1. Introduction
Northerly monsoonal winds around the eastern edge of the Siberian–Mongolia high (SM high) can drive cold air equatorward, which dissipate over a broad longitudinal extent of the western North Pacific (e.g., Dorman et al. 2004; Chang et al. 2006; Iwasaki et al. 2014). This movement of cold air manifests as a synoptic-scale equatorward cold air outbreak, which is a common feature of the East Asian winter monsoon that enhances low-level northerlies (Chang and Lau 1982; Johnson and Zimmerman 1986; Wu and Chan 1995). These strong cold surges can influence the subtropics and tropics and trigger rainfall through the Borneo vortex and cross-equatorial flow in the Maritime Continent (Chang et al. 2005; Abdillah et al. 2021; Diong et al. 2023). Abdillah et al. (2021) categorized 40-yr cold surge events into four types: South China Sea type, Philippines Sea type, both type, and blocked type. These four types will enhance low-level northerly and tropical convection at different regions. Strong MC convection can further strengthen the East Asian meridional overturning circulation (MOC) and the associated East Asian upper-level jet stream (Chang and Lau 1982; Wu and Chan 1997). Chang and Lau (1982) further suggested that cold surges and enhanced tropical convection can trigger high-level easterlies over the equatorial western Indian Ocean and westerlies over the equatorial western Pacific (i.e., impacting the Walker circulation). Besides cold surge effects on the East Asian MOC, the strength of this circulation can be influenced by intraseasonal oscillation and alter the East Asia rainfall pattern (Chen et al. 2021).
Strong low-level winds and the air–sea temperature and humidity differences along the cold surge pathway lead to the increase of the surface flux uptake over the western North Pacific (Garratt 1994; Ogino et al. 2018; Abdillah et al. 2021). In addition, large-scale subsidence over the South China Sea (SCS) is enhanced by a stronger East Asian MOC, and thus, a larger dry static energy (DSE) can be brought into the boundary layer through entrainment mixing. These changes are expected to enhance the vertical turbulent mixing and increase cloud formation within the boundary layer (Atkinson and Wu Zhang 1996; Ogino et al. 2018; Abdillah et al. 2021). Indeed, the marine boundary layer (MBL) over western North Pacific is noticeably deeper in subtropical latitudes during active cold surge season, reaching a few kilometers (Garratt 1994; Tokinaga et al. 2006; Fletcher et al. 2016; Chien et al. 2019).
Unlike stratocumulus (Sc)-topped boundary layer confined by the climate subsidence regime over cold ocean where MBL is composed of a surface layer, a mixed layer, and an entrainment zone (e.g., Wood 2012), the SCS MBL during winter is more complex because clouds develop from the top of the mixed layer in the presence of an inversion layer. As a result, MBL is defined by the height of planetary boundary layer (PBLH) or mixed layer (MLH) without clear distinction. Some studies defined the MBL height by a large vertical potential temperature gradient and/or wind shear (Heffter 1980; Seibert et al. 2000; Liu and Liang 2010; Sivaraman et al. 2013). Some studies analyzed the abrupt change of potential temperature, specific humidity, refractivity, or aerosol concentration with height to derive the MBL height (e.g., Johnson et al. 2001; Cohn and Angevine 2000; Davis et al. 2000; Wang and Wang 2014; Chien et al. 2019). Unlike terrestrial boundary layer, MBL is usually shallow and weak in diurnal variation (Garratt 1994; Liu and Liang 2010). Besides the different terminology, it is important to compare various estimates of MBL height based on in situ observations (radiosondes, lidar, ceilometer, etc.) and reanalysis data (Liu and Liang 2010; Sivaraman et al. 2013; Wang and Wang 2014; Lewis 2016; Zhang et al. 2022).
While many MBL-related studies have focused on field campaigns for studying stratocumulus to cumulus (Cu) over the eastern-central Pacific region (e.g., Wood 2012; van der Dussen et al. 2013) or over the Atlantic (Nicholls 1984; Tomassini et al. 2017; Li et al. 2022), there is a lack of MBL studies over the SCS (Johnson and Zimmerman 1986). Furthermore, the MBL over the SCS plays an important role of tropical–extratropical energy exchange and equatorward energy transport in the East Asian winter monsoon, especially during cold surges. The energy exchange and transport affect the strength of tropical convection and the East Asian MOC. Thus, a more detailed understanding of the MBL structure, turbulence, and energy budget over the SCS in winter is essential to better describe the East Asian MOC.
Cloud development is linked to turbulence processes and variations in the MBL parameters, local temperature, and moisture tendencies (Lock et al. 2000; Wood 2012; Houze 2014; Tomassini et al. 2017), while also feeds back into the energy budget (Bretherton 2015; Klein et al. 2017). However, there are still inconsistencies between cloud observation and modeling (e.g., Tomassini et al. 2017), resulting in misinterpreting the reality. During cold surges, the surface fluxes are transported upward through positive turbulent fluxes, enriching the upper MBL with moisture and favoring cloud development. The cloud radiative cooling (heating) at the cloud top (base) then drives positive buoyancy fluxes (Nicholls 1984; Houze 2014). As cold air outbreak, cloud street, open cells, and closed cells exist along the flow direction of the cold air (Atkinson and Wu Zhang 1996). Open cells usually develop when cold air passes through an area of warm sea surface temperature (SST), like the western North Pacific; closed cells occur over small surface flux regions (Atkinson and Wu Zhang 1996). However, the cloud distribution over the SCS region is not well-studied, even though large surface fluxes and large-scale descending motions are seen during cold surges.
