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    Geographical distributions of spring mean [March–May (MAM)] (a) top-of-the-atmosphere shortwave cloud radiative effect (SWCRE; W m−2) derived from CERES-MODIS (Clouds and the Earth’s Radiant Energy System–Moderate Resolution Imaging Spectroradiometer) and (b) precipitation (mm day−1) from the Global Precipitation Climatology Project during the period of 2001–16. The black solid line is the Tibetan Plateau (over 3000 m).

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    Seasonal mean cloud radiative effects (CREs) averaged over southeastern China (22°–32°N, 104°–122°E) during the period of 2001–16. Here LWCRE, SWCRE, and NCRE are the abbreviations of longwave, shortwave, and net CREs, respectively. ANN, DJF, MAM, JJA, and SON indicate the annual, winter, spring, summer, and autumn mean, respectively.

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    Spring mean (a) circulations, (b) column water vapor flux (kg m−1 s−1) and divergence (10−4 kg m−2 s−1), and (c) cloud water path (CWP; g m−2) from ERA-Interim during the period of 2001–16. In (a), the green line indicates the 200-hPa westerly jet and the vector is 850-hPa wind (m s−1). In (c), the shading is the total CWP (liquid and ice) and the blue contour line is liquid CWP with an interval of 20 (g m−2). The black solid line is the TP (over 3000 m). Here, column water vapor divergence is integrated from surface to 100 hPa. The vector data are masked below 850 hPa.

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    Simulated spring mean (a) SWCRE, (b) circulations, and (c) cloud water path (g m−2) during the period 2001–16. In (c), the shading is total CWP (liquid and ice) and the blue contour line is liquid CWP with an interval of 20 (g m−2). The black solid line is the TP (over 3000 m). The vector data are masked below 850 hPa.

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    Spring mean (a) pressure–latitude cross section of cloud water content (10−6 kg kg−1) and wind circulations averaged over 104°–122°E and (b) corresponding pressure–longitude cross section averaged over 22°–32°N. In (a), the red contour is westerly wind (m s−1) and the vector is the meridional (20 m s−1)/vertical wind (5 × 10−3 Pa s−1) component. In (b), the red contour is air temperature (°C) with intervals of 3°C and vector is zonal (m s−1)/vertical wind (2 × 10−3 Pa s−1) component. (c),(d) As in (a) and (b), but from the WRF simulation. The gray shading is the TP altitude.

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    Pentad mean variations of observed (a) precipitation (mm day−1), (b) SWCRE (W m−2), (c) 850–500-hPa mean ascending motion (hPa day−1), and (d) column water vapor flux divergence (10−4 kg m−2 s−1) averaged over 104°–122°E during the period of 2001–16. The x axis denotes the pentad number.

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    As in Fig. 6, but for the WRF simulation. In (a), the blue line is the ratio of convective rainfall amount to total rainfall amount.

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    Pentad mean variations of CREs (W m−2) in the (a) observation and (b) simulation averaged over SEC (22°–32°N, 104°–122°E) during the period of 2001–16. Here, the orange line denotes the ratio of the LWCRE magnitude to that of negative SWCRE. The vertical bars denote the standard deviation at each pentad, reflecting the interannual variability. The x axis denotes the pentad number.

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    Geographical distributions of 200-hPa westerly jet (red dashed line; m s−1), column water vapor flux (vector; kg m−1 s−1), and divergence (shading; 10−4 kg m−2 s−1) at selected pentads averaged during 2001–16. The data are from ERA-Interim. The black solid line is the TP (over 3000 m). The vector data are masked below 850 hPa.

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    Pentad mean variations of observed (black line) and simulated (blue line) (a) precipitation (mm day−1) and (b) SWCRE (W m−2) averaged over SEC (22°–32°N, 104°–122°E) in 2010. (c),(d) As in (a) and (b), but for the corresponding results in 2011. The temporal correlation coefficients are marked at the top-right corner in each subfigure. The x axis is the pentad number.

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    Pentad mean variations of (a) ERA-Interim reanalyzed vertical velocity (hPa day−1), (b) simulated vertical velocity (hPa day−1), (c) column water vapor flux divergence (10−4 kg m−2 s−1), and (d) simulated cloud water path (g m−2) averaged over SEC (22°–32°N, 104°–122°E) in 2010. (e)–(h) As in (a)–(d), but showing the corresponding results in 2011. Here, IWP denotes cloud ice water path. The x axis is the pentad number.

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    Geographical distributions of (a) CERES SWCRE (shading; W m−2), vertical velocity (Pa day−1) at 500 hPa, and wind field at 850 hPa averaged during the period of pentads 18–30 in 2010, (b) the corresponding results for 2011, and (c) the differences between 2010 and 2011. (d)–(f) As in (a)–(c), but for the corresponding results from the WRF simulation. The black solid line is the TP (over 3000 m). The vector data are masked below 850 hPa.

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    Scatterplot of SWCRE (W m−2) and vertical velocity (Pa day−1) at 500 hPa over SEC (22°–32°N, 110°–120°E). Here, the SWCRE (vertical velocity) is the difference between the 18th–30th pentad mean results in 2010 and 2011 (as shown in Figs. 11c and 11f). The grid over SEC is selected to plot the scatter figure. The regression equation and linear correlation coefficient are shown in the lower right of each panel.

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    As in Fig. 9, but from the WRF simulation.

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    Observational distribution of the 850-hPa wind (vector), zonal wind speed (red dashed line; m s−1) at 200 hPa, and precipitation (shading; mm day−1) averaged during the 18th–30th pentads of (a) 2010 and (b) 2011. The pentad number is marked at the top-right corner in each subfigure. The black solid line is the TP (over 3000 m). The vector data are masked below 850 hPa.

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Persistent Spring Shortwave Cloud Radiative Effect and the Associated Circulations over Southeastern China

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  • 1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | 2 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
  • | 3 Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York
  • | 4 School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
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Abstract

Clouds strongly modulate regional radiation balance and their evolution is profoundly influenced by circulations. This study uses 2001–16 satellite and reanalysis data together with regional model simulations to investigate the spring shortwave cloud radiative effect (SWCRE) and the associated circulations over southeastern China (SEC). Strong SWCRE, up to −110 W m−2, persists throughout springtime in this region and its spring mean is the largest among the same latitudes of the Northern Hemisphere. SWCRE exhibits pronounced subseasonal variation and is closely associated with persistent regional ascending motion and moisture convergence, which favor large amounts of cloud liquid water and resultant strong SWCRE. Around pentad 12 (late February), SWCRE abruptly increases and afterward remains stable between 22° and 32°N. The thermal and dynamic effects of Tibetan Plateau and westerly jet provide appropriate settings for the maintenance of ascending motion, while water vapor, as cloud water supply, stably comes from the southern flank of the Tibetan Plateau and South China Sea. During pentads 25–36 (early May to late June), SWCRE is further enhanced by the increased water vapor transport caused by the march of East Asian monsoon systems, particularly after the onset of the South China Sea monsoon. After pentad 36, these circulations quickly weaken and the SWCRE decreases accordingly. Individual years with spring strong and weak rainfall are chosen to highlight the importance of the strength of the ascending motion. The simulation broadly reproduced the observed results, although biases exist. Finally, the model biases in SWCRE–circulation associations are discussed.

