Land–Atmosphere Feedbacks Weaken the Cooling Effect of Soil Organic Matter Property toward Deep Soil on the Eastern Tibetan Plateau

Jing Sun aDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute of Global Change Studies, Tsinghua University, Beijing, China

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Kun Yang aDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute of Global Change Studies, Tsinghua University, Beijing, China
bNational Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

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https://orcid.org/0000-0002-0809-2371
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Hui Lu aDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute of Global Change Studies, Tsinghua University, Beijing, China

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Xu Zhou bNational Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

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Xin Li bNational Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

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Yingying Chen bNational Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

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Weidong Guo cInstitute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing, China
dJoint International Research Laboratory of Atmospheric and Earth System Sciences, Nanjing University, Nanjing, China

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Jonathon S. Wright aDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute of Global Change Studies, Tsinghua University, Beijing, China

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Abstract

Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land–atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands.

Significance Statement

The eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land–atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land–atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kun Yang, yangk@tsinghua.edu.cn

Abstract

Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land–atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands.

Significance Statement

The eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land–atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land–atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kun Yang, yangk@tsinghua.edu.cn

1. Introduction

Soil organic matter (SOM), which acts as a source and sink of carbon dioxide, can impact the amount and distribution of atmospheric carbon dioxide. Compared with these effects on the carbon cycle, however, the hydrothermal influences of SOM on coupled land–atmosphere system have received little attention. It has been demonstrated that SOM can alter soil hydrothermal properties and states along with the surface energy budget. SOM has larger soil porosity and lower thermal conductivity than mineral soil (Farouki 1981; Chen et al. 2012), but the SOM pore-size distribution parameter varies widely (2.7–12.0; Letts et al. 2000). Lawrence and Slater (2008) pointed out that SOM could cause increases in soil water content while decreases in soil temperature and surface latent heat flux in high latitudes in coupled land–atmosphere simulations, which could also be seen in offline land surface simulations. It should be noted that when exploring the impact of SOM, it is necessary to consider the large differences in the properties and vertical distributions of organic matter across regions (Letts et al. 2000; Shangguan et al. 2014). The Tibetan Plateau (TP), often referred to as the “Roof of the World,” exhibits a stark east–west contrast in SOM abundance, with much greater accumulation on the eastern TP and strong vertical heterogeneity (high content in the topsoil declining with depth; Yang et al. 2005). Moreover, soils with high SOM content on the TP have much stronger water-holding capacities (Sun et al. 2021) than on the Arctic (Lawrence and Slater 2008). Despite extensive research using offline land surface models (LSMs; Gao et al. 2015a; Zheng et al. 2015a,b; Sun et al. 2016), few studies have used coupled land–atmosphere models to explore the effects of SOM on climate over the TP.

As a remote region with strong land–atmosphere interactions (Koster et al. 2004; Xue et al. 2010, 2021; Yao et al. 2019), climate over the TP is notoriously difficult to observe and simulate reliably. Most global and regional climate models exhibit wet and cold biases over there (Su et al. 2013; Gao et al. 2015b; Yu et al. 2015; Cui et al. 2021; Xue et al. 2021), though many efforts have been made to solve this problem. Previous studies have variously attributed these wet and cold biases to coarse resolution (Lin et al. 2018; Zhou et al. 2021), misrepresented or missing physical processes (Wang et al. 2020), and inaccuracies in underlying surface conditions (Meng et al. 2018; Yue et al. 2021; Lin et al. 2021). As systematic biases that stem from model deficiencies are likely to affect climate projections (Lin et al. 2017), reducing model biases over the TP is an essential step toward effective climate risk assessment and policy development for this unique region. The SOM effect has been suggested to be one of the potential factors contributing to the global models’ large surface temperature bias over the TP (Xue et al. 2021). In this study, realistic SOM processes are incorporated into a coupled regional climate model and assessed in terms of their impacts on model biases and simulated land–atmosphere interactions.

In addition, permafrost and seasonally frozen soil are widely distributed on the TP, covering 40% and 56% of the area, respectively (Zou et al. 2017). Although permafrost is most extensive in the western TP, it also covers a considerable part of the eastern TP (Ran et al. 2021). Under global warming, one of the remarkable changes on the TP is permafrost degradation (Qiu 2008; Ran et al. 2018; Zheng et al. 2020), which can be interpreted as soil warming. Zhang et al. (2021b) found that an increase in active layer thickness was closely related to a rise in ground and air temperature. The results of offline LSM simulations in previous studies (Zhang et al. 2021a; Sun et al. 2021) suggest that SOM exerts profound influences on soil temperature, which thereby tends to affect permafrost. However, these studies did not account for the potential of SOM–atmosphere interactions to alter the net influence of SOM on the soil thermal state. Atmospheric forcing exerts strong controls on land surface energy and water budgets, with impacts that penetrate to the deep soil. Therefore, it is necessary to investigate the roles of organic matter in modulating soil temperature and the distribution of permafrost on the TP in coupled land–atmosphere simulations with realistic representations of SOM.