In this study, we focus on cold surge events and the resulting change and underlying physical explanation of the MBL structure over the SCS. The SCS is located downstream of the East Asian winter monsoon driven by the Siberian–Mongolia high over Eurasia and the Aleutian low (AL) over the northern Pacific (Fig. 1a); the upper-level westerly jet and trough over southern Japan are related to the land–sea thermal contrast between Eurasia and the Indo-Pacific warm pool. The MOC between tropical Maritime Continent and East Asian jet is shown by the climatological fields of meridional and vertical winds (υ, ω) along the blue line in Fig. 1a (Fig. 1b). Also shown in Fig. 1b are the relative humidity (RH), potential temperature (θ), and equivalent potential temperature (θe). Figure 1b shows the cold air mass from upper troposphere in the Siberian–Mongolia high flows southeastward and sinks quickly to lower troposphere as it approaches SCS. The lower-tropospheric flow in the SCS gains moist static energy (MSE) and joins the tropical ascending motion in the tropical SCS and Maritime Continent that returns to the East Asia (EA) jet in the upper troposphere. The northward flow sinks in northern SCS where the MBL is well confined below 850 hPa as shown by RH and θe. Dongsha Island (20.70°N, 116.69°E) is uniquely located at the subtropical–tropical boundary.
The climatological East Asian winter monsoon (DJF; 1981–2020) for (a) precipitable water (PW) integrated from 1000 to 100 hPa (color shading; mm), 925-hPa wind (vectors; m s−1), 500-hPa geopotential height (contours), and 200-hPa jet denoted by zonal wind speed larger than 40 m s−1 (transparent green colors). The star denotes the location of Dongsha Island. (b) The vertical–meridional cross section [along the blue line in (a)] of RH (color shading), θ (orange contour; K), θe (blue contour; K), and υ and −ω (vector; m s−1 and Pa s−1). The −ω is multiplied by a factor of 50 for display.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
Therefore, we use 10-yr high-resolution sounding data at Dongsha Island for the MBL analysis. Section 2 includes a dataset description, the procedure for energy budget analysis, and determining cold surge events. In section 3, we describe the derivation and detection method of MBL parameters to define MBL structure. Ceilometer data at Dongsha Island and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask (VFM) data are used to validate the radiosonde-derived MBL structure. Section 4 shows the large-scale circulation and MBL structure in winter and discusses the composite cold surge feature. Conclusions are made in section 5.
2. Data and methods
a. Data
1) Dongsha island regular observation
Dongsha Island is located at (20.70°N, 116.69°E) in the northern SCS, southwest of Taiwan, with an area of ∼1.8 km2. Upper air data at Dongsha Island are available from September 2009 to date (Sui et al. 2020). The sounding instruments used during the period are Vaisala RS92 and Vaisala RS41 before and after May 2016, respectively. Radiosonde is released at 0000 UTC each day, but no data are available during August 2013–April 2014. About 97.8% and 92.4% of radiosondes reached altitudes greater than 10 and 20 km, respectively. The vertical resolution of radiosonde measurements is 5–10 m and is smoothed to 5 hPa in this study to remove the noise (Sivaraman et al. 2013). Wind speed, wind direction, temperature, RH, and pressure observed by Vaisala RS41 radiosondes are used in this study to determine the MBL parameters.
Vaisala ceilometer CL31 is initially set up at Dongsha Island from May 2017 for the South China Sea Two-Island Monsoon Experiment (SCSTIMX) (Sui et al. 2020), but only data in year 2020 are currently available. CL31 emits pulse light upward with a 910-nm wavelength and collects attenuated backscatter signals due to cloud and aerosol (Morris 2016). It is also able to detect three cloud layers at most simultaneously and provide the cloud bottom height (CBH) product (Morris 2016). CL31 detection can reach up to 7 km with a 5-m vertical resolution, and the temporal resolution is 5 s (Morris 2016). To remove the noise, a 30-min running average is applied to the CL31 attenuated backscatter data and then resampled to a 10-min temporal resolution.
2) Satellite cloud products
Some primary cloud products used in this study are provided by the International Satellite Cloud Climatology Project (ISCCP). The ISCCP products include total cloud cover, cloud type, cloud-top temperature, cloud-top pressure, and cloud optical thickness. These cloud parameters are retrieved from algorithms developed by Rossow and Schiffer (1991, 1999). The ISCCP-H cloud products were produced by a series of cloud-related algorithms based on global gridded two-channel radiance data, including visible 0.65 μm and infrared 10.5 μm, merged from different geostationary and polar orbiting meteorological satellites (Young et al. 2018; Knapp et al. 2021). Cloudy regions are identified based on deviations from the cloud-free radiance values because cloud presence changes the background visible and infrared (IR) radiances (Knapp et al. 2021). The cloud amount was defined as the fraction of cloudy pixels to the total number of pixels determined within a 1° × 1° grid. Noted that ISCCP-H products are unique for using input data from full-resolution Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage and higher-resolution geostationary data; both datasets can potentially improve the retrievals. ISCCP-H products provide cloud amount for nine cloud types [i.e., stratus (St), stratocumulus, cumulus, altostratus (As), altocumulus (Ac), nimbostratus (Ns), cirrus (Ci), cirrostratus (Cs), and deep convection] with a 3-hourly temporal resolution. In this study, the ISCCP-H dataset is used, with the availability during 1984 ∼ 2017 (Young et al. 2018; Rossow et al. 2022).
We also use multiple CloudSat and CloudSat/CALIPSO combined datasets in this study. The CloudSat and CALIPSO were launched in April 2006 as a part of the United States NASA afternoon constellation (A-train) to observe the vertical structure of clouds and precipitation. CloudSat is equipped with a near-nadir-pointing 94-GHz cloud profiling radar (CPR) (Stephens et al. 2008). CloudSat CPR can penetrate thick clouds but is less sensitive to thin clouds and aerosols compared to CALIPSO lidar (Marchand et al. 2008). The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) sensor onboard can actively detect attenuated backscatter at 532- and 1064-nm wavelengths from earth surface up to 30 and 40 km, respectively, providing a vertical profile of aerosols, clouds, and their optical and physical properties (Winker et al. 2003). In addition to active sensing, CALIPSO datasets also combine passive infrared and visible imagers. CALIOP level 1 lidar data have a 5-km horizontal resolution and different vertical resolutions depending on altitude. The vertical resolution in altitude is 30 m between ∼0.5 and 8.5 km and 60 m between 8.5 and 20.1 km.