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This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).

© 2019 American Meteorological Society.

Corresponding author: Dr. Jiandong Li, lijd@mail.iap.ac.cn

Abstract

Clouds strongly modulate regional radiation balance and their evolution is profoundly influenced by circulations. This study uses 2001–16 satellite and reanalysis data together with regional model simulations to investigate the spring shortwave cloud radiative effect (SWCRE) and the associated circulations over southeastern China (SEC). Strong SWCRE, up to −110 W m−2, persists throughout springtime in this region and its spring mean is the largest among the same latitudes of the Northern Hemisphere. SWCRE exhibits pronounced subseasonal variation and is closely associated with persistent regional ascending motion and moisture convergence, which favor large amounts of cloud liquid water and resultant strong SWCRE. Around pentad 12 (late February), SWCRE abruptly increases and afterward remains stable between 22° and 32°N. The thermal and dynamic effects of Tibetan Plateau and westerly jet provide appropriate settings for the maintenance of ascending motion, while water vapor, as cloud water supply, stably comes from the southern flank of the Tibetan Plateau and South China Sea. During pentads 25–36 (early May to late June), SWCRE is further enhanced by the increased water vapor transport caused by the march of East Asian monsoon systems, particularly after the onset of the South China Sea monsoon. After pentad 36, these circulations quickly weaken and the SWCRE decreases accordingly. Individual years with spring strong and weak rainfall are chosen to highlight the importance of the strength of the ascending motion. The simulation broadly reproduced the observed results, although biases exist. Finally, the model biases in SWCRE–circulation associations are discussed.

Denotes content that is immediately available upon publication as open access.

This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).

© 2019 American Meteorological Society.

Corresponding author: Dr. Jiandong Li, lijd@mail.iap.ac.cn

1. Introduction

Clouds play significant roles in climate systems by modulating the radiative energy budget and hydrological cycle. The importance and complexity of cloud physics processes cause them to contribute the biggest uncertainty in current climate simulations and predictions (Stephens 2005; Boucher et al. 2013). Cloud formation, type, and spatial distribution are deeply affected by general circulations and then exhibit distinctive regional features (Rogers and Yau 1989; Bony et al. 2015). Southeastern China (SEC) is located in the East Asian monsoon region, which is characterized by prevailing monsoon circulations and complicated topography. Meanwhile, large amounts of continental stratus clouds and strong cloud albedo effect are distributed in SEC (Klein and Hartmann 1993; Yu et al. 2001, 2004). Due to monsoon circulations, cloud radiative characteristics over SEC also have remarkable seasonal variation, with strong winter–summer contrast (Luo et al. 2009; Li et al. 2017a). At present, climate models show evident biases of shortwave cloud radiative effect (CRE) at the top of the atmosphere (TOA) and cloud cover over SEC, where a large regional cloud albedo effect is particularly underestimated (Flato et al. 2013; F. Wang et al. 2014). These model biases significantly contribute to the uncertainties of regional cloud feedbacks and climate projections. Thus, accurate simulation of climate processes over East Asia requires a more in-depth understanding of cloud radiation characteristics over SEC.

In addition to the winter–summer monsoon period, spring is another distinctive period over SEC, where persistent spring rainfall is a unique regional climatic phenomenon (Tian and Yasunari 1998; Wan and Wu 2007). In this region, the spring rainfall contributes more than 30% of the total annual precipitation (LinHo 2008; Wu and Mao 2016) and also has obvious subseasonal and interannual variation (Pan et al. 2013; Huang et al. 2015). The anomalies of spring rainfall and relevant circulation exert great impacts on the weather, climate processes, agriculture, and socioeconomics of this region. It is noted that distinct spring shortwave CRE (SWCRE) also appears over SEC. Work by Wang et al. (2004) and Li et al. (2017a) with monthly datasets showed that strong SWCRE over SEC occurs in winter and persists until full spring. As shown in Fig. 1, spring mean SWCRE over SEC (around 104°–122°E and 22°–32°N) is the strongest at the same latitudes of the Northern Hemisphere with a maximum value of up to −120 W m−2, the intensity of which is much larger than over the eastern coasts of the Pacific (Fig. 1a). Unlike the usual amount of spring precipitation in SEC, sparse precipitation occurs over the eastern coastal regions of the Pacific where descending motion prevails (Fig. 1b). Meanwhile, the spring SWCRE averaged over SEC is the largest of the four seasons (Fig. 2). So robust spring SWCRE certainly exerts a nonnegligible force on the regional climate system. Moreover, spring is the winter–summer transition season, and cloud roles in radiation budget and atmospheric heating potentially affect the following migration of East Asian summer monsoon circulation (Guo et al. 2015; Li et al. 2015). Figure 1 also shows that the spatial pattern of SWCRE is somewhat similar to that of rainfall over the midlatitudes of the Northern Hemisphere. Hence, it is very important to investigate spring CRE over SEC for understanding the radiation budget and hydrological cycle, and their seasonal transition in East Asia.

Fig. 1.
Fig. 1.

Geographical distributions of spring mean [March–May (MAM)] (a) top-of-the-atmosphere shortwave cloud radiative effect (SWCRE; W m−2) derived from CERES-MODIS (Clouds and the Earth’s Radiant Energy System–Moderate Resolution Imaging Spectroradiometer) and (b) precipitation (mm day−1) from the Global Precipitation Climatology Project during the period of 2001–16. The black solid line is the Tibetan Plateau (over 3000 m).

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

Fig. 2.
Fig. 2.

