The states of coupling between clouds and surface or boundary layer have been investigated much more extensively for marine stratocumulus clouds than for continental low clouds, partly due to more complex thermodynamic structures over land. A manifestation is a lack of robust remote sensing methods to identify coupled and decoupled clouds over land. Following the idea for determining cloud coupling over the ocean, we have generalized the concept of coupling and decoupling to low clouds over land, based on potential temperature profiles. Furthermore, by using ample measurements from lidar and a suite of surface meteorological instruments at the U.S. Department of Energy's Atmospheric Radiation Measurement Program's Southern Great Plains site from 1998 to 2019, we have developed a method to simultaneously retrieve the planetary boundary layer (PBL) height (PBLH) and coupled states under cloudy conditions during the daytime. The new lidar-based method relies on the PBLH, the lifted condensation level, and the cloud base to diagnose the cloud coupling. The coupled states derived from this method are highly consistent with those derived from radiosondes. Retrieving the PBLH under cloudy conditions, which has been a persistent problem in lidar remote sensing, is resolved in this study. Our method can lead to high-quality retrievals of the PBLH under cloudy conditions and the determination of cloud coupling states. With the new method, we find that coupled clouds are sensitive to changes in the PBL with a strong diurnal cycle, whereas decoupled clouds and the PBL are weakly related. Since coupled and decoupled clouds have distinct features, our new method offers an advanced tool to separately investigate them in climate systems.
A large fraction of low clouds is driven by surface fluxes through the conduits of the planetary boundary layer (PBL) over land (e.g., Betts, 2009; Ek and Holtslag, 2004; Golaz et al., 2002; Teixeira and Hogan, 2002; Zheng et al., 2020; Wei et al., 2020; Santanello et al., 2018). This is a coupled cloud–surface system (Cheruy et al., 2014; Zheng and Rosenfeld, 2015; Wu et al., 1998). However, not all low clouds respond to surface forcing. Those clouds without close interactions with the local surface are considered to be in a decoupled state. Given that the PBL is, by definition, the lowest atmospheric layer influenced by the underlying surface (Stull, 1988), to what degree the PBL top overlaps with cloud bases becomes a good criterion to separate coupled and decoupled low clouds.
Conventionally, the “coupled state” of a cloud-topped marine boundary layer implies that the moist conserved variables are vertically well mixed within the PBL (Bretherton and Wyant, 1997; Dong et al., 2015; Zheng and Li, 2019; Zheng et al., 2018, 2021). However, such a definition cannot be simply applied to clouds over land since the definition and the determination methods of the PBL over land differ from those over ocean (Garratt, 1994; Vogelezang and Holtslag, 1996). The concept of coupled and decoupled states is typically used to characterize marine stratocumulus clouds due to their large-scale coverages (Nicholls, 1984). Since stratocumulus only constitutes a relatively small portion of continental clouds (Warren et al., 1986), we attempt to extend the concept of coupling and decoupling to characterize low clouds over land. Due to the relatively complex thermodynamics, the moisture conserved variables (e.g., total water mixing ratio and liquid potential temperature) may not be a constant in the coupled sub-cloud layer (Driedonks, 1982; Stull, 1988).
Following parcel theory, the lifted condensation level (LCL) has been used to diagnose a coupled cloud, based on the distance between the LCL and the cloud base (e.g., Dong et al., 2015; Glenn et al., 2020; Zheng and Rosenfeld, 2015; Zheng et al., 2020). When potential temperature and humidity are uniformly distributed in the vertical, the LCL should be consistent with the cloud base for coupled cases. However, the cloud base for coupled cases can considerably differ from the LCL over land because potential temperature and humidity have large variabilities in the vertical scale within the PBL over land (Driedonks, 1982; Guo et al., 2016, 2021; Stull, 1988; Su et al., 2017a). To address the limitation in the LCL method, we attempt to develop a remote sensing method to distinguish coupled and decoupled clouds over land.
Since the PBL height (PBLH) is the maximum height directly influenced by surface fluxes, we consider coupling with the PBL equivalent to coupling with the land surface. Thus, we use the PBLH as a critical parameter to diagnose the coupling between clouds and the land surface. The degree of coupling may thus be gauged in terms of quantitative differences between the cloud base and the PBL top. Such differences can be determined in a height coordinate system or in a potential temperature coordinate system (Kasahara, 1974). For this purpose, ground-based lidar has great potential because it can continuously track the development of the PBL (Demoz et al., 2006; Hageli et al., 2000; Sawyer and Li, 2013; Su et al., 2017b, 2018) and clouds (Clothiaux et al., 2000; Platt et al., 1994; Zhao et al., 2014) at high temporal and vertical resolutions.
