Advertisement

Boundary-Layer Meteorology

, Volume 170, Issue 2, pp 323–348 | Cite as

Biases in Model-Simulated Surface Energy Fluxes During the Indian Monsoon Onset Period

  • Tirthankar Chakraborty
  • Chandan Sarangi
  • Mithun Krishnan
  • Sachchida Nand TripathiEmail author
  • Ross Morrison
  • Jonathan Evans
Research Article

Abstract

We use eddy-covariance measurements over a semi-natural grassland in the central Indo-Gangetic Basin to investigate biases in energy fluxes simulated by the Noah land-surface model for two monsoon onset periods: one with rain (2016) and one completely dry (2017). In the preliminary run with default parameters, the offline Noah LSM overestimates the midday (1000–1400 local time) sensible heat flux (H) by 279% (in 2016) and 108% (in 2017) and underestimates the midday latent heat flux (LE) by 56% (in 2016) and 67% (in 2017). These discrepancies in simulated energy fluxes propagate to and are amplified in coupled Weather Research and Forecasting model simulations, as seen from the High Asia Reanalysis dataset. One-dimensional Noah simulations with modified site-specific vegetation parameters not only improve the partitioning of the energy fluxes (Bowen ratio of 0.9 in modified run versus 3.1 in the default run), but also reduce the overestimation of the model-simulated soil and skin temperature. Thus, use of ambient site parameters in future studies is warranted to reduce uncertainties in short-term and long-term simulations over this region. Finally, we examine how biases in the model simulations can be attributed to lack of closure in the measured surface energy budget. The bias is smallest when the sensible heat flux post-closure method is used (5.2 \(\hbox {W } \hbox {m}^{-2}\) for H and 16 \(\hbox {W } \hbox {m}^{-2}\) for LE in 2016; 0.17 \(\hbox {W } \hbox {m}^{-2}\) for H and 2.8 \(\hbox {W } \hbox {m}^{-2}\) for LE in 2017), showing the importance of taking into account the surface energy imbalance at eddy-covariance sites when evaluating land-surface models.

Keywords

Eddy covariance Energy balance closure Land-surface model Model evaluation Surface energy balance 

Notes

Acknowledgements

We gratefully acknowledge the financial support given by the Earth System Science Organization, Ministry of Earth Sciences, Government of India (Grant MM/NERC-MoES-03/2014/002) and Newton Fund to conduct this research under INCOMPASS campaign and Monsoon Mission. The INCOMPASS field campaign and A. G. Turner are supported in the UK by the NERC Project NE/L01386X/1. Jonathan G. Evan’s and Ross Morrison’s work on this project was supported by the Centre for Ecology & Hydrology (CEH) and the National Environmental Research Council (NERC), UK.

Supplementary material

10546_2018_395_MOESM1_ESM.pdf (659 kb)
Supplementary material 1 (pdf 659 KB)

