Effects of the surface coupling strength in the WRF/Noah-MP model on regional climate simulations over China

Abstract

Land–atmosphere energy and moisture exchange can strongly influence local and regional climates. However, high uncertainty exists in the representation of land–atmosphere interactions in numerical models. Parameterization of surface exchange processes is greatly affected by parameter C_zil, which, however, is typically defined as a domain-wide constant value. In this study, we examine the sensitivity of regional climate simulations over China to different surface exchange strengths via three C_zil schemes (default (without C_zil), constant (C_zil = 0.1), and dynamic canopy-height-dependent C_zil-h schemes) within a 13-km-resolution Weather Research and Forecasting model coupled with the Noah land surface model with multiparameterization options (WRF/Noah-MP). Our results demonstrate that compared to the other two schemes, the C_zil-h scheme substantially reduces land–atmosphere coupling strength overestimation, and comparison to Chinese terrestrial ecosystem flux research network (ChinaFLUX) observations reveals the capability of the C_zil-h scheme to better match observed surface energy and water variations. The results of the application of the various C_zil schemes in four typical climate zones in China demonstrate that the C_zil-h simulations achieve the closest agreement with field observations. The C_zil-h scheme can narrow the positive discrepancies in the simulated precipitation and surface fluxes and the negative biases of the land surface temperature in Northeast China, North China, eastern Northwest China, and Southwest China. In particular, the above remarkable improvements produced by the C_zil-h scheme primarily occur in areas covered with short vegetation. Additionally, the precipitation simulated with the C_zil-h scheme exhibits more intricate and uncertain changes compared with surface flux simulations due to the nonlocal impacts of the surface exchange strength resulting from atmospheric fluidity. Overall, our findings highlight the applicability of the dynamic C_zil scheme as a better physical alternative to the current treatment of surface exchange processes in atmosphere coupling models.

1 Introduction

Land–surface processes can play significant roles in cloud formation and precipitation generation (Findell and Eltahir 2003; Trier et al. 2004) by controlling energy, momentum, and mass transport to the lower atmosphere and subsequently affecting local planetary boundary layer profiles and differential surface heating (Betts et al. 1996; Los et al. 2006). The influences of local land surface characteristics such as land use, soil, and topography on precipitation occurrence and development are manifested especially in regions of strong land–atmosphere coupling (Houze 2012; Koster et al. 2003, 2004; Li et al. 2017; Pielke 2001; Zhang et al. 2020). For example, previous studies of the midwestern US drought in 1988 and flood in 1993, suggested that soil moisture (SM) conditions help to sustain extreme circumstances throughout the summer (Atlas et al. 1993; Trenberth and Guillemot 1996). These studies have underlined that land–atmosphere interactions may hold the key for improvement of weather forecasts and climate predictions.

Land–atmosphere coupling has been investigated in many observational and modeling studies, such as the calculation of land–atmosphere feedback levels based on the atmospheric moisture budget (Trenberth 1999), diagnosis of a multimodel integrated coupling coefficient (Koster et al. 2004), and assessment of the relationship between SM, evaporation, and precipitation (Dirmeyer et al. 2006; Ruiz-Barradas and Nigam 2005; Zhang et al. 2008). However, there remain high uncertainties due to the complexity of coupling processes and their strong dependence on model or reanalysis results (Koster et al. 2003, 2004; Seneviratne et al. 2010). For instance, regions of notable coupling between summer rainfall and SM in observations could not be correctly captured in the Global Land–Atmosphere Coupling Experiment due to the unreliable performance of land surface models (LSMs) in capturing the coupling strength (Dirmeyer et al. 2006; Koster et al. 2004). Similarly, Zhang et al. (2008) reported that the central United States could not be identified as a region of strong coupling between precipitation and SM in their regional simulations. Furthermore, Ruiz-Barradas and Nigam (2005) demonstrated that excessive land–atmosphere coupling in numerical models produced an excessive latent heat flux (LH), resulting in potentially incorrect feedback between SM and precipitation. These studies emphasized the critical importance of proper land–atmosphere coupling representation in atmospheric models.

The surface exchange coefficient (C_h) denotes the efficiency of energy and moisture exchange between the land surface and atmosphere and controls the coupling strength in atmospheric models (Garratt 1992; LeMone et al. 2008). Current studies (e.g., Chen et al. (1997); Chen and Zhang (2009); Gan et al. (2019); Gomez et al (2020)) have indicated that C_h potentially constitutes a major uncertainty affecting model performance, but theoretical C_h parameterization remains poorly understood. Over recent decades, there have been many theoretical and experimental studies on C_h parameterization in which many efforts have been invested into the treatment of the surface roughness length of heat or moisture (Z_ot) and momentum (Z_om) (Brutsaert 1982; Chen et al. 1997; Chen and Zhang 2009; Yang et al. 2008). The roughness length describes the characteristics of surface fields combining the effects of land cover, orography and airflow. However, differences in transfer mechanisms, i.e., heat or moisture transport through molecular diffusion and momentum transport through pressure fluctuation gradients, generate discrepancies between Z_ot and Z_om (Reijmer et al. 2004; Rigden et al. 2018). Beljaars and Viterbo (1994) suggested that employing a fixed Z_om/Z_ot ratio of 10 could contribute to improvement in the simulated surface sensible heat flux (SH) and land surface temperature (T_s). Zilitinkevich (1995) created an empirical coefficient C_zil to bridge the relationship between Z_ot and Z_om. Chen et al. (1997) recommended applying the Zilitinkevich equation with C_zil = 0.1, which yields results similar to the application of Z_om/Z_ot = 10 in most cases, but the former nonfixed formulation is physically preferable and helps reduce the forecast precipitation bias. Thereafter, C_zil = 0.1 in Zilitinkevich’s formulation has been commonly implemented in National Centers for Environmental Prediction (NCEP) operational prediction systems. Furthermore, Chen and Zhang (2009) proposed a vegetation type-dependent C_zil that could achieve more realistic application of models in terms of land–atmosphere coupling representation.

Recent studies have been limited to the responses of land–surface processes to C_h parameterization but have ignored the extent to which C_h scheme variation can truly improve regional climate simulations. The importance of surface roughness in land–surface processes affecting the atmosphere has been frequently studied, e.g., the numerical experiments of Maynard and Royer (2004) demonstrated that a reduced surface roughness plays a dominant role in African tropical deforestation affecting climate change. Therefore, it is necessary to explore the sensitivity of climate change to parameter C_h, which heavily relies on the surface roughness and directly reflects the strength of land–atmosphere coupling. Additionally, C_h parameterization with a dynamic C_zil parameter provides more applications for evaluation studies in North America (e.g., Chen and Zhang (2009); Chen et al. (2019); Zheng et al. (2015)), but C_h evaluation in China has been conducted in only a few investigations confined to changes in land–surface processes and small areas, such as individual arid and semiarid sites (Chen et al. 2010; Yang et al. 2008) and the Yangtze River basin (Huang et al. 2016). The empirical C_zil coefficient contributes the most to the uncertainty in surface energy flux estimates (Siemann et al. 2018), and coefficient specification affects areas of strong land–atmosphere coupling in both location and strength (Zheng et al. 2015). Therefore, although investigations regarding the effects of a dynamic C_zil coefficient on surface flux and precipitation simulations at the point or regional scale (e.g., Chen et al. (2010); Huang et al. (2016); Trier et al. (2011)) have been conducted, C_h parameterization still exhibits a high uncertainty and sensitivity to changes in C_zil over regions spanning various topography, land-cover, and climate change conditions, especailly in China. Certain issues regarding proper coupling strength parameterization and the influence on surface flux and regional climate simulations should be further explored.

