Diurnal Variations of the Flux Imbalance Over Homogeneous and Heterogeneous Landscapes

Research Article
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Abstract

It is well known that the sum of the turbulent sensible and latent heat fluxes as measured by the eddy-covariance method is systematically lower than the available energy (i.e., the net radiation minus the ground heat flux). We examine the separate and joint effects of diurnal and spatial variations of surface temperature on this flux imbalance in a dry convective boundary layer using the Weather Research and Forecasting model. Results show that, over homogeneous surfaces, the flux due to turbulent-organized structures is responsible for the imbalance, whereas over heterogeneous surfaces, the flux due to mesoscale or secondary circulations is the main contributor to the imbalance. Over homogeneous surfaces, the flux imbalance in free convective conditions exhibits a clear diurnal cycle, showing that the flux-imbalance magnitude slowly decreases during the morning period and rapidly increases during the afternoon period. However, in shear convective conditions, the flux-imbalance magnitude is much smaller, but slightly increases with time. The flux imbalance over heterogeneous surfaces exhibits a diurnal cycle under both free and shear convective conditions, which is similar to that over homogeneous surfaces in free convective conditions, and is also consistent with the general trend in the global observations. The rapid increase in the flux-imbalance magnitude during the afternoon period is mainly caused by the afternoon decay of the turbulent kinetic energy (TKE). Interestingly, over heterogeneous surfaces, the flux imbalance is linearly related to the TKE and the difference between the potential temperature and surface temperature, ΔT; the larger the TKE and ΔT values, the smaller the flux-imbalance magnitude.

Keywords

Convective boundary layer Diurnal variations Flux imbalance Large-eddy simulation Spatial heterogeneity 

Notes

Acknowledgements

This work was jointly supported by the National Natural Science Foundation of China (Grant: 91425303 and 41630856) and the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant: XDA19070100. H.L. acknowledges support by National Science Foundation AGS under Grants: 1419614. The major part of this work was conducted when the first author visited Boston University in 2017. We thank Professor Guido Salvucci and Dr. Angela Rigden at Boston University for their constructive comments and suggestions.

