsoon rainfall, another notable rainy season in China occurs in spring. Climatologically, the precipitation center is mainly located between the middle and lower reaches of the Yangtze River (MLYR) and the Nanling Mountains, and usually occurs in March and April. It is attributed to the low-level southwesterly jet related to the time-lag effect of seasonal warming (Tian and Yasunari 1998) and the TP’s mechanical and thermal forcing (Wan and Wu 2007). From an ISO perspective, Pan et al. (2013) determined that the quasi-biweekly oscillation of spring rainfall in southern China is predominant in most years. The strong vertical coupling and the baroclinicity related to energy conversion are the two main mechanisms for its generation and propagation. Miao et al. (2019) found that the quasi-biweekly oscillation of spring rainfall in South China is controlled by low-frequency wave trains in the mid–high latitudes and quasi-biweekly convective activities in the South China Sea.
A significant quasi-biweekly oscillation of the atmospheric heat source over the TP also exists in spring (Liu et al. 2021). As the dominant component of the atmospheric heat source in spring, the characteristics and mechanisms of the TP SH ISO and its lead–lag relationship with spring precipitation in China on the intraseasonal time scale deserve an in-depth investigation. Therefore, in this study, the aim was to detect the phase evolutions of local land–air coupling processes and large-scale atmospheric circulations related to the TP SH ISO and the mechanism linking it to the intraseasonal variation in spring rainfall in China, based on a combination of multisource datasets including a long-term TP SH estimate calculated by China Meteorological Administration (CMA) station observations, routine meteorological station observations, satellite retrievals, and reanalysis data. Note that, due to the low number and sparse distribution of stations in the western TP, the focus of this study was mainly the intraseasonal variability of surface SH over the central and eastern TP (CETP).
The rest of this paper is organized as follows. Section 2 describes the data and methods used in the study. Section 3 illustrates the characteristics and phase evolution of the CETP SH quasi-biweekly oscillation during spring, and section 4 reveals the mechanism connecting CETP SH to spring rainfall in China on the quasi-biweekly time scale. Finally, section 5 gives a summary and some further discussion.
2. Data and methods
a. Data
Datasets of surface TP SH estimates, routine meteorological station observations, satellite retrievals, and reanalysis data were employed to investigate the intraseasonal relationship between surface SH over the CETP and rainfall in China during boreal spring [March–May (MAM)]. The detailed sources are as follows:
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Daily SH estimates at 87 stations (blue dots in Fig. 1a) above 3000 m on the CETP without missing values for spring 1979–2019 were selected from the 293 stations provided by Duan et al. (2022) (available from http://data.lasg.ac.cn/TPSHLH/).
Since large uncertainties exist in the SH over the CETP among different reanalysis datasets (Wang et al. 2012), a more accurate estimate of the CETP SH compared with reanalysis data can be obtained by using the records of routine meteorological stations. Based on the similarity theory proposed by Monin and Obukhov (1954), the formula of the SH could be expressed as follows:where ρ is the air density, cp = 1005 J kg−1 K−1 is the specific heat of dry air at a constant pressure, CH is the bulk transfer coefficient for heat, Vs is the mean wind speed at the near-surface measurement level, Tg is ground skin temperature, and Ta is the air temperature at the near-surface measurement level. According to Eq. (1), this dataset was calculated based on the site observations provided by the CMA, which include 6-hourly variables of 10-m wind speed and ground skin temperature observed at 0200, 0800, 1400, and 2000 Beijing standard time, as well as daily variables of mean surface air temperature, maximum surface air temperature, minimum surface air temperature, 10-m maximum wind speed, precipitation amount, and mean surface pressure. A state-of-the-art parameterization scheme (Yang et al. 2009) that considers the diurnal variation in the CH was adopted to estimate the daily SH. -
Daily mean precipitation, surface air temperature, ground skin temperature, and surface pressure at the same 87 stations as the above SH estimate from 1979 to 2019 are archived by the CMA with an initial quality control.
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Daily radiative fluxes at the surface and total cloud fraction from 2001 to 2019 were extracted from the Clouds and the Earth’s Radiant Energy System (CERES) level 3 product, SYN1degEd4A, with a horizontal resolution of 1° × 1° (Wielicki et al. 1996), which can be obtained from the NASA Langley Research Center CERES ordering tool at https://ceres.larc.nasa.gov/data/. Three variables under all-sky conditions—namely, downward shortwave flux, upward shortwave flux, and downward longwave flux—were used to partition the surface energy balance in this study.
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Daily soil moisture data from 1979 to 2019 were derived from the European Space Agency Climate Change Initiative combined soil moisture product, version 06.1, on a 0.25° regular grid (Dorigo et al. 2017; Gruber et al. 2019). The data were accessed via https://www.esa-soilmoisture-cci.org/.
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Daily mean fields of wind, temperature, geopotential height, surface pressure, and specific humidity in the period 1979–2019 were derived from the Japanese 55-year Reanalysis (JRA-55) dataset (Kobayashi et al. 2015) with a horizontal resolution of 1.25° × 1.25° and 37 vertical layers. The JRA-55 dataset is available at the following website: http://search.diasjp.net/en/dataset/JRA55.
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Daily gridded precipitation data with a horizontal resolution of 0.25° × 0.25° from 1979 to 2015 were provided by the Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) project (Yatagai et al. 2012). This dataset covers the land area in the region 15°S–55°N, 60°–150°E, which is available at http://aphrodite.st.hirosaki-u.ac.jp/download/.
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Daily potential vorticity (PV) data with a horizontal resolution of 0.625° longitude × 0.5° latitude and 42 vertical layers were derived from version 2 of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) provided by the Global Modeling and Assimilation Office (Gelaro et al. 2017). The MERRA-2 dataset can be downloaded from the following website: https://disc.gsfc.nasa.gov/datasets?project=MERRA-2.
