Theoretical and Applied Climatology

, Volume 116, Issue 1, pp 37–49

A comparative analysis of intraseasonal atmospheric variability in OLR and 1DD GPCP rainfall data over Central Africa

  • Alain Tchakoutio Sandjon
  • Armand Nzeukou
  • Clément Tchawoua
  • François Mkankam Kamga
  • Derbetini Vondou
Original Paper

DOI: 10.1007/s00704-013-0911-3

Cite this article as:
Sandjon, A.T., Nzeukou, A., Tchawoua, C. et al. Theor Appl Climatol (2014) 116: 37. doi:10.1007/s00704-013-0911-3

Abstract

The spatial and temporal structures of the leading modes of intraseasonal atmospheric variability over Central Africa using outgoing longwave radiation (OLR) and one-degree daily Global Precipitation Climatology Project (1DD GPCP) data are compared. A spectral analysis indicates that in the two datasets, the intraseasonal variability is dominated by 20–70-day period bands with center near 40–50 days. Results from empirical orthogonal function (EOF) analysis have shown that three main spatial structures characterize the leading modes of 20–70-day intraseasonal oscillations (ISO), but they differ in the two data by the amount of variance explained by each mode. For both time series, the power spectra of all the three principal components peak around 40–50 days, indicating Madden–Julian oscillation (MJO) signal. Moreover, the cross correlation computed among the principal components indicates that there exists a relatively high positive correlation between the EOFs of similar spatial loadings. For the two datasets, an index of MJO strength was built by averaging the 30–50-day power for each day and each EOF mode. A plot of the two indices revealed a coherent onset, peak, and decay of ISO activity but with different amplitudes. The correlation coefficients computed between the ISO indices corresponding to each mode revealed that they are highly correlated, especially for the annual mean time series where the correlation coefficient reaches up to 0.88 for the Tanzanian mode. Overall, the analysis showed that the leading modes are similar, but the 1DD GPCP loadings have better spatial localization, when compared with those of OLR datasets.

Supplementary material

704_2013_911_MOESM1_ESM.pdf (73 kb)
ESM 1(PDF 72 kb)

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Alain Tchakoutio Sandjon
    • 1
    • 2
  • Armand Nzeukou
    • 2
  • Clément Tchawoua
    • 3
  • François Mkankam Kamga
    • 1
  • Derbetini Vondou
    • 1
  1. 1.Laboratory for Environmental Modeling and Atmospheric Physics, Department of Physics, Faculty of SciencesUniversity of Yaoundé 1YaoundéCameroon
  2. 2.Laboratory of Industrial Systems and Environmental Engineering, Fotso Victor Technology InstituteUniversity of DschangDschangCameroon
  3. 3.Laboratory of Mechanics, Faculty of SciencesUniversity of Yaoundé IYaoundéCameroon