Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , 5,7 19,20 , , 11 , B. D. Stocker 14,15,16,17 , A. V. Eliseev 5,6 19 23 , W. J. Riley 22 , R. Spahni 4 , M. V. Glagolev , S. N. Denisov 18 1908 1907 12,13 , M. A. Rawlins , and J. O. Kaplan 12 9 , T. Kleinen 3 , G. Chen 4 , A. Ito 2 , R. Schroeder , Q. Zhuang 8 9,10,11 , K. C. McDonald , V. Brovkin 21 3,16 , H. Tian 11 , J. R. Melton 1 , X. Zhu 8 Natural Resource Ecology Laboratory, Colorado StateEarth University, Sciences Fort Division, Collins, Lawrence CO, Berkeley USA City National College Laboratory, Berkeley, of CA, New USA York, CityInstitute University of of Botany, University New of York, New Hohenheim,Moscow York, Stuttgart, NY, State USA Germany University, Moscow, Russia Institute of Forest Science, RussianLaboratory Academy of of Sciences, Computational Uspenskoe, Geophysics, Russia Tomsk StateYugra State University, Tomsk, University, Russia Khanty-Mantsiysk, Russia Oak Ridge National Laboratory, Oak Ridge,A. TN, M. USA Obukhov Institute of Atmospheric Physics, Russian AcademyKazan of Federal Sciences, University, Kazan, Russia Department of Geography, University of Exeter,Department Exeter, of UK Geosciences, University ofInstitute Massachusetts, of Amherst, Earth MA, Surface USA Dynamics, University of Lausanne, Lausanne, Switzerland This discussion paper is/has beenPlease under refer review to for the the corresponding journal final Biogeosciences paper (BG). in BG if available. Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, School of Earth and SpaceCanadian Exploration, Centre Arizona for State Climate University, Modelling Tempe, AZ, and USA Analysis, Environment Canada,National Victoria, Institute for Environmental Studies,Max Tsukuba, Planck Japan Institute for Meteorology,Climate Hamburg, and Germany Environmental Physics, Physics Institute,Oeschger University Centre of for Bern, Climate Bern, ChangeDepartment Research, Switzerland of University Life of Sciences, Bern, Imperial Bern,International College, Switzerland Center Silwood for Park Climate Campus, and Ascot, Global UK Change Research and School of Forestry and 9 West Lafayette, IN, USA 10 11 12 13 14 15 16 17 18 19 Moscow, Russia 20 21 22 23 Received: 10 December 2014 – Accepted: 1Correspondence January to: 2015 T. – J. Bohn Published: ([email protected]) 30 JanuaryPublished 2015 by Copernicus Publications on behalf of the European Geosciences Union. B. Zhang S. Maksyutov A. Gallego-Sala WETCHIMP-WSL: intercomparison of wetland methane emissions models over West Siberia T. J. Bohn Biogeosciences Discuss., 12, 1907–1973, 2015 www.biogeosciences-discuss.net/12/1907/2015/ doi:10.5194/bgd-12-1907-2015 © Author(s) 2015. CC Attribution 3.0 License. 1 2 BC, Canada 3 4 5 6 7 8 Wildlife Sciences, Auburn University, Auburn, AL, USA Z. M. Subin Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | emissions, 4 emissions to 4 emissions, (c) the 4 ), and in situ observations erences in biogeochemical Intercomparison of Models 1 ff emissions, in terms of both ) over a century time horizon − 2 4 4 y emissions are highly sensitive 4 4 1.22 TgCH ± 1910 1909 ered from severe biases in CH fluxes per unit wetland area and compared these ff 4 flux dataset, several wetland maps, and two satellite ) of previously-frozen, labile soil carbon from thawing 4 4 is an important greenhouse gas, with approximately 34 4 ), inversions (6.06 ) largely agreed, (b) forward models using inundation products 1 1 − − y y 4 4 emissions to climate change. Large-scale biogeochemical models, 4 ) emissions from high-latitude wetlands are an important component of 4 0.54 TgCH 1.29 TgCH ± ± Process-based models are crucial for increasing our understanding of the response the global climate system. CH multi-year or multi-decaderesponses observational to long-term records climate are change. crucial for evaluating models’ 1 Introduction Methane (CH times the global warming potential(IPCC, of carbon 2013). dioxide Globally, (CO wetlands are the largest natural source of CH the atmosphere (IPCC, 2013).to Because soil temperature wetland and CH moistureChristensen conditions et (Saarnio al., et 2003; al.,2014), 1997; Moore there Friborg et et is al., al.,warming concern 2003; 2011; (Gedney that Glagolev et et they al.,is al., will particularly 2004; 2011; provide important Eliseev Sabrekov in et aof et the al., positive the world’s al., 2008; world’s high feedback wetlands Ringeval latitudes, tobeen because (Lehner et future and they and al., contain are Döll, climate 2011). nearly forecast 2004)(Serreze This half to and et risk al., because continue 2000; the experiencing IPCC, high(and more 2013). possible latitudes Adding rapid conversion to have warming to these than concerns CH is elsewhere the potential liberation of wetland CH especially those embedded withinestimating earth the system magnitudes models, of areEliseev feedbacks particularly to et important climate al., for change 2008; (e.g.,and Gedney Wetland Koven et Methane et al., Intercomparison al., 2004; 2013; of 2011). Models Wania Project However, et (WETCHIMP; as al.,as Melton shown 2013), et to in al., there the the was magnitude global wide of global Wetland disagreement and among regional large-scale wetland models CH permafrost over the next centuryet (Christensen al., et 2011; al., Schaefer 2004; et Schuur al., et 2011). al., 2008; Koven Abstract Wetlands are the world’sgas. largest The natural strong sourcesoil sensitivity of temperature of methane, and a methane moistureto has powerful emissions led greenhouse climate to to concerns environmental change.experienced about factors This potential pronounced such positive risk warming as feedbacks liberate is and large where particularly amounts thawingmodels relevant of permafrost disagree at labile as could highuncertainties carbon to potentially latitudes, in over the wetland which the magnitudeobservations. area have and next Recent and spatial 100 intensive emissions distribution(WSL) years. per field make of However, unit this campaigns emissions, global an area across idealprocess-based due and region the wetland to over a models West which scarcity to inresults Siberian assess of a of Lowland the in high-latitude performance a situ of environment.Project follow-up large-scale (WETCHIMP), Here to focused we on the the presentassessed West Wetland 21 the Siberian models and Lowland and (WETCHIMP-WSL). 5 Wetland We inversionssimulated over CH this wetland domain areas, in and terms of CH total CH results to an intensive ininundation situ products. CH We found that: (a)12 despite the year large mean scatter estimates of individual of(5.34 estimates, annual total emissions over the WSL from forward models (3.91 alone to estimate wetland areas su schemes across models had relatively smaller influence over performance; and (e) interannual timeseries of modelsto the that high lacked either latitudesdominated by or soil a single realistic thermal environmental driver emissions physics (inundationof or inversions from appropriate air and temperature), more unsaturated unlike sophisticated those peatlands forward models, tended (d) di to be 5 5 10 15 25 20 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cult, due to ffi emissions even 4 emissions. To this end, we 4 emissions models over West 4 er the opportunity to assess model observations. However, observations ff 4 1911 1912 emissions models has been di 4 emissions per unit wetland area. These discrepancies were 4 ects of soil chemical conditions such as pH, redox potential, and ff Given the potential problems of parameter uncertainty and equifinality (Tang and The prevalence of peatlands in the high-latitudes poses further challenges to In addition to these challenges at the global scale, the unique characteristics of high- Our primary goal in this study is to determine how well current global large-scale in their representationsthat of do wetland not soil explicitlya consider moisture single the conditions, uniform water rangingmore water table sophisticated from table position schemes (e.g., depth schemes that Hodson(e.g., for Bohn allow et each et for al., grid al., sub-grid 2011),Bohn cell 2007; to heterogeneity et Ringeval (e.g., in al., et Zhuang 2013; al., thebe Stocker et 2010; water highly et Riley al., table acidic al., et 2004), and 2014; al., nutrient-poor,recalcitrant Subin to 2011; and (Clymo et Kleinen much al., et et of 2014). al., al., theto Finally, 1984; available 2012; account peatland carbon Dorrepaal for soils substrate et the can can e al., be 2009). While some models attempt accurately, and toevaluation of constrain large-scale parameter wetland CH values with observations. Until recently, Zhuang, 2008; vanwetland components Huissteden are etdetermine embedded al., within which global 2009) model climate features and models, are computational it necessary is limitations to important when simulate to high-latitude peatlands when no surface water iset present al., (Saarnio 2011). etusing al., This satellite 1997; condition Friborg inundation can et productsIn al., lead as 2003; addition, to inputs Glagolev trees to anShimoyama et and wetland underestimation al., methane 2003; shrubs of Efremova emissions et are wetlandFurthermore, al., models. 2014), found area interfering the with with when detection water ofvarying varying inundation. table frequency on depth in(hummocks, the peatlands within hollows, scale (e.g., a ridges, and of peatland pools; is tens Eppinga typically of et heterogeneous, al., centimeters 2008). as Models vary a widely function of microtopography nutrient limitation (e.g., ZhuangSpahni et et al., al., 2004; 2013), Riley not et all do. al., 2011; Sabrekov et al., 2013; the sparseness of in situ and atmospheric CH centimeters below the surface, leading to anoxic conditions and CH from the West Siberian Lowland (WSL) now o assess the performance ofSiberia, 21 relative to large-scale in wetland situ CH andWe remotely-sensed examine observations both as well spatial asvariability, and inverse models. and temporal accuracy, estimate including the seasonal and relative interannual influences of environmental drivers on model modeling (Frolking etdeposits al., of 2009). highly Peatlandswaterlogged porous, are organic-rich and a soil, anoxic typeFrolking formed of et conditions, over wetland al., thousands which containing 2011). of inhibit deep Within years decomposition under the (Gorham, porous soil, 1991; the water table is often only a few and winter snowpack can thermally2008, 2010; insulate Rinke soils et (Zhang, al., 2015), 2005;climate. dampening Lawrence Several their commonly-used and sensitivities global to Slater, biogeochemical interannual models variabilityHopcroft (e.g., in et Tian al., et 2011; al., 2010; Hodsonsome et or al., all 2011; of Kleinen these et processes. al., 2012) lack representations of performance, thanks to recent intensive fieldprofiles campaigns (Glagolev (Umezawa et al., et 2011),Winderlich aircraft al., et al., 2012), 2010),Peregon and tall et al., high-resolution tower 2008, wetland 2009). inventories observations (Sheng (Sasakawa et al., etmodels 2004; capture al., the dynamics 2010; of high-latitude wetland CH due in partbiogeochemical to processes, the in part large topart variety uncertainties to the in of sparseness model of schemes in parameterizations,(Melton situ and used et observations in with al., for which 2013). to representing evaluate model hydrological performance and latitude environments pose further problemsmuch for of biogeochemical the models. northern For land example, (Smith surface is et underlain al., by 2005) permafrost,dependent which and constraints impedes stores drainage on ancient carbon carbon cycling (Koven et (Schuur al., et 2011) al., via 2008). temperature- Similarly, peat soils wetland areas and CH 5 5 15 25 10 20 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | in N, ◦ 2 N and ◦ in North- N latitude. 