The CloudSat and CloudSat/CALIPSO combined datasets include 2B-Cloud Scenario Classification lidar (2B-CLDCLASS-lidar), level 2 radiative flux and heating rate (2B-FLXHR), 2B-FLXHR-lidar, and CALIPSO VFM V3. The 2B-CLDCLASS-lidar data combine CloudSat CPR and CALIPSO lidar measurements to classify clouds into nine classical cloud types to make up the shortcomings of CloudSat that it often undetects thin high clouds and low clouds (Sassen et al. 2008; Henderson et al. 2013). Cloud information are considered valid if one of the two conditions are satisfied: (i) radar reflectivity provided in CloudSat operational geometric profile (2B-GEOPROF, Marchand et al. 2008) ≥ −22 dBZ and CPR cloud mask ≥ 20 or (ii) 2B-CLDCLASS-lidar cloud fraction > 99% (Barker 2008; Miao et al. 2019).
The 2B-FLXHR and 2B-FLXHR-lidar data are CloudSat level 2 radiative fluxes and heating rates. The data algorithms utilize delta-Eddington formulation in six shortwave bands and a constant hemisphere formulation in 12 longwave bands (L’Ecuyer et al. 2008; Henderson et al. 2013). The input datasets include 2B-CWC products (cloud water content), temperature, and humidity profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, and surface albedo and emissivity data from the International Geosphere–Biosphere Programme (IGBP) global land surface classification (L’Ecuyer et al. 2008). The input datasets from ECMWF analyses are interpolated to each of the CloudSat footprint. The description of the 2B-FLXHR data processing is also available on CloudSat website (https://www.cloudsat.cira.colostate.edu/data-products/2b-flxhr). The uncertainties of the derived radiative fluxes, compared to Earth’s radiation budget, are shown as Fig. 4 in L’Ecuyer et al. (2008). These uncertainties may come from CloudSat’s sampling time, location, and the observation period. Also, the CloudSat sensor may not detect low and/or thin clouds and lead to the errors in the outgoing shortwave radiation. In addition, CloudSat has few samples at high latitudes, so the surface albedo may be underestimated.
The CALIPSO VFM V3 is the product for daytime and nighttime. The VFM product is generated from level 1B lidar data of the CALIOP sensor based on three different algorithms including selective iterated boundary location (SIBYL), hybrid extinction retrieval algorithm (HERA), and scene classification algorithms (SCAs) (Liu et al. 2005; Winker et al. 2006, 2009). More clearly, the VFM V3 (VFM) is a classification of the atmosphere into classes of clean air, cloud, aerosol, stratospheric feature, surface, subsurface, and totally attenuated, which is stored in blocks. For classes of cloud and aerosol, the product provides a subdivision of the classes into its own subtypes. Clouds consist of nine subclasses: low-thin cloud, low-thick cloud, stratus, stratocumulus, cumulus, altocumulus, altostratus, cirrus, and deep convection (Vaughan et al. 2009). The horizontal and vertical resolutions of VFM are the same as those of CALIOP level 1 lidar data. In this study, CALIPSO VFM V3 for 2010–20 DJF is used.
3) Reanalysis products
The fifth major global reanalysis produced by European Centre for Medium-Range Weather Forecasts (ERA5) (Copernicus Climate Change Service 2017) is used in this study. It consists of hourly estimates for a large amount of atmospheric and surface variables. The information from observations is extracted from many satellite or conventional instruments (Hersbach et al. 2018, 2020). The atmospheric component is interpolated to 37 pressure levels from the 1000 up to 1 Pa. Surface variables includes 2 m-temperature, 10-m horizontal wind, mean sea level pressure, surface fluxes, and planetary boundary layer height. The calculation of the ERA5 planetary boundary layer height is based on the bulk Richardson number (Seidel et al. 2012), with a critical value of 0.25. These datasets have a horizontal grid resolution of 0.25°, corresponding to around 31 km. Currently, ERA5 covers the period from 1950 to present. More information about ERA5 characteristics can be found in Hersbach et al. (2019).
4) The IMERG precipitation
Among satellite-based precipitation products, the Global Precipitation Measurement (GPM) mission represents the next generation of global precipitation products with an advanced radar/radiometer measurement system (Hou et al. 2014), after the Tropical Rainfall Measuring Mission (TRMM; data availability: 1997–2015). The Integrated Multi-satellitE Retrievals for GPM (IMERG) final precipitation V06 product was released in March 2019 (Huffman et al. 2019a,b), with the data availability during 2000–14. It provides precipitation estimates at a spatial resolution of 0.1° grids and at half-hourly time step for the area between 60°S and 60°N. The IMERG algorithm integrates multisatellite retrievals from the passive microwave and infrared sensors, which are gridded, intercalibrated, and merged with the estimates from the GPM core observatory to form the final product. To enhance the data accuracy of IMERG, several improvements were included in the algorithm, including a homogeneous GPM–TRMM calibration, a new model-based morphing scheme, and refinements to the Kalman filter process and the quality index (Huffman et al. 2019b; Tan et al. 2019).
b. Methods
1) Energy budget
2) Identification of cold surges
SCS cold surges are identified by two criteria. First, surface temperature (Ts) at Kaohsiung (22.68°N, 120.38°E) drops more than 4°C in 2 days from day 0 to day 2 and daily minimum temperature in day 1 or day 2 falls below 14°C (Lu and Chang 2009). Second, the 925-hPa northerly wind over the SCS averaged within 110°–117.5°E along 15°N (Vscs) surges above 8 m s−1 (Chang et al. 2005) following day 0. Twenty cold surges are identified during 2010–20 DJF (Table 1). The selected cold surge events include SCS type and SCS–Philippines Sea type, which together account for 39% of high-latitude cold air outbreak events as identified in Abdillah et al. (2021). The cold surge passage is separated into three stages: presurge (day 0), surge (day V), and postsurge (day V + 1), where day V denotes the day of strongest Vscs after day 0 and day V + 1 denotes 1 day after day V.
