Geosci. Model Dev., 9, 24152440, 2016 www.geosci-model-dev.net/9/2415/2016/ doi:10.5194/gmd-9-2415-2016 Author(s) 2016. CC Attribution 3.0 License.
Anna B. Harper1, Peter M. Cox1, Pierre Friedlingstein1, Andy J. Wiltshire2, Chris D. Jones2, Stephen Sitch3,Lina M. Mercado3,4, Margriet Groenendijk3, Eddy Robertson2, Jens Kattge5, Gerhard Bnisch5, Owen K. Atkin6, Michael Bahn7, Johannes Cornelissen8, lo Niinemets9,10, Vladimir Onipchenko11, Josep Peuelas12,13,Lourens Poorter14, Peter B. Reich15,16, Nadjeda A. Soudzilovskaia17, and Peter van Bodegom17
1College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, UK
2Met Ofce Hadley Centre, Exeter, UK
3College of Life and Environmental Sciences, University of Exeter, Exeter, UK
4Centre for Ecology and Hydrology, Wallingford, UK
5Max Planck Institute for Biogeochemistry, Jena, Germany
6ARC Centre of Excellence in Plant Energy Biology, Research School of Biology, Australian National University, Canberra, Australia
7Institute of Ecology, University of Innsbruck, Austria
8Systems Ecology, Department of Ecological Science, Vrije Universiteit, Amsterdam, the Netherlands
9Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia
10Estonian Academy of Sciences, Tallinn, Estonia
11Department of Geobotany, Moscow State University, Moscow 119234, Russia
12CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del Valls, 08193 Barcelona, Catalonia, Spain
13CREAF, Cerdanyola del Valls, 08193 Barcelona, Catalonia, Spain
14Forest Ecology and Forest Management Group, Wageningen University, P.O. Box 6700 AA, Wageningen, the Netherlands
15Department of Forest Resources, University of Minnesota, Saint Paul, Minnesota, USA
16Hawkesbury Institute for the Environment, University of Western Sydney, Penrith, New South Wales, Australia
17Institute of Environmental Sciences, Leiden University, Leiden, the Netherlands
Correspondence to: Anna B. Harper ([email protected])
Received: 27 January 2016 Published in Geosci. Model Dev. Discuss.: 1 February 2016 Revised: 13 May 2016 Accepted: 20 May 2016 Published: 22 July 2016
Abstract. Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycleclimate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the rst generation UK Earth System Model. Previously, JULES represented ve plant functional types (PFTs): broadleaf trees, needle-leaf trees, C3 and C4 grasses, and shrubs. This study addresses three developments
in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These rst two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C3 and C4 grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (Vcmax), and updated the leaf
Published by Copernicus Publications on behalf of the European Geosciences Union.
Improved representation of plant functional types and physiology in the Joint UK Land Environment Simulator (JULES v4.2) using plant trait information
2416 A. B. Harper et al.: Improved plant functional types in JULES
turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area.
Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard ve PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C3 grasses in cold environments and C4 grasses that were previously over-productive.On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and62 Pg C year1, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity uxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher condence in simulations of vegetation dynamics and carbon storage.
1 Introduction
The net exchange of carbon dioxide between the vegetated land and the atmosphere is predominantly the result of two large and opposing uxes: uptake by photosynthesis and efux by respiration from soils and vegetation. CO2 can also be released by land ecosystems due to vegetation mortality resulting from human and natural disturbances, such as changes in land use practices, insect outbreaks, and res.Vegetation models are used to quantify many of these uxes, and the evolution of the terrestrial carbon sink strongly affects future greenhouse gas concentrations in the atmosphere (Friedlingstein et al., 2006; Friedlingstein, 2015; Arora et al., 2013). A subset of vegetation models also predicts both compositional and biogeochemical responses of vegetation to climate change (dynamic global vegetation models, DGVMs), one of these being the Joint UK Land Environment Simulator (JULES). ) (Best et al., 2011; Clark et al., 2011). JULES predecessor, the Met Ofce Surface Exchange Scheme (MOSES: Cox et al., 1998, 1999; Essery et al., 2001, 2003) was the land component of the Hadley Centre Global Environmental Model (HadGEM2), and JULES will represent the land surface in the next generation UK Earth System Model (UKESM). Within JULES, the TRIFFID model (Top-down Representation of Foliage and Flora Including Dynamics; Cox, 2001) predicts changes in biomass and the fractional coverage of ve plant functional types (PFTs; broadleaf trees, needle-leaf trees, C3 grass, C4 grass, and shrubs) based on cumulative carbon uxes and a predetermined dominance hierarchy. DGVMs such as JULES are essential for planning ecosystem management, understanding carbon cycleclimate feedbacks, and evaluating the potential
Table 1. Parameters used for the ve PFT experiment (JULES5). The standard PFTs are broadleaf trees (BT), needle-leaf trees (NT), C3 grass, C4 grass, and shrubs (SH). Nm was calculated by dividing the default Nl0 by Cmass (0.5 in this study), LMA was calculated as L Cmass, and sv was calculated to yield the same Vcmax,25 as
with the default ve PFTs. All other parameters were taken from Clark et al. (2011). Parameters are dened in Table A1.
BT NT C3 C4 SH
awl 0.65 0.65 0.005 0.005 0.10 Dcrit 0.09 0.06 0.10 0.075 0.10 dT 9 9 9 9 9 f0 0.875 0.875 0.900 0.800 0.900 fd 0.010 0.015 0.015 0.025 0.015 iv 0 0 0 0 0 Lmax 9 6 4 4 4
Lmin 1 1 1 1 1 LMA 0.075 0.200 0.050 0.100 0.100 N
a 1.73 3.30 1.83 3.00 3.00 Nm 0.023 0.0165 0.0365 0.030 0.030 rootd 3 1 0.5 0.5 0.5 sv 21.33 8.00 32.00 8.00 16.00
Tlow 0 10 0 13 0
Toff 5 40 5 5 5
Topt 32 22 32 41 32 Tupp 36 26 36 45 36
Vcmax,25 36.8 26.4 58.4 24.0 48.0 0.08 0.08 0.12 0.06 0.08 0 0.25 0.25 0.25 0.25 0.25 p 20 15 20 20 15 [notdef]rl 1.0 1.0 1.0 1.0 1.0 [notdef]sl 0.10 0.10 1.00 1.00 0.10
These are derived from other parameters. Here Na is g N m2.
impacts of climate change on global ecosystems. However, the use of DGVMs in ESMs is relatively rare. For example, of the nine coupled carbon cycleclimate models evaluated by Arora et al. (2013), only three distinct DGVMs interactively simulated changes in the spatial distribution of PFTs (the spatially explicit individual-based (SEIB)-DGVM, JS-BACH (the Jena Scheme for BiosphereAtmosphere Coupling in Hamburg), and JULES/TRIFFID).
Previous benchmarking studies of JULES and MOSES identied certain areas needing improvement, such as the seasonal cycle of evaporation, gross primary productivity (GPP), and total respiration in regions with seasonally frozen soils and in the tropics; too high growing season respiration; and too low GPP in temperate forests (Blyth et al., 2011); and too high GPP in the tropics (20 S20 N) (Blyth et al., 2011; Anav et al., 2013). In 21st century simulations, JULES vegetation carbon was sensitive to climate change. In particular, the tropics were very sensitive to warming, with large simulated losses of carbon stored in the Amazon forest when the climate became very dry and hot (Cox et al., 2000, 2004, 2013; Galbraith et al., 2010; Huntingford et al., 2013).
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A. B. Harper et al.: Improved plant functional types in JULES 2417
(a) (b)
(c) (d)
Figure 1. Trade-offs between leaf mass per unit area (LMA; kg m2) and (a, c) leaf nitrogen (g g1), and between LMA and (b, d) leaf life span (LL). (a, b) Parameters in the standard JULES, converted from Nl0 and l based on 0.4 kg C per kg dry mass (assumed parameter in
JULES from Clark et al., 2011). (c, d) Median values from the TRY database for the new nine PFTs. In (b) and (d), the lled circles show the observed data and the open shapes show the median values from global simulations of JULES from 1982 to 2012. Vertical and horizontal lines show the range of vales between the lower and upper quartile of data.
Based on these previous results, our study addressed three potential improvements in the parameterization and representation of PFTs in JULES. First, the original ve PFTs (Table 1) did not represent the range of leaf life spans and metabolic capacities that exists in nature, and so trees and shrubs were split into deciduous and evergreen PFTs. In a broad sense, the differences between evergreen and deciduous strategies can be summarized in a leaf economics spectrum, where leaves employ trade-offs in their nitrogen use (Reich et al., 1997; Wright et al., 2004; Fig. 1). When photosynthesis is limited by CO2, the photosynthetic capacity of a leaf is dependent on the maximum rate of carboxylation of Rubisco (Vcmax). Plants allocate about 1030 % of their nitrogen into synthesis and maintenance of Rubisco (Evans, 1989), while a portion of the remaining nitrogen is put toward leaf structural components, and hence the strong relationship between photosynthetic capacity and leaf nitrogen concentration (e.g., Meir et al., 2002; Reich et al., 1998; Wright et al., 2004) and leaf structure (Niinemets, 1999). On average, evergreen species have a lower photosynthetic capacity and respiration per unit leaf mass (Reich et al., 1997; Wright et al., 2004; Takashima et al., 2004), higher leaf mass
per unit area (LMA) (Takashima et al., 2004; Poorter et al., 2009), allocate a lower fraction of leaf N to photosynthesis (Takashima et al., 2004), and exhibit lower N loss at senescence (Aerts, 1995; Silla and Escudero, 2003; Kobe et al., 2005) than deciduous species. There is also a positive relationship between LMA and leaf life span (Reich et al., 1992, 1997; Wright et al., 2004). Leaves with high nutrient concentration tend to have a short life span and low LMA. They are able to allocate more nutrients to photosynthetic machinery to rapidly assimilate carbon at a relatively high rate (but they also have high respiration rates). Conversely, leaves with less access to nutrients use a longer-term investment strategy, allocating nutrients to structure, defense, and tolerance mechanisms. They tend to have longer life spans, low assimilation and respiration rates, but high LMAs.
