WETMETH 1.0: a new wetland methane model for implementation in Earth system models

Wetlands are the single largest natural source of methane (CH4), a powerful greenhouse gas affecting the global climate. In turn, wetland CH4 emissions are sensitive to changes in climate conditions such as temperature and precipitation shifts. However, biogeochemical processes regulating wetland CH4 emissions (namely microbial production and oxidation of CH4) are not routinely included in fully coupled Earth system models that simulate feedbacks between the physical climate, the carbon cycle, and other biogeochemical cycles. This paper introduces a process-based wetland CH4 model (WETMETH) developed for implementation in Earth system models and currently embedded in an Earth system model of intermediate complexity. Here, we (i) describe the wetland CH4 model, (ii) evaluate the model performance against available datasets and estimates from the literature, and (iii) analyze the model sensitivity to perturbations of poorly constrained parameters. Historical simulations show that WETMETH is capable of reproducing mean annual emissions consistent with present-day estimates across spatial scales. For the 2008– 2017 decade, the model simulates global mean wetland emissions of 158.6 Tg CH4 yr−1, of which 33.1 Tg CH4 yr−1 is from wetlands north of 45 N. WETMETH is highly sensitive to parameters for the microbial oxidation of CH4, which is the least constrained process in the literature.


Introduction
Wetlands are vegetated locations that are inundated with water on a permanent, seasonal, or recurrent basis (Wheeler, 1999). In the context of this study, wetlands are defined following the latest global methane (CH 4 ) budget report (Saunois et al., 2020): natural ecosystems with inundated or water-saturated soils where anoxic conditions lead to the production of CH 4 . Wetlands across the globe are the single largest natural source of atmospheric CH 4 , accounting for approximately one-third of total global emissions (Bridgham et al., 2013;Saunois et al., 2016). Estimates of global wetland CH 4 emissions over the past few decades vary between 140 and 210 Tg CH 4 yr −1 (Kirschke et al., 2013). Although different types of wetlands exist, such as bogs, fens, swamps, marshes, and floodplains (Aselmann and Crutzen, 1989;Saunois et al., 2016), the release of CH 4 from any wetland results from the balance between two biogeochemical processes (Segers, 1998): the production of CH 4 by anaerobic microbes (namely methanogens) and the oxidation of CH 4 primarily by aerobic microbes (namely methanotrophs).
Both CH 4 production and oxidation in wetlands are sensitive to changes in climate conditions. For instance, soil warming accelerates the microbial activity with a higher response for methanogenic than methanotrophic activity (Bridgham et al., 2013;Dunfield et al., 1993;Segers, 1998). At the landscape or larger scale, increased wet conditions tend to enhance methanogenic activity to the detriment of methanotrophic activity (Duval and Radu, 2018;Helbig et al., 2017;Kim, 2015). In turn, wetland CH 4 emissions can affect the global climate through changes in atmospheric CH 4 levels and associated radiative forcing (Dean et al., 2018;O'Connor et al., 2010). Analyses of ice cores suggest that CH 4 emissions from tropical and northern wetlands contributed significantly to climate changes during past glacialinterglacial transitions (Loulergue et al., 2008;Rhodes et al., 2017).
The interactions between climate conditions and wetland CH 4 emissions translate into a positive feedback loop that has the potential to amplify changes in global mean surface air temperature, which is a major concern for future climates (Dean et al., 2018;O'Connor et al., 2010). Research on feedbacks between the physical climate and biogeochemical cycles is generally conducted with three-dimensional (3-D) fully coupled Earth system models (ESMs) (Arora et al., 2013). Over the past decade, these ESMs have proven very useful to investigate and inform international climate policies, such as the accounting of carbon emissions required to avoid the risk of dangerous climate change ) and achieve the goals of the Paris Agreement (Tokarska and Gillett, 2018). However, biogeochemical processes regulating CH 4 emissions in wetlands are not commonly included in fully coupled ESM simulations.
In the past, several process-based models have been developed to investigate the response of wetland CH 4 emissions to climate variability and climate change (Hodson et al., 2011;Hopcroft et al., 2011;Pandey et al., 2017;Paudel et al., 2016;Shindell et al., 2004;Zhang et al., 2018;Zhu et al., 2015). These wetland CH 4 models are generally embedded in terrestrial or land surface models and forced with observational datasets or reanalysis products (Melton et al., 2013;Wania et al., 2013;Xu et al., 2016). A second application for wetland CH 4 models has been to quantify the climate response to wetland CH 4 emissions (Gedney et al., 2004(Gedney et al., , 2019. In this case, results from wetland CH 4 models are used in climate-carbon cycle model emulators to assess their impact on radiative forcing (Gedney et al., 2019;. These modelling studies have contributed to advance research on the possible evolution of wetland CH 4 emissions in the 21st century Shindell et al., 2004), the magnitude of their impact on the global climate (Gedney et al., 2019;, and their implications for international climate policy (Comyn-Platt et al., 2018). However, their quasi-coupling methods do not reflect the complete feedback loop between climate conditions and wetland CH 4 emissions as expected in the natural world. So far, only 1-D and 2-D models of the northern high-latitude regions have been applied for simulating the feedback between climate conditions (temperature changes) and wetland CH 4 emissions in a fully coupled mode (Schneider von Deimling et al., 2012, 2015. The implementation of process-based wetland CH 4 models in fully coupled ESMs is needed in order to advance research on wetland CH 4 -climate feedbacks in the context of global climate projections (Dean et al., 2018). In particular, this addition to Earth system modelling should be beneficial to ongoing research on the permafrost carbon feedback (Nzotungicimpaye and Zickfeld, 2017;Schuur et al., 2015) and the remaining carbon budget for achieving the goals of the Paris Agreement (Rogelj et al., 2019). This paper introduces a wetland CH 4 model developed for implementation in ESMs and currently embedded in an Earth system model of intermediate complexity (EMIC). Our study aims at developing a computationally efficient processbased model for simulating large-scale wetland CH 4 emissions constrained with sparse observations. Section 2 gives an overview of processes regulating CH 4 emissions in wetlands. Section 3 provides the model description and an outline of performed model simulations. Section 4 describes the model calibration and choice of parameter values. Section 5 presents the model performance evaluation. Section 6 describes the model sensitivity to poorly constrained parameters. Sections 7 and 8 are for discussions and conclusions, respectively.
2 Overview of processes regulating methane emissions in wetlands 2.1 Microbial production of methane Wetlands host several communities of microbes adapted to the predominant anoxic conditions of these environments (Bridgham et al., 2013). Some of these microbes are methanogens, which decompose organic matter for their metabolism and produce CH 4 as a by-product of their respiration (McCalley et al., 2014;Segers, 1998). The organic matter decomposed by methanogens in wetlands originates from litter fall, root exudates, dead plants, and dissolved organic carbon (Bridgham et al., 2013;Conrad, 2009;Girkin et al., 2018;Mitsch and Mander, 2018). In the northern permafrost region, carbon from thawed soils constitutes an additional source of organic matter to methanogens (Kwon et al., 2019;Olefeldt et al., 2013). There are three pathways through which methanogens produce CH 4 from soil organic matter (Le Mer and Roger, 2001;Segers, 1998;Whalen, 2005). The first pathway (acetotrophic methanogenesis) is operated by methanogens that rely on acetate for their metabolism, resulting in the production of both CH 4 and carbon dioxide (CO 2 ) (Bridgham et al., 2013;Whalen, 2005). The second pathway (hydrogenotrophic methanogenesis) is operated by methanogens that produce CH 4 through CO 2 reduction in the presence of hydrogen (Bridgham et al., 2013). The third pathway (methylotrophic methanogenesis) is operated by methanogens that use methylated substrates (e.g. methanol, methylamines, and dimethylsulfide) for their metabolism (Zalman et al., 2018).
Rates of CH 4 production in wetlands are generally highest in upper anoxic layers due to several factors such as the quality of organic matter and the spread of active microbial populations. For instance, in comparison to soil layers at depth, where organic matter can be recalcitrant to microbial decomposition, the organic matter in near-surface soil layers is more labile due to fresh inputs from litter fall and vegetation mortality Walz et al., 2017;Wild et al., 2016). Furthermore, observations at various sites show that methanogenic activity decreases as depth increases (Bridgham et al., 2013;Cadillo-Quiroz et al., 2006).
Increasing soil temperatures stimulate the dynamics and growth of methanogenic communities in wetlands, resulting in an increase in CH 4 production rates (Bridgham et al., 2013;Segers, 1998). However, several studies indicate that there is an optimal temperature for methanogenic activity between 25 and 30 • C (Dean et al., 2018;Dunfield et al., 1993). Other factors promoting the occurrence of CH 4 production in wetlands include the persistence of anoxic conditions as well as soil pH varying between acidic and neutral (Dunfield et al., 1993;Segers, 1998).

