Methane chemistry in a nutshell – The new submodels CH4 (v1.0) and TRSYNC (v1.0) in MESSy (v2.54.0)

Climate projections including chemical feedbacks rely on state-of-the-art chemistry-climate models (CCMs). Of particular importance is the role of methane (CH4) for the budget of stratospheric water vapor (SWV), which has an important climate impact. However, simulations with CCMs are, due to the large number of involved chemical species, computationally demanding, which limits the simulation of sensitivity studies. To allow for sensitivity studies and ensemble simulations with a reduced demand for computational resources, we introduce 5 a simplified approach to simulate the core of methane chemistry in form of the new Modular Earth Submodel System (MESSy) submodel CH4. It involves an atmospheric chemistry mechanism reduced to the sink reactions of CH4 with predefined fields of the hydroxyl radical (OH), excited oxygen (O(D)), and chlorine (Cl), as well as photolysis and the reaction products limited to water vapour (H2O). This chemical production of H2O is optionally feed back onto the specific humidity (q) of the connected General Circulation Model (GCM), to account for the impact onto SWV and its effect on radiation and stratospheric dynamics. 10 The submodel CH4 is further capable of simulating the four most prevalent CH4 isotopologues for carbon and hydrogen (CH4 and CH3D as well as CH4 and CH4), respectively. Furthermore, the production of deuterated water vapour (HDO) is, similar to the production of H2O in the CH4 oxidation, optionally feed back to the isotopological hydrological cycle simulated by the submodel H2OISO, using the newly developed auxiliary submodel TRSYNC. Moreover, the simulation of a user defined number of diagnostic CH4 ageand emission classes is possible, which output can be used for offline inverse optimization 15 techniques. The presented approach combines the most important chemical hydrological feedback including the isotopic signatures with the advantages concerning the computational simplicity of a GCM, in comparison to a full featured CCM.

using its direct radiative impact and not accounting for the water vapour (H 2 O) produced by the oxidation of CH 4 due to a set-up without chemistry. Especially in the stratosphere this additional H 2 O (stratospheric water vapor (SWV)) influences 25 among others the radiative forcing, stratospheric temperature and the ozone (O 3 ) chemistry (Stenke and Grewe, 2005;Tian et al., 2009;Solomon et al., 2010;Revell et al., 2012;Winterstein et al., 2019). The inclusion of production of H 2 O by CH 4 requires a chemical mechanism as provided by chemistry-climate models (CCMs). Current state-of-the-art CCMs include a vast amount of chemical species and reactions. By extending the chemical mechanisms, one intends to achieve an increase in accuracy of the atmospheric chemistry representation. At the same time, however, the computational demands increase. 30 Although, available computational power increases at a certain rate, too, the availability and capacity of high performance computers is a limiting factor for sensitivity studies in climate projection simulations with CCMs.
It is hence advisable to recognize both main effects of CH 4 , namely its radiative forcing and its impact on SWV, but keeping computational demands low at the same time. Therefore, our approach to simulate CH 4 includes both effects and is able to use predefined reaction partners of CH 4 , which reduces computational cost to a minimum. 35 Sections 1.1 and 1.2 introduce the sources and sinks of CH 4 , and CH 4 isotopologues and their fractionation effects, respectively. In Sect. 2 we briefly present the Modular Earth Submodel System and describe the concept of the CH4 submodel in Sect. 3. Two additional options of the CH4 submodel are explained in the subsequent Sects. 3.1 and 3.2. The coupling to the hydrological cycle with the submodel TRSYNC is introduced in Sect. 4. We show three example applications using the newly presented submodels in Sect. 5 and end with a short summary. Parts of the manuscript are based on the PhD thesis of the first 40 author (Frank, 2018).

Sources and sinks of CH 4
Methane is a GHG emitted by both, natural, and anthropogenic sources at the Earth's surface. There are basically no known chemical sources of CH 4 in the free atmosphere.
In CCMs usually predefined lower boundary conditions instead of emission fluxes are used to describe atmospheric CH 4 . 45 This approach is mainly employed due to two major problems: (1) The simulated CH 4 lifetime is not sufficiently accurate, however important for tropospheric and stratospheric chemistry. Thus, realistic climate projections with interactive chemistry and CH 4 emission fluxes are difficult. (2) Despite large ongoing efforts, current emission inventories are still subject to large uncertainties, as top-down and bottom-up inventories differ significantly (e.g. EDGAR or Saunois et al. (2016)). This mismatch indicates the dilemma, that there are a lot of open questions with respect to both, the magnitude of sources, and the sinks of 50 CH 4 .
Methane is removed from the atmosphere mainly by three photochemical reactions and is also depleted by photolysis: Another sink of CH 4 is the so called soil-loss at the Earths' surface. CH 4 is either depleted by CH 4 consuming bacteria 60 (methanotrophs), or it is removed from the air by diffusive transport into the soil, which is mostly influenced by soil water content (King, 1997). Globally, the soil-loss accounts for approximately 4 % of the total CH 4 sink (IPCC, 2013).