Second, we distinguished between tropical broadleaf evergreen trees and broadleaf evergreen trees from warm-temperate and Mediterranean climates, based on fundamental differences in leaf traits, chemistry, and metabolism (Niinemets et al., 2007, 2015; Xiang et al., 2013). For example, measured Vcmax for a given leaf N per unit area (NA) can be lower in tropical evergreen trees than in temperate broadleaf
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2418 A. B. Harper et al.: Improved plant functional types in JULES
Table 2. Updated parameters used in JULES9ALL. The new PFTs are tropical broadleaf evergreen trees (BET-Tr), temperate broadleaf evergreen trees (BET-Te), needle-leaf evergreen trees (NET), needle-leaf deciduous trees (NDT), C3 grass, C4 grass, evergreen shrubs (ESh), and deciduous shrubs (DSh).
BET-Tr BET-Te BDT NET NDT C3 C4 ESh DSh
awl 0.65 0.65 0.65 0.65 0.75 0.005 0.005 0.10 0.10 Dcrit 0.090 0.090 0.090 0.060 0.041 0.051 0.075 0.037 0.030 dT 9 9 9 9 9 0 0 9 9 f0 0.875 0.892 0.875 0.875 0.936 0.931 0.800 0.950 0.950 fd 0.010 0.010 0.010 0.015 0.015 0.019 0.019 0.015 0.015 iv 7.21 3.90 5.73 6.32 6.32 6.42 0.00 14.71 14.71 Lmax 9 7 7 7 6 3 3 4 4
Lmin 1 1 1 1 1 1 1 1 1 LMA 0.1039 0.1403 0.0823 0.2263 0.1006 0.0495 0.1370 0.1515 0.0709 N
a 1.76 2.02 1.74 2.61 1.87 1.19 1.55 2.04 1.54 Nm 0.017 0.0144 0.021 0.0115 0.0186 0.0240 0.0113 0.0136 0.0218 rootd 3 2 2 1.8 2 0.5 0.5 1 1 sv 19.22 28.40 29.81 18.15 23.79 40.96 20.48 23.15 23.15
Tlow 13 13 5 5 5 10 13 10 0
Toff 0 40 5 40 5 5 5 40 5
Topt 39 39 39 33 34 28 41 32 32 Tupp 43 43 43 37 36 32 45 36 36
Vcmax,25 41.16 61.28 57.25 53.55 50.83 51.09 31.71 62.41 50.40 0.08 0.06 0.08 0.08 0.10 0.06 0.04 0.06 0.08 0 0.25 0.50 0.25 0.25 0.25 3.0 3.0 0.66 0.25 p 15 15 20 15 20 20 20 15 30 [notdef]rl 0.67 0.67 0.67 0.67 0.67 0.72 0.72 0.67 0.67 [notdef]sl 0.10 0.10 0.10 0.10 0.10 1.00 1.00 0.10 0.10
These are derived from other parameters. Here Na is g N m2.
evergreen trees (Kattge et al., 2011), resulting in lower Vcmax and maximum assimilation rates for tropical forests (Car-swell et al., 2000; Meir et al., 2002, 2007; Domingues et al., 2007, 2010; Kattge et al., 2011). Collectively, the evergreen/deciduous and tropical/temperate distinctions resulted in a new set of nine PFTs for JULES: tropical broadleaf evergreen trees (BET-Tr), temperate broadleaf evergreen trees (BET-Te), broadleaf deciduous trees (BDT), needle-leaf evergreen trees (NET), needle-leaf deciduous trees (NDT), C3 grasses, C4 grasses, evergreen shrubs (ESh), and deciduous shrubs (DSh) (Table 2).
Lastly, several parameters relating to variation in photosynthesis and respiration have not been updated since MOSES was developed in the late 1990s. We used data on LMA (kg m2), leaf N per unit mass, Nm (kg N kg1), and leaf life span from the TRY database (Kattge et al., 2011; accessed November 2012). The new parameters for leaf nitrogen and LMA were used to calculate a new Vcmax at 25 C, and to update phenological parameters that determine leaf life span. Other parameters related to leaf dark respiration, canopy radiation, canopy nitrogen, stomatal conductance, root depth, and temperature sensitivities of Vcmax were revised based on a review of recently available observed values, which are described in Sect. 2.
The purpose of our paper is to document these changes, and to evaluate their impacts on the ability of JULES to model CO2 exchange for selected sites and globally on the scale of biomes, with a focus on the gross and net primary productivity. Specically, we explore the consequences for carbon uxes on seasonal and annual timescales of switching from the current ve PFTs to a greater number of PFTs (nine) that account for growth habit (evergreen versus deciduous) and temperate/tropical plant types.
2 Model description
Full descriptions of the model equations are in Clark et al. (2011) and Best et al. (2011). Here we briey describe relevant current equations in JULES, associated changes in terms of updated parameter values, and document new equations and parameters. The revisions discussed in our study fall into three categories: (1) changes to model physiology based on leaf trait data from TRY; (2) adjustment of parameters to account for the properties of the new PFTs (evergreen/deciduous, tropical/temperate); and (3) calibration of parameters based on known biases in the model and a review of the literature. Parameters for the standard ve PFTs and for the new nine PFTs are given in Tables 1 and 2, re-
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A. B. Harper et al.: Improved plant functional types in JULES 2419
spectively, and a summary of all parameters are in Table A1 in Appendix A. For the site-level simulations, we incrementally made changes to the model to determine whether or not changes improved the simulations. This resulted in a total of eight experiments (Table 3). The version of JULES with ve PFTs (Experiment 0) is kept as similar as possible to the conguration used in the TRENDY experiments, which are a set of historical simulations to quantify the global carbon cycle (e.g., Le Qur et al., 2014; Sitch et al., 2015) that have been included in several recent publications. In the supplement, we provide a set of recommended parameters and guidance for users who wish to run JULES with the original ve PFTs (Table A2).
2.1 JULES model
In JULES, leaf-level photosynthesis for C3 and C4 plants (Collatz et al., 1991, 1992) is calculated based on the limiting factor of three potential photosynthesis rates: Wl (light limited rate), We (transport of photosynthetic products for C3 and PEPCarboxylase limitation for C4 plants), and Wc (Rubisco limited rate) (see Supplement). We and Wc depend on
Vcmax, the maximum rate of carboxylation of Rubisco, which is a function of the Vcmax at 25 C (Vcmax,25):
Vcmax = Vmax,25fT (TC)
1 + exp
0.3
gs(Cs Ci)
1.6 , (6)
where Cs and Ci are the leaf surface and internal CO2 concentrations, respectively. The gradient in CO2 between the internal and external environments is related to leaf humidity decit at the leaf surface (D) following Jacobs (1994):
Ci [Gamma1]
Cs [Gamma1] =
TC Tupp [parenrightbig][parenrightbig][bracketrightbig]
[1 + exp(0.3(Tlow TC))]
, (1)
f0
1
D Dcrit
where Tc is the canopy temperature in Celcius, and
fT (TC) = Q0.1(TC25)10,leaf, (2) where Tupp and Tlow are PFT-dependent parameters. Q10,leaf
is 2.0.
JULES has several options for representing canopy radiation. Option 5, as described in Clark et al. (2011), includes a multi-layer canopy with sunlit and shaded leaves in each layer, two-stream radiation with sunecks penetrating below the top layer, and light-inhibition of leaf respiration. Additionally, N is assumed to decay exponentially through the canopy with an extinction coefcient, kn, of 0.78 (Mercado et al., 2007). Vcmax,25 is calculated in each canopy layer (i)
as
Vmax,25,i = neffNl0ekn(i1)/10, (3)
assuming a 10-layer canopy. The parameter Nl0 is the top-leaf nitrogen content (kg N kg C1), and neff linearly relates leaf N concentration to Vcmax,25.
Leaf dark respiration is assumed to be proportional to the Vcmax calculated in Eq. (1):
Rd = fdVcmax (4)
with a 30 % inhibition of leaf respiration when irradiance is > 10 mol quanta m2 s1 (Atkin et al., 2000; Mercado et
al., 2007; Clark et al., 2011). Plant net primary productivity (NPP) is very sensitive to fd, and since the vegetation fraction depends on NPP when the TRIFFID competition is turned on, the distribution of PFTs can also be sensitive to fd.
The parameter was modied from 0.015 (Clark et al., 2011) to 0.010 for all broadleaf tree PFTs in this study, based on underestimated coverage of broadleaf trees in previous versions of JULES. Leaf photosynthesis is calculated as
Al = (W Rd) , (5)
where W is the smoothed minimum of the three limiting rates (Wl, We, Wc), and is a soil moisture stress factor. The factor is 1 when soil moisture content of the root zone ( : m3 m3) is at or above a critical threshold ( crit), which depends on the soil texture. When soil water content drops below crit, decreases linearly until reaches the wilting point (where =0) (Cox et al., 1998).
Stomatal conductance (gs) is linked to leaf photosynthesis:
A =
. (7)
Here, [Gamma1] is the CO2 compensation point or the internal partial pressure of CO2 at which photosynthesis and respiration balance, and Dcrit is the critical humidity decit (f0 and Dcrit are PFT-dependent parameters). In JULES, the surface latent heat ux (LE) is due to evaporation from water stored on the canopy, evaporation of water from the top layer of soil, transpiration through the stomata, and sublimation of snow. Any change to LE will also impact the sensible heat and ground heat uxes, since these are linked to the total surface energy balance (Best et al., 2011).
Total plant (autotrophic) respiration, Ra, is the sum of maintenance and growth respiration (Rpm and Rpg, respectively):
Rpm = 0.012Rd
+Nr + Ns Nl
[parenrightbigg]
(8)
and
Rpg = rg(GPP Rpm), (9)
where rg is a parameter set to 0.25 (Cox et al., 1998, 1999), and the nitrogen concentration of roots, stem, and leaves are given by Nr, Ns, and Nl, respectively. When using canopy
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area when lm 2 0:
dpdt = p (1 p) for lm 2 0, (16a)
dpdt = p for lm > 2 0, (16b)
where p is the leaf growth rate.
