Field measurements have shown that cold-season methane
(CH
Cold-season carbon emissions from the Arctic tundra could potentially offset
warm-season net carbon uptake under 21st century warming climate
(Commane et al., 2017; Oechel et al., 2014, 2000; Koven et
al., 2011; Piao et al., 2008; Natali et al., 2019; Belshe et al., 2013;
Fahnestock et al., 1998; Jones et al., 1999). Field measurements have
indicated large cold-season CO
In ESM land models, soil environment influences soil microbial heterotrophic
respiration (HR) and decomposition of soil organic carbon (SOC) mainly
through applying prescribed temperature and moisture functions to modify
base decomposition rates. These functions, however, rely heavily on
empirical or semi-empirical relationships which are highly uncertain (Sierra et al., 2015, 2017; Yan et al., 2018; Moyano et
al., 2013; Tang and Riley, 2019; Rafique et al., 2016; Bhanja and Wang,
2020; Kim et al., 2019). Specifically, the temperature sensitivities of soil
carbon decomposition is often represented with a
The strong dependency of CO
We hypothesize that the underestimation of modeled cold-season CO
The paper is organized as follows. (1) We describe the study sites and the
data used in the study. (2) We present the theoretical background of
essential modules of ELMv1-ECA relevant to this study and our modifications
to the model's representations of phase-change, SOC decomposition, and
methane dynamics. (3) We then describe the model configuration and
experimental design. (4) We assess the modified phase-change scheme by
comparing simulated soil temperatures and ZCPs against observations. (5)
With the revised phase-change scheme, we analyze how the parameterization of
decomposition schemes and methane module impact simulated CO
We assembled daily observations of CO
Red dots indicate the five ABoVE flux tower sites used in this study. Cyan circles are GIPL-UAF permafrost sites.
ABoVE and CARVE provide soil temperature and moisture measurements at
various depths from 5 to 40 cm. The Permafrost Laboratory, Geophysical
Institute of University of Alaska Fairbanks (GIPL-UAF), provides daily
subsurface soil temperature observations down to various depths at
permafrost sites across
Alaska (
The observed soil moisture is only available at two or three depths that are quite different from model layer node depths and also show discontinuities in time. Thus, evaluation of ELMv1-ECA simulated liquid water content was limited. We matched soil moisture observations to the vertically closest model layer and then evaluated the simulated volumetric fraction of soil liquid water content at layers for time periods during which observations were available. In addition, we used ABoVE observed maximum soil moisture to infer site-scale soil porosity and then organic carbon content at IVO (see Sect. 3.2), which is used to prescribe thermal and hydraulic soil properties. Note that carbon substrate for respiration is simulated dynamically in the model (see Appendix B).
The E3SM land model version 1 (ELMv1-ECA) couples essential biogeophysical
and biogeochemical processes that solve terrestrial ecosystem energy, water,
carbon, and nutrient dynamics (Golaz et al., 2019; Zhu et al., 2019). In
the appendix, we describe in detail its subsurface soil thermodynamics, the
carbon decomposition module, and the methane module that are of particular
relevance to our study. Here we identify the potential problems of ELMv1-ECA
that are responsible for the underestimation of cold-season CH
We first improved ELMv1-ECA's numerical representation of coupled water and
heat transport with freeze–thaw processes via improving the phase-change
scheme. The freeze–thaw processes of soil water within ELMv1-ECA is
simulated in a decoupled way; i.e., it solves soil temperatures ignoring the
latent heat associated with phase change, determines the mass change of soil
water required to adjust the initially solved soil temperature to the
freezing point (i.e., 0
Here, we employed a phase-change efficiency and the temperature of the
freezing-point depression to effectively solve the problem of overestimating
phase-change rates within the current ELMv1-ECA modeling structure. These
modification factors are explained below. The phase-change efficiency,
introduced by Le Moigne et al. (2012) and adopted by Masson et al. (2013) and Yang et al. (2018a), introduces the
dependency of available liquid water on the phase-change rate (Le Moigne et al., 2012). The phase-change efficiency
for freezing,
As in Nicolsky et al. (2007) and Yang et al. (2018a), the occurrence of a phase-change process is then determined by the
temperature of the freezing-point depression (i.e., an virtual temperature;
see Eq. A10) instead of
We describe in detail the revised phase-change scheme in the Appendix A. In short, we improved the phase-change scheme of ELMv1-ECA by incorporating two modifications: (1) applying a phase-change efficiency to implicitly account for the heat compensation/deduction to the system from latent heat released/absorbed by soil water freezing/melting and (2) replacing the constant freezing point with the temperature of the freezing-point depression, as a virtual temperature, to determine the occurrence of phase change in subfreezing soils.
