Improved global simulations of gross primary ... - Wiley Online Library

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112, D07203, doi:10.1029/2006JD008022, 2007

Improved global simulations of gross primary product based on a separate and explicit treatment of diffuse and direct sunlight P. B. Alton,1 R. Ellis,2 S. O. Los,1 and P. R. North1 Received 12 September 2006; revised 30 October 2006; accepted 14 November 2006; published 6 April 2007.

[1] For computational expediency, regional and global land-surface models (LSMs),

especially those coupled to climate simulations, adopt simple algorithms when calculating radiative transfer (RT) and canopy photosynthesis at the vegetated land surface. Nevertheless, the interaction of sunlight with vegetation is recognized as one of the most critical processes represented in LSMs. The present study calculates global, terrestrial Gross Primary Product (GPP) with a version of the land-surface model JULES which has been modified to take explicit account of sunfleck penetration and leaf orientation within the canopy. A comparison with equivalent simulations adopting the Big Leaf (BL) or two-stream (2ST) RT scheme, indicate that current regional/global LSMs may overestimate GPP by 10% globally and up to 25% regionally. Specifically, their use of average light profiles, and consequent neglect of the dispersion in leaf irradiance, at any given height in the canopy leads to both a general overestimation of canopy light-use efficiency (LUE) and a failure to capture the LUE-enhancement under diffuse sunlight (‘‘diffuse fertilization effect’’). We also examine the current limitations of regionally/globally implemented RT schemes with respect to canopy architecture. This is done by coupling JULES to the ray-tracing numerical model FLIGHT, the latter simulating light transfer and photosynthesis in both uniform one-dimensional (1-D) canopies and 3-D tree crowns. When the distribution of leaf nitrogen (N) is configured in a manner consistent with field measurements, output from the 3-D and 1-D FLIGHT simulations is fairly similar (predicted GPP differs by 5%). Similarly, both Leaf Angle Distribution (LAD), when restricted to its observed range, and leaf-clumping appear to have a minor influence over canopy productivity. We conclude that current LSMs can radically improve their calculation of regional/global GPP by adopting a multilayer approach. This will allow the separate treatment of sunlit and shaded foliage, at discrete heights within the canopy, as well as the accurate representation of active leaf-N. Citation: Alton, P. B., R. Ellis, S. O. Los, and P. R. North (2007), Improved global simulations of gross primary product based on a separate and explicit treatment of diffuse and direct sunlight, J. Geophys. Res., 112, D07203, doi:10.1029/2006JD008022.

1. Introduction [2] The RT-scheme adopted when calculating canopy photosynthesis is identified as one of the most influential processes in determining Carbon/Water/Energy (CWE) exchange at the vegetated land surface [Knorr and Heimann, 2001; Alton et al., 2006]. Indeed, fundamental differences in the adopted RT-scheme may partially account for the large (30%) dispersion in the values of global Net Primary Product (NPP) predicted by current LSMs [Cramer et al., 2001]. Investigations at site level demonstrate that predicted 1 Climate and Land-Surface Systems Interaction Centre, Geography Department, University of Swansea, Swansea, UK. 2 Climate and Land-Surface Systems Interaction Centre, Centre of Ecology and Hydrology, Wallingford, UK.

Copyright 2007 by the American Geophysical Union. 0148-0227/07/2006JD008022

daily carbon (C) assimilation varies by 20% depending on the complexity of the adopted RT scheme [Medlyn et al., 2003]. Baldocchi and Harley [1995] underline the importance of representing three-dimensional (3-D) structure. Within their LSM called CANOAK these authors find that leaf-clumping promotes light penetration into the canopy. This increases both C-assimilation and the latent heat returned to the boundary layer (the latter by an average of 10%). To our knowledge, neither 3-D tree crowns nor leafclumping are represented in global/regional LSMs at the present time. [3] It has been hypothesized that diffuse sunlight substantially increases canopy Light-Use Efficiency (LUE; assimilated C per unit downwelling sky radiance) since an increased sharing of the radiation-load occurs across the canopy reducing the fraction of foliage which is saturated at any given time [Roderick et al., 2001; Gu et al., 2002].

