Exploring the climate variability of the tropical Indo-Pacific via advanced data sorting and signal processing techniques
Creators: name: Pike, Maxwell H.
Publisher: name: No Publisher Supplied
Year: 2022

description: This dissertation explores the multiscale climate variability of the Indo-Pacific region through application of a variety of techniques including k-means cluster analysis, empirical orthogonal function (EOF) analysis, and eigenfunctions of the Koopman operator. The underlying motivation here is to use these techniques to advance process understanding of the dynamics and thermodynamics and lifecycles of meteorological phenomena in the region, with a particular emphasis on rainfall.Understanding multiscale rainfall variability in the South Pacific Convergence Zone (SPCZ), a southeastward-oriented band of precipitating deep convection in the South Pacific, is critical for both the human and natural systems dependent on its rainfall, and for interpreting similar off-equatorial diagonal convection zones around the globe. A k-means clustering method is applied to daily austral summer (December–February) rainfall from the Tropical Rainfall Measuring Mission (TRMM) satellite to extract representative spatial patterns of rainfall over the SPCZ region over the period 1998–2013. For a k = 4 clustering, pairs of clusters differ predominantly via spatial translation of the SPCZ diagonal, corresponding to either warm or cool phases of the El Niño–Southern Oscillation (ENSO). Within each of these ENSO phase pairs, one cluster exhibits intense precipitation along the SPCZ while the other features weakened rainfall. Cluster temporal behavior is analyzed to investigate higher-frequency variability (e.g., the Madden–Julian oscillation (MJO) and synoptic-scale disturbances) that trigger deep convection where SSTs favorable. Pressure-level winds and specific humidity from the Climate Forecast System Reanalysis are composited with respect to daily cluster assignment to investigate differences between active and quiescent SPCZ conditions to reveal the conditions supporting enhanced or suppressed SPCZ precipitation, such as low-level poleward moisture transport from the equator. Empirical orthogonal functions (EOFs) of TRMM precipitation are computed to relate the ‘‘modal view’’ of SPCZ variability associated with the EOFs to the ‘‘state view’’ associated with the clusters. Finally, the cluster number is increased to illustrate the change in TRMM rainfall patterns as additional degrees of freedom are introduced. The tropical Pacific is an extremely complex dynamical system which sees processes in the ocean and atmosphere spanning a range of temporal and spatial scales from micrometers to thousands of kilometers, and fractions of a second to decades and centuries, in constant interaction with one another. These dynamic and thermodynamic processes in some instances combine to form semi-regular phenomena with predictable return periods and behaviors including the MJO and Boreal Summer Intraseasonal Oscillation (BSISO), ENSO, and rainfall features including the SPCZ and ITCZ. ENSO in particular has impacts felt globally via complex teleconnections yet many aspects of ENSO, such as its predictability, remain unresolved. In this analysis, we show how the spectral theory of dynamical systems and techniques from data science can be used to extract multiple coherent modes of variability from climate data which represent the evolution and lifecycle of several ENSO modes.The MJO, the dominant mode of tropical intraseasonal variability, is commonly identified using the Realtime Multivariate MJO (RMM) index based on joint empirical orthogonal function (EOF) analysis of near-equatorial upper and lower level zonal winds and outgoing longwave radiation. We apply the Koopman operator formalism to extract an analogue to RMM that exhibits certain features that may refine the characterization and predictability of the MJO. In particular, the Koopman eigenfunctions, with eigenvalues corresponding to mode periods, has a leading intraseasonal mode with period of ~50 days. Moreover, the amplitude of this leading intraseasonal eigenfunction exhibits a seasonal modulation clearly peaked in boreal winter. Finally, the phase space formed by the complex Koopman MJO eigenfunction exhibits a smoother temporal evolution and higher degree of autocorrelation than RMM, which may contribute to enhanced predictive skill.