The SCS cold surge events selected by surface air temperature at Kaohsiung and 925-hPa northerly wind over the SCS at 15°N during DJF 2010–20.
3. Identification of MBL
MBL structure can be divided into surface layer, mixed layer, and entrainment zone, as revealed in the vertical profiles of θ, RH, and specific humidity (qυ) from radiosonde data released at 2100 UTC 17 February 2020 at Dongsha Island that show very clear MBL structure (Fig. 2). Radiosonde-derived MLH, inversion layer, and cloud layers are determined as follows.
A snapshot of radiosonde data released at 2100 UTC 17 Feb 2020. MBL structures (mixed layer, transition layer, cloud layer, and entrainment zone) are labeled. Solid lines show the meteorological parameters, including θ (°C; orange), qυ (g kg−1; green), and RH (%; gray), and vectors show the horizontal wind (vectors). MBL parameters are labeled. Blue dots denote the cloud layers.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
a. Mixed layer
According to Johnson et al. (2001), we develop a numerical method of MLH detection. The θ and qυ profiles are examined from surface upward in three steps: 1) Δθ > 0.1 K and Δqυ < −0.1 g kg−1 is selected, where Δ represents the difference between two adjacent levels; 2) the difference of θ and qυ at each selected layer and at surface should be larger than 0.2 K and smaller than −0.5 g kg−1, respectively (i.e., θsel − θsfc < 0.2 K and qυ,sel − qυ,sfc < −0.5 g kg−1); and 3) the decrease of qυ at MLH is larger than that within MLH.
Haar wavelet transform has been used to determine MLH from lidar backscatter data by finding the largest backscatter gradient with height (Cohn and Angevine 2000; Davis et al. 2000; Brooks 2003). In this study, the dilation for Haar function for transform is 240 m [Eqs. (1) and (2) from Brooks 2003].
b. Cloud layer
Zhang et al. (2010) use RH profile from the radiosonde released at Shouxian (32.56°N, 116.78°E, 21 m above sea level) to determine the cloud layer. Cloud layers are determined if RH value at the level exceeds the threshold provided in Table 2 (Zhang et al. 2010).
c. Inversion layer
We modified the Heffter algorithm (Heffter 1980; Sivaraman et al. 2013) by including cloud information from Zhang et al. (2010) to avoid misclassification of CBH as the inversion layer. The Heffter method determines the inversion layer by a strong vertical gradient of θ, which is represented by a sudden increase of θ with height (at least 0.005 K m−1). However, the increase of θ can also be influenced by radiative heating within cloud layer at Dongsha Island in some days. Thus, the inversion layer in this study is determined if 1) the layer’s potential temperature gradient is larger than 0.005 K m−1 and 2) the layer is cloud-free.
d. Validation of MBL parameters
The CL31 measured backscattering is shown in Fig. 3. The large backscatter (reddish shaded) around 500–1000 m represents the existence of clouds. The retrieved MLH from CL31 measurements and the radiosonde-derived MLH both have similar values and trends, but CL31-MLH is more fluctuated due to much higher temporal resolution (Fig. 3). The radiosonde-derived MLH compared well (R = 0.7) with an independent MLH data derived from the CL31 (Fig. 4a). The average radiosonde-derived inversion layer value is ∼1.8 km in DJF, which is similar to that derived from global positioning system radio occultation (GPS-RO) profiles and numerical model (Chien et al. 2019).
The time–height distributions of ceilometer backscattering (10−8 Sr−1 m−1; shaded), MLH by CL31 (white crosses), MLH by radiosondes (pink dots), and inversion layer by radiosondes (black dots) in (a) January and (b) February 2020. The calendar dates (mmdd) are marked on the x axis.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
The scatterplot of (a) CL31-derived MLH compared to radiosonde-derived MLH and (b) CL31 first layer CBH product and radiosonde-derived MLH compared to radiosonde-derived CBH (red dots) in January–February 2020. The comparison between radiosonde-derived MLH and CBH (blue and green crosses) are divided into two groups: CBH < MLH (green) and CBH > MLH (blue). The linear regression of CBH < MLH group (green cross) is labeled.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
The radiosonde-derived CBH is compared with an independent CBH data derived from the CL31. The result shows that the two CBH data are well correlated (R = 0.8) but the radiosonde method underestimates CBH (Fig. 4b). The inconsistency between the two CBH datasets may be contributed by the sampling rates and the RH-derived algorithm itself. CL31 is very sensitive to some clouds passing by quickly due to its high sampling rate; however, radiosonde may not be able to detect this kind of fast-moving clouds. Also, compared to CL31 looking upward, the path of the radiosondes is influenced by the horizontal winds, so the collected data may be slightly different. In addition, though the RH-derived algorithm has been developed for decades (Poore et al. 1995; Wang and Rossow 1995; Chernykh and Eskridge 1996; Zhang et al. 2010), the adjustments of the lowest cloud height are needed for different regions. For example, the minimum CBH is set to 500 m above ground level based on the ground observation statistics in Shouxian, China (Zhang et al. 2010), while this value is set to 130 m at Dongsha Island, which is the lowest ceilometer-observed CBH in winter. The seasonal statistics of radiosonde-derived cloud parameters are compared with both CL31 cloud products and CALIPSO VFM. The heights of frequent occurrence of high RH and cloudy layer from radiosonde data are around 800–1000 m (Fig. 5a), consistent with that of large backscatter values from CL31 (Fig. 5b) and dominant low-cloud layers from CALIPSO (Fig. 6). Also, the seasonal distribution of radiosonde- and CL31-derived CBH ranges within 1088 ± 1508 m and 1100 ± 947 m, respectively. The two estimates have comparable mean values but the radiosonde-derived CBHs have a larger spread. Figure 6 shows cloud frequency distribution from CALIPSO VFM over a 3° × 3° box centered at Dongsha Island. The statistics shows dominant low cloud coverage in the winter season with a maximum cloud coverage of about 20% low thin (optical thickness τcloud < 3) clouds and 20% Sc at 1 km that together account for over 40% cloud coverage, which is comparable to the radiosonde-derived cloud frequency (∼40% at 1 km in Fig. 5a). The CALIPSO cloud product further gives the vertical extent of low clouds with CBH ∼ 770 m and CTH ∼ 1525 m (Fig. 6), similar to 628 and 1628 m from radiosonde. The slightly upward shift of height of largest cloud frequency and the overestimate of CBH retrieved from CALIPSO can be attributed from the attenuation of top-down lidar signal.