This study addresses three key questions on how SOM affects atmospheric and soil conditions in the coupled land–atmosphere system: 1) Can wet and cold biases in model simulations of the TP be mitigated by including SOM processes? 2) Are the effects of organic matter on soil hydrothermal states in coupled land–atmosphere simulations consistent with previous results based on offline LSMs? 3) If not, how might these differences affect the evolution of frozen soil? The remainder of this paper is organized as follows. The study domain, data, model, and analysis methods are introduced in section 2. The model simulations are evaluated against in situ observations in section 3, with particular focus on the atmospheric and soil responses to SOM. The physical mechanisms behind these responses are discussed in section 4, followed by a brief summary in section 5.

2. Materials and methods

a. Data

To validate the atmospheric and soil simulations, meteorological observations and soil measurements on the eastern TP (the study domain of this paper) for the year 2013 are used. Daily in situ meteorological observations, including 2-m air temperature, 2-m relative humidity, and precipitation, are obtained from the China Meteorological Administration (CMA). The locations of these meteorological stations are shown in the left panel of Fig. 1a. We also collect measurements of soil moisture and temperature from the multiscale Soil Moisture and Temperature Monitoring Network in Naqu (Yang et al. 2013; Zhao et al. 2013). There are 56 sites in this network (the right panel of Fig. 1a), where soil moisture and temperature are measured at depths of 0–5, 10, 20, and 40 cm.

Fig. 1.
Fig. 1.

(a) Terrain height (color) and locations of CMA meteorological stations and the Naqu multiscale Soil Moisture and Temperature Monitoring Network used in this study. (top right) Terrain height (color) and locations (dots) of the soil moisture/temperature sites in the Naqu network. (b)–(e) Spatial distributions of SOM mass content in four layers (surface: 0–10 cm; subsurface: 10–40 cm; layer 3: 40–100 cm; layer 4: 100–200 cm). The black line denotes the 2500-m isoline of terrain height.

Citation: Journal of Hydrometeorology 24, 1; 10.1175/JHM-D-22-0074.1

A global high-resolution soil dataset released by Dai et al. (2019a,b; hereafter referred as Dai’s dataset) provides soil information including sand, clay and SOM. Thus we use this dataset to describe the spatial pattern of SOM content in four layers (surface: 0–10 cm; subsurface: 10–40 cm; layer 3: 40–100 cm; layer 4: 100–200 cm). Figure 1b shows the distribution of surface SOM mass content across the TP, with the evident enrichment in the eastern TP relative to the western TP. This spatial heterogeneity gradually disappears with increasing soil depth (Figs. 1c–e). It is noteworthy that we focus on areas with high SOM content in the surface layer and low content in deeper layers, which are prevalent in the eastern TP, rather than wetlands, where the SOM content is high throughout the whole soil column.

b. Model descriptions

The regional climate model used in this study is the Weather Research and Forecasting (WRF) Model coupled with the Noah LSM with multiparameterization options (Noah-MP; Niu et al. 2011). The WRF Model has been widely used to simulate regional climate over the TP. The Noah-MP LSM is chosen because it provides a variety of parameterization options, making it easy to adjust the parameterization configuration and improve parameterization schemes. By default, the Noah-MP model does not consider SOM. A SOM model such as DayCent (Parton et al. 1998; Del Grosso et al. 2002) may involve many biogeochemical and biophysical processes and thus it is very complex. In this study, we simply take into account the influence of SOM on soil hydrothermal properties. A modified soil hydrothermal parameterization proposed by Sun et al. (2021) and Chen et al. (2012), which has been successfully applied to the TP in previous work, is incorporated into the Noah-MP LSM to describe the properties of SOM. This soil hydrothermal parameterization assumes that the soil is a combination of mineral soil and organic matter, and soil hydrothermal properties are weighted by their volumetric content; in particular, large SOM porosity (0.9 m3 m−3) and pore-size distribution parameter (16.6), high SOM water potential at air entry (−0.0103 m), and low SOM thermal conductivity (0.25 W m−1 K−1) are considered based on observations or the literature. The relevant equations are given in the appendix.