Microbial oxidation of methane
In wetlands, methanotrophs (CH 4 -oxidizing microbes) populate oxic portions of the soil column (Bridgham et al., 2013;Conrad, 2009;Whalen, 2005). Such oxic portions are primarily soil layers close to the surface which are in contact with the atmosphere, commonly near and above the water table (Bridgham et al., 2013;Le Mer and Roger, 2001;Segers, 1998). In the presence of vascular plants, other oxic portions of the soil column can be found near the roots due to the downward transport of oxygen (O 2 ) through plant aerenchyma (Kwon et al., 2019;Whalen, 2005). All these oxic portions of the soil column constitute the so-called oxic zone, which is predominantly made of soil layers near and above the water table (Bridgham et al., 2013;Conrad, 2009;Segers, 1998). Methanotrophs consume CH 4 that ascends from the zones of production at depth to the overlying oxic zone for their metabolism, and they primarily produce CO 2 as part of their respiration (Bridgham et al., 2013;Segers, 1998).
While O 2 has been considered for years to be the only electron acceptor involved in the microbial oxidation of CH 4 , there is a growing evidence of the occurrence of CH 4 oxidation under anoxic conditions operated by anaerobic microbes that rely on alternate electron acceptors such as nitrate and sulfate (Dean et al., 2018). However, although anaerobic CH 4 oxidation in marine environments has been well established for decades (Hoehler et al., 1994;Reeburgh, 1976), this process remains poorly investigated in wetlands despite its potential importance for the CH 4 cycle (Gauthier et al., 2015;Smemo and Yavitt, 2011).
In analogy to CH 4 production, CH 4 oxidation is influenced by changes in soil temperatures (Bridgham et al., 2013;Segers, 1998). For instance, CH 4 oxidation rates increase during the summer because of intensified microbial activity but also the availability of substantial CH 4 in response to increased soil temperatures (Segers, 1998). However, the temperature response for CH 4 oxidation is generally lower than that for CH 4 production (Bridgham et al., 2013;Dean et al., 2018;Dunfield et al., 1993;Segers, 1998).

Mechanisms transporting methane to the atmosphere
Various mechanisms exist for transporting CH 4 produced in wetlands to the atmosphere. Three transport mechanisms are well documented in the literature and generally monitored in situ (Bridgham et al., 2013;Whalen, 2005): the diffusion of CH 4 , whereby molecules of CH 4 slowly ascend the overlying water column; the ebullition of CH 4 , whereby bubbles of CH 4 rapidly ascend towards the soil surface; and the transport of CH 4 through the aerenchyma of vascular plants. However, other transport mechanisms for CH 4 in wetlands have been revealed: the hydrodynamic transport of CH 4 in the form of upwelling caused by temperature gradients primarily at nighttime (Poindexter et al., 2016), and the transport of CH 4 through tree stems (Bridgham et al., 2013;Conrad, 2009;Pangala et al., 2017) whose driving processes are still not well understood (Barba et al., 2019). Methane oxidation is highly dependent on the predominant transport mechanism for CH 4 . The water table position plays a crucial role in affecting what fraction of the produced CH 4 reaches the atmosphere (Blodau, 2002;Moore and Roulet, 1993;Segers, 1998). When the water table is well below the surface, methanotrophs may oxidize all of the diffusing CH 4 before the gas reaches the atmosphere (Segers, 1998). In the presence of vascular plants, a lower fraction of the produced CH 4 is oxidized because these plants allow the gas to bypass the oxic zone where methanotrophs are hosted (Blodau, 2002;Bridgham et al., 2013;Segers, 1998). In the case of ebullition, which often occurs episodically, CH 4 may escape to the atmosphere with reduced opportunities for oxidation (Bridgham et al., 2013;Whalen, 2005). How CH 4 oxidation relates to the transport of CH 4 through tree stems (Barba et al., 2019) or by hydrodynamic processes (Poindexter et al., 2016) is not well established. Figure 1 illustrates vertical profiles of soil organic content, CH 4 concentration, and CH 4 oxidation rates in a soil column with and without inundation at the surface based on principles outlined in the literature (Blodau et al., 2004;Whiticar and Faber, 1985). In general, the water table position determines the maximum depth at which O 2 is available in the soil column (i.e. the oxic-anoxic interface). When the surface is 6218 C.-M. Nzotungicimpaye et al.: A new wetland methane model for implementation in ESMs flooded and the water is stagnant (Fig. 1a), O 2 diffuses slowly into the soil column and may only be present in a portion of the upper soil layer which is in contact with the atmosphere. Under such predominantly anoxic conditions, CH 4 production occurs throughout the soil column and the concentration of CH 4 mirrors soil organic content -eventually with a small reduction near the surface due to CH 4 oxidation. A modest amount of ascending CH 4 may be oxidized throughout the soil column, although with highest oxidation rates near the surface where some O 2 may be available as an electron acceptor. The combination of high CH 4 production and only modest CH 4 oxidation in the soil column results in large CH 4 emissions into the atmosphere.

A synopsis of wetland methane dynamics
When the flooding recedes, O 2 becomes more prevalent in the upper soil column where the CH 4 concentration decreases following a slowdown or shutdown of CH 4 production as aerobic microbes dominate the competition for organic matter (Fig. 1b). CH 4 production persists below the oxic-anoxic interface where the concentration of CH 4 mirrors soil organic content owing to the predominant anoxic conditions. Ascending CH 4 becomes subject to substantial oxidation in the soil column with the highest oxidation rates above the oxic-anoxic interface where O 2 is abundant. The combination of decreased CH 4 production and substantial CH 4 oxidation in the soil column results in small or no CH 4 emissions into the atmosphere. Microbial production and oxidation of CH 4 are parameterized in WETMETH using a multilayer ground structure with information on the moisture distribution, the amount of organic matter (carbon content), and the average temperature in each soil layer. These soil variables are commonly simulated by ESMs. Figure 2 provides a schematic representation of WETMETH for a soil column with and without inundation at the surface. By configuration, it is considered that CH 4 emissions in WETMETH may occur not only from inundated locations but also from non-inundated ecosystems with a relatively high level of soil moisture content (Saunois et al., 2016(Saunois et al., , 2020.