References

  1. Abramowitz G, Pitman A, Gupta H, Kowalczyk E, Wang Y (2007) Systematic bias in land surface models. J Hydrometeorol 8(5):989–1001CrossRefGoogle Scholar
  2. Baldocchi D, Falge E, Gu L, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer C, Davis K, Evans R, Fuentes J (2001) Fluxnet: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull Am Meteorol Soc 82(11):2415CrossRefGoogle Scholar
  3. Bhattacharya A, Mandal M (2015) Evaluation of noah land-surface models in predicting soil temperature and moisture at two tropical sites in india. Meteorol Appl 22(3):505–512CrossRefGoogle Scholar
  4. Chakraborty S, Saha U, Maitra A (2015) Relationship of convective precipitation with atmospheric heat flux—a regression approach over an indian tropical location. Atmos Res 161:116–124CrossRefGoogle Scholar
  5. Chakraborty T, Sarangi C, Tripathi SN (2017) Understanding diurnality and inter-seasonality of a sub-tropical urban heat island. Boundary-Layer Meteorol 163(2):287–309CrossRefGoogle Scholar
  6. Charuchittipan D, Babel W, Mauder M, Leps JP, Foken T (2014) Extension of the averaging time in eddy-covariance measurements and its effect on the energy balance closure. Boundary-Layer Meteorol 152(3):303–327CrossRefGoogle Scholar
  7. Chen F, Dudhia J (2001) Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129(4):569–585CrossRefGoogle Scholar
  8. Davin EL, Maisonnave E, Seneviratne SI (2016) Is land surface processes representation a possible weak link in current regional climate models? Environ Res Lett 11(7):074027CrossRefGoogle Scholar
  9. Ek M, Mitchell K, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley J (2003) Implementation of noah land surface model advances in the national centers for environmental prediction operational mesoscale eta model. J Geophys Res Atmos 108(D22):8851CrossRefGoogle Scholar
  10. Entekhabi D, Asrar GR, Betts AK, Beven KJ, Bras RL, Duffy CJ, Dunne T, Koster RD, Lettenmaier DP, McLaughlin DB, Shuttleworth WJ (1999) An agenda for land surface hydrology research and a call for the second international hydrological decade. Bull Am Meteorol Soc 80(10):2043–2058CrossRefGoogle Scholar
  11. Falge E, Reth S, Brüggemann N, Butterbach-Bahl K, Goldberg V, Oltchev A, Schaaf S, Spindler G, Stiller B, Queck R, Köstner B (2005) Comparison of surface energy exchange models with eddy flux data in forest and grassland ecosystems of Germany. Ecol Model 188(2):174–216CrossRefGoogle Scholar
  12. Foken T (2008) The energy balance closure problem: an overview. Ecol Appl 18(6):1351–1367CrossRefGoogle Scholar
  13. Foken T, Mauder M, Liebethal C, Wimmer F, Beyrich F, Leps JP, Raasch S, DeBruin HA, Meijninger WM, Bange J (2010) Energy balance closure for the litfass-2003 experiment. Theor Appl Climatol 101(1–2):149–160CrossRefGoogle Scholar
  14. Garratt JR (1993) Sensitivity of climate simulations to land-surface and atmospheric boundary-layer treatments—a review. J Clim 6(3):419–448CrossRefGoogle Scholar
  15. Giorgi F, Avissar R (1997) Representation of heterogeneity effects in earth system modeling: experience from land surface modeling. Rev Geophys 35(4):413–437CrossRefGoogle Scholar
  16. Glenn EP, Huete AR, Nagler PL, Nelson SG (2008) Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8(4):2136–2160CrossRefGoogle Scholar
  17. Gu L, Meyers T, Pallardy SG, Hanson PJ, Yang B, Heuer M, Hosman KP, Liu Q, Riggs JS, Sluss D, Wullschleger S (2007) Influences of biomass heat and biochemical energy storages on the land surface fluxes and radiative temperature. J Geophys Res Atmos 112(D2):D02107CrossRefGoogle Scholar