The WRF simulation domain extends beyond the TP (65°–110°E and 15°–45°N) with a horizontal grid spacing of 0.09° (∼10 km) and 37 vertical levels between the surface and 50 hPa. Two coupled experiments are designed to investigate the impacts of SOM. The first experiment (WRF-CTL) excludes SOM (i.e., SOM content is set to zero in all grids). The second experiment (WRF-SOM) accounts for the distribution of the SOM vertical profile based on the Dai’s dataset, which considers the vertical heterogeneity of SOM and thus can well reflect the SOM stratification in the TP. Additional details of the WRF Model configuration are common to both experiments. To reduce initial condition-related uncertainty, we conduct an ensemble of three simulations with initialization based on conditions for 15, 20, and 25 April 2013. The coupled simulations extend until 31 August 2013 with the days prior to 31 May discarded as spinup time and the remainder (June–August) used for analysis. The WRF Model is initialized and driven by ERA5 reanalysis data (Hersbach et al. 2020). But for soil moisture initialization, we replace with offline simulations produced by the Noah-MP LSM with and without considering SOM, herein LSM-SOM and LSM-CTL, respectively. The offline Noah-MP simulations are conducted for the period of 2000–13. The high-resolution China Meteorological Forcing Dataset (CMFD; Yang and He 2011; He et al. 2020) is utilized as the meteorological forcing data. The LSM-SOM experiment uses Dai’s data of sand, clay, and SOM content. The LSM-CTL experiment sets the SOM content to zero, otherwise the same as the LSM-SOM. The purpose of this treatment is to provide soil moisture appropriate to the two different SOM conditions and shorten the spinup time in coupled simulations. Offline simulations for the summer (June–August) of 2013 are also compared with the coupled ones. Differences in soil hydrothermal properties between the two experiments (WRF-SOM minus WRF-CTL or LSM-SOM minus LSM-CTL) are shown in Fig. 2. Compared with the WRF-CTL (LSM-CTL), in the WRF-SOM (LSM-SOM), soil porosity, pore-size distribution parameter and potential increase while soil solid thermal conductivity decreases in the eastern TP. The insets in Fig. 2 are boxplots of these differences, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. Obviously, the higher the surface SOM content, the greater the magnitude of these differences.

Fig. 2.
Fig. 2.

Spatial distributions of differences in surface soil (a) porosity, (b) pore-size distribution parameter, (c) saturated water potential, and (d) solid thermal conductivity between the two experiments (WRF-SOM minus WRF-CTL or LSM-SOM minus LSM-CTL). The black line denotes the 2500-m isoline of terrain height. The insets are boxplots of these differences, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. The horizontal line inside the gray shaded box indicates the median value, the upper and lower boundaries of the gray shaded box indicate the 25th and 75th percentiles, and the ends of the upper and lower whiskers indicate maxima and minima, respectively.

Citation: Journal of Hydrometeorology 24, 1; 10.1175/JHM-D-22-0074.1

c. Analysis methods

All analyses of WRF simulations are based on three-member ensemble mean values for each coupled experiment. The accuracy of the WRF atmospheric simulations is evaluated against daily in situ meteorological observations. Model outputs at the closest grid locations to the meteorological stations are selected. Meanwhile, the 2-m air temperature is corrected by a lapse rate [−0.6°C (100 m)−1] based on elevation differences between the model and the meteorological station. For quality control, meteorological stations with more than 10 days of missing data for a given variable are excluded from that comparison. In addition, we linearly interpolate the Naqu soil moisture/temperature measured profiles to the model surface (0–10 cm) and subsurface (10–40 cm) soil layers for comparison.

3. Results

a. Reduction of WRF’s biases

Figure 3 shows comparisons of simulated and observed 2-m air temperature, 2-m relative humidity, and precipitation at the meteorological stations with no more than 10 days of missing data during June–August 2013 on the eastern TP (30°–34°N, 90°–102°E). The simulated 2-m air temperatures in the WRF-CTL experiment are much colder than observed, with a mean bias (MB) of −1.1°C averaged over all the meteorological stations (Fig. 3a). This cold bias is reduced to −0.71°C in the WRF-SOM experiment (Fig. 3b). Figures 3c and 3d show that including organic matter also substantially reduces positive biases in WRF simulations of 2-m relative humidity, as the station-averaged bias in WRF-SOM (3.5%) is more than 50% smaller than that in WRF-CTL (7.4%). As shown in Figs. 3e and 3f, both sets of coupled experiments capture the observed precipitation well, with the mean bias across the WRF-SOM (0.31 mm day−1) slightly smaller than that from the WRF-CTL (0.50 mm day−1). The smaller biases in WRF-SOM support our hypothesis that the inclusion of SOM processes can help to reduce atmospheric model biases. Accurate descriptions of these processes would therefore benefit the reliability of global climate model simulations of past, present, and potential future climates.

Fig. 3.
Fig. 3.

