• Abramowitz, G., 2012: Towards a public, standardized, diagnostic benchmarking system for land surface models. Geosci. Model Dev., 5, 819827, doi:10.5194/gmd-5-819-2012.

    • Search Google Scholar
    • Export Citation
  • Abramowitz, G., , Leuning R. , , Clark M. , , and Pitman A. J. , 2008: Evaluating the performance of land surface models. J. Climate, 21, 54685481, doi:10.1175/2008JCLI2378.1.

    • Search Google Scholar
    • Export Citation
  • Ball, J. T., , Woodrow I. E. , , and Berry J. A. , 1987: A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. Progress in Photosynthesis Research, Vol. 1, J. Biggins, Ed., Springer, 221–234.

  • Best, M. J., and et al. , 2011: The Joint UK Land Environment Simulator (JULES), model description—Part 1: Energy and water fluxes. Geosci. Model Dev., 4, 677699, doi:10.5194/gmd-4-677-2011.

    • Search Google Scholar
    • Export Citation
  • Best, M. J., and et al. , 2015: The plumbing of land surface models. J. Hydrometeor., 16, 14251442, doi:10.1175/JHM-D-14-0158.1.

  • Blyth, E. M., , Dolman A. J. , , and Wood N. , 1993: Effective resistance to sensible- and latent-heat flux in heterogeneous terrain. Quart. J. Roy. Meteor. Soc., 119, 423442, doi:10.1002/qj.49711951104.

    • Search Google Scholar
    • Export Citation
  • Boisier, J. P., and et al. , 2012: Attributing the impacts of land-cover changes in temperate regions on surface temperature and heat fluxes to specific causes: Results from the first LUCID set of simulations. J. Geophys. Res., 117, D12116, doi:10.1029/2011JD017106.

    • Search Google Scholar
    • Export Citation
  • Chen, F., , and Zhang Y. , 2009: On the coupling strength between the land surface and the atmosphere: From viewpoint of surface exchange coefficients. Geophys. Res. Lett., 36, L10404, doi:10.1029/2009GL037980.

    • Search Google Scholar
    • Export Citation
  • Clapp, R. B., , and Hornberger G. M. , 1978: Empirical equations for some soil hydraulic properties. Water Resour. Res., 14, 601604, doi:10.1029/WR014i004p00601.

    • Search Google Scholar
    • Export Citation
  • Colman, R. A., 2013: Surface albedo feedbacks from climate variability and change. J. Geophys. Res. Atmos., 118, 28272834, doi:10.1002/jgrd.50230.

    • Search Google Scholar
    • Export Citation
  • Comer, R. E., , and Best M. J. , 2012: Revisiting GLACE: Understanding the role of the land surface in land–atmosphere coupling. J. Hydrometeor., 13, 17041718, doi:10.1175/JHM-D-11-0146.1.

    • Search Google Scholar
    • Export Citation
  • Crook, J. A., , and Forster P. M. , 2014: Comparison of surface albedo feedback in climate models and observations. Geophys. Res. Lett., 41, 17171723, doi:10.1002/2014GL059280.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and et al. , 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • Desborough, C. E., 1999: Surface energy balance complexity in GCM land surface models. Climate Dyn., 15, 389403, doi:10.1007/s003820050289.

    • Search Google Scholar
    • Export Citation
  • Desborough, C. E., , and Pitman A. J. , 1998: The BASE land surface model. Global Planet. Change, 19, 318, doi:10.1016/S0921-8181(98)00038-1.

    • Search Google Scholar
    • Export Citation
  • Di Luca, A., , Flaounas E. , , Drobinski P. , , and Brossier C. L. , 2014: The atmospheric component of the Mediterranean Sea water budget in a WRF multi-physics ensemble and observations. Climate Dyn., 43, 23492375, doi:10.1007/s00382-014-2058-z.

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., 2011a: The terrestrial segment of soil moisture–climate coupling. Geophys. Res. Lett., 38, L16702, doi:10.1029/2011GL048268.

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., 2011b: A history and review of the Global Soil Wetness Project (GSWP). J. Hydrometeor., 12, 729749, doi:10.1175/JHM-D-10-05010.1.

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., , Jin Y. , , Singh B. , , and Yan X. , 2013a: Evolving land–atmosphere interactions over North America from CMIP5 simulations. J. Climate, 26, 73137327, doi:10.1175/JCLI-D-12-00454.1.

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., , Jin Y. , , Singh B. , , and Yan X. , 2013b: Trends in land–atmosphere interactions from CMIP5 simulations. J. Hydrometeor., 14, 829849, doi:10.1175/JHM-D-12-0107.1.