By jointly using lidar measurements and meteorological data from the U.S.
Department of Energy's Atmospheric Radiation Measurement (ARM) Southern
Great Plains (SGP) site (36.6
The paper is organized as follows. Section 2 describes the measurements and data. Section 3 describes the new methodology in terms of the definition and implementation. The performance of the method is demonstrated in Sect. 4, and a summary is presented in Sect. 5.
RS launches took place at least four times per day at the ARM SGP site,
usually at 00:30, 06:30, 12:30, and 18:30 local time (LT). Holdridge et al. (2011) provide technical details about the ARM RS (
There are several methods to determine PBLH from RS-measured potential temperature (
MPL backscatter profiles were collected at the SGP site from September 1998
to July 2019 with high continuity (Campbell et al., 2002). Technical details and data availability can be found at the website
The MPL can be used to detect cloud layers based on signal gradients (Platt
et al., 1994). Lidar-based methods are accurate for determining the
cloud-base height (CBH) but may miss information about the cloud top due to
the signal saturation within an optically thick cloud (Clothiaux et al.,
2000). Under this condition, the cloud radar provides a better estimation of
the cloud-top height (CTH). In this study, we directly use an existing
quality-controlled cloud product, CLDTYPE/ARSCL (
The definition of the state of cloud–surface coupling over land is a critical question. For marine stratocumulus, coupled clouds are identified when the liquid water potential temperature varies less than a certain threshold (i.e., 0.5 K) below the cloud base (Jones et al., 2011). We try to extend the concept of coupling and decoupling to clouds over land. The PBL over land is typically buoyancy driven and controlled by surface fluxes during the daytime. We consider a cloud is in the coupled state when it strongly interacts with the buoyancy fluxes within the PBL.
Figure 1 presents the idealized vertical profiles of virtual potential
temperature (
Idealized vertical profiles of virtual potential
temperature (
Following the previous studies (Jones et al., 2011; Dong et al., 2015), we
attempt to use the variations in the potential temperature within the
sub-cloud layer to diagnose the cloud coupling. For determining a suitable
threshold, we first look at several examples of profiles of
Figure 2c–d show a clear inversion layer between the cloud base and the PBL
top, and the difference in
Virtual potential temperature (
Therefore, instead of giving a height range to limit the differences between
CBH and PBLH, we consider using the differences in
As the basic framework of PBL, the slab model assumes that
Based on the variations in
Similar to the previous studies (Jones et al., 2010; Dong et al., 2015; Zheng and Rosenfeld, 2015), we identified the coupled clouds as the thermodynamics coupling between surface and cloud base. However, it is an open question whether the entire cloud layer is coupled for coupled cases. It depends on whether the liquid water potential temperature is conserved within the cloud layer, which represents a moisture adiabatic process. This issue is closely related to the cloud types. In the cloud parameterizations, the entire stratocumulus layer is considered to be well-mixed, while the cumulus-capped layer is usually partially mixed (Lock, 2000). For stratocumulus clouds, the entire cloud layer and PBL are typically fully coupled with surface, when the cloud base is coupled with surface. For the cumulus-capped PBL, the entire cloud layer may not be completely coupled, despite the coupling between cloud base and surface. The well-established parameterizations are supported by many observational studies (e.g., Betts, 1986; Storer et al., 2015; Berkes et al., 2016; de Roode and Wang. 2006; Ott et al., 2009).
Given the rapid change in clouds over land, RS observations have limitations when it comes to tracking cloud development due to the coarse temporal resolution and drifting of the balloon. We thus further developed a lidar-based method to identify the coupled states of clouds based on our new algorithm for retrieving the PBLH that can better track the diurnal variations in PBLH than conventional lidar-based approaches (Su et al., 2020). We adapted this algorithm for retrieving the PBLH and developed a new scheme to deal with cloudy conditions. Following the original method (Su et al., 2020), the rainy cases are eliminated in the quality-control process. The principles behind the PBLH algorithm are stated next for completeness.
List of parameters in the flowchart of DTDS (Fig. 4).
Our new PBLH algorithm can retrieve the PBL variability from the MPL under different thermodynamic stability (thus named the DTDS algorithm) conditions, taking into account the vertical coherence and temporal continuity of the PBLH. First, we identify the local maximum positions (LMPs; range: 0.25–4 km) in profiles of the wavelet covariance transform function derived from lidar backscatter (Brooks, 2003). These LMPs are the potential positions of the PBLH. We can use the PBLH derived from morning RS soundings as the starting point. Without morning RS soundings, the algorithm can still work well, with the lowest LMPs selected as the starting point, which reduces by 0.02–0.05 the correlation coefficient between MPL-derived and RS-derived PBLHs (Su et al., 2020).
