Climate Dynamics

, Volume 42, Issue 9–10, pp 2539–2554 | Cite as

Carbon and water flux responses to physiology by environment interactions: a sensitivity analysis of variation in climate on photosynthetic and stomatal parameters

  • William L. BauerleEmail author
  • Alex B. Daniels
  • David M. Barnard


Sensitivity of carbon uptake and water use estimates to changes in physiology was determined with a coupled photosynthesis and stomatal conductance (g s) model, linked to canopy microclimate with a spatially explicit scheme (MAESTRA). The sensitivity analyses were conducted over the range of intraspecific physiology parameter variation observed for Acer rubrum L. and temperate hardwood C3 (C3) vegetation across the following climate conditions: carbon dioxide concentration 200–700 ppm, photosynthetically active radiation 50–2,000 μmol m−2 s−1, air temperature 5–40 °C, relative humidity 5–95 %, and wind speed at the top of the canopy 1–10 m s−1. Five key physiological inputs [quantum yield of electron transport (α), minimum stomatal conductance (g 0), stomatal sensitivity to the marginal water cost of carbon gain (g 1), maximum rate of electron transport (J max), and maximum carboxylation rate of Rubisco (V cmax)] changed carbon and water flux estimates ≥15 % in response to climate gradients; variation in α, J max, and V cmax input resulted in up to ~50 and 82 % intraspecific and C3 photosynthesis estimate output differences respectively. Transpiration estimates were affected up to ~46 and 147 % by differences in intraspecific and C3 g 1 and g 0 values—two parameters previously overlooked in modeling land–atmosphere carbon and water exchange. We show that a variable environment, within a canopy or along a climate gradient, changes the spatial parameter effects of g 0, g 1, α, J max, and V cmax in photosynthesis-g s models. Since variation in physiology parameter input effects are dependent on climate, this approach can be used to assess the geographical importance of key physiology model inputs when estimating large scale carbon and water exchange.


Carbon Global Model input Sensitivity analysis Stomatal conductance Transpiration 



The MODIS MOD11C3 data were obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota ( Wind speed data were acquired as part of the activities of NASA’s Science Mission Directorate, and are archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). Humidity and solar data were acquired as part of the activities of NASA’s Science Mission Directorate and are archived and distributed by the Atmospheric Science Data Center at NASA’s Langley Research Center. This research was supported in part by the Colorado Experiment Station and USDA. Bauerle, Barnard, and Daniels were supported in part by the USDA (Grant 2009-51181-05768 and cooperative agreement 58-6618-2-0209). We thank two anonymous reviewers for excellent critical reviews.

Supplementary material

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Supplementary material (DOCX 55 kb)
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Supplementary material (DOC 2570 kb)


