Extensive and rigorous model intercomparison is of great
importance before model application due to the uncertainties in current land
surface models (LSMs). Without considering the uncertainties in forcing data
and model parameters, this study designed an ensemble of 55 296 experiments
to evaluate the Noah LSM with multi-parameterization
(Noah-MP) for snow cover events (SCEs), soil temperature (ST) and soil
liquid water (SLW) simulation, and investigated the sensitivity of
parameterization schemes at a typical permafrost site on the Qinghai–Tibet
Plateau (QTP). The results showed that Noah-MP systematically overestimates snow
cover, which could be greatly resolved when adopting the sublimation from
wind and a semi-implicit snow/soil temperature time scheme. As a result of the
overestimated snow, Noah-MP generally underestimates ST, which is mostly
influenced by the snow process. A systematic cold bias and large uncertainties
in soil temperature remain after eliminating the effects of snow,
particularly in the deep layers and during the cold season. The combination
of roughness length for heat and under-canopy (below-canopy) aerodynamic resistance
contributes to resolving the cold bias in soil temperature. In addition,
Noah-MP generally underestimates top SLW. The runoff and groundwater (RUN) process dominates the SLW
simulation in comparison to the very limited impacts of all other physical
processes. The analysis of the model structural uncertainties and
characteristics of each scheme would be constructive to a better
understanding of the land surface processes in the permafrost regions of the
QTP as well as to further model improvements towards soil hydrothermal regime modeling
using LSMs.
Introduction
The Qinghai–Tibet Plateau (QTP) is underlain by the world's largest
high-altitude permafrost, covering a contemporary area of 1.06×106 km2 (Zou et al., 2017). Under
the background of climate warming and intensifying human activities, soil
hydrothermal dynamics in the permafrost regions on the QTP has been widely
suffering from soil warming (Wang et al., 2021), soil wetting (Zhao et al.,
2019) and changes in the soil freeze–thaw cycle (Luo et al., 2020). Such changes
have not only induced a reduction in the permafrost extent, the disappearance of
permafrost patches and thickening of the active layer (Chen et al.,
2020), but they have also resulted in alterations to the hydrological cycles
(Zhao et al., 2019; Woo, 2012), changes in the ecosystem (Fountain
et al., 2012; Yi et al., 2011) and damage to infrastructure
(Hjort et al., 2018). Therefore, it is very important to monitor
and simulate the soil hydrothermal regime in order to adapt to the changes taking
place.
A number of monitoring sites have been established in the permafrost regions
of the QTP (Cao et al., 2019). However, it is inadequate to
construct the soil hydrothermal state by considering the spatial variability
of the ground thermal regime and the uneven distribution of these
observations. In contrast, numerical models are competent alternatives. In
recent years, land surface models (LSMs), which describe the exchanges of
heat, water, and momentum between the land and atmosphere
(Maheu et al., 2018), have received significant improvements
with respect to the representation of permafrost and frozen ground processes (Koven et
al., 2013; Nicolsky et al., 2007; Melton et al., 2019). LSMs are capable of
simulating the transient change in subsurface hydrothermal processes (e.g.,
soil temperature and moisture) with soil heat conduction (or diffusion) and
water movement equations (Daniel et al., 2008). Moreover, they
could be integrated with a numerical weather prediction system such as WRF
(Weather Research and Forecasting), making them effective tools to explore
comprehensive interactions between climate and permafrost
(Nicolsky et al., 2007).
Some LSMs have been evaluated and applied in the permafrost regions of the
QTP. Guo and Wang (2013) investigated near-surface permafrost and
seasonally frozen ground states as well as their changes using version 4 of the Community
Land Model (CLM4). Hu et al. (2015) applied a coupled
heat and mass transfer model to identify the hydrothermal characteristics of
the permafrost active layer in the QTP. Using an augmented
Noah LSM, Wu et al. (2018) modeled the extent of permafrost, the active layer
thickness, the mean annual ground temperature, the depth of the zero annual amplitude
and the ground ice content on the QTP in the 2010s. Despite those achievements based
on different models, LSMs are in many aspects insufficient in permafrost
regions. For one thing, large uncertainties still exist in
state-of-the-art LSMs when simulating the soil hydrothermal regime on the
QTP (Chen et al., 2019). For instance, 19 LSMs in CMIP5
overestimate snow depth over the QTP (Wei and Dong, 2015), which could
result in variations in the soil hydrothermal regime with respect to the aspects of
magnitude and vector (cooling or warming) (Zhang, 2005). Moreover, most
of the existing LSMs are not originally developed for permafrost regions:
many of their soil processes are designed for shallow soil layers
(Westermann et al., 2016), but permafrost occurs in the
deep soil; moreover, the soil column is often considered to be homogeneous, which cannot
represent the stratified soil that is common on the QTP (Yang et al.,
2005). Given the numerous LSMs and their possible deficiencies, it is necessary to
assess the parameterization schemes for permafrost modeling on the QTP,
which is helpful for identifying the influential sub-processes, for enhancing our
understanding of model behavior and for guiding the improvement of model physics
(Zhang et al., 2016).
The Noah LSM with multi-parameterization (Noah-MP) provides a
unified framework in which a given physical process can be interpreted using
multiple optional parameterization schemes (Niu et al., 2011). Due to the
simplicity in selecting alternative schemes within one modeling framework,
it has been attracting increasing attention in intercomparison work among
multiple parameterizations at point and watershed scales (Hong et al.,
2014; Zheng et al., 2017; Gan et al., 2019; Zheng et al., 2019; Chang et
al., 2020; You et al., 2020a). For example, Gan et al. (2019) carried out
an ensemble of 288 simulations from multi-parameterization schemes of six
physical processes, assessed the uncertainties in parameterizations in
Noah-MP, and further revealed the best-performing schemes for latent heat,
sensible heat and terrestrial water storage simulation over 10 watersheds
in China. You et al. (2020b) assessed the performance of Noah-MP in
simulating snow process at eight sites over distinct snow climates and
identified the shared and specific sensitive parameterizations at all sites,
finding that sensitive parameterizations contribute most of the
uncertainties in the multi-parameterization ensemble simulations.