We revisited ELMv1-ECA's representation for soil heterotrophic respiration
dynamics in subfreezing soils and then scrutinized the environmental scalars
of soil temperature and moisture. Within ELMv1-ECA's decomposition cascade
model, the environmental factors that impact the decomposition rates of soil
organic matter include soil temperature (
For wet soils, the factor that primarily limits the decomposition rates is
oxygen availability (Sierra et al., 2017; Yan et al., 2018), since
increases in soil moisture result in decreased dissolved oxygen. ELMv1-ECA
approximates oxygen stress (
ELMv1-ECA uses a
The ELMv1-ECA methane model solves the reaction and diffusion equation for
CH
Here, we first modified the ELMV1-ECA CH
Table 1 summarized all the specific modifications
made to ELMv1-ECA. These modifications involve new parameters that are all
tuneable and can be systematically optimized via calibration. Here, we seek
to reproduce the first-order cold-season process relevant to this study with
the default formation and values listed in Table
1. We also conducted sensitivity tests on three variables three key
parameters related to CH
Specific modifications made to ELMv1-ECA.
We conducted transient simulations at 30 min temporal resolution driven
by climate forcing from
Baseline simulations were conducted with ELMv1-ECA default physics, parameters, and surface datasets, i.e., OriPC_OriDecomOriCH4 using original phase-change scheme, original decomposition scheme, and methane module (Table 2). To improve the model representation of the site-level soil environment, we first examined the global soil organic matter data at the ABoVE sites by evaluating ELMv1-ECA simulated subsurface soil temperature with the topsoil temperature prescribed to observations (as did in Tao et al., 2017). Using the top soil layer as the upper boundary, the modeling system excluded potential errors induced by inaccurate meteorological forcing and vegetation cover that impact the simulation of heat transfer from the atmosphere to the shallow soil (Tao et al., 2017). Then, the accuracy of simulated soil subsurface temperature is directly determined by the factors impacting heat transfer along the “shallow-to-deep soil” gradient (Koven et al., 2013a), e.g., soil thermal properties which are mostly determined by SOC content (Tao et al., 2017; Lawrence and Slater, 2008). Results well reproduced the subsurface soil temperatures except at IVO, where summer soil temperatures were notably overestimated (see Fig. S2a). This result indicates that the SOC content at IVO was too small, leading to a large thermal conductivity, small soil porosity, and small heat capacity, altogether resulting in fast penetration of heat into the subsurface soil during summer (Tao et al., 2017; Lawrence and Slater, 2008). Thus, we derived the organic matter density at IVO based on ABoVE soil moisture data through a linear relationship between SOC content and soil porosity (i.e., Eq. 3 in Lawrence and Slater, 2008), assuming the observed maximum volumetric water content was porosity (see Fig. S3 for details). With the newly derived profile of soil organic matter density at IVO, the simulation showed large improvements in summer soil temperatures compared to that using the original global carbon dataset (see Fig. S2b). The derived SOC content is also consistent with the organic layer thickness reported in Davidson and Zona (2018). Hereafter, the simulations at IVO presented in this paper use the newly derived organic carbon data without repeated clarification.
List of designed experiments.
The representative spatial scale of the eddy flux tower is small compared to
the grid cell of global surface datasets and the climate forcing data used
by ELMv1-ECA, although the forcing dataset was interpolated to the site
scale with a bilinear or nearest-neighbor method. The site-scale vegetation
cover also shows a large diversity of vegetation types according to the
detailed vegetation survey at ABoVE flux tower footprints obtained in 2014 (Davidson and Zona, 2018). The ELMv1-ECA's default plant type
function (PFT) dataset was derived from satellite-based data by Lawrence et al. (2007). We analyzed the vegetation composition
from the closet survey plot to the flux tower and examined the rationality
of ELMv1-ECA's percentage of PFT for the site-scale simulation through
testing different PFT datasets derived from this vegetation survey (Davidson and Zona, 2018). We found that these PFT datasets
generally are not superior to the original PFT dataset, which generally
reproduced satellite-based gross primary production (GPP; Fig. S4). We thus confirmed that
ELMv1-ECA's PFT dataset was a good compromise between representing the
site-scale ecosystem and other global parameters and surface datasets within
the model. The surface CH
Table 2 lists the experiments conducted in this
study. We modified each model component (i.e., the heat transfer model,
carbon decomposition model, and methane model) serially. All the experiments
ran through 1901 to 2017 with spin-up as described earlier, although the
evaluation and optimization were conducted only using results from 2013 to
2017. We first ran simulations with the 814 environmental modifiers together
with the modified methane model with default parameterization (Table S3).