(a) Climatological mean (W m−2) and (b) standard deviation of daily surface SH over the CETP during spring (MAM) for the period 1979–2019. The blue dots indicate the locations of 87 stations used in this study. The region of the TP with terrain above 3000 m is outlined by the solid thin black contour.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
(a) Climatological mean (W m−2) and (b) standard deviation of daily surface SH over the CETP during spring (MAM) for the period 1979–2019. The blue dots indicate the locations of 87 stations used in this study. The region of the TP with terrain above 3000 m is outlined by the solid thin black contour.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
(a) Climatological mean (W m−2) and (b) standard deviation of daily surface SH over the CETP during spring (MAM) for the period 1979–2019. The blue dots indicate the locations of 87 stations used in this study. The region of the TP with terrain above 3000 m is outlined by the solid thin black contour.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
b. Methods
All analyses in the present study were based on the data in spring (MAM) over the period 1979–2019, except the time spans of the CERES and APHRODITE data, which were 2001–19 and 1979–2015, respectively. The unfiltered anomalies were obtained by removing the annual mean, three leading harmonics via the Fourier transform, daily climatology, and the synoptic fluctuations via a 5-day running mean performed on all years of the data. As a result, the seasonal cycle, interannual, interdecadal, and synoptic-scale signals were eliminated, and only subseasonal-scale signals were retained. On this basis, a bandpass filter (Duchon 1979) was applied to derive the intraseasonal component. An empirical orthogonal function (EOF) analysis was then applied to the unfiltered and filtered SH anomalies over the CETP for the MAMs of 1979–2019 to obtain the dominant modes. The dominant common periodicity of the corresponding principal component (PC) was identified by averaging the individual power spectra for the 41 spring seasons from 1979 to 2019 through power spectrum analysis (Gilman et al. 1963).
A phase composite technique (Pan et al. 2013; Hu et al. 2016) was used on the meteorological fields, based on the definition of the nine phases during a strong ISO cycle for the filtered PC time series. A strong ISO cycle was defined as one including both one positive and two negative extremes, with the peak magnitudes of the three extremes exceeding a threshold of one standard deviation. Similar to Yang and Li (2017), the lead–lag composites were utilized to demonstrate the intraseasonal connection between SH over the CETP and spring rainfall in China. The statistical significance of the correlation and composites is determined by a Student’s t test. The effective number of degrees of freedom is estimated by the methodology based on autocorrelation described by Davis (1976) and Chen (1982).
3. Characteristics and evolution of spring CETP SH ISO
a. Dominant pattern
We first examine the climatology and standard deviation of springtime (MAM) CETP SH during the period 1979–2019 (Fig. 1). It is indicated that the intensity and variation in SH over the central TP are larger than over the eastern TP. This is closely related to the semiarid climate, high altitude, and underlying surface characteristics over the central TP.
EOF analysis was performed on the nonfiltered daily CETP SH anomaly for the 41 spring seasons of 1979–2019 to identify the dominant patterns of subseasonal variability of SH over the CETP. The first two leading EOF modes explain 13.5% and 12.8% of the total variance, respectively. Although the explained variance of the first mode (EOF1) is close to that of the second mode (EOF2), the sampling error for EOF1 associated with the eigenvalue is larger than the spacing of the eigenvalues between EOF1 and EOF2. According to the rule of North et al. (1982), these modes are well separated from each other and from the rest of the EOFs. The EOF1 exhibits a large loading covering most parts of the CETP, with a maximum center in the southern corner (left-hand panel of Fig. 2a). As the time-varying magnitude of this mode is reflected by the corresponding PC1 for each spring season, PC1 can be used as the reference time series to verify the dominant intraseasonal signals of SH over the CETP. A 41-spring-averaged power spectrum analysis was applied to the time series of PC1 for the purpose of determining the distinct periods common to most of the spring seasons. The spectrum of PC1 reaches its variance peak at 8 days (right-hand panel of Fig. 2a), and the averaged power spectra analysis also demonstrates that the 7–20-day band for PC1 passes the 95% confidence level.
(a) (left) The first EOF mode of 41-spring nonfiltered daily surface SH over the CETP and (right) averaged power spectra of the corresponding PC1. (b) EOF1 of 7–20-day filtered surface SH over the CETP for the spring seasons of 1979–2019. The TP with terrain above 3000 m is outlined by the solid thin black contour. The red dotted and blue dashed lines in the right-hand panel of (a) represent the Markov red noise spectrum and a 95% confidence level, respectively. The purple rectangular frame (26°–40°N, 85°–104°E) in (b) denotes the region for calculation of area-mean variables in Figs. 3 and 4.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
(a) (left) The first EOF mode of 41-spring nonfiltered daily surface SH over the CETP and (right) averaged power spectra of the corresponding PC1. (b) EOF1 of 7–20-day filtered surface SH over the CETP for the spring seasons of 1979–2019. The TP with terrain above 3000 m is outlined by the solid thin black contour. The red dotted and blue dashed lines in the right-hand panel of (a) represent the Markov red noise spectrum and a 95% confidence level, respectively. The purple rectangular frame (26°–40°N, 85°–104°E) in (b) denotes the region for calculation of area-mean variables in Figs. 3 and 4.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
(a) (left) The first EOF mode of 41-spring nonfiltered daily surface SH over the CETP and (right) averaged power spectra of the corresponding PC1. (b) EOF1 of 7–20-day filtered surface SH over the CETP for the spring seasons of 1979–2019. The TP with terrain above 3000 m is outlined by the solid thin black contour. The red dotted and blue dashed lines in the right-hand panel of (a) represent the Markov red noise spectrum and a 95% confidence level, respectively. The purple rectangular frame (26°–40°N, 85°–104°E) in (b) denotes the region for calculation of area-mean variables in Figs. 3 and 4.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
In light of the above, EOF analysis was conducted on the daily CETP SH anomaly for the spring seasons of 1979–2019, filtered by 7–20 days. As with the unfiltered result, the first leading mode is statistically different from other EOFs, according to the method of North et al. (1982). Figure 2b presents the EOF1 of the 7–20-day filtered daily CETP SH anomaly, which explains 14.9% of the total variance. The EOF1 is characterized by a monopole pattern with large positive loadings over the entire CETP. Therefore, next, we detect the local land–air coupling processes and remote atmospheric circulations related to the 7–20-day filtered PC1 and identify their physical links to the spring rainfall in China.