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., and et al. , 2014: Intensified land surface control on boundary layer growth in a changing climate. Geophys. Res. Lett., 41, 12901294, doi:10.1002/2013GL058826.

    • Search Google Scholar
    • Export Citation
  • Dutra, E., , Schär C. , , Viterbo P. , , and Miranda P. M. A. , 2011: Land–atmosphere coupling associated with snow cover. Geophys. Res. Lett., 38, L15707, doi:10.1029/2011GL048435.

    • Search Google Scholar
    • Export Citation
  • Evans, J. P., , and McCabe M. F. , 2010: Regional climate simulation over Australia’s Murray–Darling Basin: A multitemporal assessment. J. Geophys. Res., 115, D14114, doi:10.1029/2010JD013816.

    • Search Google Scholar
    • Export Citation
  • Evans, J. P., , Ekström M. , , and Ji F. , 2012: Evaluating the performance of a WRF physics ensemble over south-east Australia. Climate Dyn., 39, 12411258, doi:10.1007/s00382-011-1244-5.

    • Search Google Scholar
    • Export Citation
  • Ferguson, C. R., , Wood E. F. , , and Vinukollu R. K. , 2012: A global intercomparison of modeled and observed land–atmosphere coupling. J. Hydrometeor., 13, 749784, doi:10.1175/JHM-D-11-0119.1.

    • Search Google Scholar
    • Export Citation
  • Findell, K. L., , and Eltahir E. A. B. , 2003a: Atmospheric controls on soil moisture–boundary layer interactions. Part I: Framework development. J. Hydrometeor., 4, 552569, doi:10.1175/1525-7541(2003)004<0552:ACOSML>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Findell, K. L., , and Eltahir E. A. B. , 2003b: Atmospheric controls on soil moisture–boundary layer interactions. Part II: Feedbacks within the continental United States. J. Hydrometeor., 4, 570583, doi:10.1175/1525-7541(2003)004<0570:ACOSML>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Finnigan, J. J., , and Raupach M. R. , 1987: Transfer processes in plant canopies in relation to stomatal characteristics. Stomatal Function, E. Zeiger, G. D. Farquhar, and L. R. Cowan, Eds., Stanford University Press, 385–429.

  • Flaounas, E., , Bastin S. , , and Janicot S. , 2011: Regional climate modelling of the 2006 West African monsoon: Sensitivity to convection and planetary boundary layer parameterisation using WRF. Climate Dyn., 36, 10831105, doi:10.1007/s00382-010-0785-3.

    • Search Google Scholar
    • Export Citation
  • Guo, Z., and et al. , 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part II: Analysis. J. Hydrometeor., 7, 611625, doi:10.1175/JHM511.1.

    • Search Google Scholar
    • Export Citation
  • Haverd, V., and et al. , 2013: Multiple observation types reduce uncertainty in Australia’s terrestrial carbon and water cycles. Biogeosciences, 10, 20112040, doi:10.5194/bg-10-2011-2013.

    • Search Google Scholar
    • Export Citation
  • Henderson-Sellers, A., , Pitman A. J. , , Love P. K. , , Irannejad P. , , and Chen T. , 1995: The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3. Bull. Amer. Meteor. Soc., 76, 489503, doi:10.1175/1520-0477(1995)076<0489:TPFIOL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hirsch, A. L., , Pitman A. J. , , and Kala J. , 2014a: The role of land cover change in modulating the soil moisture–temperature land–atmosphere coupling strength over Australia. Geophys. Res. Lett., 41, 58835890, doi:10.1002/2014GL061179.

    • Search Google Scholar
    • Export Citation
  • Hirsch, A. L., , Pitman A. J. , , Seneviratne S. I. , , Evans J. P. , , and Haverd V. , 2014b: Summertime maximum and minimum temperature asymmetry over Australia determined using WRF. Geophys. Res. Lett., 41, 15461552, doi:10.1002/2013GL059055.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and et al. , 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 11381140, doi:10.1126/science.1100217.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and et al. , 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7, 590610, doi:10.1175/JHM510.1.

    • Search Google Scholar
    • Export Citation
  • Kowalczyk, E. A., and et al. , 2013: The land surface model component of ACCESS: Description and impact on the simulated surface climatology. Aust. Meteor. Ocean J., 63, 6582.

    • Search Google Scholar
    • Export Citation
  • Kumar, S. V., and et al. , 2006: Land information system: An interoperable framework for high resolution land surface modeling. Environ. Modell. Software, 21, 14021415, doi:10.1016/j.envsoft.2005.07.004.

    • Search Google Scholar
    • Export Citation
  • Leuning, R., 1995: A critical appraisal of a combined stomatal photosynthesis model for C3 plants. Plant Cell Environ., 18, 339355, doi:10.1111/j.1365-3040.1995.tb00370.x.

