A multi-layer plant canopy model for CLM

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A multi-layer plant canopy model for CLM Gordon Bonan National Center for Atmospheric Research Boulder, Colorado, USA Mat Williams School of GeoSciences University of

1 A multi-layer plant canopy model for CLM Gordon Bonan National Center for Atmospheric Research Boulder, Colorado, USA Mat Williams School of GeoSciences University of Edinburgh Rosie Fisher and Keith Oleson NCAR CESM Land Model Working Group Meeting Boulder, Colorado 24 February 2014 NCAR is sponsored by the National Science Foundation

2 Prevailing stomatal paradigm: Ball-Berry 2 NCAR model, 1995 CSU-SiB, 1996 Photosynthesis & stomatal conductance A A c j Vcmax ( ci Γ* ) + ( 1 + / ) ( i Γ* ) ( c + Γ ) = c K o K i c i o J c = 4 2 i * ( ) A = min A, A R n c j d g = g + g Ah c s 0 1 n s s Hadley Centre, 1998

3 Stomatal optimization 3 An alternative approach models stomatal conductance by optimizing water use efficiency (A n /E) With simplifying assumptions, the Ball-Berry style model can be derived from optimization theory g s = g (1 + g 1 D s 1/2 ) A n / c s (Medlyn et al. 2011)

4 Soil-Plant-Atmosphere (SPA) model 4 A multi-layer plant canopy model: g s for each canopy layer is numerically iterated to maximize A n, within the limitations of plant water storage and soil-to-canopy water transport (ψ L > ψ Lmin ) Williams et al. (1996) Plant Cell Environ. 19:

5 A multi-layer SPA-enabled canopy model for use with CLM 5 1. Radiative transfer 2. Leaf fluxes (CO 2, H, E) Within this framework, evaluate Ball-Berry model and two different stomatal optimizations: iwue: ΔA n /Δg s > ι * and ψ L > ψ Lmin WUE: ΔA n /ΔE > ι and ψ L > ψ Lmin 3. Plant hydraulics Difference relates to VPD: ι * = ι D s 4. Soil fluxes 5. Above- and within canopy turbulence and scalar profiles Ned Patton (NCAR) Ian Harman (CSIRO)

6 Stomatal efficiency determines maximum A n and g s 6 Leaf simulations for 4 values of iota (μmol CO 2 mol 1 H 2 O) Glopnet leaf trait database (Wright et al Nature 428: )

7 Functional relationships emerge from theory 7 VPD dependence (WUE optimization) is consistent with observations and theory: g s /g sref = ln D s Leaf simulations in response to VPD Oren et al. (1999) Plant Cell Environ. 22: Katul et al. (2009) Plant Cell Environ. 32:

8 Consistent with empirical and optimal models 8 Leaf-level fluxes (top of canopy) for month of July (US- Ha1) ΔA n /Δg s optimization (Ball-Berry) (Medlyn et al. 2011) ΔA n /ΔE optimization (Ball-Berry) (Medlyn et al. 2011)

9 9 Scatter plots US-Ha1, July 2001 Ball-Berry ΔAn/Δgs opt. ΔAn/ΔE opt. Sensible heat flux Shading shows ± 1 and ± 2 std. dev. random flux error Latent heat flux GPP

10 Mean diurnal cycle US-Ha1, July Ball-Berry ΔA n / Δg s opt. ΔA n /ΔE opt. Sensible heat flux Model = red Obs = blue Latent heat flux GPP

11 Prolonged drought: US-Me2, July 2002 Dry soil that gets drier over time Ball-Berry underestimates mid-day peak latent heat flux Ball-Berry systematically underestimates GPP 11

12 Evolving drought: US-Me2, July 2005 Wet soil that gets drier over time Both stomatal models capture decline in ET as drought evolves Ball-Berry underestimates GPP as drought evolves 12

13 Site x year summary of model skill 13 Multi-layer canopy improved relative to CLM4.5 Opt. models improved relative to Ball-Berry Skill score from Taylor (2001) JGR 106D: AmeriFlux 3 DBF, 3 ENF 51 site x years

14 Model parameters 14 Symbol Description Units V cmax25 Maximum carboxylation rate at 25 C μmol m 2 s 1 r a CLM rooting distribution parameter m 1 r b CLM rooting distribution parameter m 1 Ball Berry model g 0 Minimum leaf conductance mol H 2 O m 2 s 1 g 1 Slope parameter Optimization model ψ lmin Minimum leaf water potential MPa k p Leaf-specific stem hydraulic conductance mmol H 2 O m 2 s 1 MPa 1 C p Plant capacitance mmol H 2 O MPa 1 m 2 ι Stomatal efficiency μmol CO 2 mol 1 H 2 O M T Fine root biomass g m 2 r r Fine root radius m r d Specific root density (fine root) g biomass m 3 root R r * Fine root hydraulic resistivity MPa s g mmol 1 H 2 O Parameter sensitivity analyses fail to find optimal g 0 and g 1 to minimize RMSE (tradeoff between parameters), but does find optimal ι and R r * (because these two parameters explain most of RMSE)

Multi-scale model evaluation 15 Canopy fluxes GPP, latent

heat flux Jung et al. (2011) JGR, 116, doi:10.

parameterization Profiles of light, leaf traits, and

15 Multi-scale model evaluation 15 Canopy fluxes GPP, latent heat flux AmeriFlux FLUXNET Global vegetation GPP, latent heat flux Jung et al. (2011) JGR, 116, doi: /2010jg Canopy processes Theory Numerical parameterization Profiles of light, leaf traits, and photosynthesis Leaf traits Nitrogen concentration, V cmax Kattge et al. (2009) GCB 15: Consistency among parameters, theory, processes, and observations across multiple scales, from leaf to canopy to global

16 New directions with a multi-layer plant canopy Optimization vs empirical stomatal models Functional relationships emerge from numerical optimization rather than being empirically imposed 2. Canopy turbulence (Ned Patton, NCAR; Ian Harman, CSIRO) 3. Dry deposition (biases in CLM4, Maria Val Martin, CSU) 4. BVOCs (already in CLM4.5) 5. Isotopes (already in CLM4.5) 6. Canopy chemistry how does the chemical environment affect surface fluxes?

Reconciling leaf physiological traits and canopy-scale flux data: Use of the TRY and FLUXNET databases in the Community Land Model

Reconciling leaf physiological traits and canopy-scale flux data: Use of the TRY and FLUXNET databases in the Community Land Model Gordon Bonan, Keith Oleson, and Rosie Fisher National Center for Atmospheric