The frequency of occurrence at different heights for (a) RH and cloudy layer from radiosonde at Dongsha Island for December 2019–February 2020 and (b) backscatter coefficient (βe; Sr−1) from CL31 at Dongsha Island for January–February 2020.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
The frequency of occurrence at different heights for cloud types from CALIPSO VFM within 3° × 3° box centered at Dongsha Island for December 2019–February 2020. Dominant low, thick clouds (frequency > 10%) range from 910 to 1690 m, while Sc is also dominant from 310 to 1480 m.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
4. East Asian winter monsoon and composite features of cold surge
The DJF seasonal mean flow shows the strong northerly, along the edge of Siberian–Mongolia high and Aleutian low, and column-integrated moisture is rich in MC and ITCZ regions (Fig. 1a). The East Asian MOC is clearly seen in Fig. 1b that the convection over tropics and the strong downward motion located near 30°N. The midlatitude upper-level jet core implies the large baroclinicity, and the midtroposphere subsidence and smaller peaked RH at East China Sea region (20°–30°N). Low-level northerly within PBL and large-scale subsidence over Dongsha Island (Fig. 1), located at the northern SCS region, can contribute to large surface flux uptake and convective instability.
The climatology of cloud coverage for nine cloud types over the SCS region in East Asian winter monsoon is calculated using ISCCP data during winter (DJF) 1984–2017 (Fig. 7). In the most baroclinic region near Yangtze River around 30°N, the transformation from the available potential energy to kinetic energy leads to large nimbostratus coverage (Fig. 7f). The SCS is mainly covered by low clouds (stratocumulus in Fig. 7b) due to the large-scale subsidence, in addition to the transition from stratus (St) to cumulus (Cu) from the northern (20°N) to middle (15°N) SCS (Figs. 7a,c). The southern SCS (10°N southward) is dominated by deep convection (DC, Fig. 7i) and the outflow cirrus (Ci and Cs, Figs. 7g,h), with the largest coverage of 26% and 30%, respectively.
The climatology of the ISCCP cloud coverage over the SCS region in East Asia during winter (DJF) 1984–2017 for (a) Cu, (b) Sc, (c) St, (d) Ac, (e) As, (f) Ns, (g) Ci, (h) Cs, and (i) DC.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
From the high-resolution radiosonde measurements at Dongsha Island for winter (DJF) 2010–2020, the MBL structure can be seen in Fig. 8. The statistics of the inversion depth, MLH, CTH, and CBH are shown by boxplots in Fig. 8a consisting of the first (Q1) and third quartiles (Q3), median values, and extreme values (or outliers). The median values show that MLH (550 m) is higher than LCL (318 m), CBH (∼500 m) is slightly lower than MLH, and CTH (∼1700 m) is just below the inversion (1769 m). The vertical profiles are shown as a function of normalized height (ζ). The height (z) is normalized by ζ = z/MLH for z ≤ MLH and ζ = 1 + (z − MLH)/D for z > MLH, where D is the depth of the inversion layer minus MLH. The median values of LCL, CBH, and CTH are marked relative to MLH and inversion height in the composite profiles as a function of ζ.
The MBL structure derived from radiosonde at Dongsha Island for winter (DJF) 2010 to 2020, (a) the boxplots of the inversion depth, MLH, cloud-top height (CTH), and CBH of low clouds. In the boxplot, lower and upper whiskers denote Q1 − 1.5 × (Q3 − Q1) and Q3 + 1.5 × (Q3 − Q1), respectively, where Q1 and Q3 are the first-quartile and third-quartile values, and dots are outliers. The rest of the plots show vertical profiles of composite variables in the inversion layer for the sounding profiles within Q1 and Q3 of MLH and inversion depth: (b) θ (orange line; K), q (green line; g kg−1), and horizontal winds (vector; m s−1); (c) cloud fraction (%); (d) θe and θe gradients. In (b)–(d), the profiles are composite relative to a normalized height (see the text). The normalized MLH and inversion height are marked by dashed lines, so are the median values of LCL, CBH, and CTH.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
The potential temperature gradient is negative near surface, indicating an unstable surface layer (Fig. 8b). The θ and qυ are well mixed below the LCL and remain nearly constant between LCL and MLH with θ (q) slightly increasing (decreasing) with height (Fig. 8b). The cloud frequency reaches its largest value (∼60%) at and above MLH and decreases to zero near the inversion height (Fig. 8c). The composite profiles of θe and θe gradients are shown in Fig. 8d. The θe is nearly constant within the mixed layer and decoupled layer, except a weak negative gradient [−0.5 K (100 m)−1] across the MLH, and a larger negative gradient [−1.5 K (100 m)−1] at inversion. The above results indicate the existence of abundant low clouds at Dongsha Island that is representative of northern SCS as shown in Fig. 7. Observations in tropical and subtropical oceans show that low cloud cover is well correlated with the lower-troposphere stability (LTS) that can be measured by the potential temperature of the free troposphere (700 hPa) and the surface, LTS = θ700hPa − θsfc (Slingo 1987; Klein and Hartmann 1993). The LTS is regarded as a measure of the strength of the inversion that caps the MBL by Wood and Bretherton (2006) who suggested a measure of the estimated inversion strength,
The composite evolution of the anomalous sea level pressure (SLP) and 925-hPa wind of the 20 selected cold surge cases (Table 1) are displayed in Fig. 9. Positive SLP anomalies develop over Siberia between Ural Mountains and Lake Baikal from day −2, expand to Siberia–Mongolia at day 0, and moves southward along the Tibetan periphery to southern China at surge day (day V). Note that day V is on average ∼2.5 days after day 0. Accompanied with the strengthening east–west SLP gradient from day 0 to day V, 850-hPa northerly winds over southern China and western North Pacific reach maximum at day V (Fig. 9).