The objective of this study is to assess the variability in regional climate simulations in response to the representation of land–atmosphere coupling in numerical models, and to provide potential mechanisms explaining this variability. In contrast to previous studies directly adjusting C_zil values in the Chen97 scheme (e.g., Chen and Zhang (2009); Huang et al. (2016); Zheng et al. (2015)), we introduced the Zilitinkevich equation with a dynamic C_zil coefficient into the Monin–Obukhov (M–O) scheme (hereafter referred to as C_zil-h) using the Weather Research and Forecasting model (WRF) coupled with the Noah LSM with multiparameterization options (Noah-MP) (WRF/Noah-MP). Section 2 describes the detailed land–atmosphere coupling method and the experimental setup involving the coupled WRF/Noah-MP model. Section 3 assesses the capability of C_zil-h in improving land–atmosphere coupling and its impacts on regional climate simulations. In Sect. 4, the uncertainty in the C_zil-h scheme is examined in regional climate modeling. Conclusions are outlined in Sect. 5.

2 Methodology, model and data

2.1 Land–atmosphere coupling method

The Noah-MP LSM provides lower boundary layer conditions for the planetary boundary layer scheme in the coupled atmospheric WRF model (Chen et al. 2007; Skamarock et al. 2008), which relies on the surface fluxes of SH and LH (unit: W m⁻²), as determined through the following bulk transfer equations (Garratt 1992):

$${\text{SH}} = \rho C_{p} C_{h} \left| U \right|\left( {\theta_{s} - \theta_{a} } \right) ,$$

(1)

$${\text{LH}} = \rho C_{e} \left| U \right|\left( {q_{s} - q_{a} } \right) ,$$

(2)

where ρ is the air density (kg m⁻³), C_p is the air heat capacity (J kg⁻¹ K⁻¹), and U is the wind speed (m s⁻¹). Moreover, θ_a and q_a are the potential temperature (K) and specific humidity (kg kg⁻¹), respectively, of air and θ_s and q_s are the potential temperature (K) and specific humidity (kg kg⁻¹), respectively, at the surface. C_h (unitless) is the surface exchange coefficient for SH, and C_e is the surface exchange coefficient for LH. Many studies (e.g., Högström (1967); Swinbank and Dyer (1967)) have concluded a general equivalency between C_e and C_h based on the observed high correlation between the air temperature (T_a) and specific humidity over evaporating surfaces, and this approximation has been widely adopted in numerical models (e.g., Chen et al. (1997); Huang et al. (2016); Trier et al. (2011)). Therefore, this study focuses on C_h parameterization, which is directly linked to the coupling strength and controls the inputs of the total energy flux into the lower atmosphere. Observed C_h values can be reconstituted from measurements of the variables contained in the following equation (Chen and Zhang 2009):

$$C_{h} = \frac{{{\text{SH}}}}{{\rho C_{p} \left| U \right|\left( {\theta_{s} - \theta_{a} } \right)}} .$$

(3)

Instruments at stations directly provided SH and U data, θ_a was converted from T_a observations adiabatically adjusted for the height above the surface, and θ_s was estimated from the upwelling longwave radiation following the Stephan-Boltzmann relationship (Yang et al. 2008). C_p and ρ values can be derived from T_a, relative humidity, and surface pressure observations (Allen et al. 1998; Goff 1957).

Regarding the M–O scheme within the Noah-MP LSM, C_h is parameterized as a function of the roughness length based on the M–O similarity theory (Brutsaert 1982), as follows:

$$C_{h} = \frac{{k^{2} }}{{\left[ {\ln \left( {\frac{{Z - d_{0} }}{{z_{om} }}} \right) - \psi_{m} \left( {\frac{{Z - d_{0} }}{L}} \right)} \right]\left[ {\ln \left( {\frac{{Z - d_{0} }}{{z_{ot} }}} \right) - \psi_{h} \left( {\frac{{Z - d_{0} }}{L}} \right)} \right]}} ,$$

(4)

$$L = - \frac{{\rho C_{p} u_{*}^{3} T_{{{\text{ah}}}} }}{{k \cdot g \cdot {\text{SH}}}} ,$$

(5)

where Z is the height above the ground surface (m), d₀ is the zero-displacement height (m), L is the M–O length (m), and the (Z-d₀)/L ratio is denoted as the nondimensional M–O stability parameter ζ. In addition, T_ah is the canopy air temperature (K), u_* is the friction velocity (m), k = 0.4 is the von Kaman constant, and g = 9.8 m s⁻² is the gravitational acceleration. Moreover, Ψ_m is the momentum stability function, Ψ_h is the heat stability function (Paulson 1970), Z_om is the aerodynamic roughness length (m), denoting the height at which the extrapolated wind speed reaches zero, and Z_ot is the thermal roughness length (m), representing the height at which the extrapolated T_a equals T_s.

Z_ot can be related to Z_om through an atmospheric flow function, as proposed by Zilitinkevich (1995), as follows:

$$Z_{{{\text{ot}}}} = Z_{{{\text{om}}}} \exp \left( { - kC_{{{\text{zil}}}} \sqrt {R_{e} } } \right) \;{\text{and}}\;R_{e} = \frac{{u^{*} Z_{{{\text{om}}}} }}{\upsilon } ,$$

(6)

where C_zil is an empirical coefficient, v is the kinematic molecular viscosity (~ 1.5 × 10^–5 m² s⁻¹), and R_e is the roughness Reynolds number. C_zil values are usually assumed to range from 0.01 to 1.0, with surface coupling varying from strong to weak (Chen et al. 1997; Zheng et al. 2015). C_zil in the current versions of the WRF model is assigned a value of 0.1 based on earlier model comparisons and calibrations against field data (Chen et al. 1997), but only the Chen97 option provides access to the default C_zil value of 0.1 because the M–O option assumes Z_ot = Z_om without considering C_zil.

