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Use of FLUXNET in the Community Land Model development

R. Stockli

D. M. Lawrence

G.-Y. Niu

K. W. Oleson

Peter Edmond Thornton The University of Montana

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Recommended Citation Stöckli, R., D. M. Lawrence, G.-Y. Niu, K. W. Oleson, P. E. Thornton, Z.-L. Yang, G. B. Bonan, A. S. Denning, and S. W. Running (2008), Use of FLUXNET in the Community Land Model development, J. Geophys. Res., 113, G01025, doi:10.1029/2007JG000562

This Article is brought to you for free and open access by the Numerical Terradynamic Simulation Group at ScholarWorks at University of Montana. It has been accepted for inclusion in Numerical Terradynamic Simulation Group Publications by an authorized administrator of ScholarWorks at University of Montana. For more information, please contact [email protected]. Authors R. Stockli, D. M. Lawrence, G.-Y. Niu, K. W. Oleson, Peter Edmond Thornton, Z.-L. Yang, G. B. Bonan, A. S. Denning, and Steven W. Running

This article is available at ScholarWorks at University of Montana: https://scholarworks.umt.edu/ntsg_pubs/194 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, 001025, doi:10.1029/2007JG000562, 2008 Click H ere f o r Full A rticle

Use of FLUXNET in the Community Land Model development R. Stockli/’^’^ D. M. Lawrence,"^ G.-Y. N iu/ K. W. Oleson,"^ R E. Thornton,"^ Z.-L. Yang/ G. B. Bonan/ A. S. Denning/ and S. W. Running®

Received 26 July 2007; revised 12 October 2007; accepted 21 December 2007; published 19 March 2008. [i] The Community Land Model version 3 (CLM3.0) simu1at.es land-atmosphere exchanges in response to climatic forcings. CLM3.0 has known biases in the surface energy partitioning as a result of deficiencies in its hydrological and biophysical parameterizations. Such models, however, need to be robust for multidecadal global climate simulations. FLUXNET now provides an extensive data source of carbon, water and energy exchanges for investigating land processes, and it encompasses a global range of ecosystem-climate interactions. Data from 15 FLUXNET sites are used to identify and improve model deficiencies. Including a prognostic aquifer, a bare soil evaporation resistance formulation and numerous other changes in the model result in a significantly improved soil hydrology and energy partitioning. Terrestrial water storage increased by up to 300 mm in warm climates and decreased in cold climates. Nitrogen control of photosynthesis is revealed as another missing process in the model. These improvements increase the correlation coefficient of hourly and monthly latent heat fluxes from a range of 0.5-0.6 to the range of 0.7-0.9. RMSE of the simulated sensible heat fluxes decrease by 20-50%. Primary production is overestimated during the wet season in mediterranean and tropical ecosystems. This might be related to missing carbon-nitrogen dynamics as well as to site-specific parameters. The new model (CLM3.5) with an improved terrestrial water cycle should lead to more realistic land-atmosphere exchanges in coupled simulations. FLUXNET is found to be a valuable tool to develop and validate land surface models prior to their application in computationally expensive global simulations. Citation: Stockli, R., D. M. Lawrence, G.-Y. Niu, K. W. Oleson, P. E. Thornton, Z.-L. Yang, G. B. Bonan, A. S. Denning, and S. W. Running (2008), Use of ELUXNET in the Community Land Model development, J. Geophys. Res., 113, G01025, doi: 10.1029/2007JG000562.

1. Introdiiction controls on ecosystem function and boundary layer processes r ., , , , , „ in regions where evapotranspiration as a biophysical process [2] The land surface provides a lower boundaiy to the ^ availability {Seneviratne and atmosphere tor exchanges ot radiation, heat, water, momen- ..* 1, 1 u 1 u 1 • * * , . , . . ’ . Y htocA:/;, 2007 . Furthermore the global carbon cycle interacts tum and chemical species such as CO2 . The importance of with soil and vegetation biophysics since carbon assimila­ these exchanges for the climate system is increasingly being tion and ecosystem respiration are regulated by the land recognized \Betts et al, 2000; Cox et al, 2000; Pielke, o * j 1. * u 1 n ■ n- ■ 7 n ■ 7 surtace ladiatiou, wutei uud heat balauces. 2001; tnedlinestein et at., 2003; Seneviratne et at., 2006; tttj c jir -iiii-., 7 7-1 ^ 1 7 1 1 3 Land surtaee models tor use in global climate models Betts et^ at., 2007 . .L. Storage ^ ° ot heat and water ^ on land haveu ubeen udeveloped i u over thei - i , lasti 1three .1 .1 , decades.u u They t-u range constitutes a sigmtieant memory component within the o • , u 1 1 • i- 1 1 L , from simple energy balance parameterizations to complex climate system.^ For instance, soil moisture has strong ° , schemes F including the tUll7 ; u terrestrial * i..- 1 biogeoehemieal u- u • cycle1 1 ______[Sellers et al, 1997; Friedlingstein et al., 2006] and are 'Department of Atmospheric Science, Colorado State University, Fort based on knowledge gained from field and laboratory Collins, Colorado, USA. u‘ i+ u ‘i a ' + 2^,-Climate ^ oServices, Trederal7 /J 1 Omce rxjY- ol J2 MeteorologyATT ^ , and Climatology J r-1 ^ 1 research r in plant r ^ physiology, , soil science and micrometeo- MeteoSwiss, ziirich, Switzerland. rology. However, many model components resulted from N a SA Earth Observatory, Goddard Space Flight Center, Greenbelt, relatively few observations and from idealized laboratory Maryland, USA. experiments. This leads to significant uncertainty in the ReseIXBoulde^“coteado''us^^^ Atmospheric parameterization of processes which are now employed on a '" '^ D e p ^ L rf Geolog^l Sciences, University of Texas at Austin, global scale for Studying laud-climate interaction at seasonal Austin, Texas, USA. to decadal timescales. '’Numerical Terradynamics Simulation Group, University of Montana, [4] These model uncertainties have been documented in Missoula, Montana, USA. model inter-comparison studies (e.g., PILPS [Henderson- Sellers et al., 1996; Pitman et al., 1999; Nijssen et al., Copyright 2008 by the American Geophysical Union. 0148-0227/08/2007JG000562$09.00 2003]). Large differences still exist in the simulation of

G01025 1 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE GEM DEVELOPMENT G01025 seasonal and annual evapotranspiration and runoff dynam- Reichstein et al, 2002; Turner et al, 2004; Stockli and ies [Gedney et al, 2000]. It is not elear how mueh present Vidale, 2005; Bogena et al, 2006; Friend et al, 2007] elimate model predietions are affeeted by these limitations. although it is important to remember that these observations For instanee, eoupling strength between the land surfaee are of loeal seale and ean be subjeet to potentially large and the atmosphere varies not only by region but also by the random and systematie errors [Wilson et al, 2002; Foken, used parameterization \Koster et al, 2004]. A realistie 2008]. FLUXNET is probably the most eomprehensive representation of land surfaee responses to elimatie vari­ terrestrial eeosystem data set today, and uneertainties in ability as part of global elimate simulations is important for radiation, heat, water and earbon flux measirrements ean be future elimate impaet studies. It is also mandatory in the aeeirrately quantified [Falge et al, 2001; Schmid, 2002; predietion of the global earbon balanee, with regional sinks Hollinger and Richardson, 2005; Richardson et al, 2006]. and soirrees, whieh will be part of the next generation earth Flux tower observations per se only have limited spatial system models. sealability and do not provide a gridded global eoverage. [5] The Community Land Model version 3 (CLM3.0) is a They do, however, span a global range of eeosystems where eommunity-developed land sirrfaee model maintained at we ean exereise land sirrfaee models like CLM3.0. The NCAR (National Center for Atmospherie Researeh) and importanee of individual proeesses regulating the heat, ineludes a eomprehensive set of meehanistie deseriptions of water and earbon exehanges varies by elimate. Certain soil physieal and vegetation biophysieal proeesses [Oleson proeesses may only play a role at one end of the multidi­ et al, 2004]. The model ean be extended to a full biogeo­ mensional speetrum of elimatie environments. ehemieal deseription of the terrestrial earbon-nitrogen inter- [s] In this study we use 15 FLUXNET tower sites from aetions [Thornton et al, 2007] based on the BIOME-BGC the temperate, mediterranean, tropieal, north boreal and model and vegetation biogeography with disturbanee subalpine elimate zones to interaetively assess the realism dynamies [Levis et al, 2004] based on the LPJ model. of proposed CLM3.0 enhaneements during model develop­ [e] Despite being an advaneed proeess-based land surfaee ment. Gap-filled yearly meteorologieal foreing data sets at model, CLM3.0 has known defieieneies in simulating the the tower sites are used to eonduet off-line single-point long term terrestrial hydrologieal eyele in elimate simula­ simulations. In the results seetion quality-sereened heat, tions. They ean influenee the sirrfaee elimate and vegetation water and earbon fluxes as well as soil moistirre and soil biogeography through plant-soil earbon and water dynamies temperature measurements are eompared to simulated [Dickinson et al, 2006]. In eoupled simulations with many equivalents. Several model hydrologieal defieieneies eon- feedbaek proeesses, these shorteomings ean further amplify frolling turbulent surfaee fluxes, are sueeessively identified errors from the atmospherie model, with unhealthy eon- and eorreeted with this study. It is therefore demonstrated sequenees for the simulated elimate system and land- how FLUXNET helps to reduee model biases in the atmosphere interaetions [Bonan and Levis, 2006; Hack et simulation of land sirrfaee proeesses and how it ean be a l, 2006; Lawrence et al, 2007]. The CLM model devel­ used as an effieient tool for the reevaluation of land surfaee opment eommunity has proposed a number of improved soil models like CLM3.0 during their development stage. hydrologieal and plant physiologieal formulations that rep­ resent previously missing proeesses that appear to be 2. M ethods responsible for a damped soil water storage eyele in the 2.1. Model tropies and the generally dominating fraetion of bare soil evaporation to plant transpiration [see, e.g., Lawrence et al, [9] CLM3.0 (Community Land Model Version 3 [Oleson 2007]. For details about the full set of proposed ehanges to et al, 2004]) is the land model eomponent of CCSM3 CLM, see Oleson et al. [2007]. Flere, we both evaluate how (Community Climate System Model Version 3 [Collins et these ehanges have improved the model and also elueidate al, 2006]). It ineludes meehanistie formulations of physieal, how the use of FLUXNET data has eontributed to the biophysieal and biogeoehemieal proeesses that simulate the identifieation of defieieneies in the model ineluding the terrestrial radiation, heat, water and earbon fluxes in aforementioned missing proeesses. The subset of ehanges to response to elimatie foreings. CLM3.0 provides an integrated the model evaluated in detail here inelude: (1) a Topmodel- eoupling of photosynthesis, stomata! eonduetanee, and based runoff, infiltration and aquifer model, (2) a bare soil transpiration. Therefore vegetation biophysieal proeesses evaporation resistanee and, (3) an empirieal funetion for strongly interaet with soil hydrologieal proeesses. The nitrogen eontrol of the photosynthesis-eonduetanee formu­ CLM3.0 eommunity has proposed a number of model lation. The aim of this study is to individually implement ehanges as a response to the above diseussed defieieneies and evaluate the proposed algorithms and to quantify their of the CLM3.0 eode. Three of them, in partieular, are impaet on the simulated terrestrial earbon and water eyele direetly related to simulations of the global hydrologieal on hourly to seasonal timeseales. eyele and are summarized here (full doeumentation in [7 ] Sueh a study is diffieult for a global land surfaee model Oleson et al. [2007]): due to a laek of suitable global observations [Henderson- [10] 1. Infiltration, runoff and groundwater: ATopmodel- Sellers et al, 2003]. Flowever, long-term ground-based based infiltration, saturation and runoff seheme [Beven and eeosystem observations sueh as FLUXNET [Baldocchi et Kirkby, 1979; Niu et al, 2005] infroduees eatehment-seale a l, 2001], the global network of researeh sites where the soil water dynamies from elassieal hydrologieal modeling to eddy eovarianee teehnique is used to monitor surfaee- a land sirrfaee model for global applieations. Additionally, a atmosphere exehanges of earbon, water, and energy, are a prognostie aquifer seheme [Niu et al, 2007] allows for unique data souree for proeess-based land surfaee model seasonal to inter-annual soil water storage fluetuations development [Running et al, 1999; Canadell et al, 2000; whieh involve soil depths beyond the 3.43 m deep soil of