2 ◦ N. ◦ emissions, and which 4 N. E (Fig. 1a). This region is ◦ ◦ N and used the 1 : 100 000- N (Kremenetski et al., 2003). ◦ ◦ orts to constrain model behaviors. ff N and 60 to 95 ◦ 1914 1913 , or about 25 % of the land area of the WSL, primarily 2 E; and (c) on the Yamal Peninsula north of 68 ◦ E, (b) west of the confluence of the Ob’ and Irtysh Rivers between 59 and N; and the grasslands of the Steppe south of 55 ◦ ◦ N and 64 and 70 N, with continuous permafrost occurring north of 67 erent global databases. ◦ ◦ Because the vegetative and soil conditions vary substantially across the domain, Wetlands occupy 600 000 km Two global inundation products derived from remote sensing observations were also ff and Dedysh, 2000). Permanent water bodies, ranging in size from lakes 100 km area to bog poolsthe only domain a (Lehner few and metersconcentrations Döll, across, of 2004; are lakes Repo are comingled et found: with68 (a) al., wetlands north and 2007; of throughout 80 Eppinga the et Ob’ River al., between 2008). 61 Notable and 64 61 by “Peregon2008”. Both Sheng2004map of and Romanova Peregon2008 (1977):while used Peregon2008 Sheng2004 the was used entirely 1 : the based 2 Romanova 500 on 000-scale map the north Romanova of map, 65 examined in this study: the Global Inundation Extent from Multi-Satellites (“GIEMS”; we have divided it into two halves of approximately equal size along2.2 61 Observations and inversions Table 1 lists theconsidered various four observations wetland and mapin inversions high-latitude products that wetland we over carbon used studies. theWSL: Sheng Two in WSL, et of al. this them all (2004), are denoted study. of by regional We “Sheng2004”; maps which and Peregon specific et have to al. been (2008), the denoted used The region north ofessentially this permafrost-free. line contains permafrost, while the region south of the line is scale maps ofPeregon2008 product Markov additionally delineates (1971) theThe extents and third of map various Matukhin is wetland sub-types. theet and Northern al., Circumpolar Danilov Soil 2009), Carbon an (2000)permafrost Database region. inventory (“NCSCD”; elsewhere. The Tarnocai of fourth map The carbon-richLehner is the soils, and Global including Döll, Lake and 2004), peatlands, Wetlanddi in within Database which (“GLWD”; the wetland Arctic extents are the union of polygons from four behaviors. We identify the dominanthave sources caused of them. Finally, error we and makenecessary the recommendations for model as accurate to features simulations which that of model may high-latitude features wetland are CH in the Taigawetlands are and peatlands, with Tundra peat depths zonesa ranging from total (Sheng a soil few cm et carbon todocumented over pool 5 strong al., m, of methane comprising 2004). emissions 70 PgC fromof The these (Sheng the peatlands, southern et vast particularly limit al., those majority ofGlagolev south 2004). permafrost et Numerous of (e.g., field Sabrekov al., these et studies 2012, al., have 2014; 2011; Sasakawa Friborg et al., et 2012; al., 2003; Shimoyama et al., 2003; Panikov types of observations would help improve future e Central Eurasia, spanning from 50 to 75 approximately coincident with55 continuous and permafrost; 66 the Taiga forest belt between 2 Methods 2.1 Spatial domain The West Siberian Lowland (WSL) occupies approximatelybounded 2.5 on millionkm the Westthe Central by Siberian the Plateau; Uralthe on Mountains; Altai the Mountains on North and the byThe the the East WSL grasslands Arctic by contains of Ocean; the most the andas Yenisei the of Eurasian on River western Steppe the the tributaries and South (Sheng drainage ofPermafrost by et areas the in al., various Yenisei of River, forms 2004). all the (continuous, ofmore discontinuous, Ob’ which isolated, than and drain and half into sporadic) Irtysh of covers the60 the Rivers, Arctic area as Ocean. of well theThe WSL, from region’s the major Arctic biomes Ocean south (Fig. to 1b) approximately consist of the treeless Tundra north of 66 5 5 15 25 10 20 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | grid, to ◦ 1 × ◦ N. ◦ tower observation emissions for the 4 concentrations over 4 4 resolution. ◦ 3 × ◦ cients (uniform in space over ffi spatial resolution and from daily ◦ 0.5 × ◦ 1915 1916 grid to estimate monthly CH concentrations obtained by aircraft sampling at two ◦ 4 2.5 × ◦ concentrations over the WSL relative to the concentrations 4 concentrations from the Scanning Imaging Absorption Spectrometer resolution as their prior distribution for wetland emissions within the 4 ◦ cients for wetland emissions, uniquely for each point in a 1 1 ffi × emissions, our primary reference for in situ observations was the estimate ◦ 4 cients for wetland emissions to optimize atmospheric CH relative to global surface observation networks, for both inversions. The Matthews ffi 4 We also considered emissions estimates from five inversions. Two of them were For CH The other inversions we considered were global: the “Reference” and “Kaplan” measured at the Zotino Tall Tower Observatory and three other CH versions of the Bousquet“Bousquet2011K”, et respectively; al. andby (2011) the inversion, “Bloom2010”. estimate denoted Bousquet bytransport of “Bousquet2011R” et model Bloom and al. on et a (2011) al. 3.75 used (2010), denoted the LMDZ (Li, 1999) atmospheric optimize atmospheric CH sites (Demyanskoe, Igrim, and Karasevoe) located between 58 and 63 period 1993–2009, optimizing atmosphericCH concentrations of several gasesand including Fung (1987)Bousquet2011R emissions inversion, while inventory theBousquet2011K was Kaplan inversion. the In (2002) both emissions priora cases, were region) monthly for were the coe derived wetland prior forthe each for emissions WSL of the 11 was in large boreal regions the The Asia of (in the 17 which globe. year The thecandidates region WSL record containing makes for length up investigating the ofin majority the environmental the of sensitivities drivers. the Bousquet2011 wetlands). Bloommodel, of inversions et but emissions made al. rather to thematmospheric (2010) CH optimized appealing interannual did the not variability for use parameters Atmospheric an in Chemistry atmospheric (SCIAMACHY; asatellite transport Bovensmann to simple observed et surface al., model temperaturesPrediction/National 1999) from Center relating for the on Atmospheric National Research observed the Center (NCEP/NCAR)(Kalnay weather for Envisat analyses Environmental et al., 1996)Experiment satellite and (GRACE; gravity Tapley anomaliesanomalies et are from al., indicative 2004), the of large-scale under GravityBloom2010 surface the and inversion Recovery assumption covered near-surface and the water that anomalies. period Climate gravity The 2003–2007, at 3 the WSL relative to observedlocations CH in the WSL. Winderlichprior (2012) wetland used emissions, the Kaplan within (2002) the wetland global inventory for inversion system TM3-STILT (Rödenbeck regional: “Kim2011” (Kim et al., 2011)et and al., “Winderlich2012” 2013). (Winderlich, Kim 2012; et Schuldt 2010) al. (2011) at used 1 an earlieratmospheric version of transport Glagolev2011 model (Glagolev NIES-TM et (Maksyutov al., 2007. et Kim al., 2008) et over al. thecoe (2011) period 2002– derived 12 climatological average monthly (spatially uniform) et al., 2009; Trusilovamonthly et coe al., 2010) for the year 2009. Winderlich (2012) derived 12 of Glagolev et al. (2011),product which consists we of will referfrom both to representative a as landforms “Glagolev2011”. database at The(Fig. of Glagolev2011 each 1a) over of and 2000 36 aobserved major individual emissions map to sites chamber of the over observations wetlands long-termtype. the of It average period the is emissions worth Peregon2008 2006–2010 map noting createdbased that as the by a on Glagolev2011 function applying higher-resolution product of is the maps, wetland currentlyemissions which mean undergoing from a will the revision lead Taiga zone,types to due (Glagolev a to et substantial a al., larger increasethe spatial 2013). in northern extent annual Possible of half changes high-emittinguncertainty of to wetland in emissions the our evaluation in WSL) of the model are predictions. Tundra not zone yet (in known. We consider this product’s large to monthly temporal resolution. Prigent et al.,(SSM/I) 2007; and Papa passive (ERS) etSurface Water microwave Microwave al., sensors Product Series over 2010), (“SWAMPS”; Schroederfrom the derived et active period al., (SeaWinds-on-QuikSCAT, ERS, from 2010), and 1993–2004; derived ASCAT) visible and andAMSR-E) passive microwave the sensors (AVHRR) (SSM/I, over SSMI/S, the and periodareas 1992–2013. active For were both aggregated products, from inundated native 25 km to 0.5 5 5 20 25 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | emissions estimates: CLM4Me 4 1918 1917 erent combinations of wetland maps and methane models: ff Models’ hydrologic approaches varied in other ways as well. Some (IAP-RAS The relevant hydrologic and biogeochemical features of these models are listed approach to relatetopography the (generally distributionand at of UW-VIC 1 km (SWAMPS) water determined scale). table water depths However, table depth across LPJ-WHyMe, distributions the UW-VIC from grid (GIEMS) assumed cell to and LPJ-WSL) didemissions not in include unsaturated explicit (non-inundated) waterwere wetlands; table completely IAP-RAS depth assumed formulationsimplicitly, saturated using all for soil and wetlands moisture estimating as LPJ-WSL a proxy. Most only of the considered other models used unsaturated a TOPMODEL wetlands To have someparticipating consistency modelers to across usesome the models, models GIEMS the product (DLEM, as originalto DLEM2, an and input prescribe WETCHIMP if LPJ-WSL) (time-varying) study possible.Table used Accordingly, wetland the 2. asked areas; GIEMS Several(DYPTOP-N), product these models ORCHIDEE, exclusively are (CLM4Me, SDGVM, denotedarea and LPJ-MPI, with dynamically VIC-TEM-TOPMODEL) LPX-BERN the predicted using (DYPTOP),Kirkby, code wetland 1979) topographic LPX-BERN “I” distributed information water in in table and approach; Table the 2. these TOPMODEL modelsthe However, are both long-term (Beven denoted CLM4Me mean and withso and of a by GIEMS: ORCHIDEE “T” rescaling CLM4Me tied itsin did their inundated Table so inundated areas. 2. by Thus, areasBERN, Finally, calibration LPX-BERN we to the (N), and have both remaining ORCHIDEE placed UW-VICused configurations, models did them and wetland (IAP-RAS, in all maps, four the LPJ-Bern, VISIT eitherproducts, configurations) “I” LPJ-WHyMe, alone to category LPX- or informIn in their most combination wetland cases, with schemes; thewithin topography these maps and which are were inundation inundated denoted usedand area to with LPX-BERN would determine “M” (N) vary the in consideredvarying in maximum inundated Table both time. mineral extent 2. soil a In of area staticpeatland contrast, wherever wetlands, the area. map-based LPJ-Bern, GIEMS inundated peatland LPX-BERN, area area exceeded the and a time- in Tables 2define (potential) and methane-emitting areas 3, (which we respectively. will The refer to models as “wetland” used areas). a variety of approaches to (Riley et al.,et 2011), al., DLEM 2007; (Tian2013), Eliseev et LPJ-WHyMe (Wania et al., etORCHIDEE al., 2010, al., (Ringeval 2011a, 2008), 2009a, et b, b, LPJ-Bern(denoted 2010), al., 2012), by (Spahni LPJ-WSL IAP-RAS “UW-VIC 2010), (Hodson et (GIEMS)”; (Mokhov SDGVM etother Bohn al., models. al., (Hopcroft et “UW-VIC 2011; 2011), al., (SWAMPS)” et is 2013). Zürchercalibrated another al., In instance to et addition, of 2011), match UW-VIC we al., the with and analyzedfour SWAMPS surface configurations several product. UW-VIC water using di VISIT (Ito“VISIT and (GLWD)” Inatomi, and 2012), “VISIT contributed GLWD (Sheng)” and used Sheng2004 the“VISIT Cao wetland (Sheng-WH)” (1996) maps, replaced methane respectively,model. the model LPX-BERN and Cao with (Spahni “VISIT et the model al.,of (GLWD-WH)” with 2013; LPJ-Bern Stocker and the that et al., Walter alsopeatland 2013, contributed and extent 2014) four using Heimann is configurations: Peregon2008 a (2000) “LPX-BERN”, and(DYPTOP)”, newer which inundation version which extent prescribed dynamically using GIEMS;and predicted “LPX-BERN “LPX-BERN the (N)” extentsinteractions and of between “LPX-BERN the peatlands carbonDLEM (DYPTOP-N)”, and that and which includes inundation; nitrogen soil additionally thermal cycles.et physics simulated al., DLEM2 and 2014). lateral LPJ-MPI is matter (Kleinen fluxes aa et (Liu dynamic al., newer et peatland 2012) model al., version is with 2013; a methane of (2000). Pan transport version Finally, of by VIC-TEM-TOPMODEL the the (Zhu model LPJ of et modelet Walter al., that and al., contains Heimann 2014) 1994), is TEM a (Zhuang hybrid et al., of 2004), UW-VIC and (Liang TOPMODEL (Beven and Kirkby, 1979). 