2.2 Updated leaf N, Vcmax,25, and leaf life span (Experiments 12)
Essentially, with the revised trait-based physiology, the parameter l (Eqs. 1011) and Nl0 (Eqs. 3, 1012) were replaced with LMA and Nm, respectively, from the TRY database. Nl0 and Nm both describe the nitrogen content at the top of the canopy, but the former is N per unit carbon, while the latter is the more commonly observed N per unit dry mass. Nm can be converted to Nl0 using leaf carbon content per dry mass (Cm). Historically, Cm was 0.4 in JULES (Schulze et al., 1994), but we updated it to 0.5 in all versions of JULES evaluated in this study (Reich et al., 1997; White et al., 2000; Zaehle and Friend, 2010).
We also changed the equation for Vcmax,25 from a function of Nl0 (Eq. 3) to a function of leaf N per unit area, Na, a more commonly observed leaf trait, calculated as the product of the observed leaf traits LMA (kg m2) and Nm (kg N kg1):
Na = Nm LMA (17)
and Vcmax,25 (mol CO2 m2 s1) is
Vcmax,25 = iv + svNa, (18)
where parameters iv (mol CO2 m2 s1) and sv (mol CO2 gN1 s1) were taken directly from Kattge et al. (2009; hereafter K09) (see also Medlyn et al., 1999), with two exceptions. First, the Vcmax parameterization from
K09 was based on the leaf C3 photosynthesis model. C4 plants have high CO2 concentration at the site of Rubisco, and therefore require less Rubisco than C3 plants (von Caemmerer and Furbank, 2003). C4 species typically have 3050 % as much Rubisco per unit N as C3 species (Sage and Pearcy, 1987; Makino et al., 2003; Houborg et al.,
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2420 A. B. Harper et al.: Improved plant functional types in JULES
Table 3. Experiments for the FLUXNET site-level evaluation.
Experiment number Description
0: JULES5 Five PFTs (Table 1)
1 Nine PFTs with Nm, LMA, and Vcmax,25 from TRY
2: JULES9-TRY Exp. 1 + parameters affecting leaf life span
3 Exp. 2 +f0 and Dcrit
4 Exp. 2 +
5 Exp. 2 + adjusted fd, Tupp, Tlow, and sv
6 Exp. 2 + rootd, awl
7: JULES9 All new PFT parameters (Table 2)
radiation model 5 in JULES, these are calculated as
Nl = Nl0l LAI, (10)
Nr = Nl0l[notdef]rl Lbal, (11)
Ns = Nl0[notdef]sl slh Lbal, (12)
where l is specic leaf density (kg C m2 LAI1), h is the vegetation height in meters, Lbal is the balanced leaf area index (LAI) (the seasonal maximum of LAI based on allometric relationships, Cox, 2001), [notdef]rl and [notdef]sl relate N in roots and stems to top-leaf N, and sl is 0.01 kg C m1 LAI1. In Eqs. (10)(12), Nl0, l, [notdef]rl, and [notdef]sl are PFT-dependent parameters.
The NPP is
NPP = GPP Ra. (13)
For each PFT in JULES, the NPP determines the carbon available for spreading (expanding fractional coverage in the grid cell, only relevant when the TRIFFID competition is turned on) or for growth (growing leaves or height). The net ecosystem exchange (NEE; positive ux from the land to the atmosphere) is
NEE = Reco GPP, (14)
where Reco is the total ecosystem respiration.
Phenology in JULES affects leaf growth rates and timing of leaf growth/senescence based on temperature alone (Cox et al., 1999; Clark et al., 2011). When canopy temperature (Tc) is greater than a temperature threshold (Toff), the leaf turnover rate ( lm) is equal to 0. When Tc < Toff, the turnover rate is modied as in Eq. (15a) (where Toff, 0, and dT are PFT-dependent parameters):
lm = 01 + dT (Toff Tc) for Tc Toff, (15a) lm = 0 for Tc > Toff. (15b)
The leaf turnover rate affects phenology
p = LAILbal [parenrightBig]by triggering a loss of leaf area for lm > 2 0, and a growth of leaf
A. B. Harper et al.: Improved plant functional types in JULES 2421
2013). We chose a slope (sv) for C4 to give a Vcmax,25 that is half of that for C3 grass, and set the intercept (iv) to 0.This resulted in a Vcmax,25 of 32 mol CO2 m2 s1 for C4 grass, which is similar to observed values in natural grasses (Kubien and Sage, 2004; Domingues et al., 2007) and Vcmax,25 in seven other ESMs (1338 mol CO2 m2 s1;
Rogers, 2013). Second, K09 reported a separate Vcmax,25 for tropical trees growing on oxisols (old tropical soils with low phosphorous availability) and non-oxisols. For the BET-Tr PFT, we calculated a weighted mean slope and intercept from their Table 2 to represent an average tropical soil.
The new Vcmax,25 for canopy level i is calculated as (replacing Eq. 3)
Vmax,25i = iv + svNaeKn(i1)/10. (19)
The leaf, root, and stem nitrogen contents are (replacing Eqs. 1012)
Nl = NmLMA LAI, (20)
Nr = NmLMA[notdef]rl Lbal, (21)
Ns =
level evaluation of JULES, we incrementally added these changes (Table 3).
2.3.1 Stomatal conductance (Experiment 3)
JULES stomatal conductance is related to the leaf internal CO2, where Ci/Cs is proportional to the parameters f0 and 1/Dcrit (Eq. 7). For vapor pressure decits (D) greater than Dcrit, the stomata close. For D < Dcrit, stomata gradually open in response to a reducing evaporative demand.Needle-leaf species in JULES have a lower Dcrit than other trees, grasses, and shrubs. The lower Dcrit increases the likelihood of the stomata being closed similar to Mediterranean conifers that tend to close their stomata earlier than angiosperms (Carnicer et al., 2013) and it tightly regulates the stomatal aperture, making plants more sensitive to increasing D. This is analogous to plants conserving water at the expense of assimilation. We use updated f0 and Dcrit from a synthesis of water use efciency at the FLUXNET sites (Dekker et al., 2016). Compared to the standard ve PFT parameters, the Dcrit was decreased for BET-Te, NDT,
C3 grass, and shrubs. The parameter f0 was increased for these PFTs, which increased Ci for all D < Dcrit.
2.3.2 Radiation (Experiment 4)
The light-limited photosynthesis rate (Wl) is proportional to [absorbed PAR], where is the quantum efciency
of photosynthesis (mol CO2 [mol quanta]1). We reduced from 0.08 to 0.06 for C3 grass and evergreen PFTs typical of semi-arid and arid environments, and from 0.06 to0.04 for C4 grass, where previously the model over-predicted GPP for a given PAR. Quantum efciency was set at 0.10 for NDT. These values are still within the range reported in Skillman (2008). An example of the changes is shown in the Supplement, Fig. S1. Decreasing the for BET-Te and ESh PFTs helped reduce a high bias in the GPP at low irradiances at Las Majadas (Spain a savannah site), while increasing for NDT improved the light response of GPP at Tomakai (Japan a larch site).
2.3.3 Photosynthesis and respiration parameters (Experiment 5)
The leaf dark respiration is calculated as a fraction, fd, of Vcmax (Eq. 4). In testing JULES, we found that C3 grasses were overly productive and tended to be the dominant grass type even in tropical ecosystems where we expected C4 dominance. Therefore, we increased the fd for C3 (from 0.015 to0.019) and decreased the fd for C4 (from 0.025 to 0.019) so the two grass PFTs would have similar Rd rates for a given
Vcmax.
Preliminary evaluation of JULES GPP at the FLUXNET sites in Table 4 revealed the need for a higher (lower) Vcmax,25
for the BET-Tr and NDT (BET-Te) PFTs than the mean value reported in K09. For these PFTs, the slope parameter (sv)
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Nm
Cm [notdef]sl sl h Lbal, (22)
Four phenological parameters (Toff, dT , 0, and p, Eqs. 1516) were adjusted to capture the trade-off between leaf life span and LMA. We set Toff to 5 C for deciduous trees and shrubs, to 40 C for BET-Te, NET, and ESh, and
to 0 C for BET-Tr. The latter reects the fact that many tropical evergreen tree species cannot tolerate frost (Woodward and Williams, 1987; Prentice et al., 1992). For the other evergreen PFTs, the value of 40 C ensured that plants only
lose their leaves in extremely cold environments. Second, we changed dT to 0 for grasses to attain constant leaf turnover rates (Eq. 15). This xed an unrealistic seasonal cycle in LAI of grasses and makes grasses more competitive in very cold environments (Hopcroft and Valdes, 2015). Third, we adjusted 0 for grasses and evergreen species to reect the median observed leaf life span in the TRY database. Last, we changed p from its default value of 20 to 15 year1 for the
PFTs with the thickest leaves (NET, ESh, BET-Temp, BETTrop) and to 30 year1 for the PFT with the thinnest leaves (DSh). The parameter p controls the rate of leaf growth in the spring and senescence at the end of the growing season (Eq. 16b). To reduce an overestimation of uptake during the spring with the new phenology for grass, the maximum LAI for grasses was reduced from 4 to 3.
2.3 Other updates to JULES parameters with new PFTs (Experiments 36)
Additional changes to JULES were made to account for the properties of the new PFTs, to incorporate recent observations, and to correct known biases in the model. These fall into four categories: radiation, stomatal conductance, photo-synthesis and respiration, and plant structure. For the site-
2422 A. B. Harper et al.: Improved plant functional types in JULES
Table 4. Sites used in the site simulations. Land cover is according to site PI.