b. Local land–air coupling processes
According to the definition of a strong ISO cycle introduced in section 2, there are 107 strong ISO cycles for the 7–20-day filtered PC1 during the 41 spring seasons of 1979–2019. Each cycle was classified into nine phases. Phases 1 and 9 denote the negative extremes of the filtered PC, while phase 5 represents the positive extreme. Phases 3 and 7 correspond to the anomaly value of zero, and phases 2 and 8 (phases 4 and 6) to half the negative (positive) extreme. As the result for phase 9 is very similar to that for phase 1, only the evolutions during the first eight phases are given in the following analyses. Moreover, since the EOF1 of the 7–20-day filtered daily CETP SH anomaly is characterized by a monopole pattern, 7–20-day composites averaged over the region 26°–40°N, 85°–104°E (purple frame in Fig. 2b) with respect to PC1 were constructed to investigate the phase evolutions.
Based on the SEB equation, Eq. (1), the sum of SWnet and LWdown can be regarded as the surface radiation input, which should be equal to the sum of SH, LWup, and the remainder. Thus, the composite evolutions of the quasi-biweekly SEB components from phases 1 to 8 during a strong ISO cycle with respect to the PC1 (Fig. 3) are further utilized to study the phase evolution characteristics of the surface energy budget corresponding to the first mode of spring CETP SH ISO. Similar to the SH (black line), a significant surface radiation input (brown line) and SWnet (blue line) reach their minima in phase 1 and maxima in phase 5. The LWdown (green line) is always opposite to the SWnet and imposes a negative effect on the surface radiation input. Compared with other components, the remainder (orange line), made up of LH and G, is small in magnitude and variation with phase change. The LWup (cyan line) increases after phase 3 and reaches its peak in phase 7. The above analyses indicate that the quasi-biweekly surface SH with respect to PC1 is mainly controlled by the SWnet in the framework of the SEB. As the SWnet at the surface increases, the SH also increases and reaches its peak in phase 5. After two phases, the LWup comes up to the maximum.
Composite evolution of the 7–20-day filtered surface radiation fluxes (W m−2) area averaged over the region 26°–40°N, 85°–104°E (purple rectangular frame in Fig. 2b), including the radiation input (brown), net downward shortwave radiation (blue), and downward longwave radiation (green) derived from CERES; the surface SH (black) and the upward longwave radiation (cyan) estimated by CMA station records; and the remainder made up of latent heat flux and ground heat flux (orange) from phases 1 to 9 during a strong ISO cycle with respect to the PC1. Points marked with a dot represent anomalies that are statistically significant at the 95% confidence level according to the Student’s t test.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Composite evolution of the 7–20-day filtered surface radiation fluxes (W m−2) area averaged over the region 26°–40°N, 85°–104°E (purple rectangular frame in Fig. 2b), including the radiation input (brown), net downward shortwave radiation (blue), and downward longwave radiation (green) derived from CERES; the surface SH (black) and the upward longwave radiation (cyan) estimated by CMA station records; and the remainder made up of latent heat flux and ground heat flux (orange) from phases 1 to 9 during a strong ISO cycle with respect to the PC1. Points marked with a dot represent anomalies that are statistically significant at the 95% confidence level according to the Student’s t test.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Composite evolution of the 7–20-day filtered surface radiation fluxes (W m−2) area averaged over the region 26°–40°N, 85°–104°E (purple rectangular frame in Fig. 2b), including the radiation input (brown), net downward shortwave radiation (blue), and downward longwave radiation (green) derived from CERES; the surface SH (black) and the upward longwave radiation (cyan) estimated by CMA station records; and the remainder made up of latent heat flux and ground heat flux (orange) from phases 1 to 9 during a strong ISO cycle with respect to the PC1. Points marked with a dot represent anomalies that are statistically significant at the 95% confidence level according to the Student’s t test.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
We further detect the phase evolution of precipitation, total cloud fraction, and key land–air variables at or near the surface. Figure 4 shows the composite evolutions of the 7–20-day filtered surface SH, surface air temperature, ground skin temperature, surface pressure, precipitation, surface soil moisture, total cloud fraction, and 500-hPa relative vorticity and vertical velocity with respect to PC1. As shown in Fig. 4a, the significant surface air temperature and ground skin temperature anomalies lag the SH anomaly by two phases, with their minima (maxima) appearing in phase 3 (phase 7), whereas the quasi-biweekly surface pressure anomaly leads the SH anomaly by one phase (Fig. 4b). It is also apparent that there is an antiphase relationship between the quasi-biweekly precipitation and surface SH over the CETP, which presents as a negative extreme of precipitation in phase 5 and a positive extreme in phase 1. The continuous decrease in precipitation results in a significant negative anomaly of surface soil moisture from phase 5 to 7 (Fig. 4b). Moreover, Fig. 4c illustrates that the positive SH anomaly corresponds to negative anomalies of the total cloud fraction and 500-hPa relative vorticity, and the positive anomaly of the vertical velocity at 500 hPa. Specifically, in-phase relationships exist between the quasi-biweekly rainfall and relative vorticity at 500 hPa over the CETP, as well as between the quasi-biweekly surface pressure and vertical velocity at 500 hPa. Moreover, the quasi-biweekly anomaly of the total cloud fraction leads the precipitation anomaly by one phase (Fig. 4c).
