Emergence of anthropogenic signals in the ocean carbon cycle

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The attribution of anthropogenically forced trends in the climate system requires an understanding of when and how such signals emerge from natural variability. We applied time-of-emergence diagnostics to a large ensemble of an Earth system model, which provides both a conceptual framework for interpreting the detectability of anthropogenic impacts in the ocean carbon cycle and observational sampling strategies required to achieve detection. We found emergence timescales that ranged from less than a decade to more than a century, a consequence of the time lag between the chemical and radiative impacts of rising atmospheric CO2 on the ocean. Processes sensitive to carbonate chemical changes emerge rapidly, such as the impacts of acidification on the calcium carbonate pump (10 years for the globally integrated signal and 9–18 years for regionally integrated signals) and the invasion flux of anthropogenic CO2 into the ocean (14 years globally and 13–26 years regionally). Processes sensitive to the ocean’s physical state, such as the soft-tissue pump, which depends on nutrients supplied through circulation, emerge decades later (23 years globally and 27–85 years regionally).

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Fig. 1: Venn diagram schematic of the sources of uncertainty in simulating (using an ESM approach) and observing changes in the Earth system.
Fig. 2: Percentage of ocean with emergent anthropogenic trends in ocean biogeochemical and physical variables.
Fig. 3: Global and regional ToE for globally and regionally integrated anthropogenic signals and 50% of the local anthropogenic signals for the given biogeochemical variables.
Fig. 4: ToE and signal maps for the three carbon pumps.
Fig. 5: Amplified seasonality of \(\Delta{p_{\rm{CO}_2}}\) ToE and signals.
Fig. 6: ToE and signal maps for surface versus depth-integrated chlorophyll.

Data availability

The GFDL LE data that support the findings of this study are publicly available through Globus (http://poseidon.princeton.edu).


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This work was supported by NASA award no. NNX17AI75G. Support for K.B.R. was provided by the Institute for Basic Science project code IBS-R028-D1, with additional support through NOAA award nos NA17RJ2612 and NA08OAR4320752, including support through the NOAA Office for Climate Observations and NOAA award no. NA11OAR4310066. T.L.F. acknowledges support from the Swiss National Science Foundation under grant no. PP00P2_170687. The numerical simulations were performed with the computational resources of NOAA/GFDL.

Author information

S.S. performed all the analysis and writing, with regular feedback from K.B.R., J.L.S, T.L.F., J.P.D., M.I. and R.S. The LE simulations were set up by K.B.R. and T.L.F. and performed and postprocessed by K.B.R. The sensitivity experiments and control runs were performed by R.S.

Correspondence to Sarah Schlunegger.

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Supplementary Notes 1–3, references and Figs. 1–7.

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