Parameterization of methane production
For any land location, the rate of CH 4 production in an underlying soil layer i (P i in kg C m −3 s −1 ) is parameterized as follows: where S(θ i ) is the fraction of the soil layer that is saturated with water, and C i is the amount of soil carbon (in kg C m −3 ) Figure 1. Illustrated vertical profiles of soil organic content, CH 4 concentration, and oxidation rates in a soil column with inundation at the surface (a) and without inundation at the surface (b). The vertical profiles are based on principles outlined in the literature (Blodau et al., 2004;Whiticar and Faber, 1985). As an illustrative simplification, the soil organic content is assumed to be identical in (a) and (b). Furthermore, the CH 4 concentration profile is assumed to mirror CH 4 production rates at depth within the soil column (see explanatory text in Sect. 2.4). The blue horizontal line illustrates the water table position, and the dashed red horizontal line illustrates the oxic-anoxic interface or maximum depth at which O 2 is available in the soil column. The relative magnitude of CH 4 flux in the soil column is shown by the upward arrow to the right, also characterizing the relative magnitude of CH 4 emissions into the atmosphere.
in the layer. Here we consider C i to be the aggregate of all sources of soil carbon (i.e. organic matter) such as litter fall and root exudates. The product of S(θ i ) and C i represents the organic matter (in kg C m −3 ) available for microbial decomposition under anoxic conditions. When the soil surface is not flooded (Fig. 2b), dry soil layers (S(θ i ) = 0) are assumed to be predominantly oxic and not producing CH 4 (P i = 0), mostly due to aerobic microbes dominating the competition for organic matter which results in the starvation of methanogens (Segers, 1998). The soil column is shown here with multiple layers of unequal thicknesses. The blue area at the surface of (a) represents the inundated surface area. The blue sections in the different soil layers of (a) and (b) represent water-saturated zones. For both (a) and (b), the dashed red horizontal line illustrates the oxic-anoxic interface, and the orange vertical arrow shows the relative thickness of the oxic zone or oxic zone depth (z oxic ). Larger CH 4 emissions are expected to occur when the soil surface is flooded than when it is not, due to relatively high CH 4 production and moderate CH 4 oxidation in the soil column.
The global factor r is the specific CH 4 production rate (in kg kg −1 s −1 ), which can be defined as the mass of CH 4 -C that is produced per kilogram of available soil carbon per unit of time. A meta-analysis of incubated soil samples from various anaerobic landscapes indicates that r can vary between 0.3 and 27.2 µg of CH 4 -C per gram of soil carbon per day (equivalent to the range from 3.5 × 10 −12 to 3.1 × 10 −10 kg kg −1 s −1 ) depending on the landscape type, relative water table position, and soil depth . In this first version of WETMETH, we combine all possible pathways for CH 4 production in wetlands (see Sect. 2.1) without distinguishing fast and slow pathways. Section 4.1 discusses the choice of the value for r as part of the model calibration.
The expression Q T i −T 0 10 10 , which depends on the average layer temperature T i (in kelvin, K) and a baseline temperature T 0 (273.15 K), represents the temperature dependency of CH 4 production expressed with a Q 10 coefficient as commonly done to approximate the sensitivity of biological processes to a temperature change of 10 K (Hegarty, 1973). While some biological processes double in rate with a warming of 10 K, several studies report a higher temperature sensi-tivity for CH 4 production (i.e. Q 10 > 2), although with large uncertainties (Lupascu et al., 2012;Sjögersten et al., 2018;Walz et al., 2017;Whalen, 2005). Nevertheless, a metaanalysis of temperature-response studies suggests an average Q 10 of about 4.2 for CH 4 production in pure cultures of methanogens (Hoehler and Alperin, 2014;Yvon-Durocher et al., 2014) in agreement with previous estimates (Blodau, 2002). In order to account for uncertainties with this coefficient and define the occurrence of an optimal temperature for CH 4 production (Dunfield et al., 1993;Metje and Frenzel, 2007;Schipper et al., 2014), a temperature-dependent Q 10 is considered in WETMETH. Its mathematical formula- .15 K is a reference temperature that is used to define an optimal temperature for CH 4 production ( Table 1). This formulation is defined by analogy to a mathematical expression used for soil respiration in another study (Wu et al., 2016), and it enables one to account for an optimal temperature for CH 4 production of ∼ 300.15 K (i.e. 27 • C), which is consistent with previous studies (Dunfield et al., 1993;Metje and Frenzel, 2007). Additional information on this formulation and its implications for the temperature dependency of CH 4 production are provided in Appendix A. Furthermore, CH 4 production in WETMETH is assumed to shut down in frozen soil layers, although research suggests that slow microbial activity can occur at temperatures below 273.15 K (Panikov and Dedysh, 2000;Rivkina et al., 2004). The expression exp − z i τ prod , which depends on the depth of soil layer i relative to the surface (z i in m, positive downwards), describes the declining effect of various environmental controls on CH 4 production with depth that are generally unresolved by ESMs. These environmental factors include the quality of organic matter and the spread of methanogens among other factors (Bridgham et al., 2013;Koven et al., 2015;Treat et al., 2015;Walz et al., 2017;Wild et al., 2016). Here, τ prod (in m) is a scaling parameter for CH 4 production. The choice of the value for τ prod is discussed later as part of the model calibration (see Sect. 4.1).
The total amount of CH 4 produced in the soil column (P in kg C m −2 s −1 ) is calculated as follows: where P i (in kg C m −3 s −1 ) is the rate of CH 4 production in the soil layer i from Eq. (1), dz i (in m) is the thickness of the soil layer i, and k represents the bottom-most soil layer. This amount of CH 4 (P ) is then subject to oxidation in transit to emission into the atmosphere.

Parameterization of methane oxidation and net methane emissions
Methane oxidation is parameterized based on the amount of CH 4 produced in the soil column and the relative thickness of the oxic zone. Specifically, the total amount of CH 4 oxidized in the soil column (O x in kg C m −2 s −1 ) and net CH 4 emissions to the atmosphere (E in kg C m −2 s −1 ) are calculated as which is equivalent to the following expression: where P (in kg C m −2 s −1 ) is the total amount of CH 4 produced in the soil column as defined in Eq.
(2), z oxic (in m) is the relative depth (positive downwards) to the oxic-anoxic interface (Fig. 2), and τ oxid (in m) is a scaling parameter for CH 4 oxidation. As for τ prod , the choice of the value for τ oxid is discussed as part of the model calibration (see Sect. 4.2).
Regarding z oxic , we assume that O 2 may be present in soil layers unsaturated with water as well as in a shallow oxicanoxic transition zone within the uppermost soil layer saturated with water ( Fig. 2). In this first development of WET-METH, we consider a constant thickness (z oatz ) of 0.05 m for the oxic-anoxic transition zone, with its bottom defined as the oxic-anoxic interface (Frolking et al., 2002;Singleton et al., 2018). When the soil surface is inundated, z oatz is identical to z oxic (Fig. 2a). Otherwise, z oatz is only a fraction of z oxic (Fig. 2b). The penetration of O 2 into the soil and its dynamics with changing moisture conditions can be complex depending on site-specific factors such as the soil composition (Estop-Aragonés et al., 2012) and the presence of vascular plants (Brune et al., 2000). In addition, methanotrophs may be present at depth (> 0.05 m) below the water table, probably following some adaptation to low-O 2 conditions (Singleton et al., 2018). Nevertheless, the approach applied here for z oxic is reasonable for ESMs not resolving O 2 dynamics and microbial communities in the soil.
For Eq.
The parameterization described in Eq.
(3) is a simple approach for characterizing CH 4 oxidation in the soil column. Such a parameterization is practical when there is little knowledge on the soil chemistry (e.g. O 2 and alternate electron acceptors), the dynamics of methanotrophs, and other environmental factors exerting a control on CH 4 oxidation (Blazewicz et al., 2012;Blodau, 2002;Dean et al., 2018;Kwon et al., 2019;Singleton et al., 2018;Smemo and Yavitt, 2011). Most importantly, this parameterization considers the net effect of all mechanisms transporting CH 4 from the anoxic soil layers where the gas is produced to the atmosphere. The oxidized CH 4 is assumed to produce CO 2 that becomes part of the soil respiration routinely simulated by ESMs.  ) is used here. The UVic ESCM consists of a 3-D ocean general circulation model that is coupled to a dynamic-thermodynamic sea ice model, a 2-D (vertically integrated) energy-moisture balance model for the atmosphere, and a land surface model (Weaver et al., 2001). The land surface model is a modified version of the Met Office Surface Exchange Scheme (MOSES) with 14 ground layers of unequal thickness extending down to a depth of 250 m that can simulate permafrost processes such as freezethaw dynamics (Avis et al., 2011). The top eight ground layers (∼ 10 m in total depth) are soil layers and contribute to the water cycle, whereas the bottom six ground layers are bedrock layers (Avis et al., 2011). In the hydraulically active layers, porosity and permeability are determined based on the relative abundance of prescribed sand, clay, and silt-sized particles. Water phase changes are determined over a range of soil temperatures to determine the fraction of frozen and unfrozen water in the ground (Avis et al., 2011). All components of the UVic ESCM have a horizontal grid resolution of 3.6 • in longitude and 1.8 • in latitude Weaver et al., 2001).