Seasonal mean cloud radiative effects (CREs) averaged over southeastern China (22°–32°N, 104°–122°E) during the period of 2001–16. Here LWCRE, SWCRE, and NCRE are the abbreviations of longwave, shortwave, and net CREs, respectively. ANN, DJF, MAM, JJA, and SON indicate the annual, winter, spring, summer, and autumn mean, respectively.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

Some previous work noted that spring cloud radiative characteristics over SEC are connected with regional circulations to some extent. Yu et al. (2001) suggested that the convergence below 700 hPa in winter and spring helps to the middle to low cloud formation over eastern China, where total cloud fraction peaks in spring. Work by Yu et al. (2004), Li and Gu (2006), and Zhang et al. (2013) showed that Tibetan Plateau (TP) dynamic effects contribute to the low-level convergence, ascending motion, and midlevel divergence over eastern China, favoring regional stratus clouds and high SWCRE in the cold season. Besides the TP, other regional circulations, such as the low-level southerly wind from the South China Sea, the northwest Pacific anticyclone, and the East Asian subtropical jet, also have pronounced roles in spring vertical motion and moisture supply over SEC (Liang and Wang 1998; Zhao et al. 2007; Zhu et al. 2007), which are essential for regional clouds and rainfall. Moreover, distinct subseasonal features exist in the East Asian monsoon region including SEC, where the intensity, spatial distribution, and movement timing of convection, moisture, and prevailing low-level air currents experience remarkable subseasonal variations especially in the winter–spring and spring–summer transition periods (Lau and Yang 1997; Tian and Yasunari 1998; He et al. 2008). Consequently, spring cloud radiative characteristics potentially show corresponding subseasonal variation and interannual differences as influencing circulations. However, previous studies focused on seasonal or monthly mean states of cloud radiative characteristics and mostly did not reveal subseasonal features and associations with regional circulations. Particularly for spring (March–May), the following issues remain unclear over SEC: 1) How does subseasonal SWCRE vary? 2) What types of relationships exist between SWCRE and regional circulations in spring mean and subseasonal scales? The purpose of this study is therefore to address these questions. This study investigates spring SWCRE, including the seasonal mean, subseasonal features and some interannual differences, and identifies major regional circulation conditions contributing to the occurrence and maintenance of spring SWCRE over SEC. Our analyses are conducted using satellite and reanalysis data, as well as a regional model. The regional simulation is used to verify the climatological and case-year results and to further enhance our confidence in the robustness of associated climatic features.

The remainder of this paper is organized as follows. In section 2, the data and method used are introduced. Section 3 presents the multiyear spring mean features of SWCRE and circulations over SEC, and analyzes their possible associations. Section 4 shows corresponding subseasonal variations during late winter to early summer and investigates their connections in temporal evolutions. Section 5 investigates the interannual differences of SWCRE and regional circulations with the analysis and simulation in two case years. Finally, the conclusions and discussion are presented in section 6.

2. Data and method

a. Observational and reanalysis datasets

The cloud-radiation datasets used in this study consist of two parts. The first part of the data is from satellite retrievals. Daily and monthly mean TOA radiative fluxes are from NASA’s Clouds and the Earth’s Radiant Energy System (CERES), which utilizes the most recent CERES Energy Balanced and Filled at the TOA (EBAF-TOA) Ed2.8 dataset (Doelling et al. 2013). The CERES-EBAF includes incident shortwave (SW) flux and outgoing SW and longwave (LW) radiative fluxes at the TOA under clear-sky and all-sky conditions. This dataset has been widely used to study the role of clouds and the energy cycle in the Earth climate system (Loeb et al. 2009; Wild et al. 2013). Thus, CERES-EBAF is the observational radiation dataset used herein. The aforementioned datasets have a spatial resolution of 1.0° latitude by 1.0° longitude and are available from March 2000 to February 2017.

The second part of the data is from ERA-Interim (Dee et al. 2011); these data include daily mean cloud amount, liquid, and ice water. The aforementioned cloud-radiation variables in ERA-Interim are calculated in the host assimilation model and are physically consistent with the reanalyzed meteorological variables to a large degree. ERA-Interim provides reasonable description of spatiotemporal features of cloud-radiation variables (e.g., total cloud amount, cloud vertical structures, and TOA CREs) over East Asia although some intensity biases exist (Yin et al. 2015; Li and Mao 2015; Li et al. 2017b). Another great advantage of ERA-Interim reanalysis data is their continuous and long-term states. The satellite data are limited by their operational period, instrument performance and retrieval algorithm, and cannot fully cover the spatiotemporal resolution in this study. Thus, daily cloud cover and cloud water from ERA-Interim are used to explain relevant cloud-radiation issues, particularly in the subseasonal variation and vertical distribution. The meteorological variables are also taken from ERA-Interim and show good performance in reproducing wind fields, atmospheric moisture, and precipitation (Simmons et al. 2014; Huang et al. 2016). The spatial resolution of ERA-Interim is 1.5° latitude by 1.5° longitude, with 37 vertical pressure layers in this study.

The daily mean precipitation data are from the Global Precipitation Climatology Project (GPCP) with a spatial resolution of 1.0° latitude by 1.0° longitude, which ranges from October 1996 to February 2017 (Adler et al. 2003). The monthly GPCP has a spatial resolution of 2.5° latitude by 2.5° longitude. The GPCP dataset represents the climatological state of precipitation well (Yin et al. 2004; Huffman et al. 2009). In this study, CERES satellite retrievals, ERA-Interim meteorological fields, and GPCP precipitation are used as observational references.

b. Analysis methods

CREs are widely used variables for effectively describing the bulk cloud effects on air-surface systems. Therefore, in this study, CREs are used as key study variables to analyze seasonal features of the regional cloud-radiation process. Following previous work (Ramanathan et al. 1989; Boucher et al. 2013), TOA CREs are defined as differences in TOA radiative fluxes between clear-sky and all-sky conditions:
e1
e2
e3
where OLRCS and OLR are outgoing LW radiative fluxes at the TOA under clear-sky and all-sky conditions, respectively; RSUTCS and RSUT are the corresponding outgoing SW radiative fluxes; and net CRE (NCRE) is the arithmetic sum of LWCRE and SWCRE. The radiative fluxes and CREs in this study are for TOA without a special description.