2.3 Models Among the participating models (Table 2)et were al., those 2013; of the Wania WETCHIMP et study al., (Melton 2013) that contributed CH 5 5 25 10 15 20 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | spatial ◦ 0.5 over the grid × ◦ 2 − m 1 − month 4 ered in the pathways and availability )) were analyzed at 0.5 ff 2 1919 1920 emissions (average gCH 4 ects of organic (peat) soil. In contrast, UW-VIC modeled ff usion scheme to determine the vertical profile of soil temperatures, ff Model outputs (monthly CH Models also varied in their biogeochemical schemes (Table 3). Most represented Models also varied in their soil thermal physics schemes. Most models used a 1- drew carbon substrate(or from dissolved a organic combinationtwo carbon, of configurations both in ofnitrogen root the LPX-BERN cycles. exudates case simulated Several andSDGVM) of interactions models soil included DLEM between dynamic (all carbon MPI, and vegetation the versions LPJ-WHyMe, components. LPX-BERN, DLEM2). of carbon and Some UW-VIC) CLM4Meplant LPJ and models accounted for and (LPJ-Bern, species and inhibition LPJ- of under LPX,a NPP of variety saturated ORCHIDEE, some of soil and methods,including: moisture alone taking the or conditions. median in ofobservations Finally, combination literature from models (Table values; representative 3), optimizing sites employed emissions tovalues regionally to to select match match (e.g., parameter the in UW-VIC Glagolev2011 values, situ optimizedtotal dataset emissions in parameter the to WSL) match or various globally; estimates or from optimizing inversions. global 2.4 Model simulations To be consistent with WETCHIMP’set transient al., simulation 2013), (“Experiment we focused 2-trans”,WETCHIMP our Wania models analysis provided on data the from periodgridded 1993 1993–2004, to meteorological although 2010. forcings several All non- models (Viovyspecific used and inputs the are Ciais, CRUNCEP described 2011) in as Wania et a al. common (2013). input. Model- methane production as aIAP-RAS function and of LPJ-WSL), soilIAP-RAS and and temperature, LPJ-WSL) the water explicitly availability accounted tabletable; for of depth oxidation and of (except carbon most methane for substrate. above accounted(LPJ-Bern, the Most LPJ-MPI, for water LPJ-WHyMe, (except some and degree for as LPX-BERN) of either represented methane a plant-aided production constant transport.models or (DLEM, Some DLEM2, soil-moisture-dependent and models fraction VIC-TEM-TOPMODEL) imposed ofon additional aerobic dependences soil respiration. Some pHof carbon and substrate: oxidationWH) some state. models and (UW-VIC, Models VISIT VIC-TEM-TOPMODEL,productivity di (NPP) VISIT (Sheng-WH)) as (GLWD- a related proxyLPJ-WSL, for carbon ORCHIDEE, root exudates; substrate SDGVM, others VISITsubstrate (CLM4Me, availability IAP-RAS, (GLWD) to LPJ-MPI, to and the VISIT net contentothers (Sheng)) and (DLEM, primary related residence DLEM2, carbon times LPJ-Bern, of LPJ-WHyMe, various all soil four carbon LPX-BERN reservoirs; configurations) and but VISIT usedsurface) a and linear annual interpolationSeveral average models between air current (DLEM,water-ice temperature air LPJ-MPI, phase (at temperature LPJ-WSL, change thecontained (at and a and bottom the permafrost therefore SDGVM) soil of scheme,meteorological did it did the forcings was not and driven soil not by used modelevolution column). and seasonal consider simple permafrost. vertical and analytic profile the annual of While functions soil summariesnot temperatures. to IAP-RAS consider of Additionally, DLEM estimate the and insulating the LPJ-WSL e did permafrost, seasonal peat soils, and theevaporation, dynamics thereby of adding surface another water, factor including that lake influences ice soil cover temperatures. and cell area) and monthly wetland area (km dimensional heat di proportions of(horizontal) microtopographic scale of meters. landforms (e.g., hummocks and lawns) at the resolution (resampled from nativevaried, resolution as complicating necessary).a Wetland comparison area maximal definitions among wetlandwas extent, the straightforward to either interpret. models. from Fordynamically several the of For (CLM4Me, the GIEMS models LPJ-Bern, those product thatLPJ-MPI, computed LPX-BERN or wetland ORCHIDEE, that (DYPTOP), area a SDGVM, LPX-BERN delineated and map,cell (DYPTOP-N), VIC-TEM-TOPMODEL), wetland could any potentially area emit portion methane.emitting of To provide any areas, meaningful grid estimates CLM4Me, of their ORCHIDEE, methane- and VIC-TEM-TOPMODEL defined wetland 5 5 10 15 20 25 15 10 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 4 ± N − ◦ y 4 , due E). In ff ◦ standard ± “intensities” 4 N, 70–80 E). The NCSCD N. Both of these ◦ ◦ (Winderlich2012). ◦ 1 − y 4 N, 70–90 ), and observations (3.91 ◦ emissions have with annual 1 erences are evident among 4 − ff y emissions, which dominate the 4 4 ). 2 1.22 TgCH E), east of the confluence of the Ob’ and ◦ ± 1921 1922 emissions, since it is during these months that (LPX-BERN (DYPTOP-N)) to 11.19 TgCH 4 . 1 (Kim2011) to 9.80 TgCH 4 − y 1 − 4 y N, 70–85 E) and north of the Ob’ River (61–66 4 ◦ ◦ emitted from the South and North halves of the domain, 4 ), inversions (6.06 1 N, 65–70 − ◦ y erent from the other three maps. Owing to its focus on permafrost 4 emissions, we focused on JJA CH ff 4 ) largely agreed, despite large scatter in individual estimates. Model emissions per unit area of wetland) as the ratio of average JJA CH 1 − 4 y 4 erences among observational datasets ff 0.54 TgCH ± Due to large seasonal variations in wetland areas, our analysis focused on June-July- The large degree ofbefore disagreement among using observational them datasets to is evaluate worth the addressing models. Important di 3.2 Di wetland maps (Fig.because 3). they Sheng2004 both and used Peregon2008 the are map extremely of similar, Romanova (1977) in north part of 65 with the Southern contributionindicates ranging lack from 13 of to agreementproducing 69 the % on bulk (right-hand of which column the types region’s in CH of Fig. wetlands 2), and climate conditions are Despite their large spread,inversion estimates. 15 Here out we have ofconfigurations excluded of the the LPX-BERN “WH” 17 for configurations which forwardto of nitrogen–carbon models VISIT interaction their was and fell similarities turned the within o relative to the proportions their range of counterparts of CH that were included. The wide variety in the datasets show wetlands distributed acrosssouth most of of the the WSL, withIrtysh Ob’ large Rivers River concentrations (57–62 (55–61 comparison, the GLWD map entirelyand lacks shows wetlands additional in wetland the area tundra in the region north-east north (64–67 of 67 is substantially di 1.29 TgCH estimates ranged from 2.42 TgCH (IAP-RAS). The Glagolev2011 estimatemodels, was substantially corresponding lower to than theHowever, the the 36th mean potential percentile of upward the ofa revision substantially the of higher distribution Glagolev2011 percentile of (Sect. ofrange their 2.2) distribution. model of Inversions would estimates. yielded estimates, move a 3.08 it similarly TgCH large to annual total and have strongertemperature, correlations precipitation, with or JJA inundation) environmental than factors (such annual as CH air (5.34 3 Results 3.1 Average annual total emissions A shown in Fig. 2 and Table S1 in the Supplement, 12 year mean estimates ( 2.5 Data access All data used inmodel this results, are study, available including from observational WETCHIMP-WSL (2015). products, inversions, and forward error on the mean) of annual total emissions over the WSL from forward models the majority of the year’s methaneis is emitted, the across all most models. Thus, representativevariability JJA wetland methane-emitting in area area. CH Similarly, in analyzing interannual average environmental factors. We also computed growing season CH emissions to average JJA wetland area (in m August (JJA) averages of area and CH area as their inundatedLPJ-MPI areas; reported LPX-BERN (DYPTOP), the LPX-BERNBern sum (DYPTOP-N), reported and of the peatland sumwhich area of soil peatland, and moisture inundated inundated contentand mineral mineral was SDGVM soil, reported greater soil and the than area; “wet areawith 95 for mineral % LPJ- the which soil” of the threshold (in water water chosenGIEMS. table holding was to capacity) above a minimize areas; threshold the depth, global RMS error between this area and (average CH 5 5 20 10 20 15 15 10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | N; ◦ N. We ◦ erences. N; in this ) is fairly ff ◦ 1 − ), with no clear 2 rapidly to nearly − month ff m 4 , allows comparison 4 2 − m 4 erence can be seen between ) and the mean of inversions ff 1 − month 4 erent wetland areas. We have included the ff 1923 1924 N, but Bousquet2011R has relatively stronger ◦ emissions per unit wetland area). All points along 4 distribution (mean of 0.58 TgCH /area estimate (denoted by a black star) and the mean 4 N and weaker emissions between 65 and 67 4 N). Given the numerous field studies documenting these ◦ ◦ emissions results from scatter in both area (ranging from 4 N), GIEMS gives areal extents that are 3–6 times those of ) and intensity (ranging from 1 to 8 gCH ◦ 2 ), the distribution of model estimates is substantially skewed, datasets (Fig. 4), a clear di 1 culties in detecting inundation under vegetative canopy and/or − 4 ffi month distribution; the expected upward revision of Glagolev2011 (Sect. 2.2; 4 4 emissions vs. average JJA wetland areas for the WSL as a whole (top left), the 4 emissions, JJA wetland areas, and JJA intensities, for all models, observations, 4 However, a strong area-driven bias is evident in the South (Fig. 5, bottom The two inundation products (GIEMS and SWAMPS) also exhibit large di Among the CH of spatial average intensities (CH a given line have theGlagolev2011/Peregon2008 same CH intensity but di of the inversions (denoted bythe a inversions grey to star) Peregon2008, for because reference.inversions, We (a) and set (b) the the Peregon2008 wetland area is areaCH coordinate a was for relatively not accurate available estimate for of all wetland area. JJA 200 000 to 1 200 000 km and inversions, are listed inin Table S1. model Over estimates the entire of WSL CH (Fig. 5, top left), the scatter relationship between the two. left). Although theclose model to CH both Glagolev2011 (0.67 TgCH (0.60 TgCH with most models’ estimatesthe falling inversions. well Glagolev2011’s belowmodel estimate both CH corresponds Glagolev2011 and toexact the the JJA mean 81st amount of the percentile not inversions of corresponds yet to the underestimate known) the wetland would 76th area, percentile. only withmodel Similarly, Peregon2008 raise the distribution. occupying models On that the substantially biased, the 83rd percentile. percentile other with The of hand, Glagolev2011 mean the the corresponding of model to intensity the distribution is 47th much percentile. less Even a doubling South (bottom left), andthrough the the North origin, (bottom with right). slopes A of series integer of multiples lines of (“spokes”) 1 passing gCH respect, Bousquet2011R is intermediateFinally, between Bloom2010 Glagolev2011 exhibits and relatively Winderlich2012. littleits spatial use variability of in GRACE observations emissions, as likely a due proxy to for wetlands. 