Interannual sea–air CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; flux variability from an observation-driven ocean mixed-layer scheme

Rödenbeck, Christian; Bakker, D. C. E.; Metzl, Nicolas; Olsen, Are; Sabine, Christopher L.; Cassar, Nicolas; Reum, Friedemann; Heimann, Martin

doi:10.5194/bgd-11-3167-2014

Cited by 51 publications

(103 citation statements)

References 47 publications

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“…These data streams are as follows: (i) the atmospheric CO 2 growth rate (CGR) from the National Oceanic and Atmospheric Administration (NOAA)/Earth System Research Laboratory (ESRL) atmospheric network (14), which constrains the sum of all fluxes and is determined very accurately from more than 60 monitoring stations; (ii) the atmospheric 5-y mean (negative) growth rate of O 2 /N 2 in the atmosphere from the Scripps O 2 Program (15), which relates to the combined effect of B + L and F changes, while being insensitive to changes in O (note that O 2 /N 2 has a negative trend in the atmosphere); (iii) a set of yearly mean estimates of O from observational products based on in situ partial pressure of CO 2 (pCO 2 ) surveys corrected for natural ocean CO 2 outgassing from carbon delivered by rivers (16) and using a neural network approach (17) and a diagnostic mixed-layer approach (18), and a set of decadal-mean estimates of O from inventories of carbon change in the ocean deduced indirectly from chlorofluorocarbons (CFCs) (19) combined with 14 C (20), and observed atmospheric mean CO 2 level and oceanic CO 2 and dissolved inorganic carbon observations (21); (iv) 10-y mean estimates of B from a global synthesis of changes in forest carbon stocks (22); (v) decadal mean B + L from inventory-based land carbon storage change from the RECCAP publications on regional budgets (Table S3); (vi) 5-y mean LUC emissions from two independent bookkeeping approaches constrained by observed carbon stocks (23,24). The uncertainties in each data stream are either derived directly from the original publications (when reported) or estimated from expert judgments (see details in Table S2).…”

Section: Resultsmentioning

confidence: 99%

“…(17,18,20). The constraining data tiers (Table S2) were defined as follows: tier 1, direct carbon observations (e.g., CGR); tier 2, indirect carbon observations unambiguously related to carbon quantities (e.g., O 2 /N 2 ); tier 3, direct carbon observations with an empirical (data-driven) model used to obtain global estimates, for example, the use of geostatistics to up-scale local data into global values; tier 4, indirect carbon observations not simply related to global carbon flux quantities.…”

Section: Methodsmentioning

confidence: 99%

“…34. The correlations between the uncertainties in the two data-driven estimates of O (17,18) are estimated from the series of annual fluxes of the two products by assuming that the correlation in annual fluxes within each 5-y period is an approximation of the 5-y mean flux error correlation.…”

Section: Methodsmentioning

confidence: 99%

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Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty.Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 ± 8 and 37 ± 17 Tg C·y −2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes.global carbon budget | carbon cycle | decadal variations | Bayesian fusion

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

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“…Le Quéré, C., Moriarty, R., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J. I., Friedlingstein, P., 40 Peters, G. P., Andres, R. J., Boden, T. A., Houghton, R. A., House, J. I., Keeling, R. F., Tans, P., Arneth, A., 41 Bakker, D. C. E., Barbero, L., Bopp, L., Chang, J., Chevallier, F., Chini, L. P., Ciais, P., Fader, M., Feely, R. 42…”

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“…12 We use two estimates of the ocean CO 2 sink and its variability based on interpolations of and contains quality-controlled data to 2016 (see data attribution Table A2). The SOCAT v5 were 17 mapped using a data-driven diagnostic method (Rödenbeck et al, 2013) and a combined self- …”

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Global Carbon Budget 2017

Quéré¹,

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Friedlingstein³

et al. 2017

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Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the "global carbon budget" – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on land-cover change data and bookkeeping models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of our imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2007–2016), EFF was 9.4 ± 0.5 GtC yr−1, ELUC 1.3 ± 0.7 GtC yr−1, GATM 4.7 ± 0.1 GtC yr−1, SOCEAN 2.4 ± 0.5 GtC yr−1, and SLAND 3.0 ± 0.8 GtC yr−1, with a budget imbalance BIM of 0.6 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1. Also for 2016, ELUC was 1.3 ± 0.7 GtC yr−1, GATM was 6.1 ± 0.2 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1 and SLAND was 2.7 ± 1.0 GtC yr−1, with a small BIM of −0.3 GtC. GATM continued to be higher in 2016 compared to the past decade (2007–2016), reflecting in part the higher fossil emissions and smaller SLAND for that year consistent with El Niño conditions. The global atmospheric CO2 concentration reached 402.8 ± 0.1 ppm averaged over 2016. For 2017, preliminary data indicate a renewed growth in EFF of +2.0 % (range of 0.8 % to 3.0 %) based on national emissions projections for China, USA, and India, and projections of Gross Domestic Product corrected for recent changes in the carbon intensity of the economy for the rest of the world. For 2017, initial data indicate an increase in atmospheric CO2 concentration of around 5.3 GtC (2.5 ppm), attributed to a combination of increasing emissions and receding El Niño conditions. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set

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Recent variability of the global ocean carbon sink

Landschützer

Gruber

Bakker

et al. 2014

Global Biogeochemical Cycles

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We present a new observation-based estimate of the global oceanic carbon dioxide (CO2) sink and its temporal variation on a monthly basis from 1998 through 2011 and at a spatial resolution of 1×1. This sink estimate rests upon a neural network-based mapping of global surface ocean observations of the partial pressure of CO2 (pCO2) from the Surface Ocean CO2 Atlas database. The resulting pCO2 has small biases when evaluated against independent observations in the different ocean basins, but larger randomly distributed differences exist particularly in high latitudes. The seasonal climatology of our neural network-based product agrees overall well with the Takahashi et al. (2009) climatology, although our product produces a stronger seasonal cycle at high latitudes. From our global pCO2 product, we compute a mean net global ocean (excluding the Arctic Ocean and coastal regions) CO2 uptake flux of −1.42 ± 0.53 Pg C yr−1, which is in good agreement with ocean inversion-based estimates. Our data indicate a moderate level of interannual variability in the ocean carbon sink (±0.12 Pg C yr−1, 1𝜎) from 1998 through 2011, mostly originating from the equatorial Pacific Ocean, and associated with the El Nino–Southern Oscillation. Accounting for steady state riverine and Arctic Ocean carbon fluxes our estimate further implies a mean anthropogenic CO2 uptake of −1.99 ± 0.59 Pg C yr−1 over the analysis period. From this estimate plus the most recent estimates for fossil fuel emissions and atmospheric CO2 accumulation, we infer a mean global land sink of −2.82 ± 0.85 Pg C yr−1 over the 1998 through 2011 period with strong interannual variation

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Interannual sea–air CO<sub>2</sub> flux variability from an observation-driven ocean mixed-layer scheme

Cited by 51 publications

References 47 publications

Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

Global Carbon Budget 2017

Recent variability of the global ocean carbon sink

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