  18. Guo Z, Dirmeyer PA, Koster RD, Sud Y, Bonan G, Oleson KW, Chan E, Verseghy D, Cox P, Gordon C, McGregor J (2006) GLACE: the global land-atmosphere coupling experiment. Part II: analysis. J Hydrometeorol 7(4):611–625CrossRefGoogle Scholar
  19. Hanks RJ, Ashcroft G (1980) Applied soil physics: soil water and temperature application. Springer, New YorkCrossRefGoogle Scholar
  20. Haughton N, Abramowitz G, Pitman AJ, Or D, Best MJ, Johnson HR, Balsamo G, Boone A, Cuntz M, Decharme B, Dirmeyer P (2016) The plumbing of land surface models: is poor performance a result of methodology or data quality? J Hydrometeorol 17(6):1705–1723CrossRefGoogle Scholar
  21. Ingwersen J, Steffens K, Högy P, Warrach-Sagi K, Zhunusbayeva D, Poltoradnev M, Gäbler R, Wizemann HD, Fangmeier A, Wulfmeyer V, Streck T (2011) Comparison of noah simulations with eddy covariance and soil water measurements at a winter wheat stand. Agric For Meteorol 151(3):345–355CrossRefGoogle Scholar
  22. Ingwersen J, Imukova K, Högy P, Streck T (2015) On the use of the post-closure methods uncertainty band to evaluate the performance of land surface models against eddy covariance flux data. Biogeosciences 12(8):2311–2326CrossRefGoogle Scholar
  23. Jiménez C, Prigent C, Mueller B, Seneviratne S, McCabe M, Wood E, Rossow W, Balsamo G, Betts A, Dirmeyer P, Fisher J (2011) Global intercomparison of 12 land surface heat flux estimates. J Geophys Res Atmos 116(D2):D02102CrossRefGoogle Scholar
  24. Kar G, Kumar A (2007) Surface energy fluxes and crop water stress index in groundnut under irrigated ecosystem. Agric For Meteorol 146(1):94–106CrossRefGoogle Scholar
  25. Koster RD, Dirmeyer PA, Guo Z, Bonan G, Chan E, Cox P, Gordon C, Kanae S, Kowalczyk E, Lawrence D, Liu P (2004) Regions of strong coupling between soil moisture and precipitation. Science 305(5687):1138–1140CrossRefGoogle Scholar
  26. Koster RD, Sud Y, Guo Z, Dirmeyer PA, Bonan G, Oleson KW, Chan E, Verseghy D, Cox P, Davies H, Kowalczyk E (2006) GLACE: the global land-atmosphere coupling experiment. Part I: overview. J Hydrometeorol 7(4):590–610CrossRefGoogle Scholar
  27. Leuning R, Van Gorsel E, Massman WJ, Isaac PR (2012) Reflections on the surface energy imbalance problem. Agric For Meteorol 156:65–74CrossRefGoogle Scholar
  28. Li Z, Tang H, Zhang B, Yang G, Xin X (2015) Evaluation and intercomparison of MODIS and GEOV1 global leaf area index products over four sites in North China. Sensors 15(3):6196–6216CrossRefGoogle Scholar
  29. Liebethal C, Huwe B, Foken T (2005) Sensitivity analysis for two ground heat flux calculation approaches. Agric For Meteorol 132(3–4):253–262CrossRefGoogle Scholar
  30. Liu H, Peters G, Foken T (2001) New equations for sonic temperature variance and buoyancy heat flux with an omnidirectional sonic anemometer. Boundary-Layer Meteorol 100(3):459–468CrossRefGoogle Scholar
  31. Mahrt L, Ek M (1984) The influence of atmospheric stability on potential evaporation. J Clim Appl Meteorol 23(2):222–234CrossRefGoogle Scholar
  32. Mauder M, Foken T (2006) Impact of post-field data processing on eddy covariance flux estimates and energy balance closure. Meteorol Z 15(6):597–609CrossRefGoogle Scholar
  33. Mauder M, Foken T (2011) Documentation and instruction manual of the eddy-covariance software package TK3. https://epub.uni-bayreuth.de/342/1/ARBERG046.pdf
  34. Mauder M, Cuntz M, Drüe C, Graf A, Rebmann C, Schmid HP, Schmidt M, Steinbrecher R (2013) A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agric For Meteorol 169:122–135CrossRefGoogle Scholar