3.3 Primary drivers of model spatialThe uncertainty wide disagreement among modelsJJA is CH plainly evident in Fig. 5, which plots average the spatial distributionsmajority of of Glagolev2011 emissions to and the Kim2011, region both south of of which thediscuss Ob’ assign possible River, the reasons between(Bousquet2011R 55 for and and this K, 60 regional discrepancy and inversions in Bloom2010) of have Sect.similar Kim2011 distributions coarser 4.3. between and spatial 60 The andemissions resolution Winderlich2012. 65 global between than 57 Bousquet2011R inversions and the and 60 K have While they bothof agree the that Ob’ inundation River is (61–64 most extensive in the central region north 0 in mostmay places be (particularly due inreduced to sensitivity the where di forested open regionwhile water fraction south SWAMPS is of less maintainsSWAMPS than the additionally low 10 shows Ob’ %; levels high Prigent River, inundationmay et of which along al., indicate the inundation 2007), contamination Arctic throughout of Oceanareas exhibit coastline, the most some which signal similarity of with by the the the distribution of ocean. WSL. lakes In and rivers both (Fig. datasets, 1). inundated and Winderlich2012emissions and to Bousquet2011K, the both central region of north which of the assign Ob’ the River, between majority 60 of and 65 SWAMPS. Outside of this central peak, GIEMS inundation drops o soils, it completelypermafrost excludes (approximately 60 the extensive wetlands south of the southern limit of productive southern wetlands (Sect.modeling 2.1), non-permafrost wetlands. the NCSCD seems to be inappropriate for 5 5 10 25 15 20 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ” 4 1 + − y 4 and SD of 2 0.45 TgCH − 270 000 km − emissions (which included 4 ), but had the largest scatter 2 (Fig. 6) and JJA wetland areas 4 emissions and wetland area tend to 4 , respectively). Models that determined 42 000 km 2 − emissions, partially compensated for some 4 1926 1925 N. For LPJ-WHyMe, these permafrost peatlands ered from a large negative bias ( ” group ( ◦ ff + ered from either global or local bias (which was true ff 31 000 and 34 000 km − ect on the results, since their total CH ff ) of the groups. The fact that two of the “I” models (CLM4Me and 2 , although Winderlich2012 suggested a noticeably larger contribution from 4 . Additionally, two models (LPJ-Bern and LPJ-WHyMe) from the “M” group 2 67 %). − Aside from area-driven biases, a large degree of intensity-driven scatter is evident Model inputs and formulations played a key role in determining wetland area biases. Examining the spatial distributions of annual CH peak for CH cold season months than the others (which is plausible, given reports of non-zero 3.4.1 Average seasonal cycles Models and inversions demonstrated generalcycle agreement on of the emissions shape (Fig. ofdisagreement 8, the on seasonal top the left) shape andbottom and intensities left). timing (Fig. The of 8, regional the bottom inversions seasonal right), (Kim2011 cycle despite and of wide Winderlich2012) wetland agreed area on (Fig. a July 8, accompanied by resulting reductions in CH of these models). Interestingly, several modelstwo yielded estimates regionally-optimized similar UW-VIC to simulations, thosedid of implying not the that confer a the distinct regional advantage on optimization UW-VIC. 3.4 Model temporal uncertainty and major environmental drivers in both the South and North. Indeed, the underestimation of areas in the South, wetland area dynamically usingwetland topographic maps data, (denoted butnearly by without as triangles the small in additional a input Fig. bias of 5 as and the “M the code “T” in Table 2) yielded permafrost peatlands northwere of the 60 onlynonexistent type in the of South. wetlandmethane LPJ-Bern dynamics modeled, also in used so wet themake or LPJ-WHyMe’s NCSCD, emissions inundated but emissions estimates mineral additionally were soils. simulated resulted in in While almost the larger this South, reductions allowed ina the LPJ-Bern soil given to much evaporative moisture loss. content lower These thanSouth. drier porosities would soils occur of led in to mineral peat net soils soils methane for oxidation in much of the of the intensity-driven scatterwere there. arguably However, the some result(CLM4Me, of of IAP-RAS, the area LPJ-Bern, more biases,their extreme and in global intensities LPJ-WHyMe) total that emissions scaled somebiases with their of if those their intensities the of wetland global global to maps inversions, wetland match which su models could result in local 31 000 km group, consisting of allsecond-smallest “M” SD models ( except those two, exhibited the smallest bias and (SD of 173 000 km non-inundated emissions) also su ORCHIDEE) supplied wetland areaswetlands had that little e excluded non-inundated methane-emitting (denoted by squares in Fig.to 5 their and the use code of “M” in the Table 2) NCSCD also map, yielded low which areas, omitted due non-permafrost wetlands. The “M or (Fig. 7) shows why the useAmong of inundation the data alone models resultsORCHIDEE), in the poor from model spatial performance. distributions the of “I” both CH group (CLM4Me, DLEM, DLEM2, LPJ-WSL, and Statistics of modelby performance the relative wetland to codes Glagolev2011/Peregon2008,inundation in categorized products Table 2, alone are (denotedestimated listed with the in lowest circles wetland Table in areas 4. in Fig. The the 5 south, models with and that a the bias used of code satellite “I” in Table 2) bias in the South. In comparison, the North (Fig. 5, bottom right) is relatively unbiased. of Glagolev2011’s intensitydistribution, would a place smaller bias it than for at area. only Thus, the the area bias 69th is percentile the major of driver of the CH model be strongly correlated with GIEMS (seeinundated Table 5 areas for correlations), south which of exhibits very(Sect. the low Ob’ 3.2). River, Similarly, despite the thecan large low be expanses emissions explained of of wetlands by LPJ-WHyMe there their and use LPJ-Bern of in the the NCSCD South wetland map, which only considered 5 5 25 20 10 15 15 10 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 4 cient ffi emissions, we 4 ), GIEMS fractional P emissions and the following 4 erent shapes in their seasonal cycles of ff erences in intensity. Values of the coe ), CRU precipitation ( ff 1928 1927 air oxidation) in 2004 before continuing at a much T erences matter little to the seasonal cycle of CH 4 ff rates. Some of the discrepancies in wetland area ff ects of di ff emissions displayed a wide range of interannual variability, N; the broad peak for the WSL as a whole was the result of 4 ◦ emissions peaked in July, in agreement with the regional 4 emissions comes despite wide variation in seasonal cycles of wetland area. For 4 To investigate the influence of various climate drivers on CH Aside from the above cases, the relative agreement among models on a July peak Most models’ CH computed the individual correlations between the JJA CH even after accounting for the e JJA drivers: CRU air temperature ( emissions, in part because ofarea and the water similarity table depths between within theuniversal the static strong seasonal wetlands, correlation cycles and at of in part seasonal inundated near-surface because time of air scales the temperature between nearly simulated (so intensitiesover that emissions). and cold-season wetland areas have little influence 3.4.2 Interannual variability At multi-year timeinversions’ scales total annual (shown CH for the period 1993–2010of in variation Fig. (CV) 9),BERN for models’ (N)) models and over toBousquet2011K’s the CV 0.338 period of (UW-VIC 1993–2004of 0.160 (GIEMS)) fell ranged 0.446 near with from waslargest the 0.069 25 a model % mean (LPX- CV. mean larger Bousquet2011R’s model highemissions than of CV, variability Bousquet2011R’s in was the 0.169 CV due 2002 largest inactually (Table followed part model becoming 6). by to negative CV, a a While (net andlower peak large CH over mean in drop CH value twice in frompeak the emissions 2005 and second- between to decline 2002 2009. in(notably This and LPJ-MPI, inundation LPJ-WHyMe, peak 2004, LPJ-WSL, (Fig. DLEM, and and 10)as decline VIC-TEM-TOPMODEL), as and Bousquet2011K, coincide well mirrored precipitation with this (Fig. drop ain to 11). similar proportion varying Several degrees, to models but theironly none means the dropped as or period much becamedue 2003–2007, to negative. exhibited its In extremely use contrast, of little GRACE Bloom2010, interannual as spanning a variability, proxy perhaps for wetland area. seasonality arose from several models’ usingareas static (Sects. maps 2.3 to and define 2.4). some These or di all wetland example, DLEM’s wetland area heldNovember; and steady VIC-TEM-TOPMODEL’s at wetland its areato peaked maximum in extent low August, from evapotranspiration possibly April or due through runo in CH winter emissions; Rinne etcontrast, al., both 2007; Bousquet Kim inversions peaked et inthe al., August. Bousquet2011R 2007; Unlike the inversion Panikov other and had three Dedysh, negativeJune inversions, 2000). emissions of In (net almost oxidation) every inthroughout year either not of May only its or the record.accuracy WSL These but of negative the their emissions entire seasonalleft), were boreal GIEMS cycle. widespread, Asia and region, Turning SWAMPS to displayed and quite cast the di doubt inundation on products the (Fig. 8, bottom carbon substrate in the soillinearly-interpolated or soil rapid temperatures). soil In warming contrast,was LPJ-MPI’s (the the early latter peak result is in likely of emissions of for an early VISIT, given early snow its melt. (May) TwoLPJ-Bern’s peak models late (LPJ-BERN in peak and wetland resulted UW-VIC area, (GIEMS)) fromexceptionally peaked a which, late in late in (October) August. peak peak turn, incorresponded in was wet wetland mineral the to area. soil result The a intensity, despitesubstrate late late an peak (due of peak to UW-VIC inhibition in(due (GIEMS) of to intensity, excessive NPP insulation implying under by either peat inundation) or or late surface delayed water). availability warming of of the carbon soil inversions. A few models peakedand in June: VISIT CLM4Me, (Sheng). DLEM2, LPJ-MPI,peaks Correspondingly VISIT in (GLWD) early the peaks DLEM2 in and the intensity VISIT can simulations, explain indicating the either early early availability of inundation: GIEMS exhibited aflat sharp maximum peak from in June June throughGIEMS and September. south In SWAMPS of fact, displayed about SWAMPS a 64 late-season had broad, peaks a further similar north. shape to 5 5 10 15 20 25 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . - 4 inund inund F F and -dominated , correlation air T emissions and inund inund F 4 F -dominated”. For the inund variance in a linear model, that were greater than 0.7; emissions were JJA CRU 4 4 air T . As an aside, this choice was not , for forward models and the two inund s in the correlations between simulated F ff inund F axis). Some models (all four LPX-BERN ects of peat soils, which might explain LPJ- ff 1930 1929 y ( inund can be explained by the fact that its soil temperature F air -dominated”. Other models (DLEM, LPJ-WSL, and, in and JJA GIEMS T emissions to water table depth, but at present there are air 4 air . These two drivers also exhibited nearly zero correlation T would explain the majority of CH . Therefore, we chose to examine model behavior in terms of axis) or ), and SWAMPS air inund led to consistent trade-o T F x ( inund -dominated models also lack sophisticated soil thermal physics. inund F F inund air air F T T and . Given the high interannual variability of the Bousquet2011 inversions, should serve as a general measure of the influence of both surface and air air T T or inund F and JJA GIEMS Indeed, the overarching pattern in the model correlations was