The embedding
Wetlands in the UVic ESCM are identified in grid cell areas based on soil moisture content and topography. Model grid cells in which wetlands can occur are those with unfrozen soil moisture contents greater than 65 % of the saturated moisture content in the upper soil layer for at least 1 d in a year (Avis et al., 2011). Instead of using a fixed global threshold value for topography (Avis et al., 2011), the version of the UVic ESCM used here identifies wetland coverage at the sub-grid scale following a TOPMODEL approach for global models (Gedney and Cox, 2003). Appendix B describes a minor modification applied to this TOPMODEL approach. Section 5.1 presents an evaluation of wetlands simulated by the UVic ESCM.
The UVic ESCM includes a representation of the global carbon cycle. The terrestrial carbon cycle is simulated using the Top-down Representation of Interactive Foliage and Flora including Dynamics (TRIFFID), a dynamic global vegetation model that is coupled to the land surface model (Avis et al., 2011;Meissner et al., 2003). TRIFFID defines the state of the terrestrial biosphere in terms of soil carbon as well as the structure and coverage of five plant functional types (PFTs): broadleaf trees, needleleaf trees, shrubs, C 3 grasses, and C 4 grasses (Cox, 2001;Matthews et al., 2004;Meissner et al., 2003). Terrestrial carbon gain occurs through photosynthesis that is simulated as a function of atmospheric CO 2 concentration, shortwave radiation, air temperature, humidity, and soil moisture. Soil carbon gain occurs through litter fall and vegetation mortality. The present-day permafrost carbon pool is simulated by the UVic ESCM following a method that approximates the effect of long-term freeze-thaw cycles on the vertical distribution of carbon in permafrostaffected soils, a process referred to as cryoturbation (Mac-Dougall and Knutti, 2016). Soil carbon can occur in the top six ground layers (∼ 3.35 m in total depth). Terrestrial carbon loss occurs through autotrophic respiration by plants and heterotrophic respiration by soil microbes (Matthews et al., 2004;Meissner et al., 2003). By configuration, permafrost carbon can only be lost through microbial respiration, and this heterotrophic respiration is assumed to shut down in frozen soil layers (MacDougall et al., 2012;MacDougall and Knutti, 2016). Through TRIFFID, all terrestrial carbon fluxes in the UVic ESCM are integrated with a 30 d time step (Meissner et al., 2003).
The marine carbon cycle in the UVic ESCM is represented with organic and inorganic carbon cycle models ). The organic carbon cycle is based on marine biology simulated with a nutrient-phytoplankton-zooplanktondetritus (NPZD) ecosystem model (Schmittner et al., 2008). The inorganic carbon cycle model simulates the air-sea exchange of CO 2 and ocean carbonate chemistry following the protocols of the Ocean Carbon-Cycle Model Intercomparison Project (OCMIP) (Orr, 1999;Weaver et al., 2001). Dissolved inorganic carbon is treated as a passive tracer that is subject to ocean circulation (Weaver et al., 2001). Carbonate dissolution in ocean sediments is simulated with a model of respiration in marine sediments (Archer, 1996;Eby et al., 2009).

Model simulations
For this research, three series of model simulations are performed with the UVic ESCM in its standard fully coupled mode and including WETMETH parameterizations:

Choice of model parameter values
Here, we describe the choice of three WETMETH parameters (r and τ prod for CH 4 production; τ oxid for CH 4 oxidation) as part of the model calibration. These model parameters are tuned to observations from northern high-latitude regions due to the scarcity of large-scale datasets from other regions. The model calibration against northern observations is based on the assumption that tuned parameter values will be valid across the globe, which is an important limitation as will be discussed later. Nonetheless, this approach is deemed reasonable given the present state of data availability. Section 5.1 describes northern wetlands simulated by the UVic ESCM as part of the model validation.

Methane production parameters
Parameters for CH 4 production in WETMETH are calibrated against maximum CH 4 production rates measured in laboratory incubations of soil samples from several anaerobic environments across northern high-latitude regions (> 50 • N).
These potential CH 4 production rates are obtained from a synthesis dataset, which includes information on other environmental variables such as the relative depth of the soil samples .
To allow a fair model-data comparison, measured CH 4 production rates with corresponding soil bulk density from the sites of origin are converted into kilograms of carbon per cubic metre per second (kg C m −3 s −1 ) (see Appendix C ). Furthermore, measurements from landscapes identified as uplands and lakes (in the dataset) are excluded from the dataset used in this model calibration. The remaining measurements are potential CH 4 production rates in soil samples from landscapes identified (in the dataset) as wetlands, floodplains, and lowlands across Alaska.
In order to set values for r and τ prod from Eq. (1), the depth profile of simulated CH 4 production rates across Alaska for the year 2000 is tuned to that of the measurements. By setting r to 22.8 µg CH 4 -C produced per gram of soil carbon per day (equivalent to 2.6 × 10 −10 kg kg −1 s −1 ) and τ prod to 0.75 m, we obtain a depth profile of simulated CH 4 production rates that compares fairly well to that of potential CH 4 production rates from the laboratory incubations (Fig. 3). These default values for r and τ prod are listed in Table 1. Section 6 presents a sensitivity analysis on these model parameters.

