Journal of Earth System Science

, Volume 123, Issue 1, pp 151–160 | Cite as

Impact of modified soil thermal characteristic on the simulated monsoon climate over south Asia

  • Pankaj KumarEmail author
  • Ralf Podzun**
  • Stefan Hagemann
  • Daniela Jacob

In the present study, the influence of soil thermal characteristics (STC) on the simulated monsoon climate over south Asia is analyzed. The study was motivated by a common warm temperature bias over the plains of northern India that has been noticed in several global and regional climate models. To address this warm bias and its relation to STC, two sensitivity experiments have been performed with the regional climate model REMO of the Max Planck Institute for Meteorology. The control experiment uses the standard soil thermal characteristic of the model that corresponds to a moist soil. The second experiment uses modified STC that characterize a dry soil, which is more representative of the considered region, as a large part of the region has arid, semi-arid or subtropical summer wet conditions. Both experiments were conducted over 20 years using re-analysis data as lateral boundary conditions. Results show that using the modified STC the predominant regional warm bias has reduced substantially, leading to a better and more realistic surface temperature compared to observations over south Asia. Although, the magnitude of bias has reduced, the warm bias still exists over the region suggesting that other atmospheric and land surface processes also play a role, such as aerosols and irrigation. These need to be addressed adequately in future modeling studies over the region.


Regional climate modelling REMO Indian summer monsoon soil thermal characteristics 



This research was undertaken as part of the Integrated Project called ‘HighNoon: Adaptation to changing water resources availability in Northern India with Himalayan glacier retreat and changing monsoon’. This project is funded by the European Commission, FP7, and contract number 227087. The authors acknowledge German Climate Computing Center (DKRZ) providing CPU time for REMO simulations. They also thank Tanja Blome from MPI-M for helpful discussions on soil temperature processes and their parameterization.