2 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025

Table 1. Flux Towers Used in This Study®

Number Site Lon [°E] Eat [°N] Alt (Hgt) [m] Biome Type Soil Type Years Climate Zone CarboEurope 1 Vielsakn [Aubinet et al., 2001] 6.00 50.30 450 (40) MF loam 1997-2005 Temperate 2 Tharandt [Grunwald and Bernhofer, 2007] 13.57 50.96 380 (42) ENF loam 1998-2003 Temperate 3 Castel Porziano [Valentini, 2003] 12.38 41.71 68 (25) EBF loamy sand 2000-2005 Mediterranean 4 Collelongo [Valentini, 2003] 13.59 41.85 1550 (32) DBF silt loam 1999-2003 Mediterranean 5 Kaamanen [Laurila et al, 2001] 27.30 69.14 155 (5) TUN loam 2000-2005 North boreal 6 Hyytiala [Suni et al, 2003] 24.29 61.85 181 (23) ENF loamy sand 1997-2005 Boreal 7 El Saler [dais et al., 2005] -0.32 39.35 10 (15) ENF loamy sand 1999-2005 Mediterranean

LBA 8 Santarem KM83 [Goulden et al., 2004] -54.97 -3.02 130 (64) EBF sandy clay 2001-2003 Tropical 9 Tapajos KM67 [Hutyra et al., 2007] -54.96 -2.86 130 (63) EBF clay 2002-2005 Tropical

AmeriFlux 10 Morgan Monroe [Schmid et al., 2000] -86.41 39.32 275 (46) DBF clay loam 1999-2005 Temperate 11 Boreas OBS [Dunn et al, 2007] -98.48 55.88 259 (30) ENF clay loam 1994-2005 Boreal 12 Lethbridge [Flanagan et al, 2002] -112.94 49.71 960 (4) GRA silt loam 1998-2004 Boreal 13 Fort Peck [Gilmanov et al, 2005] -105.10 48.31 634 (4) GRA sandy loam 2000-2005 Temperate 14 Harvard Forest [Urbanski et al, 2007] -72.17 42.54 303 (30) DBF sandy loam 1994-2003 Temperate 15 Niwot Ridge [Monson et al, 2002] -105.55 40.03 3050 (26) ENF clay 1999-2004 Subalpine “Biome types: mixed forest (MF), evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), tundra (TUN), evergreen broadleaf forest (EBF), grasslands (GRA). Ait is the elevation of the tower above the sea level, and Flgt is the approximate height of the wind/temperature and flux measurements above the surface.

CLM3.0. The depth of the water table is highly related to photosynthesis and therefore stomatal eonduetanee, PFT- subsurfaee runoff magnitude [Sivapalan et al, 1987; Chen dependent faetors flJS) were diagnosed from a simulation and Kumar, 2001]. During dry periods the aquifer eontrib- employing CLM-CN from a fully spun-up preindustrial utes to base-flow and provides a long-term storage for soil state of terrestrial biogeoehemistry. f{N) represents the water. It is hydraulieally eonneeted to the root zone and proportion of potential photosynthesis that is realized in therefore interaets with vegetation biophysieal state and the faee of nitrogen limitation, as predieted by CLM-CN. funetion. During rainfall events or in moist elimates the For our sim ulations/(^ is imposed on the maximum rate of water table ean rise into the model soil eolumn, whieh earboxylation Umax in n similar manner to, e.g., plant water inereases root zone soil moisture and subsurfaee runoff. It stress, as deseribed in Oleson et al. [2007, Appendix G]. also inereases infiltration sinee soil hydraulie eonduetivity Imax then modulates eanopy photosynthesis and therefore shows a highly nonlinear dependenee on soil water eontent. earbon uptake as well as eanopy eonduetanee and therefore In the original CLM3.0 the magnitude of soil water dynam­ transpiration in the model. ies is eonstrained to total soil depth, while here the aquifer aets as a buffer with a storage eapaeity varying by elimate, 2.2. Data soil, vegetation and topography. [13] FLUXNET is a global network of eurrently more [11] 2. Soil evaporation: In the original CLM3.0 an than 400 flux towers whieh operate independently or as unreasonably high fraetion of evapotranspiration eomes part of regional networks (CarboLurope, AmeriFlux, LBA, from bare soil evaporation {Lawrence et ah, 2007]. In ete.). The off-line single point simulations with CLM3.0 addition to the aheady simulated top soil humidity [Oleson were earried out at 15 FLUXNET sites eovering a range et al., 2007, equations (F1)-(F4)] a new resistanee funetion of elimatie environments listed in Table 1: temperate (5), was implemented, based on work by Sellers et al. [1992]. mediterranean (3), boreal (3), tropieal (2), north boreal Equation (F5) in Oleson et al. [2007] is an empirieal (1) and subalpine (1). Only towers providing three or more parameterization of the bare soil evaporation resistanee, years of eontinuous driver and validation data as part of the whieh was developed on a limited number of FIFE 87 publiely aeeessible AmeriFlux or CarboLurope standardized measurements. It had previously been sueeessfully used in Level 2 database have been seleeted. In order to obtain a SiB 2 and 2.5 (Simple Biosphere Model Versions 2 and 2.5 balaneed set of flux towers, only a few temperate sites eould [Sellers et al., 1996; Vidale and Stockli, 2005]). be used. On the other hand aretie and espeeially more arid [12] 3. Nitrogen limitation: Initial simulations ineluding sites with multiyear eontinuous eoverage were diffieult to the above soil hydrologieal proeesses revealed an exagger­ find. ated light response of photosynthesis, resulting in too mueh 2.2.1. Forcing Data primary produetion and slightly overestimated latent heat [14] Yearly gap-filled meteorologieal driver data were flux. Apart from soil water, temperature, humidity and ereated from level 2 flux tower data sets at 30 or 60 min radiation, leaf nitrogen eontent ean define the maximum time steps. For off-line simulations the model requires RGj rate of earboxylation in the photosynthesis formulation and (downwelling short-wave radiation; W m“^), LW j therefore stomatal opening. While prognostie nitrogen is (downwelling long-wave radiation; W m“^), (air temper­ part of the separately developed biogeoehemistry seheme ature; K), RHa (relative humidity; %), u (wind speed; m s“ ^), CLM-CN [Thornton et al, 2007], many applieations require Ps (surfaee pressure; Pa), P (rainfall rate; mm s~^). Mea­ the standard CLM. In order to simulate nitrogen eontrol on surements of these quantities at the tower referenee height