Methane oxidation parameter
Unlike for CH 4 production, there are no published largescale measurements of CH 4 oxidation rates that could be used in this research for the calibration of CH 4 oxidation. For that reason, CH 4 oxidation in WETMETH is indirectly calibrated via CH 4 emissions. A synthesis dataset of seasonal and annual CH 4 emissions from various terrestrial sites across temperate, boreal, and Arctic regions is used to this end (Treat et al., 2018). The model calibration focuses on annual CH 4 emissions from sites north of 50 • N for which many data points are available in the dataset.
While most data points are from direct measurements of CH 4 emissions, some data points are associated with different modelling methods for estimating CH 4 emissions (Treat et al., 2018). To allow a fair model-data comparison, only data points associated with direct measurements of CH 4 emissions are included in the model calibration. Furthermore, measurements from lakes, uplands, and alpine landscapes are excluded from this model calibration. In particular, the exclusion of data points from uplands and alpine landscapes sorts out measurements of terrestrial CH 4 uptake (negative CH 4 flux). The retained data points (n = 119) include measurements by chambers (85.7 %), flux towers (13.4 %), and a combination of flux towers and chambers (0.8 %).
The model calibration in this section aims at choosing a value of τ oxid from Eq. (4) such that the range (minimum -maximum) of annual CH 4 emissions across northern wetlands (> 50 • N) simulated by the UVic ESCM is compara- Figure 3. Vertical profiles of simulated and potential CH 4 production rates from wetlands across Alaska. Potential CH 4 production rates are measurements from laboratory incubations of soil samples collected from various anaerobic ecosystems . Both simulated and measured CH 4 production rates are shown here with a log-transformed axis (base-10 logarithmic scale). ble to that of annual CH 4 emissions from the data points (0.1-60.6 g CH 4 m −2 yr −1 ). By setting τ oxid to 0.0146 m, we constrain simulated CH 4 emissions from northern wetlands (specifically, grid cell CH 4 emissions divided by the inundated fraction of the grid cell) from 2000 to 2009 in the range of 0.04-65.6 g CH 4 m −2 yr −1 . This tuned value for τ oxid is listed in Table 1 and implies that ∼ 97 % of the CH 4 produced in the soil column gets oxidized in transit to emission when the soil surface is inundated. Section 6 presents a sensitivity analysis on this model parameter.
5 Evaluation of the model performance 5.1 Wetlands Figure 4 shows the latitudinal distribution of wetland areas simulated by the UVic ESCM in comparison to two global datasets. The first dataset is Global Inundation Extent from Multi-Satellites (GIEMS), which is based on remotely sensed inundation areas (Papa et al., 2010;Prigent et al., 2001Prigent et al., , 2007Prigent et al., , 2012. The second dataset is Surface Wa- ter Microwave Product Series -Global Lakes and Wetlands Database (SWAMPS-GLWD), which is based on a combination of information from satellites and maps of inundated areas in order to reduce uncertainties associated with the distribution of global wetlands . The comparison between the model and the datasets is done over 2000-2007, which is the overlap period for the datasets. Over this period, the UVic ESCM simulates an annual maximal extent of ∼ 12.6×10 6 km 2 for global wetlands, whereas GIEMS and SWAMPS-GLWD estimate ∼ 9.3×10 6 km 2 and ∼ 10.6×10 6 km 2 , respectively.
The UVic ESCM agrees better with SWAMPS-GLWD in regions north of 40 • N, although with some underestimations around 55 • N, and relatively well with GIEMS between 20 and 40 • S (Fig. 4). However, the model simulates overly small wetland areas between 20 and 30 • N when compared with both GIEMS and SWAMPS-GLWD. While our model could be underestimating wetland areas in this latitude zone, inundated areas estimated by GIEMS include rice paddies which prevail in tropical and sub-tropical regions (Prigent et al., 2007(Prigent et al., , 2012. Rice paddies are likely not represented in SWAMPS-GLWD, as there were efforts to only include natural wetlands during the development of this dataset . In comparison to GIEMS and SWAMPS-GLWD, our model simulates small wetland areas in Southeast Asia especially near Bangladesh (Figs. 5, 6).
Between 20 • N and 20 • S, the UVic ESCM simulates a bimodal distribution of the wetland extent that is consistent with the two datasets although the model simulates overly large wetland areas (Fig. 4). Unlike for GIEMS and SWAMPS-GLWD, wetlands simulated by the UVic ESCM are widespread in Amazonia as well as in West and Central Africa (Figs. 5, 6). Although the UVic ESCM could be overestimating the extent of wetlands in some of these equatorial regions, it is possible that GIEMS and SWAMPS-GLWD do not detect inundated areas in densely forested regions due to forest canopies. Recent studies suggest that tropical wetlands  are commonly underestimated in large-scale datasets (Dargie et al., 2017;Gumbricht et al., 2016).
Conversely, it is possible that the UVic ESCM overestimates tropical wetland areas due to soil hydraulic properties being unrepresented in the model. A potential cause for the overestimation of tropical wetlands in our model is the standard approach for simulating global hydrology in land surface models based on the concentration of only sand, clay, and silt in the soil. A recent study suggests that the inclusion of Ferralsols (weathered soils with micro-aggregated particles that are common in the humid tropics) in a global terrestrial model can help improve the simulation of tropical wetlands (Gedney et al., 2019).
Outside of the tropics, the UVic ESCM does a better job at simulating the distribution of wetlands in sub-Arctic and Arctic regions (Fig. 7). The model simulates the occurrence of wetlands (i.e. surface inundation) across the West Siberian Lowlands (WSL) in Russia, the Hudson Bay Lowlands (HBL) in Canada, and over other parts of northern Canada in agreement with both SWAMPS-GLWD and GIEMS (Fig. 7). However, some disagreements between the UVic ESCM and the two datasets can also be identified: (i) in comparison with GIEMS, the UVic ESCM simulates more wetland area in the Hudson Bay Lowlands (HBL) as well as widespread wetlands in parts of northern Eurasia (Figs. 7b, S2b); (ii) in comparison with SWAMPS-GLWD, the model simulates less wetland area over the WSL and northern Canada, including the HBL, and more wetland area in parts of Europe (Figs. 7c, S2c).
Statistical analyses show that (i) the UVic ESCM agrees better with SWAMPS-GLWD than with GIEMS at both the regional and global scale, and (ii) the model shows better agreement with the two datasets across northern regions than at the global scale. For details on the statistical evaluation, the reader is referred to Table S1.

Wetland methane emissions
Given the relative coarse grid resolution of the UVic ESCM, the model validation with respect to wetland CH 4 emissions focuses on large-scale emissions such as regional, zonal, and global emissions. Moreover, this model validation focuses on northern high-latitude regions because observations and estimates of wetland CH 4 emissions from other regions (e.g. the tropics) are scarce. This focus is further justified by the fact that our model better simulates the distribution of wetlands in northern high-latitude regions than in the tropics (see Sect. 5.1). Indeed, the extent of wetlands is a major control for wetland CH 4 emissions simulated by process-based models and probably the primary contributor to related uncertainties (Melton et al., 2013;Saunois et al., 2020;.