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ALTON ET AL.: IMPROVED SIMULATIONS OF GLOBAL GPP

Indeed, measured C-fluxes support the idea of increased LUE under aerosols and clouds (both diffusers of sunlight) although estimates of the LUE enhancement differ greatly [Hollinger et al., 1994; Gu et al., 1999; Gu et al., 2003; Niyogi et al., 2004; Alton et al., 2007]. Regional/global LSMs discriminate poorly between direct and diffuse sunlight. For example, MOSES [Cox et al., 1999], LPJ [Sitch et al., 2003], and SiB3 [Denning et al., 2003] all use the BL scheme where the light profile is assumed to follow Beer’s law and, implicitly, therefore all sunlight is treated as direct. Cramer et al. [2001] make a comparison of six LSMs of which four adopt the BL scheme. Medlyn et al. [2003] assess 12 large-scale models of which nine implement BL or a Radiation-Use Efficiency (RUE) scheme. Generally, RUE-schemes do not account for the fraction of diffuse sunlight (fDIF) [Gower et al., 1999]. Furthermore, even regional/global LSMs that do, in fact, allow for a fraction of diffuse sunlight, tend to use average light profiles when calculating canopy photosynthesis. In contrast, RT schemes applied to specific locations (e.g., FLUXNET sites) are relatively sophisticated in their modus operandi. For example, Norman [1981] accounts, to some extent, for the dispersion in light intensity at any given height in the canopy, by treating sunlit and shaded foliage separately. [4] The bifurcation of complex, site-specific schemes from the simpler algorithms adopted in regional/global calculations can be attributed to historical development. Regional/global LSMs, particularly those coupled to climate simulations, must be computationally economic, whereas simulations only treating specific locations can afford to be more lavish. Besides the issues of technical feasibility and increased computational times, it is instructive to ask what accuracy can be gained by implementing more complex RTschemes. Does this gain justify improving the RT-scheme beyond something as simple and convenient as the BL? In this article we identify which elements of light propagation must be represented explicitly for accurate prediction of GPP by the LSM. In the first part we calculate global GPP using an LSM modified to treat diffuse and direct sunlight separately for each layer within the canopy. To our knowledge, this is the first time that a global calculation has been conducted using an LSM that is sensitive to both diffuse and direct sunlight. The main objective is to quantify the difference in predicted GPP for schemes that distinguish explicitly between direct and diffuse sunlight and those models that either assume all direct sunlight (BL) or combine direct and diffuse sunlight in an average light profile (2ST). The global calculation is carried out with a LSM called JULES. It is difficult to isolate the effects of light transfer without examining simultaneously the role of canopy structure and leaf-N on canopy photosynthesis. For example, within the BL-scheme, active leaf-N (Rubisco) is assumed to decline vertically through the canopy in proportion to the average (exponential) light profile [e.g., Schulze et al., 1994]. Therefore in the second part of this investigation, we ascertain the role of canopy architecture with respect to productivity. Our treatment of canopy architecture comprises crown shape, leaf-clumping, the Leaf Angle Distribution (LAD) and the vertical distribution of active leaf-N. This latter phase of the investigation requires a computationally expensive ray-tracing simulation (FLIGHT) and experiments are only conducted for specific

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sites. Methods for the global and site-specific simulations are described in the following two sections.

2. Materials and Methods 2.1. Global Simulations [5] JULES is a LSM simulating CWE exchange between the land-surface and the lower atmosphere. The simulation is driven by the following meteorological variables: the downwelling fluxes of shortwave radiation (l = 0.3 2 mm; SW) and longwave radiation (l 10 mm; LW), precipitation, top-of-canopy air temperature, top-of-canopy specific air humidity, wind speed, and surface pressure. Apart from meterological driving data, input to the simulation occurs through a control file which prescribes certain timeinvariant parameters such as leaf physiology for each of five plant function types (Table 1). The energy calculation central to the model ensures that the downwelling fluxes (SW and LW) are balanced by the outgoing fluxes of sensible heat (H), latent heat (LE), ground flux (G), reflected shortwave radiation, and upwelling thermal energy R (all in Wm2). Thus SW ð1 at Þ þ LW ¼ R þ LE þ H þ G

ð1Þ

where R = sTs4, Ts is the surface temperature and s is the Stefan-Boltzmann constant [Cox et al., 1999]. A SW-albedo at is derived for each of nine tiles (five plant functional types and four nonvegetated types) and the energy balance of equation (1) is performed independently within each tile using a Penman-Monteith approach [Monteith, 1965]. Model output comprises numerous variables associated with CWE exchange (e.g., evapotranspiration, run-off, soil, and plant respiration) although this articles focuses on predicted GPP. [6] In its original form, JULES constitutes a modular version of the U.K. Meteorological Office Surface Exchange Scheme (MOSES). The latter is coupled to the Hadley Global Circulation Model for use in simulations of future climate [Cox et al., 2000]. Since its separation from MOSES, JULES has undergone several major enhancements. In its original form, JULES uses the Big Leaf approximation [Sellers, 1985; Schulze et al., 1994] to estimate canopy photosynthesis but employs the two-stream formulae [Sellers et al., 1996] to determine the shortwave albedo of the land-surface at (equation (1)). Hereafter, we refer to this scheme as BL. To improve the estimation of canopy GPP, V. R. Jogireddy et al. (An improved description of canopy light interception for use in a GCM landsurface scheme: Calibration and testing against carbon fluxes at a coniferous forest, submitted to Agriculture and Forest Meteorology, 2007, hereinafter referred to as Jogireddy et al., submitted manuscript, 2007) divide the canopy evenly into several contiguous vertical layers and canopy photosynthesis is calculated as the sum of GPP in each layer. In this case, light propagation and average leaf irradiance is estimated according to the two-stream formulation. Hereafter, we refer to this scheme as 2ST. As its name suggests, 2ST contains two separate ‘‘streams’’ for diffuse and direct sunlight but combines these two components into an average light profile when calculating canopy photosynthesis. Alton et al. [2007] find that observed

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Table 1. PFT-Dependent Vegetation Parameters Used in the Simulations of Global GPPa Property

Units

BLF

NLF

C3

C4

S

Description

Vcmax QE Tl Th canht DQcrit f0 RPAR RNIR TPAR TNIR

mmolm2s1 mol/mol K K m kg/kg – – – – –

60 0.05 281 309 30 0.090 0.88 0.10 0.55 0.05 0.25

60 0.05 271 301 20 0.060 0.88 0.10 0.55 0.05 0.25

35 0.05 273 309 1.0 0.100 0.90 0.10 0.55 0.05 0.25

25 0.05 285 315 1.0 0.075 0.80 0.10 0.55 0.05 0.25

40 0.05 273 309 2.0 0.100 0.90 0.10 0.55 0.05 0.25

Photosynthetic capacity (canopy top) Quantum efficiency incident PAR Lower temperature threshold photosynthesis Upper temperature threshold photosynthesis Vegetation height Critical humidity deficit for stomata Calibration parameter stomatal model Leaf reflectance PAR Leaf reflectance NIR Leaf transmittance PAR Leaf transmittance NIR

a

The columns BLF, NLF, C3, C4, S refer to broadleaf, needleleaf, C3 grasses, C4 grasses, and shrubs, respectively. The parameters are typical of those used to simulate CWE exchange recorded at FLUXNET sites [e.g., Alton et al., 2006] or during field campaigns such as BOREAS and FIFE [e.g., Cox et al., 1998; Cox, 2001].