3 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025

(Table 1) were used. Outliers whieh deviated na times from where eo is the elear sky atmospherie emissivity as a the median-filtered time series were removed {a is the funetion of and atmospherie vapor pressure (mb). standard deviation of the original time series and n = 4, Ea adjusts £o for elond eover. It depends on the eleamess exeept for RHa where n = 8; for m where n = 20 and for LWj index Kq, whieh ranges from 0 to 1 (full elond eover to elear where n = 16). Up to two month long sneeessive gaps were sky). Kq ean be approximated by dividing measured by filled by applying a 30 day mnning mean dinmal eyele potential downwelling solar radiation, but only during forwards and baekwards through the yearly time series. daytime. We replaeed all noetnmal Kq values where RGj Years with more than 2 month of eonseentive missing data was below 50 W m~^ with linearly interpolated values. were not used. While elear skyLJVj ean be reasonably estimated the above [15] The following exeeptions were applied to the above formulation for all-sky LJVj is a rough fix in need of some proeednre: data. Sinee elond emissivity depends on, e.g., elond type, [16] 1. RGd was not median-filtered sinee most of its water eontent and elond vertieal extent an nneertainty of variability oeenrs on dinmal timeseale. Instead the potential roughly 5-20 W m~^ is introdneed to the driver data set solar radiation as a funetion of latitude and loeal solar time, [Gabathuler et al., 2001] by using this algorithm. sealed with the annual maximum observed RGj, provided [20] The eonsistently gap-filled meteorologieal foreing an upper bound for RGj. data from the above 15 sites (and from around 50 additional [n] 2. P was neither median-filtered nor gap-filled. sites) are available from the authors (upon request also as Where provided by tower sites, daily preeipitation totals ALMA-eompliant NetCDF files). from nearby stations were used to replaee missing 30 or 2.2.2. Validation Data 60 min tower data. Daily preeipitation totals were evenly [2 1 ] Turbulent surfaee fluxes and soil physieal state distributed at night between 00:00-04:00 during days when variables from the Level 2 flux tower data sets were used no daily 30 or 60 min were available; and they were used to to validate the model during the implementation stage of augment valid 30 and 60 min data during days with partial above-deseribed modifieations. None of the validation data missing data periods. were gap-filled sinee onr intention was to look at timing and [i8] 3. For sites with no Pg, it was estimated by phase of the seasonal fluxes in response to elimatie foreings rather than to mateh the loeal-seale heat, water and earbon P, = P,„e balanee at the end of the year. LE (latent heat flux; W m~^), ( 1) Fl (sensible heat flux; W m~^), and NEE (net eeosystem where P^^ is the mean sea level pressure (101300 Pa), M is exehange; /rmol m~^ s~^) were u* filtered in order to aeeonnt for the well doenmented biases in eddy eovarianee the moleenlar weight of air (0.029 kg moU^), g is the gravitational aeeeleration (9.81 m s~^), z is the tower height measurements during periods of low tnrbnlenee [Schmid et al., 2003]: eomparisons to modeled fluxes were only above sea level (m) and R is the universal gas eonstant (8.314 J K“ ^ moU^). performed for times when the u* value was above 0.2 m s~^ (in the mean 67.4% of the data). Ideally, the u* [19] 4. For sites with no LW^i (most sites), it was estimated threshold should be site-dependent and would only need from the surfaee radiation balanee: to be applied to noetnmal data. Random uneertainties in turbulent surfaee fluxes [Hollinger and Richardson, 2005] LWa ^ R„ - RGa + RGu (2 ) were estimated based on empirieal findings by Richardson et al. [2006]. Systematie errors in measured surfaee fluxes due to failure in energy balanee elosnre were aeeonnted for where and RGu are non-gap-filled net radiation (Wm“^) by multiplying n*-sereened surfaee fluxes with the residual and npwelling solar radiation (Wm~^), a is the Stefan- of the energy balanee elosnre (as % ofi?„) for eaeh site, whieh Bolzmatm eonstant (5.67 ■ 10~* Wm~^K~^) and 7). is either was ealenlated from the regression of hourly observed R„ the eanopy temperature or soil surfaee temperature (K), versus LE and H fluxes [Wilson et al, 2002; Grunwald and depending on data availability. As a baeknp algorithm (any Bernhofer, 2007]. In all LE and H plots, the total errors were of the right hand side variables in equation (2) missing, ealenlated as the square root of the sum of the squares of most sites, again) downwelling long-wave radiation was random and systematie errors for eaeh analysis time step estimated by using the elear-sky LWj parameterization by (e.g., hourly or monthly). Table 2 presents a summary of Idso [1981], modified by an emissivify eorreetion faetor as these nneertainty estimates for eaeh site. proposed by Gabathuler et al. [2001]: [22] The model in its standard eonfignration simulates GPP (gross primary prodnetivity; ^mol m“^ s“ ^) but not R^ (3) (eeosystem respiration; /rmol m~^ s~^). In order to ealenlate NEE (net eeosystem exehange; /rmol m~^ s~^), whieh is the where: differenee between two large terms GPP and R^, both terms would need to be aeenrately prognosed. This requires a e, = 1 +0.3(1 - K o f and (4) meehanistie formulation involving prognostie earbon and nitrogen fluxes and pools as for instanee presented by Thornton et al. [2007]. In order to eompare modeled earbon uptake to observations, observed estimates of GPP were eo = 0.7 + Ba ■ 5.95 ■ 10^+T (5) empirieally derived from observed NEE, PAR (photosyn- thetieally aetive radiation; /rmol m~^ s~^), and f (5 em soil

4 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025

Table 2. Uncertainty of Observations: % of u* Filtered Data (u*), % of Energy Balance Closure (ebc), Mean Error in LE Due to Failure of Energy Balance Closure (ebc LE), Mean Random Error in LE (ran LE), Mean Error in H Due to Failure of Energy Balance Closure (ebc H), and Mean Random Error in H (ran H)

Number Site u* % ebc % ebc LE W m^^ ran LE W m ^ ebc H W m ^ ran H W m ^ 1 Vielsakn 21 73 7.0 20.2 7.5 29.7 2 Tharandt 44 78 8.4 24.5 7.7 29.8 3 Castel Porziano 34 83 8.6 25.7 13.9 36.6 4 Collelongo 41 81 8.8 28.1 12.3 37.1 5 Kaamanen 56 72 10.4 23.9 3.4 22.9 6 Hyytiala 47 72 8.3 21.5 6.2 26.1 7 El Saler 24 83 7.8 25.6 12.3 39.1 8 Santarem KM83 47 81 32.7 59.2 8.6 27.7 9 Tapajos KM67 36 81 24.9 44.9 6.5 26.3 10 Morgan Monroe 22 65 18.8 28.2 11.0 29.5 11 Boreas OBS 25 80 5.8 20.7 10.4 31.1 12 Lethbridge 42 77 7.3 21.8 8.8 34.3 13 Fort Peck 42 68 13.6 24.6 13.2 30.3 14 Harvard Forest 17 84 6.9 24.9 6.4 31.1 15 Niwot Ridge 17 76 12.7 27.6 12.3 40.7

temperature; K) using the algorithms by Desai et al. [2005]. integration seheme, eanopy intereeption ehanges, new fro­ Measured volumetrie soil moisture was eonverted to pereent zen soil and plant soil water availability parameterizations) saturation by assuming a porosity of 0.48 and by nsing the as deseribed in Oleson et al. [2007] whieh were not part of model soil layer whieh was elosest to observation depth. the original CLM3.0. [26 ] 3. CLMgw rsoil: addition of the bare soil evapora­ 2.3. Experiment tion resistanee formnlation to CLMgw (rsoil stands for soil [23] Single point model simnlations were performed for resistanee). eaeh of the 15 flnx tower sites. The original model [27 ] 4. CLM3.5: addition of a PFT-dependent nitrogen (CLM3.0) was sneeessively modified with the proposed limitation faetor to CLMgw rsoil. This simulation is equiv­ ehanges: alent to the pnblie release eode of CLM3.5. [2 4 ] 1. CLM3.0: the original and pnbliely available 2.3.1. Boundary Conditions release eode of CLM3.0. [28] Vegetation and soil parameters for eaeh site were [25] 2. CLMgw: addition of a Topmodel-based infiltra­ derived from the standard CLM3.0 PFT-dependent look-np tion, runoff and aqnifer storage formnlation to CLM3.0. tables based on vegetation type and soil type (eonstant CLMgw (gw stands for gronndwater) further ineludes all vertieal profiles of sand/elay fraetions for eaeh site) from other major updates in the model (e.g., a new eanopy Table 1. A single PFT was used for eaeh site. Visible and

5K 3.0 3.0 LE X gw X gw O gw_rsoil <> gw_rsoii 2.0 2.0 O' A 3.5 A 3.5

■oo N (0 E O o c c c c o g "S > ■o0 ■o ■g CQ ‘14A ' CO ■oc T5C B 0.5 CO 0.99

0.0 1.0 0.0 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 Standard deviation (normaiized) Standard deviation (normaiized)

Figure 1. Performanee of four model versions at 15 FLUXNET towers (numbers 1-15). Statisties in the Taylor diagram are derived from hourly simulated and observed LE and Fl fluxes. Legend: CLM3.0: red asterisks; CLMgw: green erosses; CLMgw rsoil: eyan diamonds; CLM3.5: violet triangles. In CLM3.0 H is off-seale for the two tropieal sites 8 and 9 (and therefore not shown).

5 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025

Table 3. Performance of Simulated LE and H Fluxes in Four CLM Versions (3.0, gw, gw_rsoil, 3.5): R and RMSE (W m , in Brackets) are Diagnosed on Hourly and Monthly Timescales®

