Geophysical Research Letters
Supporting Information for
Rising methane emissions from northern wetlands associated with sea ice decline
Frans-Jan W. Parmentier1,2, Wenxin Zhang1, Yanjiao Mi3,4, Xudong Zhu5, Jacobus van
Huissteden3, Daniel J. Hayes6, Qianlai Zhuang5, Torben R. Christensen1,2 and A. David McGuire7
1 Department
of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden.
2 Arctic
3 Faculty
of Earth and Life Sciences, VU University Amsterdam, Amsterdam, the Netherlands.
4 Institute of
5 Department
Research Centre, Aarhus University, Aarhus C, Denmark.
Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, USA
of Earth, Atmospheric, and Planetary Sciences and Department of Agronomy, Purdue University, West
Lafayette, Indiana, USA.
6 Environmental Sciences Division,
7 US Geological
Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
Survey, Alaska Cooperative Fish and Wildlife Research Unit, University of Alaska Fairbanks,
Fairbanks, Alaska, USA.
Contents of this file:
Text S1 to S2
Figures S1 to S5
Introduction:
This supplemental information consists of a description of the wetland map used by the
three process models included in this study, and a detailed description of those models,
as well as a discussion of the similarities and dissimilarities between them. The
supplementary figures show the mean modeled methane flux for each of the three
models, the correlations to sea ice for each model, temperature and precipitation, and
the difference in trends between sea ice-affected and unaffected areas. The latter is
equal to the subtraction of the trends shown in Figure4d from those in Figure 4c in the
main document.
Text S1. Wetland Map
For prescribing wetland area in the model simulations, we used the pan-Arctic gridded data set
developed by Hayes et al. [Hayes et al., 2011]. Wetland area was assigned to each grid cell
based on the downscaling of a one-degree grid cell fraction inundated database [Matthews and
1
Fung, 1987], where wetland area equals the product of fraction inundated and total cell area, to
the half-degree cell resolution used for the simulations. The downscaling method avoids a
simple division of the one-degree area equally among the four corresponding half-degree cells
since some of those half-degree cells may contain land cover types where wetland areas do not
or rarely occur. Rather, the method uses a prioritized list of all sub-grid “cohorts” within the
corresponding half-degree cells to preferentially distribute the one-degree wetland area
according to vegetation types that are more likely to contain wetlands. For example, boreal
needleleaf forest or shrub tundra cohorts were given higher priority than polar desert or xeric
shrublands. The underlying vegetation map was based on the Global Land Cover
Characterization [Loveland et al., 2000], where its more than 200 detailed categories were
aggregated into 13 more generalized types for which process-based models are typically
calibrated. In the downscaling method, each of 10 upland categories could potentially have
wetland associates - except for barren, ice and water types, which were excluded. The resulting
wetland map, then, provides wetland areas delineated by associated vegetation type for each
half-degree grid cell across the study domain. Since the wetland map is expressed as a simple
fraction of wetlands for each grid cell, the r-values of the correlations with sea-ice are
mathematically independent of this multiplier, apart from those areas where wetland coverage
is set to zero. However, TEM6 does simulate a low, near zero, methane uptake in the aerobic
soil of those areas, which can also affect correlations.
Text S2. Descriptions of the Models Used
S2.1. LPJ-GUESS
LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) [Smith et al., 2001] is a
process-based model of biogeochemistry and vegetation dynamics optimized to resolve
heterogeneities of vegetation structures and functions at both regional and continental scale.
In LPJ-GUESS, plant physiology, biophysical and biogeochemical processes are represented
mechanistically, based on the same formulations as the LPJ-DGVM (dynamic global vegetation
model) [Sitch et al., 2003; Gerten et al., 2004], but it differs from the generalized large-area
parameterization of vegetation structure and population dynamics used in LPJ-DGVM,
adopting instead gap model formalisms based on explicit representations of growth and
competition among cohort-averaged woody plant individuals – described by plant functional
type (PFT) – and a herbaceous understory.
In this paper, we have used an updated version of LPJ-GUESS, customized for upland and
wetland ecosystems for the Northern High Latitudes, which we refer to as LPJ-GUESS WHyMe.
In the upland ecosystem, Arctic-specific plant functional types have been adopted to
encompass deciduous and evergreen shrubs and four open-ground tundra species [Wolf et al.,
2008]. In the wetland ecosystem, the model has incorporated recent developments to LPJDGVM by Wania et al. [LPJ WHyMe v1.3.1, see Wania et al., 2009a; 2009b; 2010]with regard to
soil thawing-freezing processes, peatland hydrology, peatland PFTs, and methane dynamics.