The best CREs data are from CERES-EBAF, which begins in March 2000. Thus, in this study, only reanalyzed and simulated datasets since the beginning of the CERES satellite era are used. The spring climatological state and interannual variabilities are examined in this study. The 16-yr climatological means of the meteorological fields and cloud-radiation variables are obtained for the period of 2001–16. The spring seasonal average is calculated using the results for March, April, and May. The climatological pentad (5 day) average is used to analyze the subseasonal variation to identify the general circulation influence on regional cloud radiative effects. This procedure of pentad mean is effective for suppressing synoptic disturbances and yet retains the prominent feature of interest (Zhao et al. 2007; LinHo et al. 2008). The subseasonal period of interest is pentads 1–36, which ranges from late winter to early summer. Besides climatological states, two real case years (2010 and 2011) are selected to compare the differences in cloud-radiation and circulation over SEC. The domain of SEC is defined as 22°–32°N, 104°–122°E based on previous studies (Liang and Wang 1998; Yu et al. 2001; Wan and Wu 2007; Li et al. 2017a; Li et al. 2018), which covers the continental areas of the middle and lower Yangtze River and South China.

c. Model and experimental design

In this study, the Weather Research and Forecasting (WRF) Model (version 3.7.1) is used for the regional simulation. The WRF Model shows considerable reproducibility for the East Asian climate (e.g., Kim and Hong 2010; Z. Wang et al. 2014). In our simulation, physical packages include the WSM6 cloud microphysics scheme (Hong and Lim 2006), Kain–Fritsch convective scheme (Kain 2004), Noah land surface scheme (Chen and Dudhia 2001), Yonsei University (YSU) planetary boundary layer (PBL) scheme (Hong et al. 2006), and Rapid Radiative Transfer Model for GCMs (RRTMG) for longwave and shortwave radiation (Iacono et al. 2008). The simulation domain covers most parts of East Asia and adjacent oceans with 260 grid points along the east–west direction and 170 along the north–south direction (the buffer zone has 10 grid points). A Lambert projection is adopted, and the domain is centered over 30°N, 112.5°E. The model has a 30-km horizontal resolution and 30 vertical layers with a terrain-following sigma coordinate and prescribed model top at 50 hPa. The initial state of the atmosphere and lateral boundary conditions (updated every 6 h) are from ERA-Interim, which have a spatial resolution of 0.75° latitude by 0.75° longitude, and the daily updated sea surface temperature (SST) forcing dataset is also from ERA-Interim.

For the climatological simulation of 2001–16, one run is conducted and its initial run time is 0000 UTC 16 December. The 16-yr simulation is averaged to obtain multiyear mean spring and subseasonal results. As for each case year (2010 and 2011), an ensemble experiment with four runs is performed under different initial conditions at 0000 UTC 16 December, 0600 UTC 17 December, 1200 UTC 18 December, and 1800 UTC 19 December, and each run ends at 1800 UTC 31 August of the following year. The simulation period before 0000 UTC 1 January is considered to be the spinup time. The simulation ranging from 1 January to 31 August is used in this study. First, an arithmetic mean of four runs is taken, and then the pentad mean is obtained to analyze subseasonal variations.

3. Results

a. Climatological states of spring mean

Figure 3 presents geographical distributions of spring mean circulations and cloud water path. Here, the circulations include 200-hPa westerly jet, 500-hPa vertical velocity, 850-hPa wind, column-integrated water vapor flux, and divergence. In spring, a strong westerly jet at 200 hPa lies between the East China Sea and southern Japan, and the southern side of the entrance region of the jet is just located over SEC (Fig. 3a), which aids in regional updraft motion (Liang and Wang 1998; Holton and Hakim 2012). Low-level southwesterly wind to the southern flank of the TP and southerly wind from the west of South China Sea bring abundant water vapor into SEC, where is an obvious moisture sink region (Figs. 3a,b). Because of the above circulation distributions, considerable water vapor readily condenses as cloud water with the ascending motion. As shown in Fig. 3c, substantial cloud liquid water exists over SEC (Fig. 3c). Large SWCRE, ascending motion, water vapor sink, and cloud water are all distributed over SEC and their high value centers are around 110°E (Figs. 1a and 3). This indicates that spring circulations over SEC favor not only the occurrence of rain but also the formation of cloud water and resultant SWCRE.

Fig. 3.
Fig. 3.

Spring mean (a) circulations, (b) column water vapor flux (kg m−1 s−1) and divergence (10−4 kg m−2 s−1), and (c) cloud water path (CWP; g m−2) from ERA-Interim during the period of 2001–16. In (a), the green line indicates the 200-hPa westerly jet and the vector is 850-hPa wind (m s−1). In (c), the shading is the total CWP (liquid and ice) and the blue contour line is liquid CWP with an interval of 20 (g m−2). The black solid line is the TP (over 3000 m). Here, column water vapor divergence is integrated from surface to 100 hPa. The vector data are masked below 850 hPa.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

Figure 4 presents simulated SWCRE, circulations, and cloud water path in spring. The simulation can reproduces spring ascending motion at 500 hPa over SEC, but the ascending motion over the Indochina Peninsula is much larger than the observation. In the simulation, the low-level southerly wind from South China Sea is captured but the southwesterly wind from east to the TP is weaker (Fig. 4b), inducing a weaker transfer of water vapor over SEC. The spatial pattern of cloud water path is well reproduced by the model and its high value center is very close to the observation (Fig. 4c). Note that the simulated cloud water path is weaker than the observational reference, but the simulated cloud liquid radius is also small (not shown) and helps to intensify cloud optical depth and SWCRE over SEC and its eastern sea. The weak CWP and small cloud radius in WRF simulation show compensatory roles in determining SWCRE, resulting in a comparable intensity of simulated SWCRE relative to the observation (Figs. 1a and 4a). The simulation basically reproduces strong spring SWCRE and corresponding circulations, although biases exist.

Fig. 4.
Fig. 4.

Simulated spring mean (a) SWCRE, (b) circulations, and (c) cloud water path (g m−2) during the period 2001–16. In (c), the shading is total CWP (liquid and ice) and the blue contour line is liquid CWP with an interval of 20 (g m−2). The black solid line is the TP (over 3000 m). The vector data are masked below 850 hPa.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

Note that the high-value center of spring SWCRE is mainly distributed in western and central SEC, but stronger rainfall appears eastern SEC. This high-value distribution difference between spring SWCRE and rainfall is related to orography and land–sea effects. Li and Yu (2014) showed that compared with eastern SEC, weaker rainfall with higher frequency is dominant over western SEC because of terrain effects. Higher rainfall frequency means longer cloud duration time and larger SWCRE. Relatively, eastern SEC is more adjacent to oceans, and the abundant moisture and land–sea effects enable larger amounts of rainfall (Yuan et al. 2012). Additionally, heavy aerosol loading over western and central SEC also contributes to small cloud radius and strong SWCRE (Li et al. 2017a).

SWCRE at subtropical and midlatitudes highly depends on low- to midlevel cloud water content (Cess et al. 1990; Zhang et al. 2005). We further analyzed vertical distributions of spring meridional mean cloud water and winds to examine the relationship among vertical circulations, cloud water, and SWCRE. As clearly shown in Fig. 5a, the westerly jet core is around 30°N, and descending motion and northerly wind occur north to 30°N. The obvious ascending motion and southerly wind appear over 20°–30°N (the SEC latitudinal zone) and just correspond to large cloud water content between 850 and 500 hPa, the magnitude of which is much larger than at same tropical longitudes (Fig. 5a). From the pressure–longitude cross section averaged over 22°–32°N (Fig. 5b), the strongest ascending motion occurs at low to midlevels immediately adjacent to the TP (104°–110°E). The large cloud water appears between the area east of the TP and the middle reaches of the Yanzi River, and extends easterly to ocean regions around 122°E. Moreover, cloud water is mainly distributed below 500 hPa and the isotherm of 0°C (Fig. 5b), showing that cloud water over SEC mainly consists of liquid water. At the same time, the westerly wind over the TP and eastern China is conducive to the transfer of cloud water downstream.