  1. Abrams MD (1998) The red maple paradox. Bioscience 48:355–364CrossRefGoogle Scholar
  2. Alton P (2011) How useful are plant functional types in global simulations of the carbon, water, and energy cycles? J Geophys Res 116 (G01030). doi: 10.1029/2010JG001430
  3. Alton P, North PR, Los SO (2007a) The impact of diffuse sunlight on canopy light-use efficiency, gross photosynthetic product and net ecosystem exchange in three forest biomes. Glob Chang Biol 13:776–787CrossRefGoogle Scholar
  4. Alton P, Mercado L, North P (2007b) A sensitivity analysis of the land-surface scheme JULES conducted for three forest biomes: biophysical parameters, model processes, and meteorological driving data. Glob Biogeochem CY 20 (GB1008). doi: 10.1029/2005GB002653
  5. Aphalo PJ, Jarvis PG (1993) An analysis of Ball’s empirical model of stomatal conductance. Ann Bot 72:321–327CrossRefGoogle Scholar
  6. Baldocchi DD, Wilson KB (2001) Modeling CO2 and water vapor exchange of a temperate broadleaved forest across to decadal time series. Ecol Model 142:155–184CrossRefGoogle Scholar
  7. Ball JT, Woodrow IE, Berry JA (1987) A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggins J (ed) Progress in photosynthesis research 7th international congress. Kluwer, Boston, pp 221–224CrossRefGoogle Scholar
  8. Bauerle WL, Bowden JD (2011) Separating foliar physiology from morphology reveals the relative roles of vertically structured transpiration factors within red maple crowns and limitations of larger scale models. J Exp Bot 62:4295–4307CrossRefGoogle Scholar
  9. Bauerle WL, Bowden JD, Wang GG, Shahba MA (2009) Exploring the importance of within-canopy spatial temperature variation on transpiration predictions. J Exp Bot 60:3665–3676CrossRefGoogle Scholar
  10. Bauerle WL, Oren R, Way DA, Qian SS, Stoy PC, Thornton PE, Bowden JD, Hoffman FM, Reynolds RF (2012) Photoperiodic regulation of the seasonal pattern of photosynthetic capacity and the implications for carbon cycling. P Natl Acad Sci USA 109(22):8612–8617CrossRefGoogle Scholar
  11. Berry JA, Beerling DJ, Franks PJ (2010) Stomata: key players in the earth system, past and present. Curr Opin Plant Biol 13:233–240CrossRefGoogle Scholar
  12. Bonan GB, Levis S, Kergoat L, Oleson KW (2002) Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models. Glob Biogeochem CY 16. doi: 10.1029/2000GB001360
  13. Bonan GB, Lawrence PJ, Oleson KW, Levis S, Jung M, Reichstein M, Lawrence DM, Swenson SC (2011) Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data. J Geophys Res 116:G02014. doi: 10.1029/2010JG001593 Google Scholar
  14. Bonan GB, Oleson KW, Fisher RA, Lasslop G, Reichstein M (2012) Reconciling leaf physiological traits and canopy flux data: use of the TRY and FLUXNET databases in the Community Land Model version 4. J Geophys Res 117 (G02026). doi: 10.1029/2011JG001913
  15. Bowden JD, Bauerle WL (2008) Measuring and modeling the variation in species-specific transpiration in temperate deciduous hardwoods. Tree Physiol 28:1675–1683CrossRefGoogle Scholar
  16. Caird MA, Richards JH, Donovan LA (2007) Nighttime stomatal conductance and transpiration in C3 and C4 plants. Plant Physiol 143:4–10CrossRefGoogle Scholar
  17. Chen H, Dickinson RE, Dai Y, Zhou L (2011) Sensitivity of simulated terrestrial carbon assimilation and canopy transpiration to different stomatal conductance and carbon assimilation schemes. Clim Dyn 36:1037–1054CrossRefGoogle Scholar
  18. Colello G, Grivet C, Sellers P, Berry JA (1998) Modeling of energy and CO2 flux in a temperate grassland ecosystem with SiB2: May–October 1987. J Atmos Sci 55:1141–1169CrossRefGoogle Scholar
  19. Cox P, Huntingford C, Harding R (1998) A canopy conductance and photosynthesis model for use in a GCM land surface scheme. J Hydrol 212–213:79–94CrossRefGoogle Scholar
  20. dePury DGG, Farquhar GD (1997) Simple scaling of photosynthesis from leaves to canopies without the errors of big leaf models. Plant, Cell Environ 20:537–557CrossRefGoogle Scholar
  21. Egea G, Verhoef A, Vidale PL (2011) Towards an improved and more flexible representation of water stress in coupled photosynthesis-stomatal conductance models. Agric For Meteorol 151:1370–1384CrossRefGoogle Scholar