Furthermore, a dynamic vegetation type-dependent C_zil scheme was proposed by Chen and Zhang (2009), and multiyear Ameriflux data and the least squares regression method were employed to determine parameter C_zil as a function of the canopy height h (unit: m):

$$C_{{{\text{zil}}}} = 10^{{\left( { - 0.4h} \right)}} .$$

(7)

2.2 Coupled WRF/Noah-MP model and experimental design

In this study, the WRF 3.9.1 model coupled with Noah-MP (WRF/Noah-MP) was applied to investigate the sensitivity of regional climate simulations to land–atmosphere coupling. As a state-of-the-art atmospheric modeling system, the WRF model (Skamarock et al. 2008) contains full physical processes and multiple parameterization options and thus usually functions as a common framework applied to physical scheme improvement, regional climate simulation, and numerical weather prediction. The Noah-MP LSM (Niu et al. 2011), the successor of the Noah LSM (Chen et al. 1996), is applied as an augmented land surface module in the coupled WRF model. Similar to the WRF model, multiple physics scheme options are available in Noah-MP.

The WRF/Noah-MP experiments in this study were initiated at 00:00 UTC on 1 February and ran until 00:00 UTC on 1 September (i.e., the vegetation growing season) between 2003 and 2012, in which the first month of each year was treated as the model spin-up time. As shown in Fig. 1a, the model was integrated over a domain covering the entire China land surface with a central point located at 37° N and 102.5° E. The horizontal resolution was 13 km with 399 × 344 grid points. The grid vertically contained 36 terrain-following eta levels with the model top occurring at 100 hPa. Atmospheric lateral boundary conditions and initial atmospheric fields were retrieved from 6-hourly 0.75° × 0.75° European Centre for Medium-Range Weather Forecast Interim Reanalysis (ERA-Interim) data (Dee et al. 2011).

Fig. 1

a Weather Research and Forecasting model coupled with the Noah land surface model with multiparameterization options (WRF/Noah-MP) modeling domain. The shaded contours indicate Moderate Resolution Imaging Spectroradiometer (MODIS) land cover/land use classification data within the International Geosphere-Biosphere Program (IGBP). The values in parentheses indicate the canopy height (unit: m). The dark circles denote the locations of the eight ChinaFLUX sites: Changbaishan (Cha), Changling (Cng), Dangxiong (Dan), Dinghushan (Din), Duolun (Du2), Haibei Alpine (HaM), Qianyanzhou (Qia), and Siziwang Banner (Sw2). b Division of wet and dry climate zones in China. The shaded contours indicate the atmospheric aridity index, which is the ratio of the annual accumulated precipitation to the potential evapotranspiration. The blue rectangles indicate the four typical climate regions selected in this study. NE: Northeast China (120–135° E, 42.5–50° N); NC: North China (110–123° E, 35–41° N); ENW: eastern Northwest China (99–109° E, 32.5–40° N); SW: Southwest China (98–111°E, 22–28°N)

The physical parameterization schemes employed in this study include the Noah-MP LSM (Niu et al. 2011), Thompson microphysics (Thompson et al. 2008), Kain-Fritsch convection (Kain 2004), Yonsei University planetary boundary layer (Hong et al. 2006), and rapid radiative transfer models (Iacono et al. 2008). The Noah-MP LSM provides two options to calculate C_h: the M–O (Brutsaert 1982) and Chen97 (Chen et al. 1997) schemes. Both of these options are based on the M–O similarity theory and heavily rely on the aerodynamic (Z_om) and thermal (Z_ot) roughness lengths and atmospheric stability. The primary differences lie in the treatment of the roughness length, i.e., the Chen97 scheme accounts for the discrepancy between Z_ot and Z_om through parameter C_zil (usually C_zil = 0.1, so Z_ot = Z_om/10), while the M–O scheme considers identical roughness lengths but the zero-displacement height. In this study, we performed model experiments by incorporating the Z_om/Z_ot relationship of Eq. (6) with the dynamic C_zil coefficient of Eq. (7) into the M–O scheme.

Three cases were designed to simulate the different responses of regional climate simulations to land–atmosphere coupling: Case 1 (Default; C_zil ~ 0) adopted the original M–O option to calculate C_h, Case 2 (C_zil; C_zil = 0.1) adopted a typical constant C_zil value, and Case 3 (Newczil; C_zil-h) adopted a dynamic canopy height dependent C_zil.

2.3 Validation datasets

Surface meteorological data (V3.0), including over 2000 stations from 2003 to 2012 operated by the China Meteorological Administration (CMA), were utilized to derive regional C_h observations and verify the model performance. The variables involved include hourly precipitation and T_a as well as T_s, surface wind speed, surface humidity and surface pressure at 6-hourly intervals. With the use of Cressman-type interpolation, we horizontally interpolated the on-site observations to obtain gridded data at the 399 × 344 grid points, which exhibited the same resolution as that of the regional climate simulations. The SH data required to calculate C_h observations were obtained from the monthly 0.0833° FLUXNET-Model Tree Ensemble (MTE) dataset provided by the Max Planck Institute for Biogeochemistry. The gridded FLUXNET-MTE dataset was integrated considering 253 global FLUXNET eddy covariance observations and the MTE machine learning algorithm (Jung et al. 2009). FLUXNET-MTE products have been widely employed as a proxy for observations in analysis of land–atmosphere energy exchange and validation of land surface modeling at regional and global scales (Bonan et al. 2011; Gan et al. 2019; Zhang et al. 2019). It should be noted that this gridded dataset provides no values in areas of western China due to the lack of observation stations, and the unevenly distributed flux towers considered to train the model tree can create nonnegligible uncertainty in the product. Additionally, we collected measurements at eight flux tower sites (Table 1) from the Chinese terrestrial ecosystem flux research network (ChinaFLUX) to reconstruct C_h and evaluate surface energy and water simulation variations. The collected 30-min flux observations were located in areas spanning different land-cover types (grasslands, forestlands, and wetlands) and climatic regimes (arid, semiarid, semihumid, and wet climatic regions).

Table 1 General information on the eight ChinaFLUX sites considered in this study

In addition, the atmospheric aridity index (AI), which is the ratio of the annual accumulated precipitation to the potential evapotranspiration, was utilized to determine dry and wet climatic regions (Hulme 1996; Middleton and Thomas 1992). AI values were derived using monthly 0.5° precipitation and potential evapotranspiration data from 1983 to 2012 in the Climate Research Unit (CRU)-TS3.23 dataset acquired from the University of East Anglia. Extreme arid, arid, semiarid, semiarid and semihumid, and humid climate zones were defined according to 0 ≤ AI < 0.05, 0.05 ≤ AI < 0.20, 0.20 ≤ AI < 0.50, 0.50 ≤ AI < 0.65, and AI ≥ 0.65, respectively.

3 Results

3.1 Model verification

Prior to employing the WRF model to explore the characteristics and influences of land–atmosphere coupling on the local climate, the model skill was examined considering available field observations. Here, we evaluated default M–O simulations against on-site observations in terms of T_a and precipitation. Three statistics commonly considered to quantify the consistency between model outputs and field observations [e.g., Brovkin et al. (2013); Dai et al. (2019)] were adopted, namely, the Pearson correlation coefficient, mean bias error, and root mean square error (RMSE).

