Heartbeat of the Southern Oscillation explains ENSO climatic resonances

Abstract

The El Niño‐Southern Oscillation (ENSO) nonlinear oscillator phenomenon has a far reaching influence on the climate and human activities. The up to 10 year quasi‐period cycle of the El Niño and subsequent La Niña is known to be dominated in the tropics by nonlinear physical interaction of wind with the equatorial waveguide in the Pacific. Long‐term cyclic phenomena do not feature in the current theory of the ENSO process. We update the theory by assessing low (>10 years) and high (<10 years) frequency coupling using evidence across tropical, extratropical, and Pacific basin scales. We analyze observations and model simulations with a highly accurate method called Dominant Frequency State Analysis (DFSA) to provide evidence of stable ENSO features. The observational data sets of the Southern Oscillation Index (SOI), North Pacific Index Anomaly, and ENSO Sea Surface Temperature Anomaly, as well as a theoretical model all confirm the existence of long‐term and short‐term climatic cycles of the ENSO process with resonance frequencies of {2.5, 3.8, 5, 12–14, 61–75, 180} years. This fundamental result shows long‐term and short‐term signal coupling with mode locking across the dominant ENSO dynamics. These dominant oscillation frequency dynamics, defined as ENSO frequency states, contain a stable attractor with three frequencies in resonance allowing us to coin the term Heartbeat of the Southern Oscillation due to its characteristic shape. We predict future ENSO states based on a stable hysteresis scenario of short‐term and long‐term ENSO oscillations over the next century.

Resilience of the Copepod Oithona similis to Climatic Variability: Egg Production, Mortality, and Vertical Habitat Partitioning

“…The physical and biological systems at L4 are strongly influenced by the annual cyclic motion of the Earth and by decadal processes (Smyth et al, 2014). DFSA is a highly accurate and efficient estimation signal analysis approach designed to resolve these cyclic Earth-system processes (Smyth et al, 2014;Barnes et al, 2015;Tarran and Bruun, 2015;Bruun et al, 2017). Here, DFSA was used to construct a predictive dynamic model of the seasonal and decadal properties of how the Oithona spp.…”

“…The physical and biological systems at L4 are influenced by natural cyclic variability on seasonal and decadal scales. Following this natural cyclic variability, the stable hysteresis assumption (Bruun et al, 2017) was applied to the DFSA-derived baseline Oithona spp. dynamic to predict the 2018 dynamic had typical environmental conditions prevailed.…”

Reconstrucción dendrohidrológica de escurrimientos en la subregión hidrológica Coahuayana, estado de Jalisco

“…En el presente estudio, la serie de madera temprana evidenció una correlación de -0.47 (p < 0.01) con el SOI enero-junio y de 0.43 con MEI, lo cual corrobora que ENSO ejerce una influencia positiva en la precipitación y en consecuencia, favorece los escurrimientos invierno-primavera en la subregión hidrológica Coahuayana del estado de Jalisco. La influencia de ENSO definió picos significativos de 2.21 y 11.58 años, en los que el de 2.21 años queda incluido en las frecuencias dominantes de ENSO (Bruun et al, 2017), mientras que el de 11.58 años puede asociarse a frecuencias dominantes del PDO (Mantua y Hare, 2002).…”

Anderson localization and extreme values in chaotic climate dynamics

“…This can lead to phenomena such as convective self-aggregation and spatial clumping transport processes [33]. Recently there has been a resurgence of interest to assess wave properties of geophysical climate systems (Delplace, et al, 2017;Bruun et al, 2017;Skkala and Bruun, 2018) [20,34,35]. In these analyses universal and scale invariant properties help to identify the dynamics and the resulting wave processes.…”

“…The Extreme Value Process (EVP) of any system can be characterised by the shape parameter ξ that represents the extreme edge of the hysteresis characteristic [17][18][19][20] and it is widely used in design engineering such as flood prevention work to assess extrapolation and risk properties. The EVP distribution is justified from a linear renormalisation and extremal types or max stable limit law based on geometric universality [21][22][23][24].…”