LEH Number Site 3.0 gw gw rsoil 3.5 3.0 gw gw rsoil 3.5 Hourly 1 Vielsakn 0.68 (43.0) 0.84 (57.1) 0.88 (46.1) 0.90 (35.9) 0.79 (67.0) 0.71 (67.1) 0.74 (63.9) 0.83 (54.2) 2 Tharandt 0.65 (44.3) 0.74 (51.4) 0.79 (37.2) 0.79 (35.2) 0.80 (75.3) 0.75 (75.3) 0.82 (60.8) 0.86 (55.4) 3 Castel Porziano 0.23 (89.7) 0.46 (86.8) 0.73 (66.5) 0.70 (61.6) 0.87 (102.1) 0.86 (93.3) 0.86 (78.4) 0.89 (68.8) 4 Collelongo 0.57 (70.7) 0.76 (87.2) 0.81 (67.7) 0.81 (55.3) 0.72 (91.2) 0.66 (109.0) 0.73 (97.7) 0.81 (83.0) 5 Kaamanen 0.71 (40.6) 0.81 (40.2) 0.86 (27.2) 0.88 (25.2) 0.76 (37.9) 0.70 (39.0) 0.72 (40.2) 0.74 (39.3) 6 Hyytiala 0.65 (39.6) 0.80 (32.5) 0.83 (28.7) 0.84 (28.3) 0.81 (59.1) 0.82 (50.9) 0.82 (52.5) 0.87 (47.6) 7 El Saler 0.42 (68.4) 0.53 (65.4) 0.66 (50.2) 0.67 (49.5) 0.89 (95.7) 0.88 (87.7) 0.89 (81.1) 0.91 (75.4) 8 Santarem KM83 0.52 (157.8) 0.74 (135.2) 0.77 (127.1) 0.76 (111.9) 0.59 (166.1) 0.41 (125.1) 0.39 (118.0) 0.68 (92.0) 9 Tapajos KM67 0.47 (147.2) 0.78 (132.6) 0.78 (131.0) 0.81 (100.2) 0.45 (146.7) 0.03 (120.7) -0.02 (120.5) 0.45 (83.1) 10 Morgan Monroe 0.55 (89.8) 0.66 (102.5) 0.78 (76.5) 0.85 (58.5) 0.56 (111.7) 0.53 (98.7) 0.57 (88.7) 0.73 (75.1) 11 Boreas OBS 0.41 (49.6) 0.53 (45.7) 0.69 (35.6) 0.79 (41.2) 0.84 (68.4) 0.86 (65.1) 0.85 (63.7) 0.83 (70.8) 12 Lethbridge 0.48 (53.7) 0.50 (56.4) 0.72 (40.1) 0.81 (32.5) 0.74 (82.1) 0.75 (81.5) 0.75 (78.7) 0.76 (74.3) 13 Fort Peck 0.67 (60.6) 0.74 (58.9) 0.80 (48.0) 0.79 (47.5) 0.71 (63.3) 0.66 (69.1) 0.60 (72.9) 0.71 (63.2) 14 Harvard Forest 0.67 (62.8) 0.78 (68.8) 0.86 (46.6) 0.89 (37.5) 0.59 (96.7) 0.51 (104.5) 0.67 (88.0) 0.79 (73.8) 15 Niwot Ridge 0.49 (66.1) 0.61 (63.0) 0.73 (46.4) 0.71 (47.6) 0.84 (96.7) 0.85 (85.9) 0.86 (78.9) 0.88 (72.5) Monthly 1 Vielsakn 0.73 (22.0) 0.91 (36.9) 0.96 (29.0) 0.96 (23.1) 0.85 (30.3) 0.81 (22.0) 0.85 (20.0) 0.88 (19.6) 2 Tharandt 0.83 (15.9) 0.88 (25.6) 0.93 (13.6) 0.93 (11.2) 0.88 (35.0) 0.84 (33.4) 0.89 (20.2) 0.88 (21.9) 3 Castel Porziano -0.21 (44.2) 0.05 (51.3) 0.79 (37.3) 0.81 (31.3) 0.97 (41.3) 0.96 (44.5) 0.96 (34.4) 0.97 (28.6) 4 Collelongo 0.61 (37.1) 0.88 (47.1) 0.92 (33.8) 0.92 (26.9) 0.76 (73.4) 0.71 (47.0) 0.73 (37.1) 0.79 (29.2) 5 Kaamanen 0.88 (16.2) 0.91 (18.6) 0.95 (11.4) 0.96 (10.4) 0.92 (14.4) 0.89 (13.2) 0.90 (17.0) 0.90 (17.9) 6 Hyytiala 0.89 (14.2) 0.94 (11.1) 0.97 (7.4) 0.97 (8.6) 0.88 (28.7) 0.92 (21.0) 0.92 (23.4) 0.91 (24.9) 7 El Saler 0.31 (29.3) 0.54 (22.4) 0.71 (18.3) 0.72 (18.4) 0.95 (47.1) 0.95 (36.6) 0.95 (34.5) 0.95 (36.1) 8 Santarem KM83 0.33 (85.5) 0.65 (65.0) 0.69 (57.9) 0.66 (55.1) 0.43 (72.9) 0.03 (71.7) 0.04 (71.7) 0.18 (44.1) 9 Tapajos KM67 0.03 (65.5) 0.65 (76.8) 0.69 (74.2) 0.68 (55.5) 0.36 (61.4) -0.23 (60.2) -0.29 (58.1) -0.22 (40.8) 10 Morgan Monroe 0.74 (41.7) 0.85 (53.4) 0.95 (33.1) 0.95 (27.8) 0.26 (48.6) 0.41 (37.7) 0.41 (37.7) 0.43 (25.2) 11 Boreas OBS 0.75 (18.0) 0.76 (17.1) 0.89 (12.0) 0.96 (20.8) 0.93 (18.5) 0.95 (18.7) 0.95 (16.7) 0.92 (33.9) 12 Lethbridge 0.77 (22.0) 0.71 (23.8) 0.83 (19.5) 0.92 (12.4) 0.90 (26.5) 0.91 (26.7) 0.91 (24.8) 0.92 (21.2) 13 Fort Peck 0.79 (32.4) 0.81 (29.4) 0.84 (27.1) 0.83 (28.5) 0.84 (22.5) 0.74 (31.5) 0.59 (35.7) 0.79 (27.0) 14 Harvard Forest 0.70 (28.1) 0.86 (23.7) 0.96 (11.5) 0.95 (14.5) 0.30 (39.8) 0.23 (46.3) 0.55 (33.9) 0.49 (32.4) 15 Niwot Ridge 0.50 (26.3) 0.72 (21.2) 0.90 (13.5) 0.88 (14.4) 0.72 (41.4) 0.80 (30.0) 0.84 (22.8) 0.82 (23.9) “Bold numbers show the best of the four model versions for each diagnostic and site.

near-infrared soil albedos were set to arbitrary values of 2.3.3. Analysis 0.18/0.36 for a dry top soil and 0.09/0.18 for a saturated top [30] Hourly model output from the last spin-up eyele was soil due to a laek of in-situ information at most sites. Stem used for the analysis. At sites where 30 min measiuements Area Index was set to 0.08. Vegetation top/bottom heights were available they were averaged to 60 min values. were 35 m/1 m for tropieal forests, 20m/10m for other forests, and 1 m/0.1 m for short vegetation. A elimatologieal 3. Results monthly Leaf Area Index for eaeh site eame from the 1982- 2001 EFAI-NDVI data set [Stockli and Vidale, 2004]. Sinee [31] Comparisons between modeled and observed LE and our intent was to perform a proeess-based analysis of a H in Figure 1 (Taylor Diagram) and Table 3 (R and RMSE) global model, PFT-dependent model parameters were not provide a quiek overview of performanee ehanges aeross tuned to site-speeifie and speeies-speeifie eonditions. model versions: the original CLM3.0; modifieations using a 2.3.2. Initial Conditions and Spin-Up groundwater seheme (CLMgw), addition of a bare soil [29] The model was initialized from its standard arbitrary evaporation resistanee (CLMgw rsoil) and further addition initial eonditions of 283 K vegetation, ground and soil of PFT-dependent nitrogen limitation faetor in the final temperatures, 30% (CLM3.0) 40% (CLMgw, CLMgw rsoil, model version (CLM3.5). (In the Taylor diagram [Taylor, CLM3.5) volumetrie soil water eontent and with empty 2001], four statistieal quantities are geometrieally ground snow and eanopy intereeption water stores. Spin-up eonneeted: the eorrelation eoeffieient R, standard deviation was aehieved by repeating the full range of available years of observations <7o, standard deviation of the model cr„, and five times for eaeh site (five spin-up eyeles). Mean yearly the eentered pattem root-mean-square error E'. The polar latent and sensible heat fluxes were within 0.1 W m~^ of axis displays R and the radial axes display the standard those from the previous spin-up eyele after a single spin-up deviation of the modeled variable divided by the standard eyele (similar to PILPS 2a spin-up eriteria [Chen et al., deviation of the observed variable cjJ cJo. The geomefrie 1997]). More arid elimates would need longer spin-up times relationship of this diagram is sueh that the distanee sinee the water table there takes longer to adjust (see, e.g., between the 1.0 value of the X-axis and the plotted value the global simulations by Oleson et al. [2008]). Neverthe­ show E' and thus is a measiue for the absolute model error. less, surfaee fluxes are not affeeted by variations of a very Root-mean-square error E is given by: E ^ E + E', whereE deep water table in sueh areas. is the mean bias. The foiu statistieal moments are eonneeted

