Climate Dynamics

, Volume 48, Issue 7–8, pp 2315–2337 | Cite as

Prediction of a thermodynamic wave train from the monsoon to the Arctic following extreme rainfall events

  • T. N. Krishnamurti
  • Vinay Kumar


This study addresses numerical prediction of atmospheric wave trains that provide a monsoonal link to the Arctic ice melt. The monsoonal link is one of several ways that heat is conveyed to the Arctic region. This study follows a detailed observational study on thermodynamic wave trains that are initiated by extreme rain events of the northern summer south Asian monsoon. These wave trains carry large values of heat content anomalies, heat transports and convergence of flux of heat. These features seem to be important candidates for the rapid melt scenario. This present study addresses numerical simulation of the extreme rains, over India and Pakistan, and the generation of thermodynamic wave trains, simulations of large heat content anomalies, heat transports along pathways and heat flux convergences, potential vorticity and the diabatic generation of potential vorticity. We compare model based simulation of many features such as precipitation, divergence and the divergent wind with those evaluated from the reanalysis fields. We have also examined the snow and ice cover data sets during and after these events. This modeling study supports our recent observational findings on the monsoonal link to the rapid Arctic ice melt of the Canadian Arctic. This numerical modeling suggests ways to interpret some recent episodes of rapid ice melts that may require a well-coordinated field experiment among atmosphere, ocean, ice and snow cover scientists. Such a well-coordinated study would sharpen our understanding of this one component of the ice melt, i.e. the monsoonal link, which appears to be fairly robust.


Heat flux convergence Global WRF Arctic ice 



This research was supported by NASA Grant 13-WEATHER13-0021. We are thankful to Dr. Robert Ross and Ms. Darlene Oosterhof for editing.


  1. Archambault HM, Bosart LM, Eyser DK, Cordeira JM (2013) A climatological analysis of the extratropical flow response to recurving Western North Pacific tropical cyclones. Mon Weather Rev 141:2325–2346CrossRefGoogle Scholar
  2. Cavalieri DJ, Parkinson CL, Gloersen P, Zwally HJ (1996) Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave data, Version 1. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder. doi: 10.5067/8GQ8LZQVL0VL Google Scholar
  3. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107CrossRefGoogle Scholar
  4. Hong S-Y, Pan H-L (1996) Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon Weather Rev 124:2322–2339CrossRefGoogle Scholar
  5. Houze RA Jr, Rasmussen KL, Medina S, Brodzik SR, Romatschke U (2011) Anomalous atmospheric events leading to the summer 2010 floods in Pakistan. Bull Am Meteorol Soc 92:291–298CrossRefGoogle Scholar
  6. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain–Fritsch scheme. The representation of cumulus convection in numerical models. Am Meteorol Soc Meteorol Monogr 46:165–170Google Scholar
  7. Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471CrossRefGoogle Scholar
  8. Kelman I (2001) The autumn 2000 floods in England and flood management. Weather 56:346–360CrossRefGoogle Scholar
  9. Kratz DP, Stackhouse PW, Wong T, Sawaengphokhai P, Wilber AC, Gupta SK, Loeb NG (2014) Earth radiation budget at top-of-atmosphere [in “State of the Climate in 2013”]. Bull Am Meteorol Soc 95:S30–S32CrossRefGoogle Scholar
  10. Krishnamurti TN, Xue J, Bedi HS, Ingles K, Oosterhof D (1991) Physical initialization for numerical weather prediction over the tropics. Tellus A 43:53–81CrossRefGoogle Scholar
  11. Krishnamurti TN, Rohaly G, Bedi HS (1994) On the improvement of precipitation forecast skill from physical initialization. Tellus A 46:598–614CrossRefGoogle Scholar
  12. Krishnamurti TN, Jha B, Bedi HS, Mohanty UC (2000) Diabatic effects on potential vorticity over the global tropics. J Meteorol Soc Jpn 78:527–542Google Scholar
  13. Krishnamurti TN, Vijaya Kumar TSV, Rajendran K, Hopkins A (2003) Antecedents of the flooding over Southeastern England during October 2000. Weather 58:367–370CrossRefGoogle Scholar
  14. Krishnamurti TN, Krishnamurti R, Das S, Kumar V, Jayakumar A, Simon A (2015a) A pathway connecting the monsoonal heating to the rapid arctic ice melt. J Atmos Sci 72:5–34CrossRefGoogle Scholar
  15. Krishnamurti TN, Kumar V, Simon A, Thomas A, Bhardwaj A, Das S, Senroy S, Roy-Bhowmik SK (2015b) March of buoyancy elements during extreme rainfall over India. Clim Dyn. doi: 10.1007/s00382-016-3183-7
  16. Lau WKM, Kim K-M (2012) The 2010 Pakistan flood and Russian heat wave: teleconnection of 501 hydro-meteorological extremes. J Hydrometeorol 13:392–403CrossRefGoogle Scholar
  17. Lin Y-L, Rareley RD, Orville HD (1983) Bulk parameterization of the field in a cloud model. J Clim Appl Meteorol 22:1065–1092CrossRefGoogle Scholar
  18. Oort AH (1977) The interannual variability of atmospheric circulation statistics. NOAA Professional Paper 8, 76 ppGoogle Scholar
  19. Orvik KA, Skagseth O (2005) Heat flux variations in the eastern Norwegian Atlantic current toward the Arctic from moored instruments, 1995–2005. Geophys Res Lett 32:L14610. doi: 10.1029/2005GL023487 CrossRefGoogle Scholar
  20. Schroeder D, Feltham DL, Flocco D, Tsmados M (2014) September Arctic sea ice minimum predicted by spring melt pond fraction. Nat Clim Change. doi: 10.1038/nclimate2203 Google Scholar
  21. Tao W-K, Anderson D, Chern J, Estin J, Hou A, Houser P, Kakar R, Lang S, Lau W, Peters-Lidard C, Li X, Matsui T, Shen B-W, Shi J-J, Zeng X (2009) Goddard multi-scale modeling systems with unified physics. Ann Geophys 27:3055–3064CrossRefGoogle Scholar
  22. Zhang X, He J, Zhang J, Polaykov I, Gerdes R, Inoue J, Wu P (2012) Enhanced poleward moisture transport and amplified northern high-latitude wetting trend. Nat Clim Change 3:47–51. doi: 10.1038/nclimate1631 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Earth, Ocean and Atmospheric ScienceFlorida State UniversityTallahasseeUSA

Personalised recommendations