Figure 2 shows that the simulated summer T_a and precipitation patterns agreed well with those of the observations across China from 2003 to 2012, with spatial correlations of 0.86 and 0.78, respectively. The simulated T_a pattern could capture the observed large-scale pattern, although colder simulation values occurred in the southwest of China and warmer simulation values occurred in the northwest of China. The modeled precipitation pattern resembled to the observed pattern involving small rainfall amounts in the northwest and large rainfall amounts in the southeast, despite an overestimated magnitude observed in most areas. Furthermore, the model capacity for the simulation of the temporal evolution of T_a and precipitation was assessed in four typical climate regions in China (the blue rectangles in Fig. 1b). As shown in Fig. 3, the model reproduced the observed T_a and precipitation variability levels well and mostly yielded significantly positive correlations higher than 0.8 exceeding the 95% confidence level. However, the simulations in Northeast China, North China, and Southwest China exhibited cold biases, while those in eastern Northwest China tended to exhibit warm biases. Additionally, the model generated excessive rainfall in these four regions, especially in Southwest China, with a large positive deviation, which may be attributed to the influences of the Asian summer monsoon and plateau topography (Song et al. 2010; Wang et al. 2014). Accurate T_a and precipitation estimation has remained a challenge. In particular, general precipitation overestimation has occurred with many atmospheric models (Liu et al. 2017; Wang et al. 2015). The simulation accuracy could be improved through the reduction in errors in model boundary conditions and further development of critical physical parameterizations, e.g., boundary layer and radiative transfer schemes for T_a simulation (Wang and Zeng 2011; Wang et al. 2015) and cloud microphysical and cumulus schemes for precipitation simulation (Gao et al. 2020; Liu et al. 2017).

Fig. 2

Spatial patterns of a the near-surface air temperature and b precipitation in the field observations retrieved from the China Meteorological Administration (OBS) and the simulations with the WRF/Noah-MP model using the default M–O scheme (WRF), in addition to the differences (WRF-OBS) in summer from 2003 to 2012

Fig. 3

Temporal evolution of the near-surface air temperature and precipitation in the field observations obtained from the China Meteorological Administration (OBS) and the simulations with the WRF/Noah-MP model using the default M–O scheme (WRF) for a, b Northeast China, c, d North China, e, f eastern Northwest China and g, h Southwest China from March to August during the 2003–2012 period. R, MBE, and RMSE denote the Pearson correlation coefficient, mean bias error, and root mean square error, respectively

In general, the WRF model employed in this study exhibits a good performance in capturing the features and temporal evolution of observed large-scale spatial patterns, and verification analysis has indicated that WRF model application in sensitivity experiments can provide helpful information regarding the modification characteristics of the regional climate under different coupling strength extents.

3.2 Impacts of C _zil on the land–atmosphere coupling strength

The various C_zil schemes adopted in surface layer parameterization through the exchange efficiency of land–atmosphere energy and moisture may produce pronounced effects on the evolution of weather and climate systems (Chen and Zhang 2009; LeMone et al. 2008; Trier et al. 2011). Therefore, we first comparatively analyzed the simulated C_h in the different C_zil experiments based on field observations prior to assessing the impacts of land–atmosphere coupling on the regional climate.

C_h observations were calculated with Eq. (3) based on surface flux and meteorological measurements. Figure 4a–c shows general overestimations in most areas of China in 2003–2012 summers over observation-derived C_h values. Considering Fig. 4a–c and d, e, the C_zil-h scheme generally produced the closest C_h values with the observations and remarkably reduced the positive C_h bias in the default M–O and C_zil = 0.1 experiments, which suggests improvement in the application of the dynamic C_zil scheme in representing land–atmosphere coupling. Furthermore, the C_h simulations exhibited notable variability across the various vegetation types. As shown in Fig. 4f, a large increase in C_h from a smooth sparsely vegetated surface with short canopies to a rough densely vegetated surface with high canopies occurred, as revealed by the observations. Particularly, the C_zil-h scheme performed the best in areas covered by short vegetation (e.g., barren, crop, grassland, and shrubland areas) with the smallest mean bias error of 0.37 and exhibited a similar behavior of coupling strength modification to that of the default M–O scheme in grids with tall vegetation (e.g., mixed forestland areas), thereby producing overestimated but reasonable C_h values.

Fig. 4

Comparisons of the surface exchange coefficient, C_h (plotted at a log10 scale), derived from the field observations of the China Meteorological Administration (OBS), and calculated with the WRF/Noah-MP model using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes in summer from 2003 to 2012. a–c Differences between the simulations and observations, d, e differences between the simulations, and f regional averaged C_h values for the typical land-cover types. Bias: the mean deviation in the simulations from the observations; Bias shortveg: the discrepancy between the simulated and observed values averaged over short vegetation areas with canopy heights < 5 m. The green bars in f indicate the canopy height

The results in this study are generally consistent with the findings obtained using offline Noah-MP simulations over China (Zhang et al. 2021) and employing FLUXNET observations and numerical models in North America (Chen and Zhang 2009; Chen et al. 2019). Overall, the adopted C_zil-h scheme can help improve the representation of land–atmosphere coupling in coupled atmospheric models and thus influence land–atmosphere energy and moisture transport. Hence, this study next focuses on the extent of the influence of these processes on surface and atmospheric conditions.

3.3 Impacts of C _zil on surface energy and water variations

The land–atmosphere coupling (C_h) representation in the atmospheric model was verified to be improved by the C_zil-h scheme in Sect. 3.2. Furthermore, the potential skill of the C_zil-h scheme in replicating the observed surface energy and water components was evaluated. Figure 5 shows the daily evolution trends of SH, LH, soil temperature (ST), and SM averaged over the ChinaFLUX grassland sites (Cng, Dan, Du2, HaM, and Sw2; Table 1) and forestland sites (Cha, Din, and Qia) from March to August. ST and SM were simulated in four soil layers (0–0.1, 0.1–0.4, 0.4–1, and 1–2 m), and the observed values were measured at 0.05 m except at the Sw2 site (0‒0.3 m). The simulated ST and SM were aggregated to match the observed soil layer. The model could favorably capture the daily variability in the observed surface energy and water fluxes. However, the SH, LH, ST, and SM values were generally overestimated in all C_zil experiments. In contrast, the simulations involving the C_zil-h scheme agreed better with the measurements at the ChinaFLUX grassland sites, with minimum RMSE values for SH, LH, and ST of 14.55 W m⁻², 18.38 W m⁻², and 1.41 °C, respectively. The various C_zil schemes produced minor differences in the simulated SM values. Furthermore, the diurnal surface heat fluxes at the ChinaFLUX grassland and forestland sites are shown in Fig. 6. The different C_zil schemes yielded larger SH and LH diurnal amplitudes in the simulations than those reflected in the flux measurements, especially during the daytime. However, the C_zil-h scheme substantially reduced the obtained SH overestimation with the default M–O and C_zil = 0.1 schemes, which corresponds to the effect of the C_zil-h scheme in weakening excessively strong coupling (Fig. 4). Comparatively, the modeled LH values with the three C_zil schemes exhibited negligible discrepancies in both grassland and forestland areas.