As in Fig. 3, but for the 7–20-day filtered (a) surface SH (black; W m−2), surface air temperature (red; K), and ground skin temperature (blue; K), (b) surface pressure (yellow; hPa), precipitation (green; mm day−1), and surface soil moisture (purple; m3 m−3), and (c) total cloud fraction (cyan; %), 500-hPa relative vorticity (brown; 10−6 s−1), and 500-hPa vertical velocity (orange; Pa s−1) with respect to PC1. Points marked with a dot represent anomalies that are statistically significant at the 95% confidence level according to the Student’s t test.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
As in Fig. 3, but for the 7–20-day filtered (a) surface SH (black; W m−2), surface air temperature (red; K), and ground skin temperature (blue; K), (b) surface pressure (yellow; hPa), precipitation (green; mm day−1), and surface soil moisture (purple; m3 m−3), and (c) total cloud fraction (cyan; %), 500-hPa relative vorticity (brown; 10−6 s−1), and 500-hPa vertical velocity (orange; Pa s−1) with respect to PC1. Points marked with a dot represent anomalies that are statistically significant at the 95% confidence level according to the Student’s t test.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
As in Fig. 3, but for the 7–20-day filtered (a) surface SH (black; W m−2), surface air temperature (red; K), and ground skin temperature (blue; K), (b) surface pressure (yellow; hPa), precipitation (green; mm day−1), and surface soil moisture (purple; m3 m−3), and (c) total cloud fraction (cyan; %), 500-hPa relative vorticity (brown; 10−6 s−1), and 500-hPa vertical velocity (orange; Pa s−1) with respect to PC1. Points marked with a dot represent anomalies that are statistically significant at the 95% confidence level according to the Student’s t test.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
To sum up, a strong local land–air coupling process links the quasi-biweekly SH to precipitation over the CETP. The presence of an anomalous cold high near the surface causes air to sink, reduces the cloud cover, and inhibits convection. As a result, more shortwave radiation is allowed to reach the surface, leading to the maximum of SH with a monopole pattern over the CETP, as well as the minimum of low-level relative vorticity. In addition to the SH heating the lower atmosphere, the dry soil condition induced by deficient rainfall is also favorable to the increase in ground skin temperature and surface air temperature. The warm surface causes air to rise, creating an anomalous warm low near the surface. This warm low increases the total cloud cover and low-level relative vorticity, leading to the positive rainfall anomaly and negative SH anomaly. Finally, a strong ISO cycle of SH is completed.
c. Large-scale atmospheric circulations
In this section, we further investigate the evolutions of large-scale atmospheric circulations related to the first leading mode of spring CETP SH ISO. Figure 5 presents the composite evolution of the 7–20-day filtered divergence, wind fields, and horizontal components of the wave activity flux at 200 hPa with respect to the PC1 for phases 1–8 of 107 strong ISO cycles. The upper-level circulations indicate that there is a quasi-biweekly Rossby wave train propagating eastward and southeastward continuously, affecting the characteristics of SH ISO over the TP. At the beginning of the ISO cycle (phase 1; Fig. 5a), the Rossby wave train is characterized by multiple enclosed anomalous anticyclonic and cyclonic centers located over northwestern Africa (A1), northeastern Africa (C1), the northern Arabian Sea (A2), TP (C2), and MLYR (A3), with strong divergence or convergence between. From phase 1 to phase 5, the cyclonic center C2 moves from the central TP to the MLYR, while the anticyclonic center A2 shifts from the Middle East to the central TP (Figs. 5a–e). This process corresponds to the increase in the SH ISO intensity over the CETP and its maximum in phase 5. Note that a new anomalous cyclonic circulation (C3) is generated over the Mediterranean Sea in phase 4, while the anticyclonic center A3 moves southeastward to southern China and then the northwest Pacific in phase 5. Afterward, a series of cyclonic and anticyclonic circulations (C3, A1, C1, A2, and C2) move continuously eastward and southeastward from phase 6 to 8 (Figs. 5f–h). When the cyclone C1 controls the whole TP, the CETP SH ISO reaches another negative extreme, indicating that a complete ISO cycle ends. A similar Rossby wave train also appears at 500 hPa (figure not shown).
Composite evolution of the 7–20-day filtered divergence (shading; 10−6 s−1), winds (black vectors; m s−1), and horizontal components of the wave activity flux (purple vectors; m2 s−2) at 200 hPa with respect to the PC1 for (a)–(h) phases 1–8, respectively. The TP with terrain above 3000 m is outlined by the solid red curve. Stippling denotes regions where the divergence anomalies are significant at the 95% confidence level according to the Student’s t test. Only significant wind vectors (at least one component) are plotted. The letters “A” and “C” indicate anticyclone and cyclone, respectively.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Composite evolution of the 7–20-day filtered divergence (shading; 10−6 s−1), winds (black vectors; m s−1), and horizontal components of the wave activity flux (purple vectors; m2 s−2) at 200 hPa with respect to the PC1 for (a)–(h) phases 1–8, respectively. The TP with terrain above 3000 m is outlined by the solid red curve. Stippling denotes regions where the divergence anomalies are significant at the 95% confidence level according to the Student’s t test. Only significant wind vectors (at least one component) are plotted. The letters “A” and “C” indicate anticyclone and cyclone, respectively.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Composite evolution of the 7–20-day filtered divergence (shading; 10−6 s−1), winds (black vectors; m s−1), and horizontal components of the wave activity flux (purple vectors; m2 s−2) at 200 hPa with respect to the PC1 for (a)–(h) phases 1–8, respectively. The TP with terrain above 3000 m is outlined by the solid red curve. Stippling denotes regions where the divergence anomalies are significant at the 95% confidence level according to the Student’s t test. Only significant wind vectors (at least one component) are plotted. The letters “A” and “C” indicate anticyclone and cyclone, respectively.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
The fields of quasi-biweekly divergence also reveal that the evolution of the 7–20-day ISO of spring CETP SH is closely associated with the eastward and southeastward propagation of a Rossby wave train along the pathway from northwestern Africa to southern China via the TP in the upper troposphere. The higher-level divergence in phase 1 (or convergence in phase 5) over the CETP (Figs. 5a,e) lead to the rising (or sinking) movement of airflow at 500 hPa and negative (or positive) anomalies of surface pressure (Figs. 4b,c), which corresponds to the negative (or positive) phase of spring CETP SH ISO (Fig. 4a). As the Rossby wave train propagates, upper-layer divergence and convergence centers alternately appear over the CETP, corresponding to the transition from negative to positive phase. Furthermore, the eastward and southeastward propagations of this Rossby wave train during the whole ISO cycle are clearly illustrated by the horizontal components of the wave activity flux (purple vectors in Fig. 5). The significant wave activity fluxes show eastward or southeastward movements from North Africa to South China via the TP in all phases. The reason why the intensity of the wave activity flux over East Asia is stronger than in other regions is that the Rossby wave propagates more slowly and intensifies near the exit of the Asian jet (Naoe et al. 1997; Naoe and Matsuda 1998).