References

  1. Aubinet M, Vesala T, Papale D (eds) (2012) Eddy covariance: a practical guide to measurement and data analysis. Springer, DordrechtGoogle Scholar
  2. Beare RJ, Cortes MAJ, Cuxart J, Esau I, Golaz C, Holtslag AAM, Khairoutdinov M, Kosovic B, Lewellen D, Lund T, Lundquist J, McCabe A, Macvean MK, Moene A, Noh Y, Poulos G, Raasch S, Sullivan PP (2006) An intercomparison of large-eddy simulations of the stable boundary-layer. Boundary-Layer Meteorol 118:247–272CrossRefGoogle Scholar
  3. Cheng GD, Li X, Zhao WZ, Xu ZM, Feng Q, Xiao SC, Xiao HL (2014) Integrated study of the water–ecosystem–economy in the Heihe River Basin. Nat Sci Rev 1(3):413–428CrossRefGoogle Scholar
  4. Crosman ET, Horel JD (2010) Sea and lake breezes: a review of numerical studies. Boundary-Layer Meteorol 137:1–29CrossRefGoogle Scholar
  5. Darbieu C, Lohou F, Lothon M, Vilà-Guerau de Arellano J, Couvreux F, Durand P, Pino D, Patton EG, Nilsson E, Blay-Carreras E, Gioli B (2015) Turbulence vertical structure of the boundary layer during the afternoon transition. Atmos Chem Phys 15:10071–10086CrossRefGoogle Scholar
  6. De Roo F, Mauder M (2017) The influence of idealized surface heterogeneity on virtual turbulent flux measurements. Atmos Chem Phys Discuss.  https://doi.org/10.5194/acp-2017-498 Google Scholar
  7. Eder F, De Roo F, Kohnert K, Desjardins RL, Schmid HP, Mauder M (2014) Evaluation of two energy balance closure parametrizations. Boundary-Layer Meteorol 151:195–219CrossRefGoogle Scholar
  8. Eder F, Schmidt M, Damian T, Traumner K, Mauder M (2015a) Mesoscale eddies affect near-surface turbulent exchange: evidence from lidar and tower measurements. J Appl Meteorol 54:189–206CrossRefGoogle Scholar
  9. Eder F, De Roo F, Rotenberg E, Yakir D, Schmid HP, Mauder M (2015b) Secondary circulations at a solitary forest surrounded by semi-arid shrubland and their impact on eddy-covariance measurements. Agric For Meteorol 211–212:115–127CrossRefGoogle Scholar
  10. Finnigan JJ (2000) Turbulence in plant canopies. Annu Rev Fluid Mech 32:519–571CrossRefGoogle Scholar
  11. Finnigan JJ, Clement R, Malhi Y, Leuning R, Cleugh H (2003) A re-evaluation of long-term flux measurement techniques part I: averaging and coordinate rotation. Boundary-Layer Meteorol 107:1–48CrossRefGoogle Scholar
  12. Foken T (2008) The energy balance closure problem: an overview. Ecol Appl 18:1351–1367CrossRefGoogle Scholar
  13. Foken T, Wimmer F, Mauder M, Thomas C, Liebethal C (2006) Some aspects of the energy balance closure problem. Atmos Chem Phys 6:4395–4402CrossRefGoogle Scholar
  14. Foken T, Aubinet M, Finnigan JJ, Leclerc MY, Mauder M, Paw UKT (2011) Results of a panel discussion about the energy balance closure correction for trace gases. Bull Am Meteorol Soc 92(4):ES13–ES18CrossRefGoogle Scholar
  15. Gao ZQ, Horton R, Liu HP (2010) Impact of wave phase difference between soil surface heat flux and soil surface temperature on soil surface energy balance closure. J Geophys Res 115:D16112.  https://doi.org/10.1029/2009JD013278 CrossRefGoogle Scholar
  16. Gao ZM, Liu HP, Katul GK, Foken T (2017) Non-closure of the surface energy balance explained by phase difference between vertical velocity and scalars of large atmospheric eddies. Environ Res Lett 12(3):034025CrossRefGoogle Scholar
  17. Goulart A, Degrazia G, Rizza U, Anfossi D (2003) A theoretical model for the study of convective turbulence decay and comparison with large-eddy simulation data. Boundary-Layer Meteorol 107:143–155CrossRefGoogle Scholar
  18. He Y, Monahan AH, McFarlane NA (2013) Diurnal variations of land surface wind speed probability distributions under clear-sky and low-cloud conditions. Geophys Res Lett 40:3308–3314CrossRefGoogle Scholar