that models that Some of the The relative strengths of the correlations between models’ CH inund air dominated models do have such formulations. InDLEM2, addition, and inundated LPJ-WSL fractions were of DLEM, explicitlyLPJ-MPI driven by does GIEMS. account Unlike the for other the three thermal models, e VISIT’s strong correlation with scheme is a simple linearand interpolation annual between current average airVISIT’s air temperature soil at temperature the temperature at surface “WH” has the configurations a bottom of 1.0 of VISITHeimann correlation the to (2000) with soil the had air default column;model. a temperature. SDGVM configurations, as lower Comparing also the a lacks correlation the model soilare result, with freeze-thaw of completely dynamics. air Walter saturated IAP-RAS and and temperature assumes holds all than wetlands their the areas Cao constant in (1996) time; as a result, its CH models (except for DLEM2) havethat in common account is that for they lack soil soil thermal freeze/thaw formulations processes; conversely, most of theMPI’s low non- (slightly negative) correlation with air temperature. emissions have no dependenceon on air temperature. moisture LPX-BERN’s ora high relative inundation, correlation insensitivity and with of air strong CH temperature dependence is the result of too few sites withsensitivity multi-year is observations reasonable. in Nitrogen–carbon the interaction region (LPX-BERN to (N) and determine LPX-BERN whether this low F lacked physical and biochemical formulations appropriatestronger to the high correlations latitudes exhibited withor inundation more or sophisticated air models. One temperature characteristic than that either most the of inversions the we hesitate to treatHowever, their them as lack an ofalso accurate strong should not depiction correlations exhibit of with strong correlations wetland either with behavior driver one in driver. might the imply WSL. that models either the South or the North, DLEM2 and LPJ-MPI) were “F inundated areaBousquet2011 ( inversions, over thefour period additional model 1993–2004 configurations (Table(GIEMS-WH), that S2). we VISIT Here did we (SHENG-WH), notdrivers included LPX-BERN, show in yielding and previous LPX-BERN-DYPTOP. the The sections: VISIT two highest correlations with JJA CH since this means that we have denoted them as “T other models and inversions,models no had driver small explained enoughcorrelations the contributions were from majority negative, due one of to or the the small the negative variance. correlation other between A driver that few the resulting T with each othervariations over in the water WSL table position andrainfall, are the evapotranspiration, driven by South and the and drainage) same hydrologic North that factors halves (snowmelt, drive (Table variations 7). Because in simulations, all fourRAS, VISIT ORCHIDEE, simulations, and SDGVM) and, had in correlations with either the South or the North, IAP- Neither of the two Bousquet2011 inversions exhibited strong correlations with either subsurface moisture conditions on methaneexplicitly emissions, driven even by for models that were not correlations with JJA CRU with an endorsement of GIEMSto over GIEMS); SWAMPS it (which simply resulted yielded in qualitatively better similar separation results amongdrivers models. varied widely, as shown inleft) the as scatter well plots as inbetween the Fig. 12. South Over and the North entireemissions WSL halves (top (bottom and left and right), the low correlation 5 5 20 10 15 25 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , inund erent F ff and air T in the South. air T -dominated or sedge-dominated, ect on LPX-BERN’s temporal variability emissions, but also led to attribution of ff erent methane emissions parameters ff 4 sphagnum 1932 1931 erences between UW-VIC and other models in their culties in detecting inundation under forest canopies ff ffi emissions that resulted from using satellite inundation ects, greater evaporative cooling, and longer residence 4 ff erences arising from optimizing surface water dynamics to di ff erence in behavior between UW-VIC (GIEMS) and UW-VIC (SWAMPS) ff It is therefore important for modelers – both forward and inverse – to use Finally, UW-VIC (GIEMS) had small negative correlations with both as incould Lupascu potentially et be applied.inundated al., When extents, using modelers 2012) inundation mustlarge products to be rivers, to sure using which constrain either(Carroll a simulated to di dataset et mask such out al.,appropriate as permanent to 2009); these GLWD lakes forms (Lehner or and of and surface better, water. Döll, to 2004) implement or MOD44W carbon cycling processes that are in which methaneas emissions have Tarnocai a etpermafrost dependence wetlands. al. on Ideally, water (2009) modelersthe table would high-resolution may depth. be map Maps of able be Peregon toalso such et inappropriate draw identifies al. (2008) on the unless that a major not restricting global only sub-types version delineates (e.g., simulations wetlands, of but to accurate wetland mapsLehner such and asparameter Döll Peregon or (2004) et asand in a al. to reference their (2008), for account model Sheng evaluating for prognostically-computed development, et the wetland whether existence al. areas; of (2004), as non-inundated or a portions static within these input wetlands not only caused underestimationthe of majority total of CH theis region’s emissions not to unique the topresent permafrost the throughout zone WSL, in the the as highBrown North. et the latitudes al., This collocation (Tarnocai 1998). issue Indeed, et of inet al., permafrost, their al. analysis 2009; lakes, of (2013) Lehner and the Hudsonmodels found and inundation Bay (CLM4Me, Lowland that is Döll, DLEM, (HBL), 2004; and three Melton area LPJ-WSL), of was although the not whether examined. this fourcarbon Given was lowest from present due thawing emissions concerns to permafrost a over estimates overto the bias the were avoid potential in next under- century from liberation or (Koven of over-estimating “I” et emissions labile al., from 2011), permafrost it wetlands. is crucial (DYPTOP-N)) appeared to have only ain minor e the North but led to a slight reduction in correlation with products far outweighed the di likely the resulthad of been its initially surface calibratedextents water using of formulation. the GIEMS SWAMPS UW-VIC’scorresponding in product; surface insulating the the water much e North dynamics largertimes, resulted thus inundated in lowering correlations substantiallyThe with deeper large both di surface observed water,implies inundation that with and the air di temperature. selection of biogeochemical parameters. 4 Discussion 4.1 Long-term means and spatial distributions The most striking finding,the in substantial terms bias of long-term in means CH and spatial distributions, was underrepresented by SWAMPS;Lehner and and the Döll,or 2004), high via which concentrations the do same of carbonet not cycling lakes al., necessarily processes 2004). there as emit wetlandswith The (e.g., methane (e.g., visible practical Walter at et or di the al., high-frequencycompound 2006; same these microwave Pace problems. rates sensors In the (e.g., case Sippel of the and WSL, Hamilton, equating wetlands 1994) with inundation products orimportant inaccurate component wetland ofextent wetland maps at models, high to but latitudes,inundated it given peatlands delineate both clearly that the is wetlands. large exist a expanses Inundation there poor of (Sect. proxy strongly-emitting is 2.1) for partially- an wetland that were missed by GIEMS and 5 5 25 20 15 10 10 15 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | - inund F ect reduces ff emissions, a longer 4 erence between the two ff emissions to zero because 4 ects on CH ect on LPX-BERN in the South. ff ff erence to the sensitivities of emissions ff -dominated model, treated all wetlands in air erence in this study, but warrant further 1933 1934 T ff ectively set non-inundated CH ff ects of these features. Changes in vegetation community ect might vary with model implementation. ff ect performance. However, our 12 year study period was likely ff ff emissions from unsaturated peatlands. LPJ-WSL, an emissions from an approximate doubling of current rates without . It is not clear if this is an inherent di 4 4 air T ects of snow and peat, and relationships between emissions and water ff their static map asof soil if moisture they variability. were Thewater relative table inundated, insensitivity position thereby of similarly eliminatingalthough LPX-BERN’s reduced there emissions the the are to contribution contribution too of few observations soil to moisture say variability, whether this is unreasonable. No additional features thatstorage are in poorly UW-VIC constrained.therefore was performed The poorly optimized dynamic in the surface for UW-VIC water (GIEMS) the configuration. SWAMPS inundation product, and dominated model, e it did not simulateAt wetlands the outside other of extreme, the IAP-RAS, time-varying a GIEMS inundated area. Realistic soil thermal physics. Mostone of driver the (LPJ-WSL, models DLEM, that LPJ-MPI, were highly VISIT, and correlated SDGVM) with Accurate lacked this representations feature. ofcorrelations peat (LPJ-WSL, DLEM, soils. VISIT, and Again, SDGVM) lacked many thisRealistic feature. of CH the models with high – – – – Other features may warrant further study. Replacing the Cao (1996) model with the Thus, it appears that the following model features are desirable for reliable model of Waltercorrelation and with Heimann (2000) modestly lowered VISIT’s otherwise extreme Ringeval et al., 2011;vegetation Riley growth et in al., responsereduce 2011) inundated to extents have future via found increased climate a evapotranspiration. change This “wetland drying can feedback”, e lower in water which tables and historical study period, along withof longer species observational compositions records (including and observations soil carbon densities), would be necessary. Other model features made little di too short tocomposition may see become the moreWang, important e 2008; in Kaplan end-of-century and projections New, 2006). (e.g., In Alo particular, and recent studies (Koven et al., 2011; investigation. For example, whether modelsand/or contained dynamic community vegetation (phenology composition)components or did dynamic not a peatland (peat accumulation and loss) formulations or just an artifactthe of Walter their parameter and values Heimann inSimilarly, model VISIT, nitrogen–carbon but is it interaction more might had imply appropriate that a for small applications e at high latitudes. end-of-century CH the feedback to onlychemical a changes 20–30 % in increase peator soils, with in the drainage response feedback. for to Similarly, mining hydrologicprojections disturbances and such or (e.g., as agricultural Strack permafrost purposes,dynamic et thaw vegetation may al., or be peatland 2004). schemes important However, and in to their e end-of-century properly assess the accuracy of simulations of boreal wetlands: to environmental drivers, at least overfully the trust 12 the year Bousquet2011 period inversions, of itthat this seems simulate reasonable study. Even high-latitude-specific to if processes assume we are that do the morethan not models likely to the be correct other in models. thispotential regard future These climate sensitivities change (e.g., have Riley a et al., bearing 2011; on Koven et models‘ al., responses 2011). to Again, the size of the e 4.2 Temporal variability, environmental drivers, and modelAnother features notableformulations finding appropriate to was the highinundation that latitudes or exhibited air more models extreme temperature correlationsother than that words, with either high-latitude biogeophysical inversions lacked processes orinsulating – more physical specifically, e soil sophisticated freeze/thaw, and models. the In table depth biochemical in peatlands – make a substantial di 5 5 25 20 10 25 20 15 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erences ff emissions. 