Site name Location Simulated years Land cover Dominant PFT(s)
BR-Ma2 Manaus, Brazil 20022005 Evergreen broadleaf forest 100 % BETBR-Sa1 Santarem (Tapajs Forest, KM67), Brazil 20022004 Evergreen broadleaf forest 100 % BETBR-Sa3 Santarem (Tapajs Forest, KM77), Brazil 20012005 Pasture 20 % BET, 75 % C4, 5 % soil
DE-Tha Tharandt, Germany 19982006 Needle-leaf evergreen forest 100 % NETES-ES1 El Saler, Spain 19992006 Needle-leaf evergreen forest 100 % NETES-LMa Las Majadas, Spain 20042006 Closed shrub 33 % Temp-BET, 33 % C3, 33 % ESh
FI-Hyy Hyytil, Finland 19982002 Needle-leaf evergreen forest 100 % NETFI-Kaa Kaamanen, Finland 20002005 Wetland (simulated as C3 grass) 80 % C3 grass, 20 % bare soil
JP-Tom Tomakai, Japan 20012003 Needle-leaf deciduous plantation 10 % BDT, 10 % NET, 80 % NDT US-Bo1 Bondville, IL, US 19972006 Crop (rotating C3/ C4) 40 % C3, 40 % C4, 20 % soil
US-FPe Fort Peck, MT, US 20002006 Grassland (C3) 80 % C3 grass, 20 % bare soil US-Ha1 Harvard, MA, US 19952001 Broadleaf deciduous forest 100 % BDT
US-MMS Morgan Monroe Forest, US 20002004 Broadleaf deciduous forest 100 % BDTUS-Ton Tonzi, CA, US 20012006 Woody savannah 33 % BDT, 33 % C3, 33 % DSh
Figure 2. Vcmax,25 for the new nine PFTs (black), from the comparable PFT from the TRY data (Kattge et al., 2009) (green), and from the standard ve PFTs (red). Asterisks indicate the Vcmax,25 for
JULES9 prior to calibration based on the FLUXNET sites. The standard deviation reported in Kattge et al. (2009) are also shown for the observations with the vertical lines. BET-Tr Tropical broadleaf evergreen trees, BET-Te Temperate broadleaf evergreen trees, BDT Broadleaf deciduous trees, NET Needle-leaf evergreen trees, NDT Needle-leaf deciduous trees, C3G C3 grass, C4G C4 grass, ESh Evergreen shrubs, DSh Deciduous shrubs.
was adjusted to result in the nal Vcmax,25 for each PFT (black bars, Fig. 2), using the mean 1 standard deviation
of Vcmax,25 from K09 as an upper limit.
Tupp and Tlow were also modied, as optimal Vcmax can occur at temperatures near 40 C (Medlyn et al., 2002), and the previous optimal temperature for Vcmax was 32 C for broadleaf trees (BT) and 22 C for NT. A study of seven broadleaf deciduous tree species found Topt for Vcmax ranging from 35.9 C to > 45 C (Dreyer et al., 2001), and maximum
Vcmax can occur at temperatures of at least 38 C in the Amazon forest (B. Kruijt, personal communication, 2015). There-
fore, we changed Topt from 32 to 39 C for all broadleaf trees and from 22 to 33 and 32 C for NET and NDT, respectively. C3 grass Topt was decreased from 32 to 28 C to help reduce the high productivity bias in grasses.
Additionally, the ratio of nitrogen in roots to leaves ([notdef]rl) was updated following the relationships in Table 1 of
Kerkhoff et al. (2006). However, instead of assigning a separate [notdef]rl for each PFT, we assigned the mean values for trees/shrubs and grasses (0.67 and 0.72, respectively).
2.3.4 Plant structure (Experiment 6)
There is evidence that larch trees (NDT) can be tall with a relatively low LAI compared to needle-leaf evergreen trees (Ohta et al., 2001; Hirano et al., 2003) and compared to broadleaf deciduous trees (Gower and Richards, 1990). In JULES, canopy height (h) is proportional to the balanced LAI, Lb:
h =
Lbwl1b. (23)
The parameter awl relates the LAI to total stem biomass, and for trees it is 0.65. Hirano et al. (2003) found h = 15 m
and maximum LAI = 2.1, which would imply awl = 0.91,
and Ohta et al. (2001) found h = 18 m and LAI = 3.7, im
plying awl = 0.75. Therefore, we adjusted awl for NDT to
0.75, which was an important change for allowing NDT to out-compete BDT in high latitudes.
We also changed the root depths, although these changes were constrained by the 3 m deep soil in the standard JULES setup. Previously, root depths were 3 m for broadleaf trees, 1 m for needle-leaf trees, and 0.5 m for grasses and shrubs (Best et al., 2011). With the new PFTs, roots are shallower for BET-Te and BDT (2 m), and deeper for NET (1.8 m), NDT (2 m), and shrubs (1 m) (Zeng, 2001).
awl
aws sl
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A. B. Harper et al.: Improved plant functional types in JULES 2423
Figure 3. (a) Dominant vegetation type from the ESA LC_CCI data set, aggregated to the new nine PFTs. (b) Color-coded map of global biomes, based on World Wildlife Fund biomes.
3 Methods
3.1 Data
We analyzed leaf Nm, specic leaf area (= 1/ LMA), and
leaf life span from the TRY database (accessed in November 2012). Data were translated from species level to both the standard ve and new nine PFTs based on a look-up table provided by TRY, and screened for duplicate entries. We only selected entries with measurements for both LMA and Nm. This resulted in 9372 LMA /Nm pairs and 1176 leaf life span measurements (Supplement).
To evaluate the model performance we used GPP from the model tree ensemble (MTE) of Jung et al. (2011), MODIS NPP from the MOD17 algorithm (Zhao et al., 2005; Zhao and Running, 2010), and GPP and NEE from 13 and 14 FLUXNET sites (Table 4). Using the net exchange of CO2 observed at the FLUXNET sites, NEE was partitioned into GPP and Reco. Assuming that nighttime NEE = Reco, Reco
was estimated as a temperature function of nighttime NEE (Reichstein et al., 2005; Groenendijk et al., 2011).
3.2 Model simulations
We performed two sets of simulations to evaluate the impacts of the new PFTs in JULES v4.2. First, site-level simulations used observed meteorology from 14 FLUXNET towers these include the nine original sites benchmarked in the study of Blyth et al. (2011), plus an additional ve to represent more diversity in land cover types and climate. The vegetation cover was prescribed as in Table 4, and vegetation competition was turned off. The changes described in Sect. 2.2 and 2.3 were incrementally added to evaluate the effect of each group of changes (Table 3). Full results are shown in the Supplement, but for the main text we focus the discussion on JULES with ve PFTs (JULES5); JULES with nine PFTs and updated Nm, LMA, Vcmax,25, and leaf life span from the TRY database (JULES9TRY); and JULES with nine PFTs and all updated parameters described in Sect. 2.3 (JULES9ALL). These are, respectively, Experiments 0, 2, and 7 in Table 3.
Soil carbon takes more than 1000 years to equilibrate in
JULES, so we used an accelerated method that only requires 200300 years of spin-up (depending on the site). JULES has four soil pools (decomposable and resistant plant material, long-lived humus, and microbial biomass), and the decom-
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2424 A. B. Harper et al.: Improved plant functional types in JULES
posable material pool has the fastest turnover rate (equivalent to 10 year1) (Clark et al., 2011). For each experiment,
soil carbon was spun-up using accelerated turnover rates in the three slower soil pools for the rst 100 years. The rates of the resistant, humus, and biomass pools were increased by a factor of 33, 15, and 500, respectively, so all pools had the same turnover time as the fastest pool. This resulted in unrealistically depleted soil carbon pools. The second step of the spin-up was to multiply the pool sizes by these same factors, and then allow the soil carbon to spin-up under normal conditions for an additional 100200 years.
Second, global simulations were conducted for JULES5 and JULES9ALL. It could be argued that similar model improvements might be gained with the original ve PFTs with improved parameters. We tested this hypothesis with a third global experiment, JULES5ALL, with ve PFTs but improved parameters (Table A2). The global simulations followed the protocol for the S2 experiments in TRENDY (Sitch et al., 2015), where the model was forced with observed annual-average CO2 (Dlugokencky and Tans, 2013), climate from the CRU-NCEP data set (v4, N. Viovy, personal communication, 2013), and time-invariant fraction of agriculture in each grid cell (Hurtt et al., 2011). Vegetation cover was prescribed based on the European Space Agencys Land Cover Climate Change Initiative (ESA LC_CCI) global vegetation distribution (Poulter et al., 2015, processed to the JULES 5 and nine PFTs by A. Hartley) (Fig. 3a). JULES did not predict vegetation coverage in this study, which enabled us to evaluate JULES GPP and NPP given a realistic land cover. The evaluation of vegetation cover and updated competition for nine PFTs will be evaluated in a follow-up paper. Since the land cover was prescribed based on a 2010 map, we also set the agricultural mask based on land use in 2010, and enforced consistency between the two maps such that fraction of agriculture could not exceed the fraction of grass in each grid cell. During the spin-up (300 years with 100 years of accelerated turnover rates as at the sites), we used atmospheric CO2 concentration from 1860 and recycled climate from 1901 1920. The transient simulation (with time-varying CO2 and climate) was from 19012012. The model spatial resolution was N96 (1.875 longitude 1.25 latitude).
3.3 Model evaluation
The model evaluation is presented in two stages. First, using the site-level simulations, we evaluated GPP and NEE with the root mean square error (RMSE) and correlation coefcient, r, based on daily and monthly averaged uxes, respectively. Site history can result in non-zero annual NEE, but JULES maintains annual carbon balance, so it is not realistic to expect the simulated annual NEE to match the observations. Therefore, we compared anomalies of NEE instead.
We summarized the changes in RMSE and r using relative improvements for each experiment in Table 4, i. The statistics were calculated such that positive values denote an
improvement compared to JULES5 (Experiment 0):
RMSE_reli =
RMSE5pfts RMSEi
RMSE5pfts , (24)
r_reli =
rir5pfts
r5pfts . (25)
Second, we compared the model from global simulations to biome-averaged uxes in eight biomes based on 14 World Wildlife Fund terrestrial ecoregions (Olson et al., 2001) (Fig. 3b, Table S3). Fluxes were averaged for the land in each biome in both the model and the observations. We evaluated seasonal cycles of GPP from the MTE (Jung et al., 2011), and annually averaged GPP (from the MTE) and NPP (from MODIS). The tropical forest biome includes regions of tropical grasslands and pasture in the ESA LC_CCI data set, the BET-Tr PFT is dominant in only 38 % of the biome and grasses occupy 36 %. Therefore, we only included the grid cells where the dominant PFT in the ESA data is BET-Tr. The extratropical mixed forest biome has a large coverage of agricultural land, and as a result 46 % of the biome is C3 grass, while BDT and NET only cover 14 and 8 % of the biome, respectively. We omitted grid cells with > 20 % agriculture in 2012 to calculate the biome average uxes.