canopy photosynthesis is reproduced much better once JULES takes account of the dispersion in leaf irradiance at any given height in the canopy rather than averaging irradiance into a mean light profile. The calculation is carried out at several discrete heights within the canopy by simulating sunfleck penetration and explicitly modelling leaf orientation. Hereafter, we refer to this third scheme as SF. It is important to note that all three of the aforementioned schemes refer to changes in the calculation of canopy GPP. Thus when we refer to a specific RT-scheme it is with respect to leaf irradiance and the light profile used in the calculation of photosynthesis. All three RT-schemes employ the two-stream formulae to compute the surface albedo entering the energy balance of equation (1). [7] In its usual configuration, JULES is driven for a single location (e.g., a FLUXNET site) with hourly meteorological data [e.g., Harris et al., 2004]. A reconfiguration of the model ‘‘front-end’’ (R. J. Ellis et al., manuscript in preparation, 2007) allows the simulation to be run for multiple gridded land points. In this paper, we drive JULES with the GSWP2 data set (meteorological data at 3-hourly intervals for all land points of 1 sq.deg.) in order to derive global GPP. The GSWP2 data set is a hybrid product, combining reanalysis and observations, and nominally spans 10 years [Dirmeyer et al., 1999]. Our global simulations are limited, for computational expediency, to a 2-year period. January 1986 to December 1986 (incl.) provides a spin-up for the LSM and GPP is integrated over the period January 1987 to December 1987 (incl.). For land points of 1 sq.deg., the dominant PFT, the fraction of bare ground and the Leaf Angle Index (LAI) at 10 day intervals are taken from Los et al. [2000]. Soil type and its SWalbedo, the latter as a single time-averaged value, are taken from the GSWP2 1° data set and are based on Wilson and Henderson-Sellers [1985]. The corresponding hydrological and thermal properties originate from the same source. [8] We conduct four simulations with JULES in order to estimate global GPP: (1) BL, (2) 2ST with direct sunlight only, (3) 2ST with estimated fDIF, (4) SF with estimated f DIF. For simulations 3 and 4, fDIF is estimated at each time step according to the ratio of observed surface radiance (SW in the driving data) and the top-of-atmosphere solar radiance [Roderick et al., 2001]. [9] The BL scheme assumes an exponential vertical decline in light through the canopy (i.e., Beer’s law).

Therefore implicitly, all sunlight is direct in this scheme. Active leaf-N (equivalently the photosynthetic capacity at light saturation or the Rubisco concentration in the leaves) is assumed to be proportional to the average light profile. With these assumptions, canopy GPP is linearly dependent on leaf photosynthesis, the multiplicative factor being equal to (1exp(kextLAI))/kext [e.g., Schulze et al., 1994]. Here, kext is the time-averaged extinction coefficient. We use kext = 0.5 for all land points in order to be consistent with previous global simulations conducted with MOSES for future climate [Cox et al., 2000]. For simulations using a multilayer RT scheme (i.e., simulations 2 –4 above), we find five layers provides sufficient numerical convergence (GPP changing by 1%) and we adopt this number of canopy divisions hereafter. The allocation parameter for active leafN (krub) determines the exponential distribution of Rubisco in a manner analogous to kext for the light profile [Hirose and Werger, 1987]. We set krub to 0.15 in the multilayer schemes in order to be consistent with the relatively shallow gradient in active leaf-N measured for tree canopies [Dang et al., 1997; Carswell et al., 2000; Lewis et al., 2000; Meir et al., 2002]. Note that as implied above, our BL scheme uses krub = kext = 0.5. In all cases, leaf photosynthetic rate is calculated within each foliage layer according to a colimitation submodel which takes account of light incidence and photosynthetic capacity due to Rubisco concentration [Collatz et al., 1991]. After the scale-up of assimilation from leaf to canopy level, GPP is multiplied by a soil moisture factor which is unity when all soil layers are at or above their critical soil moisture threshold. The multiplicative factor falls to zero as the volumetric soil moisture content reaches the wilting point in all soil layers. The critical and wilting points are ‘‘standard’’ values adopted in the GSWP intercomparison project for LSMs [Dirmeyer et al., 1999]. Leaf stomatal conductance follows from a simplified Leuning submodel which depends on leaf-surface humidity deficit [Cox et al., 1998]. The minimum and maximum temperatures for the colimitation formulation of leaf photosynthesis (Tl and Th), as well as the parameters controlling leaf stomatal conductance (DQcrit and f0), are listed in Table 1. 2.2. Site Simulations [10] The role of canopy architecture in determining canopy productivity is ascertained using the Forest Light (FLIGHT) simulation [North, 1996]. This is a ray-tracing, numerical model which employs Monte Carlo techniques to

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Table 2. Properties of Three Sites Selected for 3-D Ray-Tracing With FLIGHTa Value Property

Units

Zotino

Harvard

Manaus

Designation Location Biome Latitude Longitude Primary Vegetation Leaf Area Index Green Fraction