Fig. 5

Comparisons of the daily sensible heat flux (SH), latent heat flux (LH), soil temperature (ST), and soil moisture (SM) from March to August between the ChinaFLUX observations (OBS) and the WRF/Noah-MP simulations using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes. The values in a are averaged over the ChinaFLUX grassland sites (Cng, Dan, Du2, HaM, and Sw2) in the respective available years (Table 1), and the values in b are averaged over the forestland sites (Cha, Din, and Qia). The root mean square errors (RMSEs) for the default M–O, C_zil = 0.1, and C_zil-h simulations are sequentially displayed in each panel

Fig. 6

Diurnal comparisons of the sensible heat flux (SH) and latent heat flux (LH) between the ChinaFLUX observations (OBS) and the WRF/Noah-MP simulations using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes. The values in a–d are averaged over the ChinaFLUX grassland sites (Cng, Dan, Du2, HaM, and Sw2) in the respective available years (Table 1), and the values in e–h are averaged over the forestland sites (Cha, Din, and Qia)

3.4 Responses of the regional climate simulations to C _zil in the four typical climate zones of China

Thus far, we demonstrated that the C_zil-h scheme contributed significantly to improvement in the WRF/Noah-MP model performance in representing land–atmosphere coupling and regenerating surface fluxes. Furthermore, the C_zil-h scheme was applied to four typical climate zones in China (the blue rectangles in Fig. 1b), and the behavior in replicating the observed regional climate was assessed. Northeast China, North China, and eastern Northwest China are located in dry–wet transition zones with notable land–atmosphere interactions (Huang et al. 2017; Li et al. 2017; Ma and Fu 2003) and predominantly covered with short vegetation (e.g., crop and grass), and Southwest China located in a humid zone was primarily chosen because of the high coverage of tall vegetation (e.g., mixed forestland).

Figure 7 shows the differences in summertime precipitation between the simulations involving the three C_zil schemes and the CMA field observations from 2003 to 2012. The different C_zil schemes generally produced similar climatological precipitation spatial patterns but varying precipitation intensity levels. The simulated precipitation magnitudes were larger than those of the observations, whereas the C_zil-h scheme decreased the positive deviations in the simulations with the other two C_zil schemes and provided notable improvement in northeastern Northeast China, southern North China, southwestern and eastern Northwest China, and northeastern Southwest China. Compared to the default M–O and C_zil = 0.1 simulations, the C_zil-h scheme reduced the warm T_a biases in eastern Northwest China and the cool T_s biases in Northeast China, North China and Southwest China (Figs. 8, 9), exhibiting minimal mean deviations from the CMA observations. Figures 10, 11 show the discrepancies in SH and LH between the WRF/Noah-MP simulations and the FLUXNET-MTE observations. The C_zil-h scheme generally narrowed the mean biases of the surface fluxes simulated with the other two C_zil schemes and attained a higher consistency with the observations. These features correspond to the skill of the C_zil-h scheme in significantly reducing the observed C_h overestimation with the default M–O and C_zil = 0.1 schemes (Fig. 4). The C_zil-h scheme, through mitigation of the land–surface coupling efficiency (Fig. 4), transferred fewer surface heat fluxes into the atmosphere (Figs. 10, 11) and generated a corresponding increase in T_s (Fig. 9) and decrease in T_a (Fig. 8), which consequently occurred through the atmospheric planetary boundary layer affecting atmospheric precipitation (Fig. 7). Generally, with increasing precipitation, a high water availability results in a wetter land surface, leading to greater evaporative cooling and a subsequent reduction in the land–air temperature contrast (ΔT) (Lian et al. 2017; Mildrexler et al. 2011; Wang and Zeng 2011). Therefore, the reduced precipitation in the C_zil-h experiment (Fig. 7) in turn promoted a larger ΔT with increasing T_s (Fig. 9) and decreasing T_a (Fig. 8). Additionally, remarkable improvements in the four climate zones primarily occurred in the short vegetation areas with canopy heights < 5 m (Fig. 1a).

Fig. 7

Spatial differences in precipitation between the field observations retrieved from the China Meteorological Administration (OBS) and the WRF/Noah-MP simulations using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes in a Northeast China, b North China, c eastern Northwest China and d Southwest China in summer from 2003 to 2012. Bias: the mean discrepancy between the simulations and observations

Fig. 8

Spatial differences in the near-surface air temperature between the field observations retrieved from the China Meteorological Administration (OBS) and the WRF/Noah-MP simulations using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes in a Northeast China, b North China, c eastern Northwest China and d Southwest China in summer from 2003 to 2012. Bias: the mean discrepancy between the simulations and observations

Fig. 9

Spatial differences in the land surface temperature between the field observations obtained from the China Meteorological Administration (OBS) and the WRF/Noah-MP simulations using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes in a Northeast China, b North China, c eastern Northwest China and d Southwest China in summer from 2003 to 2012. Bias: the mean discrepancy between the simulations and observations

Fig. 10

Spatial differences in the sensible heat flux between the FLUXNET-MTE observations (OBS) and the WRF/Noah-MP simulations using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes in a Northeast China, b North China, c eastern Northwest China and d Southwest China in summer from 2003 to 2012. Bias: the mean discrepancy between the simulations and observations

Fig. 11

Spatial differences in the latent heat flux between the FLUXNET-MTE observations (OBS) and the WRF/Noah-MP simulations using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes in a Northeast China, b North China, c eastern Northwest China and d Southwest China in summer from 2003 to 2012. Bias: the mean discrepancy between the simulations and observations

Atmospheric circulations affecting precipitation were analyzed through moisture fluxes and divergences vertically integrated from 1000 to 300 hPa (Fig. 12). The C_zil treatment in surface layer parameterization notably affects water vapor transport. The differences between the various C_zil simulations exhibited a distinct seasonality and regionality, with more remarkable moisture transport deviations in summer and over eastern China. The moisture divergence in the C_zil-h simulations increased over North China, eastern Northwest China, eastern Northeast China, and Southwest China in summer (Fig. 12c, d), which indicates that the net water inflow decreased. Subsequently, as shown in Fig. 7, the C_zil-h scheme reduced the wet biases obtained in the precipitation simulations in the four regions. Additionally, atmospheric convective conditions in terms of the convective available potential energy (CAPE), planetary boundary layer height (PBLH), and 2-m specific humidity (Fig. 13) were analyzed to reveal the potential influence of convective activity on precipitation based on the different C_zil schemes. CAPE represents the amount of energy available for convection, and is an indicator of atmospheric instability. Compared to the default M–O and C_zil = 0.1 schemes, the C_zil-h scheme considerably reduced the atmospheric instability energy over a wide area, which is unfavorable for the onset of convective triggering and precipitation occurrence. The elevated PBLH simulated with the C_zil-h scheme enhanced water vapor mixing in the lower atmosphere, in addition to greater water vapor evaporation due to a higher T_s (Fig. 9), leading to a conspicuous reduction in the near-surface humidity. These unfavorable convective conditions could potentially reduce the precipitation intensity in the C_zil-h experiments in the four climate zones. Additionally, significant CAPE changes potentially indicated a better representation of summer precipitation through convection-permitting modeling (Kurkute et al. 2020; Li et al. 2019; Liu et al. 2017). This inspired us to employ a convection-permitting model in future studies to explore the precipitation simulation improvements achievable with the C_zil-h scheme.