The climatic forcing for these processes was retrieved from version 3.2 of the CRU climate
dataset [CRU, University of East Anglia Climatic Research Unit (CRU) et al., 2013].
We ran LPJ-GUESS WHyMe only for the peatland fraction of each grid cell (0.5×0.5 degree) to
simulate daily methane production (via diffusion, plant-mediated and ebullition pathways) and
oxidation in response to soil temperature and water table depth. To produce methane, part of
the litter, soil carbon and exudate carbon pools supply carbon substrates as a potential carbon
2
pool available for methanogenesis. All of the potential carbon is allocated to each soil layer in
proportion to plants’ root biomass distribution among layers. Diffusion of methane underneath
water uses the Crank-Nicolson numerical scheme to solve the diffusion equation for gas
transport via molecular diffusion within the soil. The gas diffused from the water and air
interface is instead derived from a robust approach based on the difference between gas
concentration measured in the surface water and the equilibrium in the atmosphere and gas
exchange coefficient [for more details, see Wania et al., 2010]. Plant-mediated transport occurs
via the flood-tolerant C3 graminoid PFT, with the gas flux through vascular plants being related
to the cross-sectional area of tillers available to transport gas. Diffusion and plant-mediated
transport of oxygen are also simulated to determine how much methane is oxidized. Ebullition
of gas is simulated based on Henry’s Law, the ideal gas law and findings from recent field and
laboratory experiments. The water table depth is updated daily in response to precipitation,
snowmelt, evapotranspiration and surface runoff using the hydrology scheme [Granberg et al.,
1999; Wania et al., 2009a].
S2.2. TEM6
The Terrestrial Ecosystem Model (TEM) is a process-based ecosystem model that simulates
terrestrial carbon, water, and nutrient dynamics and their interactions in plants and soils [Raich
et al., 1991; McGuire et al., 1992; Melillo et al., 1993]. The model version of TEM used here,
TEM6 [McGuire et al., 2010; Hayes et al., 2011], is comprised of several modules, mainly
including a carbon and nitrogen dynamic module (CNDM), soil thermal module (STM),
hydrological module (HM), and methane dynamic module (MDM). These modules are closely
coupled to simulate soil thermal/hydrological regimes and biogeochemical processes including
carbon and methane cycles in terrestrial ecosystems. Here, we only introduce the MDM that is
directly relevant to this study. In TEM6, methane-related processes including methane
production, oxidation, and transport are explicitly represented in MDM [Zhuang et al., 2004].
Methane production (oxidation) is modeled as an anaerobic (aerobic) process in saturated
(unsaturated) soil layers below (above) simulated water table, and these two biotic processes
are formulated as functions of changing soil conditions (e.g., substrate availability, soil
temperature, moisture, pH, etc.) as adjusted by climate and vegetation dynamics. Three
transport pathways (diffusion, ebullition and plant-mediated transport) are represented to
simulate the methane transport from soil column to the atmosphere. The model was developed
to estimate net methane exchange at a daily time step.
To make spatially and temporally explicit estimates of daily methane fluxes at each grid cell (0.5
degree by 0.5 degree) in this study, we used the environmental driver data and simulation
protocol from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project
[Huntzinger et al., 2013; Wei et al., 2014] including monthly climate fields (shortwave radiation,
temperature, precipitation, water vapor), potential vegetation, and soil texture [Zhuang et al.,
2003]. To run the model at a daily time step, we converted monthly climate data into daily data
using a downscaling statistical algorithm for precipitation and a simple linear interpolation for
other climate data [Zhuang et al., 2004]. The static vegetation and soil datasets were used to
assign calibrated vegetation- and soil-specific parameters to each grid cell. Based on the
regional wetland area map described above, we assumed methane emissions occurred only in
the wetland part of a 0.5-degree grid cell while methane consumption only in the upland part,
and grid-cell-mean methane flux is determined as area weighted mean of wetland and upland
ecosystems. The wetland/inundation fraction dataset was used to calculate the proportions of
wetland and upland ecosystems in each grid cell.
3
S2.3. Peatland-VU
Peatland-VU is a process-based model designed to simulate methane (CH4) and carbon dioxide
(CO2) fluxes from a column of peat soil [van Huissteden et al., 2006; Mi et al., 2014]. The latest
version of Peatland-VU consists of six submodels: a CH4 submodel, a CO2 submodel, an organic
production submodel, a soil physics submodel, a hydrology submodel, and a gross primary
production (GPP) submodel. The CH4 submodel is based on Walter and Heimann [Walter and
Heimann, 2000]. CH4 fluxes depend on CH4 production in the anaerobic soil zone, its
consumption by methanotrophic bacteria in the aerated zone and the different transport
pathways to the atmosphere. The boundary of the anaerobic/aerated zones is defined by the
ground water table, which is simulated by the hydrology submodel.