6 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025 by: E'^ ^ + (To — la^cFaR ) General ehanges in LE and H deereased RMSE eompared to CLM3.0. For the two tropieal on the hourly and monthly timeseale eovering all sites are sites hourly R for H beeomes worse in CLMgw and diseussed first, followed by a elose inspeetion of results at CLMgw rsoil and inereases again with CLM3.5. Hourly individual sites eneompassing temperate, north boreal, and monthly RMSE for those sites signifieantly deereases mediterranean and the tropieal eeosystems. by around 35-45%. Only small ehanges in R and RMSE on both hourly and monthly timeseale ean be seen for the two 3.1. Latent Heat Flux grasslands Fort Peek and Lethbridge. They eannot make use [32] The R of LE shown in Figiue la inereases with the of the groundwater if the water table falls below their new groundwater seheme (CLMgw, green erosses) eom­ shallow rooting depth, whieh is most likely the ease at pared to the original CLM3.0 (red asterisks). However, the those two sites. variability of LE is now exaggerated eompared to observa­ 3.3. Temperate tions. The inelusion of the bare soil evaporation resistanee [34] Morgan Monroe State Forest is a deeiduous temper­ (CLMgw rsoil, eyan diamonds) generates more realistie LE ate broadleaf forest in Indiana (USA). Monthly mean variability, resulting in a higher R than with the groundwa­ ter formulation alone. A further improvement in both measured LE (Figure 2e; blaek) displays a elear seasonal eyele with a growing season between May and Oetober. eorrelation and variability is aehieved with the nitrogen limitation (CLM3.5, violet triangles). R for hoiuly LE at H (Figure 2d; blaek) peaks before leaf emergenee in Mareh and April [Schmid et al, 2003]. most sites inereases from around 0.4-0.7 (CLM3.0) and [35] CLM3.0 shows exeess LE in winter and too low LE 0.5-0.8 (CLMgw) to 0.7-0.9 (CLMgw_rsoil and in summer. This results in a too low modeled variability CLM3.5). For all 15 sites hoiuly and monthly LE has a of hourly LE and exaggerated variability of hourly H higher R and a lower RMSE for CLM3.5 eompared to (Figure 2b; red). The modeled root zone soil moisture is low CLM3.0. Some sites display substantial improvements: e.g., throughout the year eompared to observed soil moisture the mediterranean site Castelporziano (R inereases from (Figure 2a; blaek and red). The simulated soil moisture 0.23 to 0.70 for hourly LE and from —0.21 to 0.81 for profile (Figure 3a; CLM3.0) provides insight into the monthly LE) and the tropieal site KM67 (R for hourly LE inereases from 0.47 to 0.81; and for monthly LE from 0.03 proeesses responsible for these results: an impermeable and dry soil layer is formed after a few years of spin-up to 0.68). Similarly, temperate eeosystems show a steady improvement (e.g., Vielsalm or Morgan Monroe). High and inhibits further infiltration and water storage at deeper soil moisture levels. The main reason for this effeet is found latitude eeosystems (e.g., Kaamanen and Hyytiala) are in the delieate interplay between soil physies and the aheady well simulated by CLM3.0, but they also slightly numerieal solution of the vertieal soil water transfer. As improve. RMSE deereases at all sites (exeept for Vielsalm at diseussed in Stockli et al. [2007], the exponential relation­ the monthly timeseale) from CLM3.0 to CLM3.5 on both ship between soil hydraulie eonduetivity and soil water hourly and monthly timeseale. The grassland sites Leth­ eontent in a finite differenee numerieal solution of Darey’s bridge and Fort Peek improve on both hourly and monthly equation ean ereate a feedbaek below eertain soil water timeseale, but to a lesser extent than forest sites. levels whieh sueeessively deeouples upper from lower soil 3.2. Sensible Heat Flnx layers through further inhibition of infiltration. We ean see [33] The ehanges in H, shown in Figure lb, are not as in Figure 3a that this “vieious loop” eannot be broken even easily generalized as LE, although the model ehanges by long preeipitation events during spring. appear to result in an overall improvement. Even though [36] It was ehosen to improve the physieal and biophys­ ehanges in LE are almost fully eompensated by opposite ieal proeesses in order to support a stable numerieal solution ehanges in H, the new hydrology formulations do not affeet of soil water dynamies as doenmented in Oleson et al. R of H as mueh. This is due to a number of faetors. First of [2007]: a Topmodel-based surfaee and subsurfaee runoff all, H is smaller than LE for most sites. Seeondly, while LE seheme [Niu et al, 2005] eoupled to a prognostie ground­ ean eompletely be shut down by soil moisture, H is strongly water seheme [Niu et al, 2007] are meehanistie formula­ eoupled to net shortwave radiation through skin tempera­ tions of soil water dynamies whieh were not in the original ture, largely independent of the state of subsurfaee hydrol­ CLM3.0. The new groundwater seheme (Figure 3b; ogy [Betts, 2004]. R is a good indieator for phase but not for CLMgw) inereases soil moisture to a range where the magnitude in this ease. For sites like Castelporziano, where numerieal solution provides a more stable interaetion the R of LE inereased substantially, R of H remains eonstant between infiltration and seasonal water storage: there are (R inereases from 0.87 to 0.89; hourly timeseale). But no more dry impermeable soil layers. Seasonal LE and H RMSE of H deereases from 102.1 W m~^ to 68.8 W m~^. fluxes in CLMgw have a more realistie variability (Figure 2b; Remaining high RMSE values should also be viewed with green erosses) and R for hourly LE rises from 0.55 in respeet to uneertainties in observed fluxes (Table 2). This CLM3.0 to 0.66 in CLMgw. It stays nearly eonstant for result suggests that the mean error and variability of H was H (0.56 to 0.53). Although soil moisture in the upper 30 em improved with the new hydrology, and not the timing and does not signifieantly inerease (Figure 2a; green line), the phase of H. Indeed, in Figure lb R values remain roughly new seheme has inereased summer LE (Figure 2f; red and the same for all four model versions. But the spread in the green lines) due to a higher soil moisture availability in radial direetion deereases and sueeessively moves symbols lower depths (Figure 3b; CLMgw). But it also has inereased eloser towards observed variability at the 1.0 are by use of off-season LE for the same reason (Figure 2e; red and green the new formulations. Several sites aetually show a slightly lines). worse R with the new hydrology, but they still have a

7 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025

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00 06 12 18 24 00 06 12 18 24 FEB 2003 AUG 2003 Figure 2. Model diagnostics at a temperate deeiduous forest (Morgan Monroe State Forest, USA) during 2003: (a) soil moisture relative to saturation at 30 em depth; (b) Taylor diagram with hoiuly statisties of LE and H fluxes; (e) monthly LE fluxes; (d) monthly H fluxes; (e) diurnal eyele of LE fluxes in February; (f) diurnal eyele of LE fluxes in August. Error bars show estimated uneertainties of observed turbulent fluxes. Legend: observations: blaek plus signs; CLM3.0: red asterisks; CLMgw: green erosses; CLMgw rsoil: eyan diamonds; CLM3.5: violet biangles.

8 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025

a) 1 ' ** ^ i| I •> V ^|| -0.5

- 1.0

■e -1.5

- 2.0

-2.5

-3.0

0 .4.2

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 2003

Figure 3. Simulated soil moisture profiles at a temperate deeiduous forest (Morgan Monroe State Forest, USA) during 2003. Model versions: (a) CLM3.0; (b) CLMgw; (e) CLMgw rsoil.

[37 ] The implementation of a more realistie soil water eonditions. Addition of the empirieally derived bare soil treatment reveals a defieieney of the model: given enough resistanee [Sellers et al, 1992] offers a eonstraint for bare soil water and low leaf-eoverage during off-season periods, soil evaporation fluxes during periods of low leaf eoverage CLMgw simulates exeessive bare soil evaporation eom­ in deeiduous forests. pared to observations. The same problem was present in [38] It leads to a signifieant improvement of the simulated CLM3.0 but it was mostly hidden by the generally dry soil terrestrial water eyele in CLMgw rsoil (Figure 3e): the water

9 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025 table rises above the bottom of the soil water eolumn and analyzed. The 2004 snowmelt at Kaamanen serves as an now displays dynamies within the biophysieally aetive root example for the hydrologieal impaets of the new formula­ zone. The root zone soil moisture is more eomparable to tions in the simulations CLMgw and CLMgw rsoil and observed values (Figure 2a; eyan line). Coneurrently hourly CLM3.5. and seasonal H and LE fluxes show a more realistie [42] Modeled snowmelt at the end of April 2004 releases seasonal variability (Figirres 2b-2f; eyan diamond sym­ 250 mm of water (not shown) whieh ean be distributed bols/lines). Aquifer water storage provides a meehanism for between the different terrestrial water storage (TWS) eom- making winter and spring preeipitation available as soil ponents: surfaee runoff, drainage runoff, soil water and moisture during summer in order to sustain transpiration evapotranspiration. Figure 4d summarizes the TWS as a (Figirre 2f, eyan line). R for hoiuly LE rises from 0.66 in funetion of storage ehange over time. A rise in TWS CLMgw to 0.78 CLMgw rsoil and for Fl it rises from 0.53 indieates water fluxes into the eeosystem (e.g., snow aeeu- to 0.57 (Table 3). A similar improvement ean be seen on the mulation, soil water storage), negative direetions are water monthly timeseale, where R of LE rises from 0.85 to 0.95 losses from the eeosystem (transpiration, runoff). CLM3.0 and RMSE is eut by around 30%. The remaining range of loses around 250 mm of water at the time of snowmelt, RMSE values on the order of 20-40 W m~^ is eomparable eoneurrently with the inerease in aeeumulated total runoff to stoehastie observation uneertainties (Table 2). Those (Figure 4f). Both CLMgw and CLMgw rsoil simulate illustrated ehanges in soil hydrology have a very similar aeeumulated snowmelt runoff on the order of 100-150 mm, impaet on surfaee energy partitioning at other temperate mueh less than CLM3.0. The rest of the snowmelt water is forests like Vielsalm, Tharandt and Flarvard Forest (not kept in the eeosystem due to enhaneed infiltration, the shown). implementation of a fraetional impermeable area and water [39] The site-observed inerease in Fl during Mareh and storage physies of the new formulation. The soil resistanee April before leaf emergenee eannot be reprodueed by the parameterization CLMgw rsoil on top of CLMgw does model. It still simulates exeessive LE during this time not further affeet TWS, and R values are mostly unehanged period, mostly from bare soil evaporation (not shown). on both hourly and monthly timeseale (Table 3). Exeessive The implementation of nitrogen limitation in the final model off-season soil evaporation is not a problem here, mainly version CLM3.5 has a small effeet on soil moisture and beeause of snow eoverage, frozen soils and a low atmo­ mostly affeets energy partitioning during summer. It leads to spherie evaporative demand during these periods. A more higher eorrelations of LE and Fl with observed values stable ealeulation of the water table depth in CLM3.5 [see (Figure 2b), with hourly R for LE and Fl inereasing to Oleson et al., 2007, Appendix C] further ereates a more gradual 0.85 and 0.73, respeetively. Compared to CLMgw rsoil response of drainage runoff after snowmelt (Figure 4f; monthly R values do not improve but RMSE values for violet line). Surfaee runoff (Figure 4e) ehanges from LE and Fl deerease by another 20-30%. CLM3.0 to CLMgw as a result of the enhaneed infiltration formulation. While tower-seale runoff measurements are not 3.4. North Boreal available, Oleson et al. [2008] demonstrate that, indeed, the [40] North boreal wetlands like the Kaamanen tundra site new model signifieantly improves aretie and boreal snow­ in northern Finland are eharaeterized by a generally low melt runoff magnitude and phase. evaporative demand, a short growing season and a hydro­ logieal eyele whieh is dominated by snow and frozen soil 3.5. Mediterranean physieal proeesses {Laurila et al., 2001]. [43] The summer-dry mediterranean elimate at Castelpor­ [41] Surfaee heat and water fluxes in this elimatie envi­ ziano (Italy) provides an important exereise for the new soil ronment are less sensitive to the hydrologieal defieieneies in hydrology. As shown in detail for the temperate elimate CLM3.0 (Figures 4a and 4b). Soil temperatures do not differ zone, CLM3.0 has severe shorteomings in simulating sea­ mueh between model versions (Figure 4e) and the annual sonal soil water storage. In Castelporziano summer drought eyele of soil temperature eompares well to observed values generally lasts from June until Oetober. Most of the yearly whieh indieates that the snow eover duration and snow preeipitation falls during winter and spring. Summer 2003 in thermal properties are reasonably simulated at Kaamanen: Europe was exeeptionally dry and hot \Schdr et al., 2004] 30 em soil temperature remains roughly at freezing from with redueed evapotranspiration (resulting in a higher bowen January-April in 2004; during this period snow eoverage ratio) and higher surfaee temperatures throughout the eonti- and soil freezing proeesses are thermally deeoupling the soil nent {Zaitchik et al., 2006]. Castelporziano showed 47% less from the atmosphere. Flowever, soil temperatures tend to be GPP and 3.5K higher air temperatures from Jul-Sep 2003 too warm during summer and too eold during the fall. This eompared to 2002 [Ciais et al., 2005]. temperature bias may be due to the laek of an insulating [44] Figure 5b summarizes the performanee of simulated organie soil layer in CLM. Lawrence and Slater [2008] LE and Fl at Castelporziano for the four model versions suggest a method in whieh soil organie matter ean be treated during the heat wave in 2003. During 2003 CLM3.0’s R for in CLM, whieh generally results in somewhat eooler sum­ hourly LE is quite low at 0.22, but inereases to 0.37 for mer soil temperatures. Flourly LE and Fl of CLM3.0 show R CLMgw, 0.77 for CLMgw_rsoil and 0.75 for CLM3.5 values of 0.71 and 0.76, respeetively (Table 3 and Figures (Table 3 shows statisties for the frill time period 2000- 4a and 4b), and only slightly improve/worsen to 0.88 and 2005). R values for hourly and monthly H remain high for 0.74 in CLM3.5. Even though turbulent surfaee fluxes are all model versions. Figure 5b shows that the new surfaee insensitive to improvements in eold elimate soil hydrology, hydrology improves R for hourly LE and ereates a more ehanges in the whole terrestrial water eyele will now be realistie variability for H. The surfaee energy balanee at