Isotopologues of CH 4
A powerful and common method in the investigation of the CH 4 budget is the study of CH 4 isotopologues. Production and removal of CH 4 cause fractionation effects, which lead to distinct isotopological signals in the atmosphere. These isotopic 65 signatures provide potentially additional insights into the role of specific CH 4 sources and depleting reactions, and are already widely used in the context of CH 4 (Hein et al., 1997;Fletcher et al., 2004;Monteil et al., 2011;Rigby et al., 2012;Nisbet et al., 2016;Schaefer et al., 2016).
Fundamentally, the stable isotopologues of CH 4 form with respect to the most abundant stable isotopes of hydrogen and of carbon. The stable isotopes of hydrogen are 1 H and 2 H (deuterium, D), and for carbon, carbon-12 ( 12 C) and carbon-13 ( 13 C).

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This results in the first order stable isotopologues 12 CH 4 , 13 CH 4 , and CH 3 D. The relative abundances of higher substituted and mixed isotopologues (e.g. CH 2 D 2 or 13 CH 3 D) are less than 0.0007% (compared to 0.0616% of CH 3 D) (Stolper et al., 2014) and hence neglected.
The chemical fractionation is based on the fact that isotopologues of the same molecule have different reaction rates, i.e. they react with different speed or probability. This difference in reaction rates is described as the so called Kinetic Isotope 75 Effect (KIE) and becomes apparent during the chemical reaction of a specific molecule X: with X L being its light (major), and X H its heavy (minor) isotopologue. E and P/P' denote the reaction partner(s) and product(s), respectively. The value of the KIE is thereby defined as the ratio of the reaction rates k L and k H (Bigeleisen, 2005) and 80 its inverse is called the fractionation factor α: The KIEs of the sink reactions of CH 4 have been, among others, determined by Saueressig et al. (1995Saueressig et al. ( , 1996Saueressig et al. ( , 2001 and Crowley et al. (1999) in laboratory measurements (see Table 1). Since the KIEs of CH 4 isotopologues are partly temperature dependent, the KIEs are described by two parameters A and B and are calculated as   with T being the temperature in [K].
The largest KIE and therefore strongest fractionation effect is found for the reaction with Cl, which especially influences the isotopic composition of CH 4 in the middle and upper stratosphere Bergamaschi et al., 1996).
Conversely, the reaction with O( 1 D) shows the lowest KIE, which furthermore does not show any temperature dependence.

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The KIE of the reaction with OH is temperature dependent with respect to deuterated methane (CH 3 D) but not with respect to methane containing 13 C ( 13 CH 4 ) (Saueressig et al., 2001). Nair et al. (2005) estimated the rate coefficients of the photodissociation of CH 4 and its major isotopologues for planet Mars, which results in a calculated KIE= 1.005 for CH 3 D and a negligible isotopic fractionation for the 13 C isotopologue (Nixon et al., 2012). There is, especially for deuterium, a non-negligible fractionation during the soil-loss for CH 4 (Snover and Quay, 2000;Maxfield et al., 2008). An average value for the overall soil-loss 95 is estimated as KIE soil CH3D = 1.0825 and KIE soil 13 CH4 = 1.0196 (Snover and Quay, 2000;Holmgren, 2006;Maxfield et al., 2008). Chemistry (EMAC) model is a numerical chemistry and climate simulation system that includes sub-models describing tropospheric and middle atmosphere processes and their interaction with oceans, land and human influences .  2010)) enables the user to tag selected chemical elements, without modifying the underlying standard chemical mechanism of MECCA. It can be applied for simulating isotopologues of trace gases with respect to selected isotopes. In order to do so, rare and abundant isotopologues of the species of interest (e.g., those containing atomic hydrogen (H)) are created in an extended set of reactions in the same chemical mechanism.