To further explore the phase evolution of vertical structure related to the spring CETP SH ISO, the pressure–longitude cross sections of quasi-biweekly geopotential height, air temperature, and airflow composites averaged along the latitudinal band of 28°–38°N (the TP sector) during a strong ISO cycle with respect to the PC1 are presented in Fig. 6. Consistent with the Rossby wave train that propagates eastward in the upper troposphere (Fig. 5), the vertical structure also displays an alternating distribution of significant positive and negative geopotential height anomaly centers moving slowly eastward. The eastward movement of geopotential height anomaly centers leads to changes in the surface thermal conditions and air temperature anomalies over the TP. In the negative phase (phase 1; Fig. 6a), the negative geopotential height anomaly covers the whole TP, accompanied by strong descending motion over the western TP and ascending motion over the eastern TP. From phase 2 to 3 (Figs. 6b,c), the subsidence begins to control the whole TP, since the center of the geopotential height anomaly shifts to the eastern TP. A significant negative temperature anomaly manifests as a band tilting eastward with decreasing height and extending from the lower to upper layer (Fig. 6c). In phase 4, the descending motion over the CETP continues to strengthen (Fig. 6d) and reaches a maximum (Fig. 4c). An anomalous cold high occupies the CETP, allowing more shortwave radiation to reach the surface (Fig. 4c). With the center of the positive geopotential height anomaly moving eastward and controlling the whole TP in phase 5 (positive phase; Fig. 6e), the CETP SH ISO reaches its peak, resulting in warming near the surface. Afterward (Figs. 6f–h), the warm air covers the TP, leading to the development of upward motion. However, the increase in cloud cover and rainfall is not conducive to the maintenance of the positive SH anomaly (Fig. 4). Thus, the CETP SH ISO enters a negative phase again, completing a cycle.
Pressure–longitude cross sections of composite 7–20-day filtered geopotential height (contoured every 5 gpm), air temperature (shading; K), and airflow [vectors, zonal wind (m s−1) and vertical velocity (−200 × ω; Pa s−1)] averaged along the latitudinal band of 28°–38°N during a strong ISO cycle with respect to the PC1 for (a)–(h) phases 1–8, respectively. Only the values of geopotential height, air temperature, and airflow (at least one component) that are statistically significant at the 95% confidence level are plotted. The topography is shaded gray.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Pressure–longitude cross sections of composite 7–20-day filtered geopotential height (contoured every 5 gpm), air temperature (shading; K), and airflow [vectors, zonal wind (m s−1) and vertical velocity (−200 × ω; Pa s−1)] averaged along the latitudinal band of 28°–38°N during a strong ISO cycle with respect to the PC1 for (a)–(h) phases 1–8, respectively. Only the values of geopotential height, air temperature, and airflow (at least one component) that are statistically significant at the 95% confidence level are plotted. The topography is shaded gray.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Pressure–longitude cross sections of composite 7–20-day filtered geopotential height (contoured every 5 gpm), air temperature (shading; K), and airflow [vectors, zonal wind (m s−1) and vertical velocity (−200 × ω; Pa s−1)] averaged along the latitudinal band of 28°–38°N during a strong ISO cycle with respect to the PC1 for (a)–(h) phases 1–8, respectively. Only the values of geopotential height, air temperature, and airflow (at least one component) that are statistically significant at the 95% confidence level are plotted. The topography is shaded gray.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
In summary, the transitions between negative and positive phases of quasi-biweekly CETP SH are jointly determined by the local land–air coupling over the TP and an eastward-propagating Rossby wave train. This Rossby wave train is characterized by a series of enclosed anomalous anticyclonic and cyclonic centers along the pathway from North Africa, to central Asia, to the TP in the upper and middle troposphere. As the wave train propagates east, it causes corresponding changes in vertical motion, air temperature, surface pressure, cloud cover, and precipitation anomalies over the TP, leading to the transitions between negative and positive phases of CETP SH ISO via local land–air coupling processes. In contrast, the Rossby wave propagation is more important for the development stage of quasi-biweekly CETP SH, while the weakening stage is affected by the local land–air coupling processes.
4. Relationship with spring rainfall in China
Pan et al. (2013) pointed out that the quasi-biweekly oscillation of spring rainfall in southern China is dominant and exists in most years. Therefore, it is essential to reveal the relationship between TP SH and spring rainfall in China on the quasi-biweekly time scale and the underlying mechanism.
Figure 7 presents the zonal propagations of the 7–20-day filtered precipitation averaged from 28° to 38°N with respect to the PC1 derived from the lead–lag correlation. The maximum spring CETP SH ISO, characterized by a monopole pattern, corresponds to the negative rainfall anomaly in situ on lag day 0, which is consistent with the antiphase relationship between the quasi-biweekly precipitation and surface SH over the CETP revealed by the phase composite analysis (see Fig. 4). Significant eastward propagations of precipitation anomalies from the west to the CETP can be clearly seen, with the greatest correlation located over the CETP. The indication is that the spring TP SH is closely associated with the precipitation in both local and downstream areas on the quasi-biweekly time scale, and this relationship can be a contemporaneous one or an advanced one.