  19. Huang J, Lee X, Patton EG (2008) A modelling study of flux imbalance and the influence of entrainment in the convective boundary layer. Boundary-Layer Meteorol 127(2):273–292CrossRefGoogle Scholar
  20. Inagaki A, Letzel MO, Raasch S, Kanda M (2006) Impact of surface heterogeneity on energy imbalance: a study using LES. J Meteorol Soc Jpn 84:187–198CrossRefGoogle Scholar
  21. Kanda M, Inagaki A, Letzel MO, Raasch S, Watanabe T (2004) LES study of the energy imbalance problem with eddy covariance fluxes. Boundary-Layer Meteorol 110:381–404CrossRefGoogle Scholar
  22. Kristensen L, Mann J, Oncley SP, Wyngaard JC (1997) How close is close enough when measuring scalar fluxes with displaced sensors. J Atmos Ocean Technol 14:814–821CrossRefGoogle Scholar
  23. Lee X (1998) On micrometeorological observations of surface-air exchange over tall vegetation. Agric For Meteorol 91:39–49CrossRefGoogle Scholar
  24. Leuning R, van Gorsel E, Massman WJ, Isaac PR (2012) Reflections on the surface energy imbalance problem. Agric For Meteorol 156:65–74CrossRefGoogle Scholar
  25. Li X, Cheng GD, Liu SM, Xiao Q, Ma MG, Jin R, Che T, Liu QH, Wang WZ, Qi Y, Wen JG, Li HY, Zhu GF, Guo JW, Ran YH, Wang SG, Zhu ZL, Zhou J, Hu XL, Xu ZW (2013) Heihe Watershed Allied Telemetry Experimental Research (HiWATER): scientific objectives and experimental design. Bull Am Meteorol Soc 94:1145–1160CrossRefGoogle Scholar
  26. Li X, Yang K, Zhou YZ (2016) Progress in the study of oasis-desert interactions. Agric For Meteorol 230–231:1–7CrossRefGoogle Scholar
  27. Liu SM, Xu ZW, Song LS, Zhao QY, Ge Y, Xu TR, Ma YF, Zhu ZL, Jia ZZ, Zhang F (2016) Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agric For Meteorol 230–231:97–113CrossRefGoogle Scholar
  28. Mahrt L (1998) Flux sampling errors for aircraft and towers. J Atmos Ocean Technol 15:416–429CrossRefGoogle Scholar
  29. Mahrt L (2010) Computing turbulent fluxes near the surface: needed improvements. Agric For Meteorol 150(4):501–509CrossRefGoogle Scholar
  30. Moeng C, Dudhia J, Klemp J, Sullivan P (2007) Examining two-way grid nesting for large eddy simulation of the PBL using the WRF model. Mon Weather Rev 135(6):2295–2311CrossRefGoogle Scholar
  31. Nadeau DF, Pardyjak ER, Higgins CW, Fernando HJS, Parlange MB (2011) A simple model for the afternoon and early evening decay of convective turbulence over different land surfaces. Boundary-Layer Meteorol 141:301–324CrossRefGoogle Scholar
  32. Oncley SP, Foken T, Vogt R, Kohsiek W, DeBruin HAR, Bernhofer C, Christen A, van Gorsel E, Grantz D, Feigenwinter C, Lehner I, Liebethal C, Liu H, Mauder M, Pitacco A, Ribeiro L, Weidinger T (2007) The energy balance experiment EBEX-2000. Part I: overview and energy balance. Boundary-Layer Meteorol 123:1–28CrossRefGoogle Scholar
  33. Patton E, Sullivan P, Moeng C (2005) The influence of idealized heterogeneity on wet and dry planetary boundary layers coupled to the land surface. J Atmos Sci 62:2078–2097CrossRefGoogle Scholar
  34. Pino D, Jonker H, Vilà-Guerau De Arellano J, Dosio A (2006) Role of shear and the inversion strength during sunset turbulence over land: characteristic length scales. Boundary-Layer Meteorol 121:537–556CrossRefGoogle Scholar
  35. Rannik U, Vesala T (1999) Autoregressive filtering versus linear detrending in estimation of fluxes by the eddy covariance method. Boundary-Layer Meteorol 91(2):259–280CrossRefGoogle Scholar
  36. Raupach MR, Shaw RH (1982) Averaging procedures for flow within vegetation canopies. Boundary-Layer Meteorol 22:79–90CrossRefGoogle Scholar