Nevertheless, there is little research on the intercomparison of soil
hydrothermal processes in the permafrost regions. In this study, an ensemble
experiment of 55 296 scheme combinations was conducted at a typical
permafrost monitoring site on the QTP. The simulated snow cover events
(SCEs), soil temperature (ST) and soil liquid water (SLW) of the Noah-MP model
was assessed, and the sensitivities of the parameterization schemes at different
depths were further investigated. This study could be expected to present a
reference for soil hydrothermal simulation in the permafrost regions on the
QTP.
This article is structured as follows: Sect. 2 introduces the study site,
the atmospheric forcing data, the design of the ensemble simulation experiments and the
sensitivity analysis methods; Sect. 3 describes the ensemble simulation
results of the SCEs, ST and SLW, and explores the sensitivity and interactions of
parameterization schemes; Sect. 4 discusses the schemes in each physical
process, and Sect. 5 concludes the main findings.
Methods and materialsSite description and observation datasets
The Tanggula observation station (TGL) lies in the continuous permafrost regions
of the Tanggula Mountains, on the central QTP (33.07∘ N, 91.93∘ E; 5100 m a.s.l.; Fig. 1). This site is a typical permafrost site on the
plateau with a sub-frigid and semiarid climate (Li et al., 2019), filmy and
discontinuous snow cover (Che et al., 2019), sparse grassland (Yao et al.,
2011), coarse soil (Wu and Nan, 2016; He et al., 2019), and thick active
layer (Luo et al., 2016), which are common features in the permafrost
regions of the plateau. According to the observations from 2010 to 2011, the
annual mean air temperature of the TGL site was -4.4∘C. The annual
precipitation was 375 mm, 80 % of which was concentrated between May
and September. Alpine steppe with low height is the main land surface, and this land surface type
covers about 40 %–50 % of the region (Yao et
al., 2011). The active layer thickness is about 3.15 m (Hu et al., 2017).
The atmospheric forcing data, including wind speed and direction; air
temperature, relative humidity and pressure; downward shortwave and longwave
radiation; and precipitation, were used to drive the model. The abovementioned variables were measured at a height of 2 m and covered the period from 10 August 2010 to 10 August 2012 with a temporal resolution of 1 h. Daily soil temperature and liquid moisture at depths of 5, 25,
70, 140, 220 and 300 cm from 10 August 2010 to 9 August 2011
were utilized to validate the simulation results.
Location and geographic features of the study site. (a) Location of the
observation site and permafrost distribution (Zou et al., 2017). (b) Topography of the Qinghai–Tibet Plateau. (c) Photo of the Tanggula
observation station.
Ensemble experiments of Noah-MP
The offline Noah-MP LSM v1.1 was assessed in this study. The default Noah-MP model
consists of 12 physical processes that are interpreted by multiple optional
parameterization schemes. These sub-processes include the following: the vegetation model
(VEG), canopy stomatal resistance (CRS), the soil moisture factor for stomatal
resistance (BTR), runoff and groundwater (RUN), the surface layer drag
coefficient (SFC), supercooled liquid water (FRZ), frozen soil permeability
(INF), the canopy gap for radiation transfer (RAD), snow surface albedo (ALB),
the precipitation partition (SNF), the lower boundary of soil temperature (TBOT) and the
snow/soil temperature time scheme (STC) (Table 1). Details about the
processes and optional parameterizations can be found in Yang et
al. (2011a).
VEG(1) is adopted in the VEG process, in which the vegetation fraction is
prescribed according to the NESDIS/NOAA 0.144 degree monthly 5-year
climatology green vegetation fraction (https://ral.ucar.edu/solutions/products/wrf-noah-noah-mp-modeling-system, last access: 27 March 2021), and the monthly leaf area
index (LAI) was derived from the Advanced Very High-Resolution Radiometer
(AVHRR; https://www.ncei.noaa.gov/data/, last access: 27 March 2021, Claverie et al., 2016).
Previous studies have confirmed that Noah-MP seriously overestimates the snow
events and underestimates soil temperature and moisture on the QTP
(Jiang et al., 2020; Li et al., 2020; Wang et al., 2020), which
can be greatly resolved by considering the sublimation from wind (Gordon
scheme) and a combination of roughness length for heat and under-canopy (below-canopy) aerodynamic resistance (Y08–UCT scheme) (Zeng et al., 2005; Yang et al., 2008; Li
et al., 2020). For a more comprehensive assessment, we added two physical
processes based on the default Noah-MP model – i.e., the snow sublimation from
wind (SUB) and the combination scheme process (CMB) (Table 1). In the two
processes, users can choose to turn on the respective Gordon and Y08–UCT schemes
(described in the study of Li et al., 2020) or not. As a result,
55 296 total combinations are possible for the 13 processes, and orthogonal
experiments were carried out to evaluate their performance in soil
hydrothermal dynamics.
The Noah-MP model was modified to consider the vertical heterogeneity in the
soil profile by setting the corresponding soil parameters for each layer.