CH4 production is temperature dependent and linearly related to substrate availability. The soil
temperature gradient is calculated using thermal balance equation. The thermal diffusivity is
estimated from the volumetric heat capacity and thermal conductivity. The substrate is the
sum of labile soil organic matters (SOM), which in turn is closely related to gross primary
production (GPP), calculated by the GPP submodel.
CH4 oxidation is temperature sensitive and calculations are based on the CH4 concentration.
The model simulates CH4 transport in three ways: transport by diffusion above and below the
water table, transport by bubble formation (ebullition) below the water table, and transport
through plants.
In this study, Peatland-VU is run to simulate circumpolar CH4 emission from wetlands,
representing the wetland area percentage in 1 x 1 degree grid cells. For each grid cell, the model
is parameterized separately for peatland, floodplain soils and other wetland soils (tundra gley
soils). The latter two are parameterized as fine-grained mineral soils with an organic horizon
that is too thin to classify as histosol. Although the floodplain soils comprise a relatively small
area, they may show high CH4 fluxes [van Huissteden et al., 2005; Mi et al., 2014], and are,
therefore, modeled separately. The organic horizon thickness in these soils has been set at 0.4
m, while for peat soils the entire soil column (1.5 m) is considered to consist of peat. For all soils,
a 0.2 m thickness of low bulk density topsoil has been assumed (acrotelm). For calculating the
soil temperature profile, the lower boundary of the profile is set at 20 m below surface; the
lowest horizon in the profile is assumed to extend to this depth, except for peat soils where a
transition between peat and mineral material is assumed to occur at 2 m depth.
The soil classes have been derived from the FAO/UNESCO world soil map [FAO, 1971]. Climate
data input for the model consists of daily mean air temperature at 2 meter, daily precipitation
and evaporation, derived from the ERA-Interim reanalysis [Dee et al., 2011]. Snow cover
thickness was obtained from ERA-Land [Albergel et al., 2013; Balsamo et al., 2013] due to a
known processing error in the quality of the snow cover thickness data between 2002 and 2010
in ERA Interim. The model has been run with a two-year spin-up. The modeling framework
calculates an area-weighted average flux over all three soil types for each grid cell.
S2.4 Commonalities Between the Three Models Used in This Study
Good agreement between the models may be an indication of the robustness of our
conclusions, if the models simulate methane emissions independently enough. Indeed, there
are substantial differences between the implementation of the various processes among the
4
models. For example, the methane subroutine employed by LPJ-GUESS has been developed
independently from the other two models (Wania et al., 2010), where methane production is
simulated as a fixed percentage of respiration, and oxidation according to oxygen availability.
On the other hand, TEM6 and Peatland-VU do share components originally proposed by Walter
and Heimann (2000). Nevertheless, these two models show the largest differences both in
spatial pattern and flux amplitude. This seems surprising, since both models employ a Q10
relationship with temperature to simulate methane production, and a Michaelis-Menten
function to simulate methane oxidation (van Huissteden et al., 2006; Zhuang et al., 2004).
However, these models require – in addition to temperature – NPP and water table as external
forcing, and they both use completely different subroutines to accomplish this (Mi et al., 2014;
van Huissteden et al., 2009; Zhuang et al., 2004). This means that both the available substrate
for production, and the balance between oxidation and production will be different between
these models. Since these factors are known to exhibit large spatial variability, the response of
these models to a change in environmental conditions will also show quite different spatial
variability. Moreover, both models simulate the diffusion of air temperature into the soil from
their respective climate forcing in different ways, leading to a somewhat different soil
temperature even when the two would be forced with the same air temperature.
Although this demonstrates that the models approach the simulation of methane emissions
differently, some agreement is to be expected: the main processes generating methane fluxes
from wetland soils are well known and therefore a description of these processes in models is
expected to converge to some extent. Large scale studies as the one by Olefeldt et al. (2013) –
who analyzed measurements from 303 different sites – clearly show that a non-linear function,
such as the Q10 relationship used by TEM6 and Peatland-VU, is an accurate approximation of
the sensitivity of methane emissions to an increase in temperature. This explains why
agreement between the models exists: they may not be perfect, but the assumptions regarding
temperature sensitivity are not dissimilar from those obtained from measurements, which all
three models have been tested against (van Huissteden et al., 2006; Wania et al., 2010; Zhuang
et al., 2004).