Then, we selected the environmental modifiers that provided satisfactory
performance in simulating CO
We define the early cold season as September and October; the cold-season
period as September to May, which includes the two shoulder seasons (both
thawing and freezing) as consistent with Zona et al. (2016); and the
warm season from June to August. We define the zero-curtain period (ZCP) as
the set of successive days when the soil temperature is within the range of
[
To evaluate ELMv1-ECA simulated soil temperature and moisture, we calculated
the RMSE for each soil layer, i.e.,
Here the modeled active layer thickness (ALT), i.e., maximum thaw depth
during an annual cycle, is computed as the bottom depth of the deepest
thawed soil layer (i.e., with a maximum annual temperature above
0
We used Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe,
1970) to examine the performance of the ELMv1-ECA simulated time series of
CH
We optimized the model simulations through two steps. Specifically, we first
evaluated the simulations using (814) environmental modifiers to the base
decomposition rate that assembled commonly used empirical soil temperature-
and moisture-dependency functions (Table S2). These simulations used the
newly modified methane model with the default parameters (Table S3). We
selected the common decomposition schemes that provided satisfactory results
of CO
Further, among the common parameterizations of environmental modifiers and
CH
In general, we use NSE to evaluate the model's performance in capturing
seasonality (i.e., time series) of CH
We first evaluated the simulated daily soil temperature profiles against the observations from ABoVE and GIPL-UAF at the four site locations. Then, we examined improvements in simulations of soil temperature, soil moisture, and the durations of ZCPs by employing the newly revised phase-change scheme (i.e., NewPC_OriDecomOriCH4; Table 2).
Results for the BES/CMDL and IVO site are shown in
Fig. 3; results for other sites are shown in Fig. S4 in the Supplement. At BES/CMDL, the baseline (i.e.,
OriPC_OriDecomOriCH4; Table 2)
simulated soil temperatures (Ts) with the default phase-change scheme
(Ts_OriPC; blue lines; Fig. 2a)
decrease rapidly in fall due to the overestimated freezing rate (i.e., the
slope of decreasing liquid water fraction), notably underestimating the
duration of the ZCP (bluish shaded area). Consequently, liquid water
saturation (
Comparison of multi-year (2013–2017) averaged daily soil
temperatures observed (Ts_Obs, black) and simulated with the
original (Ts_OriPC, blue) and improved (Ts_NewPC, red) phase-change schemes at BES/CMDL
Comparison between observed and ELMv1-ECA simulated durations of
ZCP for the original (OriPC; open diamonds) and improved (NewPC; solid
circles) phase-change schemes over four annual cycles (July to June) from
2013 to 2017. “ly” means model layer. Simulated ZCP durations with NewPC
demonstrate significant improvements compared to OriPC (solid dots vs. open
diamonds), especially for the fourth to the deepest layer above
permafrost. Note that a zero-day ZCP means that the maximum daily temperature
during an annual cycle is below 0
Simulated ZCP durations with the revised phase-change scheme (NewPC)
demonstrated notable improvements over the baseline (original) phase-change
scheme (OriPC) (solid circles vs. open diamonds)
(Fig. 3), showing greatly reduced mean absolute
errors (MAEs) (Table 3). For example, at 12 cm depth
(fourth layer), the relative improvements in MAE of the ZCP durations were
65 %, 65 %, 66 %, and 50 % for the four site locations
(Table 3). The largest improvement in MAE was as
large as 65 d for the sixth layer at BES/CMDL, with a relative
improvement of 84 % (Table 3). This large improvement stems from the
better-estimated ALT at this site; the OriPC simulated sixth-layer
temperature remained below freezing, leading to a zero-day ZCP (diamonds on
the
Mean absolute error (MAE) of simulated ZCP (days) with the original
phase-change scheme (Ori_PC) and newly resized phase-change
scheme (NewPC), and the relative improvement (%) of using the new
phase-change scheme compared to the baseline results, calculated as 100 %
The deeper active layer simulated by NewPC implies more soil water storage
capacity, resulting in lower soil moisture in shallow soil layers and higher
soil water in deep layers (
The changes to model representations of phase change led to large reductions in soil temperature bias. The relative improvements in RMSE of simulated soil temperatures during September and October (i.e., the 2 months that the ZCPs usually cover) generally increased with depth for surface layers (within about 20 cm of the surface, i.e., first to fourth layer) and were above 80 % for the intermediate layers (fifth to eight) at all the sites (Fig. 4). At the two Barrow sites where observed soil temperatures were available, the relative improvements for the deepest (13th) layer were 72.6 % and 71.1 %, on average, for the early winter and annual cycle, respectively. Therefore, incorporating the new phase-change scheme also resulted in improved bottom temperature boundary conditions, which is critical for accurately simulating permafrost dynamics (Sapriza-Azuri et al., 2018). Improvements between September and December and the whole annual cycle also increased with soil depth, showing site-averaged reductions in RMSEs ranging from 47 % to 63 % and from 36 % to 46 % for the two periods, respectively. The whole cold-season period (September to May) showed, on average, 44 % to 53 % reduction in RMSEs from the first to sixth layer at relatively warmer sites (i.e., ATQ and IVO) and a reduction from 19 % to 69 % for the top 13 layers for the two Barrow sites. Also, after the freezing process ends, simulated deeper soil layer temperatures were underestimated (e.g., December through April). This bias might be caused by underestimated snow depth (Fig. S9) possibly resulting from inaccurate forcing (particularly snowfall), land cover, microtopography, and/or windblown snow redistribution.