The soil hydraulic parameters, including the porosity, saturated hydraulic
conductivity, hydraulic potential, the Clapp–Hornberger parameter b, the field
capacity, the wilt point and the saturated soil water diffusivity, were determined
using the pedotransfer functions proposed by Hillel (1980), Cosby et al. (1984), and Wetzel and Chang (1987) (Eqs. S1–S7 in the Supplement), in which the sand and
clay percentages were based on Hu et al. (2017) (Table S1). In
addition, the simulation depth was extended to 8.0 m to cover the active
layer thickness of the QTP. The soil column was discretized into 20 layers (Table S1),
whose depths follow the default scheme in CLM 5.0 (Lawrence et
al., 2018). Due to the inexact match between observed and simulated depths,
the simulations at 4, 26, 80, 136, 208 and 299 cm were
compared with the observations at 5, 25, 70, 140, 220 and 300 cm, respectively. A 30-year spin-up was conducted in every simulation to
reach equilibrium soil states.
The physical processes and options in the Noah-MP LSM (Yang et al., 2011a).
Physical processesOptionsVegetation model (VEG)(1) Table LAI, prescribed vegetation fraction(2) Dynamic vegetation(3) Table LAI, calculated vegetation fraction(4) Table LAI, prescribed max vegetation fractionCanopy stomatal resistance (CRS)(1) Jarvis(2) Ball–BerrySoil moisture factor for stomatal resistance (BTR)(1) Noah(2) CLM(3) SSiBRunoff and groundwater (RUN)(1) SIMGM with groundwater(2) SIMTOP with equilibrium water table(3) Noah (free drainage)(4) BATS (free drainage)Surface layer drag coefficient (SFC)(1) Monin–Obukhov (M–O)(2) Chen97Supercooled liquid water (FRZ)(1) Generalized freezing-point depression(2) Variant freezing-point depressionFrozen soil permeability (INF)(1) Defined by soil moisture, more permeable(2) Defined by liquid water, less permeableCanopy gap for radiation transfer (RAD)(1) Gap = F(3D structure, solar zenith angle)(2) Gap = zero(3) Gap =1- vegetated fractionSnow surface albedo (ALB)(1) BATS(2) CLASSPrecipitation partition (SNF)(1) Jordan91(2) BATS: Tsfc<Tfrz+2.2 K(3) Tsfc<TfrzLower boundary of soil temperature (TBOT)(1) Zero heat flux(2) Soil temperature at 8 m depthSnow/soil temperature time scheme (STC)(1) Semi-implicit(2) Fully implicitSnow sublimation from wind (SUB)(1) No(2) YesCombination scheme by Li et al. (2020) (CMB)(1) No(2) Yes
The abbreviations used in the table are as follows: BATS (Biosphere–Atmosphere Transfer Model), CLASS (Canadian Land Surface
Scheme), SIMGM (Simple topography-based runoff and Groundwater Model),
SIMTOP (Simple Topography-based hydrological model) and SSiB (Simplified Simple
Biosphere model).
Methods for sensitivity analysis
The simulated snow cover events (SCEs) were quantitatively evaluated using
the overall accuracy index (OA) (Toure et al., 2016):
OA=a+da+b+c+d,
where a represents the positive hits, b represents the false alarm, c represents the
misses and d represents the negative hits. The value of the OA ranges from 0 to
1. A higher OA signifies better performance. Ground albedo was used as an
indicator for snow events due to a lack of snow depth observations. The days
when the daily mean albedo is greater than the observed mean value of the
warm and cold season (0.25 and 0.30, respectively) are identified as snow
cover.
The root mean square errors (RMSEs) between the simulations and observations
were adopted to evaluate the performance of Noah-MP in simulating soil
hydrothermal dynamics.
To investigate the degree of influence of each physical process on the SCEs, ST
and SLW, we firstly calculated the mean OA (for SCE) and the mean RMSE (for ST
and SLW) (Y¯ji) of the jth parameterization schemes (j=1, 2,
…) in the ith process (i=1, 2, …). The
maximum difference in Y¯ji (ΔOA or ΔRMSE) was then defined to quantify the degree of influence of the ith process (i=1, 2, …) (Li et al., 2015):
ΔOA or ΔRMSE=Y¯maxi-Y¯mini,
where Y¯maxi and Y¯mini are the largest and the
smallest Y¯ji in the ith process, respectively. For a given
physical process, a high ΔOA or ΔRMSE
signifies a large difference between parameterizations, indicating high
sensitiveness of the ith process for SCEs and ST/SLW simulation.
The sensitivities of physical processes were determined by quantifying the
statistical distinction level of performance between parameterization
schemes. The independent sample (two-tailed) t test was adopted to identify
whether the distinction level between two schemes was significant, and the significance of the distinction level
between three or more schemes was tested using a Tukey test. The Tukey
test has been widely used due to its simple computation and statistical
features (Benjamini, 2010). Detailed descriptions of this method
can be found in Zhang et al. (2016), Gan et al. (2019) and You et al. (2020a). A process can be considered sensitive when the schemes show a
significant difference. Moreover, schemes with a large mean OA and a small mean
RMSE were considered favorable for SCEs and ST/SLW simulation, respectively.
We distinguished the differences of the parameterization schemes at the 95 %
confidence level.
ResultsGeneral performance of the ensemble simulation
The performance of Noah-MP for snow simulation was firstly tested by
conducting an ensemble of 55 296 experiments. Due to a lack of snow depth
measurements, ground albedo was used as an indicator of snow cover. Figure 2 shows the monthly variations in observed ground albedo and the simulation
results of the ensemble simulations. The ground albedo was extremely
overestimated with large uncertainties when considering the snow options in
Noah-MP, indicating an overestimation of snow depth and duration. This
overestimation continued until July.
Monthly variations in ground albedo at the TGL site for the observations
(Obs) and the ensemble simulation (Sim). The light blue shading represents
the standard deviation of the ensemble simulation.