Finally, the main reason for the similarity between the models can be derived from careful
examination of the environmental conditions. As shown in Figures S3 and S4, there is a strong
and very similar correlation between near-surface air temperature and sea ice for all three
climate forcing datasets used as input, while no strong patterns appear for precipitation. This
means two things: both that the change in methane emissions following sea ice decline is
strongly temperature driven, rather than through changes in precipitation, but also that the
agreement among the various climate forcings causes the models to show similar spatial
correlations. The closer similarity between LPJ-GUESS and TEM6 originates from the fact that
the reanalysis product CRU-NCEP (Wei et al., 2014) that is used by TEM6 is a combination of
both the NCEP/NCAR reanalysis product and the CRU climate dataset, of which the latter is
used by LPJ-GUESS. Thus, even though TEM6 and LPJ-GUESS simulate methane emissions
distinctly different, they both show similar patterns due to the commonalities in the forcing
data.
Similar patterns in the correlations, however, are not the same as a similar response to a
change in temperature: amplitudes and long-term trends may differ. Indeed, the models vary
quite a lot in the size of the amplified fluxes following sea ice decline (1.5–2.4 Tg CH4 yr-1, 0.4–
5
0.7 Tg CH4 yr-1 and 1.8–4.1 Tg CH4 yr-1 higher by 2005-2010 for LPJ, TEM6 and Peatland-VU
respectively). This shows that there is no convergence in terms of magnitude. Besides, the
newly added panel to Figure 4 shows that the average trend in temperature between the
climate forcing datasets is much more dissimilar than the trend in the modeled fluxes. This
suggests that the models do not agree because they respond in the exact same way to an
imposed temperature change.
Given the temperature and precipitation input from the climate forcing, it is clear that sea-ice
affected areas have seen stronger warming than areas not affected by sea ice-decline, while no
clear association with precipitation is present. If the models represent local hydrology correctly,
these results show that methane emissions have risen, especially in the autumn, due to higher
temperatures. The models do not agree on the size of this increase, however. Thus, while our
analyses provide support for the direction of this change, they do not provide a consistent
estimate for its size.
Supplementary Figures:
Figure S1. Total methane emissions from May to October for each of the three models,
averaged over the period 1981 to 2010.
6
Figure S2. Correlations between sea-ice decline and modeled methane emissions across the
Arctic for the months May to October during 1981-2010. Correlations are shown per month as
an average of all models, and for each model separately. The color of each grid cell shows how
well modeled methane emissions in that point correlate to the fraction of sea ice within a radius
of 2000 km, after detrending both time series. Sea ice is plotted as a simple linear trend in cover
fraction, to indicate areas of high retreat. Note that a high correlation does not necessarily
indicate high emission trends in that point. When trends did not pass the desired significance
level, these were set to zero (p > 0.05).
7
Figure S3. Correlations between sea-ice decline and near-surface air temperature across the
Arctic for the months May to October during 1981-2010. Correlations are shown per month for
each climate forcing dataset used by the models (CRU: LPJ-GUESS, MsTMIP: TEM6, ERAInterim: Peatland-VU). The color of each grid cell shows how well temperature in that point
correlates to the fraction of sea ice within a radius of 2000 km, after detrending both time
series. Sea ice is plotted as a simple linear trend in cover fraction, to indicate areas of high
retreat. Note that a high correlation does not necessarily indicate high temperature trends in
8
that point. When trends did not pass the desired significance level, these were set to zero (p >
0.05).
Figure S4. Correlations between sea-ice decline and precipitation across the Arctic for the
months May to October during 1981-2010. Correlations are shown per month for each climate
forcing dataset used by the models (CRU: LPJ-GUESS, MsTMIP: TEM6, ERA-Interim: PeatlandVU). The color of each grid cell shows how well precipitation in that point correlates to the
fraction of sea ice within a radius of 2000 km, after detrending both time series. Sea ice is
plotted as a simple linear trend in cover fraction, to indicate areas of high retreat. Note that a
9
high correlation does not necessarily indicate high precipitation trends in that point. When
trends did not pass the desired significance level, these were set to zero (p > 0.05).
Figure S5. Difference in (a) near-surface air temperature and (b) the change in modeled
methane emissions in sea ice-affected vs. sea ice-unaffected areas, considering the trend in
sea-ice decline from 1991 to 2010 (regions shown separately in Figure 3). Temperature is shown
as the difference in °C, while methane emissions are expressed as the percentage change, both
relative to the averages of 1981-1990. Apart from August temperature in ERA-Interim, the
differences shown are significant following individual t-tests between the two regions for each
month and climate forcing or model (p<<0.05).
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