4 concentrations 4 emissions may have 4 orts like the revision of ff ects would require a much longer ff emissions in the WSL are not strongly 4 ciently similar snow, soil, and water table ffi 1936 1935 fluxes, 80 % of which were obtained south of 4 ects of microbial and vegetative parameters. ff erences among model formulations. As shown in ff over time scales long enough for changes in the long- air T emissions to long-term temperature changes. Unfortunately, the version 4 cient of variability (CV), which resulted in net negative annual emissions over ffi cult due to the many larger di erent types and locations of observations were used: Glagolev2011 was based on The global inversions were also subject to uncertainties. For example, while the The large discrepancy between the spatial distributions of emissions from Some of the scatter in model sensitivities to drivers may come from di Other features that were not investigated here could have potentially large impacts ff ffi formulations in order to isolate the e of ORCHIDEE used in2013; this Wania study et al., and 2013) in didORCHIDEE’s the not correlation use with original acclimatization. WETCHIMP Acclimatization study likely would (Melton lower et al., on the response ofis high-latitude wetlands acclimatization, to in futureterm which climate mean change. soil soil One temperature. microbial such This(Koven feature feature communities et has al., gradually been 2011; explored adaptwetland in Ringeval to CH the et ORCHIDEE the al., model long- 2010), where it greatly reduced the response of term mean to be aset large as al. interannual anomalies. (2011) Another featuredynamic is explored vegetation, by the a Koven liberation robust evaluation of of ancient these labile e carbon stored in permafrost. As with study period. 4.3 Future needs for observations andThe inversions wide disagreement amongour estimates ability from to observations assess and modelcan have inversions performance. on hampers Given emissions the estimates large (not only influence in that the wetland WSL, maps but over larger areas, as shown the WSL inet 2004, al. was (2006) substantially higher noted than that the their highest inversions model were CV. Bousquet more sensitive to the interannual correlated with eithertemporal inundation behaviors or aircoe must temperature, be the Bousquet2011 evaluated inversions’ with caution. The reference inversion’s Bousquet2011 inversions imply that wetland CH version of Glagolev2011and (Glagolev Bousquet2011K et both used al.,di the 2010) Kaplan as (2002) distribution its as prior, their while prior.the Second, Winderlich2012 Ob’ River; while Winderlich2012 was based on atmospheric CH been influenced by assumedburning, emission which rates were fromGlagolev2011 not fossil will fuel adjusted certainly extractionwould during help and benefit biomass the in from incorporating resolving inversion. observationsto some E reduce over long uncertainties discrepancies, time in but periods their long-term and all means. wider estimates areas Glagolev2011 and Kim2011Bousquet2011K (concentrated (concentrated in in the North)inversions’ the posterior may estimates be South) reflect due their and to prior several distributions: Winderlich2012 reasons. Kim2011 First, used and the an earlier in situ chamber measurements of CH by Petrescu et al., 2010), caresatellite must or be map taken products to such select asin appropriate the the maps. WSL) GLWD Ideally, (which should global omitted be the validatedsuch against northernmost as more wetlands Sheng2004 intensively ground-truthed and regional Peregon2008 maps discrepancies where between such the maps GIEMS exist. and Similarly,would SWAMPS resolving require remote the verification sensing against inundation products independent observations. observed at towers near orfrom north the of same the years. Ob’ Finally, River. Third, the observations Winderlich2012 were wetland not CH taken in the values ofplant-aided parameters transport, related which toHuissteden, 2011) methane recent have found production, to studies be methaneInvestigation particularly (Riley oxidation, influential of over and et these wetland CH parameters al.,di over 2011; the WSL Berritella in and a van model intercomparison can be Sects. 3.3 and 3.4.2, theinfluence methods over of the biogeochemical model parameter resultsas selection than sophisticated had the soil far presence less thermal orof absence physics. of a Such major a subset features comparison such of would the require models examination that have su 5 5 25 20 10 15 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er ff ), and 1 − y 4 seeps (Etiope 4 1.22 TgCH ± ciently long periods of record to assess ), inversions (6.06 ffi cients that optimized total emissions over 1 ffi − y 1938 1937 4 ) largely agreed. However, it was clear that reliance 1 − emissions over the region. Models and inversions y 4 4 0.54 TgCH ± emissions from 21 large-scale wetland models, including the 1.29 TgCH 4 ± emissions, inundated area, and total wetland area over the West 4 emissions, and the factors that most directly influence these emissions (e.g., er a single year of posterior emissions. Long records of in situ observations ff 4 Another general limitation of inversions and observations, distinct from estimates of At the other extreme, the Bloom2010 inversion exhibited almost no spatial or Siberian Lowland (WSL),of individual over estimates, thefrom forward mean period models (5.34 estimates 1993–2004. of Despite annual the total large emissions scatter over the WSL models from thedatasets of WETCHIMP CH project, to 5 inversions and several observational Plotnikovo/Bakchar Bog in theGlagolev WSL; Panikov et and al., Dedysh, 2011). 2000;to Friborg Indeed, evaluate et the LPX-BERN’s al., paucityobservations 2003; relatively would of low also long be sensitivity inet helpful, as to situ al., winter water records 2007; emissionsas are limited Kim table sparsely to our et depth. sampled the ability al., (Rinne magnitude Year-round networks 2007; of winter in Panikov emissions and the (Fig.promise Dedysh, WSL 8). in 2000) The (Sasakawa this regard, recent and et as implementationcomprehensive inversions their al., of observations disagree observations tower 2010; are of both Winderlich emissionsas multi-year et seeps, and from year-round. pipe al., non-wetland More leaks, methane 2010)inversions and (although lakes, sources show pipe most leaks such some of arebe now which being beneficial have considered; in so Berchet increasing far et the not al., accuracy 2014), been of would accounted inversions. for in 5 Conclusion We compared CH variability of wetland emissionsBousquet2011 than inversions to underestimated their the long-term mean;Another mean, accordingly, possibility thereby it is raising is the that possible CV. the that monthly the coe on satellitebiases inundation in products long-termlargely alone mean agreed to CH on thesome delineate outliers timing wetlands in of theinaccuracies the caused timing in seasonal of substantial cycle simulating peaksinversions of the in emissions timing also simulated over of inundated displayed theBousquet2011 snow area reference WSL, a melt inversion indicated but was and potential wide more drainage than range twice rates. the Models of CVs and of interannual all but variability: one model, the CV of the all of boreal Asia were notinteracting with optimal wetlands over elsewhere the may WSL not alone,A have since been further the in possibility, given phase environmental credence with drivers by those theemissions in reference over the inversion’s consistent WSL. all net of negative of boreal Asia the in May inversionbackground and June, methane (e.g., is that concentrations atmosphericemissions. errors advected Finally, in other OH other from methane components sources concentrations, elsewhere)might that were influenced have methane not been wetland accounted oxidation for attributed in to rates, the inversion wetlands; for example: geological CH long-term mean emissions, ismodel the sensitivities lack of to su environmentalinversions and drivers the and SWAMPS climate inundationthis change. product issue are The at long Bousquet2011 the enoughthe global to WSL. Regional begin scale, inversions to but such address as the Kim2011 Bousquet2011 and inversions Winderlich2012, which are might not o optimized for observations (3.91 soil temperature and water table depth) only exist in a handful of locations (e.g., the inversion must also bemodel questioned, or given that account it forinversion’s methane did coarse oxidation not resolution, in use these the anuseful atmosphere. characteristics atmospheric in When transport prevented our combined Bloom2010 study with for from anything the being other than comparing long-term mean emissions. more spatially accurateonly estimates o for theof WSL CH than the Bousquet2011 inversions, temporal variability. This mightwetland be inundation an and artifact water of table using GRACE levels. data The as spatio–temporal a accuracy proxy for of this et al., 2008), leaks from gas pipelines (Ulmishek, 2003), or lakes (Walter et al., 2006). 5 5 15 10 25 20 10 20 15 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 emissions 4 ects of snow ff erent from those of ff 1940 1939 culties in detecting inundation under forest canopies. To ffi T. J. Bohn and J. R. Melton jointly conceived and designed this study The WETCHIMP project received support for collaboration from the emissions to environmental drivers. 4 Forward and inverse models shouldas use inputs the or best asderived available wetland targets inundation maps, for either products optimizationmisclassifying of large are areas dynamic of a wetland high-latitudethe poor peatlands schemes. water that Satellite- table proxy can is emit for below methanewhose when the wetland surface, carbon (b) extent, cycling often due including dynamics permanent to water can bodies, (a) be substantially di improve the accuracyinformation of from global satellite products wetland and canonical map maps. productsModels may must account require for combining relationships emissions between emissions from and non-inundated water table wetlands, depth. withModels realistic should implement realisticaccount soil for thermal the presence physics of and peat snow soils schemes, atMulti-year and high and latitudes. multi-decade observationalassessing and whether inversion model products simulations are captureCH crucial the for correct sensitivities of wetland wetlands; and (c) di – – – – Based on our findings, we have the following recommendations for simulating CH The Supplement related to this. doi:10.5194/bgd-12-1907-2015-supplement article is available online at Author contributions. with input from J.models. O. T. Kaplan. J. J. Bohn,results A. R. Ito, for Melton T. the Kleinen, provided other R. the non-WETCHIMPSWAMPS Spahni, dataset. models. results B. M. D. R. from Stocker, V. Schroeder B. the Glagolev andon Zhang, provided original fossil and K. methane the X. WETCHIMP C. seeps. Glagolev S. Zhu McDonald et Maksyutov provided providedet provided al. the the (2011) Kim al. et dataset al. (2008) and (2011)processed inversion information wetland and and the map. Peregon reformatted V. resultsthe Brovkin of results. provided all T. the models, J.manuscript observations, Winderlich Bohn with and contributions and (2012) inversions; from J. inversion. and all R. co-authors. analyzed T. Melton J.Acknowledgements. collaborated Bohn on all figures. T. J. Bohn prepared the Partnership Initiative (NEESPI)supported for by the grantEngineering use 1216037 and of fromJ. Education computational R. the for Melton resources. USCanada Sustainability was (NSERC) T. National visiting supported (SEES) J. Post-Doctoral ScienceMinistry by Fellowship. Post-Doctoral Bohn T. Kleinen Foundation a of Fellowship was was National (NSF) supportedR. Education program. by Science Science, Spahni the and and German was EngineeringCommission supported Research