Figure 3 illustrates the ensemble-simulated and observed annual cycle of ST
and SLW at the TGL site. The ensemble experiments basically captured the
seasonal variability of ST, whose magnitude decreased with soil depth. In
addition, the simulated ST in the snow-affected season (October–July) showed
relatively wide uncertainty ranges, particularly in the shallow layers. This
indicates that the selected schemes perform very differently with respect to snow
simulation, resulting in large uncertainties in shallow STs. The simulated
ST were generally smaller than the observations with relatively large gaps
during the snow-affected season. This indicates that the Noah-MP model
generally underestimates the ST, especially during the snow-affected months.
As the observation equipment can only record the liquid water, the soil
liquid water (SLW) was evaluated against simulations from the ensemble
experiments (Fig. 3). The Noah-MP model generally underestimated surface (5 and 25 cm) and deep (220 and 300 cm) SLW (Fig. 3g, h, k, l).
However, Noah-MP tended to overestimate the SLW in the middle layers of 70 and 140 cm. Moreover, the simulated SLW exhibited relatively wide
uncertainty ranges, particularly during the warm season (Fig. 3).
Monthly soil temperature (ST, in ∘C) and soil liquid water
(SLW, in %) at (a, g) 5 cm, (b, h) 25 cm, (c, i) 70 cm, (d, j) 140 cm, (e, k) 220 cm and (f, l) 300 cm at the TGL site. The light blue shading represents the
standard deviation of the ensemble simulation. The black line and the symbols
represent the ensemble mean of simulations with STC(1) and SUB(2).
The maximum difference in the mean overall accuracy (OA) for
albedo (ALB-ΔOA) in each physical process (a) annually and during the (b) cold and (c) warm seasons at the TGL site.
Sensitivity of physical processesDegree of influence of physical processes
Figure 4 compares the influence scores of the 13 physical processes based
on the maximum difference in the mean OA over 55 296 experiments using the
same scheme, for SCEs at the TGL site. On the whole, the SUB and STC processes
had the largest scores for the whole year as well as during both the warm
and cold seasons, and the other processes showed a value of less than 0.05
(Fig. 4a, b, c). Moreover, the SUB process had a consistent influence on
SCEs, whereas the influence of STC differed with season. In the cold season,
the score of the SUB process (0.28) was 2 times more than that of the STC
process (Fig. 4b), indicating the relative importance of snow sublimation
for SCE simulation during the cold season. When it came to the warm
season, the influence score of SUB (0.25) did not change much, whereas that of
STC increased to 0.26 and showed a similar influence on SCE simulation to
SUB.
The maximum difference in the mean RMSE for (a, c, e) soil
temperature (ST-ΔRMSE, in ∘C) and (b, d, f) soil
liquid water (SLW-ΔRMSE, in %) in each physical process (a, b) annually and during the (c, d) warm and (e, f) cold seasons at
different soil depths at the TGL site.
Figure 5 compares the influence scores of the 13 physical processes at
different soil depths, based on the maximum difference in the mean RMSE over
55 296 experiments using the same scheme, for ST and SLW at the TGL site. The
snow-related processes, including the STC, SUB and SNF processes, showed the
largest ST-ΔRMSE at all layers, followed by the RAD, SFC and
RUN processes, whereas the ST-ΔRMSE values of the other seven physical
processes were less than 0.5 ∘C, among which the influence of the CRS and
BTR processes were negligible. Moreover, the FRZ, INF and TBOT processes
had larger influence scores during the cold season than during the warm season, and the
scores of TBOT were greater in deep soils than shallow soils. During the
warm season, the physical processes generally showed more influence on
shallow soil temperatures. When it came to the cold season, the influence
of the physical processes on deep layers obviously increased and was comparable
to that on shallow layers, implying relatively higher uncertainties in
Noah-MP during the cold season.
Most of the ΔRMSE values for SLW are less than 5 %, indicating
that all of the physical processes have limited influence on the SLW, among
which CRS, BTR, ALB, SNF and CMB showed the smallest effects (Fig. 5b, d, f). During the warm season, the RUN process, along with the STC
and SUB processes, dominated the performance of SLW simulation, especially
in the shallow layers (5, 25 and 70 cm; Fig. 5d). During the cold season,
however, the RUN process dominated the SLW simulation, with a great decline
in the dominance of STC and SUB processes.
Sensitivities of physical processes and the general behaviors of
parameterizations
To further investigate the sensitivity of each process and the general
performance of the parameterizations, an independent sample (two-tailed) t test and a Tukey test were conducted to establish whether the differences
between parameterizations within a physical process were significant (Figs. 6, 7). For a given sub-process, any two schemes labeled with different
letters behave significantly differently, and this sub-process can, therefore,
be identified as sensitive; otherwise, the sub-process is considered
insensitive. For simplicity, schemes with insensitive sub-process are not
labeled. Moreover, schemes with the letters late in the alphabet have
smaller mean RMSEs and outperform those with letters that appear early in the
alphabet. The following outlines an example of this process using the two schemes in the CRS process, hereafter CRS(1) and CRS(2),
in Fig. 6. For the annual and warm season, CRS(1) and CRS(2)
were labeled with “B” and “A”, respectively. In the cold season, neither of
them were labeled with letters. As described above, the CRS process was
sensitive for SCE simulation during the annual and warm season, and CRS(1)
outperformed CRS(2). However, it was not sensitive during the cold season.
Consistent with the degrees of influence shown in Fig. 4, the performance difference
between schemes of the STC and SUB processes for SCE simulation were significantly
greater than for other processes. Most other physical processes showed
significant but limited differences. Schemes in the BTR and TBOT processes,
however, showed no significant difference with respect to performance. Specifically, the
performance order was as follows: STC(1) > STC(2), SUB(2) > SUB(1), SFC(2) > SFC(1), ALB(2) > ALB(1) and CMB(2) > CMB(1) at both the annual and seasonal scales. RAD showed no
obvious difference during the warm season, whereas RAD(3) outperformed RAD(1)
and RAD(2) during the cold season. For SNF, SNF(3) generally excelled over SNF(1) and
SNF(2), especially during the warm season.