    • Search Google Scholar
    • Export Citation
  • Lorenz, R., , and Pitman A. J. , 2014: Effect of land–atmosphere coupling strength on impacts from Amazonian deforestation. Geophys. Res. Lett., 41, 59875995, doi:10.1002/2014GL061017.

    • Search Google Scholar
    • Export Citation
  • Lorenz, R., , Pitman A. J. , , Hirsch A. L. , , and Srbinovsky J. , 2015: Intraseasonal versus interannual measures of land–atmosphere coupling strength in a global climate model: GLACE-1 versus GLACE-CMIP5 experiments in ACCESS1.3b. J. Hydrometeor., 16, 22762295, doi: 10.1175/JHM-D-14-0206.1.

    • Search Google Scholar
    • Export Citation
  • Loveland, T. R., , Reed B. C. , , Brown J. F. , , Ohlen D. O. , , Zhu Z. , , Yang L. , , and Merchant J. W. , 2000: Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens., 21, 13031330, doi:10.1080/014311600210191.

    • Search Google Scholar
    • Export Citation
  • Medlyn, B. E., and et al. , 2011: Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biol., 17, 21342144, doi:10.1111/j.1365-2486.2010.02375.x.

    • Search Google Scholar
    • Export Citation
  • Miralles, D. G., , Teuling A. J. , , van Heerwaarden C. C. , , and Vila-Guerau de Arellano J. , 2014: Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci., 7, 345349, doi:10.1038/ngeo2141.

    • Search Google Scholar
    • Export Citation
  • Oleson, K. W., and et al. , 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-478+STR, 257 pp., doi:10.5065/D6FB50WZ.

  • Peters-Lidard, C. D., and et al. , 2007: High-performance earth system modeling with NASA/GSFC’s land information system. Innovations Syst. Software Eng., 3, 157165, doi:10.1007/s11334-007-0028-x.

    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., 2003: The evolution of, and revolution in, land surface schemes designed for climate models. Int. J. Climatol., 23, 479510, doi:10.1002/joc.893.

    • Search Google Scholar
    • Export Citation
  • Puma, M. J., , Koster R. D. , , and Cook B. I. , 2013: Phenological versus meteorological controls on land–atmosphere water and carbon fluxes. J. Geophys. Res. Biogeosci., 118, 1429, doi:10.1029/2012JG002088.

    • Search Google Scholar
    • Export Citation
  • Qu, X., , and Hall A. , 2006: Assessing snow albedo feedback in simulated climate change. J. Climate, 19, 26172630, doi:10.1175/JCLI3750.1.

    • Search Google Scholar
    • Export Citation
  • Raupach, M. R., 1989a: A practical Lagrangian method for relating scalar concentrations to source distributions in vegetation canopies. Quart. J. Roy. Meteor. Soc., 115, 609632, doi:10.1002/qj.49711548710.

    • Search Google Scholar
    • Export Citation
  • Raupach, M. R., 1989b: Applying Lagrangian fluid mechanics to infer scalar source distributions from concentration profiles in plant canopies. Agric. For. Meteor., 47, 85108, doi:10.1016/0168-1923(89)90089-0.

    • Search Google Scholar
    • Export Citation
  • Raupach, M. R., 1994: Simplified expressions for vegetation roughness length and zero-place displacement as functions of canopy height and area index. Bound.-Layer Meteor., 71, 211216, doi:10.1007/BF00709229.

    • Search Google Scholar
    • Export Citation
  • Santanello, J. A., , Peters-Lidard C. D. , , and Kumar S. V. , 2011: Diagnosing the sensitivity of local land–atmosphere coupling via the soil moisture–boundary layer interaction. J. Hydrometeor., 12, 766786, doi:10.1175/JHM-D-10-05014.1.

    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., , Mintz Y. , , Sud Y. C. , , and Dalcher A. , 1986: A Simple Biosphere Model (SiB) for use within general circulation models. J. Atmos. Sci., 43, 505531, doi:10.1175/1520-0469(1986)043<0505:ASBMFU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., and et al. , 2013: Impact of soil moisture–climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment. Geophys. Res. Lett., 40, 52125217, doi:10.1002/grl.50956.

    • Search Google Scholar
    • Export Citation
  • Shuttleworth, W. J., 2007: Putting the “vap” into evaporation. Hydrol. Earth Syst. Sci., 11, 210244, doi:10.5194/hess-11-210-2007.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and et al. , 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D68S4MVH.