Fig. 12

Spatial difference in moisture flux transport (vector, unit: kg (m s)⁻¹) and moisture flux divergence (shading; unit: 10^–6 kg (m² s)⁻¹) between the simulations using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes in spring and summer from 2003 to 2012. The moisture flux values are derived by multiplying the specific humidity and vector wind and vertically integrated from 1000 to 300 hPa

Fig. 13

Spatial differences in a–d the convective available potential energy (CAPE), e–h planetary boundary layer height (PBLH), and i–l 2-m air specific humidity (Q) between the simulations using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes in spring and summer from 2003 to 2012

Previous studies (e.g., Huo et al. (2021); Koster et al. (2003); Li et al. (2018)) have reported that many factors in the Earth system influence precipitation changes and establish feedbacks between atmospheric processes and land surface fluxes. Similarly, the precipitation responses to the different C_zil schemes are complicated and do not exactly correspond to the obtained C_h variation patterns (Fig. 4). This suggests that the changes in surface heat fluxes driving the evolution process of the boundary layer alter atmospheric circulations and further affect the formation of clouds and precipitation (Trier et al. 2011; Zheng et al. 2015). Furthermore, to comprehensively understand the physical mechanisms of the changes in precipitation induced by the C_zil-h scheme, Table 2 quantitatively analyzes the differences in simulations between the various C_zil schemes in the four climate zones by jointly considering thermal and dynamical factors. The C_zil-h scheme simulated less precipitation than did the default M–O scheme in Northeast China, North China, eastern Northwest China, and Southwest China in summer. The above decrease in precipitation is associated with horizontal transport of less water vapor from moisture source areas such as the Bay of Bengal and South China Sea (Table 2 and Fig. 12), evaporation of less water vapor from drier surfaces, thus vertically weakening LH, and stabilization of the lower atmosphere and thermal convection suppression due to a lower CAPE. The SH values decreased with decreasing coupling strength (C_h) in the C_zil-h experiments, which, along with lower surface heat fluxes transferred into the atmosphere, led to a large increase in T_s and a small decrease in T_a. A corresponding rise in ΔT caused PBLH deepening under thermal instability conditions. Additionally, clouds can inhibit shortwave radiation from reaching the surface (Huo et al. 2021; Molod et al., 2015). Therefore, less precipitation and fewer cloudy days contributed to the above PBLH deepening. Water vapor conditions can also notably contribute to PBLH changes by regulating the energy balance near the surface (Kurkute et al. 2020; Lian et al. 2017; Wang et al. 2015). The C_zil-h scheme generated intensified precipitation in spring, which corresponded to more water vapor available for precipitation from enhanced horizontal moisture transport, higher humidity and increased LH under favorable convective conditions. Compared to the C_zil = 0.1 simulations, the C_zil-h scheme produced less precipitation across the four regions in summer and Northeast China and Southwest China in spring and increased precipitation amounts in North China and eastern Northwest China in spring. The responses of precipitation changes to the surface and atmospheric components simulated with the C_zil-h scheme against the C_zil = 0.1 scheme were generally consistent with those simulated against the default M–O scheme.

Table 2 Differences in precipitation (Precip), surface exchange coefficient (C_h at a log10 scale), near-surface air temperature (T_a), land surface temperature (T_s), land–air temperature contrast (ΔT), 2-m specific humidity (Q), sensible heat flux (SH), latent heat flux (LH), 10-m u/v-wind components (U10/V10), u/v-wind moisture fluxes vertically integrated from 1000 to 300 hPa (IUQ/IVQ), planetary boundary layer height (PBLH), and convective available potential energy (CAPE) between the simulations involving the default M–O, C_zil = 0.1, and C_zil-h schemes in the four typical climate zones (the blue rectangles in Fig. 1b) from 2003 to 2012

Additionally, according to Table 2, the large discrepancies in ΔT between the C_zil simulations primarily arose from the contribution of T_s, and the T_a values varied slightly between the different C_zil schemes. For example, the difference in summer ΔT in North China between the C_zil-h and default M–O schemes was 1.58 °C, in which the mean bias for T_s reached 1.39 °C (a contribution of 87.97%) and that for T_a reached − 0.20 °C. These results demonstrate the important role of C_h in controlling T_s, and Yang et al. (2011) indicated that C_h is the most important factor in modeling T_s. Hence, considerable efforts by LSM modelers have invested to improve the model performance in reproducing T_s through C_h parameterization (Chen et al. 2011; Gomez et al. 2020).

4 Discussion

4.1 Sensitivity of the surface flux and precipitation simulations to the C _zil -h scheme and uncertainty in C _zil -h scheme application

In this study, we verified that C_zil in surface layer parameterization could be slightly adjusted with the WRF coupled model to improve regional climate simulations in China. Compared to the control experiments without considering C_zil or directly setting a constant value of 0.1, the dynamic C_zil scheme depending on the vegetation type (i.e., C_zil-h) could achieve improvements in simulating the land–atmosphere coupling strength, thus affecting the atmospheric planetary boundary layer through the exchange of heat and moisture and altering regional climate simulations. However, the impacts of the C_zil-h scheme on the simulations notably varied with the land-cover type, variables, and season, etc. Specifically, the C_zil-h scheme could reduce the observed overestimation in both the default M–O and C_zil = 1 experiments in regard to short vegetation canopies, but the C_zil-h scheme behaved similarly to the default M–O scheme in regard to high canopies. Furthermore, the behavior of the C_zil-h scheme was inconsistent among all surface and atmospheric variables. Therefore, this section quantifies the sensitivity of the simulations to the C_zil-h scheme by analyzing the discrepancies between the various C_zil experiments and examines the deficiencies of applying the C_zil-h scheme in numerical models.