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MESSy and its application in EMAC has been used in multiple studies (see the special issue in Atmospheric Chemistry and Physics https://www.atmos-chem-phys.net/special_issue22.html) and includes several submodels from contributing institutions. Further information on EMAC, MESSy and its submodels can be found in Jöckel et al. (2010Jöckel et al. ( , 2016, on the web-site https://www.messy-interface.org/, or accompanying papers documenting the specific submodels.

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The MESSy submodel CH4 aims to close the gap between the operational modes of EMAC as a GCM without chemistry and as a CCM with the comprehensive chemical mechanisms of MECCA and SCAV. The basic concept of the submodel is to limit the chemical mechanism to the loss-processes of methane and use predefined fields of the reaction partners OH, O( 1 D) and Cl to reduce the computational demands. The predefined fields are taken either from existing simulation results with detailed chemistry, or from other data sources (e.g. reanalyses or projections). If CH4 is included in an EMAC CCM 130 simulation (which is possible in the MESSy framework), the CH4 submodel can also be coupled to the reactant fields, which are on-line calculated during the same simulation by the chemical mechanism (i.e. MECCA). Although this does not save computational requirements, such a simulation configuration can be used, for example, if output of one of the additional options of the CH4 submodel are desired together with a coupled comprehensive chemical mechanism. Same applies for the photolysis rate of CH 4 , which can be predefined or on-line calculated by the submodel JVAL (Sander et al., 2014).
135 Figure 1 visualizes the conceptual differences between the MESSy submodel CH4 (left) and a CCM simulation with MECCA (right). MECCA simulates the entire chemical mechanism and therefore also includes the feedback onto the reaction partners (depicted in yellow) of CH 4 . Additionally, there is also a secondary feedback by the products from the CH 4 sink reactions (  The chemical mechanism in CH4 is reduced to the sink reactions of CH4 and gives optional feedback to H2O only. In MECCA a complete chemical mechanism is included which feeds back among others on H2O and other products of the CH4 sink reactions. Reaction partners are depicted in yellow, whose feedback is included in MECCA. The reaction partners in the CH4 submodel are predefined fields without feedback. on. GCMs include CH 4 foremost for its radiative impact as a greenhouse gas, but also for its influence on stratospheric water vapor (SWV, e.g. Monge-Sanz et al. (2013) is, however, only a rough approximation as analyzed by Frank et al. (2018).
Note that soil loss is not explicitly included in the CH4 submodel, since the concept of dry deposition is already part of the EMAC submodel DDEP (Kerkweg et al., 2006a). An example how to use DDEP to simulate the soil loss of CH 4 is included in the supplement of this paper.
The submodel CH4, with its four sink reactions of CH 4 , is considerably computationally cheaper, compared to a fully 150 interactive chemistry simulation using MECCA, which represents (depending on the chosen set-up) several hundred reactions (e.g., more than 300 in the base simulations of Earth System Chemistry integrated Modelling (ESCiMo) project (Jöckel et al., 2016)). For example, a reference set-up with MECCA requires about 250 node-h 1 per simulated year, while a set-up with the CH4 submodel without MECCA requires only 30 node-h per year (these numbers are calculated for simulations conducted on the high performance computer (HPC) Mistral at the Deutsches Klimarechenzentrum (DKRZ)).

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First simulations using the CH4 submodel are presented in studies by Eichinger et al. (2015a, b), it was included in the simulations of the ESCiMo project (Jöckel et al., 2016) and it has been used for the CH 4 forecast system presented by Nickl et al. (2019).

Option I: Age and Emission classes
The CH 4 submodel includes an option for simulating age and emission classes. These classes, which can be specified by the 160 user via namelist, enable a precise distinction between CH 4 source sectors and/or regions (emission classes), as well as further insight into the CH 4 distribution over time (age classes). The term "emission class" denotes thereby a CH 4 -like tracer defined by the CH4 submodel. The assignment of specific emission fluxes (sectors and regions) to the tracers of the emission classes is handled by the submodel OFFEMIS (Kerkweg et al., 2006b). In our present application example these classes are subject to emissions being a combination of an emission sector (like wetlands, biomass burning, anthropogenic etc.) and a region (e.g.