– – – deg deg – m2m2 –

Zo Siberia Boreal needleleaf 60.8N 89.4E Scots Pine 2.0 0.6

Hv N. America Temperate broadleaf 42.5N 72.2W Oak, Maple 4.5 0.95

Mn Amazonia Tropical broadleaf 2.6S 60.2W Mixed species 5.5 1.0

a Sources are as follows: Wirth et al. [1999] and Lloyd et al. [2002] for Zotino; Williams et al. [1996], Wofsy et al. [1993] and Goulden et al. [1996] for Harvard; Carswell et al. [2000] and Williams et al. [1998] for Manaus.

sample light propogation and leaf irradiance in both uniform 1-D vegetation layers and heterogeneous 3-D canopies. The recent implementation of a colimitation leaf photosynthesis module into FLIGHT allows canopy GPP to be calculated by ray-tracing and stored in a look-up table (LUT) for retrieval by JULES [Alton et al., 2005]. Values are stored according to canopy temperature, sky radiance, solar zenith angle, fDIF, and the CO2 concentration internal to the leaf. The colimitation module implemented in FLIGHT is identical to that contained within JULES and permits a direct comparison between values of canopy GPP generated by the two models. For 3-D simulations conducted with FLIGHT, tree crowns are represented by geometric primitives (ellipses and cones) with a range of prescribed dimensions (e.g., radius and height). In all cases, foliage is represented by volume-averaged parameters such as LAI and the scattering phase function. The computational demand is too great to produce LUTs suitable for all land points. Therefore we limit our investigation to three locations which vary greatly in canopy structure, LAI, and latitude. These sites are amongst the best studied FLUXNET locations with biophysical properties that are welldocumented in the literature (Table 2). For each of the three sites, both 1-D and 3-D canopies are configured within FLIGHT. Canopy GPP is parsed, via a LUT, from FLIGHT to JULES and the LSM is run, as for the global simulations, over the 1986 – 1987 period using GSWP2 climatic data. GPP is integrated for the growing season of 1987 (Julian Day 120– 270 for the temperate and boreal sites, Zotino and Harvard, and Julian Day 1– 365 for the tropical location, Manaus). For computational expediency we adopt a constant LAI over these integration periods (Table 2). The fraction of diffuse sunlight, fDIF, is derived at each time step as per the global simulations 3 and 4. [11] For the 3-D simulations, ellipsoid crowns are configured with a minor (horizontal) radius of 2, 5, and 5 m at Zotino, Harvard, and Manaus, respectively, A major (vertical) radius of 5 m is adopted at all sites. The crown base is positioned randomly between a minimum and maximum height of 4 and 12 m at Zotino, 10 and 14 m at Harvard, and 12.5 and 27.5 m at Manaus. For 3-D simulations, cover fraction of vegetation is 0.6, 0.95, and 1.0 at Zotino, Harvard, and Manaus, respectively. These dimensions approximate structural properties recorded at the sites. All other aspects of FLIGHT are matched as closely as possible to the parameters contained within JULES. Thus soil albedo is taken from the GSWP2 database while reflective and transmission properties for the leaves originate from

Table 2. Baldocchi and Harley [1995] claim that the clumping of leaves plays an important role in predicted GPP at their temperate, broadleaf site. Therefore for our broadleaf, temperate site, Harvard, a clumped 3-D canopy is also configured. For this clumped configuration, spheres of radius 1.5 m are used, all other properties of the FLIGHT simulation remaining the same. [12] A spherical LAD is adopted for all PFTs in the global simulations. Using JULES it is difficult to test the influence of LAD on GPP since options exists for only a spherical LAD or horizontal leaves within the LSM (other LADs complicate the analytic calculation of light transfer considerably). Horizontal leaves are unlikely to be encountered except perhaps as shade leaves situated near the canopy floor. The default LAD for FLIGHT is a spherical distribution. However, in separate experiments, we recreate 1-D and 3-D LUTs for each of our three sites using (1) a planophile LAD (mean leaf angle (MLA) of 27° for the leaf normal to the vertical) and (2) an erectophile LAD (MLA = 64°). These LADs can be compared with the standard spherical distribution where MLA = 57°. The aforementioned experiments embrace the range of LAD encountered for most species [Campbell and Norman, 1998; Falster and Westoby, 2003] and are alluded to as the probable range of LAD hereafter.

3. Results [13] Results for the global and site simulations are presented separately in the following two sections. The emphasis of the current investigation is on C-assimilation. However, our experience in fitting eddy-covariance fluxes in the past [Alton et al., 2006] indicates that when canopy GPP is predicted correctly, there is a significant improvement in the modelled energy-balance (i.e., latent and sensible heat) through the concommitant change in simulated transpiration. 3.1. Global Simulations [14] The results from this section are presented in three figures, intended to illustrate the difference between the new RT scheme (SF) and its predecessors (BL and 2ST). Figure 1 shows global GPP calculated with the SF scheme (simulation 4 in section 2.1). Figure 2 depicts the difference in GPP between SF and BL (simulation 1), the latter corresponding approximately to the state-of-the-art calculation for global GPP. Figure 3 presents GPP from all four simulations against latitude and compares against average

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Figure 1. Global GPP. Values (kgm2yr1) are predicted by a modified (sunfleck) version of the landsurface model JULES. A ‘‘temperature’’ color table is used, scaled from 0.0 (black) to 3.0 kgm2yr1 (red).

