Boundary-Layer Meteorology

, Volume 139, Issue 1, pp 121–141 | Cite as

The Simulation of the Opposing Fluxes of Latent Heat and CO2 over Various Land-Use Types: Coupling a Gas Exchange Model to a Mesoscale Atmospheric Model

  • Mark Reyers
  • Andreas Krüger
  • Christiane Werner
  • Joaquim G. Pinto
  • Stefan Zacharias
  • Michael Kerschgens
Open Access


A mesoscale meteorological model (FOOT3DK) is coupled with a gas exchange model to simulate surface fluxes of CO2 and H2O under field conditions. The gas exchange model consists of a C3 single leaf photosynthesis sub-model and an extended big leaf (sun/shade) sub-model that divides the canopy into sunlit and shaded fractions. Simulated CO2 fluxes of the stand-alone version of the gas exchange model correspond well to eddy-covariance measurements at a test site in a rural area in the west of Germany. The coupled FOOT3DK/gas exchange model is validated for the diurnal cycle at singular grid points, and delivers realistic fluxes with respect to their order of magnitude and to the general daily course. Compared to the Jarvis-based big leaf scheme, simulations of latent heat fluxes with a photosynthesis-based scheme for stomatal conductance are more realistic. As expected, flux averages are strongly influenced by the underlying land cover. While the simulated net ecosystem exchange is highly correlated with leaf area index, this correlation is much weaker for the latent heat flux. Photosynthetic CO2 uptake is associated with transpirational water loss via the stomata, and the resulting opposing surface fluxes of CO2 and H2O are reproduced with the model approach. Over vegetated surfaces it is shown that the coupling of a photosynthesis-based gas exchange model with the land-surface scheme of a mesoscale model results in more realistic simulated latent heat fluxes.


C3 single leaf photosynthesis Latent heat flux Net ecosystem exchange Stomatal conductance Sun/shade model 



This research was supported by the Transregional Collaborative Centre 32 “Pattern in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation” (Transregio/SFB32), funded by the Deutsche Forschungsgemeinschaft (DFG). Thanks for providing observations go to TR32 participants Alexander Graf and Anke Schickling, and to Marius Schmidt of the University of Cologne. We gratefully acknowledge GuidoWaldhoff of the TR32 project “Database and Data Management” for preparing the land-use classification. The authors thank three anonymous reviewers for their detailed comments and suggestions.

Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution,and reproduction in any medium, provided the original author(s) and source are credited.


