Fine Pattern of Natural Modes in Sea Surface Temperature Variability: 1985–2003

Abstract

A natural mode refers, in this study, to a periodic oscillation of sea surface temperature (SST) that is geophysically significant on a global, regional, or local scale. Using a newly developed harmonic extraction scheme by Chen, which has the advantage of being space-time decoupled and fully data adaptive, a variety of natural modes have been recovered from global monthly SST data for the period of 1985-2003. Among them, the eight most significant modes are identified as primary modes, whose spatial patterns are presented, along with their phase distributions. At seasonal time scales, a 4-month primary mode is uncovered in addition to the well-documented annual and semiannual cycles. At interannual time scales, the dominant El Niño-Southern Oscillation (ENSO) variability is found to be composed of at least five primary modes, with well-defined central periods around 18, 25, 32, 43, and 63 months. At time scales beyond ENSO, a decadal SST signal with an average period of 10.3 yr is observed. A unique contribution of this study is the derivation and presentation of fine patterns of natural SST modes and signals in joint dimensions of time, space, period, and phase, leading to several findings and conclusions that are of potential importance: 1) The degree of separability and regularity of the sub-ENSO modes is surprising, and thus reveals new details on the nature of this event.2) The midlatitude counterparts of the equatorial interannual and decadal SST modes/signals are found in the two hemispheres with a frequency shift toward longer periods. The "shadows" of the Pacific Ocean's ENSO modes are also observed with some detail in the Atlantic and the Indian Oceans. All of these provide direct evidence that teleconnections exist between the equatorial and extratropical oceans, as well as among the tropical Pacific, tropical Atlantic, and tropical Indian Oceans, possibly as a result of the "atmospheric bridge." 3) A sharply opposite anisotropy is observed in the spatiotemporal pattern between the interannual modes and decadal signals, implying that they are potentially of a categorical difference in origin. 4) Locality or regionality is a fundamental feature for most of the SST modes. Treating the interannual or decadal variability as a single ENSO or Pacific decadal oscillation mode appears to be an oversimplification, and may lead to inappropriate interpretations. The results herein represent an improved knowledge of the natural variability in sea surface temperature, which will hopefully help to enhance the understanding of natural fluctuations of the global/regional climate system in the context of ocean-atmosphere interaction.

New Insights into Annual and Semiannual Cycles of Sea Level Pressure

“…With the availability of a complete spatiotemporal dataset P(x, y, t), the retrieved A 1,2 (x, y) and u 1,2 (x, y) are supposed to carry full information of the annual and semiannual SLP variability in terms of amplitude and phase, as evidenced in Chen and Quartly (2005) and Chen and Li (2008) with sea surface temperature and sea surface height for the global ocean.…”

“…Thus, amphidromes are naturally linked with amplitude and phase, which, in combination with periodicity, constitute the three basic parameters to characterize the so-called harmonic signal. This concept can also be extended to nontidal oceanographic and atmospheric variables with a periodic nature (Chen and Quartly 2005;Zhang et al 2008). Amphidromes are therefore considered as critical nodal points that define the fundamental pattern of the spatiotemporal variation of a geophysical property.…”

Interannual Modality of Upper-Ocean Temperature: 4D Structure Revealed by Argo Data

“…Despite the diversity in El Niño/La Niña definition and their geographical pattern, it is helpful to keep in mind that these events occur about 55% of the time so that more often than not the tropical oceans are regarded as being in one phase or the other and average conditions are less common (Trenberth 1997). The El Niño/La Niña associated with the Southern Oscillation are known to be the dominant signals in SST variability, ranging from intraseasonal to multidecadal in the tropical Pacific (e.g., Chen and Li 2008;Chen et al 2010a). The nature of complex year-to-year SST changes clearly requires a comprehensive characterization of the interannual variability at the air-sea interface.…”

Multitime scale variations of sea surface temperature in the China seas based on the HadISST dataset

“…As one of the fundamental variables to describe the coupled ocean-atmosphere system, sea surface temperature (SST) is, on one hand, a surface indicator or manifestation of many geophysical and biological processes in the ocean, such as ocean currents, planetary waves, ocean primary productivity, and marine fisheries, and on the other hand, it constitutes the bottom boundary condition of the atmosphere and plays a key role in the air-sea exchanges of mass, momentum, and energy (Chen and Li, 2008). Many significant progresses have been made during the past 30 a or so in characterizing low-frequency variations of SST in the Pacific, Indian and Atlantic Oceans, especially in identifying its principal modes at intraseasonal-tomultidecadal periods (Lau and Weng, 1999;Yeh and Kirtman, 2004;Solomon and Jin, 2005;Mochizuki and Kida, 2006;Frankignoul and Sennechael, 2007;Chen and Li, 2008).…”

“…Many significant progresses have been made during the past 30 a or so in characterizing low-frequency variations of SST in the Pacific, Indian and Atlantic Oceans, especially in identifying its principal modes at intraseasonal-tomultidecadal periods (Lau and Weng, 1999;Yeh and Kirtman, 2004;Solomon and Jin, 2005;Mochizuki and Kida, 2006;Frankignoul and Sennechael, 2007;Chen and Li, 2008). China has complicated features in its near-coastal continental shelf distribution, and the SST of the offshore exhibits its own variation characteristic influenced by many factors, such as the continent climate, the coastal shape, and the nearshore current oscillation.…”