Figure 2. GPP difference BL-SF. The difference in GPP (kgm2yr1) predicted by JULES with two different representations of radiative transfer in the canopy, Big Leaf and sunfleck (the latter as shown in Figure 1). A ‘‘temperature’’ color table is used, scaled from 0.0 (black) to 1.0 kgm2yr1 (red). 5 of 12

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Figure 3. Analysis of global GPP by latitudinal band. (a) GPP integrated over 10° latitudinal bands (Gt/yr) predicted using the Big Leaf (BL) and sunfleck (SF) schemes; (b) the same quantity using the two-stream scheme (2ST) assuming either all direct sunlight (DIR) or an estimated fraction of diffuse sunlight (f DIF); (c) the ratio of integrated GPP (over 10° latitudinal bands) for the Big leaf and sunfleck schemes (BL/SF) and the corresponding ratio for the two-stream and sunfleck schemes (2ST/SF). For these ratios both 2ST and SF use f DIF. The ratios are plotted against the mean values, per latitudinal band, of LAI (divided by a factor 10 for clarity) and f DIF; (d) the ratio of GPP from the Big leaf and sunfleck schemes (BL/SF) plotted against time-averaged LAI. For Figure 3d, landpoints are ascribed to a dominant PFT (Table 3) and the GPP-ratio averaged over bins of 0.5 in LAI. values of fDIF and LAI. The salient points from these figures can be summarized as follows: [15] 1. Global GPP calculated with the SF simulation is 118 Gt/yr. Assuming a NPP-to-GPP ratio of 0.42 [Ruimy et al., 1996], this coincides with the lower end of the range indicated by CO2 measurements and previous LSM simulations (107 – 167 Gt/yr) [Knorr and Heimann, 2001; Cramer et al., 2001]. The distribution of GPP by PFT, for the SF simulation, agrees well with the measurements collated by Knorr and Heimann [2001] although GPP for C4 grasses may be somewhat underestimated (Table 3). [16] 2. Global GPP derived with BL (simulation 1) and 2ST (simulation 3), the latter with estimated f DIF, are 131 Gt and 129 Gt, respectively. These values are 11% and 10% higher, respectively, than the corresponding SF simulation. [17] 3. For regions of higher mean LAI, differences between BL and SF are more pronounced. For example, GPP predicted by the BL scheme for the tropics is up to 25% higher than equivalent output from SF. The difference in output between 2ST and SF appears to be more or less independent of latitudinal band. [18] 4. The 2ST scheme produces very similar output independent of the value of f DIF (typical difference 3%). [19] 5. The mean global value for f DIF lies between 0.4 and 0.7. Therefore the correct treatment of diffuse sunlight in LSMs can be considered important.

3.2. Site Simulations [20] For the three sites (Zotino, Harvard, and Manaus), Figure 4 shows predicted GPP against the N-allocation parameter krub. The left panels in the figure reveal the role of canopy structure in C-assimilation by coupling the output from 1-D and 3-D FLIGHT simulations to JULES. Panels on the right correspond to multilayer simulations (2ST and SF) for the same sites. Except for cases where leaf-N is not conserved, the multilayers simulations are matched as closely as possible to the 1-D simulations with FLIGHT. The range illustrated for krub in Figure 4 is large (0.0 –2.0) to show, with completeness, the response of canopy photosynthesis to the vertical distribution in active leaf-N. However, we define krub = 0.0– 1.0 as the ‘‘current range’’ for the leaf-N distribution adopted in LSMs. This range encompasses simulations that assume a uniform N-distribution (krub = 0) as well as those that assume a N-gradient that is fully light-acclimated (i.e., krub = kext where the timeaveraged extinction coefficient kext is usually in the range 0.5– 1.0). For tree canopies, the gradient of photosynthetic capacity or that of leaf-N itself, is found to be significantly shallower than the corresponding average light profile. Therefore we also define a probable range for krub which is 0.15 and 0.5 based on field measurements [Dang et al., 1997; Carswell et al., 2000; Lewis et al., 2000; Meir et al., 2002]. Note that many LSMs are parameterized in such a way that total leaf-N is not necessarily conserved between

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Table 3. Distribution of Global GPP Predicted With the Sunfleck Simulationa PFT (1)

Predicted GPP, kg/m2 (2)

Predicted NPP, kg/m2 (3)

Measured NPP, kg/m2 (4)

Fraction of Landpoints (5)

Fraction of Predicted GPP (6)

BLF NLF C3 C4 S Mixed Soil

2.23 1.08 0.77 0.23 0.26 1.09 0.00

0.94 0.45 0.32 0.10 0.11 0.46 0.00

0.55 – 1.05 0.30 – 0.60 0.30 – 0.45 0.25 – 0.60 0.05 – 0.10 – –

0.10 0.12 0.18 0.14 0.18 0.19 0.09

0.33 0.12 0.17 0.04 0.04 0.29 0.00

a In order to compare with measurements of NPP (column 4) collated by Knorr and Heimann [2001], we assume a NPP-to-GPP ratio of 0.42 [Ruimy et al., 1996]. Note that GPP and NPP are expressed per unit area. Column 5 refers to the fraction of 1 arc-degree land points dominated by the PFTs listed in column 1 (see Table 1 for designation of PFTs). The corresponding contribution to predicted global GPP is indicated in column 6. The PFT is deemed ‘‘dominant’’ if it covers 75% of the grid-square area. Remaining land points are classified as ‘‘soil’’ if they contain no vegetation or as ‘‘mixed’’ if no PFT dominates.

simulations. For example, the N-gradient (through krub) and the Rubisco-limited photosynthetic capacity of the top leaves (through Vcmax) are prescribed independently within the JULES input control file. Therefore Figure 4 illustrates the canopy response for both simulations that do and those that do not conserve total leaf-N. Table 4 indicates various ratios of GPP, from our site simulations, for both the current and probable range of krub. We summarize our findings from Figure 4 and Table 4 as follows: (1) GPP predicted by the 1-D, 3-D, and 3-D (leaf-clumped) FLIGHT simulations differ only moderately (8%). (2) Predicted GPP changes modestly over the probable range in LAD (4 – 5%). (3) GPP predicted by the SF simulation is fairly close to the value produced in the FLIGHT 1-D simulation for the current range in krub (difference in GPP 8%). (4) For the current range in krub, 2ST produces much higher values of GPP compared to the SF simulation (9 – 18%). (5) For the probable range in krub, model output varies by 16% (assuming conservation of leaf-N). For the current range in krub the corresponding variation is 35%. (6) If active leaf-N is not conserved, model output from the 2ST and SF simulations can drop by up to 18% for krub increasing across its probable range and by up to 47% for krub increasing across its current range.