Fig. 5.
Fig. 5.

Spring mean (a) pressure–latitude cross section of cloud water content (10−6 kg kg−1) and wind circulations averaged over 104°–122°E and (b) corresponding pressure–longitude cross section averaged over 22°–32°N. In (a), the red contour is westerly wind (m s−1) and the vector is the meridional (20 m s−1)/vertical wind (5 × 10−3 Pa s−1) component. In (b), the red contour is air temperature (°C) with intervals of 3°C and vector is zonal (m s−1)/vertical wind (2 × 10−3 Pa s−1) component. (c),(d) As in (a) and (b), but from the WRF simulation. The gray shading is the TP altitude.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

The simulation can capture major vertical distributions of cloud water and wind as shown in Figs. 5c and 5d. Compared to the observation, the simulated central intensity and location of westerly jet are stronger and more southerly, and the ascending motion between 10° and 15°N is also too large (Fig. 5c). Besides the low-level atmosphere, large cloud water content is also distributed between 300 and 400 hPa and extends eastward to the East China Sea (Fig. 5d), where this considerable high-level cloud water complicates cloud overlap (Fig. 4a). The TP is a heat source in spring relative to the same latitudes over eastern China (Yeh et al. 1957; Wu et al. 2007). As shown in Figs. 5b and 5d, a temperature gradient from west to east exists in the middle atmosphere around 104°–110°E, which can increase the stability at low to midlevels over SEC (Wu and Zhang 1998; Zhang et al. 2013). Previous work (Yu et al. 2001, 2004; Li and Gu 2006) suggested that the TP can reduce the westerly wind and then low-level convergence and midlevel divergence appear on the lee side of the TP. These thermal and dynamic roles of the TP limit updraft motion in low to midlevels but aid the accumulation of cloud water. Because of the above-mentioned circulation distributions, spring cloud water over SEC is mainly located in the low to midlevels. The work by Luo et al. (2009) also shows that low to middle clouds during March to May are the dominant cloud types over eastern China with the almost same domain as this study. As a result, large amounts of liquid cloud reflect shortwave radiation and cause strong SWCRE over SEC.

b. Subseasonal variation

Given that some subseasonal features cannot be reflected in spring mean (March to May) results, we used the pentad mean variation to further investigate the SWCRE and relevant circulations over SEC. This study focuses on the spring and therefore pentads 1–48 (early January to late September) are selected for analysis in this study. The vertical velocity between 850 and 500 hPa is averaged to represent the low-to-midlevel vertical motion. Ascending motion and water vapor supply are the major circulation conditions for the formation of large-scale clouds and rainfall. Figure 6 presents the observed subseasonal variation of rainfall, SWCRE, and the two general circulations mentioned above. Some researchers (He et al. 2007, 2008; Zhao et al. 2007; Zhu et al. 2011) have pointed out that the subtropical spring rain (around pentads 12–24), the establishment of the South China Sea monsoon (around pentads 25–28), and the following northward movement of summer monsoon rain (after pentad 28) consecutively occur over Asian regions, and the wind field and moisture then show dramatic variations during late winter to early summer over SEC. These features are also very clearly seen in pentad mean variation of rainfall (Fig. 6a). Note that the intensity of rainfall, SWCRE, ascending motion, and water vapor convergence abruptly increases around pentad 12 (late February) in Fig. 6, and the corresponding low-level southerly wind and westerly jet synchronously reduce (not shown). This abrupt change almost simultaneously appears with the reversal of temperature gradient between the Indochina Peninsula and the western Pacific to the east of the Philippines in late February (Tian and Yasunari 1998). After pentad 12, the low-to-midlevel ascending motion increases up to −50 hPa day−1 and persists until pentad 36 (late June) in Fig. 6c. During the same period, SWCRE and water vapor convergence gradually increase and then further intensify starting at pentad 16 (mid-March), when SWCRE intensity reaches up to −110 W m−2. After pentad 24 (late April), SWCRE and water vapor continue to increase until pentad 36, and afterward reduce quickly (Figs. 6b and 6d). The cloud water content shows similar variations to those described above (not shown). As listed in Table 1, the temporal correlations between area-averaged SWCRE and updraft velocity (water vapor convergence) during pentads 1–36 are over 0.84 in the observation and simulation, showing that the two circulation conditions are essential to maintain strong SWCRE over SEC.

Fig. 6.
Fig. 6.

Pentad mean variations of observed (a) precipitation (mm day−1), (b) SWCRE (W m−2), (c) 850–500-hPa mean ascending motion (hPa day−1), and (d) column water vapor flux divergence (10−4 kg m−2 s−1) averaged over 104°–122°E during the period of 2001–16. The x axis denotes the pentad number.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

Table 1.

Temporal correlations between 850–500-hPa mean vertical velocity (hPa day−1)/column moisture divergence (10−4 kg m−2 s−1) and SWCRE (W m−2)/precipitation (mm day−1) during pentads 1–36 for the observed and simulated data. The data are averaged over SEC (22°–32°N, 104°–122°E) during the period of 2001–16. “Obs.” and “Sim.” are the abbreviations for observation and simulation.

Table 1.

Figure 7 is simulated pentad mean variations. The simulation can well reproduce subseasonal variation of SWCRE to the north of 20°N. The simulation accurately captures the abrupt increase in SWCRE, ascending motion, and water vapor convergence at pentad 12, and their further intensification at pentads 16 and 24 (Figs. 7b–d). In contrast, simulated SWCRE around the SEC latitudinal zone is weaker during pentads 1–20, and rainfall, ascending motion, and water vapor convergence to the south of 20°N are stronger after pentad 24. Moreover, large rainfall remains after pentad 36, which is associated with strong convections from WRF cumulus parameterization used in this study. In the simulation, ascending motion and water vapor supply over tropical regions to south of SEC (the South China Sea) are also greatly enhanced since pentad 24, but the corresponding SWCRE is much weaker than that over SEC. Note that large SWCRE stably exists at SEC latitudinal bands (22°–32°N) during pentads 25–36, and does not retreat southward and further intensify northward as rainfall (Figs. 6b and 7b). This difference of subseasonal variations between SWCRE and rainfall is relevant to respective cloud-precipitation features over subtropical (SEC) and tropical (South China Sea) monsoon regions. Stratiform precipitation with large area and long cloud duration time accounts for a considerable proportion over SEC and markedly increases after the onset of South China Sea monsoon; by comparison, although the convective precipitation amount over South China Sea is dramatically enhanced after pentad 24, its precipitation area quickly decreases, indicative of short duration time (Hu et al. 2011). As clearly shown in the simulation (Figs. 7a,b), the convective rainfall ratio during pentads 25–36 is up to 0.9 over South China Sea, and then cloud lifetime and resultant SWCRE are not as large as over SEC.