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continents or countries). One tracer, for example, thus traces anthropogenic CH 4 emitted from Africa, as shown in Sect. 5.1.
These additional diagnostic tracers are transported identical to the master CH 4 tracer of the CH4 submodel and also experience the same sink reactions.
The time period represented by one age class can be chosen by the user. How the age and emission classes evolve over time is depicted in Fig. 2. Methane of each emission class is propagated through a specific number of age classes. The emitted CH 4 170 of a specific emission class is added to the tracer which corresponds to the first age class. After the selected time span it moves to the next "older" age class until it reaches the oldest. The oldest age class represents the background, since CH 4 does not proceed further.
It is further selectable which age evolving method is applied. The CH4 submodel offers three options: (1) CH 4 is passed on in one step after a user-defined time-span, (2) CH 4 is continuously passed on with respect to an user-defined time-span, and (3) 175 CH 4 is passed on monthly with fixed-lag.
We define the state vector for emission class i and age classes 1 to N as: The first two options are implemented according to  After the defined length of time, the age classes proceed to the next "older" age class. The last class represents the background CH4, where the CH4 is only subject to transport and the chemically defined sink reactions, but not propagated to an older age class, which is indicated by the circled arrow.
with ∆f i being the tendency of f i , ∆t being the time step length, and M being a matrix defining the ageing step according to the chosen option. For option (1) this matrix looks like This moves the current values of one age class tracer after a user-defined time-span to the next older one. The implementation of this option is not conform with a Leapfrog time stepping with Asselin-filter and might cause numerical oscillations 185 with negative values. It was implemented solely for testing purposes during development, but it is not recommended for real applications. The ageing step matrix M for option (2) with α = ∆t T andT being the user-defined time-span indicating the binning width of the age class. This option carries out a quasi-continuous update of the age classes, as it moves at every time step a fraction of the current age class to the next.

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The third option is implemented for usage by a fixed-lag Kalman filter for inverse optimization. With this option, one age class represents one month and at the end of one month all CH 4 of one age class moves to the next. This option is specifically implemented to be conform with the Leapfrog time stepping (c.f. option (1)).
In order to reduce numerical errors, the age and emission classes are continuously constrained (i.e., in each model time step) to sum up to the master tracer and are scaled appropriately, if the sum deviates.

Option II: Isotopologues
Additional to solving the basic CH 4 kinetics, the submodel CH4 further allows for the simulation of CH 4 isotopologues, which are a potent diagnostic measure in the source and sink attribution. The submodel CH4 is able to simulate the abundant and first order rare isotopologues and defines these as tracers additional to the master tracer. Higher substituted isotopologues are neglected. The user can choose, whether isotopologues are simulated with respect to carbon (methane containing 12 C ( 12 CH 4 ) 200 and 13 CH 4 ), or hydrogen (CH 4 (containing 1 H isotopes only) and CH 3 D), or both. The abundant (with 12 C or 1 H isotopes only) and rare (with 13 C or D) isotopologues are thereby simulated in parallel. During the simulation it is taken care that each isotopologue family sums up to the master tracer CH 4 tracer of the CH4 submodel (CH4_fx). The isotopic signatures of CH 4 emission sources are included by splitting the emission fluxes into an abundant and a rare fraction. This is handled via the OFFEMIS namelist (Kerkweg et al. (2006b), see example namelists in the supplement).