4. Discussion 4.1. Is the Choice of RT-Scheme Important in Predicting Global GPP? [21] The RT-schemes selected for our global simulations (BL, 2ST, and SF) manifest differences of 10% in their estimate of global GPP. For particular regions or conditions of sky radiance the discrepancies can be more pronounced. For the tropics, for example, GPP predicted with the BL scheme is up to 25% higher than the corresponding output from the SF simulation (Figure 3). Likewise, for the temperate forest, Harvard, GPP integrated over the growing season is 18% higher in the 2ST scheme compared to equivalent value from the SF simulation (Table 4). As direct sunlight predominates the discrepancy between output from the 2ST and SF simulations increases. Thus for dense canopies in the tropics, simulated GPP in the 2ST and SF schemes differ by 30% for cloudless days. The 10– 30% discrepancies described above are large within the context of the C-cycle, especially when compared to anthropogenic sources of atmospheric CO2 (equivalent to 10% global NPP [Schlesinger, 1997]).

Figure 4. GPP (kgm2yr1) predicted for three selected sites (Zotino, Harvard, and Manaus) using output from FLIGHT (left) and (right) output from the two-stream (2ST) and sunfleck (SF) schemes. FLIGHT simulates both 1-D and 3-D canopies and spherical leaf clumps are also configured for Harvard. For Manaus, a 1-D FLIGHT canopy possessing an erectophile LAD is also shown (dashed line bottom-right panel). All FLIGHT simulations conserve leafN. The multilayer simulations (2ST and SF) marked by ‘‘N’’ in parentheses also conserve leaf-N. In all cases, GPP is plotted against the nitrogen allocation coefficient krub. We use an interval of 0.5 in krub with an additional simulation conducted at krub = 0.15 (the value adopted in our global simulations with 2ST and SF). The key, only shown for Harvard, applies to all sites. GPP is the integral over the growing season as defined in the text (section 2.2).

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Table 4. Site-Specific Simulations Examining the Role of Canopy Architecture (3-D Structure, LAD, and Active Leaf-N)a GPP-Ratio Site

3D/1D

3D(Cl.)/1D

2ST/SF

1D/1D(E)

1D/1D(P)

1D/SF

Zotino

0.99 – 1.00 (0.97 – 1.05) 1.02 – 1.05 (0.97 – 1.06) 1.00 – 1.05 (0.95 – 1.08)

– – 0.95 – 0.97 (0.95 – 0.98) – –

1.13 – 1.13 (1.12 – 1.14) 1.18 – 1.18 (1.13 – 1.18) 1.11 – 1.12 (1.09 – 1.13)

1.00 – 1.00 (1.00 – 1.01) 1.00 – 1.00 (0.99 – 1.00) 1.00 – 1.01 (0.95 – 1.02)

0.97 – 0.97 (0.97 – 0.98) 0.99 – 1.02 (0.98 – 1.02) 1.01 – 1.03 (0.99 – 1.04)

0.93 – 0.94 (0.92 – 0.96) 0.93 – 0.96 (0.92 – 0.99) 0.96 – 1.00 (0.94 – 1.02)

Harvard Manaus

a The ratio of predicted GPP is given for various simulations. These are designated as follows: 1D, 1D(E), 1D(P), 3D, and 3D(Cl.) all refer to JULES coupled with FLIGHT (Cl., E, and P denote leaf-clumps, erectophile LAD, and planophile LAD, respectively); 2ST and SF correspond to output from the two-stream and sunfleck simulations, respectively. In each case, the minimum and maximum ratio of GPP is given for the range krub = 0.15 – 0.5 and for the range krub = 0 – 1 (the latter in parentheses). All simulations tabulated below conserve total leaf-N.

[22] In terms of increasing complexity, accuracy, and computational time, we order the RT-schemes treated in this work as follows: BL, 2ST, SF, FLIGHT-1D, FLIGHT3D. We justify this ranking thus. The BL scheme calculates photosynthesis for a single, flat leaf and scales to canopylevel assuming that leaf-N is fully acclimated to the time-averaged light profile. The assumption of complete light-acclimation simplifies the calculation of canopy photosynthesis by allowing a linear scale-up from leaf to canopy level [e.g., Schulze et al., 1994]. No separate treatment of different foliage layers, nor of sunlit and shaded leaves within the canopy, is undertaken within the BL scheme. Within the 2ST scheme, light transfer and leaf-N are treated separately and canopy GPP constitutes the sum of photosynthesis calculated for all layers constituting the canopy (Jogireddy et al., submitted manuscript, 2007). Leaf-N is prescribed and does not necessarily decline as the average light profile. The SF scheme also constitutes a multilayer model but, in contrast to the 2ST scheme, it treats the dispersion in leaf irradiance at any given height within the canopy rather than using an average light profile [Alton et al., 2007]. FLIGHT uses ray-tracing and Monte Carlo techniques to sample both direct and diffuse leaf-irradiance. Light incidence is sampled for all locations within the canopy and for leaves of different inclinations [Barton and North, 2001; Alton et al., 2005]. Flight 3-D differs from FLIGHT 1-D by placing foliage within crowns rather than adopting a uniform vegetation layer. Table 5 provides an overview of the differences between the various RT-schemes. [23] The differences in output from our global simulations can be explained as follows. The BL and 2ST schemes assume average vertical light profiles within the canopy. Thus the dispersion in leaf irradiance, at any given height within the vegetation layer, is zero within the model. For any given time step in the simulation this approach max-