Fig. 7.
Fig. 7.

As in Fig. 6, but for the WRF simulation. In (a), the blue line is the ratio of convective rainfall amount to total rainfall amount.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

The above subseasonal variations of SWCRE are also reflected in their regional mean time series. As shown in Fig. 8, the observed and simulated SWCRE dramatically intensify around pentad 12 and then gradually increase. The maximum of observed and simulated SWCRE occurs in pentads 33 (mid-June) and 28 (late May), with values of −128 and −120 W m−2, respectively. The earlier peak time of simulated SWCRE arises from the high-value position bias of SWCRE, which is east in comparison with the observation. During pentads 12–36, LWCRE also increases and offsets with SWCRE, and then NCRE intensity stably remains over −60 W m−2. As illustrated in Fig. 2, Fig. 8 further shows that SWCRE over SEC is much larger than LWCRE and dominates regional CREs. The intensity ratio of LWCRE to SWCRE is only 0.2 before pentad 12 (Fig. 8). Afterward, with the increase in ascending motion and high cloud, the ratio of LWCRE gradually increases and exceeds 0.4 at around pentad 36. The simulated interannual variabilities of SWCRE and NCRE are larger than the observations. Another interesting feature is that the interannual variability of SWCRE is very close to NCRE during pentads 1–30, indicating that SWCRE dominates interannual variability of CREs over SEC.

Fig. 8.
Fig. 8.

Pentad mean variations of CREs (W m−2) in the (a) observation and (b) simulation averaged over SEC (22°–32°N, 104°–122°E) during the period of 2001–16. Here, the orange line denotes the ratio of the LWCRE magnitude to that of negative SWCRE. The vertical bars denote the standard deviation at each pentad, reflecting the interannual variability. The x axis denotes the pentad number.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

Based on the pentad mean variations mentioned above, Fig. 9 presents several important pentads to show geographical distributions of water vapor and ascending motion affecting SWCRE. In pentads 12, 18, and 24, the westerly jet stays between eastern China and Japan, and obvious moisture convergence and ascending motion occur over SEC. The water vapor mainly comes from the westerly wind south to the TP and the southerly wind from the South China Sea. At pentad 28 (mid-May) when the South China Sea monsoon has been established, the water vapor transport by the southwesterly wind from the Bay of Bengal is greatly enhanced. SWCRE over SEC then further increases since pentad 28. Afterward, the west Pacific subtropical high extends westward and advances northward. In pentad 38, the subtropical ridge is close to the middle and lower reaches of the Yanzi River and suppresses strong updraft motion; consequently, moisture convergence, updraft motion, and SWCRE weaken. The simulation basically reproduces the aforementioned circulation distributions, but some evident biases of moisture transport exist. Particularly, moisture transfer by the southwest air current is weak over SEC in pentad 18 but too strong in pentads 28, 33, and 38 over the Indochina Peninsula, the South China Sea, and south of Japan (Fig. A1). The overestimated moisture leads to large rainfall over tropical regions south of SEC.

Fig. 9.
Fig. 9.

Geographical distributions of 200-hPa westerly jet (red dashed line; m s−1), column water vapor flux (vector; kg m−1 s−1), and divergence (shading; 10−4 kg m−2 s−1) at selected pentads averaged during 2001–16. The data are from ERA-Interim. The black solid line is the TP (over 3000 m). The vector data are masked below 850 hPa.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

According to the results shown in Figs. 6, 7, and 9, large SWCRE over SEC is closely associated with regional water vapor supply and low-to-midlevel ascending motion, which exhibit pronounced stage features. Around pentad 12, ascending motion, moisture supply, and SWCRE over SEC abruptly increases, which is caused by the reversal of thermal contrast between the Indochina Peninsula and the western Pacific to east of the Philippines in late February. The subsequent pentad mean variations of SWCRE over SEC are roughly divided into two stages during late winter to early summer. In stage 1 (pentads 12–24), regional ascending motion is mainly caused by the TP thermal and dynamic roles and westerly jet while water vapor as cloud water supply stably comes from the southern flank of the TP and South China Sea. In stage 2 (pentads 25–36), SWCRE intensity is strongly affected by the march of East Asian monsoon systems (the tropical South China Sea monsoon and subtropical East Asian monsoon). Particularly, after the onset of the South China Sea monsoon (around pentads 24–28), the greatly intensified moisture transport from the Bay of Bengal increases cloud water supply and then enhances SWCRE over SEC.

c. Case studies in 2010 and 2011

Spring circulation conditions have remarkable interannual variation over SEC, and regional cloud radiative characteristics including SWCRE readily exhibit interannual differences. Here, spring SWCRE and relevant circulations are further examined in two real case years using satellite and reanalyzed datasets as well as WRF regional simulation. The most severe drought in the last 30 years occurred persistently over SEC from January to May 2011, but the precipitation amount was relatively larger in 2010; the corresponding general circulations showed a sharp contrast between the two years (Sun and Yang 2012). Furthermore, these two years are the only pair of continuous years with the occurrence of contrasting precipitation in spring since the CERES-MODIS satellite era. Thus, 2010 and 2011 are selected as the strong and weak case years. During the period of 2001–16, the normalized standard deviations of spring mean precipitation averaged over SEC are 0.92 and −2.46 in 2010 and 2011, respectively, and the corresponding values of SWCRE are 1.6 and −0.46, respectively, showing large interannual differences in the two case years. In this section, pentad 1–36 is selected for the analysis of subseasonal variations.