Research through by (CarboPerm-Project, the Council thesupported BMBF Swiss of FP7 National by Grant project Sciencewere ERC No. Past4Future Foundation supported (grant FP7 and 03G0836C). by No. by US projectX. National the 243908). Zhu Science EMBRACE European B. were Foundation (grantM. (EaSM: D. supported AGS-1243220). V. Stocker No. by Q. Glagolev the was 282672). ZhuangCompetitiveness was US and Improvement H. supported Department Program. Tian of byEnvironment S. Research Energy a and and Maksyutov with Technology grant Development was B. projectJapan. Fund in supported No. (ERTDF), Zhang S. accordance Ministry DE-SC0007007. by N. of withNSh-3894.2014.5 Environment Grant Denisov the and A-1202 and Tomsk by of State A. the University V. Russian Eliseev Foundation were for supported Basic by Research the grant Russian 15-05-02457. President grant COST Action TERRABITESof (ES0804). Washington We and thank NASA Dennis grant NNX11AR16G P. Lettenmaier from at the Northern the University Eurasian Earth Science while the CV of the Bloom2010 inversion was essentially zero. Summer CH from the Bousquet2011air inversions temperature exhibited orrealistic inundation. only methane-soil Models moisture weak relationships thatcorrelations similarly correlations accounted tended with with to for both have summer influences inundation soil low to of and thermal moderate temperature air physics and temperature, and moisture, due and in in part part to to the the insulating competing e emissions from high-latitude wetlands: and peat soils.inundation- In or temperature-dominated contrast, (either models inundationmore or than lacking temperature 50 accounted % these for of formulations the variance). tended to be either 5 5 10 20 25 30 15 15 10 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | emissions from northern 4 1942 1941 , J., Machida, T., Parker, R., Sasakawa, M., Spahni, R., Stocker, B. 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Yes Uniform Yes Yes Farr et al. (2007), f climatology Temporal Resolution Static map West SiberiaStatic map 1 : 2 500 000 north West Siberia of Static map 1 : 2 500 000 Northern Static map Globalaggregated to monthly 1 : 1 000 000 aggregated to monthly climatology climatology + + + + + + + + + a a T Yes TOPMODEL No Yes M,M M,M M,M b 2006–2010 Monthly Domain 2nd half of 20th Century 2nd half of 20th Century 2nd half of 20th Century 2nd half of 20th Century 1993–2004 Daily, 1992–2013 Daily, 2003–2007 Annual Global1993–2009 Monthly Global1993–2009 3 Monthly Global2002–2007 1 Monthly 2009 1 Monthly ETOPO (2006), e Sheng2004 M,M Sheng2004 M,M – T Yes Uniform No No –for peatland fraction T––I Yes T TOPMODEL Yes Yes Yes TOPMODEL Yes Yes – Tfor peatland fraction Yes TOPMODEL Yes No NDP017 , , d d c c c c c g g , , f f concentrations e 4 1956 1955 ASTER ASTER Hydro1K 2v2 Hydro1K – Peregon2008 – Peregon2008 Amante and Eakins (2009), d inundated non-peatland wetlands inundated non-peatland wetlands Inundation Topography Maps Code Siberia, 2006–2010; statistical modelfunction of of wetland fluxes type as applied toet map al. of (2008). Peregon SCIAMACHY atmospheric CH regional maps ofDanilov Markov (2000), (1971),Supplemented Matukhin and with peat and Romanova cores. et al. (1977). regional map of Romanovatypes et al. (1977). identified Wetland validation. by remote sensing andpermafrost field region. Over the WSL,of based Fridland on (1988) maps and Naumov (1993). visible (AVHRR) and active(ERS) microwave (SSM/I) sensors. and passive (Bovensmann et al., 1999),temperatures NCEP/NCAR (Kalnay surface etgravity al., anomalies 1996), (Tapley et and al., 2004). GRACE union of four global datasets. tive (SeaWinds-on-QuikSCAT, ERS, andand ASCAT) passive (SSM/I, SSMI/S) microwave sensors. Global inversion using LMDZFung (1987) with inventory Matthews as and the wetland prior. Global inversion using LMDZ withKaplan emissions (2002) as from the wetland prior. prior in WSL, Fung et al. (1991) elsewhere. Regional inversion over West Siberia,(2002) with as Kaplan the wetland prior. 1993–2010 –1993–2010 –1993–2010 – – – – Sheng2004 M,M GLWD Sheng2004 M,M M,M 1993–2004 GIEMS1993–2010 – Hydro1K – GLWD M,M 1993–2010 SWAMPS SRTM 1993–2004 GIEMS SRTM 1993–2010 – ETOPO1 1993–2010 –1993–2010 GIEMS for ETOPO1 Hydro1K (2013), c VISIT (SHENG) VISIT (GLWD-WH) VISIT (SHENG-WH) TOPMODEL VISIT (GLWD) UW-VIC (SWAMPS) SDGVMUW-VIC (GIEMS) 1993–2004 – ETOPO LPX-BERN (DYPTOP-N) LPX-BERN (DYPTOP) LPX-BERN (N) ORCHIDEE 1993–2004 GIEMS Hydro1K LPJ-Bern 1993–2004LPJ-MPI GIEMSLPJ-WHyMe –LPJ-WSL 1993–2010 1993–2004 – –LPX-BERN 1993–2004 GIEMS NCSCD 1993–2010 GIEMS for Hydro1K – M – Yes NCSCD – M Uniform I Yes Yes Yes No Microtopography Yes Yes n/a No No DLEMDLEM2IAP-RAS 1993–2004 GIEMS 1993–2004 GIEMS 1993–2004 – – – – – – CDIAC I I Yes Yes Uniform Uniform No Yes No Yes Tarnocai et al. (2009) Map of wetlands across the northern circumpolar Papa et al. (2010) Remote sensing inundation product based on Lehner and Döll (2004) Global lake and wetland map. Wetlands were the Schroeder et al. (2010) Remote sensing inundation product based on ac- Bousquet et al. (2006) Bousquet et al. (2006) Schuldt et al. (2013) Zhu et al. (2014) VIC-TEM- (2012) (2011) (2013) Stocker et al. (2014) (2010) (2011), Zürcher et al. (2013) (2012) (2009a, b; 2010) (2011) (2013), Stocker et al. (2013), 2011a, b, 2012) 2011a,b, 2012) (2007), Eliseev et al. (2008) Participating models and their relevant hydrologic features. Observations and inversions used in this study. Inventory Inversions CLM4Me and ORCHIDEE are listed ashttp://cdiac.esd.ornl.gov/ndps/ndp017.html, “I” due to tuning/rescaling of inundated areas to match GIEMS, thus destroying contribution of topography. 4 4 a b VIC-TEM- TOPMODEL VISIT Ito and Inatomi SDGVMUW-VIC Hopcroft et al. Bohn et al. ORCHIDEE Ringeval et al. LPJ-Bern Spahni et al. LPJ-MPILPJ-WHyMe Kleinen et al. Wania et al. LPJ-WSLLPX-BERN Hodson et al. Spahni et al. DLEMDLEM2 Tian etIAP-RAS al. (2010, Tian et al. (2010, Mokhov et al. Model ReferenceCLM4ME Riley et Configuration al. (2011) CLM4ME Period Contributing 1993–2004 Areas – Observational Constraints GIEMS – Unsaturated – I CH Glagolev2011CH Glagolev et al. (2011)Bloom2010 In situ flux sampling along transect spanning West Bloom et al. (2010) Global optimization of relationship between Inundation Extent Global Inundation Extent from Multi-Satellites (GIEMS) NameWetland Maps Sheng2004Peregon2008 Reference Sheng et al. (2004)Northern Circumpolar Soil Carbon Wetland Database Peregon et al. map(NCSCD) (2008) of Description WSL based on digitization Wetland of map of WSL based on digitization of Temporal Global Lake and Wetland Database (GLWD) Surface Water Microwave Product Series (SWAMPS) Bousquet2011RBousquet2011K Bousquet et al. (2011), Kim2011 Bousquet et al. (2011), Winderlich2012 Kim et al. (2011) Winderlich (2012), Global inversion, with Glagolev et al. (2010) as Table 2. Table 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 21 112 15 95 − − ) 2 km 3 Parameter Selection total Scaled to global total total Scaled to global total Scaled to global total Scaled to global total Scaled to global total various sites Glagolev2011 Glagolev2011 4287 17330 109 34 389 355 46 327 12 153 105 270 31 321 − − − − Saturated NPP Inhibition NoNoNo Literature No Literature Literature Literature oxidation) oxidation) oxidation) oxidation) ects ects ects ects ff ff ff ff 4 4 4 4 7446 113 93 250 306 291 136 66 − − − N) and Northern halves, for the period Nitrogen–Carbon Cycle Interaction upland CH upland CH upland CH upland CH ◦ 61 < emissions from subsets of the participating Dynamic Vegetation 4 1958 1957 State ) Average Jun-Jul-Aug Contributing Area (10 1 − pH Redox a month 4 (TgCH 4 0.45 0.16 0.89 0.59 0.24 388 − root exudates, in proportion to net primary productivity. C Substrate Source = aerobic /R anaerobic R WSL South North WSL South North soil carbon pool; “NPP” = Estimates of June-July-August CH Participating models and their relevant biogeochemical features. 1.30 0.34 1.17 0.85 0.18 1.10 0.45 0.16 0.15 633 Mean Bias SD Mean Bias SD Mean Bias SD Mean Bias SD Mean Bias SD Mean Bias SD + SubsetIT Average Jun-Jul-AugM CH M 1.10 1.42 0.14 1.32 0.46 0.37 0.36 0.82 1.01 0.22 0.81 0.69 0.14 0.02 0.46 0.97 0.61 0.64 0.31 0.34 0.39 0.40 682 605 4 325 294 Sources: “Cpool” LPJ-BernLPJ-MPILPJ-WHyMeLPJ-WSLLPX-BERN ConstantLPX-BERN (DYPTOP) Constant ConstantLPX-BERN Constant (N) NPP and Cpool Constant ConstantLPX-BERN (DYPTOP-N) NPP and Cpool Cpool Constant NPP NoORCHIDEE and Cpool Constant No Cpool No NPPSDGVM and Cpool No NPPUW-VIC(GIEMS) No and Cpool Yes No NPP andUW-VIC(SWAMPS) Cpool No No Variable Yes No No No VariableVIC-TEM-TOPMODEL Yes No No Variable NoVISIT(GLWD) No Variable Variable No No Yes Yes No CpoolVISIT(GLWD-WH) Yes NPP No NPPVISIT(Sheng) Yes Yes NPP No Variable No Cpool VariableVISIT(Sheng-WH) Yes No Yes No Yes Variable Cpool Variable NPP No No Yes No No No Yes No Yes Yes Yes No Cpool NPP No Yes Yes Optimized to various Yes No sites; No No Yes Literature; No Scaled No to Yes global No No Optimized No to No various sites; No No No Optimized Literature No No to various Optimized sites; No No to various No sites; No Optimized to various Literature sites; No Yes (only a No Yes (only a No Yes Yes (only Yes a No No Yes (only a Literature and Optimized to Optimized to sites in Optimized to sites Optimized in to Literature various sites Model CLM4MeDLEMDLEM2IAP-RAS Variable Variable Variable n/a Cpool NPP and Cpool NPP and Cpool Cpool Yes Yes Yes Yes Yes Yes No No Yes No No No No Yes No No No No No Optimized to Optimized various to Optimized sites various to No sites various sites Literature; Scaled to global ∗ Table 4. models, over the entire WSL and its Southern ( Table 3. 1993–2004. Biases were computed with respect to the Glagolev2011/Peregon2008 estimates. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | emissions and GIEMS 4 emissions, 1993–2004. 4 1960 1959 cients of Variation (CV) of annual CH ffi 0.03 LPX-BERN (DYPTOP-N) 0.28 VISIT (GLWD) 0.62 − Spatial correlations between simulated average annual CH Temporal Coe CLM4MeDLEMDLEM2 0.115IAP-RAS LPJ-WSLLPJ-Bern 0.242 0.140LPJ-MPI 0.091 LPX-BERN (N) LPX-BERNLPJ-WHyMe (DYPTOP-N) 0.087 ORCHIDEE 0.127 0.076 SDGVM 0.195 UW-VIC VISIT (SWAMPS) (Sheng) UW-VIC (GIEMS) 0.208 0.069 0.197 VIC-TEM-TOPMODEL VISIT (GLWD) 0.149 0.113 0.163 0.338 Bousquet2011K 0.118 Bousquet2011R 0.171 0.160 0.446 Model CV Model CV Model CV LPJ-BernLPJ-MPI 0.56 0.01 ORCHIDEE SDGVM 0.61 0.09 VISIT (Sheng) 0.65 ModelCLM4Me CorrelationDLEM 0.69 ModelDLEM2IAP-RAS 0.70 0.21 LPJ-WHyMe LPJ-WSL LPX-BERN (N) Correlation 0.45 Model 0.41 0.97 UW-VIC (GIEMS) VIC-TEM-TOPMODEL 0.41 0.44 UW-VIC (SWAMPS) Correlation 0.11 Table 6. inundated area. Table 5. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 (blue) taken from 2 Dominant land cover at 1 km > (b) Limits of domain (brown) and peatland (a) 1962 1961 0.100.11 1.00 0.440.280.120.10 1.00 0.68 0.44 0.220.06 1.00 0.05 1.00 1.00 0.87 0.34 1.00 0.61 1.00 − − − − − − − WSLCRU T JJACRU P JJA SWAMPS JJA CRUGIEMS T JJA JJA 1.00 CRU 0.14S P JJA SWAMPS JJACRU T JJA GIEMSCRU JJA P JJA 0.66SWAMPS JJA GIEMS JJA 1.00 CRU TN JJA CRU PCRU 1.00 JJA T JJACRU SWAMPS P JJA JJA SWAMPS GIEMS JJA JJA GIEMS JJA 1.00 CRU T 0.32 JJA CRU P JJA SWAMPS JJA 0.60 GIEMS JJA 1.00 Map of the West Siberian Lowland (WSL). Temporal correlations among environmental drivers, 1993–2004. Figure 1. distribution (cyan), takenLehner from and Sheng Döll etsampling al. (2004); sites from (2004); permafrost Glagolev lakes et zone al. of boundaries (2011) area denoted after with Kremenetski red circles. et al. (2003); CH 25 km derived from MODIS-MOD12Q1 500 m land cover classification (Friedl et al., 2010). Table 7. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0.72 / 0.28 0.44 / 0.56 0.44 / 0.56 0.36 / 0.64 0.54 / 0.46 0.63 / 0.37 0.41 / 0.59 0.69 / 0.31 0.39 / 0.61 0.56 / 0.44 0.62 / 0.38 0.21 / 0.79 0.16 / 0.84 0.57 / 0.43 0.18 / 0.82 0.83 / 0.17 0.13 / 0.87 0.33 / 0.67 0.19 / 0.81 0.50 / 0.50 0.33 / 0.67 0.79 / 0.21 0.60 / 0.40 0.35 / 0.65 South / North ) N ◦ 1 12 6 11.19 > (