  35. Maussion F, Scherer D, Mölg T, Collier E, Curio J, Finkelnburg R (2014) Precipitation seasonality and variability over the Tibetan Plateau as resolved by the High Asia Reanalysis. J Clim 27(5):1910–1927CrossRefGoogle Scholar
  36. Meyers TP, Hollinger SE (2004) An assessment of storage terms in the surface energy balance of maize and soybean. Agric For Meteorol 125(1):105–115CrossRefGoogle Scholar
  37. Mitchell K, Ek M, Wong V, Lohmann D, Koren V, Schaake J, Duan Q (2005) The community Noah land-surface model (LSM) user’s guide, version 2.7. 1. NOAA/NCEP DocGoogle Scholar
  38. Mohan M, Bhati S (2011) Analysis of WRF model performance over subtropical region of Delhi, India. Adv Meteorol 621:235Google Scholar
  39. Moncrieff JB, Massheder J, De Bruin H, Elbers J, Friborg T, Heusinkveld B, Kabat P, Scott S, Soegaard H, Verhoef A (1997) A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide. J Hydrol 188:589–611CrossRefGoogle Scholar
  40. Moncrieff J, Clement R, Finnigan J, Meyers T (2004) Averaging, detrending, and filtering of eddy covariance time series. Handbook of micrometeorology. Springer, Berlin, pp 7–31Google Scholar
  41. Nakai T, Shimoyama K (2012) Ultrasonic anemometer angle of attack errors under turbulent conditions. Agric For Meteorol 162:14–26CrossRefGoogle Scholar
  42. Neftel A, Spirig C, Ammann C (2008) Application and test of a simple tool for operational footprint evaluations. Environ Pollut 152(3):644–652CrossRefGoogle Scholar
  43. Nemunaitis-Berry KL, Klein PM, Basara JB, Fedorovich E (2017) Sensitivity of predictions of the urban surface energy balance and heat island to variations of urban canopy parameters in simulations with the WRF model. J Appl Meteorol Climatol 56(3):573–595CrossRefGoogle Scholar
  44. Niemelä S, Räisänen P, Savijärvi H (2001) Comparison of surface radiative flux parameterizations. Part I: longwave radiation. Atmos Res 58(1):1–18CrossRefGoogle Scholar
  45. Oncley SP, Foken T, Vogt R, Kohsiek W, DeBruin H, Bernhofer C, Christen A, Van Gorsel E, Grantz D, Feigenwinter C, Lehner I (2007) The energy balance experiment EBEX-2000. Part I: overview and energy balance. Boundary-Layer Meteorol 123(1):1–28CrossRefGoogle Scholar
  46. Panda J, Sharan M (2012) Influence of land-surface and turbulent parameterization schemes on regional-scale boundary layer characteristics over northern India. Atmos Res 112:89–111CrossRefGoogle Scholar
  47. Papale D, Reichstein M, Aubinet M, Canfora E, Bernhofer C, Kutsch W, Longdoz B, Rambal S, Valentini R, Vesala T, Yakir D (2006) Towards a standardized processing of net ecosystem exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3(4):571–583CrossRefGoogle Scholar
  48. Patil M, Waghmare R, Halder S, Dharmaraj T (2011) Performance of noah land surface model over the tropical semi-arid conditions in western India. Atmos Res 99(1):85–96CrossRefGoogle Scholar
  49. Patil M, Kumar M, Waghmare R, Dharmaraj T, Mahanty N (2014) Evaluation of Noah-LSM for soil hydrology parameters in the Indian summer monsoon conditions. Theor Appl Climatol 118(1–2):47–56CrossRefGoogle Scholar
  50. Paul S, Ghosh S, Oglesby R, Pathak A, Chandrasekharan A, Ramsankaran R (2016) Weakening of Indian summer monsoon rainfall due to changes in land use land cover. Sci Rep 6:32177CrossRefGoogle Scholar
  51. Pielke RA (2001) Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev Geophys 39(2):151–177CrossRefGoogle Scholar