Biases of summer (a),(b) 2-m air temperature, (c),(d) 2-m relative humidity, and (e),(f) precipitation simulations from the WRF-CTL and WRF-SOM experiments relative to in situ observations at meteorological stations on the eastern TP (30°–34°N, 90°–102°E).

Citation: Journal of Hydrometeorology 24, 1; 10.1175/JHM-D-22-0074.1

b. Spatial patterns of SOM impacts on atmospheric conditions

Figure 4 shows differences in simulated atmospheric variables between the two coupled experiments. The 2-m air temperature increases over most of the eastern TP when SOM is taken into account (Fig. 4a), further increasing near-surface air saturation vapor pressure. By contrast, 2-m specific humidity decreases (Fig. 4b). Relative humidity is defined as the ratio of actual vapor pressure to saturation vapor pressure, so 2-m relative humidity is substantially reduced in the WRF-SOM experiment in comparison to the WRF-CTL one (Fig. 4c). This reduction in humidity provides a plausible explanation for decreases in precipitation that span much of this region (Fig. 4d). Complexities and subtleties involving moisture source distributions and mechanisms for changes in precipitation are beyond the scope of this paper. Instead, we focus on how changes in atmospheric forcing of the land surface (i.e., near-surface air conditions and precipitation) affect the soil hydrothermal state (see section 4b). Boxplots provided as insets in Figs. 4a–4c divide grid cells of the TP into four classes by surface SOM mass content (<2%, 2%–4%, 4%–6%, and ≥6%) to help clarify any dependence of atmospheric feedback strength on SOM. Larger surface SOM contents are typically associated with larger differences in 2-m air temperature and specific/relative humidity, although these differences tend to stabilize when SOM content exceeds 4%. The reasons for this asymptotic behavior are explored in section 4.

Fig. 4.
Fig. 4.

Spatial distributions of differences in summer (a) 2-m air temperature, (b) 2-m specific humidity, (c) 2-m relative humidity, and (d) precipitation between the two coupled experiments (WRF-SOM minus WRF-CTL). The gray line denotes the 2500-m isoline of terrain height. The insets are boxplots of these differences, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. The horizontal line inside the gray shaded box indicates the median value, the upper and lower boundaries of the gray shaded box indicate the 25th and 75th percentiles, and the ends of the upper and lower whiskers indicate maxima and minima, respectively.

Citation: Journal of Hydrometeorology 24, 1; 10.1175/JHM-D-22-0074.1

c. Spatial patterns of SOM impacts on soil conditions

Larger SOM contents are associated with enhanced soil water-holding capacity, and therefore often lead to larger soil liquid water content in Figs. 5a–5d, consistent with previous findings in Zheng et al. (2015a) and Sun et al. (2021). In addition, since SOM accumulates mainly in the surface layer in the eastern TP, the SOM-induced increase in soil liquid water content is much greater in the surface layer than in other ones (insets in Figs. 5a–d). The exception is wetlands, which are not the focus of this study. As for the effect of SOM on soil temperature in the coupled land–atmosphere system, which is shown in Figs. 5e–5h, the sign of soil temperature differences between WRF-SOM and WRF-CTL changes from positive to negative as soil depth increases. When SOM is included in the coupled model, the top two soil layers (surface and subsurface) generally become warmer, with the most pronounced differences in the southeastern part of the TP (Figs. 5e,f). By contrast, the bottom two soil layers (layer 3 and layer 4) are cooler in WRF-SOM than in WRF-CTL (Figs. 5g,h). Insets in Figs. 5e–5h show variations in soil temperature differences across the four classes of surface SOM content. Surface soil temperature is increased in most locations where surface SOM content is large (>4%), while layer-3 and layer-4 soil temperatures are typically reduced relative to WRF-CTL. The latter suggests that organic matter helps to protect frozen soils. The subsurface soil temperature shows a slight increase. Based on CoupModel simulations at the Tanggula station (located in the central TP), Zhou et al. (2013) found that soil organic matter could prevent the active layer from deepening in summer, consistent with our results for the bottom two soil layers.

Fig. 5.
Fig. 5.

Spatial distributions of differences in summer (a)–(d) soil liquid water content, and (e)–(h) soil temperature in four layers (surface: 0–10 cm; subsurface: 10–40 cm; layer 3: 40–100 cm; layer 4: 100–200 cm) between the two coupled experiments (WRF-SOM minus WRF-CTL). The gray line denotes the 2500-m isoline of terrain height. The insets are boxplots of these differences, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. The horizontal line inside the gray shaded box indicates the median value, the upper and lower boundaries of the gray shaded box indicate the 25th and 75th percentiles, and the ends of the upper and lower whiskers indicate maxima and minima, respectively.