n r e h 9.80 ) 10 N). Error bars on the model t 1 r − ◦ r 8.74 o y

N 61 4 7.87 H < C

g 8 T (

s n 6.98 6.47 o 6.73 i s s 5.77 i 5.05 5.69 m 5.49 6 9 5.34 e

2 l . ) 5.08 a 1

N 4.77 u 0 1964 1963 4.62 ◦ n ± 4 . n 1 2 4.01 1 a 9

6 . n 3.72 ± 3 < 4 a 3.59 3.71 8 ( e 3.28

0 . M n 3.07 3 r e h 2.42 t u 2 o S 0 DLEM DLEM2 LPJ-MPI SDGVM IAP-RAS LPJ-WSL LPJ-Bern CLM4Me Kim2011 ORCHIDEE LPJ-WHyMe Bloom2010 VISIT (GLWD) LPX-BERN (N) Glagolev2011 VISIT (SHENG) Winderlich2012 Bousquet2011R Bousquet2011K UW-VIC (GIEMS) Observational datasets related to wetland areas. For SWAMPS and GIEMS, areas Mean annual emissions from the WSL, from inversions (green), observation-based UW-VIC (SWAMP) VIC-TEM-TOPMODEL LPX-BERN (DyPTOP-N) Model ensemble mean Figure 3. shown are the June-July-August (JJA) average inundated area over the period 1993–2004. estimates (red), andemissions forward from models the (blue).results southern The indicate half the hatched interannual ofinversions portions SD and the of observational of domain estimates the indicate the southern (latitude thefractions and uncertainty bars of northern given the indicate in emissions. total those emissions Error the studies.are Numeric contributed displayed bars in by on the the the right-hand southern column. and northern halves of the domain Figure 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 2 − m 1 − y 4 800 N) ◦ I T M 700 61 > 600 UW-VIC (SWAMP) VIC-TEM-TOPMODEL VISIT (GLWD) VISIT (SHENG) Inversions/Peregon2008 Glagolev2011/Peregon2008 Northern ( 5.0 4.0 3.0 2.0 1.0 7.0 6.0 500 wetland) ) and JJA wetland area 2 emissions (gCH 1 km 4 − 3 400 300 month Mean JJA (10 LPJ-WSL LPX-BERN (N) LPX-BERN (DyPTOP-N) ORCHIDEE SDGVM UW-VIC (GIEMS) 4 200 100 CLM4Me DLEM DLEM2 IAP-RAS LPJ-Bern LPJ-MPI LPJ-WHyMe 0 5 0 5 0 5 0 5 0

......

2 2 1 1 0 0 3 3 4 /month) CH (Tg JJA Mean 800 1200 1965 1966 N) ◦ I T M I T M emissions (TgCH 700 61 , allow comparison of spatial average intensities (CH < 4 2 1000 − 600 Whole region m 1 − Southern ( 800 2.0 1.0 7.0 6.0 5.0 4.0 3.0 500 wetland) wetland) 2 2 km km 3 3 400 month 600 4 300 7.0 6.0 5.0 4.0 3.0 2.0 1.0 Mean JJA (10 Mean JJA (10 400 200 100 200 0 Model estimates of JJA CH 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0

......

Observation- and inversion-based estimates of annual CH 1 1 0 0 3 3 2 2 1 1 0 0 3 3 2 2 4 4 /month) CH (Tg JJA Mean /month) CH (Tg JJA Mean ), for the entire WSL (top left) and the Southern (bottom left) and Northern (bottom 2 km 3 right) halves, forinteger the multiples of period 1 gCH 1993–2004. Lines passing through the origin, with slopes of Figure 5. (10 emissions per unit wetland area).alone Circles denote (corresponding models to that used codethat satellite used inundation “I” products topographic in information, Table with“T” 2) or in to without Table 2). inundation delineate Squares productsinundation wetlands. denote products (corresponding models Triangles (corresponding to that denote to code used code models “M” wetland in maps Table with 2). or without topography or of grid2007 cell (Kim2011),(Bousquet2011K area). and R). 2003–2007 For inversions, (Bloom2010), averages 2009 are (Winderlich2012), over and the 1993–2004 following periods: 2002– Figure 4. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | of grid cell area). 1 − y 2 − m 4 emissions (gCH 4 1968 1967 Maps of average JJA wetland area (fraction of grid cell area) from participating Maps of simulated average annual CH Figure 7. models. Figure 6. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 Dec Nov UW-VIC (GIEMS) UW-VIC (SWAMP) VIC-TEM-TOPMODEL VISIT (GLWD) VISIT (SHENG) Oct 2010 VISIT (GLWD) VISIT (SHENG) Bloom2010 Bousquet2011K Bousquet2011R Sep 2009 ) from participating Aug 4 2008 Jul 2007 Months Jun 2006 LPJ-WSL LPX-BERN (N) LPX-BERN (DyPTOP-N) ORCHIDEE SDGVM May Apr 2005 Mar 2004 emissions (TgCH Feb ORCHIDEE SDGVM UW-VIC (GIEMS) UW-VIC (SWAMP) VIC-TEM-TOPMODEL CLM4Me DLEM DLEM2 LPJ-Bern LPJ-MPI 2003 4 Intensity Jan

1 2 7 5 3 6 0

4

4 ) d n a l t e w m r e p H C g (

2

2002

4 y t i s n e t n i n o i s s i m e H C Years 2001 Dec Dec 1970 1969 2000 Nov Nov Winderlich2012 Kim2011 Bousquet2011K Bousquet2011R Oct Oct 1999 Sep Sep 1998 LPJ-MPI LPJ-WHyMe LPJ-WSL LPX-BERN (N) LPX-BERN (DyPTOP-N) Aug Aug 1997 Jul Jul 1996 Months Months Jun Jun 1995 May May Apr Apr 1994 Mar Mar emissions and wetland areas have been normalized relative to their peak CLM4Me DLEM DLEM2 IAP-RAS LPJ-Bern 1993 4 Feb Feb SWAMPS GIEMS 2 2 8 6 0 4 of wetland area; lower right), with satellite inundation products and inversions for

CH4 emissions Wetland area

12 10 14

Timeseries of simulated annual total CH

Average whole-domain seasonal cycles (1993–2004) of normalized monthly CH 4 Jan Jan ) g T ( s n o i s s i m e H C l a u n n 2 A

−

1.0 1.0 0.2 0.2 0.8 0.8 0.0 0.0 0.6 0.6 0.4 0.4 (normalized) (normalized)

m 4 Wetland area Wetland H C s n o i s s i m e 4 models, Bousquet etal. (2011), al. and Bloom (2011) (2010) inversion. the Reference and Kaplan inversions from Bousquet et Figure 9. emissions (top), normalized(gCH monthly wetland areas (lower left), and monthly intensities reference. CH values. Figure 8. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2010 2010 2010 VISIT (GLWD) VISIT (SHENG) GIEMS SWAMPS 2009 2009 2009 2008 2008 2008 2007 2007 2007 ), with JJA inundated areas 2006 2006 2006 2 km 2005 2005 2005 3 2004 2004 2004 ORCHIDEE SDGVM UW-VIC (GIEMS) UW-VIC (SWAMP) VIC-TEM-TOPMODEL 2003 2003 2003 C) and precipitation (mm). ◦ 2002 2002 2002 Years Years 2001 2001 2001 1972 1971 2000 2000 2000 1999 1999 1999 1998 1998 1998 LPJ-MPI LPJ-WHyMe LPJ-WSL LPX-BERN (N) LPX-BERN (DyPTOP-N) 1997 1997 1997 1996 1996 1996 1995 1995 1995 1994 1994 1994 CRU JJA Air Temperature CRU JJA Precip DLEM2 IAP-RAS LPJ-Bern CLM4Me DLEM 1993 1993 1993 0

75 55 65 70 50 60 80

Timeseries of simulated JJA wetland areas (10

200 600 800 400 (mm) Precip JJA CRU

Timeseries of CRU JJA air temperature (

1600 1400 1200 1000

15.5 14.5 13.5 12.5 16.0 15.0 14.0 13.0 ) m k 0 1 ( a e r a d n a l t e w A J J

CRU JJA Air Temperature (C) Temperature Air JJA CRU

2 3 Figure 11. from GIEMS and SWAMPS products for reference. Figure 10. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | - 4 inund ), for F 4 emissions. Symbol 4 emissions (expressed as correlation between 4 1973 ) vs. influence of air temperature on model CH 4 -dominated” denote correlation thresholds above which inundated area or air T Influence of inundation on model CH JJA GIEMS inundatedemissions area (expressed and as JJAthe correlation CH entire between WSL JJA (top) CRU and air the Southern temperature and and Northern JJA halves CH of the domain (bottom). “ Figure 12. dominated” and “ shapes and colors are the same as in Fig. 5. air temperature, respectively, explain more than 50 % of the variance of CH