Figure 10 presents pentad mean precipitation and SWCRE averaged over SEC in 2010 and 2011. Compared to the observation, the simulation well reproduces subseasonal variations of rainfall and SWCRE over SEC in 2010, with temporal correlation of 0.78 and 0.86, respectively (Figs. 10a and 10b). In contrast, the temporal correlation of SWCRE in 2011 is only 0.55 because the simulated SWCRE is much weaker before pentad 22. During the strong year (2010), strong ascending motion appears early, with the development height reaching up to 400 hPa in pentads 9 and 13, and corresponds well to large moisture convergence and cloud liquid water path (Figs. 10b and 11a–d). SWCRE (less than −110 W m−2) and moisture convergence are relatively stable until pentad 18; afterward, SWCRE markedly increases up to −130 W m−2 while the moisture convergence also gradually increases and the ascending motion becomes continuous and deep (Figs. 10b and 11a–d). The simulation also performs well in subseasonal variations of ascending motion and moisture convergence in 2010. During the weak year (2011), deep ascending motion before pentad 24 is much weaker than that of 2010, corresponding to weaker moisture convergence and cloud liquid water path during the same period (Figs. 11e–h). Before pentad 18 of 2011, considerable ascending motion still appears in low to midlevels (Fig. 11e) and helps to maintain SWCRE (Fig. 10d). Compared to the observation in 2011, the simulated moisture convergence before pentad 18 is close to the observation (Fig. 11g), but the simulated low-to-midlevel ascending motion is obviously weaker (Figs. 11e,f), which can partly explain the weaker SWCRE in the simulation. As shown in Fig. 11, although the simulated intensity has some deviation, the simulation basically captures the timing of strong ascending motion in 2011. Note that the contrast of cloud liquid water path between 2010 and 2011 is larger than cloud ice water path (Figs. 11d and 11h), indicating that cloud liquid water is more sensitive to the ascending motion.

Fig. 10.
Fig. 10.

Pentad mean variations of observed (black line) and simulated (blue line) (a) precipitation (mm day−1) and (b) SWCRE (W m−2) averaged over SEC (22°–32°N, 104°–122°E) in 2010. (c),(d) As in (a) and (b), but for the corresponding results in 2011. The temporal correlation coefficients are marked at the top-right corner in each subfigure. The x axis is the pentad number.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

Fig. 11.
Fig. 11.

Pentad mean variations of (a) ERA-Interim reanalyzed vertical velocity (hPa day−1), (b) simulated vertical velocity (hPa day−1), (c) column water vapor flux divergence (10−4 kg m−2 s−1), and (d) simulated cloud water path (g m−2) averaged over SEC (22°–32°N, 104°–122°E) in 2010. (e)–(h) As in (a)–(d), but showing the corresponding results in 2011. Here, IWP denotes cloud ice water path. The x axis is the pentad number.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

To examine the geographical distribution of the interannual differences, the observed and simulated mean of pentads 18–30 is selected as the spring mean period when the simulation performs relatively better and the interannual contrast is also large. Figure 12 presents the SWCRE, circulations, and relevant differences averaged during pentads 18–30 in 2010 and 2011. In 2010 (strong year), there is strong ascending motion and SWCRE over SEC as well as a strong low-level southwesterly from the Bay of Bengal and a southerly from the South China Sea (Figs. 12a,c). In contrast, the SWCRE and southwesterly over SEC are much weaker in 2011 (weak year) (Figs. 12b,e). In the strong spring rainfall case year (2010), the larger SWCRE (up to −60 W m−2) corresponds to a larger updraft velocity (less than −30 Pa day−1) at 500 hPa over SEC, the southwesterly (southerly) brings abundant water vapor into SEC, and the northwesterly from Siberia and northern China enters this region and causes a strong low-level convergence (Figs. 12c,f). The distributions of the regional circulation and SWCRE over SEC are closely related to the pattern of large-scale circulation in both case years. Relative to the pattern that occurs in 2010, the westerly jet (>40 m s−1) at 200 hPa and the northwestern Pacific subtropical high are weak, and their center positions shift eastward in 2011. This circulation pattern in 2011 produces a weaker southwesterly, ascending motion and precipitation over SEC (Fig. 12; see also Fig. A2 in the appendix). The strong circulation differences between these two case years were mainly caused by La Niña and the North Atlantic Oscillation in 2010–11 (Sun and Yang 2012). The strong differences between the two case years are also very clear in the domain mean values, as listed in Table 2. For example, the SWCRE differences are −31.6 and −35.2 W m−2 for the observation and simulation, respectively, and the vertical velocity differences at 500 hPa are −37.8 and −33.4 hPa day−1, respectively (Table 2). The observed and simulated differences of cloud liquid water path are 45.4 and 38.0 g m−2, respectively and much larger than their counterparts of cloud ice water path, with the values of 8.3 and 6.9 g m−2, respectively, showing that cloud water differences between 2010 and 2011 are mainly contributed by liquid phase cloud.

Fig. 12.
Fig. 12.

Geographical distributions of (a) CERES SWCRE (shading; W m−2), vertical velocity (Pa day−1) at 500 hPa, and wind field at 850 hPa averaged during the period of pentads 18–30 in 2010, (b) the corresponding results for 2011, and (c) the differences between 2010 and 2011. (d)–(f) As in (a)–(c), but for the corresponding results from the WRF simulation. The black solid line is the TP (over 3000 m). The vector data are masked below 850 hPa.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

Table 2.

Observed and simulated cloud radiation and meteorological variables averaged over SEC (22°–32°N, 104°–122°E) during the 18th–30th pentad in 2010 and 2011. Here, the differences are between the corresponding values in 2010 and 2011.

Table 2.

The above results show that the ascending motion is one of key general conditions relating the regional circulation to the SWCRE. Water vapor is condensed to cloud water through the lifting role of the ascending motion, the maintenance of which provides stable conditions for cloud formation. Thus, the domain 22°–32°N, 110°–120°E where large SWCRE contrast exists is selected to examine the scatter relationship between the SWCRE and ascending motion at 500 hPa in pentads 18–30 of 2010 and 2011. In the observations, the larger SWCRE (points with negative value) nearly corresponds to the ascending motion (points with negative value), and the linear correlation is up to 0.70 (Fig. 13a). In the simulation, the value of most points is less than zero, the linear correlation is 0.64, and the regressive slope is also very close to the observation (Fig. 13b). In addition to the updraft motion, water vapor convergence supplies the source of cloud water, and then the intensity of the moisture convergence over SEC affects the regional SWCRE to some extent. As for the scatter relationship between the SWCRE and column moisture convergence in the above region, the linear correlation coefficients are 0.51 and 0.55 for the observation and simulation, respectively (not shown). Water vapor convergence still corresponds well to larger SWCRE in the observation; however, the simulation poorly captures the good observed scatter distribution and underestimates the difference of water vapor convergence between the two case years. The above analysis shows that the contribution of the updraft motion to cloud water and SWCRE is likely larger than the air moisture in the simulation. In Asian monsoon regions, water vapor transportation is mainly driven by the regional circulation (Wang et al. 2017) and the vertical motion is the prominent influencing factor for the spring moisture budget over SEC (Li et al. 2018). In this sense, the WRF model has broadly reproducibility in interannual differences of above cloud radiative characteristics over SEC. According to the results in Figs. 1013, favorable spring general circulations cause stronger ascending motion over SEC in the strong case year, and then produce more cloud liquid water and larger SWCRE relative to the weak case year. Consequently, the interannual differences of SWCRE in other cases very likely contribute to the anomalous ascending motion over SEC.