  1. Adegoke JO, Pielke R, Carleton AM (2007) Observational and modelling studies of the impacts of agricultural-related land use change on planetary boundary layer processes in the central US. Agric For Meteorol 142: 203–215CrossRefGoogle Scholar
  2. Atkin OK, Bruhn D, Hurry VM, Tjoelker MG (2005) The hot and the cold: unravelling the variable response of plant respiration to temperature. Funct Plant Biol 32: 87–105CrossRefGoogle Scholar
  3. Baldauf M, Förstner C, Klink S, Reinhardt T, Schraff C, Seiffert A, Stephan K (2009) Kurze Beschreibung des Lokal-Modells Kürzesfrist COSMO-DE (LMK) und seiner Datenbank auf dem Datenserver des DWD. Deutscher Wetterdienst, Geschäftsbereich Forschung und Entwicklung, Offenbach, Germany, 72 ppGoogle Scholar
  4. 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: Biggens J (eds) Progress in photosynthesis research IV. Martinus Nijhoff, Dordrecht, pp 221–224Google Scholar
  5. Beyrich F, Mengelkamp HT (2006) Evaproation over heterogeneous land surface: EVA_GRIPS and the LITFASS-2003 experiment—an overview. Boundary-Layer Meteorol 121: 5–32CrossRefGoogle Scholar
  6. Beyschlag W, Ryel RJ (1999) Canopy modelling. In: Pugnaire F, Valladares F (eds) Handbook of plant functional ecology. Marcel Dekker, New York, pp 771–804Google Scholar
  7. Bonan GB (1995) Land–atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model. J Geophys Res 100: 2817–2831CrossRefGoogle Scholar
  8. Brücher W (1997) Numerische Studien zum Mehrfachnesting mit einem nicht-hydrostatischen modell. Dissertation, University of CologneGoogle Scholar
  9. Brücher W, Kerschgens M, Steffany F (1994a) On the generation of synthetic wind roses in orographocally structured terrain. Theor Appl Climatol 48: 203–207CrossRefGoogle Scholar
  10. Brücher W, Kerschgens M, Steffany F (1994b) Synthetik wind climatologies. Meteorol Z 3:183-186Google Scholar
  11. Brücher W, Kessler C, Kerschgens M, Ebel A (2001) Simulation of traffic-induced air pollution on regional to local scales. Atmos Environ 34(27): 4675–4681CrossRefGoogle Scholar
  12. Bunnell FL, Tait DEN, Flanagan PW, Van Cleve K (1977) Microbial respiration and substrate weight loss, I, A general model of the influences of abiotic variables. Soil Biol Biochem 9: 33–40CrossRefGoogle Scholar
  13. Collatz GJ, Ball JT, Griver C, Berry JA (1991) Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agric For Meteorol 54: 107–136CrossRefGoogle Scholar
  14. Dai Y et al (2003) The common land model. Bull Am Meteorol Soc 84: 1013–1023CrossRefGoogle Scholar
  15. de Arellano JVG, Gioli B, Miglietta F, Jonker HJJ, Baltink HK, Hutjes RWA, Holtslag AAM (2004) The entrainment process of carbon dioxide in the atmospheric boundary layer. J Geophys Res 109: 110–124Google Scholar
  16. de Pury 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
  17. Farquhar GD, von Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149: 78–90CrossRefGoogle Scholar
  18. Fesquet C, Drobinski P, Barthlott C, Dubos T (2009) Impact of terrain heterogeneity on near-surface turbulence structure. Atmos Res 94: 254–269CrossRefGoogle Scholar
  19. Garcia-Amorena I, Wagner F, van Hoof TB, Gomez Manzaneque FG (2006) Stomatal responses in deciduous oaks from southern Europe to the anthropogenic atmospheric CO2 increase; refining the stomatal-based CO2 proxy. Rev Palaeobot Palynol 141: 303–312CrossRefGoogle Scholar
  20. Górska M, Vilà-Gueraude Arellano J, LeMone MA, van Heerwarden CC (2008) mean and flux horizontal variability of virtual potential temperature, moisture, and carbon dioxide: aircraft observations and LES study. Mon Weather Rev 136: 4435–4451CrossRefGoogle Scholar
  21. Graf A, Prolingheuer N, Schickling A, Schmidt M, Schneider K, Schüttemeyer D, Herbst M, Huisman JA, Weihermüller L, Scharnagl B, Steenpass C, Harms R, Vereecken H (2010a) Temporal downscaling of soil CO2 efflux measurements based on time-stable spatial patterns. Vadose Zone J. doi: 10.2136/vzj2009.0152
  22. Graf A, Schüttemeyer D, Geiß H, Knaps A, Möllmann-Coers M, Schween JH, Kollet S, Neininger B, Herbst M, Vereecken H (2010b) Boundedness of turbulent temperature probability distributions, and their relation to the vertical profile in the convective boundary layer. Boundary-Layer Meteorol 134: 459–486CrossRefGoogle Scholar
  23. Harley PC, Thomas RB, Reynolds JF, Strain BR (1992) Modelling photosynthesis of cotton grown in elevated CO2. Plant Cell Environ 15: 271–282CrossRefGoogle Scholar
  24. Heinemann G, Kerschgens M (2005) Comparison of methods for area-averaging surface energy fluxes over heterogenous land surfaces using high-resolution non-hydrostatic simulations. Int J Climatol 25: 379–403. doi: 10.1002/joc.1123 CrossRefGoogle Scholar
  25. Houborg RM, Soegaard H (2004) Regional simulation of ecosystem CO2 and water vapor exchange for agricultural land using NOAA AVHRR and Terra MODIS satellite data. Application to Zealand, Denmark. Remote Sens Environ 93: 150–167CrossRefGoogle Scholar
  26. Hübener H, Schmidt M, Sogalla M, Kerschgens M (2005) Simulating evapotranspiration in a semi-arid environment. Theor Appl Climatol 80: 153–167CrossRefGoogle Scholar
  27. Jacobsen I, Heise E (1982) A new economic method for the computation of the surface temperature in numerical models. Beitr Phys Atmos 55: 28–41Google Scholar