  37. Rizza U, Miglietta M, Degrazia G, Acevedo O, Marques Filho E (2013) Sunset decay of the convective turbulence with large-eddy simulation under realistic conditions. Phys A 392:4481–4490CrossRefGoogle Scholar
  38. Schalkwijk J, Jonker HJJ, Siebesma AP (2016) An investigation of the eddy-covariance flux imbalance in a year-long large-eddy simulation of the weather at Cabauw. Boundary-Layer Meteorol 160:17–39CrossRefGoogle Scholar
  39. Sorbjan Z (1997) Decay of convective turbulence revisited. Boundary-Layer Meteorol 82:501–515CrossRefGoogle Scholar
  40. Steinfeld G, Letzel M, Raasch S, Kanda M, Inagaki A (2007) Spatial representativeness of single tower measurements and the imbalance problem with eddy-covariance fluxes: results of a large-eddy simulation study. Boundary-Layer Meteorol 123:77–98CrossRefGoogle Scholar
  41. Stoy PC, Mauder M, Foken T, Marcolla B, Boegh E, Ibrom A, Arain MA, Arneth A, Aurela M, Bernhofer C, Cescatti A, Dellwik E, Duce P, Gianelle D, van Gorsel E, Kiely G, Knohl A, Margolis H, McCaughey H, Merbold L, Montagnani L, Papale D, Reichstein M, Saunders M, Serrano-Ortiz P, Sottocornola M, Spano D, Vaccari F, Varlagin A (2013) A data-driven analysis of energy balance closure across FLUXNET research sites: the role of landscape scale heterogeneity. Agric For Meteorol 171–172:137–152CrossRefGoogle Scholar
  42. Talbot C, Bou-Zeid E, Smith J (2012) Nested mesoscale large-eddy simulations with WRF: performance in real test cases. J Hydrometeorol 13(5):1421–1441CrossRefGoogle Scholar
  43. Twine TE, Kustas WP, Norman JM, Cook DR, Houser PR, Meyers TP, Prueger JH, Starks PJ, Wesely ML (2000) Correcting eddy-covariance flux underestimates over a grassland. Agric For Meteorol 103:279–300CrossRefGoogle Scholar
  44. Wang JM, Wang WZ, Liu SM, Ma MG, Li X (2009) The problems of surface energy balance closure: an overview and case study. Adv Earth Sci 24:705–713 (in Chinese) Google Scholar
  45. Wilson K, Goldstein A, Falge E, Aubinet M, Baldocchi D, Berbigier P, Bernhofer C, Ceulemans R, Dolman H, Field C, Grelle A, Ibrom A, Law BE, Kowalski A, Meyers T, Moncrieff J, Monson R, Oechel W, Tenhunen J, Valentini R, Verma S (2002) Energy balance closure at FLUXNET sites. Agric For Meteorol 113:223–243CrossRefGoogle Scholar
  46. Wohlfahrt G, Widmoser P (2013) Can an energy balance model provides additional constraints on how to close the energy imbalance? Agric For Meteorol 169:85–91CrossRefGoogle Scholar
  47. Xu ZW, Liu SM, Li X, Shi SJ, Wang JM, Zhu ZL, Xu TR, Wang WZ, Ma MG (2013) Intercomparison of surface energy flux measurement systems used during the HiWATER-MUSOEXE. J Geophys Res Atmos 118:13140–13157CrossRefGoogle Scholar
  48. Xu ZW, Ma YF, Liu SM, Shi WJ, Wang JM (2017) Assessment of the energy balance closure under advective conditions and its impact using remote sensing data. J Appl Meteorol 56(1):127–140CrossRefGoogle Scholar
  49. Zhang N, Wang XY, Peng Z (2014) Large-eddy simulation of mesoscale circulations forced by inhomogeneous urban heat island. Boundary-Layer Meteorol 151(1):179–194CrossRefGoogle Scholar
  50. Zhu X, Ni G, Cong Z, Sun T, Li D (2016) Impacts of surface heterogeneity on dry planetary boundary layers in an urban-rural setting. J Geophys Res Atmos 121:12164–121179CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Department of Earth and EnvironmentBoston UniversityBostonUSA
  4. 4.Laboratory for Atmospheric Research, Department of Civil and Environmental EngineeringWashington State UniversityPullmanUSA
  5. 5.Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijingChina
  6. 6.CAS Center for Excellence in Tibetan Plateau Earth SciencesChinese Academy of SciencesBeijingChina

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