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The reaction rates of the CH 4 isotopologues with their reaction partners are adjusted with respect to the KIE factors, e.g.:   In principal, if EMAC is applied in GCM mode, only the master hydrological cycle is present (see Fig. 3, inner solid blue cycle). Adding MECCA or CH4 to the set-up expands the model into a CCM, or a simple "CH 4 -only" CCM, respectively (red solid circle). The chemistry submodels use water vapor as a chemical tracer (first green star) and calculate the contribution from CH 4 oxidation (second green star). This chemical feedback onto water vapor was already implemented as an option in previous 230 EMAC versions. By including the isotopological submodels into the set-up, H2OISO doubles the hydrological cycle for the water isotopologues and CH4 or MECCA_TAG create the chemical tracers of the water isotopologues (outer dashed circles).
This results in several physical and chemical H 2 O isotopologue tracers. While the master chemical process adds its feedback directly to the specific humidity of the hydrological cycle (there is no need for a chemical water tracer), the synchronization of the physical isotopological tracers in the isotopic hydrological cycle (H2OISO) and the chemical isotopological tracers (CH4 or 235 MECCA_TAG) is done via the new auxiliary submodel TRSYNC. In brief, TRSYNC guarantees that the physical H 2 O tracers (incl. their isotopologues) receive also the correct tendencies of the corresponding chemical tracers. Since isotopological water vapor tracers of MECCA_TAG and the HDO tracer created by CH4 are transported in EMAC in the same way as every other tracer, they are subject to some of the physical processes, but not to all hydrological fractionation effects. Thus, at the first synchronization point the chemical tracer is synchronized to represent the current value of the physical tracer. In the following, chemical tendencies including fractionation effects are calculated and are added via the second synchronization point to the physical tracer. By doing so, chemical and physical fractionation processes are strictly separated and the tendencies of the chemical tracers represent the chemical tendencies in addition to the previous physical fractionations in the current time step.
Water vapor in the physical hydrological cycle (regarding ECHAM5 and H2OISO) are defined in units of kg of the tracer per kg of moist air (kg kg −1 moist air ), while the chemical tracers are defined in mol mol −1 dryair . This also holds for the corresponding 245 isotopologue tracers. Parameterizations of physical processes in ECHAM5 are by design formulated with specific humidity (per moist air). Conversely, chemical reactions are necessarily calculated with species concentrations. This requires the individual chemical and physical isotopologue tracers, which have, for the sake of correct process formulations, distinct units, and motivated the development of the auxiliary submodel TRSYNC in order to be able to synchronize these tracers accordingly and in a common way for CH4 and MECCA_TAG, respectively.

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In addition to that, the application of MECCA_TAG creates the basis to investigate various other isotopes in the interactive chemical mechanism. While CH4 feedbacks on H 2 O with respect to hydrogen isotopes only, MECCA_TAG can also be used to simulate oxygen isotopes ( 16 O, 17 O and 18 O) in the chemical mechanism. It is therefore also possible to couple MECCA_TAG with oxygen isotopes to the corresponding oxygen related isotopologue tracers in H2OISO. Last but not least, for MECCA_TAG tracer names are not standardized. Therefore, the namelist of the submodel TRSYNC can be adjusted 255 according to the actual tracer names used in MECCA_TAG.

Example applications
The following examples are simulations carried out with EMAC in a GCM-like mode including the newly presented CH4 and TRSYNC submodels. Other involved MESSy submodels are OFFEMIS (Kerkweg et al., 2006b) and DDEP (Kerkweg et al., 2006a). OFFEMIS manages the emissions of CH 4 from prescribed sources. It reads predefined fields with emission data and 260 adds these fluxes to the chemical tracers. DDEP simulates the dry deposition for gases and aerosols and is used in the present context to simulate the soil-loss of CH 4 , which is not done in the CH4 submodel itself.
Monthly mean sink fields are used in the simulation set-up in the examples below. Higher frequencies are technically possible, this would, however, increase the computational demands due to the larger amount of data read from disk. Monthly mean fields smooth the diurnal cycle, which is especially strong in OH. However, in order to investigate long-term global trends 265 of CH 4 , which has a tropospheric lifetime of 8-10 years, variations on time scales of less than one month are negligible and monthly mean fields are assumed to suffice for such applications. Furthermore in the examples, photolysis rates are calculated by the submodel JVAL in the presented examples, but predefined data can be used as well.
The H2OISO submodel (Eichinger, 2014;Eichinger et al., 2015a) simulates the stable water isotopologues with respect to H and D, as well as 16 O, 17 O and 18 O. Overall, it represents a second hydrological cycle, which includes water isotopologues and convective clouds, during vertical diffusion, and during evaporation from the ocean (evaporation from soil, biosphere and snow are not considered to have a significant fractionation).
We simulated the years 1989 to 2012 and applied a specified dynamics set-up to represent the reanalyzed meteorology of this time. Specified dynamics means here that the prognostic variables divergence, vorticity, temperature and (logarithm of) 275 surface pressure are nudged by Newtonian relaxation towards ECMWF ERA-Interim reanalysis data (Dee et al., 2011).