The composite fields of the anomalous SLP (shading; hPa) and 850-hPa wind (vector) of the 20 selected cold surges from 6-day lead (day −6) to postsurge (3–4 day lag) relative to day 0 at every 2 days. The anomalies are relative to the 1981–2020 DJF climatology.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
The passage of cold surge through the SCS can be divided into three stages: presurge (day 0), surge (day V), and postsurge (day V + 1) stages. The composite fields of ERA5 marine boundary layer height (MBLH), 925-hPa wind, and precipitation of the 20 selected cold surge cases at the three stages are shown in Fig. 10. The ERA5 MBLH is defined as the layer of the bulk Richardson number reaching 0.25. The Richardson number is the ratio of the buoyancy term gθ−1dθ/dz and the shear term (∂u/∂z)2. The ERA5 MBLH is defined when the horizontal wind speed decreases drastically with height and/or the strong inversion exists.
The composite of GPM IMERG total precipitation (shading; mm day−1), ERA5 MBLH (contour; m), and ERA5 925-hPa wind (vector; m s−1) of the 20 selected cold surges at three stages: (a) presurge, (b) surge, and (c) postsurge. The map area is the box shown in Figs. 11d–f.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
Low-level winds are very weak at the presurge stage (Figs. 9d and 10a), and the rainband is located at south China (Fig. 10a). Due to the anomalous southerly that decreases the vertical shear, and the surface heating that increases the vertical potential temperature gradient (Figs. 11a and 12), the ERA5 MBLH at the presurge stage is only ∼600 m, which is the lowest MBLH among the three stages (Fig. 10). At the surge stage, strong precipitation occurs over the northern SCS related to cyclonic flow (Figs. 9e and 10b); the SCS 925-hPa northerly wind reaches a maximum. At postsurge, that is, 1 day after the surge stage, precipitation weakens, the northerly wind slightly decreases; the ERA5 MBLH over the SCS generally remains at 1 km but reaches 1.2 km over the northern SCS (Fig. 10c). The figure shows that rainband migrates southward from Southern China to northern SCS from the presurge to surge stages and then gradually vanishes at the postsurge stage while rainfall develops over southern SCS upon the development of cold surges. These features in the northern SCS will be discussed again later with the radiosonde data.
The composite evolution of anomalous RH (shading; %), θ (red contour; CI: 2 K), θe (blue contour; CI: 2 K) and MOC (vector) as in Fig. 1b at (a) presurge, (b) surge, and (c) postsurge.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
The cold surge composite of (a) equivalent potential temperature (θe; shading; K), potential temperature (θ; red contour; K), and specific humidity (q; black contour; g kg−1) and (b) cloud frequency (shading; %), inversion layer (gray line), MLH (purple line), and horizontal winds (vector; m s−1), (c) the q difference at the inversion, Δqinv = qsfc − q700hPa, and (d) LTS, EIS, and ECTEI, from day −2 to day 7 (x axis) from radiosonde data at Dongsha Island. In (a) and (b), the left axis denotes the pressure levels (hPa), and the right axis shows the corresponding height (m). Day 0 represents the presurge stage, days 2–3 represent the surge stage, and days 3–4 represent the postsurge stage.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
The meridional–vertical distribution of anomalous MOC and boundary layer features associated with the evolution of cold surges is shown in Fig. 11. In the presurge stage, the East Asian MOC (Fig. 11a) is weaker and the embedding frontal rain is associated with the anomalous cyclonic flow in southern China to the northern SCS (Fig. 10). Warm-humid air and upward motion around 25°N and the warm and humid MBL at 0°–10°N is capped by subsiding dry air (Fig. 11a). At surge and postsurge stages, the cold air spread equatorward by the strong low-level northerly and the cold air mass near Siberia is weaker (Figs. 9e,f). The tropical and lower branch of MOC becomes strengthened as characterized by active convection and ascending motion in the tropics and enhanced subtropical subsidence in the surge and postsurge stages (Figs. 11b,c), compared to that of the presurge (Fig. 11a). In addition, the weakening of the southward migrating frontal system may lead to indirect thermal circulation and weak subsidence at northern SCS. Corresponding to the development of cold surge, the front weakens as it moves from 25° to 20°N that is accompanied with a large temperature drop and northerly wind surge in the low troposphere over the northern SCS.