Citation: Journal of Hydrometeorology 24, 1; 10.1175/JHM-D-22-0074.1

For further validation of the LSM performance, we compare offline Noah-MP simulations with measurements at the 56 soil moisture/temperature sites. The Noah-MP simulations are performed at the soil sites for the period 2000–13, following the approach outlined by section 2 and Sun et al. (2021). When soil texture measurements are available, the Dai’s data are replaced with those measurements. Soil moisture and temperature simulations and measurements are compared for the year 2013. Figure 6 shows seasonal variations of observed and Noah-MP simulated surface and subsurface soil liquid water contents and soil temperatures in 2013 for the 56 soil moisture/temperature sites separated into three classes (0%–4%, 4%–6%, and ≥6%) according to observed surface SOM mass content. As shown in Figs. 6a and 6b, the soil tends to hold more liquid water at sites with higher surface SOM content, which can be well captured by the Noah-MP simulations (Figs. 6e,f). The evolution of soil temperature is also consistent between the measurements and simulations (Figs. 6c,d,g,h): soil temperatures at sites with higher surface SOM content are lower, especially in the subsurface layer (10–40 cm). The agreement between measurements and offline simulations at these 56 sites confirms the reliability of the LSM we used to consider SOM effects. It is worth noting that differences in observed soil temperature and liquid water content are the results of a combination of different SOM contents, site locations, meteorological conditions and so on, and thus the observed and WRF simulated results are not comparable. Since it is not possible to reveal the effect of land–atmosphere feedbacks based solely on observations or land surface simulations, sensitivity experiments are undertaken to analyze the SOM effects and their mechanisms using a coupled climate model in this study.

Fig. 6.
Fig. 6.

Seasonal variations of (a)–(d) observed and (e)–(h) Noah-MP simulated surface (0–10 cm) and subsurface (10–40 cm) soil liquid water contents and soil temperatures in 2013 for the 56 soil moisture/temperature sites separated into three classes (0%–4%, 4%–6% and ≥6%) according to observed surface SOM mass content.

Citation: Journal of Hydrometeorology 24, 1; 10.1175/JHM-D-22-0074.1

4. Discussion

The results outlined in section 3 indicate that SOM-induced changes in the near-surface air and soil are vertically asymmetric, with warming and drying overlying cooling and moistening. This raises the following question: by what means does organic matter produce these responses?

a. How does SOM affect atmospheric conditions?

Land surface conditions, such as land use/cover, ground/soil temperature, and soil moisture, have important influences on energy and water exchanges between the atmosphere and land surface (Santanello et al. 2011; Mahmood et al. 2014; Xue et al. 2017; Ganeshi et al. 2020; Li et al. 2020; Xue et al. 2010, 2021). Surface soil wetness, defined as the ratio of surface soil liquid water content to porosity, is a key variable that can be altered by the impacts of SOM on soil properties (Sun et al. 2021).

Figure 7a shows the spatial pattern of differences in surface soil wetness between the WRF-SOM and WRF-CTL experiments. Surface soil wetness decreases over much of the TP when SOM is included, primarily because soil porosity increases (Fig. 2a) more than liquid water content (Fig. 5a). The most pronounced differences are located in the eastern TP. The inset in Fig. 7a likewise shows decreases in surface soil wetness with increasing SOM content, although again this effect tends to stabilize for SOM contents exceeding 4%. Changes in surface soil wetness directly impact the surface energy budget and the atmosphere in turn (Sellers et al. 1997; Guo et al. 2006; Koster et al. 2006; Sakaguchi and Zeng 2009). As shown in Figs. 7b–7d, SOM-related decreases in surface soil wetness on the eastern TP are associated with smaller latent heat fluxes, larger sensible heat fluxes, and warmer ground temperatures. Further investigation shows that changes in evaporation are the main reason for decreases in latent heat flux, while changes in transpiration can be ignored. Differences in ground heat flux are negligible and therefore not shown here. The insets of Figs. 7b–7d show that the variation of land surface heat fluxes and ground temperature with organic matter content is similar to that of surface soil wetness. These differences tend to stabilize when organic matter content exceeds 4%, helping to explain the similar asymptotic behavior of changes in near-surface air variables (Figs. 4a–c).

Fig. 7.
Fig. 7.

Spatial distributions of differences in summer (a) surface soil wetness, (b) latent heat flux, (c) sensible heat flux, and (d) ground temperature between the two coupled experiments (WRF-SOM minus WRF-CTL). The insets are boxplots of these differences, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. The horizontal line inside the gray shaded box indicates the median value, the upper and lower boundaries of the gray shaded box indicate the 25th and 75th percentiles, and the ends of the upper and lower whiskers indicate maxima and minima, respectively.

Citation: Journal of Hydrometeorology 24, 1; 10.1175/JHM-D-22-0074.1

b. How does SOM affect soil conditions?

