Diagnosis of Free and Convectively Coupled Equatorial Waves
A methodology for diagnosis of free and convectively coupled equatorial waves (CCEWs) is reviewed and illustrated for Kelvin and mixed Rossby–gravity (MRG) waves. The method is based on prefiltering of the geopotential and horizontal wind, using three-dimensional normal mode functions of the adiabatic linearized equations of a resting atmosphere, followed by space–time power and cross-spectral analysis applied to the normal-mode-filtered fields and the outgoing long-wave radiation (OLR) to identify spectral regions of coherence. The methodology is applied to geopotential and horizontal wind fields produced by European Centre for Medium-Range Weather Forecasts interim reanalysis and OLR data produced by the National Oceanic and Atmospheric Administration. The same type of data simulated by two climate models that participated in the fifth phase of the climate model intercomparison project are also used. Overall, simulation of free and CCEWs was achieved by the models with moderate success. Kelvin and MRG waves were identified in the space–time spectral domains, using both observationally based and climate model datasets. Other nonequatorial waves, classified as tropical depression and extratropical storm track activity, along with the Madden–Julian oscillation were also observed. However, significant deviations were also evident in the models, which may help identification of deficiencies in the models’ simulation schemes for some physical processes. Therefore, this diagnosis method should be a useful procedure for climate model validation and model benchmarking.
KeywordsConvectively coupled equatorial waves Hough functions Vertical normal modes Kelvin wave Mixed Rossby–gravity wave
This work was supported by the National Foundation for Science and Technology (FCT) within project CLICURB (EXLC/AAG-MAA/0383/2012). C.A.F.M. was supported by the FCT under grant SFRH/BPD/76232/2011. We are grateful to the Beijing Climate Center and to the Max Planck Institute for Meteorology for providing the atmospheric datasets used in this study. The CMIP5 datasets were obtained from its data portal at http://pcmdi9.llnl.gov/. ERA interim data were obtained from the ECMWF data server. Interpolated OLR data were provided by the NOAA/OAR/ESRL PSD, Boulder, CO, USA, from their website at http://www.esrl.noaa.gov/psd/.
- Andrews DG, Holton JR, Leovy CB (1987) Middle atmosphere dynamics. Academic Press, LondonGoogle Scholar
- Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. https://doi.org/10.1002/qj.828 CrossRefGoogle Scholar
- Liebmann B, Smith CA (1996) Description of a complete (interpolated) outgoing longwave radiation dataset. Bull Am Meteorol Soc 77:1275–1277Google Scholar
- Lin JL, Kiladis GN, Mapes BE, Weickmann KM, Sperber KR, Lin W, Wheeler MC, Schubert SD, Genio AD, Donner LJ, Emori S, Gueremy JF, Hourdin F, Rasch PJ, Roeckner E, Scinocca JF (2006) Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: convective signals. J Clim 19:2665–2690. https://doi.org/10.1175/JCLI3735.1 CrossRefGoogle Scholar
- Sneddon IN (1972) The use of integral transforms. McGraw-Hill, New York, p 539Google Scholar
- Trenberth KE, Berry JC, Buja LE (1993) Vertical interpolation and truncation of model-coordinate data. Technical Note. NCAR/TN-396+STR, NCAR, p 54Google Scholar
- von Storch H, Zwiers FW (2003) Statistical analysis in climate research. Cambridge University Press, CambridgeGoogle Scholar