  28. Jarvis P (1976) The interpretation of leaf water potential and stomatal conductance found in canopies in the field. Philos Trans R Soc Lond Ser B Biol Sci 273: 593–610CrossRefGoogle Scholar
  29. Konrad W, Roth-Nebelsick A, Grein M (2008) Modelling of stomatal density response to atmospheric CO2. J Theor Biol 253: 638–658CrossRefGoogle Scholar
  30. Kothavala Z, Arain MA, Black TA, Verseghy D (2005) The simulation of energy, water vapor and carbon dioxide fluxes over common crops by the Canadian Land Surface Scheme (CLASS). Agric For Meteorol 133: 89–108CrossRefGoogle Scholar
  31. Kvifte G, Hegg K, Hansen V (1983) Spectral distribution of solar radiation in the Nordic countries. J Clim Appl Meteorol 22: 143–152CrossRefGoogle Scholar
  32. LeMone MA, Chen F, Alfieri JG, Tewari M, Geerts B, Miao Q, Grossman RL, Coulter RL (2007) Influence of land cover and soil moisture on the horizontal distribution of sensible and latent heat fluxes in southeast Kansas during IHOP_2002 and CASES-97. J Hydrometeorol 8: 68–87CrossRefGoogle Scholar
  33. Maurer B, Heinemann G (2006) Validation of the “Lokal-Modell” over heterogeneous land surfaces using aircraft-based measurements of the REEEFA experiment and comparison with micro-scale simulations. Meteorol Atmos Phys 91: 107–128CrossRefGoogle Scholar
  34. Mengelkamp HT, Beyrich F, Heinemann G, Ament F, Bange J, Berger F, Bösenberg J, Foken T, Hennemuth B, Heret C, Huneke S, Johnsen KP, Kerschgens M, Kohsiek W, Leps JP, Liebethal C, Lohse H, Maider M, Meijninger W, Raasch S, Simmer C, Spiess T, Titterbrand A, Uhlenbrock J, Zittel P (2006) Evaporation over a heterogeneous land surface—the EVA-GRIPS Project. Bull Am Meteorol Soc 87: 775–786CrossRefGoogle Scholar
  35. Niyogi D, Alapaty K, Raman S, Chen F (2009) Development and evaluation of a coupled photosynthesis –based gas exchange evapotranspiration model (GEM) for mesoscale weather forecasting applications. J Appl Meteorol Climatol 48: 349–368CrossRefGoogle Scholar
  36. Noilhan J, Planton S (1989) A simple parameterization of land surface processes for meteorological models. Mon Weather Rev 117: 536–549CrossRefGoogle Scholar
  37. Norby RJ, Luo Y (2004) Evaluating ecosystem responses to rising atmospheric CO2 and global warming in a multi-factor world. New Phytol 162: 281–293CrossRefGoogle Scholar
  38. Pielke RA, Avissar R (1990) Influence of landscape structure on local and regional climate. Landsc Ecol 4: 133–155CrossRefGoogle Scholar
  39. Pielke RA, Niyogi D (2009) The role of landscape processes within the climate system. In: Otto JC, Dikaum R (eds) Landform-structure, evolution, process control: proceedings of the international symposium on Landforms organised by the research training group 437, vol 115. Springer, Berlin, p 67Google Scholar
  40. Pinto JG, Neuhaus CP, Krüger A, Kerschgens M (2009) Assessment of the wind gust estimates method in mesoscale modelling of storm events over West Germany. Meteorol Z 18: 495–506CrossRefGoogle Scholar
  41. Ryel RJ, Beyschlag W, Caldwell MM (1993) Foliage orientation and carbon gain in two tussock grasses as assessed with a new whole-plant gas-exchange model. Funct Ecol 7: 115–124CrossRefGoogle Scholar
  42. Sellers PJ, Berry JA, Collatz GJ, Field CB, Hall FG (1992) Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sens Environ 42: 187–216CrossRefGoogle Scholar
  43. Sellers PJ et al (1996) A revised land surface parameterisation (SiB2) for atmospheric GCMs. Part I: model formulation. J Clim 9: 676–705Google Scholar
  44. Shao Y, Sogalla M, Kerschgens M, Brücher W (2001) Effects of land-surface heterogeneity upon surface fluxes and turbulent conditions. Meteorol Atmos Phys 78: 157–181CrossRefGoogle Scholar
  45. Sogalla M, Krüger A, Kerschgens M (2006) Mesoscale modelling of interactions between rainfall and the land surface in West Africa. Meteorol Atmos Phys 91: 211–221CrossRefGoogle Scholar
  46. Szeicz G (1974) Solar radiation for plant growth. J Appl Ecol 11: 617–636CrossRefGoogle Scholar
  47. Tricker PJ, Trewin H, Kull O, Clarkson GJJ, Eensalu E, Tallis MJ, Colella A, Doncaster CP, Sabatti M, Taylor G (2005) Stomatal conductance and not stomatal density determines the long-term reduction in leaf transpiration of poplar in elevated CO2. Oecologia 143: 652–660CrossRefGoogle Scholar
  48. Waldhoff G (2010) Land use classification of 2009 for the Rur catchment. doi: 10.1594/GFZ.TR32.1
  49. Werner C, Correia O, Ryel RJ, Beyschlag W (2001) Effects of photoinhibition on whole-plant carbon gain assessed with a photosynthesis model. Plant Cell Environ 24: 27–40CrossRefGoogle Scholar
  50. Xiu A, Pleim JE (2001) Development of a land surface model, part I: application in a mesoscale meteorological model. J Appl Meteorol 40: 192–209CrossRefGoogle Scholar
  51. Zhan X, Kustas WP (2001) A coupled model of land surface CO2 and energy fluxes using remote sensing data. Agric For Meteorol 107: 131–152CrossRefGoogle Scholar

Copyright information

© The Author(s) 2010

Authors and Affiliations

  • Mark Reyers
    • 1
  • Andreas Krüger
    • 1
  • Christiane Werner
    • 2
  • Joaquim G. Pinto
    • 1
  • Stefan Zacharias
    • 1
  • Michael Kerschgens
    • 1
  1. 1.Institute for Geophysics and MeteorologyUniversity of CologneCologneGermany
  2. 2.Experimental and Systems EcologyUniversity of BielefeldBielefeldGermany

Personalised recommendations