To ensure good continuity, we select the closest LMP to the earlier position of the PBLH. Different stages of PBL development are considered. DTDS-derived PBLHs likely increase during the growth stage and decrease during the decaying stage, but the algorithm is also able to identify decreases during the growth stage or increases during the decaying stage based on the selection scheme described by Su et al. (2020). There are multiple step signals in the backscatter profiles when complex aerosol structures (e.g., the residual layer) are present, leading to multiple LMPs. Based on temporal continuity, we select the appropriate LMP as the position of the PBL top. However, PBLH retrievals still suffer from relatively low accuracies under stable conditions because of the weak vertical mixing and residual layer.
Clouds induce strong step signals in the lidar backscatter, further
considerably affecting PBLH retrievals. Su et al. (2020) only considered
cases where the low cloud top coincided with the previous PBL top, excluding
other low-cloud cases (
The flowchart of the updated DTDS algorithm. In this
diagram,
For the DTDS algorithm, five empirical parameters are used, including
The LCL is calculated from surface meteorological data (relative humidity,
temperature, pressure) at the SGP site based on an exact expression (Romps,
2017). Specifically, Romps (2017) proposed an exact, explicit, analytic
expression for LCL as a function of surface meteorology. Compared to the
previous approximate expressions, some of which may have an uncertainty in
the order of hundreds of meters, the Romps expression can be considered as
the precise value. The uncertainty of empirical vapor pressure data may lead
to a bias of
After determining the coupling or decoupling state of a cloud, we retrieve
After retrieving
The states of coupling and decoupling are diagnostic parameters rather than explicit expressions. Similar to the other methods for retrieving PBLH (e.g., Brooks, 2003; Liu and Liang, 2010), multiple empirical parameters are used to determine PBLH. Table 1 lists the five empirical parameters in the algorithm. These parameters are related with three factors, including LCL, PBLH, and CBH. The sensitivity to the selection of these parameters is presented. The detailed impacts of variations in these parameters on the retrievals of cloud coupling and PBLH will be discussed in this section.
Note that we used the CTH and
Similar to previous studies, we can also use the LCL as the standard to
identify coupled clouds (Dong et al., 2015; Zheng and Rosenfeld, 2015). We
assume a cloud is coupled if
Commission errors and omission errors of coupled cloud
identifications
Despite the coarse temporal resolution, the RS-derived PBLH can be a good
criterion to use to distinguish between coupling and decoupling. If we
consider a coupled cloud as a cloud where CBH
Moreover, we test the sensitivity of selecting these empirical parameters.
Figure 6 presents the commission errors and omission errors in the
identifications of coupled clouds for selecting different values of
empirical parameters. Among these parameters,
Commission errors (red line) and omission errors (blue
line) of coupled cloud identifications for selecting different values of
empirical parameters (
As a by-product of this method, we also pay attention to the PBLH
retrievals under cloudy conditions. Figure 7 presents the mean absolute
biases and correlation coefficients between PBLH derived from lidar and
radiosonde for selecting different values of empirical parameters. To match
the scope of this study, we only analyze the low-cloud conditions. For
retrieving PBLH under cloudy conditions,
Red lines indicate the mean absolute biases between PBLH
derived from lidar and radiosonde for selecting different values of
empirical parameters (
In short, selections of these empirical parameters are based on the overall
relationship between cloud and PBL under the coupled and decoupled states.
In our method, the selection of
Figure 8 illustrates four examples of PBLH retrievals and cloud states
derived from the DTDS algorithm for 27 October 2011, 31 July 2002, 19 March 2000, and 1 May 2012. Figure 8a depicts coupled shallow cumulus occurring at
noontime at the PBL top. With a weak surface flux of
Daily backscatter profiles:
The identification accuracy, or disparity between different methods, is
evaluated in terms of the selected criteria, for which the identification
method based on
Figure 5 also compares the accuracy between the DTDS and LCL methods. Based
on the LCL alone, we cannot choose an appropriate criterion to achieve a
lower commission error and omission error simultaneously. Thus, we do not
use the LCL as the single standard to detect the coupling and decoupling of
low clouds in our study. As diagnostic parameters, different methods
inevitably produce different results regarding coupling and decoupling.