Application of the CH4 submodel for inverse optimization of CH 4 emission inventories
Current estimates of CH 4 emission inventories still include large uncertainties. In order to reduce these, new estimates of inventories must be able to represent temporal and spatial resolutions in greater detail (e.g., seasonal cycle, distinct regions). One statistical method to estimate CH 4 emission strengths is the fixed-lag Kalman Filter, which performs an inverse optimization 280 of the emission inventory by comparing simulated and observed mixing ratios of a trace gas (see e.g., Bruhwiler et al. (2005)).
This "off-line" inversion algorithm requires data from a forward simulation including temporal and spatial information of the simulated CH 4 tracer.
In order to provide the necessary data, the CH4 submodel with the option of age and emission classes is applied. The combination of chosen regions and emission sectors in this example results in 48 emission classes altogether. These 48 emission 285 classes are simulated with 5 age classes for ages up to 1, 2, 3, 4, and ≥5 months since emission release. Figure 4 shows exemplarily the evolution of one emission class (i.e., anthropogenic emissions in Africa) from age class to age class. Panel Northern Hemisphere (NH). Eventually, the fifth (i.e. the last age class) shows the accumulated background of all CH 4 from anthropogenic African sources. Applied is an a priori emission inventory.
Overall, the temporal evolution of the age classes in Fig. 4 confirms that the 5 age classes in this set-up sufficiently track the spread of CH 4 towards a fairly uniform distribution, which is a prerequisite for a successful application of the inverse optimization method.

Simulating CH 4 isotopologues
We further present a simulation using the CH4 submodel, which includes all four CH 4 isotopologues. For this simulation, we applied a global a posteriori emission inventory provided by Dominik Brunner (pers. communication) and a set of isotopic emission signatures prepared from data from literature (see Table S1 in the supplement). Figure 5 shows zonal mean climatolo-  North-South gradient. In the stratosphere CH 4 becomes isotopically enriched towards higher altitudes. This can be ascribed to 305 fractionation processes, as heavier CH 4 isotopologues likely remain when CH 4 is exposed to oxidation during the ascend in the troposphere.
These simulation results compare well to observations. For example isotopic observations from the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) sampling sites (White et al., , 2017 and airborne samples taken during the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project 310 (Umezawa et al., 2012) verify the North-South gradient. The values of signature of 13 C in CH 4 (δ 13 C(CH 4 )), for example, are within the uncertainty of the CONTRAIL observations. The signature of D in CH 4 (δD(CH 4 )) is isotopological depleted in D compared to the CONTRAIL observations, however, still capture the gradient well (not shown). The vertical gradient (i.e. isotopical enrichment in the stratosphere) can be verified by comparing with balloon borne observations by Röckmann et al. (2011). Our simulation results are thereby within the local and temporal uncertainties (not shown). Note that an optimization 315 with respect to source signatures are yet to be made and requires an optimized emission inventory. However, the capturing of the respective gradients indicates that the isotopical fractionation is sufficiently implemented.

Coupling of the CH 4 isotopologues to the isotopological hydrological cycle
The previously shown results were achieved with the CH4 submodel including the option to simulate CH 4 isotopologues. The produced HDO (by oxidation of CH 3 D) is connected via the TRSYNC submodel to the isotopological hydrological cycle rep-320 resented by the H2OISO submodel. We carried out an additional simulation in which we applied MECCA and MECCA_TAG to simulate the atmospheric chemistry and the CH 4 isotopologues instead of the CH4 submodel. In this simulation TRSYNC connects the produced HDO likewise to the isotopological water tracers of H2OISO.
In Figure 6 we compare the results obtained with submodel CH4 (left) and those obtained with the submodel MECCA_TAG it is observed that the EMAC model underestimates the H 2 O mixing ratio (see Figs. 6a and 6c). This is associated with a too cold tropopause in EMAC, where a temperature bias of −2 to −6 K is detected in the upper troposphere (Jöckel et al., 2016).
This reduces the H 2 O transported into the stratosphere since more gas phase H 2 O freezes and sediments. Comparing oxidation chain of CH 3 D, and (2) the HD, produced in the troposphere and propagating into the stratosphere, which is not included in the simplified chemistry, but represents an additional source of HDO. For an accurate simulation of stratospheric 340 HDO this source needs to be considered as well in future simulations.

Summary
The submodel CH4 provides a reduced chemical set-up focusing on the CH 4 sink reactions, using predefined data of reaction partners, and optionally includes the feedback on SWV. This reduces the computational demands for sensitivity simulations of climate projections without neglecting the main source of chemically induced SWV. 345 We presented two additional options of the CH4 submodel. The age and emission classes allow the inverse optimization of emission inventories using a fixed-lag Kalman filter. The simulation of CH 4 isotopologues provides further insight into the variability and distribution of CH 4 from its source (via emission signatures and fractionation effects) to its sink (