Considering that the experiments designed in this study are greatly affected by the land-cover type, we analyzed C_h, SH, T_s and precipitation, which are strongly influenced by the different C_zil schemes, considering three dominant vegetation types in China. Figure 14 and Table 3 indicate the relative deviations between the simulations using the different C_zil schemes. The values for grasslands, croplands, and forestlands were extracted from eastern Northwest China, North China, and Southwest China, respectively (Fig. 1). A relative deviation in C_h of approximately – 10% (− 12%) between the C_zil-h and default M–O simulations occurred in grassland (cropland) areas, which was double (quadruple) the relative deviation between the C_zil-h and C_zil = 0.1 simulations. These results indicate the marked influence of the C_zil coupling coefficient on the simulation results and the substantial reduction obtained with the C_zil-h scheme in the C_h overestimation generated by the default M–O scheme, corresponding to the results in Fig. 4. The C_zil-h scheme generated an average relative deviation of − 8.12% in C_h over the control experiments for short vegetation canopies, and the weakened coupling strength resulted in reduced surface SH (an average relative deviation of − 6.82%) inputs into the atmosphere and a higher T_s (an average relative deviation of 5.28%). The C_h simulations in the C_zil-h experiments at the forestland sites exhibited large positive deviations (relative deviation > 36%) against the C_zil = 0.1 simulations, consequently resulting in significantly higher SH values (relative deviation > 16%) and negative T_s deviations (< − 8%). The precipitation generated with the C_zil-h scheme in grassland and cropland areas produced a mean negative deviation of − 4.69% over the control experiments. Although the C_h values simulated with the C_zil-h scheme in forestland areas exhibited small negative deviations (< 0.2%) against the default M–O scheme, these minor biases in the land–atmosphere coupling strength could result in distinctively different precipitation levels, especially in summer, with relative deviations reaching up to 60%. This probably occurs because local available moisture and energy are affected by the enhancement in land–atmosphere exchange efficiency as vegetation proliferates from spring to summer (Chen and Zhang 2009; Zhang et al. 2021) and the influence of horizontal moisture flux transport (Fig. 12).

Fig. 14

Deviations in C_h (plotted at a log10 scale), sensible heat flux (SH), precipitation (Precip), and land surface temperature (T_s) between the simulations using the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes in a, b grassland, c, d cropland, and e, f forestland areas from 2003 to 2012. ND: (Newczil – Default)/|Newczil|× 100, NC: (Newczil – Czil)/|Newczil|× 100

Table 3 Differences in surface exchange coefficient (C_h at a log10 scale), precipitation (Precip), sensible heat flux (SH), and land surface temperature (T_s) between the simulations involving the default M–O (Default), C_zil = 0.1 (Czil), and C_zil-h (Newczil) schemes in grassland, cropland, and forestland areas from 2003–2012

Theoretically, the fundamental reason for the differences in the simulations between the various C_zil schemes is that the C_zil-h scheme can directly relate the surface coupling strength to terrestrial ecosystems through Eq. (7), thus affecting the Z_om/Z_ot ratio, which is critical in C_h calculation. C_zil values should be assigned according to the various vegetation types, with C_zil decreasing with increasing canopy height, and C_h should increase through enhancement of the energy and moisture exchange efficiency, which affects the atmospheric lower boundary conditions (Chen et al. 2019; Trier et al. 2011; Zheng et al. 2015). The coupling strength of tall vegetation canopies with rougher surfaces is 10 times higher than that of low vegetation (Chen and Zhang 2009). However, the C_zil value changes little in areas with canopy heights > 5 m. For example, the C_zil difference within the canopy height range from 19 to 20 m is only 1.51 × 10^–8, but the difference reaches 0.24 within the canopy height range from 1‒2 m. A close-to-zero C_zil values for high vegetation results in equivalent aerodynamic and thermal roughness lengths according to Eqs. (6), (7). Therefore, the C_zil-h scheme produced a positive deviation similar to the default M–O scheme.

In fact, there remain many deficiencies in applying the C_zil-h scheme to atmospheric coupling models in regional climate simulation. First, regarding the canopy height and Z_om, which played important roles in the experiments, we adopted default Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover classification data in the model due to the unavailability of observations across China. However, choosing Sud and Smith (1985) as an example, it was indicated that a decrease in surface roughness from 45 to 0.02 cm considerably reduced precipitation in the Sahara Desert, and the impact was comparable to that of surface albedo enhancement. Therefore, more accurate data employed in the C_zil-h scheme can contribute C_h parameterization improvement and can provide more valuable detailed information. Second, the C_zil-h scheme improved the land–atmosphere coupling strength mainly in regard to short vegetation canopies, and the simulations considering high vegetation were similar to those with the default M–O scheme. Comparatively, the C_zil = 0.1 scheme produced more similar C_h values to the observations. Therefore, based on extensive evaluation of the C_zil-h simulation results, it may be necessary to consider further improvement and optimization of the C_zil-h scheme in future studies. Additionally, adjustment of land–surface physical processes can substantially affect climate change (Maynard and Royer 2004; Pielke 2001; Trier et al. 2011). Moreover, changes in surface water and energy processes can result from land–surface feedbacks to atmospheric anomalies (Findell and Eltahir 2003; Wang and Zeng 2011). Thorough exploration of the differences in land–atmosphere responses and feedbacks between the various C_zil schemes could help to better explain the impacts of C_zil on the land surface and atmosphere and could contribute to C_zil-h scheme improvement.

4.2 Impacts and uncertainties in atmospheric dynamics on the representation of the land–atmosphere coupling strength

The representation of land–atmosphere coupling in the WRF/Noah-MP model by adjusting C_zil based on the vegetation type can be improved, and regional climate simulations can subsequently be modified (Sect. 3.4). Furthermore, near-surface atmospheric dynamic conditions, by influencing surface roughness elements and aerodynamic properties, interact with dynamic changes in the land–atmosphere coupling strength (C_h) in a complicated manner (Rigden et al. 2018; Zhang et al. 2012). Therefore, this section analyzes the effect of atmospheric dynamics on C_h, which in turn impacts climate simulations, and examines the influence of uncertainties in atmospheric dynamics on the representation of land–atmosphere coupling in numerical models.

