Research interests
- Asian Monsoon variability, ENSO and climate change
- Climate tropical and extratropical interactions
- Climate time scale interactions
- Climate studies using climate (coupled) models and observations
- Statistics, data analysis and scientific computing
- Statistical and linear algebra software development
Representative research contributions
- Understanding tropical climate teleconnections:
Understanding how the differents oceanic tropical basins interfer with ENSO is an important topic of current climate research. Understanding these relationships using global coupled models currently dominates my research activities. A good example of how this can be done is detailed in
- Assessing the role of time scale interactions in tropical variability:
Understanding how different time scales interact with each other is another current challenge of current climate research. This is also fundamental for climate prediction and predictability of different climate phenomena like ENSO or the South Asian monsoon. An illustration for this topic is detailed in
- Assessing the role of the extratropics on tropical variability:
Understanding how the extratropics may influence the tropical variability is a challenging topic in climate research. The following papers suggest that the Southern Hemisphere climate has a significant influence on the Indian monsoon, ENSO and the tropical Indian Ocean dipole via SST subtropical dipole modes in the Indian, Atlantic and Pacific oceans
Terray, P., F. Chauvin and H. Douville (2007) "Impact of southeast Indian Ocean Sea Surface Temperature anomalies on monsoon-ENSO-dipole variability in a coupled ocean-atmosphere model", Climate Dynamics, Vol. 28: 553-580, doi, pdf.
Terray, P. (2011) "Southern Hemisphere extra-tropical forcing: A new paradigm for El Niño-Southern Oscillation", Climate Dynamics, Vol. 36: 2171-2199, doi, pdf.
Cretat, J., P. Terray, S. Masson and K.P. Sooraj (2017) "Intrinsic precursors and timescale of the tropical Indian Ocean Dipole : Insights from partially decoupled experiments", in press in Climate Dynamics, doi, pdf.
- Assessing the future of the South Asian Monsoon in the context of global warming:
Understanding how the South Asian monsoon will evolve in the future is a serious scientific and socio-economic concern since many recent studies have demonstrated the weakening of large-scale tropical circulation under anthropogenic forcing. Two illustrations for this topic are detailed in
Sooraj, K.P., P. Terray and M. Mujumdar (2015) "Global warming and the weakening of the Asian summer monsoon circulation: Assessments from the CMIP5 models", Climate Dynamics, Vol. 45: 233-252, doi, pdf.
- Algorithms for Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) with missing values or any choice of positive weights:
PCA and SVD play a fundamental role in multivariate statistics. Generalization of these methods/concepts when missing values are present or positive weights are introduced are useful in different fields such as climatology or computer vision and have received much attention recently, with renewed interest in variable-projection approaches. I have been contributing to some aspects of this movement. Effective methods for such calculations emerged for example in
Terray, P. (1995) "Space/Time structure of monsoons interannual variability", Journal of Climate, 8: 2595-2619, doi, pdf.
- Refined tests of statistical significance in composite analysis:
Composite analysis is a classical statistical tool in climatology. Its purpose is to highlight the space-time evolution of a time series or a gridded dataset (for instance surface temperature) according to the variations of a given index time series (for instance a Niño index). While it is easy to compute composite means, assessing the significance of these composite maps is a more difficult task. This is often done with the help of a classical Student's two sample t-test, where one sample consists of the years belonging to one group and the second sample of the other years. In the usual context of statistical inference, this procedure is used to test the hypothesis of equal population means on the basis of two random samples independently drawn from two normal populations with a common variance, but possibly different means. The assumptions of random selection and normality are essential for the validity of the test. Since these two assumptions are invalid with most climate data, an alternative procedure for significance testing of the composite results is required. The following article suggests an alternative approach