Distinction level for overall accuracy (OA) of snow cover events
(SCEs) annually and during the warm and cold seasons at the TGL site. The limits of the
boxes represent the upper and lower quartiles, and the lines in the boxes indicate the
median values.
All of the physical processes showed sensitivities for ST and SLW simulation to
varying magnitudes except for the BTR and CRS processes in most layers.
For ST, the performance difference between schemes of the STC, SUB and SNF processes
were obviously greater than other processes, indicating the importance of
snow on ST, followed by the RAD, SFC and RUN processes. The performance
order was as follows: STC(1) > STC(2), SUB(2) > SUB(1),
SNF(3) > SNF(1) > SNF(2), RAD(3) > RAD(1) > RAD(2) and SFC(2) > SFC(1). For SLW, the RUN, STC
and SUB processes showed significant and higher sensitivities than other
physical processes, especially during the warm season and in the shallow
layers (Figs. 5, 8). Consistent with that of ST, the performance order for the SLW
simulation was as follows: STC(1) > STC(2) and SUB(2) > SUB(1). For the RUN process, the performance order for both ST and SLW
simulation generally followed RUN(4) > RUN(1) > RUN(3) > RUN(2) as a whole, among which RUN(1) and RUN(4) showed
similar performance during both warm and cold seasons. During both warm and
cold seasons, the performance order for the ST simulations was SFC(2) > SFC(1) for SFC process, FRZ(2) > FRZ(1) for FRZ
process and RAD(3) > RAD(1) > RAD(2) for RAD process
(Figs. S2, S3), which is somewhat similar to SLW simulations in the shallow
and deep layers.
For ST, both FRZ and INF showed higher sensitivities during the cold season,
especially in shallow soils for FRZ and deep soils for INF. FRZ(2) / INF(1)
outperformed FRZ(1) / INF(2) for the whole year with respect to ST simulation.
Specifically, FRZ(1) / INF(2) performed better in shallow soils during the
warm season, whereas its performance was worse during the cold season compared with
FRZ(2) / INF(1). For SLW, FRZ(2) / INF(2) generally preceded FRZ(1) / INF(1) in
shallow and deep soils (5, 25, 220 and 300 cm), whereas its performance was worse in the
middle soil layers (140 and 220 cm).
For ST simulation, the performance sequence for RAD and SNF was RAD(3) > RAD(1) > RAD(2) and SNF(3) > SNF(1) > SNF(2), respectively. For SLW simulation, the sequence became
complicated. However, RAD(3) and SNF(3) still outperformed the other two
schemes, respectively. ALB(2) was superior to ALB(1) for both ST and SLW
simulation. The influence of TBOT on soil hydrothermal dynamics arose in deep soils
and during cold season, and TBOT(1) excelled over TBOT(2). CMB(2) outperformed
CMB(1) for ST simulation and for SLW simulation in shallow and deep
soils (5, 25 and 300 cm).
Distinction level for the RMSE of ST at different layers
annually and during the warm and cold seasons in the ensemble simulations at the TGL site.
Limits of the boxes represent the upper and lower quartiles, and the lines in the boxes
indicate the median values.
Same as in Fig. 7 but for SLW.
Uncertainty interval of ground albedo at the TGL site in the dominant
physical processes (STC and SUB) for SCE simulation.
Influence of snow cover and the surface drag coefficient on soil hydrothermal
dynamics
The influence of snow on soil temperature is firstly investigated. The
dominant role of STC and SUB in the simulation of SCEs has been identified
(Figs. 4, 6). Interactions between the two physical processes are further
analyzed here. Figure 9 compares the uncertainly intervals of the two
physics. The duration of snow cover is the longest when STC(2) + SUB(1),
followed by when STC(1) + SUB(1). Simulations considering SUB(2) generally have
a short snow duration. Among the four combinations, STC(1) + SUB(2) is in
best agreement with the measurements.
Given the good performance of STC(1) + SUB(2) in simulating SCEs, the
influence of snow on soil hydrothermal dynamics is investigated by comparing
the total ensemble mean ST and SLW with those adopting STC(1) + SUB(2) (Fig. 3). It can be seen that the ensemble mean ST values of simulations adopting STC(1)
and SUB(2) are generally higher than the total ensemble means, especially
during the spring and summer (March–August). In January and February in the shallow
layers (5, 25 and 70 cm), STC(1) + SUB(2) had a lower ST and showed an
insulation effect on ST for these 2 months. As a whole, however, snow
cover has a cooling effect on ST. In addition, along with the improved SCEs
and elevated ST, STC(1) + SUB(2) induced moister soil with a higher SLW (Fig. 3).
Monthly soil temperature (ST, in ∘C) at (a) 5 cm, (b) 25 cm, (c) 70 cm, (d) 140 cm, (e) 220 cm and (f) 300 cm for the SFC process with or without consideration of the CMB(2) and STC(1) + SUB(2) processes.
The SFC and CMB processes use different ways of calculating the surface drag
coefficient, which greatly influences the surface energy partitioning and,
thus, the ST and SLW. The influence of the surface drag coefficient is assessed by
comparing the soil temperature before and after considering the combined
scheme, CMB(2), and the effect of snow, STC(1) + SUB(2) (Fig. 10). SFC(2)
tended to produce a higher ST than SFC(1), especially during the warming
period (January–August). When adopting the combined Y08–UCT scheme,
CMB(2), the cold bias was significantly resolved. The performance order
was as follows: SFC(2) + CMB(2) > SFC(2) > SFC(1) + CMB(2) > SFC(1). However, considerable underestimations of ST still
exist in all layers due to the poor representation of snow process. After
eliminating the effects of snow (STC(1) + SUB(2), dash lines in Fig. 10),
the simulated ST increased accordingly, except in January and February.