  52. Pitman A (2003) The evolution of, and revolution in, land surface schemes designed for climate models. Int J Climatol 23(5):479–510CrossRefGoogle Scholar
  53. Prasad R, Sharma A, Mehrotra P (2016) Ground water year book Uttar Pradesh (2014-2015) Retrieved from: http://cgwb.gov.in/Regions/GW-year-Books/GWYB-2014-15/GWYB%202014-15%20U.P.pdf
  54. Radell DB, Rowe CM (2008) An observational analysis and evaluation of land surface model accuracy in the Nebraska Sand Hills. J Hydrometeorol 9(4):601–621CrossRefGoogle Scholar
  55. Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grünwald T, Havránková K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival J, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakir D, Valentini R (2005) On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob Change Biol 11(9):1424–1439CrossRefGoogle Scholar
  56. Rodell M, Houser P, Jambor U, Gottschalck J, Mitchell K, Meng C, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M, Entin J (2004) The global land data assimilation system. Bull Am Meteorol Soc 85(3):381CrossRefGoogle Scholar
  57. Roxy MK, Ritika K, Terray P, Murtugudde R, Ashok K, Goswami B (2015) Drying of Indian subcontinent by rapid Indian Ocean warming and a weakening land-sea thermal gradient. Nat Commun 6:7423CrossRefGoogle Scholar
  58. Ruppert J, Thomas C, Foken T (2006) Scalar similarity for relaxed eddy accumulation methods. Boundary-Layer Meteorol 120(1):39–63CrossRefGoogle Scholar
  59. Saha A, Ghosh S, Sahana A, Rao E (2014) Failure of CMIP5 climate models in simulating post-1950 decreasing trend of Indian monsoon. Geophys Res Lett 41(20):7323–7330CrossRefGoogle Scholar
  60. Sahu L, Sheel V, Pandey K, Yadav R, Saxena P, Gunthe S (2015) Regional biomass burning trends in india: analysis of satellite fire data. J Earth Syst Sci 124(7):1377–1387CrossRefGoogle Scholar
  61. Samala BK, Nagaraju C, Banerjee S, Kaginalkar A, Dalvi M (2013) Study of the Indian summer monsoon using WRF–ROMS regional coupled model simulations. Atmos Sci Lett 14(1):20–27CrossRefGoogle Scholar
  62. Schotanus P, Nieuwstadt F, De Bruin H (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26(1):81–93CrossRefGoogle Scholar
  63. Seneviratne SI, Stöckli R (2008) The role of land-atmosphere interactions for climate variability in europe. Climate variability and extremes during the past 100 years. Springer, Berlin, pp 179–193CrossRefGoogle Scholar
  64. Sharma BR, Amarasinghe U, Ambili GK (2010) Tackling water and food crisis in South Asia: insights from the Indo-Gangetic Basin. Synthesis report of the Basin Focal Project for the Indo-Gangetic Basin (No. H044046). International Water Management InstituteGoogle Scholar
  65. Siderius C, Hellegers P, Mishra A, van Ierland E, Kabat P (2014) Sensitivity of the agroecosystem in the ganges basin to inter-annual rainfall variability and associated changes in land use. Int J Climatol 34(10):3066–3077CrossRefGoogle Scholar
  66. Stoy PC, Mauder M, Foken T, Marcolla B, Boegh E, Ibrom A, Arain MA, Arneth A, Aurela M, Bernhofer C, Cescatti A (2013) A data-driven analysis of energy balance closure across FLUXNET research sites: the role of landscape scale heterogeneity. Agric For Meteorol 171:137–152CrossRefGoogle Scholar
  67. Suni T, Guenther A, Hansson H, Kulmala M, Andreae M, Arneth A, Artaxo P, Blyth E, Brus M, Ganzeveld L, Kabat P (2015) The significance of land-atmosphere interactions in the Earth system—iLEAPS achievements and perspectives. Anthropocene 12:69–84CrossRefGoogle Scholar