  22. Ehleringer J, Pearcy RW (1983) Variation in quantum yield for CO2 uptake among C3 and C4 plants. Plant Physiol 73:555–559CrossRefGoogle Scholar
  23. Farquhar G, von Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C-3 species. Planta 149:78–90CrossRefGoogle Scholar
  24. Farquhar GD, von Caemmerer S, Berry JA (2001) Models of photosynthesis. Plant Physiol 125:42–45CrossRefGoogle Scholar
  25. Friend AD (2001) Modelling canopy CO2 fluxes: are ‘big-leaf’ simplifications justified. Glob Ecol Biogeogr 10:603–619CrossRefGoogle Scholar
  26. Friend AD, Geider RJ, Behrenfeld MJ, Still CJ (2009) Photosynthesis in global-scale models. In: Laisk A, Nedbal L, Govindjee (eds) Photosynthesis in silico: understanding complexity from molecules to ecosystems. Springer, Dordrecht, pp 465–497CrossRefGoogle Scholar
  27. Gutschick VP, Simonneau T (2002) Modelling stomatal conductance of field-grown sunflower under varying soil water content and leaf environment: comparison of three models of stomatal conductance to leaf environment and coupling with an abscisic acid based model of stomatal response to soil drying. Plant, Cell Environ 25:1423–1434CrossRefGoogle Scholar
  28. Hallgren WS, Pitman AJ (2000) The uncertainty in simulations by a global biome model (BIOME3) to alternative parameter values. Glob Chang Biol 6:483–495CrossRefGoogle Scholar
  29. Hanson PJ, Amthor JS, Wullschleger SD et al (2004) Oak forest carbon and water simulations: model intercomparison and evaluations against independent data. Ecol Monogr 74:443–489CrossRefGoogle Scholar
  30. Harley PC, Sharkey TD (1991) An improved model of C3 photosynthesis at high CO2: reversed O2 sensitivity explained by lack of glycerate reentry into the chloroplast. Photosyn Res 27:169–178Google Scholar
  31. Harley P, Tenhunen JD (1991) Modeling the photosynthetic response of C3 leaves to environmental factors. In: Boote KJ, Loomis RS (eds) Modeling crop photosynthesis: from biochemistry to canopy. Special publication of the American Society of Agronomy, Madison, pp 17–39Google Scholar
  32. IPCC (Edited by R.K. Pachauri, and A. Reisinger) (2007) Climate change 2007: synthesis report. Contribution of working group I, II, and III to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  33. Kattge J, Knorr W, Raddatz T, Wirth C (2009) Quantifying photosynthetic capacity and nitrogen use efficiency for earth system models. Glob Chang Biol 15:976–991CrossRefGoogle Scholar
  34. Leuning R (1995) A critical appraisal of a combined stomatal-photosynthesis model for C3 plants. Plant, Cell Environ 18:339–355CrossRefGoogle Scholar
  35. Lin Y-S, Medlyn BE, Ellsworth DS (2012) Temperature responses of leaf net photosynthesis: the role of component processes. Tree Physiol 32:219–231CrossRefGoogle Scholar
  36. Long SP, Postl WF, Bolhár Nordenkampf HR (1993) Quantum yields for uptake of carbon dioxide in C3 vascular plants of contrasting habitats and taxonomic groupings. Planta 189:226–234CrossRefGoogle Scholar
  37. Manzoni S, Vico G, Katul G, Fay PA, Polley HW, Palmroth S, Porporato A (2011) Optimizing stomatal conductance for maximum carbon gain under water stress: a meta-analysis across plant functional types and climates. Funct Ecol 25:456–467CrossRefGoogle Scholar
  38. Medlyn BE (2004) MAESTRO retrospective. In: Mencuccini M, Grace J, Moncrieff JB, McNaughton K (eds) Forests at the land–atmosphere interface. CAB International, Wallingford, pp 105–121CrossRefGoogle Scholar
  39. Medlyn BE, Berbigier P, Clement R et al (2005) Carbon balance of coniferous forests growing in contrasting climates: model-based analysis. Agric For Meteorol 131:97–124CrossRefGoogle Scholar
  40. Medlyn BE, Pepper DA, O’grady AP, Keith H (2007) Linking leaf and tree water use with an individual-tree model. Tree Physiol 27:1687–1699CrossRefGoogle Scholar
  41. Medlyn BE, Duursma RA, Eamus D, Ellsworth DS, Prentice IC, Barton CVM, Crous KY, De Angelis P, Freeman M, Wingate L (2011) Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob Chang Biol 17:2134–2144CrossRefGoogle Scholar