4 Results
4.1 Data analysis of leaf traits
With the previous ve PFTs, only the needle-leaf tree PFT occupied the slow investment end of the leaf economics spectrum (high LMA and low Nm) (Fig. 1). The new PFTs were given the median Nm and LMA from the TRY data set (Fig. 1c), and these exhibit a range of deciduous and evergreen strategies, although there is substantial overlap between PFTs. The needle-leaf evergreen trees, evergreen shrubs, and temperate broadleaf evergreen trees have low Nm and thick leaves, but their NA (shown in the legend of Fig. 1a,c) is relatively high (> 2 g m2), which has been long known for species with long leaf life spans (> 1 year) (Reich et al., 1992). These traits on aggregate indicate that they use the slow investment strategy of growing thick leaves with low rates of photosynthesis per unit investment of biomass.
Compared to the evergreen PFTs, the deciduous shrubs and broadleaf deciduous trees have higher Nm, thinner leaves, lower NA (1.31.7 g N m2), and leaf life spans of less than 6 months. The tropical broadleaf evergreen trees have a moderate Nm and leaf thickness, with an average life span of 11 months, reecting a mixture of successional stages in the database. The grasses have the shortest leaf life spans.C4 grasses have high LMA, low Nm, and a high NA; while the thinner C3 grasses have a high Nm and low NA. Figure 1 also shows the impacts of changing the phenological parameters (Toff, dT , 0, and p, Eqs. 1516) on median leaf life
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A. B. Harper et al.: Improved plant functional types in JULES 2425
Table 5. Comparison of simulated and observed annual GPP and NPP at FLUXNET sites, listed in order from most to least productive. Units: g C m2 year1. Results are color-coded so blue shows when there is an improvement. The GPP and NPP are based on similar data processing between the FLUXNET observations and model. Sources: 1 Malhi (2009); 2 Gower and Richards (1990), assuming 0.5 gC g1 biomass.
Site GPP JULES5 JULES9 OBS NPP JULES5 JULES9 OBS
BR-Sa1 2671 2795 3314 600 850 1048 1440 130
1
1
2
(JULES9TRY) (Experiments 1 and 2, respectively, in Table 3) at the sites listed in Table 4. The results are summarized in Fig. 4, where yellows and reds indicate increased correlation (Fig. 4a, b) or reduced RMSE (Fig. 4c, d) in each experiment compared to JULES5. Using the Nm, LMA, and Vcmax,25 data from TRY improved the seasonal cycle of GPP at the two tropical forest sites, the evergreen savannah, and the crop site, and decreased the daily RMSE at one NET site (Tharandt), all grass sites, and the NDT site (Tomakai) (Experiment 1, Fig. 4). Enforcing the LMAleaf life span relationship further improved the seasonal cycle at both savannah sites, the two natural C3 grass sites (the seasonal cycle was worse at the crop site), and the NDT site, and further reduced RMSE at the deciduous savannah site and one BDT site (Harvard) (Experiment 2, a.k.a. JULES9TRY). In comparison, applying all parameter changes summarized in Table 3 further reduced the RMSE at every site except the two tropical forests and further increased r at every site except the tropical forests and the evergreen savannah (Experiment 7, a.k.a. JULES9ALL).
Overall, the carbon and energy exchanges were best captured with JULES9ALL. Compared to JULES5, the RMSE for GPP in JULES9ALL decreased by more than 40 % at Kaamanen (C3 grass), Tharandt (NET), and Tomakai (NDT); the daily RMSE of NEE decreased at eight sites; and r increased for NEE at 11 sites. The only sites without an improvement in either metric for NEE were Manaus (BET-Tr) and Bondville (Crop). The improvements to NEE were large at Tharandt (r from 0.61 to 0.76), Fort Peck C3 grass (0.05 to 0.38), and
Tomakai (0.09 to 0.93), and RMSE for NEE decreased by more than 35 % at Kaamanen and Tomakai. Respiration and latent heat uxes are discussed in the supplemental material.
On an annual basis, GPP was higher in JULES9ALL than in JULES5 at every site except for the Tapajs K77 pasture,
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BR-Ma2 2848 3225 3285 835 867 1198 1011 140
BR-Sa3 3318 2116 1623 1125
DE-Tha 1364 1876 1923 547 700 1004
JP-Tom 1306 1361 1723 641 691 747 1100
ES-ES1 1164 1087 1458 383 513 404
US-MMS 1135 1234 1445 463 603 693
US-Ha1 1229 1438 1433 531 686 851
US-Bo1 896 1006 1233 568 457 591
ES-LMA 1095 1257 1133 305 500 644
FI-Hyy 1124 1465 1084 324 605 834
US-Ton 818 794 924 256 365 405
US-FPe 238 368 354 185 88 192
FI-Kaa 633 512 297 126 359 311
span during a 30-year global simulation, where now JULES captures the observed leaf life spans.
Based on the new NA, Vcmax,25 was updated using the new parameters iv and sv (Eq. 18; Fig. 2). The values calculated from the TRY data are shown with asterisks, and these were used in the JULES9TRY experiments. The black bars show the nal Vcmax,25 after adjusting sv for the two broadleaf evergreen tree PFTs and the needle-leaf deciduous trees (see Sect. 2.3.3). Within the trees, the temperate broadleaf evergreen PFT has the highest Vcmax,25, while the needle-leaf deciduous and tropical broadleaf evergreen PFTs have the lowest. Because the JULES C3 and C4 PFTs are assumed representative of natural vegetation, they have relatively low Vcmax,25 (compared to the range from K09 for C3). The NA calculated from median Nm and LMA in this study (1.19 g N m2) is lower than the average NA reported in K09 (1.75 g N m2). However, the C3Vcmax,25
(51.09 mol CO2 m2 s1) is close to values reported for European grasslands (41.9 6.9 mol CO2 m2 s1 and
48.6 3.5 mol CO2 m2 s1 for graminoids and forbs, re
spectively, in Wohlfahrt et al., 1999). In comparison to
JULES5, the new Vcmax,25 is higher for all PFTs except for
C3 grass. Previously, the Vcmax,25 was lower than the ob-served range for all non-tropical trees, but now the Vcmax,25
for all PFTs is within the range of observed values.
4.2 Site-level simulations
In most cases, the higher Vcmax from trait data increased the GPP and NPP, and resulted in higher respiration uxes due to both autotrophic (responding to higher GPP) and heterotrophic (responding to higher litterfall due to higher NPP) respiration. First, we compared JULES with ve PFTs (JULES5) to JULES with nine PFTs and the TRY data
2426 A. B. Harper et al.: Improved plant functional types in JULES
Table 6. (a) Area-weighted GPP from each biome (g C m2 year1). The biome total GPP from MTE is given in Pg C year1 to give perspective of each biomes role in the global total. (b) Area-weighted NPP from each biome (g C m2 yr1).
(a) Biome JULES5 JULES9 JULES5-ALL MTE MTE total
Tropical forest 2403 217 2295 191 2505 217 2244 297 49.9
Tropical forest: only BET-Tr. 2924 144 2955 147 3279 178 2790 273
Tropical savannah 1355 244 1268 223 1320 237 1111 257 21.9
Extratropical mixed forests 947 147 1082 158 1119 167 1119 212 2.9 (13.4*)
Boreal and coniferous forests 514 99 597 118 645 122 650 203 12.1
Temperate grasslands 420 145 465 138 477 140 509 184 8.1
Deserts and shrublands 82 48 91 46 91 47 283 200 4.9
Tundra 86 20 94 20 101 20 279 233 1.9
Mediterranean woodlands 324 147 407 136 405 140 510 190 1.5(b) Biome JULES5 JULES9 JULES5-ALL MODIS17
Tropical forest 956 144 1007 125 951 143 786 352
Only BET-Tr. 1141 101 1233 103 1109 126 929 315
Tropical savannah 527 158 591 143 584 152 451 319
Extratropical mixed forests 586 93 631 104 640 110 563 231
Boreal and coniferous forests 307 65 358 77 385 80 350 155
Temperate grasslands 180 94 243 89 242 90 304 247
Deserts and shrublands 16 29 35 29 33 29 111 133
Tundra 52 14 61 13 65 13 136 94
Mediterranean Woodlands 118 94 201 89 195 89 324 184
* Value for EMF (extra-tropical mixed forest) biome when agricultural mask is not applied.
compared to JULES5. The simulated NPP was too low in JULES5 at both sites. In JULES9ALL, the NPP was too high at Manaus (by 187 g C m2 year1) and too low at Tapajs (by 396 g C m2 year1).
At the two BDT sites (Harvard and Morgan Monroe), the peak summer GPP was closer to observations in JULES9ALL.
GPP was very well reproduced at Harvard (BDT), where the average JJA temperature was 4 C cooler than at Morgan Monroe (29 C compared to 33 C), and, due to differences in the soil parameters, the soil moisture stress factor was higher ( > 0.8 at Harvard compared to 0.5 < < 0.7 at Morgan Monroe). At Morgan Monroe, the observed GPP was nearly zero from NovemberMarch, but all versions of JULES simulated uptake during NovemberDecember, when the average temperatures were still above freezing, possibly due to leaves staying on the trees for too long in the model.The RMSE of NEE decreased (Fig. 5b), but the amplitude of the seasonal cycle was too small at both BDT sites.