  1. Anders I and Rockel B 2009 The influence of prescribed soil type distribution on the representation of present climate in a regional climate model; Clim. Dyn. 33(2–4) 177–186.CrossRefGoogle Scholar
  2. Bhaskaran B, Jones R G, Murphy J M and Noguer M 1996 Simulations of the Indian summer monsoon using a nested regional climate model: Domain size experiments; Clim. Dyn. 12 573–587.CrossRefGoogle Scholar
  3. Brohan P, Kennedy J J, Harris I, Tett S F B and Jones P D 2006 Uncertainty estimates in regional and global observed temperature changes: A new dataset from 1850; J. Geophys. Res. 111 D12106, doi:  10.1029/2005JD006548.CrossRefGoogle Scholar
  4. Bollasina M and Nigam S 2011 The summertime ‘heat’ low over Pakistan/northwestern India: Evolution and origin; Clim. Dyn. 37 957–970.CrossRefGoogle Scholar
  5. Boos W R and Kuang Z 2013 Sensitivity of the south Asian monsoon to elevated and non-elevated heating; Nature Scientific Reports 3 1192, doi:  10.1038/srep01192.Google Scholar
  6. Chakraborty A, Nanjundiah R S and Srinivasan J 2002 Role of Asian and African orography in Indian summer monsoon; Geophys. Res. Lett. 29(20) 1989, doi:  10.1029/2002GL015522.CrossRefGoogle Scholar
  7. Christensen J H, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R and Kolli R K et al. 2007 Regional Climate Projections, Chapter 11; In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds) Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K B, Tignor M and Miller H L, Cambridge University Press, Cambridge, UK and New York, USA.Google Scholar
  8. Das S K, Shekhar M S and Singh G P 2006 Simulation of Indian summer monsoon circulation and rainfall using RegCM3; Theor. Appl. Climatol. 86 161–172.CrossRefGoogle Scholar
  9. Davies H C 1976 A lateral boundary formulation for multi-level prediction models; Quart. J. Roy. Meteorol. Soc. 102 405–418.Google Scholar
  10. Dee D P, Uppala S M, Simmons A J, Berrisford P, Poli P and Kobayashi S et al. 2011 The ERA-interim reanalysis: Configuration and performance of the data assimilation system; Quart. J. Roy. Meteorol. Soc. 137 553–597, doi:  10.1002/qj.828.CrossRefGoogle Scholar
  11. Dobler A and Ahrens B 2010 Analysis of the Indian summer monsoon system in the regional climate model COSMO-CLM; J. Geophys. Res. 115 D16101, doi:  10.1029/2009JD013497.CrossRefGoogle Scholar
  12. Dümenil L and Todini E 1992 A rainfall-runoff scheme for use in the Hamburg climate model; In: Advances in Theoretical Hydrology: A Tribute to James Dooge (ed.) O’Kane J P, European Geophysical Society Series on Hydrological Sciences (Amsterdam: Elsevier Press), pp. 129–157.Google Scholar
  13. Giorgi F 1990 Simulation of regional climate using a limited area model nested in a general circulation model; J. Climate 3 941–963.CrossRefGoogle Scholar
  14. Giorgi F et al. 2001 Regional Climate Information – evaluation and projection. Climate Change; The Scientific Basis, Contribution of Working Group I to the Third Assessment report of IPCC.Google Scholar
  15. Gordon B 2002 Ecological climatology: Concepts and applications; Cambridge University Press, 678p.Google Scholar
  16. Hagemann S 2002 An improved land surface parameter dataset for global and regional climate models; Report 336, Max-Planck-Institute for Meteorology, Hamburg.Google Scholar
  17. Hageman S and Dümenil Gates L 2003 Improving a subgrid runoff parameterization scheme for climate models by the use of high resolution data derived from satellite observations; Clim. Dyn. 21 349–359.CrossRefGoogle Scholar
  18. Jacob D 2001 A note to the simulation of the annual and interannual variability of the water budget over the Baltic Sea drainage basin; Meteorol. Atmos. Phys. 77(1–4) 61–74.CrossRefGoogle Scholar
  19. Jacob D 2009 Regional Climate Models: Linking global climate change to local impacts; The Springer Encyclopedia of Complexity and Systems Science, pp. 7591–7602.Google Scholar
  20. Jacob D and Podzun R 1997 Sensitivity studies with the Regional Climate Model REMO; Meteor. Atmos. Phys. 63 119–129.CrossRefGoogle Scholar
  21. Jacob D et al. 2012 Assessing the transferability of the Regional Climate Model REMO to different COordinated Regional Climate Downscaling EXperiment (CORDEX) Regions; Atmosphere 3 181–199.CrossRefGoogle Scholar
  22. Kang I S, Jin K, Wang B, Lau K M, Shukla J, Krishnamurthy V, Schubert S D, Wailser D E, Stern W F, Kitoh A, Meehl G A, Kanamitsu M, Galin V Y, Satyan V, Park C K and Liu Y 2002 Intercomparison of the climatological variations of Asian summer monsoon precipitation simulated by 10 GCMs; Clim. Dyn. 19 383–395.CrossRefGoogle Scholar
  23. Kesler E 1969 On the distribution and continuity of water substance in atmospheric circulations; Meteor. Monogr. 32 84.Google Scholar
  24. Koster R D et al. 2004 Regions of strong coupling between soil moisture and precipitation; Science 305(5687) 1138–1140.CrossRefGoogle Scholar
