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Nonlin. Processes Geophys., 25, 89-97, 2018
https://doi.org/10.5194/npg-25-89-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
07 Feb 2018
Tipping point analysis of ocean acoustic noise
Valerie N. Livina1, Albert Brouwer2, Peter Harris1, Lian Wang1, Kostas Sotirakopoulos1, and Stephen Robinson1 1National Physical Laboratory, Teddington, Middlesex TW11 0LW, UK
2Preparatory Commission of the Comprehensive Nuclear-Test-Ban Treaty Organization, Vienna, Austria
Abstract. We apply tipping point analysis to a large record of ocean acoustic data to identify the main components of the acoustic dynamical system and study possible bifurcations and transitions of the system. The analysis is based on a statistical physics framework with stochastic modelling, where we represent the observed data as a composition of deterministic and stochastic components estimated from the data using time-series techniques. We analyse long-term and seasonal trends, system states and acoustic fluctuations to reconstruct a one-dimensional stochastic equation to approximate the acoustic dynamical system. We apply potential analysis to acoustic fluctuations and detect several changes in the system states in the past 14 years. These are most likely caused by climatic phenomena. We analyse trends in sound pressure level within different frequency bands and hypothesize a possible anthropogenic impact on the acoustic environment. The tipping point analysis framework provides insight into the structure of the acoustic data and helps identify its dynamic phenomena, correctly reproducing the probability distribution and scaling properties (power-law correlations) of the time series.

Citation: Livina, V. N., Brouwer, A., Harris, P., Wang, L., Sotirakopoulos, K., and Robinson, S.: Tipping point analysis of ocean acoustic noise, Nonlin. Processes Geophys., 25, 89-97, https://doi.org/10.5194/npg-25-89-2018, 2018.
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Short summary
We have applied tipping point analysis to a large record of ocean acoustic data to identify the main components of the acoustic dynamical system: long-term and seasonal trends, system states and fluctuations. We reconstructed a one-dimensional stochastic model equation to approximate the acoustic dynamical system. We have found a signature of El Niño events in the deep ocean acoustic data near the southwest Australian coast, which proves the investigative power of the tipping point methodology.
We have applied tipping point analysis to a large record of ocean acoustic data to identify the...
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