Concluding that SOM affects the atmosphere mainly by modifying the partitioning of surface heat fluxes, we turn to the mechanisms by which SOM affects the soil in the coupled land–atmosphere system. Previous studies have pointed out that SOM has important impacts on the physical and hydrothermal properties of soil (Letts et al. 2000). Essentially, SOM can help to moisten soil by enhancing its water-holding capacity, but it may also reduce shallow soil water content by reducing precipitation (Fig. 4d). SOM effects on soil temperature can likewise be separated into two components. On the one hand, the heat absorbed by a soil layer is partly used for its own warming and partly transferred to its adjacent soil layers. The latter depends on soil thermal conductivity. The high surface SOM content leads to a decrease in surface soil thermal conductivity (Fig. 2d), which favors more heat retention in the surface soil layer and less heat transfer down to the deeper soil layers, and then causes higher soil temperature in the surface layer and lower soil temperature in the bottom two layers. Thus, SOM contributes to shallow soil warming and deep soil cooling by decreasing the thermal conductivity and thus reducing the thermal inertia of soil. On the other hand, the SOM-induced warmer ground through modulating surface energy budget can further heat the soil, and this warming effect is so strong that it can influence the deep soil. As a result, for the surface soil layer, the effects of the above two components are consistent and both manifest as warming, while for the bottom two soil layers, the warming effect of the second component is masked by the cooling effect of the first one (Figs. 5e–h).

Soil temperature differences between coupled and offline simulations classified by surface SOM content are shown in Fig. 8 as blue and black boxplots, respectively. It is clear that there are evident discrepancies between coupled and offline results. In the offline simulations, SOM-induced increases in surface soil temperatures are suppressed because atmospheric conditions are identical for both the LSM-SOM and LSM-CTL experiments. The offline simulations may therefore underestimate SOM-induced warming of the shallow soil (Fig. 8a) while overestimating the cooling of the deep soil (Figs. 8b–d). These results suggest that land–atmosphere feedbacks enhance the warming effect of SOM on the shallow soil while weakening its cooling effect on the deep soil, and the SOM protective effect on frozen soil may have been overestimated in early offline simulations. Despite differences in magnitude, surface soil layer is dominated by the warming effect (through both land–atmosphere feedbacks and altered soil properties), while the bottom two soil layers are dominated by the cooling effect in both the offline and coupled simulations. The subsurface soil layer serves as a transition zone in coupled simulations, with both warming and cooling effects playing comparable roles. By contrast, this layer is controlled by the SOM cooling effect in offline simulations due to the lack of coupled feedbacks between the land and atmosphere.

Fig. 8.
Fig. 8.

Boxplots of soil temperature differences in four layers (surface: 0–10 cm; subsurface: 10–40 cm; layer 3: 40–100 cm; layer 4: 100–200 cm) in the TP in coupled (blue; WRF-SOM minus WRF-CTL) and offline (black; LSM-SOM minus LSM-CTL) simulations, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. The horizontal line inside the box indicates the median value, the upper and lower boundaries of the box indicate the 25th and 75th percentiles, and the ends of the upper and lower whiskers indicate maxima and minima, respectively.

Citation: Journal of Hydrometeorology 24, 1; 10.1175/JHM-D-22-0074.1

In short, the inclusion of organic matter in the coupled land–atmosphere model reduces surface soil wetness. Associated changes in the surface energy budget can lead to a warmer and drier atmosphere. Compared with the atmosphere, the processes by which organic matter affects the soil are more complex, with both SOM–soil and SOM–atmosphere–soil feedbacks playing influential roles. A schematic illustration of the physical mechanisms behind SOM impacts on the atmosphere and soil is provided in Fig. 9. As an insulator, SOM tends to mitigate the effects of global warming on deep soil and slow simulated historical and projected permafrost degradation (Xu and Wu 2021). The responses and feedbacks of SOM to climate change, especially in permafrost zones of the TP, warrant thorough investigation in future studies (Zhao et al. 2018).

Fig. 9.
Fig. 9.

Schematic diagram of SOM impacts on the atmosphere and soil. The color map of the TP represents the distribution of surface SOM mass content: the redder the color, the higher the SOM content; the bluer the color, the lower the SOM content.

Citation: Journal of Hydrometeorology 24, 1; 10.1175/JHM-D-22-0074.1

5. Conclusions

Large amounts of SOM have accumulated on the eastern TP, but most previous studies have focused on the effects of organic matter on soil hydrothermal states using offline LSM simulations. As a result, little is known about how these large amounts of SOM affect land–atmosphere interactions.