Relative improvement in the RMSE of simulated soil temperature
with the new phase-change scheme (RMSE_Ts_NewPC) compared to that with the original scheme (RMSE_Ts_OriPC), calculated as 100 %
Simulations with the new phase-change scheme also show improved agreements
between simulated and observed soil temperatures during the spring thawing
season compared to the baseline results (red vs. blue in Fig. 2). Compared
to observations, the newly simulated soil temperatures were still slightly
underestimated during the thawing season (i.e., May) at all four sites,
showing later onset of thawing indicated by the timing when warming soil
temperatures cross 0
The improved simulations of soil temperature, liquid water content, and ZCP
duration greatly impacted soil HR and methane production but did not
necessarily guarantee improvements in CO
Here we evaluate the simulated monthly CO
Scatter plot between the Nash–Sutcliffe efficiency (NSE)
of simulated monthly CH
Observed and simulated monthly CO
The improved phase-change scheme, and thus improved simulations of ZCP
durations and soil temperature and moisture, resulted in greatly improved
performance for CO
Figure S8 illustrates the uncertainty associated with the model
representations of environmental influences on heterotrophic respiration and
methane parameters. The optimal simulations at the study sites used
either the modified ELMv1-ECA moisture scalar or Yanetal (see Table S6), i.e., two
groups of moisture-dependency functions implemented for each soil layer. For
the Sierra et al. (2015) empirical moisture functions, the influence of
liquid moisture content on heterotrophic respiration is uniformly applied to
all active soil layers, even though the soil properties (e.g., porosity and
saturated soil water potential) are quite different vertically. ELMv1-ECA's
moisture scalars (including the original scheme) that use soil water
potential, in contrast, reasonably explained the varying influence along
with the vertical soil profile (i.e., relationships between soil liquid
water content and soil temperature varies with soil clay fraction as
demonstrated by Fig. 1 in Niu and Yang, 2006). The Yanetal moisture functions
also used soil-layer-dependent porosity and clay content to calculate
relevant parameters (Yan et al., 2018). The simulations with moisture
functions documented in Sierra et al. (2015) generally overestimated
CO
Reducing the minimum soil water potential
The optimal simulations used Daycent2 temperature-dependency function at ATQ
and
The extended ZCPs, the revised environmental modifier to decomposition, and
the modified CH
Comparison of multi-year (2013–2017) averaged monthly mean
net CO
Throughout this section, we only retain and discuss the identified optimal
simulation results (i.e., ELM_NewPC_Optimized)
for each site. To better verify the cold-season contribution of CH
The new simulation results with the optimal parameterization showed greatly
enhanced performance in terms of capturing the averaged seasonal cycle (red;
Fig. 8), especially for the cold-season months
(September to May; Fig. 8), reducing site-averaged
MAEs in cold-season total CH
Multi-year (2013–2017) averaged cumulative CH
Total CH
The optimized simulations showed larger improvements in cold-season CO
The observed multi-year averaged annual net CO
Through trend analysis between 1950 and 2017, we found that the ZCP
durations showed increasing trends at all three sites, with ZCP trends
increasing with depth (Table 6). At ATQ, a warmer
site than BES/CMDL and BEO, the trends of ZCP durations increase from 0.12
to 0.49 d yr
Historical trend of ZCP durations (days year
Historical trend (1950–2017) in site-scale heterotrophic
respiration, CH
In this study, we improved ELMv1-ECA simulated subsurface soil temperature,
zero-curtain period durations, and cold-season CH
With the revised phase-change scheme, the updated ELMv1-ECA greatly improved site-scale simulations of soil temperature, soil moisture, and zero-curtain period. Specifically, the RMSE of daily subsurface soil temperature was substantially reduced compared to the baseline simulation, showing site-averaged improvements ranging from 58 % to 87 % over the early cold season (September to October) and from 36 % to 46 % over the annual cycle for soil layers within the active layer. The evaluation of simulated liquid water content with the new phase-change scheme, although limited by the availability of observations, showed a relative reduction in RMSE as high as 43 % for the fifth layer at ATQ and site-averaged improvements of 15 % and 21 % for the fourth and fifth layer, respectively. Simulated ZCP durations were also greatly improved, with, e.g., relative reductions in MAEs of 65 %, 65 %, 66 %, and 50 % for the fourth layer (about 12 cm) at BES/CMDL, BEO, ATQ, and IVO, respectively.