4.2.2 Needle-leaf forests
The seasonal cycle of GPP improved at the needle-leaf forests, but JULES9ALL underestimated GPP during midsummer at the larch site (Tomakai) and during the summer at a Mediterranean site (El Saler), and overestimated summertime GPP at a cold conifer site (Hyytil). Although there was a large improvement in the seasonal cycle at El Saler in JULES9ALL, the GPP was still underestimated during the dry
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El Saler (NET), Tonzi (savannah), and Kaamanen, and NPP was higher at every site except for Tapajs K77, El Saler, and Kaamanen (Table 5). Total GPP was improved at every site except for Hyytil (NET) and Las Majadas (savannah), where annual GPP was too high in JULES5, and at El Saler and Tonzi, where the modeled GPP was too low. However, for every site except Hyytil, JULES9ALL was within the range of observed annual GPP. We now explore some site-specic aspects of the carbon cycle results.
4.2.1 Broadleaf forests
Both GPP and NPP were higher in JULES9ALL than JULES5 for broadleaf forests due to a higher Topt of Vcmax and a higher Vcmax,25. Simulated GPP was similar to observations in the absence of soil moisture stress. The increase in GPP occurred year-round at Manaus, but only during the wet season at Tapajs K67 (Fig. 5). GPP was similar in all JULES simulations during the dry season (October December), when soil moisture decits limited photosynthesis. The soil moisture stress factor, , was < 0.7 during these months, while it was > 0.87 all year at Manaus (recall that a higher indicates less stress). The reduction in GPP during the dry season at both sites is in contrast to the observations, which show an increase from AugustDecember. As a result, the simulated seasonal cycle of GPP was incorrect at both sites, and although the annual total GPP was closer to observations, the monthly RMSE was higher in JULES9ALL
A. B. Harper et al.: Improved plant functional types in JULES 2427
(a) GPP correlation (b) NEE correlation
7: All 6: 2+structure
5: 2+A/Resp 4: 2+radiation
3: 2+gs 2: 1+LL 1: 9 PFTs + Nm,
LMA, Vcmax,25
7: All 6: 2+structure
5: 2+A/Resp 4: 2+radiation
3: 2+gs 2: 1+LL 1: 9 PFTs + Nm,
LMA, Vcmax,25
(c) GPP RMSE (d) NEE RMSE
7: All 6: 2+structure
5: 2+A/Resp 4: 2+radiation
3: 2+gs 2: 1+LL 1: 9 PFTs + Nm,
LMA, Vcmax,25
7: All 6: 2+structure
5: 2+A/Resp 4: 2+radiation
3: 2+gs 2: 1+LL 1: 9 PFTs + Nm,
LMA, Vcmax,25
Figure 4. Relative changes in daily RMSE (Eq. 24) and monthly correlation coefcients (Eq. 25) for the JULES experiments in Table 4 compared to JULES5. Yellows and reds indicate an improvement in JULES compared to the FLUXNET observations.
months of JuneOctober. During this period, reduced to a minimum of 0.17 in August, and the GPP was too low by an average 1.83 g C m2 d1. At all sites there was shift toward stronger net carbon uptake during the summer months with the new PFTs, which increased the correlation with ob-served NEE. At El Saler, the RMSE of NEE increased due to a change in the seasonal cycle of leaf dark respiration (Rd,
Eq. 8) resulting from the higher Topt. At Hyytil, the RMSE of NEE increased due to higher rates of soil respiration during the winter months (Fig. S3; where soil respiration is the difference between total and autotrophic respiration).
Compared to JULES5 (with a needle-leaf PFT), both GPP and respiration were improved with the new NDT PFT at Tomakai, primarily due to an improved seasonal cycle of GPP with the deciduous phenology (Experiment 2). In JULES5, the LAI at the site was 6.0 m2 m2, compared to a summer maximum of 3.5 m2 m2 with the deciduous phe
nology and to a reported average LAI of larch of 3.8 m2 m2 (Gower and Richards, 1990). The new deciduous PFT also improved the seasonal cycle of NEE, and reduced errors in LE and SH (Fig. S4). The magnitude of maximum summertime GPP was still underestimated, but this could be because the site is a plantation, where trees are evenly planted to optimize the incoming radiation, rather than a natural larch forest.
4.2.3 Grasses
GPP and NEE were improved for temperate grasslands (Kaamanen and Fort Peck) and NEE was improved at a tropical pasture (Tapajs K77). Compared to JULES5, productivity in JULES9ALL was higher at a temperate C3 site (Fort Peck), and lower at a cold C3 site (Kaamanen) and the tropical C4 site. In terms of GPP, these changes brought JULES9ALL closer to the observations (Table 5). With the new PFT parameters, grasses had higher year-round LAI due to the removal of phenology, and GPP increased earlier in the year at Kaamanen, Bondville, and Fort Peck in JULES9ALL compared to JULES5. Net uptake also occurred 12 months earlier in JULES9ALL (compared to JULES5), which decreased
RMSE and increased r for NEE at the three natural grassland sites. JULES9ALL underestimated productivity at Bondville (crop site), but this is not surprising given that the PFT is meant to represent natural grasses. There is a separate crop model available for JULES (Osborne et al., 2015).
The Tapajs K77 pasture was not included in the set of sites with GPP/Reco partitioning. The simulated GPP was lower in JULES9ALL than in JULES5 due to the lower quantum efciency (Fig. S3c). The seasonal cycle of NEE was close to that observed during most months (Fig. 5b), and in terms of r and RMSE JULES9ALL were better than JULES5.
In JULES5, the GPP and NPP were higher at the Tapajs K77 pasture than at the Tapajs K67 forest site despite being driven by the same meteorology (Table 5). In JULES9ALL,
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2428 A. B. Harper et al.: Improved plant functional types in JULES
Manaus Tapajos K67 Las Majadas Tonzi
(a)
At Las Majadas, the GPP increased in JULES9ALL (compared to JULES5) during the wet spring (JanuaryApril) due to high GPP from the BET-Te and C3 grass PFTs. The former had a higher year-round LAI ( 4.6 m2 m2), Vcmax,25, and
Topt for Vcmax compared to the BT from the ve PFTs (which simulated maximum summer LAI of 3.8 m2 m2). For C3 grass, the new Vcmax,25 and Topt were lower in JULES9ALL, but the removal of phenology (setting dT to 0) increased the LAI during the cool, mild winter months when photosynthesis could still occur. Grid-cell mean GPP was also slightly higher during the hot, dry summer, again owing to the BETTe PFT. The simulated seasonality NEE was similar to obser-
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Figure 5.
GPP was higher at the forest site than at the pasture, and the NPP was similar.
4.2.4 Mixed vegetation sites
Las Majadas and Tonzi are savannah sites dominated by evergreen and deciduous plants, respectively (assumed in the simulations to be an equal mix of trees, shrubs, and C3 grass,
Table 4). Both GPP and NPP were better simulated with JULES9ALL at both sites, and the annual GPP was within the range of the observations (although it was too high at Las Majadas and too low at Tonzi).
A. B. Harper et al.: Improved plant functional types in JULES 2429
(b)
Manaus Tapajos K67 Las Majadas Tonzi
JULES9-TRY
Figure 5. (a) Monthly mean uxes of GPP. Observations standard deviation from FLUXNET are shown with triangles and vertical lines.
The three JULES simulations are JULES5 with standard ve PFTs (JULES5, red); JULES with nine PFTs and new LMA, Nm, and Vcmax,25 from TRY (JULES9TRY, orange); JULES9-TRY plus new parameters for the PFTs as discussed in Sect. 2.3 (JULES9ALL, blue). Also shown are the daily root mean square error (RMSE) based on daily uxes and the correlation coefcient (r) based on monthly mean uxes for all years of the simulations. Site information is given in Table 3. All units are in g C m2 d1. (b) As in (a) but for monthly anomalies of NEE.
vations (r = 0.70), but the AprilMay uptake was too strong
and contributed to an overestimation of the annual GPP.At Tonzi, GPP was similar to observations except during
AprilJuly, when it was too low. The modeled photosynthesis began to decline after March, coinciding with a rapid increase in simulated soil moisture stress and stomatal resistance. Moving from a generic to a deciduous shrub resulted in a large decrease in simulated GPP at this site. The shrub LAI decreased from 3.3 m2 m2 to a maximum of
1.5 m2 m2, and the Vcmax,25 for the DSh was slightly lower
than the Vcmax,25 for the generic shrub. Slightly compensating for the lower shrub GPP was a higher broadleaf tree GPP, with a higher Vcmax,25 and Topt compared to the previous values in JULES5.
4.3 Global results
In this section, we analyze the impact of the PFT-specic biases and improvements on biome-scale GPP and NPP uxes in global simulations. The area-weighted uxes are displayed in Table 6 and Fig. 6 for the biomes shown in
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2430 A. B. Harper et al.: Improved plant functional types in JULES
JULES9-ALL JULES5
Over the tropical savannah biome, the GPP decreased in JULES9ALL compared to JULES5 due to lower productivity from C4 grasses, and GPP was within the uncertainty range of the MTE GPP, although slightly higher. The overestimation occurred during most of the year (Fig. 7b), except during the late dry season/early wet season (OctoberDecember).Although C4 grasses had a lower NPP in JULES9ALL, a signicant fraction of the biome is composed of C3 grass, BDT, ESh, and DSh in the ESA data, which all had higher NPP in JULES9ALL. For this reason, biome-scale NPP was higher in JULES9ALL than in JULES5, and simulated NPP was 140 g C m2 year1 higher than the MOD17 value. In the temperate grasslands biome, both GPP and NPP were higher in JULES9ALL compared to JULES5, and closer to the
MTE and MOD17 values. However, compared to the MTE, the JULES9 GPP increased 1 month early, it was too low in the mid-summer, and it declined too slowly in the autumn.
The biome-scale GPP in the extratropical mixed forests improved in JULES9ALL compared to JULES5, and was very close to the MTE estimate. The simulated GPP was overestimated during the autumn (SeptemberOctober) and underestimated during the winter. Simulated NPP was very close to the MOD17 NPP in JULES5, but it is too high by 100 g C m2 year1 in JULES9ALL. The predominant
vegetation types in the boreal and coniferous forests biome are NET (26 % coverage), C3 grass (20 %), and
NDT (14 %). Shrubs, deciduous broadleaf trees, and bare soil cover the remaining 40 % of the biome. There was a large increase in summertime GPP in this biome, bringing JULES9ALL closer to the MTE GPP than JULES5. The NPP increased in JULES9, compared to JULES5, and was within 10 g C m2 year1 of the MOD17 NPP.