In this study, two numerical experiments using the WRF Model coupled with the Noah-MP LSM have been completed to investigate SOM influences in the coupled land–atmosphere system. The results demonstrate that SOM warms and dries the near-surface air by decreasing surface soil wetness, and then modulating surface energy budget. The inclusion of SOM improves WRF Model performance with respect to near-surface air variables and precipitation in this region (Fig. 3). As for the soil, SOM tends to increase liquid water content but decrease surface soil wetness and warm the shallow soil while cooling the deep soil by both land–atmosphere feedbacks and altered soil properties. The wetting and cooling effects of SOM on deep soil are captured by both observations and offline land surface simulations. However, the impacts of SOM are shown to differ in coupled land–atmosphere simulations relative to offline LSM simulations owing to the influence of atmospheric feedbacks. The increase in shallow soil liquid water content in coupled simulations is reduced due to less precipitation than that in offline ones, but the inclusion of SOM increases soil liquid water content in both coupled and offline simulations. Compared with the offline ones, SOM-induced soil temperatures in the coupled simulations are warmer due to increased ground temperature. We therefore conclude that land–atmosphere feedbacks weaken the cooling effect of SOM toward deep soil on the TP. It is important to note that these findings are from one regional climate model and do not apply to wetlands.

In summary, the inclusion of SOM warms and dries the near-surface air while moistening and cooling the deep soil during summer. The soil cooling is expected to slow the degradation of permafrost, but this effect is not as strong as that estimated through offline land surface modeling. Sun et al. (2021) found the SOM warming effect on the subsurface soil in the cold season based on Noah-MP offline simulations. Further analysis of the SOM influence in the cold season is thus worth carrying out in the future when the model’s capability is improved over the TP. In the context of global warming, changes in the environment over the TP (e.g., warming and moistening) are expected to change the nature, amount, and spatial distribution of SOM, which may in turn alter the effects of SOM on the coupled hydrothermal states of the atmosphere and soil. These possibilities should be explored in future work.

Acknowledgments.

This work was supported by National Key Research and Development Program of China (Grant 2018YFA0605400) and Basic Science Center for Tibetan Plateau Earth System (BCTPES; NSFC project 41988101).

Data availability statement.

The ERA5 reanalysis data are available at ECMWF (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). The CMA observations are available at National Meteorological Information Center (http://data.cma.cn/en/?r=data/detail&dataCode=A.0012.0001) after registration. The global high-resolution soil data are obtained from http://globalchange.bnu.edu.cn/research/soil5.jsp. The soil moisture/temperature data are available at https://data.tpdc.ac.cn/en/data/6557f9ac-f752-474e-a156-3cfc28434cad/.

APPENDIX

Soil Hydrothermal Functions

Following Sun et al. (2021) and Chen et al. (2012), the soil hydrothermal properties considering the SOM are calculated as follows:
VSOM=ρP(1θs,min)MSOCρSOC(1MSOC)+ρP(1θs,min)MSOC,
θs=(1VSOM)×θs,min+VSOM×θs,SOM,
θs,min=0.4890.00126×(%sand),
b=(1VSOM)×bmin+VSOM×bSOM,
bmin=2.91+0.159×(%clay),
Ks=(1VSOM)×Ks,min+VSOM×Ks,SOM,
Ks,min=7.0556×106.884+0.0153(%sand),
ψs=(1VSOM)×ψs,min+VSOM×ψs,SOM,
ψs,min=10.0×101.880.0131(%sand),
λsolid=λqtzqtz(1VSOM)λSOMVSOMλo(1qtz)(1VSOM), and
Csolid=(1θs)VSOMCSOM+(1θs)(1VSOM)Cmin,
where VSOM is the volume content of SOM; ρp and ρSOC are the density of mineral soil (2700 kg m−3) and organic carbon (130 kg m−3), respectively; MSOC is the organic carbon mass content (kg kg−1); %sand and %clay are the percentage (%) of sand and clay after removing SOM, respectively. The terms θs, b, Ks, and ψs represent soil porosity (m3 m−3), pore-size distribution parameter (–), saturated hydraulic conductivity (m s−1), and water potential (m), respectively, with subscripts min and SOM referring to mineral soil and SOM. Here, θs,SOM and ψs,SOM are taken as 0.9 m3 m−3 and −0.0103 m. The term qtz is the quartz volume content taken as %sand/2; λsolid, λqtz, λSOM, and λo are the thermal conductivity of solid soil, quartz (7.7 W m−1 K−1), SOM (0.25 W m−1 K−1), and other soil particles (2.0 W m−1 K−1), respectively; Csolid, CSOM, and Cmin are the thermal heat capacity of solid soil, SOM (2.5 × 106 J m−3 K−1), and mineral soil (2.0 × 106 J m−3 K−1), respectively. For soils with SOM content exceeding 6% (not exceeding 2%), the values of bSOM and Ks,SOM are set as 16.6 (2.7) and 10−4.67 m s−1 (2.8 × 10−4 m s−1). For soils with SOM content between 2% and 6%, it is assumed that the bSOM value increases linearly from 2.7 to 16.6 with the SOM content, while the log10(Ks,SOM) value decreases linearly from log10(2.8 × 10−4) to −4.67.