10 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025

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Figure 4. Model diagnostics at a north boreal tundra eeosystem (Kaamanen, Finland) during 2004: (a) monthly LE fluxes; (b) monthly H fluxes; (e) soil temperature at 30 em; (d) terrestrial water storage; (e) aeeumulated sirrfaee runoff; (f) aeeumulated total runoff. Error bars show estimated uneertainties of observed turbulent fluxes. Legend: observations: blaek plus signs; CLM3.0: red asterisks; CLMgw: green erosses; CLMgw rsoil: eyan diamonds; CLM3.5: violet biangles.

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Figure 5. Model diagnostics at a mediterranean hardwood forest (Castel Porziano, Italy) for 2003: (a) soil moisture at 30 em depth; (b) Taylor diagram showing statisties from hourly LE and H fluxes; (e) monthly LE fluxes; (d) monthly H fluxes; (e) terrestrial water storage; (f) modeled versus NEE- derived GPP. Error bars show estimated uneertainties of observed turbulent fluxes. Legend: observations: blaek plus signs; CLM3.0: red asterisks; CLMgw: green erosses; CLMgw rsoil: eyan diamonds; CLM3.5: violet triangles.

12 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025

Castelporziano is dominated by H. In eomparison to a more [49] Figures 6e and 6d show that aeeumulated LE and H humid eeosystem, improving LE at a dry eeosystem only fluxes are simulated aeenrately during the wet season with has a small effeet on the dinmal eourse of H. However, as CLM3.0, but the observed eontinuous inerease in aeeumu­ ean be seen in Figure 5b, a better simulation of LE ean shift lated water flux throughout the dry season from August to the absolute magnitude and therefore seasonal variability of Deeember eannot be sustained, resulting in a very high H towards observed values. bowen ratio during this latter period. CLMgw, CLMgw rsoil [45] CLM3.0 simulates a low magnitude and damped and CLM3.5 provide remedy for this defieieney: R for seasonal eourse of soil moisture eompared to observed soil hourly LE steadily inereases from 0.52 to 0.76 (Table 3). moisture at 30 em depth (Figiue 5a). It eoineides with a low R for hourly H deereases from 0.59 to 0.41 for CLMgw and simulated TWS magnitude (the range between the minimum inereases again to 0.68 for CLM3.5. The generally low H at and the maximum TWS during a year) of around 60 mm this site (within the nneertainty range of observations) (Figure 5e). Coneurrently LE is almost eompletely absent in renders the eorrelation eoeffieient as an unsuitable measure the summer months from May to August (Figure 5e), result­ for performanee eomparisons (this is even more evident at ing in a too high H during this time period (Figure 5d). This the other tropieal site KM67). RMSE is a more robust result explains the exaggerated variability and low eorrela­ measure. On the hourly timeseale it deereases signifieantly tion of CLM3.0 and CLMgw displayed in Figure 5b. While from 166.1 W m “^ to 92.0 W m“^. the addition of groundwater storage (CLMgw) rises TWS [50] Little differenee is found between the aquifer water magnitude to 120 mm, it eannot overeome the unrealistie storage formulation only (CLMgw) and the use of an drought stress during summer months. The bare soil resis­ additional bare soil evaporation resistanee formulation tanee eonstrains off-season evaporation losses (Figure 5e; (CLMgw rsoil). For instanee, R for LE rises from 0.74 to CLMgw rsoil) and augments TWS magnitude to over 0.77 on the hourly timeseale. Constant and high leaf 300 mm. The soil water storage eapability of the ground­ eoverage at this site provides a radiation-driven proeess water seheme beeomes effeetive when the soil model’s for the eontrol of exeessive bare soil evaporation, so the numeries and physies show a more stable interaetion. addition of the missing resistanee term is not eritieal for this [46] The new hydrology of CLMgw rsoil is able to evergreen tropieal eeosystem. supply the extensive water demand at this eeosystem during [51] A eomparison between modeled and measured soil the dry summer 2003. A storage defieit of around 100 mm moisture at 20 em depth in Figure 6a does not provide mueh persists into the next year (Figure 5e). Although the off­ evidenee for why dry season LE is enhaneed in season observed soil moisture levels eorrespond well to CLMgw rsoil eompared to CLM3.0; most of the model those modeled in CLMgw rsoil, the model’s soil at 30 em enhaneements seem to influenee lower soil depths. There still dries out too mueh during summer. Deeper soil levels were no soil moisture measurements reported for depths aet as the large TWS buffer in this ease. Reichstein et al. below 1 m. The modeled TWS eyele in Figure 6b provides [2003] notes that the site’s vegetation has aeeess to insight into the relevant hydrologieal proeesses: while topographieally indueed groundwater (lateral groundwater CLM3.0 has a very low TWS magnitude of less than reeharge), whieh was not simulated here. 100 mm, CLMgw and CLMgw rsoil push TWS magnitude [47 ] Similarly to LE, modeled GPP (Figure 5f) beeomes to 400 mm. Seasonal soil water storage with sueh a high more realistie from CLM3.0 to CLMgw rsoil during sum­ eapaeity is important for a tropieal eeosystem sinee plant mer. But GPP and LE are now overestimated during other biophysieal frinetioning in a seasonally dry elimate depends parts of the year. The new sun-shade eanopy seheme on long-term soil moisture dynamies. This is supported by implemented by Thornton and Zimmermann [2007] has a observational evidenee: da Rocha et al. [2004] show that more realistie light intereeption parameterization for eanopy- the Amazonian rainforest at KM83 ean sustain transpiration integrated photosynthesis but depends on the quantifieation throughout the dry season sinee it has aeeess to deep soil of nitrogen as a eonfrolling faetor for this proeess. The water. standard model does not inelude nitrogen eontrols on pho­ [5 2 ] As already shown for the mediterranean site, tosynthesis. After soil hydrology is fixed in CLMgw rsoil CLMgw rsoil with the more realistie soil water eyele leads we now find that GPP is overestimated. PFT-dependent Umax to overestimated LE and GPR Ineluding the paramefrie sealing faeto rs/(^ simulating nitrogen limitation are pre­ nitrogen limitation f{N) in CLM3.5 results in a more sented in Oleson et al. [2007] and applied in CLM3.5. As a realistie LE and H balanee for both wet and dry season result of the deereased light response (Figure 5f, violet (Figures 6e and 6d). R for hourly LE remains roughly triangles), GPP and LE slightly deerease during spring and eonstant (0.76, eompared to 0.77 for CLMgw rsoil), but autumn. However, this newly introdneed formulation alone RMSE is redueed by around 15 W m~^. On the other hand, eannot aeeonnt for the exaggerated fluxes. GPP (and to a R for hourly H signifieantly inereases from 0.39 to 0.68 and lesser extent also LE) is still highly overestimated during RMSE is redueed by 26 W m~^. GPP is still overestimated the wet season. during the wet season. However, a slightly more realistie light response of GPP (Figures 6e and 6f) is aehieved. In a 3.6. Tropical high light environment sueh as the Amazon, stomatal [48] The evergreen tropieal broadleaf forest site KM83 eonduetanee during daylight is mostly eonstrained by the south of Santarem (Brazil) represents a eonstant hot and maximum rate of earboxylation. The faetor J{N) has the humid elimate \da Rocha et al., 2004]. 70% of the annual largest absolute effeets on GPP for these eeosystems. LE preeipitation oeeur within the seven month long wet season (and thus the surfaee energy partitioning) is influeneed to a from January to July. lesser extent, as LE is also eontrolled by the boundary layer

13 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025

300

E E.

S 200 I Q_Jl P CO"O w 100 o 0.4 ■o0 OBS 3.0 o c X X X gw gw_rsoil AAA 3.5 -100 F M A J J A S O J FMAMJ JASOND 2002 2002

6 * 10* 6 * 10’

4*10’

2* 10’

JFMAMJJASOND JFMAMJJASOND 2002 2002

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I t

10

0.