SFC(2) and SFC(2) + CMB(2) overestimated STs from March to July in the shallow
layers (5 and 25 cm), resulting in good agreement of deep STs with
observations. In contrast, the simulated STs in the shallow layers (5 and 25 cm) by SFC(1) and SFC(1) + CMB(2) were basically consistent with
observations from March to July, whereas a large cold bias remained in the deep
layers.
DiscussionSnow cover on the QTP and its influence on the soil hydrothermal regime
Snow cover in the permafrost regions of the QTP is thin, patchy and
short-lived (Che et al., 2019), and its influence on soil temperature and the
permafrost state is usually considered weak (Jin et al., 2008; Zou et al.,
2017; Wu et al., 2018; Zhang et al., 2018; Yao et al., 2019). However, our
ensemble simulations showed that the surface albedo is extremely
overestimated with respect to both magnitude and duration (Fig. 2), implying an extreme
overestimation of snow cover, which is consistent with studies using
Noah-MP model (Jiang et al., 2020; Li et al., 2020; Wang et al., 2020) and
widely found in other state-of-the-art LSMs (Wei and Dong, 2015) for the QTP.
Great efforts to resolve the overestimation of snow cover in LSMs include
considering the vegetation effect (Park et al., 2016), the snow cover
fraction (Jiang et al., 2020), blowing snow (Xie et al., 2019) and the
fresh snow albedo (Wang et al., 2020). Our results illustrated the
superiority of considering the snow sublimation from wind (SUB(2)) and using a
semi-implicit snow/soil temperature time scheme (STC(1)) (Figs. 4, 6, 9)
when simulating snow cover on the QTP. This is consistent with previous
conclusions that accounting for the loss resulting from wind contributes to
improving the snow cover days and depth (Yuan et al., 2016) and that STC(1) has more
rapid snow ablation than STC(2) (You et al., 2020a).
The impacts of snow cover on soil temperature with respect to magnitude and vector
(cooling or warming) depend on its timing, duration and depth (Zhang et
al., 2005). In January and February, the ground heat flux mainly goes
upward, and the warming effect of simulated snow can be related to the
overestimated snow depth that prevents heat loss from the ground. During the
spring and summer, when snow melts, a cooling effects occurs, mainly
because the considerable energy that is used to heat the ground is reflected due to
the high albedo of snow. With the improvement of snow (STC(1) + SUB(2)), the
originally overestimated snow melts and infiltrates into the soil, resulting
in improved SLWs (Fig. 3). A higher soil temperature also contributed to
the SLWs according to the freezing-point depression equation, in which SLW
exponentially increases with soil temperature for a given site (Niu and Yang,
2006).
Discussions on the sensitivity of physical processes to soil hydrothermal
simulationCanopy stomatal resistance (CRS) and soil moisture factor for stomatal
resistance (BTR)
The biophysical BTR and CRS processes directly affect the canopy stomatal
resistance and, thus, plant transpiration (Niu et al., 2011). The
transpiration of plants could impact the ST and SLW through its cooling effect
(Shen et al., 2015) and the water balance in the root zone
(Chang et al., 2020). However, the annual transpiration of the alpine
steppe is weak due to the shallow effective root zone and lower stomatal
control in this dry environment (Ma et al., 2015), which may
explain the indistinctive or very small difference among the schemes of the
BTR and CRS processes for SCEs (Fig. 6), ST (Fig. 7) and SLW (Fig. 8).
Runoff and groundwater (RUN)
In the warm season, different SLWs would result in a difference in the
surface energy partitioning and, thus, different soil temperatures. RUN(2) had
the worst performance for simulating ST and SLW (Figs. 7, 8) among the
four schemes, likely due to its higher estimation of soil moisture (Fig. S1)
and, thus, greater sensible heat and smaller ST (Gao et al., 2015). Likewise,
RUN(4) was on par with RUN(1) with respect to the simulation of ST in most layers due
to the very small difference in the SLW of the two schemes (Figs. 8, S1). For the
whole soil column, RUN(4) surpassed RUN(1) and RUN(2) for SLW simulation,
both of which define surface and subsurface runoff as functions of the groundwater
table depth (Niu et al., 2005, 2007). This is in keeping with
the study of Zheng et al. (2017), which found that soil-water-storage-based
parameterizations outperformed groundwater-table-based parameterizations
in simulating the total runoff in a seasonally frozen and high-altitude
Tibetan river. Moreover, RUN(4) is designed based on the infiltration-excess
runoff (Yang and Dickinson, 1996) in spite of the
saturation-excess runoff in RUN(1) and RUN(2) (Gan et al., 2019), which is
more common in arid and semiarid areas like the permafrost regions of the QTP
(Pilgrim et al., 1988). In the cold season, much of the liquid
water freezes into ice, which would greatly influence the thermal
conductivity of frozen soil considering that the thermal conductivity of ice is
nearly 4 times that of the equivalent liquid water. Therefore, the impact
of RUN is important for the soil temperature simulations in both the warm and
cold seasons (Figs. 5, 7).
Surface layer drag coefficient (SFC and CMB)
SFC defines the calculation of the surface exchange coefficient for heat
and water vapor (CH), which greatly impact the energy and water balance and,
thus, the temperature and moisture of soil (Zeng et al., 2012; Zheng et al.,
2012). SFC(1) adopts the Monin–Obukhov similarity theory (MOST) in its
general form, whereas SFC(2) uses the improved MOST modified by Chen et
al. (1997). In SFC(1), the roughness length for heat (Z0h) is taken as
the same as the roughness length for momentum (Z0m; Niu et
al., 2011). SFC(2) adopts the Zilitinkevich approach for Z0h
calculation (Zilitinkevich, 1995). The difference between SFC(1)
and SFC(2) has a great impact on the CH value. Several studies have reported
that SFC(2) shows better performance for the simulation of sensible and
latent heat on the QTP (Zhang et al., 2016; Gan et al., 2019). The
results of the t test in this study showed remarkable distinctions between the
two schemes, where SFC(2) was dramatically superior to SFC(1) (Figs. 7,
8). SFC(2) produces lower CH values than SFC(1) (Zhang et al., 2014), resulting in
less efficient ventilation and greater heating of the land surface (Yang et
al., 2011b) as well as a substantial improvement in the cold bias of Noah-MP in this
study (Figs. 7, 10).
