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Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
Published on: October 21, 2016
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A data-driven approach for discovering the most probable transition pathway for a stochastic carbon cycle system
Jianyu Chen1, Jianyu Hu1, Wei Wei1
1Center for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
Chaos (Woodbury, N.Y.)
|December 1, 2022
Summary
Marine carbon stocks face tipping points. This study models ocean carbonate systems to identify transition pathways and understand how random carbon inputs influence these critical shifts, aiding in recognizing climate change impacts.
Area of Science:
- Earth and Environmental Sciences
- Climate Science
- Oceanography
Background:
- Natural systems can exhibit tipping points, leading to abrupt state shifts.
- Marine carbon stocks are crucial for climate regulation and susceptible to environmental changes.
- Understanding these transitions is vital for predicting climate change impacts.
Purpose of the Study:
- To model a stochastic carbonate system in the upper ocean.
- To analyze transition phenomena and identify tipping points.
- To investigate the influence of random carbon input rates on these transitions.
Main Methods:
- Utilized Onsager-Machlup action functional theory.
- Employed a neural shooting method to calculate transition pathways.
- Applied a physics-informed neural network to determine optimal transition times.
Main Results:
- Calculated the most probable transition pathway between metastable and oscillatory states.
- Determined the impact of external random carbon input rates on transition pathways.
- Computed the optimal transition time towards maximum carbonate concentration.
Conclusions:
- Provides a framework for recognizing tipping points in stochastic ocean systems.
- Offers insights into how noise-affected carbon inputs affect transition dynamics.
- Highlights the importance of transition time in carbonate system stability.
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