Deserts/shrublands and tundra are both dry environments with annual-average GPP of 280 g C m2 year1 accord
ing to the MTE data set. Although GPP increased in both biomes in JULES9ALL relative to JULES5, it was much lower than the MTE value. In the tundra biome, GPP was underestimated during the entire growing season, and it was underestimated all year in the desert biome. The simulated NPP was also signicantly lower than MOD17 in these two biomes, although it was slightly improved in JULES9ALL.
These results indicate that the JULES plants struggle in extremely cold and arid environments.
In the Mediterranean woodlands, GPP increased by 90 g C m2 year1 and NPP increased by 80 g C m2 year1 in JULES9ALL compared to JULES5, but both uxes were still 100 g C m2 year1 lower than the MTE GPP and
MOD17 NPP. The simulated GPP (in JULES9ALL) was close to the MTE value during most of the year except the dry season, when it declined more in the model than in the MTE estimate.
On a global scale, JULES9ALL had a similar GPP but higher NPP compared to JULES5 (Fig. 8). In both simulations, the global GPP was 128129 Pg C year1 (average from 20002012), compared to the MTE average of
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Figure 6. Annual GPP and NPP for the eight biomes shown in Fig. 3b. Biome abbreviations are D deserts, M Mediterranean woodlands, TU tundra, TG temperate grasslands, TS tropical savannahs, BCF boreal and coniferous forests, EMFs extra-tropical mixed forests, TF tropical forests.
Fig. 3, and seasonal cycles are shown in Fig. 7. GPP increased in JULES9ALL compared to JULES5 in all extratropical biomes, but it decreased in the two biomes with signi-cant coverage by C4 grass. For all biomes, the representation of GPP in JULES9ALL was closer to the observed (MTE) value. NPP increased in every biome, and this was an improvement (relative to MOD17) in ve biomes (boreal and coniferous forests, temperate grasslands, deserts/shrublands, tundra, and Mediterranean woodlands), but NPP was too high in tropical biomes and extratropical mixed forests.
In the tropical forests, the biome-averaged GPP and NPP increased in JULES9ALL compared to JULES5, and both uxes were 200 g C m2 yr1 higher than their respective
observational value. The seasonality of rainfall in the tropics has a hemispheric dependence. Splitting the biome into the Northern and Southern hemispheres revealed that the seasonal cycle in Fig. 7a was most similar to the Southern Hemisphere in terms of the climate and uxes. In both hemispheres, the JULES GPP was higher than the MTE GPP during the transition period from the wet to the dry season and the early dry season. This is in contrast to the results at the two Brazilian FLUXNET sites, where JULES GPP was lower than that observed during the dry season.
Most of the differences between JULES5ALL and JULES9ALL were in the tropics (Fig. 9, Table 6). The global GPP was relatively high (135 Pg C year1) in JULES5ALL (compared to 127 Pg C year1 for JULES9ALL), primarily because Vcmax for the generic broadleaf tree was much higher than for the tropical broadleaf evergreen PFT, based on the data from K09. Although tropical GPP was higher in JULES5ALL compared to JULES9ALL, the NPP in tropical forests was lower and closer to the values from MODIS NPP. The reason was the differences in leaf nitrogen, which increased respiratory costs in JULES5ALL compared to
JULES9ALL. Both NA and Nm were higher for the broadleaf tree PFT than for the tropical evergreen broadleaf tree PFT.
A. B. Harper et al.: Improved plant functional types in JULES 2431
Figure 7. Area-averaged seasonal cycles of GPP from the biomes shown in Fig. 3b, comparing JULES5, JULES9, and the Jung et al. (2011) MTE. Also shown are the temperature and precipitation from the CRU-NCEP data set used to force the JULES simulations. The gray shading in the GPP plots shows the MTE GPP 1 standard deviation based on the area-averaged standard deviations of monthly uxes for each grid
cell.
122 8 Pg C year1. GPP was higher in JULES9ALL com
pared to JULES5 in the core of the tropical forests, but lower in tropical/subtropical South America, Africa, and Asia. These are regions with signicant grass coverage (Fig. 3a), especially C4 grasses. Poleward of 30 , GPP was higher in
JULES9ALL due to higher productivity in trees. In JULES5, the global NPP (55 Pg C year1) was close to the value from MODIS NPP (54 Pg C year1). In JULES9ALL, the NPP was higher than JULES5 almost everywhere (except for southern Brazil where C4 grasses are dominant), and the global NPP was 62 Pg C year1.
5 Discussion
5.1 Impacts of trait-based parameters and new PFTs
Including trait-based data on leaf N, Vcmax,25, and leaf life span improved the seasonal cycle of GPP at seven sites, es-
pecially sites with C3 grass and NDT. Parameterizing leaf life span correctly has been shown to be important, even within biomes (Reich et al., 2014). Our study conrms this, as the simulation of GPP improved at fewer sites in the simulations without the improved leaf life span. However, compared to the standard ve PFTs, the RMSE of GPP was only improved at four sites in JULES9TRY. Despite this, the new PFTs with the new trait data include observed trade-offs between leaf structure and life span. These trade-offs are important for enabling JULES to represent observed vegetation distribution and for predictions of future uxes.
Incorporating more data and accounting for evergreen and deciduous habits further improved the model, as indicated by the closer model-data comparison obtained with JULES9ALL at both the site and global level. The distinction between the tropical and temperate broadleaf evergreen trees provided mixed results. While there was no improvement in the seasonal cycles at the two tropical forest sites, both GPP and the
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2432 A. B. Harper et al.: Improved plant functional types in JULES
Figure 8. Global maps of carbon cycle uxes from 2000 to 2012. The observation sources are MTE (GPP) and MODIS MOD17 (NPP, 20002013).
seasonal cycle of NEE were improved at the warm-temperate evergreen savannah site (Las Majadas). This study has laid the groundwork for further improvements to JULES GPP and plant respiration by incorporating trait-based physiological relationships and allowing for a exible number of PFTs. Future development can focus on more biome-specic data-model mismatches than was possible with the generic set of ve PFTs.
The nine PFTs were chosen as they represent the range of deciduous and evergreen plant types with minimal externally determined bioclimatic limits. The distinction between tropical and temperate broadleaf evergreen trees account for the important differences between these types of trees (e.g., a lower Vcmax for a given NA in tropical broadleaf evergreen trees: Kattge et al., 2009). The comparison of JULES5ALL and JULES9ALL indicates that even using improved parameters with ve PFTs based on the TRY data and the literature reviewed in this study will give improved productivity uxes in JULES. However, an important caveat is JULES was not run with dynamic vegetation for this analysis. The additional PFTs enable more diverse and specic dynamic responses to climate change.
5.2 Future development priorities
The biome-level evaluation of GPP and NPP provides insight into potential areas for improvement in JULES: in particular boreal forests, tundra, Mediterranean woodlands and desert/xeric shrublands (Fig. S6). GPP was systematically underestimated in regions experiencing seasonal soil moisture stress, such as the tropical forests, summer at Morgan Monroe, and the dry season at El Saler. A similar result was seen with the arid biomes and in the Mediterranean biome during summer. The fact that the model did not match the seasonal cycle of GPP at the two tropical forest sites with improved parameters indicates that processes such as the representation of plant water access and/or soil hydraulic properties need to be addressed in JULES. However, the dry season bias was not present when JULES was compared to the biome-scale MTE GPP. This underscores the complexity of modeling tropical forest productivity and the need to evaluate multiple data sources. High latitude grasses were underproductive, which also contributed to an underestimation of soil carbon (not shown). Further development of a tundra-specic PFT(s) could improve the carbon cycle in these regions.
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A. B. Harper et al.: Improved plant functional types in JULES 2433
Figure 9. Differences between modeled and observed GPP (observed MTE) and NPP (observed MOD17). (a, b) JULES with the standard ve PFTs and default parameters; (c, d) JULES with ve PFTs and improved parameters; (e, f) JULES with nine PFTs and improved parameters.
A side effect of the trait-based parameters was increased respiration, and comparison to both FLUXNET sites and the MTE suggest it is now too high for most biomes. Total ecosystem respiration was higher than that observed at Manaus, Harvard, Morgan Monroe, Tharandt, Hyytil, Kaamanen, Las Majadas, and Tonzi (75 % of the sites with respiration data) (Fig. S3). As this study has focused primarily on improving the GPP, the next step should be to include a more mechanistic representation of growth and maintenance respiration in JULES to improve the net productivity (e.g., using data from Atkin et al., 2015). Comparison to the MTE respiration also suggests that JULES soil respiration is too high during the winter in the temperate and boreal biomes. In the latter, both versions of JULES predicted positive respiration ux during the winter, while the MTE product showed negligible uxes (Fig. S5). The average winter temperatures in the biome were < 13 C, yet soil respiration continued during
these months because the Q10 soil respiration scheme has a very slow decay of soil respiration ux at sub-zero temperatures (see Fig. 2 of Clark et al., 2011). A similar result was seen at Hyytil (Fig. S3b), which further indicates that wintertime respiration might be too high.
Last, the simulation of GPP could be further improved by replacing the static Vcmax,25 per PFT. Simultaneous with this
study, there is work to include temperature acclimation for photosynthesis JULES, which is more realistic than a set Topt for each PFT. Also, the data exhibit large within-PFT variation in Vcmax,25 (Fig. 2) and photosynthetic capacity can depend on the time of year. Recent work relating photosynthetic capacity to climate variables, environmental factors, and soil conditions shows promise for better capturing the dynamic nature of this parameter (e.g., Verheijen et al., 2013; Ali et al., 2015; Maire et al., 2015).
6 Conclusions
We evaluated the impacts on GPP, NEE, and NPP of new plant functional types in JULES. All changes were evaluated in version 4.2 with the canopy radiation model 5 option (Clark et al., 2011). At the base of the new PFTs was inclusion of new data from the TRY database. Nm and LMA replaced the parameters Nl0 and l. These were used to calculate new Vcmax,25, which was higher for all of the new
PFTs compared to the original ve, except for C3 grasses.