Both SFC(1) and SFC(2) could not produce the diurnal variation in Z0h
(Chen et al., 2010). CMB offers a scheme that considers the diurnal
variation in Z0h in bare ground and under-canopy turbulent exchange in
sparse vegetated surfaces (Li et al., 2020). Consistent with previous
studies on the QTP (Chen et al., 2010; Guo et al., 2011; Zheng et al., 2015;
Li et al., 2020), the simulated ST generally followed SFC(2) + CMB(2) > SFC(2) > SFC(1) + CMB(2) > SFC(1)
with/without removing the overestimation of snow (Fig. 10), indicating that
CMB(2) contributes to resolving the cold bias of the LSMs. However, none of the
four combinations could satisfactorily reproduce the shallow and deep STs
simultaneously. When the snow was well-simulated, SFC(2) + CMB(2) performed
the best in the deep layers at the cost of overestimating the shallow STs.
Meanwhile, SFC(1) + CMB(1) showed the best agreement in the shallow layers with a
considerable cold bias in the deep layers, which could be related to the
overestimated frozen soil thermal conductivity (Luo et al., 2009; Chen et
al., 2012; Li et al., 2019).
Supercooled liquid water (FRZ) and frozen soil permeability (INF)
FRZ and INF describe the unfrozen water and permeability of frozen soil, and they
had a larger influence on ST and SLW during the cold season than during the warm season, as
expected (Fig. 5). Specifically, FRZ treats liquid water in frozen soil
(supercooled liquid water) using two forms of the freezing-point depression
equation. FRZ(1) takes a general form (Niu and Yang, 2006), whereas
FRZ(2) exhibits a variant form that considers the increased surface area of
icy soil particles (Koren et al., 1999). FRZ(2) generally
yields more liquid water compared with FRZ(1) (Fig. S2). INF(1) uses soil
moisture (Niu and Yang, 2006), whereas INF(2) employs only the liquid
water (Koren et al., 1999) to parameterize soil hydraulic
properties. INF(2) generally produces more impermeable frozen soil than
INF(1), which is also found in this study (Fig. S3). For the whole year,
INF(1) surpassed INF(2) with respect to simulating STs, which may be related to the more
realistic SLWs produced by INF(1) for the whole soil column (Fig. S3).
Canopy gap for radiation transfer (RAD)
RAD treats the radiation transfer process within the vegetation and adopts
three methods to calculate the canopy gap. RAD(1) defines the canopy gap as a
function of the 3D vegetation structure and the solar zenith angle, RAD(2)
employs no gap within canopy and RAD(3) treats the canopy gap from unity
minus the vegetation fraction (Niu and Yang, 2004). The RAD(3) scheme allows
the most solar radiation to penetrate to the ground, followed by the RAD(1) and RAD(2)
schemes. As it is an alpine grassland, there is a relative low LAI at the TGL site
and, thus, quite a high canopy gap. Therefore, schemes with a larger canopy gap could
realistically reflect the environment. Consequently, the performance
decreased in the following order for the
ST and SLW simulation: RAD(3) > RAD(1) > RAD(2).
Snow surface albedo (ALB) and precipitation partition (SNF)
The ALB describes two ways of calculating snow surface albedo, in which
ALB(1) and ALB(2) adopt the scheme from the BATS and CLASS LSMs, respectively.
ALB(2) generally produces lower albedo than ALB(1), especially when the
ground is covered by snow (Fig. S4). As a result, higher net radiation is absorbed
by the land surface and more heat is available for heating the soil in
ALB(2), which is beneficial for counteracting the cooling effect of
overestimated snow on the ST (Fig. S5). Along with the higher ST, ALB(2)
outperformed ALB(1) with respect to SLW simulation, likely due to more snowmelt water
offsetting the dry bias in Noah-MP (Fig. S5).
The SNF defines the snowfall fraction of precipitation as a function of
surface air temperature. SNF(1) is the most complicated of the three
schemes, in which the precipitation is considered rain (snow) when the surface
air temperature is greater (less) than or equal to 2.5 ∘C (0.5 ∘C),
otherwise it is recognized as sleet, whereas SNF(2) and SNF(3) simply
distinguish rain or snow by judging whether the air temperature is above the respective thresholds of 2.2 and 0 ∘C or not. The significant difference between the
three schemes for SCE simulation during the warm season is consistent with
the large difference in the snowfall fraction in this period (Figs. 6, S6).
SNF(3) is the most rigorous scheme and produces the minimum amount of snow,
followed by SNF(1) and SNF(2) with limited difference (Fig. S6). This
specifically explains the superiority of SNF(3) for ST and SLW simulation (Figs. 7, 8).
Lower boundary of soil temperature (TBOT) and the snow/soil temperature
time scheme (STC)
The TBOT process adopts two schemes to describe the soil temperature boundary
conditions. TBOT(1) assumes zero heat flux at the bottom of the model,
whereas TBOT(2) adopts the soil temperature at 8 m depth (Yang
et al., 2011a). In general, TBOT(1) is expected to accumulate heat in the
deep soil and produce higher ST than TBOT(2). In this study, the two
assumptions performed significantly differently, especially in the deep soil layers
and during the cold season. Although TBOT(2) is more representative of
realistic conditions, TBOT(1) surpassed TBOT(2) in this study. This can be
related to the overall underestimation in the model, which can be alleviated
by TBOT(1) due to heat accumulation (Fig. S7).
