LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Pulido, M.; Polavarapu, S.; Shepherd, T.G.; Thuburn, J. (2012)
Publisher: Royal Meteorological Society
Languages: English
Types: Article
Subjects:
Identifiers:doi:10.1002/qj.932
There is a current need to constrain the parameters of gravity wave drag (GWD) schemes in climate models using observational information instead of tuning them subjectively. In this work, an inverse technique is developed using data assimilation principles to estimate gravity wave parameters. Because mostGWDschemes assume instantaneous vertical propagation of gravity waves within a column, observations in a single column can be used to formulate a one-dimensional assimilation problem to estimate the unknown parameters. We define a cost function that measures the differences between the unresolved drag inferred from observations (referred to here as the ‘observed’ GWD) and the GWD calculated with a parametrisation scheme. The geometry of the cost function presents some difficulties, including multiple minima and ill-conditioning because of the non-independence of the gravity wave parameters. To overcome these difficulties we propose a genetic algorithm to minimize the cost function, which provides a robust parameter estimation over a broad range of prescribed ‘true’ parameters. When real experiments using an independent estimate of the ‘observed’ GWD are performed, physically unrealistic values of the parameters can result due to the non-independence of the parameters. However, by constraining one of the parameters to lie within a physically realistic range, this degeneracy is broken and the other parameters are also found to lie within physically realistic ranges. This argues for the essential physical self-consistency of the gravity wave scheme. A much better fit to the observed GWD at high latitudes is obtained when the parameters are allowed to vary with latitude. However, a close fit can be obtained either in the upper or the lower part of the profiles, but not in both at the same time. This result is a consequence of assuming an isotropic launch spectrum. The changes of sign in theGWDfound in the tropical lower stratosphere, which are associated with part of the quasi-biennial oscillation forcing, cannot be captured by the parametrisation with optimal parameters.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Alexander MJ, Geller M, McLandress C, Polavarapu S, Preusse P, Sassi F, Sato K, Eckermann S, Ern M, Hertzog A, Kawatani Y, Pulido M, Shaw TA, Sigmond M, Vincent RA, Watanabe S. 2010. Recent developments in gravity wave effects in climate models, and the global distribution of gravity wave momentum flux from observations and models. Q. J. R. Meteorol. Soc. 136: 1103 -1124.
    • Allen SJ, Vincent RA. 1995. Gravity wave activity in the lower atmosphere: Seasonal and latitudinal variations. J. Geophys. Res. 100: 1327 -1350.
    • Brunet G, Shapiro M, Hoskins BJ, Moncrieff M, Dole R, Kiladis GN, Kirtman B, Lorenc AC, Mills B, Morss R, Polavarapu S, Rogers D, Schaake J, Shukla J. 2010. Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction. Bull. Amer. Meteorol. Soc. 91: 1397 -1406.
    • Butchart N, Scaife AA, Bourqui M, de Grandpre´ J, Hare SHE, Kettleborough J, Langematz U, Manzini E, Sassi F, Shibata K, Shindell D, Sigmond M. 2006. Simulations of anthropogenic change in the strength of the Brewer-Dobson circulation. Climate Dyn. 27: 727 -741, DOI: 10.1007/s00382-006-0162-4
    • Butchart N, Cionni I, Eyring V, Shepherd TG, Waugh DW, Akiyoshi H, Austin J, Bru¨hl C, Chipperfield MP, Cordero E, Dameris M, Deckert R, Frith SM, Garcia RR, Gettelman A, Giorgetta MA, Kinnison DE, Li F, Mancini E, McLandress C, Pawson S, Pitari G, Plummer DA, Rozanov E, Sassi F, Scinocca JF, Shibata K, Tian W. 2010. Chemistryclimate model simulations of 21st century stratospheric climate and circulation changes. J. Climate 23: 5349 -5374.
    • Charbonneau P. 2002. 'An introduction to genetic algorithms for numerical optimization'. Technical Note TN-450+IA. NCAR: Boulder, MA.
    • Charron M, Manzini E. 2002. Gravity waves from fronts: Parameterization and middle atmosphere response in a general circulation model. J. Atmos. Sci. 59: 923 -941.
    • Evensen G. 2003. The Ensemble Kalman Filter: Theoretical formulation and practical implementation. Ocean Dyn. 53: 343 -367. DOI: 10.1007/s10236-003-0036-9
    • Giering R, Kaminski T. 1997. Recipes for adjoint code construction. ACM Trans. Math. Software 24: 437 -474.
    • Golberg DE. 1989. Genetic algorithms in search, optimization and machine learning. Addison-Wesley: Boston, USA.
    • Hertzog A, Boccara G, Vincent RA, Vial F, Cocquerez P. 2008. Estimation of gravity wave momentum flux and phase speeds from quasiLagrangian stratospheric balloon flights. Part II: Results from the Vorcore campaign in Antarctica. J. Atmos. Sci. 65: 3056 -3070.
    • Hines CO. 1997. Doppler spread parametrization of gravity-wave momentum deposition in the middle atmosphere. Part 1: Basic formulation. J. Atmos. Sol. Terr. Phys. 59: 371 -386.
    • Hurrell J, Meehl GA, Bader D, Delworth TL, Kirtman B, Wielicki B. 2009. A unified modeling approach to climate system prediction. Bull. Amer. Meteorol. Soc. 90: 1819 -1832.