Composite evolution of the MBL structure during the passage of cold surges through Dongsha Island is shown in Fig. 12 based on radiosonde data. The mixed layer deepens from presurge (day 0) and reaches a height of ∼1.0 km at the postsurge stage (days 3–4), while the inversion layer is abruptly lifted to ∼2.5 km during the surge stage before gradually decreasing to ∼2 km during the postsurge stage. At the presurge stage, the shallow mixed layer contains warm and moist air but less cloud development (Fig. 12). Following the rapid decrease of temperature and moisture, the MBL becomes well-mixed and extensive low clouds develop up to 50%–70% during the surge and postsurge stages (Fig. 12). The three estimates of inversion properties in Fig. 12 show a clear evolution of the MBL during the passage of cold surges. In the presurge stage, inversion is weaker than the seasonal mean (EIS < 2.2), moisture gradient Δqinv = qsfc − q700hPa is larger, and cloud top entrainment instability is satisfied (ECTEI ≤ 0). During the surge to postsurge, inversion is relatively stronger (EIS > 2.2), moisture gradient is weaker, and ECTEI is larger than the seasonal mean value (ECTEI > 0.27). The evolution of EIS and ECTEI is positively correlated with the cloud amount shown in Fig. 12b.
Focusing on low-troposphere energy budgets [Eqs. (1)–(3)] in the northern SCS (15°–20.5°N, 112.5°–117.5°E), Fig. 13 shows large tropical–extratropical energy exchanges during the surge and postsurge stages by the horizontal negative MSE advection, including zonal (about 150 W m−2) and meridional cold and dry advection (about −500 W m−2), while surface fluxes (about 420 W m−2) are the primary sources of warming (∼70 W m−2) and moistening (∼350 W m−2). Precipitation is nearly 0 at presurge and postsurge (Fig. 10), but the area averaged budget in the postsurge stage shows a net condensation 〈L(c − e)〉 ≈ L × P ≤ 50 W m−2, where P denotes the total precipitation. In Figs. 13a and 13c, the residual terms include 850-hPa turbulent fluxes and the radiation (i.e.,
The composite column integrated budget of (a) DSE (s), (b) latent energy (Lqυ), and (c) MSE (h) from 850 to 1000 hPa, averaging over northern SCS (15°–20.5°N, 112.5°–117.5°E) at presurge, surge, and postsurge. Each budget term is shown on the label. Note that
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
Noted that the estimated quantities contain uncertainties because the ERA5 budget is not closed (Mayer et al. 2021). The imbalanced budgets arise due to certain inconsistencies among observed variables and assimilated variables involving parameterized physical processes. In addition, GPM IMERG precipitation is used in budget analysis. Inconsistencies between these datasets can cause further budget imbalance.
The budget analysis shows that large-scale advection plays an important role on MBL evolution during cold surges. Figures 14 and 15 show the vertical profiles of large-scale advection during cold surge passages. At day 0 (presurge stage), Dongsha Island experiences large MSE near surface and weak advection. The negative meridional DSE (MSE) advection peaks at Dongsha Island at days 2–3 and followed by nearly well-mixed situation at days 3–4 (Fig. 15a). This phenomenon indicates that the large-scale advection triggers strong sea surface fluxes and turbulent mixing. The large-scale downward motion also brings high DSE after day 2 (Fig. 14d), which corresponds to the strengthening of East Asian MOC.
Composite large-scale (a) DSE, (b) its zonal advection (shaded) and u wind (contour; m s−1), (c) meridional advection and υ wind (contour; m s−1), and (d) vertical advection and pressure velocity (contour; Pa s−1) at Dongsha Island during cold surges (centered at day 0). The x axis denotes days before or after cold surge happens, and y axis denotes the pressure levels.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
As in Fig. 14, but for MSE.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
To describe the cloud features and estimate the cloud radiative effect during cold surges, we use CloudSat and CALIPSO data to resolve the cloud vertical structure. These datasets show the following characteristics: 1) low-level clouds, especially low, thick clouds and Sc dominate at surge and postsurge stages (Figs. 16b and 17b,c); 2) no deep convection is observed at the postsurge stage (Figs. 16b and 17c); and 3) cirrus cloud increases significantly at the surge stage and slightly decreases at postsurge (Fig. 17). The clouds that are detected by CALIPSO but undetected by CloudSat may be attributed to the sensor sensitivity, indicating that the clouds may be too (optically) thin for CloudSat CPR to detect, such as the cumulus at the postsurge stage. These features mentioned above can be linked to the strengthening of the East Asian MOC. Tropical convection is enhanced and pushed southward when cold surge occurs. The convection-generating cirrus clouds are brought northward by the upper branch of the East Asian MOC, as indicated by the increasing cirrus amount over the SCS. The lower branch of the East Asian MOC contributes to the strong turbulent mixing from sea surface and favors low-cloud development. The presence of clouds influences the radiation budget. From CloudSat dataset, the radiative heating rate is around −1 K day−1 below 15 km at the presurge stage (Fig. 16c). In contrast, cloud radiative heating (∼0.5 K day−1) near low cloud bottom and cooling (∼−1 K day−1) at low cloud top can increase instability at the postsurge stage (Fig. 16d). Note that the difference between these CloudSat and CALIPSO datasets can be attributed to the time passing by and the equipped sensor sensitivity.
(a),(b) Frequency of cloud types and (c),(d) vertical profiles of radiative heating rate (K day−1) over northern SCS (110°–120°E, 18.70°–22.70°N). (a),(c) At presurge; (b),(d) at postsurge. The results are provided by CloudSat 2B datasets.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
CALIPSO VFM cloud-type frequency over northern SCS (110°–120°E, 18.70°–22.70°N) at (a) presurge stage, (b) surge stage, and (c) postsurge stage.