Northern high-latitude emissions
The UVic ESCM simulates total CH 4 emissions from northern wetlands that are in the range of recent estimates. Over the 2013-2014 period, the model simulates mean annual emissions of 33.2 Tg CH 4 yr −1 for wetlands north of 45 • N ( Table 2). These CH 4 emissions are consistent with estimates from recent upscaled flux measurements (UFMs) over the same period based on a random forest (RF) algorithm and three wetland maps (Peltola et al., 2019): 30.6 ± 9.2 Tg CH 4 yr −1 (RF-DYPTOP), 31.7 ± 9.4 Tg CH 4 yr −1 (RF-PEATMAP), and 37.6 ± 11.8 Tg CH 4 yr −1 (RF-GLWD) ( Table 2). Table S2 shows that the UVic ESCM has no preferential agreement with one of the three UFMs. Figure 8 shows the spatial distribution of simulated CH 4 emissions in comparison to the three UFMs. When compared with each other, the three UFMs exhibit substantial differences primarily attributed to the distinct wetland distributions (Peltola et al., 2019). Considering the general pat- Table 2. Mean annual wetland CH 4 emissions simulated by the UVic ESCM in comparison to estimated emissions from the literature. All emissions are reported in teragrams of methane per year (Tg CH 4 yr −1 ), and uncertainties are provided for estimates from the literature. Three periods are used to allow a fair comparison between the UVic ESCM and estimates from the literature where possible: 2008-2017, as in the latest global CH 4 budget report (Saunois et al., 2020); 2013-2014, as for recent upscaled flux measurements across the northern high latitudes (Peltola et al., 2019);and1993-2004, as for the WETCHIMP model ensemble (Melton et al., 2013). Principal methods used in the different references for estimates are reported in the last column: top-down (TD) methods including inverse models (IM), and bottom-up (BU) methods including upscaled measurements (UM) and process-based models (PM).  (Bohn et al., 2015). For tropical wetlands, the range between the process-based models is 85-184 Tg CH 4 yr −1 (Melton et al., 2013). tern and magnitude of wetland CH 4 emissions, the UVic ESCM agrees with either two or all three UFMs over key source regions such as the Hudson Bay Lowlands (HBL), the West Siberian Lowlands (WSL), western Europe, and southcentral Canada (Fig. 8).
The UVic ESCM simulates less CH 4 emissions over parts of northeastern Canada and Fennoscandia compared with the UFMs (Fig. 8). However, the three UFMs do not necessarily agree on both the distribution and magnitude of wetland CH 4 emissions in these regions. Furthermore, the UVic ESCM does not simulate wetland CH 4 emissions in southern Eurasia (45-60 • N; 40-135 • E), whereas the three UFMs suggest that CH 4 can be emitted from sporadic wetlands in this region (Fig. 8). Overall, the mismatch between the UFMs and our model in terms of northern CH 4 emissions can be primarily attributed to differences in the wetland extent but also to the spatial distribution of soil carbon simulated by the UVic ESCM (MacDougall and Knutti, 2016).
The UVic ESCM is also evaluated with respect to wetland CH 4 emissions over the 2000-2009 and 2008-2017 decades, which are both reference periods for the latest global CH 4 budget report (Saunois et al., 2020).  (37.4 ± 7.2 Tg CH 4 yr −1 ) from data-constrained model ensembles over the same region (Treat et al., 2018).  (Table 2). These wetland CH 4 emissions correspond to the upper limit of bottomup estimates (2-18 Tg CH 4 yr −1 ) from the latest global CH 4 budget report (Saunois et al., 2020). Figure 9 shows seasonal cycles of CH 4 emissions from wetlands north of 45 • N over the 2013-2014 period as simulated by the UVic ESCM and estimated from the three UFMs (Peltola et al., 2019). The pattern and magnitude of simulated seasonal emissions compare well to that of the UFMs. For both the model and UFMs, minimal emissions vary between 0.2 and 0.6 Tg CH 4 per month and occur in December, whereas peak emissions are well below 10 Tg CH 4 per month and occur in July (Fig. 9). However, simulated peak emissions (∼ 8.5 Tg CH 4 per month) are relatively higher than peak emissions for the UFMs (range of best estimates from 5.6 to 7.5 Tg CH 4 per month). Moreover, in comparison to the three UFMs, the UVic ESCM simulates lower CH 4 emissions between December and May but higher CH 4 emissions between July and September (Fig. 9).
The UVic ESCM simulates the occurrence of wetland CH 4 emissions during the non-growing season. For wetlands north of 45 • N, our model simulates total emissions of 2.1 Tg CH 4 yr −1 between November and March. The UFMs predict total emissions of 4.6-10.2 Tg CH 4 yr −1 during these cold months (Peltola et al., 2019). For wetlands north of 60 • N, the UVic ESCM simulates emissions of 1.2 Tg CH 4 yr −1 from October through May in agreement with recent estimates (1.6 ± 0.1 Tg CH 4 yr −1 ) from dataconstrained model ensembles for these months (Treat et al., 2018). Based on our calculations, the three UFMs predict about 3.5-4.5 Tg CH 4 yr −1 emitted from wetlands north of 60 • N between October and May. Overall, this analysis shows that WETMETH is capable of simulating nonnegligible CH 4 emissions from northern wetlands during cold months as emphasized by recent studies (Treat et al., 2018;Zona et al., 2016).

Global emissions
The UVic ESCM simulates total emissions of 155.1 and 158.6 Tg CH 4 yr −1 from global wetlands over the 2000-2009 and 2008-2017 decades, respectively. According to the latest global CH 4 budget report, these wetland emissions are in the mid-range of bottom-up estimates (102-179 and 102-182 Tg CH 4 yr −1 ) but close to the lower limit of top-down estimates (153-196 and 155-200 Tg CH 4 yr −1 ) over the two respective decades (Saunois et al., 2020). Previous bottom-up estimates are significantly high (Melton et al., 2013;Saunois et al., 2016), which is primarily due to possible double counting of emissions from wetlands and other inland water areas (Saunois et al., 2020;Thornton et al., 2016) in addition to uncertainties associated with the extent of wetlands and model parameterizations (Melton et al., 2013). Table 2 summarizes the comparison between the model results and estimates from the latest global CH 4 budget report for the 2008-2017 decade. Figure 10 shows the spatial distribution of simulated wetland CH 4 emissions over the 2001-2004 period in comparison to three process-based model ensembles: GCP-CH4 ), WetCHARTs (Bloom et al., 2017, and WETCHIMP (Melton et al., 2013). The UVic ESCM simulates few CH 4 -emitting areas over Southeast Asia in comparison to the three model ensembles. The potential underestimation of wetland CH 4 emissions in that region is associated with the relatively few wetland areas simulated by the UVic ESCM (see Sect. 5.1). In tropical Africa, our model simulates too many CH 4 -emitting locations in comparison with  the model ensembles (Fig. 10), which is also associated with the distribution of simulated wetlands (see Sect. 5.1). Nevertheless, the UVic ESCM simulates the occurrence of wetland CH 4 emissions in key source regions such as the Amazon and Congo River basins, South Sudan (Sudd swamps), and the Indonesian islands (Fig. 10). For the Amazon and Congo River basins, however, the UVic ESCM simulates lower wet-land CH 4 emissions than predicted by the model ensembles (Fig. 10). This can be due to either the consideration of an optimal temperature for CH 4 production (around 27 • C) in our model unlike many other process-based models or the fact that model parameters in this study are tuned to northern estimates.  Figure 11a shows the latitudinal distribution of simulated wetland CH 4 emissions compared with the model ensembles. Interestingly, although GCP-CH4 and WetCHARTs are based on the same wetland dataset (SWAMPS-GLWD) (Bloom et al., 2017;Poulter et al., 2017), their zonal wetland CH 4 emissions are very different, especially near the Equator and across northern high-latitude regions (Fig. 11a).
Using the three model ensembles as reference, the UVic ESCM simulates significantly lower wetland CH 4 emissions around the Equator (Fig. 11a), despite the fact that the model simulates overly large equatorial wetland areas (Fig. 4). In fact, wetland emission intensities (emissions per unit of wetland area) by the UVic ESCM are lower than those by the model ensembles between 10 • S and 10 • N (Fig. 11b) due to relatively large wetland areas but small CH 4 emissions in equatorial regions (cf. Fig. 4 and Fig. 11a). As previously discussed, the relatively small CH 4 emissions simulated by the UVic ESCM in equatorial regions can be associated with either the optimal temperature for CH 4 production considered in WETMETH but not in most other process-based models or the fact that model parameters in this study are tuned to northern estimates.
Furthermore, the UVic ESCM simulates more wetland CH 4 emissions between 10 and 20 • N than the three model ensembles (Fig. 11a), and this can be attributed to the widespread wetlands in West and Central Africa simulated by our model (Figs. 5, 6). In addition, the UVic ESCM simulates significantly less wetland CH 4 emissions between 20 and 35 • N in comparison to the WETCHIMP ensemble (Fig. 11a), and this can be attributed to the relatively small wetland areas simulated by the UVic ESCM in Southeast Asia where some models include agricultural wetlands such as rice paddies. Moreover, wetland emission intensities by the UVic ESCM feature low variability with latitude unlike the three model ensembles (Fig. 11b). Such a relative lack of variability can be attributed to two factors: (i) both wetland areas and CH 4 emissions simulated by the UVic ESCM feature relatively low variability with latitude compared with the datasets and model ensembles (Figs. 4,11a); and (ii) as previously discussed, our model likely simulates overly large wetland areas but overly small CH 4 emissions around the Equator, implying a lack of variability across tropical latitudes.
Despite the various discrepancies between the UVic ESCM and both model ensembles regarding the distribution of wetland CH 4 emissions in the tropics, our model simulates mean annual CH 4 emissions from tropical wetlands that are in the range of estimates from the literature (Table 2). For the 1993-2004 period, the UVic ESCM simulates tropical wetland CH 4 emissions of 105.5 Tg CH 4 yr −1 , whereas the WETCHIMP ensemble predicts 126 ± 31 Tg CH 4 yr −1 (Melton et al., 2013). Another study suggests a lower mean value (90 ± 77 Tg CH 4 yr −1 ) for wetland CH 4 emissions in the tropics, although with large uncertainties (Sjögersten et al., 2014). Indeed, several studies indicate that wetland CH 4 emissions in the tropics are highly uncertain due to limited ground-based measurements and poorly delimitated wetland extent (Dargie et al., 2017;Gumbricht et al., 2016;Hu et al., 2018;Pangala et al., 2017;Saunois et al., 2020).