imizes radiation-sharing across the foliage and produces a relatively high canopy LUE. FLIGHT and the SF scheme simulate leaf irradiance more realistically then either the BL or 2ST schemes by taking explicit account of both sunfleck penetration and leaf orientation. Consequently, for any given time step, a greater fraction of foliage is lightsaturated and the LUE in FLIGHT and the SF scheme is lower compared to the corresponding simulation with BL or 2ST. For the more complex RT-schemes, FLIGHT and SF, the drop in LUE is greatest when direct sunlight predominates [see Alton et al., 2006, Figure 4]. This behavior is consistent with the sizeable enhancement in canopy LUE (up to a factor of 2) observed under cloud and high aerosol loading at FLUXNET sites [Hollinger et al., 1994; Gu et al., 2003; Niyogi et al., 2004; Alton et al., 2007]. The internal calculation within 2ST distinguishes between direct and diffuse sunlight. However, given that both the direct and diffuse components are represented by average light profiles, 2ST predicts similar values of GPP irrespective of the value of f DIF at each time step. This is demonstrated most clearly in Figure 3b where GPP predicted by 2ST under direct sunlight is only 3% less than the corresponding 2ST simulation using fDIF. Taking account of the nature of sky radiance (direct or diffuse) within LSMs is evidently important since, as a global average, diffuse sunlight dominates sky radiance for approximately half the year (Figure 3c). Of the RT schemes we have implemented at a global level, SF provides the most realistic calculation of GPP. This is corroborated in Figure 4 where output from sophisticated ray-tracing (FLIGHT 1-D) and the SF simulation are fairly convergent (difference in GPP 8%). Some of the discrepancy between the two schemes (3%) can be accounted for by the method employed by the LSM to retrieve values of GPP from the FLIGHT LUT (bilinear interpolation). The remaining difference (5%) arises from the assumption, in the multilayer schemes (SF and 2ST),

Table 5. Overview of the Radiative Transfer (RT) Schemes Treated in This Worka Property Sky radiance RT N-Profile Surface albedo

Big Leaf

Multilayer

Sunfleck

FLIGHT

all direct average light profile (Beer’s Law) / average light two-stream

direct and diffuse average light profile (two-stream) prescribed two-stream

direct and diffuse explicit leaf irradiance (direct) and two – stream (diffuse) prescribed two-stream

direct and diffuse explicit leaf irradiance (direct and diffuse) prescribed ray-tracing

a We indicate the assumptions within each scheme with respect to sky radiance (diffuse and/or direct sunlight), the vertical profile of active leaf nitrogen (N-profile), and the calculation of surface albedo.

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that the diffuse light is isotropic. In contrast, FLIGHT takes account of the directionality of the diffuse component. [24] Our results are approximately consistent with the findings of Medlyn et al. [2003] who show that predictions of daily C-assimilation vary by 20% depending on the complexity of the selected RT scheme (BL, multilayer, and RUE schemes are considered). Notably, these authors argue that, over longer integration periods (e.g., monthly), differences in model output may be less pronounced. Potentially, cross-calibration between certain RT-schemes may reduce or temporarily remove discrepancies in predicted GPP. However, in order to be able to predict the response of the land-surface to a changing climate, the argument for a more mechanistic representation of light interception is compelling. It is noteworthy that certain global simulations, subject to future climate, predict the die-back of the Amazonian forest and that these predictions are based on a LSM employing the BL scheme [Cox et al., 2000]. It is within the tropics that we find that the BL algorithm overestimates GPP most severely (25%) with respect to the more realistic simulation using SF. [25] Historically, increased computation has been viewed as an impediment in the adoption of more sophisticated RT schemes into global climate models [e.g., Collatz et al., 1991; Gower et al., 1999]. However, for our global simulations, execution times with the multilayer schemes are only double those required with the BL scheme. The time calculating CWE exchange itself, as opposed to reading forcing data, increases by no more than a factor of 3 using the more sophisticated algorithm. 4.2. Do Assumptions of Leaf-N Influence Predicted Global GPP? [26] The answer to this question depends on the range adopted for the N-allocation parameter krub and whether active leaf-N is conserved between simulations (section 3.2). If total active leaf-N is conserved, output from the multilayer site simulations (2ST and SF) varies only moderately (4 – 11%) for the more restrictive range in leaf-N (krub = 0.15 – 0.5). However, within the current spectrum of regional/global LSMs, where leaf-N can assume a uniform or even a fully light-acclimated distribution, GPP can vary by 47% between simulations when leaf-N is not conserved. Therefore regional/global LSMs, in their present configuration, depend sensitively on the selected distribution in leafN or photosynthetic capacity. We feel that the influence of the leaf-N profile, in predicted canopy C-assimilation, is perhaps somewhat understated in previous studies [e.g., Friend, 2001]. Our results reveal the usefulness of multilayer models where the Rubisco gradient is prescribed independently of the light profile. In some regional/global LSMs, replacement of the BL approach by sunlit-shade schemes is already occurring but not all the replacement schemes appear to be multilayer [e.g., De Pury and Farquhar, 1997; Liu et al., 2002; Dai et al., 2004]. We feel that dividing the canopy into several discrete layers is essential, not only for accurate representation of light transfer but for the prescription of realistic Rubisco gradients based on field measurements. [27] The BL scheme assumes, implicitly, that the Rubisco distribution is fully light-acclimated, i.e., krub = kext. For our global simulations we have prescribed kext = 0.5 at all time