The higher Vcmax,25 resulted in higher GPP. The GPP did not increase for C4 grasses due to a lower quantum efciency, or for cold grasslands due to a lower optimal temperature for
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2434 A. B. Harper et al.: Improved plant functional types in JULES
Vcmax. Increases in NPP generally followed on from the increases in GPP.
A trade-off between LMA and leaf life span was enforced by changing parameters relating to leaf phenology, growth and senescence. The new parameter values changed the turnover rate of leaves on trees in the spring and fall, therefore altering the leaf life span in JULES in a manner consistent with observations. In JULES9TRY, the median leaf life span of grasses and shrubs were reduced, which improved the seasonal cycle at the relevant sites (Las Majadas, Tonzi, Fort Peck, Kaamanen, and Tomakai). The exception was the Bondville crop site.
Including the full range of updated parameters (in JULES9ALL) resulted in an improved seasonal cycle of GPP at 10 sites and reductions to daily RMSE at 11 sites (out of 13 sites with GPP data) compared to JULES9TRY. The annual
GPP was within the range of the FLUXNET observations at every site except for one (Hyytil). On a biome scale, we compared GPP to the MTE product of Jung et al. (2011) and NPP to the MODIS17 product. GPP was improved in JULES9 for all eight biomes evaluated, although for the tundra and desert/shrubland biome the GPP was much lower than the MTE value. The global NPP was slightly higher than that observed, but JULES9 was closer to MOD17 in most biomes the exceptions being the tropical forests, savannahs, and extratropical mixed forests where JULES9 was too high. The biome-averaged NPP from JULES9 was within the range of MOD17 NPP for all biomes.
Overall, the simulation of gross and net productivity was improved with the nine PFTs. The present study can be thought of as a bottom-up approach to improving JULES uxes, with new parameters being based on large observationally based data sets. The next step for improving PFTs in JULES is to evaluate the nine PFTs when the dynamic vegetation is turned on. This will be addressed in a follow-up paper. A complimentary, top-down method for reducing uncertainty in JULES is to optimize PFT parameters based on minimizing errors between simulated and observed uxes.This is currently being done with adJULES, an adjoint version of JULES (Raoult et al., 2016). Future model development within JULES will have more exibility for improving the model with more PFTs, and the improvements presented in this study increase our condence in using JULES in carbon cycle studies.
7 Code availability
The simulations discussed in this manuscript were done using JULES version 4.2. This can be accessed through the JULES FCM repository: https://code.metoffice.gov.uk/trac/jules
Web End =https://code.metofce.gov.uk/trac/ https://code.metoffice.gov.uk/trac/jules
Web End =jules (registration required). For further details, see https://code.metoffice.gove.uk/trac/jules/wiki/9PFTs
Web End =https: https://code.metoffice.gove.uk/trac/jules/wiki/9PFTs
Web End =//code.metofce.gove.uk/trac/jules/wiki/9PFTs . An example with the nine PFTs and parameters in this paper is provided for Loobos in the documentation directory of the JULES trunk. Summary tables of the traits LMA, Nm, and leaf life span are included in the Supplement.
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A. B. Harper et al.: Improved plant functional types in JULES 2435
Appendix A
Table A1. List of parameters and symbols in the text.
Symbol Units Equation Description Default value
Al kg C m2 s1 5 Leaf-level photosynthesisawl kg C m2 24 Allometric coefcientaws 24 Ratio of total to respiring stem carbonbwl 24 Allometric exponent 1.667
Ci Pa 6 Internal leaf CO2 concentrationCmass kg C [kg biomass]1 23 Leaf carbon concentration per unit mass 0.5 for this study
Cs Pa 6 Leaf surface CO2 concentrationDcrit kg kg1 7 Critical humidity decitdT 16 Rate of change of leaf turnover with temperaturef0 7 Stomatal conductance parameterfd 4 Leaf dark respiration coefcientgs m s1 6 Leaf-level stomatal conductanceiv mol CO2 m2 s1 19 Intercept for relationship between NA and Vcmax,25kn 3, 20 Extinction coefcient for nitrogen 0.78 h m 13, 23, 24 Canopy heightLbal m2 m2 12, 13, 2224 Balanced leaf area index (maximum LAI given the plants height)
Lmax m2 m2 Maximum LAI Lmin m2 m2 Minimum LAI
LMA kg m2 18, 21, 22 Leaf mass per unit area (new parameter)
Na kg N m2 18 Leaf nitrogen per unit areaneff mol CO2 m2 s1 kg C [kg N]1 3 Constant relating leaf nitrogen to Rubisco carboxylation capacity
Nl0 kg N [kg C]1 3 Top-leaf nitrogen concentration (old parameter, mass basis) Nm kg N kg 1 18, 2123 Top-leaf nitrogen concentration (new parameter)
Nl kg N m2 11, 21 Total leaf nitrogen concentration Nr kg N m2 12, 22 Total root nitrogen concentration
Ns kg N m2 13, 23 Total stem nitrogen concentration p 17 Phenological state (LAI/Lbal)
Q10,leaf 2 Constant for exponential term in temperature function of Vcmax 2
Ra kg C m2 s1 8 Total plant autotrophic respiration
Rd kg C m2 s1 4, 5 Leaf dark respirationrg 10 Growth respiration coefcient 0.25 rootd m e-folding root depthsv mol CO2 g N1 s1 19 Slope between NA and Vcmax,25
Tlow C 1 Upper temperature parameter for Vcmax Toff C 16 Threshold temperature for phenology
T b
opt C Optimal temperature for VcmaxTupp C 1 Upper temperature parameter for Vcmax
Vcmax,25 mol m2 s1 1, 9 The maximum rate of carboxylation of Rubisco at 25 CW kg C m2 s1 5 Smoothed minimum of the potential limiting rates of photosynthesis mol CO2 [mol PAR photons]1 Quantum efciency 5 Soil moisture stress factor Pa 7 CO2 compensation point 0 [360 days]1 16 Minimum leaf turnover rate lm [360 days]1 16 Leaf turnover rate p [360 days]1 17 Leaf growth rate 20 [notdef]rl 12, 22 Ratio of nitrogen concentration in roots and leaves[notdef]sl 13, 23 Ratio of nitrogen concentration in stems and leaves sl kg C m2 LAI1 13, 23 Live stemwood coefcient 0.01 L kg C m2 LAI1 11, 12 Specic leaf density (old parameter)
Default values only provided for non-PFT-dependent parameters.
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2436 A. B. Harper et al.: Improved plant functional types in JULES
Table A2. New trait-based parameters for ve PFTs that are consistent with the data used in this study. Used in the JULES5ALL experiments.
BT NT C3 C4 SH
Nm 0.0185 0.0117 0.0240 0.0113 0.0175
LMA 0.1012 0.2240 0.0495 0.1370 0.1023 sv 25.48 18.15 40.96 20.48 23.15 iv 6.12 6.32 6.42 0.00 14.71
Vcmax,25 53.84 53.88 55.08 31.71 56.15
Toff 5 40 5 5 40
dT 9 9 0 0 9 0 0.25 0.25 3.0 3.0 0.66 p 20 15 20 20 15 Lmin 1 1 1 1 1
Lmax 9 7 3 3 4
Dcrit 0.09 0.06 0.051 0.075 0.037 f0 0.875 0.875 0.931 0.800 0.950 fd 0.010 0.015 0.019 0.019 0.015 rootd 3 2 0.5 0.5 1 Tlow 5 0 10 13 0
Topt 39 32 28 41 32
Tupp 43 36 32 45 36 0.08 0.08 0.06 0.04 0.08 [notdef]rl 0.67 0.67 0.72 0.72 0.67
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A. B. Harper et al.: Improved plant functional types in JULES 2437
The Supplement related to this article is available online at http://dx.doi.org/10.5194/gmd-9-2415-2016-supplement
Web End =doi:10.5194/gmd-9-2415-2016-supplement .
Acknowledgements. We gratefully acknowledge all funding bodies. AH was funded by the NERC Joint Weather and Climate Research Programme and NERC grant NE/K016016/1. The study has been supported by the TRY initiative on plant traits (http://www.try-db.org
Web End =http://www.try-db.org ). The TRY initiative and database is hosted, developed, and maintained by J. Kattge and G. Bnisch (Max Planck Institute for Biogeochemistry, Jena, Germany). TRY is currently supported by DIVERSITAS/Future Earth and the German Centre for Integrative Biodiversity Research (iDiv) HalleJenaLeipzig. O. K. Atkin acknowledges the support of the Australian Research Council (CE140100008). Met Ofce authors were supported by the Joint DECC/Defra Met Ofce Hadley Centre Climate Programme (GA01101). V. Onipchenko was supported by RSF (RNF) (project 14-50-00029). J. Peuelas acknowledges support from the European Research Council Synergy grant ERCSyG-2013-610028, IMBALANCE-P, and N from the advanced grant ERC-AdG-322603, SIP-VOL+. We also thank Andrew
Hartley (UK Met Ofce), who processed the ESA Land Cover data to the 5 and nine PFTs, and Nicolas Viovy (IPSL-LSCE), who kindly provided the CRU-NCEP driving data.
Edited by: J. KalaReviewed by: two anonymous referees
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Copyright Copernicus GmbH 2016
Abstract
Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycle-climate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the first generation UK Earth System Model. Previously, JULES represented five plant functional types (PFTs): broadleaf trees, needle-leaf trees, C<sub>3</sub> and C<sub>4</sub> grasses, and shrubs. This study addresses three developments in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These first two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C<sub>3</sub> and C<sub>4</sub> grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (V<sub>cmax</sub>), and updated the leaf turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area.<br><br>Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard five PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C<sub>3</sub> grasses in cold environments and C<sub>4</sub> grasses that were previously over-productive. On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes - the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and 62 Pg C year<sup>1</sup>, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity fluxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher confidence in simulations of vegetation dynamics and carbon storage.
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