Model sensitivity to poorly constrained parameters
We performed a set of 30 model runs with perturbed parameter values (sensitivity runs) over the 2000-2009 decade in order to analyze the model sensitivity to poorly constrained parameters (T ref , r, τ prod , z oatz , and τ oxid ). For each parameter, we increased or decreased the default value by 10 %, 20 %, and 30 % while holding values for other parameters constant  Our results show that the model sensitivity varies with the different parameters and across regions (Fig. 12). Among the five poorly constrained parameters, the UVic ESCM is most sensitive to perturbations of the two parameters for CH 4 oxidation (z oatz and τ oxid ) at both the global and regional scale. For z oatz , a decrease (increase) of the default parameter value by 10 %-30 % results in an augmentation (reduction) of default wetland CH 4 emissions by 41 %-179 % (29 %-64 %) at both the global and regional scale (Fig. 12jl). For τ oxid , a decrease (increase) of the default parameter value by 10 %-30 % implies a reduction (augmentation) of default wetland CH 4 emissions by 32 %-77 % (37 %-120 %) at both the global and regional scale (Fig. 12m-o).
The UVic ESCM is also very sensitive to perturbations of T ref , but this sensitivity is more pronounced for tropical regions than northern regions (Fig. 12a-c). We recall that T ref is used to define an optimal temperature for CH 4 production in WETMETH through the Q 10 formulation (see Sect. 3.1.1 and Appendix A). For northern regions, a decrease (increase) of T ref by 10 %-30 % results in a reduction (augmentation) of default wetland CH 4 emissions by 5 %-21 % (3 %-5 %). For tropical regions, however, a decrease (increase) of T ref by 10 %-30 % results in a reduction (augmentation) of default wetland CH 4 emissions by 34 %-82 % (33 %-75 %). Globally, a decrease (increase) of T ref by 10 %-30 % results in a reduction (augmentation) of default wetland CH 4 emissions by 26 %-66 % (24 %-55 %). The model sensitivity to perturbations of r is linear across all regions (Fig. 12d-f). Lastly, the model is least sensitive to perturbation of τ prod across the globe (Fig. 12g-i).

WETMETH in the spectrum of wetland methane models
A recent study reviewed 40 models of CH 4 emissions in terrestrial ecosystems (predominantly rice paddies and natural wetlands) and classified them into three categories based on their level of complexity: relatively simple models, relatively mechanistic models, and mechanistic models . Relatively simple models are those that simulate net CH 4 emissions based on soil carbon or other environmental factors without explicit representations for the different CH 4 production and oxidation pathways as well as mechanisms transporting CH 4 to the atmosphere. Relatively mechanistic models are those that account for at least one transport mechanism for CH 4 release in addition to representing CH 4 production and oxidation with simple functions. Mechanistic models are more comprehensive and explicitly simulate different pathways for both CH 4 production and oxidation, more than two mechanisms for CH 4 release, and their environmental controls. Based on this classification, WETMETH is a relatively simple model in the sense that it does not distinguish pathways for CH 4 production and oxidation as well as the various mechanisms transporting CH 4 to the atmosphere. Although some wetland CH 4 models are claimed to be embedded in ESMs , none of these models are currently run in fully coupled models with feedbacks between climate conditions and the global carbon cycle. Most of these models are rather implemented in dynamic vegetation models or uncoupled land surface components of climate models (Arora et al., 2018;Eliseev et al., 2008;Hodson et al., 2011;Riley et al., 2011;Ringeval et al., 2011;Wania et al., 2009). Nonetheless, relatively simple models present the ideal level of complexity for the current generation of ESMs. More complex models generally imply detailed soil chemistry for O 2 and alternate electron acceptors Wania et al., 2010), different carbon substrates and their effects on CH 4 production (Grant, 1998;Lovley and Klug, 1986), and an explicit representation of the dynamics of different microbial communities (Grant, 1998;Xu et al., 2015), which all require comprehensive soil chemistry or model parameters that are currently not common in ESMs . Process parameterizations in mechanistic models generally imply too many degrees of freedom, making it difficult to constrain model parameters against sparse observations. Furthermore, mechanistic models may be too computationally demanding for fully coupled ESM runs without a proportional benefit for large-scale simulations of wetland CH 4 emissions.
The particularity of WETMETH among relatively simple models is that the model accounts for an optimum temperature for CH 4 production, a calibration of depth-dependent CH 4 production rates against potential CH 4 production rates from laboratory incubations, dynamic CH 4 oxidation based on the vertical distribution of soil moisture, and the potential for CH 4 emissions in non-inundated ecosystems with a relatively high level of soil moisture content. In conclusion, WETMETH is simple enough to be compatible with ESMs and yet complex enough to implicitly simulate biogeochemical processes regulating wetland CH 4 emissions.