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steps to be consistent with previous climate simulations. For our simulations based on multilayer RT schemes (2ST and SF), krub is ascribed an explicit value of 0.15 in order to be consistent with field measurements. This difference in Rubisco gradient might appear to be the reason why the BL scheme predicts higher values of GPP than the corresponding SF simulation (BL is light-acclimated but SF is not). However, for all our global simulations, the Rubisco concentration at the top of canopy is prescribed according to the value of Vcmax contained in Table 1. Thus our BL scheme contains a smaller quantity of total active leaf-N than either the SF or 2ST simulations. An increase in Vcmax, so as to compensate for the steeper (light-acclimated) Rubisco gradient in BL, would almost certainly accentuate the differences in output between BL and SF (i.e., GPP increases for BL). At first sight, the tendency for the BL scheme to overestimate GPP, with respect to the SF simulation, appears LAI-related (e.g., 25% over-estimation in the tropics). However, Figure 3d indicates a strong dependency on PFT in this regard. Indeed, overestimation by the BLscheme is greatest for PFTs where Vcmax is relatively large and the sky radiance is high through the growing season. 4.3. Is it Necessary to Represent 3-D Canopy Structure in Global Simulations? [28] Our investigation of 3-D/1-D structure is only possible for three sites. These locations span a wide range in latitude, foliage density, and green fraction (Table 2). Therefore we make some tentative inferences of how a global simulation using a 3-D canopy would differ from the 1-D representations currently used in regional/global LSMs. The importance of representing 3-D structure depends to some extent on the assumed Rubisco gradient. Deviations between the FLIGHT 1-D and 3-D simulations increase for a steeper vertical decline in active leaf-N (Figure 4). For the probable range in krub (0.15 – 0.5), predicted GPP from the 1-D and 3-D simulations are fairly close (5%). With the less restrictive range in leaf-N currently implemented in regional/global LSMs (krub = 01), the discrepancy is 8%. These differences are relatively minor compared with, for example, the choice of RT scheme. Thus configuring 3-D structure in LSMs could be considered of secondary importance in the context of global simulations of C-assimilation. Somewhat surprisingly even highly heterogeneous landscapes, such as that characterizing our boreal site Zotino (fGREEN = 0.6), appear to be adequately represented by a uniform 1-D vegetation layer. [29] The modeling study of Baldocchi and Harley [1995] suggests an increase of ’10% in average, predicted latent heat release when a clumped foliage, as opposed to a turbid medium, is adopted in their CANOAK land-surface simulations of temperate broadleaf forest. Predicted GPP appears to be up to 30% higher in their clumped scheme. The authors attribute the LUE-enhancement to gaps which allow sunlight to illuminate leaves that in the turbid medium, would be in shade. Our FLIGHT simulations for the site Harvard (also temperate broadleaf) suggest that placing foliage in clumps has a relatively minor impact on output from the LSM (GPP changes by 5%; Table 4). Moreover, we record a slight decrease, rather than the sizeable increase noted by Baldocchi and Harley, in canopy productivity when clumps are introduced into the 3-D simulation with

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FLIGHT. We attribute this reduction in LUE to an increased dispersion in leaf irradiance, at any given height in the canopy, when leaves are bunched. Although the simulations conducted by Baldocchi and Harley account for sunlit/ shaded fractions and a gradient in Rubisco, they use a mean leaf inclination, rather than an explicit range of leaf orientations, in heir model. This may account, to some extent, for the apparent contradiction with the current study. [30] The study by Baldocchi and Harley [1995] also indicates that the change in average leaf orientation may be important. However, we find that, within the probable range of LAD (MLA = 27– 64° [Campbell and Norman, 1998; Falster and Westoby, 2003]), model output varies very little (2%). The spherical LAD, usually assumed in regional/global LSMs, appears adequate at the present time. 4.4. Uncertainties and Limitations of the Current Study [31] The exact biophysical properties adopted in our simulations may change the absolute values of GPP generated by the LSM but the relative differences that we identify between the RT schemes appear to be quite robust. For example, we reduce LAI at each time step within JULES by 20% (the uncertainty in LAI) and recalculate GPP using the 2ST and SF schemes. This is done for a random sample of 2000 land points (analogous to our global simulations 3 and 4). In both simulations total GPP, integrated across the subset, is reduced by 9% compared to simulations adopting the original LAI. However, as before, 2ST overestimates GPP with respect to the SF simulation by 10%. The representation of stomatal conductance might have a large influence on predicted C-assimilation. Replacement of the current (Leuning-type) stomatal algorithm in JULES with the Ball-Berry algorithm [Collatz et al., 1991] reduces total GPP for the aforementioned subset by just 3%. As before, 2ST overestimates GPP by 10% with respect to the SF scheme. Additionally, we check that our inferences are not dependent on the exact climatic data used to force the model. Using GSWP2 climatic data for years 1988 and 1989, as spin-up and integration periods, respectively, we reach the same conclusion as we do for the original period 1986 – 1987. Indeed, GPP integrated across the land point subset appears to be invariant with respect to the year conducted for the simulation. Interannual variability in predicted GPP for any given land point is 16% (median average) but GPP integrated across the whole subset varies by