Zonal propagations of the 7–20-day filtered precipitation along the latitudes between 28° and 38°N calculated by the lead–lag correlation with respect to the PC1. The y axis is lag days. The dotted areas are the regions above 95% in the significance tests.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Zonal propagations of the 7–20-day filtered precipitation along the latitudes between 28° and 38°N calculated by the lead–lag correlation with respect to the PC1. The y axis is lag days. The dotted areas are the regions above 95% in the significance tests.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Zonal propagations of the 7–20-day filtered precipitation along the latitudes between 28° and 38°N calculated by the lead–lag correlation with respect to the PC1. The y axis is lag days. The dotted areas are the regions above 95% in the significance tests.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
The connections between the CETP SH and spring rainfall in China on the quasi-biweekly time scale can be established based on the lead–lag composites, which are widely utilized in multiple studies (e.g., Yang and Li 2016, 2017; Hu et al. 2019). First, a total of 49 positive and 49 negative extremes of which the absolute values are greater than 1.5, were identified from the normalized PC1 time series. The days, including day 0, when each maximum (or minimum) occurs, and n days ahead as well as n days back, were considered as the period of a positive (or negative) case. Then, the lead–lag composites of various anomaly fields on days 0 and +n for positive (or negative) cases were calculated. Since the composites for negative cases virtually presents a mirror image of the composites for positive cases (figures not shown), the differences between them (positive minus negative) on days 0 and +n were used to intensify the signals reported in the following analyses.
The lead–lag composite maps of the 7–20-day filtered precipitation with respect to the normalized PC1 during the spring seasons are presented in Fig. 8. On day 0 (Fig. 8a), negative rainfall anomalies appear over most parts of the CETP and the regions around the MLYR. One or two days later (Figs. 8b,c), the negative anomaly center of the rainfall is weakened and moves to South China. At the same time, positive rainfall anomalies start to develop around the MLYR. These positive anomalies further intensify and reach a maximum on day +5 (Figs. 8d–f), which then abate and move southeastward to South China (Figs. 8g,h). It is demonstrated that, on the fifth day after the maximum of the SH ISO occurs in the CETP during spring, the maximum center of the 7–20-day filtered precipitation anomaly is located in the MLYR. Then, on the seventh day, it moves to South China.
Spatial distributions of the 7–20-day filtered precipitation (mm day−1) from day 0 to +7 calculated by lead–lag composites with respect to the normalized PC1. Only the values that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Spatial distributions of the 7–20-day filtered precipitation (mm day−1) from day 0 to +7 calculated by lead–lag composites with respect to the normalized PC1. Only the values that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Spatial distributions of the 7–20-day filtered precipitation (mm day−1) from day 0 to +7 calculated by lead–lag composites with respect to the normalized PC1. Only the values that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
In light of the above, we next need to address the possible mechanism responsible for the relationship between TP SH and spring precipitation in China on the quasi-biweekly time scale through analyzing the water vapor transport, the configuration between high and low levels, and PV evolutions, focusing on days 0 to +7. Figure 9 presents the composites of moisture flux and its convergence vertically integrated from the surface to 100 hPa during the period from day 0 to +7. To begin with, a wide range of water vapor divergence over the MLYR on day 0 (Fig. 9a) corresponds to the negative precipitation anomalies in situ (Fig. 8a). One day later, the water vapor from the western North Pacific moves westward to the eastern TP, causing local moisture convergence (Fig. 9b). This water vapor pathway further leads to the formation of moisture convergence over the MLYR (Fig. 9c). Subsequently, the moisture convergence is intensified by another water vapor pathway from the northern Indian Ocean (Fig. 9d), reaching its maximum on day +4 (Fig. 9e). Then, the water vapor transport weakens from day +5 and the moisture convergence center moves to South China (Figs. 9f–h).
As in Fig. 8, but for the vertically integrated moisture flux convergence (shading; 10−6 kg m−2 s−1) and moisture flux (vectors; kg m−1 s−1) from the surface to 100 hPa. Only the values of moisture flux (at least one component) and moisture flux convergence that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
As in Fig. 8, but for the vertically integrated moisture flux convergence (shading; 10−6 kg m−2 s−1) and moisture flux (vectors; kg m−1 s−1) from the surface to 100 hPa. Only the values of moisture flux (at least one component) and moisture flux convergence that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
As in Fig. 8, but for the vertically integrated moisture flux convergence (shading; 10−6 kg m−2 s−1) and moisture flux (vectors; kg m−1 s−1) from the surface to 100 hPa. Only the values of moisture flux (at least one component) and moisture flux convergence that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
The corresponding atmospheric circulations in the high and low levels are illustrated in Figs. 10 and 11. Figure 10 shows the composite patterns of 7–20-day filtered divergence and winds at 200 hPa from day 0 to +7. Consistent with Fig. 5e, there is an anticyclone and cyclone covering the TP and downstream regions, where the anticyclonic center is located over the western TP and the cyclonic center over the MLYR, with a convergence between them (Fig. 10a). As indicated by the southeastward-propagating Rossby wave train in Fig. 5, this anticyclone–cyclone pairing moves slowly southeastward along the waveguide, accompanied by a divergence anomaly passing through the western TP and reaching the CETP and the north of the MLYR on day +5 (Fig. 10f). Due to the continuity of the air, an abnormal updraft is inevitably induced in situ, forming a large-scale circulation background field conducive to rainfall development. Subsequently, the anticyclonic center moves from the MLYR to South China on day +7 (Fig. 10h). The spatial distributions of quasi-biweekly horizontal winds at 700 hPa are given by the vectors in Fig. 11. The evolutions of the low-level circulation are characterized by the anomalous anticyclone that originally controls the MLYR (Figs. 11a,b) and moves eastward to the Pacific Ocean (Figs. 11c,d), along with the cyclonic anomaly to the west of it that shifts gradually eastward to occupy the MLYR and South China (Figs. 11e–h).