  42. Misson L, Panek JA, Goldstein AH (2004) A comparison of three approaches to modeling leaf gas exchange in annually drought-stressed ponderosa pine forest. Tree Physiol 24:529–541CrossRefGoogle Scholar
  43. Monteith JL (1965) Evaporation and environment. In: Symposium of the society for experimental biology, pp 205–224Google Scholar
  44. Niinemets U, Anten NPR (2009) Packing the photosynthetic machinery: from leaf to canopy. In: Laisk A, Nedbal L, Govindjee (eds) Photosynthesis in silico: understanding complexity from molecules to ecosystems. Springer, Dordrecht, pp 363–399CrossRefGoogle Scholar
  45. Ogle K, Lucas RW, Patrick Bentley L, Cable JM, Barron-Gafford GA, Griffith A, Ignace D, Jenerette GD, Tyler A, Huxman TE, Loik ME, Smith SD, Tissue DT (2012) Differential daytime and night-time stomatal behavior in plants from North American deserts. New Phytol 194:464–476CrossRefGoogle Scholar
  46. Oleson KM, Lawrence DM, Bonan G, Flanner MG, Kluzek E, Lawrence PJ, Levis S, Swenson SC, Thornton PE (2010) Technical description of version 4.0 of the Community Land Model (CLM) NCAR, Technical Note. NCAR/TN-478+STR: 257Google Scholar
  47. Perry DA, Oren R, Hart SC (2008) Forest ecosystems, 2nd edn. The Johns Hopkins University Press, Baltimore, p 632Google Scholar
  48. Reichstein M, Tenhunen JD, Roupsard O, Ourcival J-M, Rambal S, Miglietta F, Peressotti A, Pecchiari M, Tirone G, Valentini R (2002) Severe drought effects on ecosystem CO2 and H2O fluxes in three Mediterranean evergreen ecosystems: revision of current hypotheses? Glob Chang Biol 8:999–1017CrossRefGoogle Scholar
  49. Reynolds RF, Bauerle WL, Wang Y (2009) Simulating carbon dioxide exchange rates of deciduous tree species: evidence for a general pattern in biochemical changes and water stress response. Ann Bot 104:775–784CrossRefGoogle Scholar
  50. Sala A, Tenhunen JD (1996) Simulations of canopy net photosynthesis and transpiration in Quercus ilex L. under the influence of seasonal drought. Agric For Meteorol 78:203–222CrossRefGoogle Scholar
  51. Sellers PJ, Randall DA, Collatz GJ, Berry JA, Field CB, Dazlich DA, Zhang C, Collelo GD, Bounoua L (1996) A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: model formulation. J Clim 9:676–705CrossRefGoogle Scholar
  52. Sellers PJ, Dickinson RE, Randall DA, Betts AK, Hall FG, Berry JA, Collatz GJ, Denning AS, Mooney HA, Nobre CA, Sato N, Field CB, Henderson-Sellers A (1997) Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 275:502–509CrossRefGoogle Scholar
  53. Thuiller W, Albert C, Araujo MB, Berry PM, Cabeza M, Guisan A, Hickler T, Midgley GF, Paterson J, Schurr FM, Sykes MT, Zimmermann NE (2008) Predicting global change impacts on plant species’ distributions: future challenges. Perspect Plant Ecol 9:137–152CrossRefGoogle Scholar
  54. Tuzet A, Perrier A, Leuning R (2003) A coupled model of stomatal conductance, photosynthesis and transpiration. Plant, Cell Environ 26:1097–1116CrossRefGoogle Scholar
  55. Walters RS, Yawney HW (1990) Acer rubrum L., red maple. In: Silvics of North America. Agriculture Handbook II. Hardwoods. U.S. Dept. Agr., Washington, DC, p 654Google Scholar
  56. Wang Y-P, Jarvis PG (1990a) Description and validation of an array model—MAESTRO. Agric For Meteorol 51:257–280CrossRefGoogle Scholar
  57. Wang Y-P, Jarvis PG (1990b) Influence of crown structural properties on PAR absorption, photosynthesis, and transpiration in Sitka spruce: application of a model (MAESTRO). Tree Physiol 7:297–316CrossRefGoogle Scholar
  58. Wullschleger SD (1993) Biochemical limitations to carbon assimilation in C3 plants—a retrospective analysis of A/ci curves for 109 species. J Exp Bot 44:907–920CrossRefGoogle Scholar
  59. Zaehle S, Sitch S, Smith B, Hatterman F (2005) Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Glob Biogeochem CY 19 (GB3020). doi: 10.1029/2004GB002395

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • William L. Bauerle
    • 1
    Email author
  • Alex B. Daniels
    • 1
  • David M. Barnard
    • 1
  1. 1.Department of Horticulture and Landscape ArchitectureColorado State UniversityFort CollinsUSA

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