Based upon the improved simulations of soil temperature and moisture with
the new phase-change scheme, the optimized parameterization for SOC
decomposition scheme and the revised methane module, the site-averaged mean
annual errors of cold-season emissions were reduced by 72 % and 70 %
for CH
Although showing improvements compared to baseline results, the new
simulations generally overestimated the contribution of the early-cold-season (September and October) CO
The underestimated emissions during post-ZCP months (October to November) may be
caused by the lack of sudden bursts of CO
Given the persistent warming and the continued more severe warming in the
cold season (Box et al., 2019), we envision continuing increases in
cold-season CO
ELMv1-ECA approximates the subsurface heat transfer process with a one-dimensional heat diffusion equation:
ELMv1-ECA incorporates freeze–thaw processes of soil water in a decoupled
way. Specifically, the model determines the onset of thawing or freezing by
soil temperature initially solved at time step
The rate of phase change is initially assessed from the heat excess (or
deficit) needed to change the estimated temperature to the freezing point.
Specifically, the model first computes the energy (
The
To improve this scheme, we can incorporate soil water freezing phase change into equation (Eq. A1) and rewrite the heat transfer equation as (Eq. A7) or (Eq. A8),
Thus, we revised the phase-change scheme mainly through incorporating a
phase-change efficiency (
Then, through multiplying the initially estimated mass change (
Here,
ELMv1-ECA explicitly simulates carbon cycle dynamics (both plant and soil) and accounts for the limitation of nutrient (i.e., nitrogen and phosphorus) availability for plant growth and the nutrient competition between plants and microbes (Burrows et al., 2020; Zhu et al., 2019; Golaz et al., 2019; Zhu et al., 2020). The ELMv1-ECA uses a century-like soil carbon decomposition cascade model with vertically resolved soil biogeochemistry (Koven et al., 2013b) and explicitly accounts for the influence of substrate and nutrient availability on soil respiration (both root and microbes) (Zhu et al., 2019).
Within the ELMv1-ECA century decomposition cascade model, the respiration
fractions are parameterized as the fraction of the decomposition carbon flux
out of each carbon pool, including litter and soil organic matter. The base
decomposition rate is modified by a function representing environmental
controls on soil decomposition, which accounts for the impacts of individual
factors including temperature (
We use a Q
The depth scalar
The ELMv1-ECA methane model includes the representations of CH
The
The ultimately estimated CH
Vascular plant aerenchyma tissues serve as diffusive pathways to transport
CH
In ELMv1-ECA, methane emissions through aerenchyma were turned off when the
soil temperature is below 0
The
Another key variable that is highly uncertain is snow resistance to gas emissions. When snow is present, the upper boundary layer resistance to gas emissions is added by a snow resistance accounting for diffusion through the snow based on the Millington–Quirk expression (Riley et al. 2011). Specifically, the gaseous and aqueous diffusivity in snow is calculated by (Eq. C5)
The observations used in this study are available at
The supplement related to this article is available online at:
JT assembled observations, developed the methodology, conducted model simulations, analyzed results, and wrote and revised the paper. QZ and WJR contributed to the experiment design and to editing the original and revised manuscript. RBN edited the original and revised manuscript and provided valuable discussion and guidance.
The authors declare that they have no conflict of interest.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We are grateful for valuable discussions with Jinyun Tang, Roisin Commane, Xiyan Xu, Kai Yang, and Chenghai Wang. We thank the anonymous reviewers for their helpful comments.
This research has been supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (grant no. DE-SC0019063).
This paper was edited by Christian Beer and reviewed by two anonymous referees.