As in Fig. 8, but for the divergence (shading; 10−6 s−1) and winds (vectors; m s−1) at 200 hPa. Only the values of horizontal winds (at least one component) and divergence that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
As in Fig. 8, but for the divergence (shading; 10−6 s−1) and winds (vectors; m s−1) at 200 hPa. Only the values of horizontal winds (at least one component) and divergence that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
As in Fig. 8, but for the divergence (shading; 10−6 s−1) and winds (vectors; m s−1) at 200 hPa. Only the values of horizontal winds (at least one component) and divergence that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
As in Fig. 8, but for the potential vorticity (shading; 10−6 K m2 kg−1 s−1) and winds (vectors; m s−1) at 700 hPa. Only the values of horizontal winds (at least one component) and potential vorticity that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
As in Fig. 8, but for the potential vorticity (shading; 10−6 K m2 kg−1 s−1) and winds (vectors; m s−1) at 700 hPa. Only the values of horizontal winds (at least one component) and potential vorticity that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
As in Fig. 8, but for the potential vorticity (shading; 10−6 K m2 kg−1 s−1) and winds (vectors; m s−1) at 700 hPa. Only the values of horizontal winds (at least one component) and potential vorticity that are statistically significant at the 95% confidence level are plotted.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Since the slope of the TP usually intersects with the isentropic surfaces, the TP is an important source of atmospheric PV. As PV is a physical quantity characterizing both dynamic and thermodynamic properties of the atmosphere, it is often used as a diagnostic tool to reveal the mechanisms responsible for the impacts of the TP on weather and climate in downstream regions (e.g., Ortega et al. 2017; Wu et al. 2020; Zhang et al. 2021). Next, we further explore the evolution and mechanism of the TP PV anomaly affecting the quasi-biweekly precipitation in China through investigating the lower-level spatial distribution of the quasi-biweekly PV (Fig. 11) as well as its vertical structure (Fig. 12).
Pressure–longitude cross sections (28°–38°N) of the 7–20-day filtered potential vorticity (shading; 10−6 K m2 kg−1 s−1), airflow [vectors, zonal wind (m s−1) and vertical velocity (−50 × ω; Pa s−1)], and diabatic heating (contours; K day−1) for day 0 to +7 derived from lead–lag composites with respect to the normalized PC1. Only the values of potential vorticity, diabatic heating, and airflow (at least one component) that are statistically significant at the 95% confidence level are plotted. The topography is shaded gray.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Pressure–longitude cross sections (28°–38°N) of the 7–20-day filtered potential vorticity (shading; 10−6 K m2 kg−1 s−1), airflow [vectors, zonal wind (m s−1) and vertical velocity (−50 × ω; Pa s−1)], and diabatic heating (contours; K day−1) for day 0 to +7 derived from lead–lag composites with respect to the normalized PC1. Only the values of potential vorticity, diabatic heating, and airflow (at least one component) that are statistically significant at the 95% confidence level are plotted. The topography is shaded gray.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Pressure–longitude cross sections (28°–38°N) of the 7–20-day filtered potential vorticity (shading; 10−6 K m2 kg−1 s−1), airflow [vectors, zonal wind (m s−1) and vertical velocity (−50 × ω; Pa s−1)], and diabatic heating (contours; K day−1) for day 0 to +7 derived from lead–lag composites with respect to the normalized PC1. Only the values of potential vorticity, diabatic heating, and airflow (at least one component) that are statistically significant at the 95% confidence level are plotted. The topography is shaded gray.
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
In the positive phase of the first mode of CETP SH ISO, a large loading of negative PV anomalies is concentrated near the MLYR at 700 hPa (Fig. 11a). As can be seen from Fig. 12a, there is a positive PV anomaly near the surface and a negative PV anomaly in the upper troposphere of the western TP, which corresponds to the upward movement and positive diabatic heating anomaly. Moreover, the anomaly centers of negative diabatic heating and PV appear near the surface and in the lower troposphere of the eastern TP and the MLYR, accompanied by a strong downdraft and an upper-level positive PV anomaly. It is noteworthy that significant southerly anomalies start to show up on the east side of the TP on day +1 (Fig. 11b), which is also the western branch of the anticyclonic circulation occupying the MLYR. Due to the existence of vast terrain, the southerlies would form a cyclonic wind shear through lateral friction, as suggested in previous studies (Thorpe et al. 1993; Zhang et al. 2021), which benefits the generation of a positive PV anomaly in situ. At the same time, the positive anomalies of PV and diabatic heating in the western TP expand eastward, while the intensity of the negative anomalies of diabatic heating and PV over the east of the TP is decreased (Fig. 12b). Subsequently, the positive PV anomaly at 700 hPa near the eastern side of the TP gradually extends eastward and strengthens (Figs. 11c–e), then occupies the MLYR (Fig. 11f). In the meantime, a band of positive PV anomalies tilting eastward with decreasing height moves eastward to the eastern TP and the MLYR. This stimulates the occurrence and development of local vertical ascending motion and positive diabatic heating anomalies (Figs. 12c–f), resulting in the maximum of quasi-biweekly precipitation in MLYR (Fig. 8f). In addition, according to the SHAP mechanism (Wu et al. 2015), the high value of CETP SH with a monopole pattern surely causes the local upward movement (Figs. 12d,e) and transports sufficient water vapor from the oceans to East Asia (Figs. 9d,e). It is indicated that both the TP’s thermal effect and the frictional forcing on its east side contribute to the occurrence and evolution of quasi-biweekly precipitation in the MLYR.
In general, the composite analyses demonstrate that, five (seven) days after the peak of the first mode of quasi-biweekly CETP SH, the maximum springtime quasi-biweekly rainfall will occur in the MLYR (South China). There are three vital factors that affect the development of quasi-biweekly precipitation in China—namely, the TP’s thermal effect, the TP’s topographic frictional forcing and generation of PV, and the southeastward-propagating Rossby wave train in the upper troposphere. When the TP SH reaches its maximum, it inevitably heats the lower-level atmosphere, resulting in the occurrence of ascending motion. Due to the SHAP effect, sufficient water vapor is transported from the oceans to East Asia, providing favorable moisture conditions for precipitation in China. The Rossby wave train in the upper layer propagates southeastward and causes the changes in lower-layer circulation in China. One of the most noteworthy features is the presence of a southerly anomaly on the east side of the TP. The frictional forcing caused by the topography on the eastern side of the TP further induces a cyclonic wind shear response and the enhancement of positive PV anomalies that ultimately cover the MLYR, which is then followed by the occurrence of the maximum quasi-biweekly rainfall in situ. With the upper-level Rossby wave train moving farther to the southeast, the water vapor convergence zone shifts to South China with a positive precipitation anomaly center.