Although we consider the method based on
Figure 9a–b present the occurrence frequencies of the CBH and the CTH at different heights. Despite the same variation ranges, clouds are mostly coupled if the CBH is lower than 1 km, while decoupled clouds dominate if the CBH is higher than 3 km. Figure 9c–d show the changes in the coupled fraction (ratio of coupled cases to total cases) with different CBHs and CTHs. The coupled fraction is about 90 % if the CBH is lower than 1 km and decreases to 2 % for CBHs above 3 km. Although the CBHs for coupled cases are generally less than 3 km, CTHs for coupled cases can be much higher. Coupled clouds still account for around 10 % of the cases with CTHs above 6 km.
The height-dependent occurrence frequencies of
The relationships between
Figure 10 shows scatter plots between CBH, CTH, PBLH, and LCL for coupled and decoupled clouds. For coupled clouds, there is a generally strong correlation between CBH, LCL, and PBLH, contrary to the weak relationships of decoupled cases. The relationship between CTH and RS-derived PBLH is complicated. For shallow cumulus clouds, their tops can be considered as PBL tops for the coupled state, while the cloud top is considerably above the position of the PBL top for active cumulus clouds. We also note that the accuracy of CTH retrievals is generally lower than the accuracy of CBH retrievals (Clothiaux et al., 2000). As CTH is not a criterion for cloud coupling, the accuracy of CTH would not affect the identification of coupled cloud but may affect the PBLH retrievals for the coupled cloud cases. Meanwhile, despite the laser-based detection of CBH being considered the standard method (Platt et al., 1994; Clothiaux et al., 2000; Lim et al., 2019), the CBH retrievals from ceilometer or lidar still bear some uncertainties, which can potentially lead to a mean bias of 0.1 km (Silber et al., 2018). In our method, a systematic increase of 0.1 km in the CBH can lead to an increase of 2.1 % in omission errors and a decrease of 1 % in commission errors.
After identifying the coupling state of clouds, it is feasible to retrieve
the PBLH under cloudy conditions. In particular, the DTDS-derived PBLH needs
to resort to the cloud position for coupled cloud cases. For decoupled cloud
cases, on the other hand, the PBLH below clouds is sought to avoid cloud
interference. For coupled clouds, DTDS-derived PBLHs show a strong
correlation with RS-derived PBLHs with a correlation coefficient (
In this study, we proposed a novel method for distinguishing between coupled and decoupled low clouds over land. Based on the understanding of PBL processes and quantitative analyses, we developed a lidar-based method (DTDS) to identify the coupling state of low clouds over the SGP site. In practice, we identified a coupled cloud when the position of the cloud base was generally close to or lower than the previous position of the PBL top, with the LCL serving as an additional restriction. Compared to using the LCL alone, the coupled states identified by the DTDS method show better consistency with the results derived from radiosondes, with about 10 % differences between the lidar-based retrievals and radiosonde results.
Not only the coupled state but also the PBLH under cloudy conditions is retrieved by the method. A long-lasting problem with lidar retrieval of PBLH is either incapability of retrieval or large uncertainties induced by the occurrence of low clouds (e.g., Chu et al., 2019; Hageli et al., 2000; Lewis et al., 2013); we address this issue by separately considering the coupled and decoupled state of low clouds. Specifically, in coupled conditions, the position of the coupled cloud serves as a good reference for identifying the PBLH. In decoupled conditions, the large backscatter and step signals from clouds would be excluded to avoid interfering with the retrievals. With our method, cloudy conditions are well handled.
With the new method, we study the difference of cloud–PBL interactions in coupled and decoupled conditions. In contrast to the sensitive responses of coupled clouds to changes in the PBLH and buoyancy, the decoupled clouds and the PBLH are weakly related. Due to their different relationships with the PBL, a robust differentiation between the coupled and decoupled low clouds is critical for further investigating the coupled land–atmosphere system and aerosol–cloud interactions. Our methodology paves a solid ground for such pursuits.
All these datasets are publicly available at the ARM archive
TS, YZ, and ZL conceptualized this study. TS carried out the analysis, with comments from other co-authors. TS, YZ, and ZL interpreted the data and wrote the manuscript.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We acknowledge the provision of radiosonde, MPL data, surface meteorological data, and cloud products by the U.S. Department of Energy's ARM program. We thank the two anonymous reviewers for their comments.
This research has been supported by the U.S. Department of Energy (grant no. DE-SC0018996) and the National Science Foundation (grant nos. AGS2126098 and AGS1837811).
This paper was edited by Yun Qian and reviewed by Xiquan Dong and two anonymous referees.