Two time discretization strategies are implemented in the STC process –
STC(1) adopts the semi-implicit scheme, whereas STC(2) uses the fully implicit
scheme – to solve the thermal diffusion equation in first soil or snow layers
(Yang et al., 2011a). STC(1) and STC(2) are not strictly
physical processes, they are different upper boundary conditions of the soil column
(You et al., 2020a). The differences between STC(1) and STC(2) were
significant (Fig. 7). The impacts of the two options on ST are remarkable
(Fig. 6), particularly in the shallow layers and during the warm season
(Fig. 5). In addition, STC(1) outperformed STC(2) in the ensemble-simulated
ST (Fig. 7), because STC(1) greatly alleviated the cold bias in Noah-MP (Fig. S8) by producing a higher OA for SCEs (Fig. 6).
Perspectives
This study analyzed the characteristics and general behavior of each
parameterization scheme of Noah-MP at a typical permafrost site on the QTP,
hoping to provide a reference for simulating the permafrost state on the QTP. We
identified the systematic overestimation of snow cover, cold bias and dry
bias in Noah-MP, and discussed the role of snow and the surface drag coefficient
on soil hydrothermal dynamics. Further tests at another permafrost site (BLH
site; 34.82∘ N, 92.92∘ E; 4,659 m a.s.l.) basically
showed consistent conclusions with those from the TGL site (see the Supplement for details), indicating that relevant results and methodologies can be
practical guidelines for improving the parameterizations of physical
processes and testing their uncertainties towards soil hydrothermal modeling
in the permafrost regions of the plateau. Although the site that we selected may
be representative of the typical environment on the plateau, continued
investigation with a broad spectrum of climate and environmental conditions
is required to make a general conclusion at a regional scale.
Conclusions
An ensemble simulation using multi-parameterizations was conducted using the
Noah-MP model at the TGL site, aiming to present a reference for simulating
soil hydrothermal dynamics in the permafrost regions of the QTP using LSMs. The
model was modified to consider the vertical heterogeneity in the soil, and
the simulation depth was extended to cover the whole active layer. The
ensemble simulation consists of 55 296 experiments, combining 13
physical processes (CRS, BTR, RUN, SFC, FRZ, INF, RAD, ALB, SNF, TBOT, STC,
SUB and CMB), each with multiple optional schemes. On this basis, the
general performance of Noah-MP was assessed by comparing simulation results
with in situ observations, and the sensitivity of snow cover event, soil
temperature and moisture at different depths of the active layer to the
parameterization schemes was explored. The main conclusions of the study are as follows:
Noah-MP tends to overestimate snow cover, which is most influenced by
the STC and SUB processes. Such overestimation can be greatly resolved by
considering the snow sublimation from wind, SUB(2) and the semi-implicit
snow/soil temperature time scheme, STC(1).
Soil temperature is largely underestimated by the overestimated snow cover
and, thus, dominated by the STC and SUB processes. Systematic cold bias and
large uncertainties in soil temperature still exist after eliminating the
effects of snow, particularly in the deep layers and during the cold season.
The combination of the Y08 and UCT schemes contributes to resolving the cold bias of soil
temperature.
Noah-MP tends to underestimate the soil liquid water content. Most physical
processes have a limited influence on the soil liquid water content, among which
the RUN process plays a dominant role during the whole year. The STC and SUB
process have a considerable influence on topsoil liquid water during the
warm season.
Code availability
The original source code of the offline 1D Noah-MP LSM v1.1 is available at
https://ral.ucar.edu/solutions/products/noah-multiparameterization-land-surface-model-noah-mp-lsm
(last access: 23 February 2021). The modified Noah-MP LSM considering vertical heterogeneity in the soil profile, snow sublimation from wind, and
the combination of roughness length for heat and under-canopy aerodynamic
resistance can be downloaded from 10.5281/zenodo.4555449 (Li, 2021).
Data availability
The 1-hourly forcing data, daily soil temperature and liquid water content
at the TGL and BLH sites are available at
10.17632/h7hbd69nnr.2 (Li, 2020). Soil texture data can be obtained from
10.1016/j.catena.2017.04.011 (Hu et al., 2017). The AVHRR
LAI data can be downloaded from https://www.ncei.noaa.gov/data/
(last access: 27 March 2021, Claverie et al., 2016).
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-14-1753-2021-supplement.
Author contributions
TW and XL conceived the idea and designed the model experiments. XL
performed the simulations, analyzed the output and wrote the paper. JC
helped to compile the model in a GNU/Linux (CentOS 7.0) environment. XW, XZ,
GH and RL contributed to conducting the simulation and interpreting
the results. YQ provided the observations of atmospheric forcing and soil
temperature. CY and JH helped with downloading and processing the AVHRR LAI
data. JN and WM provide guidelines for the visualization. All authors revised
and polished the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The
authors thank the Cryosphere Research Station on the Qinghai–Tibet Plateau, CAS,
for providing field observation data and Guohui Zhao for providing
access to supercomputing resources. The authors are also grateful to Sizhong Yang
and the two anonymous reviewers for their insightful and constructive comments
and suggestions that greatly improved the quality of the paper.
Financial support
This research has been supported by the CAS “Light of West China” program, the National Natural Science Foundation of China (grant nos. 41690142, 41771076, 41961144021 and 42071093), the CAS “Hundred Talents program” (Sizhong Yang) and the National Cryosphere Desert Data Center Program (grant no. E0510104).
Review statement
This paper was edited by Juan Antonio Añel and reviewed by two anonymous referees.
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