(0 1 5 ;.X Ai £ ^. ,++¥++++***+* w<0 O2 ;+*+

0 JFMAMJJASOND 0 200 400 600 800 2002 OBS: Solar In (W/m2)

Figure 6. Model diagnostics at a tropical evergreen forest (Santarem KM83, Brazil) during 2002: (a) soil moisture at 20 em depth; (b) terrestrial water storage; (e) aeeumulated LE fluxes; (d) aeeumulated H fluxes; (e) modeled versus NEE-derived GPP; (f) mean light response curves for modeled and NEE- derived GPP (binned by ineoming solar radiation). Error bars show estimated uneertainties of observed turbulent fluxes. Legend: observations: blaek plus signs; CLM3.0: red asterisks; CLMgw: green erosses; CLMgw rsoil: eyan diamonds; CLM3.5: violet biangles.

14 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025 vapor pressure gradient, bare soil evaporation and aerody- rainfall (dry season) or large atmospherie demands (summer namieal properties. season) substantially improve. [59] While a prognostie aquifer model \Niu et al., 2005, 4. Discussion 2007] provides the physieal framework for simulating large seasonal TWS fluetuations, the size of TWS magnitude [5 3 ] Turbulent heat and water fluxes of the original depends on a dynamieally varying set of involved soil and CLM3.0 show signifieant biases in tropieal, mediterranean vegetation proeesses. The new hydrologieal formulations and temperate elimatie environments. These biases result enhanee TWS by 200-300 mm eompared to the original from a poor representation of soil moisture storage and its CLM3.0, with quite benefieial effeets for the simulated interaetion with seasonal variations of the surfaee elimate. surfaee energy and water balanees in seasonally dry eli­ Modeled plant transpiration generally shuts down during mates. This result is highly eonsistent with eomparisons either summer or dry seasons due to a laek of soil moisture between modeled and GRACE estimates of TWS at eateh­ supply. Observations from the 15 flux tower sites, however, ment seale presented by Oleson et al. [2008]. They show indieate that plants ean sustain their physiologieal funetion that CLM3.5 enhanees TWS magnitude by 50-300 mm during seasonal-seale and longer term drought periods. eompared to CLM3.0, with improved eorrelations and Subsurfaee hydrologieal proeesses on whieh these plants substantial deereases in RMSE. largely depend therefore need to be properly represented in [60 ] In northem boreal regions like Kaamanen, however, land surfaee models in order to simulate the terrestrial TWS magnitude deereases by around 100 mm when earbon and water eyele {Reichstein et al., 2002]. This groundwater storage is added (Figure 4e). This behavior is requirement gains further importanee in view of the pre­ opposite to what one would expeet. As in warm elimates, dieted temperature and preeipitation ehanges in future eli­ soil water storage funetion of eold elimates not only mate seenarios, whieh eould severely affeet eeosystem depends on storage eapaeity, but elosely interaets with the funetion during hotter and drier summer periods [Seneviratne dominant hydrologieal proeesses through time-delayed et al., 2006]. feedbaeks: the analysis shows that snowmelt water ean be 4.1. Terrestrial Water Storage stored in spring after soil thaw and should not eompletely run off into rivers like in the original formulation. Soil [54] To aehieve a higher water storage eapaeity in a land moisture storage seems to dampen the seasonal eourse of surfaee model, the total soil depth and other soil parameters TWS at Kaamanen. While adding groundwater does not are often modified as a first guess. The above findings, mueh affeet turbulent surfaee fluxes in eold elimates however, suggest that soil water storage eapaeity is a (Figures 4a and 4b) it eould lead to improvements in high dynamie quantity. It does not primarily depend on soil latitude runoff timing and magnitude (Figures 4e and 4f). physieal parameters. It rather results from a eonsistent This is doenmented in Oleson et al. [2008] by eomparison interplay between the soil and vegetation biophysieal of global simulated versus observed river diseharge and parameterizations on one side and the soil numerieal runoff. seheme on the other side: they both depend on eaeh other in order to provide a realistie simulation of the terrestrial 4.2. Nitrogen Limitation water eyele. [61 ] Results from the mediterranean and tropieal sites [55] At the mediterranean and temperate sites only small suggest that the enhaneed and more realistie water storage improvements in surfaee fluxes result from the implemen­ proeesses in the model ean lead to exeessive transpiration. tation of larger soil water storage eapaeity by use of a The addition of a parameterized nitrogen eontrol for pho­ prognostie aquifer seheme. Soil water infiltration and stor­ tosynthesis deereases light sensitivity of stomatal opening age are both still largely inhibited by exeessive bare soil as expeeted (Figures 5f, 6e, and 6f). The need for this evaporation during off-season periods in those eeosystems. parameterization only beeame evident after the new soil Further addition of a bare soil evaporation resistanee finally hydrology and the new eanopy integration seheme was results in a realistie TWS magnitude and eoneurrently in a implemented: maximum photosynthesis rates in CLM3.0 substantial inerease of turbulent flux R and deerease in were fixed, based on observed values. Low soil moisture RMSE at most sites. Figures 3a-3e illustrate the underlying levels furthermore limited the plant physiologieal aetivity in soil hydrologieal proeesses: most elimates. Parameterized nitrogen eontrol beeame a [56] 1. A dry soil ean eontinuously inhibit vertieal soil neeessity with the new hydrologieal modifieations. While moisture fluxes and thus deerease seasonal water storage by nitrogen is an important eonfrolling faetor for most terres­ hydrologieally deeoupling upper from lower soil layers. trial eeosystems {f{N) ranging from 0.60-0.84 in Oleson et [57 ] 2. Extending the storage pool by implementing a al. [2007]), our results suggest that it mostly affeets the prognostie aquifer breaks the infiltration barrier by provid­ surfaee energy and water balanee in environments with high ing ample soil moisture to the root zone but TWS remains at GPP. Tropieal broadleaf forests have the lowest diagnosed a low seasonal magnitude (e.g.. Figure 5e). nitrogen limitations among the 16 PFTs (highest f{N) = [58] 3. Bare soil evaporation during off-season periods 0.84). Flowever, they mostly operate at high light levels, was identified as the main proeess whieh dampens TWS resulting in the largest nifrogen-eonfrolled deereases in GPP magnitude for deeiduous vegetation in temperate and med­ in absolute terms. In eomparison to GPP, LE is less sensitive iterranean elimate zones. With a more realistie off-season to ehanges in stomatal eonduetanee through nitrogen eontrol bare soil evaporation TWS beeomes positive during the beeause LE is a eomposite of transpiration and bare soil winter or wet season when moisture is stored in the soil. As evaporation. The latter is independent of nitrogen availabil­ a eonsequenee transpiration fluxes during months of low ity. LE is further eontrolled by boundary layer aerodynamieal

15 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025 resistances, whieh only indirectly and weakly influenee eapaeity: TWS magnitude inereases in tropical, mediterra­ GPP. Nevertheless, results show a positive effeet of the nean and temperate elimates and deereases in eold elimates. newly introduced f{N) on R and RMSE of hourly and This result was mainly aehieved by introduction of meeh­ monthly LE and H fluxes. The two tropieal sites KM67 anistie hydrologieal proeesses and neither by extending the and KM83 show the largest deerease in RMSE (Table 3) by soil depth nor by modifying soil hydraulie parameters. In ineluding/(A^ eompared to simulations with ehanges in soil support of this eonelusion [Gulden et al, 2007] find that a hydrology alone. Boundary layer proeesses for these eeo­ model with a prognostie aquifer is less sensitive to the systems are expeeted to benefit from this model enhance­ largely unknown and spatially variable set of soil hydraulie ment in eoupled simulations. parameters eompared to a model with a deep soil alone. The nneertainty in the prescription of soil physieal parameters in 4.3. Open Questions land surfaee models should therefore be mitigated by use of [62 ] Figiues 5f and 6f show that yet another proeess more meehanistie formulations for soil water storage. Fur­ might be missing. LE and GPP are still overestimated thermore this result justifies and faeilitates eomparisons during the wet season at the mediterranean and the tropieal between tower sites with similar vegetation but different site. LE is less of a problem than GPP sinee LE is also soils. driven by the atmospherie vapor pressiue gradient and [66] 3. Nitrogen eontrol of photosynthesis (and therefore siufaee layer aerodynamics. It furthermore is composited stomatal opening and transpiration) is needed in order to from plant transpiration and bare soil evaporation, the latter eorreetly partition energy into turbulent heat and water being unrelated to stomatal funetioning. Suggestions for fluxes in environments with high GPR This missing proeess missing proeesses are, e.g., a prognostie dry season phe­ was only uncovered after soil hydrologieal modifieations nology (whieh ean also vary in tropieal eeosystems [Myneni led to a better simulated subsurfaee water balanee and the et al., 2007]) and dynamie allocation of leaf strueture and new eanopy integration seheme ereated a more realistie light photosynthates [Dickinson et al., 2002], whieh are not response of photosynthesis. The original CLM3.0 was simulated in the standard CLM. Simulations for mediterra­ providing the right results for the wrong reasons: stomates nean and tropieal FLUXNET sites employing CLM3.5 with in tropical and mediterranean eeosystems were seasonally its full biogeoehemistry seheme [Thornton et al, 2007] closing due to missing water supply while observations eould shed some light into these open questions. Figures 2e indieate that photosynthesis in those eeosystems is not so and 2d show that CLM3.5 still eannot represent the Fl peak sensitive to drought effeets. diuing Mareh and April just before leaf emergenee in [67 ] 4. Despite above improvements CLM3.5 still over­ temperate forests. This problem is common to many land estimates GPP during the wet season in mediterranean and siufaee models and might be related to model defieieneies tropical eeosystems. Although the surfaee energy partition­ in either phenology (too early leaf emergenee) or surfaee ing is less sensitive to stomatal response than GPP, we litter eover (too mueh bare soil evaporation) and should be hypothesize that drought phenology or biogeoehemieal addressed in future studies. feedbaeks involving the full terrestrial earbon-nitrogen eyele eould be responsible for these differences. Loeal-seale 5. Conclusion and speeies-speeifie soil and vegetation properties and furthermore the general underestimation of eddy eovarianee [63 ] The Community Land Model version 3 ineludes fluxes might explain some differences between observed meehanistie representations of terrestrial radiation, heat, and modeled turbulent fluxes [Wilson et al, 2002; Foken et water and earbon exehange proeesses, whieh have been al, 2006]. The steady reduction of RMSE into the range of developed from laboratory experiments and field studies. observation nneertainty (or below; e.g., for monthly fluxes Defieieneies in the CLM3.0 soil hydrology have been at Kaamanen: RMSE = 10-20 W m~^) in boreal, northem revealed from long-term elimate simulations, with some­ boreal and temperate elimates as a result of the new times negative effeets on surfaee elimate and plant bioge­ meehanistie formulations is a strong indieator for the ography. In this study new algorithms for removing these success of CLM’s new hydrology. In seasonally dry and defieieneies were tested in off-line simulations at 15 FLUX­ tropical elimates most uncertainty may still be on the NET tower sites. model’s side, sinee monthly RMSE ranges between 30- [64 ] 1. The prognostie aquifer seheme [Niu et al., 2007] 50 W m~^), whieh is larger than estimated errors in extends the soil storage pool of CLM3.0, but this enhance­ observations. ment only beeomes effeetive when bare soil evaporation is [68] 5. A land surfaee model should as a first step inelude curtailed by the application of an empirieal bare soil a realistie set of meehanistie formulations, whieh was the resistanee term [Sellers et al., 1992]. Soil water storage in focus of this study. This leads to a better understanding of models like CLM strongly depends on the interplay the role of eeophysiologieal drivers sueh as water, light and between soil numeries (nonlinear state-parameter depen­ nitrogen in eonfrolling photosynthesis at a range of eeosys­ denee) and terrestrial biophysics. In this ease exeessive tems, and it thus helps to either support or invalidate some off-season bare soil evaporation in deeiduous eeosystems of our above hypotheses. It further makes the model suitable inhibited groundwater storage by sueeessively reducing for global predictive applieations aeross a range of spatial long term soil moisture levels below a threshold at whieh and timeseales. As noted by Abramowitz [2005], there are, hydraulie eonduetivity allows for vertieal water transfer in however, still considerable opportunities for improvements the finite differenee soil water seheme. in sueh models. In a second step, the many empirieal model [65 ] 2. As a eonsequenee of these two enhaneements, parameters should be eonstrained in order to further reduee CLM3.5 now ineludes a more dynamie soil water storage model uneertainty. The eurrently developed standardized.