  68. Tang J, Wang S, Niu X, Hui P, Zong P, Wang X (2016) Impact of spectral nudging on regional climate simulation over CORDEX East Asia using WRF. Clim Dyn 12:69–84Google Scholar
  69. Trenberth KE, Fasullo JT, Kiehl J (2009) Earth’s global energy budget. Bull Am Meteorol Soc 90(3):311–323CrossRefGoogle Scholar
  70. Turner A, Bhat G, Evans J, Marsham J, Martin G, Parker D, Taylor C, Bhattacharya B, Madan R, Mitra A, Mrudula G (2015) Interaction of convective organization and monsoon precipitation, atmosphere, surface and sea (INCOMPASS). In: EGU General Assembly Conference Abstracts, vol 17, p 3957Google Scholar
  71. Turner AG, Annamalai H (2012) Climate change and the South Asian summer monsoon. Nat Clim Change 2(8):587–595CrossRefGoogle Scholar
  72. Twine TE, Kustas W, Norman J, Cook D, Houser P, Meyers T, Prueger J, Starks P, Wesely M (2000) Correcting eddy-covariance flux underestimates over a grassland. Agric For Meteorol 103(3):279–300CrossRefGoogle Scholar
  73. Ukkola A, De Kauwe M, Pitman A, Best M, Abramowitz G, Haverd V, Decker M, Haughton N (2016) Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts. Environ Res Lett 11(10):104012CrossRefGoogle Scholar
  74. Unnikrishnan C, Rajeevan M, Rao SVB (2017) A study on the role of land-atmosphere coupling on the south Asian monsoon climate variability using a regional climate model. Theor Appl Climatol 127:949–964CrossRefGoogle Scholar
  75. Velde R, Su Z, Ek M, Rodell M, Ma Y (2009) Influence of thermodynamic soil and vegetation parameterizations on the simulation of soil temperature states and surface fluxes by the Noah LSM over a Tibetan plateau site. Hydrol Earth Syst Sci 13(6):759–777CrossRefGoogle Scholar
  76. Venkata Ramana M, Krishnan P, Kunhikrishnan P (2004) Surface boundary-layer characteristics over a tropical inland station: seasonal features. Boundary-layer Meteorol 111(1):153–157CrossRefGoogle Scholar
  77. Vickers D, Mahrt L (1997) Quality control and flux sampling problems for tower and aircraft data. J Atmos Ocean Technol 14(3):512–526CrossRefGoogle Scholar
  78. Vishnu S, Francis P (2014) Evaluation of high-resolution WRF model simulations of surface wind over the west coast of India. Atmos Ocean Sci Lett 7(5):458–463CrossRefGoogle Scholar
  79. Waghmare R, Dharmaraj T, Patil M (2012) Noah-LSM simulation on various soil textures in tropical semi-arid regions. Soil Sci 177(11):664–673CrossRefGoogle Scholar
  80. Webb EK, Pearman GI, Leuning R (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R Meteorol Soc 106(447):85–100CrossRefGoogle Scholar
  81. Wilczak JM, Oncley SP, Stage SA (2001) Sonic anemometer tilt correction algorithms. Boundary-Layer Meteorol 99(1):127–150CrossRefGoogle Scholar
  82. Wilson K, Goldstein A, Falge E, Aubinet M, Baldocchi D, Berbigier P, Bernhofer C, Ceulemans R, Dolman H, Field C, Grelle A (2002) Energy balance closure at FLUXNET sites. Agric For Meteorol 113(1):223–243CrossRefGoogle Scholar
  83. Yamashima R, Matsumoto J, Takata K, Takahashi HG (2015) Impact of historical land-use changes on the Indian summer monsoon onset. Int J Climatol 35(9):2419–2430CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Centre for Environmental Science and EngineeringIIT KanpurKanpurIndia
  2. 2.School of Forestry and Environmental StudiesYale UniversityNew HavenUSA
  3. 3.Civil Engineering DepartmentIIT KanpurKanpurIndia
  4. 4.Pacific Northwest National LaboratoryRichlandIndia
  5. 5.Environmental Engineering and Management ProgrammeIIT KanpurKanpurIndia
  6. 6.Centre for Ecology and HydrologyWallingfordUK

Personalised recommendations