  25. Koster R D et al. 2006 GLACE: The global land-atmosphere coupling experiment. Part I: Overview; J. Hydromet. 7(4) 590–610.CrossRefGoogle Scholar
  26. Kumar P, Kumar K R, Rajeevan M and Sahai A K 2007 On the recent strengthening of the relationship between ENSO and northeast monsoon rainfall over south Asia; Clim. Dyn. 28 649–660.CrossRefGoogle Scholar
  27. Kumar P, Wiltshire A, Mathison C, Asharaf S, Ahrens B, Lucas-Picher P, Christensen J H, Gobiet A, Saeed F, Hagemann S and Jacob D 2013 Down scaled climate change projections with uncertainty assessment over India using a high resolution multi-model approach; Sci. Total Environ. 468–469 S18–S30, doi:  10.1016/j.scitotenv.2013.01.051.CrossRefGoogle Scholar
  28. Lee E, Sacks W J, Chase T N and Foley J A 2011 Simulated impacts of irrigation on the atmospheric circulation over Asia; J. Geophys. Res. 116 D08114, doi:  10.1029/2010JD014740.CrossRefGoogle Scholar
  29. Li Chengfeng and Michio Yanai 1996 The onset and interannual variability of the Asian summer monsoon in relation to land–sea thermal contrast; J. Climate 9 358–375, doi:  10.1175/1520-0442(1996)009<0358:TOAIVO>2.0.CO;2.CrossRefGoogle Scholar
  30. Lucas-Picher P, Christensen J H, Saeed F, Kumar P, Asharaf S, Ahrens B, Wiltshire A, Jacob D and Hagemann S 2011 Can regional climate models represent the Indian Monsoon?; J. Hydrometeor. 12 849–868.CrossRefGoogle Scholar
  31. Nordeng T E 1994 Extended versions of the convective parametrization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics; ECMWF Research Department, Technical Memorandum No. 206, October 1994, European Centre for Medium Range Weather Forecasts, Reading, UK, 41p.Google Scholar
  32. Ramanathan V, Chung C, Kim D, Bettge T, Buja L, Kiehl J T, Washington W M, Fu Q, Sikka D R and Wild M 2005 Atmospheric brown clouds: Impacts on south Asian climate and hydrological cycle; PNAS 102(15) 5326–5333, doi:  10.1073/pnas.0500656102.CrossRefGoogle Scholar
  33. Rajeevan M et al. 2006 High resolution daily gridded rainfall data for the Indian region: Analysis of break and active monsoon spells; Curr. Sci. 91(3) 296–306.Google Scholar
  34. Rajeevan M and Nanjundiah R S 2009 Coupled model simulations of 20th century climate of the Indian summer monsoon; Current Trends in Science: Platinum Jubilee Special (ed.) Mukunda N, Indian Academy of Sciences, pp. 537–568.Google Scholar
  35. Ratnam J V and Kumar K K 2005 Sensitivity of the simulated monsoons of 1987 and 1988 to convective parameterization scheme in MM5; J. Climate 18 2724–2743.CrossRefGoogle Scholar
  36. Rechid D and Jacob D 2006 Influence of monthly varying vegetation on the simulated climate in Europe; Meteorol. Z. 15 99–116.CrossRefGoogle Scholar
  37. Rechid D, Raddatz T J and Jacob D 2008a Parameterization of snow-free land surface albedo as a function of vegetation phenology based on MODIS data and applied in climate modeling; Theor. Appl. Climatol. 95 245–255, doi:  10.1007/s00704-008-0003-y.CrossRefGoogle Scholar
  38. Rechid D, Hagemann S and Jacob D 2008b Sensitivity of climate models to seasonal variability of snow-free land surface albedo; Theor. Appl. Climatol. 95 197–221, doi:  10.1007/s00704-007-0371-8.CrossRefGoogle Scholar
  39. Roeckner E et al. 1996 The atmospheric general circulation model ECHAM-4: Model description and simulation of present-day climate; Max Planck Institute for Meteorology, Report No. 218.Google Scholar
  40. Rupa Kumar K et al. 2006 High-resolution climate change scenarios for India for the 21st century; Curr. Sci. 90(3) 334–345.Google Scholar
  41. Saeed F, Hagemann S and Jacob D 2009 Impact of irrigation on the south Asian summer monsoon; Geophys. Res. Lett. 36 L20711.CrossRefGoogle Scholar
  42. Siebert S, Döll P, Hoogeveen J, Faures J M, Frenken K and Feick S 2005 Development and validation of the global map of irrigation areas; Hydrol. Earth Syst. Sci. 9 535–547, doi:  10.5194/hess-9-535-2005.CrossRefGoogle Scholar
  43. Simmons A J and Burridge D M 1981 An energy and angular momentum conserving vertical finite-difference scheme and hybrid vertical coordinates; Mon. Weather Rev. 109 758–766.CrossRefGoogle Scholar
  44. Tiedtke M 1989 A comprehensive mass flux scheme for cumulus parameterization in large scale models; Mon. Weather Rev. 117 1779–1800.CrossRefGoogle Scholar
  45. Turner A G and Annamalai H 2012 Climate change and the south Asian summer monsoon; Nature Climate Change 2 587–595, doi:  10.1038/nclimate1495.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2014

Authors and Affiliations

  • Pankaj Kumar
    • 1
    Email author
  • Ralf Podzun**
    • 1
  • Stefan Hagemann
    • 1
  • Daniela Jacob
    • 1
    • 2
  1. 1.Max Planck Institute for MeteorologyHamburgGermany
  2. 2.Climate Service CenterHamburgGermany

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