5. Summary and discussion
In the present study, the first leading EOF mode of quasi-biweekly (7–20-day) surface SH over the CETP during the spring seasons from 1979 to 2019 was derived from a long-term TP SH estimate calculated by CMA station observations using an advanced parameterization scheme. The phase evolutions of local land–air coupling processes and large-scale atmospheric circulations associated with the first mode were also investigated based on multisource datasets, including routine meteorological station observations, satellite retrievals, and reanalysis data. In addition, we further detected the mechanism linking CETP SH to spring rainfall in China on the quasi-biweekly time scale.
The first leading mode of quasi-biweekly CETP SH explains 14.9% of the total variance, which shows a monopole pattern in which the largest loading is located centrally. Phase composite analyses indicated the existence of a strong local land–air coupling process during a strong ISO cycle with respect to the PC1. To begin with, the anomalous cold high near the surface benefits the descending motion, reduces cloud cover, and hinders precipitation. Accordingly, more shortwave radiation is allowed to reach the surface, leading to the positive phase of the first mode of quasi-biweekly CETP SH. In addition to the SH heating the lower atmosphere, the dry soil condition due to a lack of precipitation is also conducive to an increase in surface air temperature. This makes the air rise and creates an anomalous warm low near the surface, which results in an increase in cloud cover and rainfall. Consequently, the negative phase is achieved and a cycle is completed.
An eastward-propagating Rossby wave train also contributes to the transition between positive and negative phases, which is characterized by a series of enclosed anomalous anticyclonic and cyclonic centers along the pathway from North Africa, to central Asia, to the TP in the upper and middle troposphere. When this wave train moves eastward, the circulations over the CETP will change accordingly, affecting the intraseasonal variability of SH via land–air coupling. Taking the mechanism of positive phase formation as an example (Fig. 13a), the presence of an upper-level anticyclone and convergence center over the CETP leads to a strong downdraft in situ, which inhibits convection and reduces cloud cover. The resultant elevated shortwave radiation input and drier soil conditions push the first mode of quasi-biweekly CETP SH into the positive phase.
Schematic diagram illustrating the mechanisms for (a) generating the maximum of quasi-biweekly CETP SH with a monopole pattern and (b) determining the leading relationship between CETP SH and spring rainfall in China on the quasi-biweekly time scale. The red curved arrows on the east side of the TP in the lower troposphere indicate a cyclonic circulation manufactured by the topographic friction, which enhances the positive PV anomaly over the MLYR. This is followed by the maximum of the quasi-biweekly rainfall over the MLYR (①) and then the rainfall anomaly in South China (②).
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Schematic diagram illustrating the mechanisms for (a) generating the maximum of quasi-biweekly CETP SH with a monopole pattern and (b) determining the leading relationship between CETP SH and spring rainfall in China on the quasi-biweekly time scale. The red curved arrows on the east side of the TP in the lower troposphere indicate a cyclonic circulation manufactured by the topographic friction, which enhances the positive PV anomaly over the MLYR. This is followed by the maximum of the quasi-biweekly rainfall over the MLYR (①) and then the rainfall anomaly in South China (②).
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
Schematic diagram illustrating the mechanisms for (a) generating the maximum of quasi-biweekly CETP SH with a monopole pattern and (b) determining the leading relationship between CETP SH and spring rainfall in China on the quasi-biweekly time scale. The red curved arrows on the east side of the TP in the lower troposphere indicate a cyclonic circulation manufactured by the topographic friction, which enhances the positive PV anomaly over the MLYR. This is followed by the maximum of the quasi-biweekly rainfall over the MLYR (①) and then the rainfall anomaly in South China (②).
Citation: Journal of Climate 36, 19; 10.1175/JCLI-D-22-0892.1
In spring, the peak of the first mode of quasi-biweekly CETP SH leads the maximum of the quasi-biweekly rainfall in the MLYR (South China) by approximately 5 (7) days, as revealed by lead–lag composites. Figure 13b presents the mechanism responsible for this leading relationship between CETP SH and spring rainfall in China on the quasi-biweekly time scale. Three essential factors contribute to the intensification of quasi-biweekly precipitation in China—namely, the TP’s thermal effect, the TP’s topographic frictional forcing and generation of PV, and the southeastward-propagating Rossby wave train in the upper troposphere. As the CETP SH reaches its peak, it uniformly heats the lower atmosphere, resulting in the occurrence of a local updraft. Owing to the SHAP effect, abundant water vapor is transported from the oceans to China. The upper-level Rossby wave train propagates southeastward and changes the lower-layer atmospheric circulation. The lower-layer southerly anomaly on the east side of the TP develops into an anomalous cyclonic circulation owing to the effect of topographic friction. This leads to the enhancement and expansion of the positive PV anomaly that ultimately covers the MLYR. All of the above lead to the maximum of the quasi-biweekly rainfall in the MLYR. As the upper-level Rossby wave train propagates farther to the southeast, the water vapor convergence zone, as well as the rainfall anomaly center, moves to South China. This suggests that the CETP monopole SH warming could be a good indicator for predicting intraseasonal variations in spring rainfall over China.
In this study, we investigated the influence of TP thermal forcing on spring quasi-biweekly rainfall in China via multisource data diagnosis. However, the TP SH sensitivity experiments in the form of quasi-biweekly oscillation needs to be conducted by using general circulation models (GCMs) or coupled GCMs to completely separating the effect of SH from that of Rossby wave train on spring rainfall in China, which our group intends to do in future work. In addition, according to the PV equation proposed by Hoskins et al. (1985), analysis of the PV budget also needs to be done to determine precisely which process contributes to the PV tendency.
Acknowledgments.
This research was jointly supported by the Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant XDB40030204) and the National Natural Science Foundation of China (42275032 and 42175076).
Data availability statement.
No datasets were generated or analyzed during the current study.
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