A reversible-jump Markov chain Monte Carlo algorithm to estimate paleo surface CO2 fluxes linking temperature to atmospheric CO2 concentration time series

Future goals and strategies for mitigating ongoing climate changes rely on the understanding of the global carbon cycle and its connections to climate. Evidence from ice cores regarding past atmospheric CO2 and temperature changes through glacial-interglacial oscillations provide crucial insight int...

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Published inComputers & geosciences Vol. 196; p. 105838
Main Authors Castrogiovanni, L., Sternai, P., Piana Agostinetti, N., Pasquero, C.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.02.2025
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ISSN0098-3004
1873-7803
DOI10.1016/j.cageo.2024.105838

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Abstract Future goals and strategies for mitigating ongoing climate changes rely on the understanding of the global carbon cycle and its connections to climate. Evidence from ice cores regarding past atmospheric CO2 and temperature changes through glacial-interglacial oscillations provide crucial insight into the natural variability of carbon cycling. However, poor constraints on atmospheric CO2 input and output fluxes limit our quantitative understanding of late Pleistocene carbon cycling and climate changes. In this study, we describe an inversion method based on a reversible-jump Markov chain Monte Carlo (rj-McMC) algorithm and a general formulation of the geological carbon cycle to estimate paleo-fluxes of CO2. We present results from two synthetic tests and a real case study based on data from the ice core of Dome Fuji. Results from synthetic tests demonstrate the capability of the algorithm to retrieve reliable estimates of atmospheric CO2 input and output fluxes inverting the time derivative of the atmospheric CO2 record and using its temperature time series as a further constraint. Results from the Dome Fuji case study underscore systematic pulses of input CO2 fluxes into the atmosphere during deglaciations predating peaks of T and output CO2 fluxes by ∼2.5 kyrs. The retrieved surface source and sink CO2 fluxes as well as future applications of the algorithm presented here will provide new insights to assess past climate driving mechanisms and inform projections of future climatic trajectories. •Retrieved time histories of atmospheric input and output CO2 fluxes over the last 340 kyrs.•Systematic input pulses of CO2 into the atmosphere during the deglaciations.•Systematic peaks of atmospheric CO2 output fluxes 2.5 kyrs after the peak of input pulses.•Rj-McMC algorithm successfully applied to ice core data for paleoclimate investigations.
AbstractList Future goals and strategies for mitigating ongoing climate changes rely on the understanding of the global carbon cycle and its connections to climate. Evidence from ice cores regarding past atmospheric CO2 and temperature changes through glacial-interglacial oscillations provide crucial insight into the natural variability of carbon cycling. However, poor constraints on atmospheric CO2 input and output fluxes limit our quantitative understanding of late Pleistocene carbon cycling and climate changes. In this study, we describe an inversion method based on a reversible-jump Markov chain Monte Carlo (rj-McMC) algorithm and a general formulation of the geological carbon cycle to estimate paleo-fluxes of CO2. We present results from two synthetic tests and a real case study based on data from the ice core of Dome Fuji. Results from synthetic tests demonstrate the capability of the algorithm to retrieve reliable estimates of atmospheric CO2 input and output fluxes inverting the time derivative of the atmospheric CO2 record and using its temperature time series as a further constraint. Results from the Dome Fuji case study underscore systematic pulses of input CO2 fluxes into the atmosphere during deglaciations predating peaks of T and output CO2 fluxes by ∼2.5 kyrs. The retrieved surface source and sink CO2 fluxes as well as future applications of the algorithm presented here will provide new insights to assess past climate driving mechanisms and inform projections of future climatic trajectories. •Retrieved time histories of atmospheric input and output CO2 fluxes over the last 340 kyrs.•Systematic input pulses of CO2 into the atmosphere during the deglaciations.•Systematic peaks of atmospheric CO2 output fluxes 2.5 kyrs after the peak of input pulses.•Rj-McMC algorithm successfully applied to ice core data for paleoclimate investigations.
Future goals and strategies for mitigating ongoing climate changes rely on the understanding of the global carbon cycle and its connections to climate. Evidence from ice cores regarding past atmospheric CO₂ and temperature changes through glacial-interglacial oscillations provide crucial insight into the natural variability of carbon cycling. However, poor constraints on atmospheric CO₂ input and output fluxes limit our quantitative understanding of late Pleistocene carbon cycling and climate changes. In this study, we describe an inversion method based on a reversible-jump Markov chain Monte Carlo (rj-McMC) algorithm and a general formulation of the geological carbon cycle to estimate paleo-fluxes of CO₂. We present results from two synthetic tests and a real case study based on data from the ice core of Dome Fuji. Results from synthetic tests demonstrate the capability of the algorithm to retrieve reliable estimates of atmospheric CO₂ input and output fluxes inverting the time derivative of the atmospheric CO₂ record and using its temperature time series as a further constraint. Results from the Dome Fuji case study underscore systematic pulses of input CO₂ fluxes into the atmosphere during deglaciations predating peaks of T and output CO₂ fluxes by ∼2.5 kyrs. The retrieved surface source and sink CO₂ fluxes as well as future applications of the algorithm presented here will provide new insights to assess past climate driving mechanisms and inform projections of future climatic trajectories.
ArticleNumber 105838
Author Sternai, P.
Piana Agostinetti, N.
Pasquero, C.
Castrogiovanni, L.
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Keywords Rj-McMC algorithm
Bayesian inference
Glacial-interglacial climate variability
Inverse theory
Paleo surface CO2 fluxes
Language English
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SubjectTerms algorithms
Bayesian inference
carbon
carbon cycle
carbon dioxide
case studies
climate
Glacial-interglacial climate variability
global carbon budget
ice
Inverse theory
Markov chain
Paleo surface CO2 fluxes
Pleistocene epoch
Rj-McMC algorithm
temperature
time series analysis
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Title A reversible-jump Markov chain Monte Carlo algorithm to estimate paleo surface CO2 fluxes linking temperature to atmospheric CO2 concentration time series
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