16 of 19 G01025 STOCKLI ET AL.: USE OF FLUXNET IN THE CLM DEVELOPMENT G01025 gap-filled and bias-corrected FLUXNET Synthesis data set References involving more than 200 tower sites (D. Papale and Abramowitz, G. (2005), Towards a benchmark for land surface models, M. Reichstein, personal eommunieation, 2007) provides a Geophys. Res. Lett, 32, E22702, doi:I0.I029/2005GE0244I9. global set of observations suitable for a data assimilation Aubinet, M., B. Chermanne, M. Vandenhaute, B. Longdoz, M. Yemaux, and E. Laitat (2001), Long term carbon dioxide exchange above a mixed exereise aimed at reducing parameter uneertainty in land forest in the Belgian ardennes, Agric. For. Meteorol., 108{A), 293-315. surfaee models. Baldocchi, D., et al. (2001), FLUXNET: A new tool to study the temporal [69] While at first the small number of 15 FLUXNET and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Sac., 52(11), 2415-2433. towers seems to be inappropriate for testing a globally Betts, A. K. (2004), Understanding hydrometeorology using global models. applicable land siufaee model, we demonstrate that focusing Bull. Am. Meteorol. Sac., 85, I673-I688. on only four sites aheady effectively helps to identify and Betts, A. K., R. L. Desjardins, and D. Worth (2007), Impact of agriculture, correct for major missing soil hydrologieal and vegetation forest and cloud feedback on the surface energy budget in boreas, Agric. For. M eteorol, 142(2-4), 156-169. biophysieal proeesses in the model. As aheady shown by Betts, R. A., P. M. Cox, and F. I. Woodward (2000), Simulated responses of Stockli and Vidale [2005], sueh a modeling framework with potential vegetation to doubIed-C02 climate change and feedbacks on offline simulations allows for computationally inexpensive near-surface temperature. Global Ecol. Biogeogr, 9, 171-180. Beven, K. J., and M. J. Kirkby (1979), A physically based, variable con­ researeh and development of land surfaee models. FLUX­ tributing area model of basin hydrology. Hydro! Set Bull, 24(1), 43 -6 9 . NET provides valuable observations of quantities at time­ Bogena, H., K. Schulz, and H. Vereecken (2006), Towards a network of seales whieh are relevant in elimate simulations. Despite observatories in terrestrial environmental research, G eoscl, 9, 109- 114. lacking global eoverage, FLUXNET statistically inherits the Bonan, G. B., and S. Levis (2006), Evaluating aspects of the community land whole global set of eeosystems and elimate zones. Although and atmosphere models (CLM3 and CAM3) using a Dynamic Global individual sites differ in absolute magnitude and timing of Vegetation model, J. Glim., 79(11), 2290-2301. heat, water and earbon fluxes, they show similar patterns for Canadell, J. G., et al. (2000), Carbon metabolism of the terrestrial bio­ sphere: A multitechnique approach for improved understanding. Ecosys­ sites within eertain eeosystem and elimate zones. Similarly, tems, 3, I I 5 - I3 0 . model defieieneies become visible as eonsistent patterns of Chen, J., and P. Kumar (2001), Topographic influence on the seasonal and time and phase shifts on diurnal and seasonal timeseales interannual variation of water and energy balance of basins in North America,/. Glim., 14(9), I989-20I4. aeross a number of sites, whieh was demonshated here. Chen, T. H., et al. (1997), Cabauw experimental results from the project for While this study explored hourly-seasonal terreshial pro­ intercomparison of land-surface parameterization schemes, J. Glim., 10, eesses, there is an inereased number of FLUXNET sites I I 9 4 - I2 I 5 . Ciais, P., et al. (2005), Europe-wide reduction in primary productivity with 10 years or longer eoverage whieh allow a similar caused by the heat and drought in 2003, Nature, 437, 529-533. analysis for the interannual timeseale. Collins, W. D., et al. (2006), The Community Climate System Model ver­ [?o] Oleson et al. [2008] further shows that, indeed, those sion 3 (ccsm3), J. Glim., 19(11), 2I22-2I43. identified and eorreeted proeesses at loeal seale are appli­ Cox, P. M., R. A. Betts, C. D. Jones, S. A. Spall, and I. J. Totterdell (2000), Acceleration of global warming due to carbon-cycle feedbacks in a cable to the global seale and lead to improvements in the coupled climate model. Nature, 408, 184-187. simulation of the terrestrial water eyele. This might be a step da Rocha, H. R., M. E. Goulden, S. D. Miller, M. C. Menton, E. D. V. O. towards an answer in the debate on land-atmosphere eou­ Pinto, H. C. de Freitas, and A. M. E. S. Figueira (2004), Seasonality of water and heat fluxes over a tropical forest in eastern amazonia, Ecol. pling strength \Koster et al., 2006; Guo et al., 2006]. Appl, 14(4), 2 2 -3 2 . Dirmeyer et al. [2006] find that turbulent surfaee fluxes Desai, A. R., P. V. Bolstad, B. D. Cook, K. J. Davis, and E. V. Carey (2005), and planetary boundary layer proeesses still respond very Comparing net ecosystem exchange of carbon dioxide between an old- growth and mature forest in the upper Midwest, USA, Agric. For. differently to soil moisture states among models. Flowever, Meteorol, 128(1-2), 3 3 -5 5 . models should be able to reproduce the basic relationships Dickinson, R. E., et al. (2002), Nitrogen controls on climate model evapo­ in land-atmosphere interaetions found in observational- transpiration, J. Glim., 15, 278-295. based analysis data sets [Betts, 2004]. A more realistie Dickinson, R. E., K. W. Oleson, G. Bonan, F. Hoffinan, P. Thornton, M. Vertenstein, Z. E. Yang, and X. B. Zeng (2006), The Community seasonal-interannual hydrology in a land surfaee model is Land Model and its climate statistics as a component of the Community also a prerequisite for the funetioning of dynamie vegetation Climate System model, / Glim., 79(11), 2302-2324. [Bonan and Levis, 2006] and biogeoehemieal model com­ Dirmeyer, P. A., R. D. Koster, and Z. C. Guo (2006), Do global models properly represent the feedback between land and atmosphere?, J. Hydro- ponents [Thornton et al, 2007]. m eteorol, 7(6), I I7 7 - II 9 8 . [71 ] The new and publiely available Community Land Dunn, A. E., C. C. Barford, S. C. Wofsy, M. E. Goulden, and B. C. Daube Model CLM3.5 ineludes all the above improvements. Its (2007), A long-term record of carbon exchange in a boreal black spmce application within the Community Climate System Model forest: means, responses to interannual variability, and decadal trends. Global Change Biol, 13(3), 577-590. should have benefieial impaets on the simulated global Falge, E., et al. (2001), Gap filling strategies for long term energy flux data earbon and water eyele. sets, Agric. For. M eteorol, 107, 7 1 -7 7 . Flanagan, L. B., L. A. Wever, and P. J. Carlson (2002), Seasonal and interannual variation in carbon dioxide exchange and carbon balance in [72] Acknowledgments. We acknowledge the NASA Energy and a northem temperate grassland. Global Change Biol, 8, 599-615. Water Cycle Study (NEWS) grant NNG06CG42G as the main funding Foken, T. (2008), The energy balance closure problem - an overview, Ecol. source of this study. We acknowledge the coordinating efforts by the A p p l, in press. CarboEurope IP, AmeriFlux and LBA projects as part of FLUXNET. Foken, T, F. Wimmer, M. Mauder, C. Thomas, and C. Eiebethal (2006), Meteorological driver and validation data have been collected and prepared Some aspects of the energy balance closure problem, Atmos. Chem. by the individual site Pis and their teams. We would like to thank Marc Phys., 6, 4395-4402. Aubinet (Vielsahn), Christian Bernhofer (Tharandt), Riccardo Valentini Friedlingstein, P., J. L. Dufresne, P. M. Cox, and P. 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