    • Kalnay E. 2002. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press: Cambridge, UK.
    • Karlsson B, McLandress C, Shepherd TG. 2009. Inter-hemispheric mesospheric coupling in a comprehensive middle atmosphere model. J. Atmos. Solar-Terr. Phys. 71: 518 -530. DOI: 10.1016/j.jastp.2008.08.006
    • Li F, Austin J, Wilson J. 2008. The strength of the Brewer-Dobson circulation in a changing climate: Coupled chemistry-climate model simulations. J. Climate 21: 40 -57.
    • Lindzen RS. 1981. Turbulence and stress owing to gravity wave and tidal breakdown. J. Gheophys. Res. 86: 9707 -9714.
    • Manzini E, McFarlane NA. 1998. The effect of varying the source spectrum of a gravity wave parameterization in a middle atmosphere general circulation model. J. Geophys. Res. 103: 31523 -31539.
    • McFarlane NA. 1987. The effect of orographically excited gravity wave drag on the general circulation of the lower stratosphere and troposphere. J. Atmos. Sci. 44: 1775 -1800.
    • McLandress C, Scinocca JF. 2005. The GCM response to current parameterizations of non-orographic gravity wave drag. J. Atmos. Sci. 62: 2394 -2413.
    • McLandress C, Shepherd TG. 2009. Simulated anthropogenic changes in the Brewer-Dobson circulation, including its extension to high latitudes. J. Climate 22: 1516 -1540.
    • Orr A, Bechtold P, Scinocca JF, Ern M, Janiskova´ M. 2010. Improved middle atmosphere climate and forecasts in the ECMWF model through a non-orographic gravity wave drag parametrization. J. Climate 23: 5905 - 5926.
    • Palmer TN, Shutts GJ, Swinbank R. 1986. Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through on orographic gravity wave drag parametrization. Q. J. R. Meteorol. Soc. 112: 1001 - 1039.
    • Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rodwell MJ. 2008. Toward seamless prediction: Calibration of climate change projections using seasonal forecasts. Bull. Amer. Meteorol. Soc. 89: 459 - 470.
    • Phillips TJ, Potter GL, Williamson DL, Cederwall RT, Boyle JS, Fiorino M, Hnilo JJ, Olson JG, Xie S, Yio JJ. 2004. Evaluating parameterizations in general circulation models. Climate simulation meets weather prediction. Bull. Amer. Meteorol. Soc. 85: 1903 - 1915.
    • Preusse P, Eckermann SD, Ern M. 2008. Transparency of the atmosphere to short horizontal wavelength gravity waves. J. Geophys. Res. 113: D24104. DOI: 10.1029/2007JD009682
    • Pulido M, Thuburn J. 2005. Gravity wave drag estimation from global analyses using variational data assimilation principles. I: Theory and implementation. Q. J. R. Meteorol. Soc. 131: 1821 - 1840.
    • Pulido M, Thuburn J. 2006. Gravity wave drag estimation from global analyses using variational data assimilation principles. II: A case-study. Q. J. R. Meteorol. Soc. 132: 1527 - 1543.
    • Pulido M, Thuburn J. 2008. The seasonal cycle of gravity wave drag in the middle atmosphere. J. Climate 21: 4664 - 4679.
    • Randel WJ, Garcia R, Wu F. 2008. Dynamical balances and tropical stratospheric upwelling. J. Atmos. Sci. 65: 3584 - 3595.
    • Ren S, Polavarapu S, Shepherd TG. 2008. Vertical propagation of information in a middle atmosphere data assimilation system. Geophys. Res. Lett. 35: L06804, DOI: 10.1029/2007GL032699
    • Rodwell MJ, Palmer TN. 2007. Using numerical weather prediction to assess climate models. Q. J. R. Meteorol. Soc. 33: 129 - 146.
    • Scinocca JF. 2002. The effect of back-reflection in the parametrization of non-orographic gravity-wave drag. J. Meteorol. Soc. Japan 80: 939 - 962.
    • Scinocca JF. 2003. An accurate spectral non-orographic gravity wave drag parameterization for general circulation models. J. Atmos. Sci. 60: 667 - 682.
    • Sigmond M, Scinocca JF. 2010. The influence of the basic state on the Northern Hemisphere circulation response to climate change. J. Climate 23: 1434 - 1446.
    • Song I-S, Chun H-Y. 2008. A Lagrangian spectral parameterization of gravity wave drag induced by cumulus convection. J. Atmos. Sci. 65: 1204 - 1224.
    • Stainforth DA, Aina T, Christensen C, Collins M, Faull N, Frame DJ, Kettleborough JA, Knight S, Martin A, Murphy JM, Piani C, Sexton D, Smith LA, Spicer RA, Thorpe AJ, Allen MR. 2005. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433: 403 - 406.
    • Warner CD, McIntyre ME. 1996. On the propagation and dissipation of gravity wave spectra through a realistic middle atmosphere. J. Atmos. Sci. 53: 3213 - 3235.
    • Xu Q. 1996. Generalized adjoint for physical processes with parameterized discontinuities. Part I: Basic issues and heuristic examples. J. Atmos. Sci. 53: 1123 - 1142.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article