In this study, we referred to Zhang et al. (2012) to construct a dynamic comprehensive variable u²/u*, in which the surface wind speed (u) considerably changes the structure and morphology of roughness elements (e.g., the plant height and density), while the friction velocity (u*) can represent the interaction between near-surface airflow and roughness elements (Rigden et al. 2018; Zilitinkevich et al. 2008). Moreover, the M–O stability parameter ζ (the calculation is described in Sect. 2.1), which comprehensively considers the thermodynamic influences on atmospheric stability and functions as a determinant in C_h parameterization (Grachev et al. 1998; Yang et al. 2001), was analyzed. Figure 15 shows scatter plots between C_h and u²/u*, ζ and precipitation simulated with the C_zil-h scheme in grassland, cropland, and forestland areas (the coverage is equivalent to that mentioned in Sect. 4.1). The correlations between C_h and u²/u* are significantly positive (p < 0.05) in the three vegetation regions, indicating enhanced effects of airflow movement on surface exchange processes. The C_h and u²/u* values in forestlands attain a higher correlation coefficient value of 0.64 than those in grasslands and croplands (~ 0.4), suggesting that C_h of high vegetation canopies is highly susceptible to atmospheric dynamic conditions. The scatter points between C_h and u²/u* are highly dispersed in grasslands and croplands, whereas in forestlands, the variations in u²/u* are more concentrated presumably due to the decrease in surface wind speed resulting from the blocking effect of dense canopies (Maynard and Royer 2004; Pielke 2001). Thus, the climatic average u²/u* values in the forestland areas (55.98 m s⁻¹) are lower than those in the grassland (90.23 m s⁻¹) and cropland areas (85.51 m s⁻¹), despite the larger C_h values in the forestland areas (an average lg(C_h) value of − 1.61) than those in the cropland (− 2.01) and grassland (− 2.08) areas. C_h and ζ are negatively correlated. ζ mostly yields negative values, denoting unstable atmospheric stratification (ζ < − 0 0.01), and the larger the value of |ζ|, the higher the instability. Accordingly, the coupling strength increase (larger C_h values). As the atmosphere stabilizes (ζ > 0 0.01), C_h tends to decrease and the land–atmosphere coupling strength weakens, while − 0.01 < ζ < 0.01 indicates a near-neutral atmosphere. The atmosphere over forestland areas exhibits the highest instability with a broader |ζ| range from 0–1.5 × 10⁴, leading to the most efficient land–atmosphere coupling with a lg(C_h) range from − 2.6 to − 0.8. However, the lowest atmospheric instability occurs in grassland areas, with sparse values of |ζ| over 10⁴, which are smaller than those in forestland and cropland areas, thus corresponding to a modest coupling strength. The C_h and precipitation values are relatively dispersed but still exhibit significant positive correlations (p < 0.05). The enhanced coupling strength due to an increase in C_h through heat and water exchange facilitates precipitation increase. Moreover, sufficient rainfall enables vegetation to flourish, which in turn increases C_h by affecting the surface roughness (Chen and Zhang 2009; Zhang et al. 2013).

Fig. 15

Scatter plots for a grassland, b cropland, and c forestland areas between the 6-hourly C_h (plotted at a log10 scale) and the dynamic comprehensive variable (u²/u*), Monin–Obukhov stability parameter (ζ) and precipitation simulated with the C_zil-h scheme in summer from 2003 to 2012. u²/u* is the ratio of the surface wind speed (u) squared and friction velocity (u*), and ζ is the ratio of the reference height and Monin–Obukhov length (the detailed calculation expressed as Eq. (5)). The Pearson correlation coefficient between the simulations and observations (R) is displayed in each panel

The complicated interactions between atmospheric dynamic conditions and land–atmosphere coupling strength variability result in challenges in the accurate characterization of land–atmosphere interactions in numerical models. The influences of atmospheric dynamics on land–surface processes are closely connected not only to the surface roughness geometry, such as the plant height and density, but also to the dynamic responses of vegetation to airflow (e.g., the flexibility of crops and grass). However, the intricate processes of dynamic roughness changes affecting C_h have yet to be implemented in current models (Gomez et al. 2020; Zhang et al. 2012; Zilitinkevich et al. 2008). Moreover, the variations in Z_om, a key factor influencing C_h parameterization, are manifested due to the notable dependence of vegetation growth on the precipitation variability (Zhang et al. 2013). In contrast, the Z_om values in this study were determined as a function of only the vegetation type, similar to the approach in most current numerical models (e.g., Trier et al. (2011)), and thus, the introduction of dynamic Z_om data (e.g., monthly values) in the model may improve the simulation results.

5 Conclusions

In this study, we investigated the sensitivity of regional climate simulations over China from 2003 to 2012 to the parametrization of land–atmosphere coupling in a 13-km resolution WRF/Noah-MP model. The surface coupling strength in the model was greatly affected by parameter C_zil, which is typically defined as a constant, through dynamic adjustment based on the land-cover type. Thus, we designed three C_zil schemes, namely, default M–O (C_zil ~ 0), constant C_zil (set to 0.1) and dynamic canopy-height-dependent C_zil (C_zil-h) schemes, and the simulations were compared to observations retrieved from more than 2000 meteorological stations and eight ChinaFLUX sites. The main conclusions of this study are summarized as follows.

The remarkable differences in land–atmosphere coupling strength can be modeled with the three C_zil schemes. The default M–O scheme substantially overestimates the observation-derived C_h, while the C_zil = 0.1 scheme slightly reduces the positive deviation. In contrast, the C_zil-h scheme produces the lowest C_h deviation from the observations in regard to short vegetation canopies (e.g., cropland, grassland, and shrubland areas) and yields C_h values similar to those calculated with the default M–O scheme in regard to high canopies (e.g., mixed forestland areas). Additionally, a comparison to ChinaFLUX observations indicates the capability of the C_zil-h scheme in yielding a better match to the observed surface energy and water variations. In general, application of the C_zil-h scheme can achieve improved representation of land–atmosphere coupling in numerical models.

C_zil adjustment with the vegetation type can modulate the exchange efficiency of land–atmosphere energy and moisture and subsequently alter regional climate simulations through the atmospheric planetary boundary layer. The results obtained through the application of the various C_zil schemes in four typical climate zones in China indicate that the C_zil-h simulations attain the closest agreement with the field observations. The C_zil-h scheme narrows the positive discrepancies in the simulated precipitation and surface fluxes and the negative biases of T_s in Northeast China, North China, eastern Northwest China, and Southwest China over the default M–O and C_zil = 0.1 schemes. This occurs because the C_zil-h scheme, through mitigation of the land–surface coupling efficiency, transfers lower surface fluxes into the atmosphere and correspondingly increases T_s. The elevated PBLH simulated with the C_zil-h scheme enhances water vapor mixing in the lower atmosphere, in addition to greater moisture evaporation due to a higher T_s, resulting in a notable reduction in near-surface humidity. The decreased precipitation in the C_zil-h experiments is associated with horizontal transport of less water vapor from moisture source areas, evaporation of less moisture from drier surfaces, thus vertically weakening LH, and lower atmosphere stabilization and thermal convection suppression attributed to CAPE reduction.

Although the C_zil-h scheme achieves a competitive skill in representing land–atmosphere coupling, the deficiencies and uncertainties in the application of the dynamic C_zil-h scheme in atmospheric coupled models, e.g., the accuracy of canopy height and Z_om data, regional scheme applicability, and dynamic responses of vegetation to airflow, must be further explored and resolved by performing a large number of experiments. These efforts may be helpful in accurately characterizing land–atmosphere coupling and further optimizing C_h parameterization. Additionally, using a convection-permitting model to further explore the impacts of different coupling strengths on precipitation simulations will be the focus of our future work.