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  • Fig. 1.

    (a) Terrain height (color) and locations of CMA meteorological stations and the Naqu multiscale Soil Moisture and Temperature Monitoring Network used in this study. (top right) Terrain height (color) and locations (dots) of the soil moisture/temperature sites in the Naqu network. (b)–(e) Spatial distributions of SOM mass content in four layers (surface: 0–10 cm; subsurface: 10–40 cm; layer 3: 40–100 cm; layer 4: 100–200 cm). The black line denotes the 2500-m isoline of terrain height.

  • Fig. 2.

    Spatial distributions of differences in surface soil (a) porosity, (b) pore-size distribution parameter, (c) saturated water potential, and (d) solid thermal conductivity between the two experiments (WRF-SOM minus WRF-CTL or LSM-SOM minus LSM-CTL). The black line denotes the 2500-m isoline of terrain height. The insets are boxplots of these differences, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. The horizontal line inside the gray shaded box indicates the median value, the upper and lower boundaries of the gray shaded box indicate the 25th and 75th percentiles, and the ends of the upper and lower whiskers indicate maxima and minima, respectively.

  • Fig. 3.

    Biases of summer (a),(b) 2-m air temperature, (c),(d) 2-m relative humidity, and (e),(f) precipitation simulations from the WRF-CTL and WRF-SOM experiments relative to in situ observations at meteorological stations on the eastern TP (30°–34°N, 90°–102°E).

  • Fig. 4.

    Spatial distributions of differences in summer (a) 2-m air temperature, (b) 2-m specific humidity, (c) 2-m relative humidity, and (d) precipitation between the two coupled experiments (WRF-SOM minus WRF-CTL). The gray line denotes the 2500-m isoline of terrain height. The insets are boxplots of these differences, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. The horizontal line inside the gray shaded box indicates the median value, the upper and lower boundaries of the gray shaded box indicate the 25th and 75th percentiles, and the ends of the upper and lower whiskers indicate maxima and minima, respectively.

  • Fig. 5.

    Spatial distributions of differences in summer (a)–(d) soil liquid water content, and (e)–(h) soil temperature in four layers (surface: 0–10 cm; subsurface: 10–40 cm; layer 3: 40–100 cm; layer 4: 100–200 cm) between the two coupled experiments (WRF-SOM minus WRF-CTL). The gray line denotes the 2500-m isoline of terrain height. The insets are boxplots of these differences, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. The horizontal line inside the gray shaded box indicates the median value, the upper and lower boundaries of the gray shaded box indicate the 25th and 75th percentiles, and the ends of the upper and lower whiskers indicate maxima and minima, respectively.

  • Fig. 6.

    Seasonal variations of (a)–(d) observed and (e)–(h) Noah-MP simulated surface (0–10 cm) and subsurface (10–40 cm) soil liquid water contents and soil temperatures in 2013 for the 56 soil moisture/temperature sites separated into three classes (0%–4%, 4%–6% and ≥6%) according to observed surface SOM mass content.

  • Fig. 7.

    Spatial distributions of differences in summer (a) surface soil wetness, (b) latent heat flux, (c) sensible heat flux, and (d) ground temperature between the two coupled experiments (WRF-SOM minus WRF-CTL). The insets are boxplots of these differences, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. The horizontal line inside the gray shaded box indicates the median value, the upper and lower boundaries of the gray shaded box indicate the 25th and 75th percentiles, and the ends of the upper and lower whiskers indicate maxima and minima, respectively.

  • Fig. 8.

    Boxplots of soil temperature differences in four layers (surface: 0–10 cm; subsurface: 10–40 cm; layer 3: 40–100 cm; layer 4: 100–200 cm) in the TP in coupled (blue; WRF-SOM minus WRF-CTL) and offline (black; LSM-SOM minus LSM-CTL) simulations, binned into four classes (<2%, 2%–4%, 4%–6%, and ≥6%) according to surface SOM mass content. The horizontal line inside the box indicates the median value, the upper and lower boundaries of the box indicate the 25th and 75th percentiles, and the ends of the upper and lower whiskers indicate maxima and minima, respectively.

  • Fig. 9.

    Schematic diagram of SOM impacts on the atmosphere and soil. The color map of the TP represents the distribution of surface SOM mass content: the redder the color, the higher the SOM content; the bluer the color, the lower the SOM content.

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