  • Swenson, S. C., , and Lawrence D. M. , 2014: Assessing a dry surface layer–based soil resistance parameterization for the Community Land Model using GRACE and FLUXNET-MTE data. J. Geophys. Res. Atmos., 119, 10 29910 312, doi:10.1002/2014JD022314.

    • Search Google Scholar
    • Export Citation
  • Tawfik, A. B., , and Dirmeyer P. A. , 2014: A process-based framework for quantifying the atmospheric preconditioning of surface triggered convection. Geophys. Res. Lett., 41, 173178, doi:10.1002/2013GL057984.

    • Search Google Scholar
    • Export Citation
  • Wang, Y. P., , Kowalczyk E. , , Leuning R. , , Abramowitz G. , , Raupach M. R. , , Pak B. , , van Gorsel E. , , and Luhar A. , 2011: Diagnosing errors in a land surface model (CABLE) in the time and frequency domains. J. Geophys. Res., 116, G01034, doi:10.1029/2010JG001385.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 36 36 2
PDF Downloads 26 26 2

Evaluating Land–Atmosphere Coupling Using a Resistance Pathway Framework

View More View Less
  • 1 ARC Centre of Excellence for Climate System Science and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia
  • | 2 CSIRO Oceans and Atmosphere Flagship, CSIRO, Canberra, Australian Capital Territory, Australia
© Get Permissions
Restricted access

Abstract

This paper presents a methodology for examining land–atmosphere coupling in a regional climate model by examining how the resistances to moisture transfer from the land to the atmosphere control the surface turbulent energy fluxes. Perturbations were applied individually to the aerodynamic resistance from the soil surface to the displacement height, the aerodynamic resistance from the displacement height to the reference level, the stomatal resistance, and the leaf boundary layer resistance. Only perturbations to the aerodynamic resistance from the soil surface to the displacement height systematically affected 2-m air temperature for the shrub and evergreen boreal forest plant functional types (PFTs). This was associated with this resistance systematically increasing the terrestrial and atmospheric components of the land–atmosphere coupling strength through changes in the partitioning of the surface energy balance. Perturbing the other resistances did contribute to changing the partitioning of the surface energy balance but did not lead to systematic changes in the 2-m air temperature. The results suggest that land–atmosphere coupling in the modeling system presented here acts mostly through the aerodynamic resistance from the soil surface to the displacement height, which is a function of both the friction velocity and vegetation height and cover. The results show that a resistance pathway framework can be used to examine how changes in the resistances affect the partitioning of the surface energy balance and how this subsequently influences surface climate through land–atmosphere coupling. Limitations in the present analysis include grid-scale rather than PFT-scale analysis, the exclusion of resistance dependencies, and the linearity assumption of how temperature responds to a resistance perturbation.

Corresponding author address: Annette L. Hirsch, Institute for Atmospheric and Climate Science, ETH Zürich, Universitätstrasse 16, 8092 Zurich, Switzerland. E-mail: annette.hirsch@env.ethz.ch

Current affiliation: Institute for Atmospheric and Climate Science, ETH Zürich, Zurich, Switzerland.

Abstract

This paper presents a methodology for examining land–atmosphere coupling in a regional climate model by examining how the resistances to moisture transfer from the land to the atmosphere control the surface turbulent energy fluxes. Perturbations were applied individually to the aerodynamic resistance from the soil surface to the displacement height, the aerodynamic resistance from the displacement height to the reference level, the stomatal resistance, and the leaf boundary layer resistance. Only perturbations to the aerodynamic resistance from the soil surface to the displacement height systematically affected 2-m air temperature for the shrub and evergreen boreal forest plant functional types (PFTs). This was associated with this resistance systematically increasing the terrestrial and atmospheric components of the land–atmosphere coupling strength through changes in the partitioning of the surface energy balance. Perturbing the other resistances did contribute to changing the partitioning of the surface energy balance but did not lead to systematic changes in the 2-m air temperature. The results suggest that land–atmosphere coupling in the modeling system presented here acts mostly through the aerodynamic resistance from the soil surface to the displacement height, which is a function of both the friction velocity and vegetation height and cover. The results show that a resistance pathway framework can be used to examine how changes in the resistances affect the partitioning of the surface energy balance and how this subsequently influences surface climate through land–atmosphere coupling. Limitations in the present analysis include grid-scale rather than PFT-scale analysis, the exclusion of resistance dependencies, and the linearity assumption of how temperature responds to a resistance perturbation.

Corresponding author address: Annette L. Hirsch, Institute for Atmospheric and Climate Science, ETH Zürich, Universitätstrasse 16, 8092 Zurich, Switzerland. E-mail: annette.hirsch@env.ethz.ch

Current affiliation: Institute for Atmospheric and Climate Science, ETH Zürich, Zurich, Switzerland.

Save