Limitations for WETMETH
The developed wetland CH 4 model is associated with several limitations, which are linked to either its level of complexity or the scarcity of large-scale datasets for model calibration: 1. The present state of global wetland modelling assumes generic wetlands without distinguishing their different types (Melton et al., 2013;Poulter et al., 2017). Like many other large-scale models of the current generation, WETMETH would not be appropriate for investigating the contribution from particular wetland types to regional or global CH 4 emissions (Aselmann and Crutzen, 1989).
2. As WETMETH is not based on a comprehensive soil biochemistry module and does not include the different pathways for CH 4 production and oxidation, the model is not suited for investigating the role of specific biological and chemical controls on wetland CH 4 emissions (Bridgham et al., 2013;Kwon et al., 2019).
3. WETMETH does not simulate the contribution from wetland-specific vegetation species to CH 4 emissions, although some of these species can either lead to high emissions (e.g. sedges are vascular plants that can transport CH 4 through their aerenchyma) or low emissions (e.g. mosses are non-vascular plants that have been shown to develop a symbiotic relationship with methanotrophs) (Bridgham et al., 2013;Chen and Murrell, 2010).
4. Ebullition and aerenchyma of vascular plants allow CH 4 produced in wetlands to escape to the atmosphere with little opportunity for oxidation (Segers, 1998;Whalen, 2005). Moreover, stems of woody trees are important conduits for CH 4 emissions in Amazonia, a major source region in the world (Pangala et al., 2017). By considering the net effect of all mechanisms transporting CH 4 to the atmosphere, WETMETH presents a limitation for investigating the relative contribution of transport mechanisms to CH 4 emissions across regions and at the global scale.
5. Methane produced in northern wetlands can be stored underneath frozen soil during the winter and be released abruptly upon spring thaw (Mastepanov et al., 2013;Song et al., 2012). WETMETH does not currently feature such a storage of CH 4 in the soil column, which is probably more relevant for small-scale (site) and shortterm (daily) than large-scale (regional) and long-term (seasonal) emissions (Fig. 9).
6. While the existence of an optimal temperature for CH 4 production in wetlands is relatively well established in the literature (Dean et al., 2018), there are currently no estimates of such an optimal temperature for different climate zones across the globe. Previous studies suggest a range of 25-30 • C for such an optimal temperature based on measurements of CH 4 production in northern wetlands (Dunfield et al., 1993;Metje and Frenzel, 2007). In WETMETH, we use a global value for this optimal temperature (∼ 27 • C) which is assumed to be valid for CH 4 production in both tropical and extratropical wetlands (see Sect. 3.1.1 and Appendix A). However, our sensitivity analysis suggests that, in the present-day climate, wetland CH 4 emissions in the tropics are much more dependent on the optimal temperature for CH 4 production than wetland CH 4 emissions in the boreal and Arctic regions (see Sect. 6). The optimal temperature for CH 4 production in WETMETH, along with other factors such as areal wetland extents, contributes to inter-model differences in simulated wetland CH 4 intensities in the tropics (see Fig. 11b).
7. As presented in this study, poorly constrained WET-METH parameters are tuned to estimates from northern high-latitude regions because large-scale datasets from other regions are scarce (see Sect. 4). A strong limitation comes with the assumption that the chosen parameter values are representative for CH 4 production and oxidation across the globe. However, the applied model calibration remains a reasonable approach given the scarcity of observations for wetland CH 4 production, oxidation, and emissions at the global scale.
Despite these limitations and the model simplicity, WET-METH is skilful when it comes to the simulation of mean seasonal, annual, and decadal wetland CH 4 emissions at the regional, hemispheric, and global scale (see Sect. 5.2). The implementation of WETMETH in a fully coupled ESM should advance research on the interactions between climate change and wetland CH 4 emissions in the context of global climate projections. This paper introduces WETMETH -a process-based wetland CH 4 model developed for implementation in ESMs. WETMETH is currently embedded in the UVic ESCM, a fully coupled EMIC. WETMETH is a computationally efficient model, applicable globally, and of appropriate complexity with respect to the current state of wetland CH 4 modelling. Unconstrained model parameters are tuned to potential CH 4 production rates from incubated soil samples and CH 4 emissions from northern wetlands due to the scarcity of large-scale datasets from other regions. Nevertheless, WET-METH skilfully reproduces estimates of mean annual CH 4 emissions over the past few decades at the regional, hemispheric, and global scale. Despite the importance of tropical wetlands in the global CH 4 budget (Kirschke et al., 2013;Saunois et al., 2016) and climate change (O'Connor et al., 2010;, their areal extent and associated CH 4 emissions remain highly uncertain in both the literature and modelling work (including this study) due to a combination of limited ground-based measurements and process understanding (Pangala et al., 2017;Saunois et al., 2020;Sjögersten et al., 2014) as well as a low accuracy from remotely sensed products, especially over the dense rainforests of Indonesia, Amazonia, and the Congo River basin where new peatlands continue to be discovered to date (Dargie et al., 2017). Largescale wetland mapping is a field of ongoing research (Tootchi et al., 2019), and further model development should focus on the improvement of wetland simulations in the tropics. In parallel, a compilation of tropical wetland CH 4 measurements from various sources into synthesis datasets would be beneficial for constraining wetland CH 4 processes in largescale models.
The inclusion of wetland CH 4 processes in a fully coupled ESM allows one to advance the research on the feedback between climate change and wetland CH 4 emissions. The implementation of WETMETH in the UVic ESCM constitutes an ideal tool for investigating interactions between climate conditions and wetland CH 4 emissions from decadal to longer timescales. Of particular importance is the permafrost carbon feedback to climate change, in which CH 4 emissions from northern wetlands are expected to play an important role (Nzotungicimpaye and Zickfeld, 2017).
Appendix A: Temperature-dependent Q 10 coefficient for methane production Figure A1 illustrates the different shapes of the temperature-dependency function for CH 4 production Q T i −T 0 10 10 ; T 0 = 273.15 K across a range of temperatures when considering (i) a constant Q 10 of 4.2 and (ii) a temperature-dependent Q 10 coefficient given by where T ref = 308.15 K. The temperature-dependent Q 10 (T i ) implies an optimal temperature for CH 4 production in WETMETH of around 300.15 K. When Q 10 (T i ) decreases to reach negative values, its value in WETMETH is set to 10 −3 to represent a very small methanogenic response to temperature changes (Fig. A1). Figure A1. (a) Differences between a constant Q 10 coefficient and a temperature-dependent Q 10 (T i ) coefficient, and (b) implications for the temperature-dependency expression for CH 4 production (Q 10 [(T i − T 0 )/10]). The temperature-dependent coefficient Q 10 (T i ) = 1.7+2.5[tanh(0.1(308.15−T i ))] allows one to account for uncertainties in the Q 10 coefficient and to define an optimal temperature for CH 4 production of around 300.15 K (dashed vertical line). The freezing point of water is shown at 273.15 K (continuous vertical line). The TOPMODEL approach implemented in the UVic ESCM is based on the formulation by Gedney and Cox for global land surface models (Gedney and Cox, 2003). This approach combines the simulated hydrology with a prescribed topographic index to determine the occurrence of wetlands (surface inundation) and soil moisture heterogeneity at the subgrid scale. The occurrence of wetlands is simulated in an area whose local topographic index ( ) satisfies the following condition: where min is a lower threshold that can be related to undersaturated conditions, and max is an upper threshold that can be related to oversaturated conditions. In the initial work by Gedney and Cox, min depends on the transmissivity of the entire soil column (T (0)), the transmissivity of the soil column below the mean water table depth (z w ) of the grid box (T (z w )), and the mean topographic index ( mean ). It is calculated as min = ln T (0) T (z w ) + mean . While mean is static and prescribed with a topographic index map, both transmissivities (T (0) and T (z w )) are simulated and non-static for a specific grid cell. Hence, min is a non-static and grid-dependent threshold. Unlike min , max is a static and global threshold. This threshold is applied to constrain the occurrence of wetlands in areas of stagnant water based on the assumption that locations where the water table rises well above the surface would be characterized by streamflow.
For the current study, a minor modification is applied to the above TOPMODEL approach. The revision consists of using a non-static and grid-dependent max instead of a static and global threshold. Following the formulation by Comyn-Platt et al. (2018), an expression for max that depends on min is currently used in the UVic ESCM. This threshold is defined as follows: where range is a global tuning parameter ( range = 0.93 in the version of the UVic ESCM used in this study). In summary, unlike the initial work by Gedney and Cox (2003), the modified TOPMODEL approach considers two non-static and grid-dependent thresholds ( min and max ) for the identification of wetlands across the globe.
Appendix C: Unit conversion for potential methane production rates Here, we describe steps followed for converting units of maximum CH 4 production rates measured in laboratory incubations from a soil weight basis (µg C g DW −1 h −1 ) to a soil volume basis (kg C m −3 s −1 ). This unit conversion relies on the soil bulk density (BD in g cm −3 ) from the site of origin. The following two steps illustrate the applied unit conversion. In the first step, the potential CH 4 production rates (P d,0 ) are converted from micrograms of carbon per gram dry weight per hour (µg C g DW −1 h −1 ) to micrograms of carbon per cubic centimetre per hour (µg C cm −3 h −1 ) as follows: Then, the conversion of P d,1 from micrograms of carbon per cubic centimetre per hour (µg C cm −3 h −1 ) to kilograms of carbon per cubic metre per second (kg C m −3 s −1 ) is done as follows: where δ encompasses the conversion factors from micrograms (µg) to kilograms (kg) and from cubic centimetres (cm −3 ) to cubic metres (m −3 ) (δ = 10 −3 kg m −3 ); and γ is the number of seconds per hour (γ = 3600 s).