Fig. 13.
Fig. 13.

Scatterplot of SWCRE (W m−2) and vertical velocity (Pa day−1) at 500 hPa over SEC (22°–32°N, 110°–120°E). Here, the SWCRE (vertical velocity) is the difference between the 18th–30th pentad mean results in 2010 and 2011 (as shown in Figs. 11c and 11f). The grid over SEC is selected to plot the scatter figure. The regression equation and linear correlation coefficient are shown in the lower right of each panel.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

4. Conclusions and discussion

This study shows that SWCRE over SEC (22°–32°N, 104°–122°E) exhibits strong and persistent features in spring. As for the spring (March–May) mean, the intensity of SWCRE over SEC is up to −110 W m−2, being the largest at the same latitude bands of the Northern Hemisphere. In this region, strong spring SWCRE is mainly caused by large and stable cloud liquid water, which is closely associated with persistent water vapor transport and low-to-midlevel ascending motion. SWCRE over SEC also has distinctive subseasonal variation and its high value stays over SEC during pentads 12–36 (late February to late June). Around pentad 12, ascent motion, moisture supply, and SWCRE over SEC abruptly increases, which is accompanied by the reversal of thermal contrast between the Indochina Peninsula and western Pacific to east of the Philippines on late February. During pentads 12–24 (late February to late April), the TP thermal and dynamic roles and high-level westerly jet provide appropriate settings for the stable occurrence of continuous ascending motion over SEC, while water vapor is transferred from south of the TP and the South China Sea. During pentads 25–36 (early May to late June), SWCRE is strongly affected by the march of East Asian monsoon systems (the tropical South China Sea monsoon and subtropical East Asian monsoon) and its intensity further increases. Particularly, after the onset of South China Sea monsoon (around pentads 24–28), the greatly intensified moisture transport from the Bay of Bengal increases cloud water supply and then enhances SWCRE over SEC. After pentad 36, the above circulations favoring the maintenance of cloud water and SWCRE quickly weaken and then large SWCRE cannot remain over SEC. These subseasonal results indicate that the spring SWCRE over SEC is not just affected by specific circulations (e.g., the dynamical and thermal roles of the TP, westerly jet) but is also deeply influenced by the seasonal march of the East Asian monsoon system. In the observed case analysis, stronger ascending motion, larger amounts of cloud liquid water, and SWCRE occur during a strong spring rainfall year, and the differences in SWCRE between strong and weak spring rainfall years are well related to the strengths of the anomalous ascending motion over SEC.

The regional model (WRF) can basically reproduce large spring SWCRE over SEC and its persistence during late winter to early summer. In contrast, the simulated association of SWCRE with ascending motion is closer than water vapor convergence. As shown in Figs. 9 and A1, considerable simulation biases exist over the East Asian monsoon regions regarding the geographical distribution and intensity of moisture transport and convergence. Similar deficiencies of moisture simulation are also common in current state-of-the-art climate models (Sperber et al. 2013; Yang et al. 2015) and closely relevant to the uncertainties of physics parameterizations (e.g., convection, cloud precipitation, and planet boundary layer) and numerical algorithms. The complicated topography in the TP and surrounding areas further increases above simulation difficulties. To well capture the roles of water vapor in spring SWCRE over SEC, more model work is needed to carefully select and tune model parameterization schemes regarding moisture and cloud water. Both observation and simulation show that subseasonal evolution of SWCRE is not exactly same with rainfall over SEC and South China Sea, reflecting that ambient conditions influencing SWCRE and rainfall are different over these regions. Besides cloud water and associated regional circulations, SWCRE is also sensitive to the microcloud radius, which is highly modulated by aerosols as cloud condensation nuclei (Gettelman and Sherwood 2016). A large number of aerosols are distributed over SEC and readily produce remarkable contrasts of cloud microphysical characteristics (e.g., cloud number concentration and radius) and radiative properties relative to the adjacent oceans (Li et al. 2016). Thus, more cloud microphysics analysis and well-designed simulations should be conducted to explore climatic mechanisms resulting in the maintenance of spring SWCRE over SEC.

The case studies clearly show that circulations (e.g., the East Asian westerly jet, the northwestern Pacific subtropical high, and the low-latitude tropical circulation) around East Asia have obvious interannual variations, which affect the distribution, strength, and variability of CREs over SEC. To identify individual roles of these circulations in regional SWCRE, it is very necessary to quantitatively examine the associations between these circulations and SWCRE in the interannual scale. Additionally, CERES satellite and ERA-Interim reanalysis data used in this study still have some uncertainties in describing regional cloud–radiation variables, especially in their quantitative aspects. As for further work, more comparative analysis with model and observational data should be conducted to obtain reasonable physical consistency results for East Asian cloud–radiation processes.

Acknowledgments

This research was jointly supported by the National Key R&D Program of China (2017YFA0603503), the National Science Foundation of China (91737306, 41730963, and 41876020), the Priority Research Program of the Chinese Academy of Sciences (QYZDY-SSW-DQC018), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant XDA17010105), and a grant (to University at Albany) from the Office of Science (BER), U.S. Department of Energy. CERES-MODIS cloud properties and CERES EBAF products used in this study are produced by the NASA CERES Team, available at http://ceres.larc.nasa.gov.

APPENDIX

Geographical Distribution of Circulations in the Simulation and Case Years

Figure A1 shows 200-hPa zonal wind speed, column water vapor flux, and divergence simulated from WRF Model at selected pentads averaged during 2001–16. Figure A1 clearly shows the simulated biases of air moisture regarding its spatial location and intensity. The possible reasons are discussed in the main text. To show the differences of large-scale circulations in two case years, Fig. A2 presents 850-hPa wind, 200-hPa zonal wind speed, and precipitation averaged from the 18th–36th pentads of 2010 and 2011.

Fig. A1.
Fig. A1.

As in Fig. 9, but from the WRF simulation.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

Fig. A2.
Fig. A2.

Observational distribution of the 850-hPa wind (vector), zonal wind speed (red dashed line; m s−1) at 200 hPa, and precipitation (shading; mm day−1) averaged during the 18th–30th pentads of (a) 2010 and (b) 2011. The pentad number is marked at the top-right corner in each subfigure. The black solid line is the TP (over 3000 m). The vector data are masked below 850 hPa.

Citation: Journal of Climate 32, 11; 10.1175/JCLI-D-18-0385.1

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