Citation: Monthly Weather Review 152, 9; 10.1175/MWR-D-23-0238.1
From the CloudSat radiative heating rate profiles (Figs. 17c,d), we can estimate the radiation term in the energy budget shown in Fig. 13, with the equation ∂T/∂t = (ρCp)−1∂F/∂z, where ∂T/∂t is the heating rate, ρ is the air density, Cp is heat capacity, and ∂F/∂z is the radiative fluxes varying with height. The radiation term is calculated as the difference between the net radiative fluxes at 850 hPa and those at 1000 hPa. The radiation term is 12 W m−2 at the presurge stage, −4 W m−2 at the surge stage, and −1 W m−2 at the postsurge stage. Noted that although the radiation flux difference between 850 and 1000 hPa is small at surge and postsurge stages, the abundant low clouds coincide with the strongest cooling–heating contrast below 1 km at surge and postsurge stages (Figs. 12b and 16a,b).
As the radiation term is estimated, we can now include this information to estimate the 850-hPa turbulent fluxes,
The estimation of each residual term in DSE, latent heat energy (Lq), and MSE and the surface turbulent fluxes at presurge, surge, and postsurge stages under the assumption of the closed budget. For MSE and DSE, each residual term is estimated by the formulas
To estimate the budget uncertainties, we compare the 850-hPa MSE fluxes derived from the MSE budget [
5. Conclusions
Monsoonal northerlies originating from the Siberian–Mongolia high bring cold and dry air to East Asia, the SCS, and the Philippines Sea. The MBL structure is maintained by strong surface fluxes due to low-level negative MSE advection and entrainment under a strong inversion layer. A synthesis of high-resolution radiosondes at Dongsha Island and satellite cloud products shows that the MBL in the northern SCS is confined within ∼1800 m by large-scale subsidence with ∼60% cloud frequency at a 1-km height, consisting of stratocumulus (28%), cumulus (20%), stratus (12%), and nimbostratus (14%) coverages, while the MLH is at 550 m. The abundant low cloud cover corresponds to the strong inversion that is quantified by LTS = 17.8, EIS = 2.2, and ECTEI = 0.274, consistent with the previous studies about the relation between low cloud cover and inversion strength as a function of SST. The MBL structure and the low cloud correlated indices suggest an important role of MBL mixing and low cloud feedback over the SCS that connect the boreal winter MOC and tropical convection over Maritime Continent.
Cold surge influences on the MBL over the northern SCS are shown in three stages: presurge, surge, and postsurge. At the presurge stage when the Siberian–Mongolia high is strengthening, the mixed layer is thin and inversion is weak in the presence of anomalous large-scale warm and moist advection in the southern part of a front over South China. Both turbulent fluxes and inversion are weak at this stage that do not favor low-cloud development due to cloud-top entrainment instability (ECTEI < 0). At the surge stage, thick precipitating low clouds develop over northern SCS, accompanied by strong low-level northerly and strong inversion that is lifted to ∼2.5 km on average. The precipitation decays at the postsurge stage, but cold and dry advection persists, and, the MLH reaches a maximum because of strong surface turbulent fluxes, while the inversion layer lowers (∼2.0 km) due to the subsidence.
At the surge and postsurge stages, the cooling and drying tendencies in the low troposphere are primarily attributed to meridional advection and balanced by surface latent heat fluxes, zonal advection, and surface sensible heat fluxes, in descending order. Total surface fluxes at the postsurge stage are 10 times larger than those at the presurge stage such that a strong turbulent flux converges within the MBL. Since a large portion of turbulence fluxes is to balance the advective cooling and drying, the remaining turbulent mixing is not the only important source for the MBL development. The abundant low cloud cover suggests that cloud-radiative forcing and the cloud evaporative cooling within the MBL need be considered in future study.
In this study, we analyze the MBL structure and quantify the underlying physical processes that influence the MBL over the northern SCS during East Asian winter monsoon and cold surges. This is a follow-up study of the SCSTIMX conducted in 2018. The study is performed to provide a background for the special observations by the National Taiwan University (NTU) research vessel (RV), NOR1 cruises to SCS in March 2022. The ongoing data assimilation experiment of in situ observations during the cruises will provide deeper insights into the turbulent processes and MBL evolution. Furthermore, we hope to encourage more MBL studies over the SCS such as those relating to low-level moistening of intraseasonal oscillation and tropical waves.
Acknowledgments.
Sincere thanks go to all participants in the SCSTIMX field experiments and the co-PIs of the SCSTIMX follow-up projects, Po-Hsiung Lin, Sen Jan, and Ming-Huei Chang. We acknowledge the captain and crew of the NTU RVs, OR1, and NOR1 for assisting the field campaign. Chung-Hsiung Sui and Kuan-Yun Wang were supported by the National Science and Technology Council, Taiwan, under Grants MOST 110-2119-M-002-012, MOST 110-2111-M-002-015, NSTC 111-2111-M-002-011-, and NSTC 112-2111-M-002-009-. Mong-Ming Lu is supported by MOST 111-2111-M-002-007- and NSTC 112-2111-M-002-010-.
Data availability statement.
Data requests of radiosonde and ceilometer at Dongsha Island can be sent to Central Weather Administration in Taiwan. Further information about these two datasets is available at https://doi.org/10.3319/TAO.2019.11.29.02. The ISCCP satellite products are publicly accessible on its website (https://isccp.giss.nasa.gov/products/onlineData.html). CloudSat datasets can be obtained from its website (https://www.cloudsat.cira.colostate.edu/). CALIPSO VFM V3 can be downloaded from NASA Langley Atmospheric Science Data Center DAAC (NASA/LARC/SD/ASDC 2018; https://www-calipso.larc.nasa.gov/). GPM IMERG final precipitation V06 product can be downloaded from NASA Earthdata system (https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_06/summary). ERA5 is available through the EU-funded Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/). MERRA-2 datasets are provided by Goddard Earth Sciences Data and Information Services Center (GES DISC) and available from NASA Earthdata system (https://disc.gsfc.nasa.gov/datasets/M2T3NVRAD_5.12.4/summary).
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