Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean &amp;lt;i&amp;gt;p&amp;lt;/i&amp;gt;CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; Mapping intercomparison (SOCOM)

Rödenbeck, Christian; Bakker, D. C. E.; Gruber, Nicolas; Iida, Yosuke; Jacobson, A. R.; Jones, S. D. M.; Landschützer, Peter; Metzl, Nicolas; Nakaoka, Shin‐Ichiro; Olsen, Are; Park, Geun-Ha; Peylin, P.; Rodgers, Keith B.; Sasse, Tristan P.; Schuster, Ute; Shutler, J. D.; Valsala, Vinu; Wanninkhof, Rik; Zeng, Jiye

doi:10.5194/bg-12-7251-2015

Cited by 210 publications

(248 citation statements)

References 64 publications

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“…The application of distinct interpolation techniques in recent years has led to the publication of several global pCO 2 products that are largely based on the same observational dataset (i.e., SOCAT; Bakker et al, 2016). To quantify the uncertainty introduced by the choice of the pCO 2 field, Rödenbeck et al (2015) applied an identical parameterization of the CO 2 exchange at the air-water interface to 14 pCO 2 data products. The global F CO 2 ranged from −1.36 to −1.96 Pg C yr −1 , and the relative difference (∼ 30 %) is thus slightly larger than the one attributed to different formulations of k and wind products (20 %, ignoring NCEP2) calculated here.…”

Section: Discussionmentioning

confidence: 99%

“…The remainder is estimated to be taken up in roughly equal shares by the land and the ocean. In past decades, the magnitude of the ocean carbon sink was mainly estimated from global ocean biogeochemistry models and atmospheric inverse models but the recent increase in oceanic CO 2 measurements and the creation of the Surface Ocean CO 2 Atlas (SOCAT) database Pfeil et al, 2013;Sabine et al, 2013) has opened new research avenues, including the possibility to monitor the temporal evolution of the global oceanic carbon sink based on surface ocean CO 2 measurements (Land-schützer et al, 2016;Rödenbeck et al, 2015). The exchange of CO 2 through the air-seawater interface can be estimated from the surface ocean CO 2 measurements using a relationship of the form…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty in the global oceanic CO2 uptake induced by wind forcing: quantification and spatial analysis

et al. 2018

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Abstract. The calculation of the air-water CO 2 exchange (F CO 2 ) in the ocean not only depends on the gradient in CO 2 partial pressure at the air-water interface but also on the parameterization of the gas exchange transfer velocity (k) and the choice of wind product. Here, we present regional and global-scale quantifications of the uncertainty in F CO 2 induced by several widely used k formulations and four wind speed data products (CCMP, ERA, NCEP1 and NCEP2). The analysis is performed at a 1 • × 1 • resolution using the sea surface pCO 2 climatology generated by Landschützer et al. (2015a) for the 1991-2011 period, while the regional assessment relies on the segmentation proposed by the Regional Carbon Cycle Assessment and Processes (REC-CAP) project. First, we use k formulations derived from the global 14 C inventory relying on a quadratic relationship between k and wind speed (k = c · U 10 2 ; Sweeney et al., 2007;Takahashi et al., 2009;Wanninkhof, 2014), where c is a calibration coefficient and U 10 is the wind speed measured 10 m above the surface. Our results show that the range of global F CO 2 , calculated with these k relationships, diverge by 12 % when using CCMP, ERA or NCEP1. Due to differences in the regional wind patterns, regional discrepancies in F CO 2 are more pronounced than global. These global and regional differences significantly increase when using NCEP2 or other k formulations which include earlier relationships (i.e., Wanninkhof, 1992;Wanninkhof et al., 2009) as well as numerous local and regional parameterizations derived experimentally. To minimize uncertainties associated with the choice of wind product, it is possible to recalculate the coefficient c globally (hereafter called c * ) for a given wind product and its spatiotemporal resolution, in order to match the last evaluation of the global k value. We thus performed these recalculations for each wind product at the resolution and time period of our study but the resulting global F CO 2 estimates still diverge by 10 %. These results also reveal that the Equatorial Pacific, the North Atlantic and the Southern Ocean are the regions in which the choice of wind product will most strongly affect the estimation of the F CO 2 , even when using c * .

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty in the global oceanic CO2 uptake induced by wind forcing: quantification and spatial analysis

et al. 2018

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“…Finally, we point to latest products from compilations and syntheses of oceanic and atmospheric CO 2 data collected by a large international community (Röden-beck et al, 2015;Bakker et al, 2016). Data products like air-sea CO 2 flux of specified ocean regions (biomes), as derived in Rödenbeck et al (2015), in combination with data of nutrient concentrations and O 2 will likely put new light on those parameters that determine variations of the elemental stoichiometry (C : N: P : O 2 ) in model results of inorganic and organic matter cycling.…”

Section: Examples Of Recent Advances In Data Availabilitymentioning

confidence: 99%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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Abstract. To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling.We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time-and spacedependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.

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“…The definition of the coastal/open-oceanic boundary varies strongly from one study to the other (Walsh, 1988;Laruelle et al, 2013), with a potentially large impact on the shelf CO 2 budget (Laruelle et al, 2010). Here, we use a very wide definition for this boundary (i.e., 300 km width or 1000 m depth) to secure spatial continuity between our new shelf pCO 2 product and those already existing for the open ocean (Landschützer et al, , 2016Rödenbeck et al, 2015). Our approach leads to the first continuous and monthly resolved pCO 2 maps over the 1998-2015 period across the global shelf region, permitting us to study the seasonal dynamics of these regions in relationship to that of the adjacent open ocean.…”

Section: Introductionmentioning

confidence: 99%

“…Although differing in their details (see, e.g., Rödenbeck et al, 2015, for an overview), these products typically have a nominal spatial resolution of 1 • and monthly temporal resolution. By filling in the spatial and temporal gaps, these products greatly facilitate the calculation of the air-sea CO 2 exchange, as they do not require separate assumptions about the surface ocean pCO 2 in areas lacking data.…”

Section: Introductionmentioning

confidence: 99%

Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation

Laruelle

Landschützer

Gruber³

et al. 2017

Biogeosciences

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Abstract. In spite of the recent strong increase in the number of measurements of the partial pressure of CO 2 in the surface ocean (pCO 2 ), the air-sea CO 2 balance of the continental shelf seas remains poorly quantified. This is a consequence of these regions remaining strongly under-sampled in both time and space and of surface pCO 2 exhibiting much higher temporal and spatial variability in these regions compared to the open ocean. Here, we use a modified version of a two-step artificial neural network method (SOM-FFN; to interpolate the pCO 2 data along the continental margins with a spatial resolution of 0.25 • and with monthly resolution from 1998 to 2015. The most important modifications compared to the original SOM-FFN method are (i) the much higher spatial resolution and (ii) the inclusion of sea ice and wind speed as predictors of pCO 2 . The SOM-FFN is first trained with pCO 2 measurements extracted from the SOCATv4 database. Then, the validity of our interpolation, in both space and time, is assessed by comparing the generated pCO 2 field with independent data extracted from the LD-VEO2015 database. The new coastal pCO 2 product confirms a previously suggested general meridional trend of the annual mean pCO 2 in all the continental shelves with high values in the tropics and dropping to values beneath those of the atmosphere at higher latitudes. The monthly resolution of our data product permits us to reveal significant differences in the seasonality of pCO 2 across the ocean basins. The shelves of the western and northern Pacific, as well as the shelves in the temperate northern Atlantic, display particularly pronounced seasonal variations in pCO 2, while the shelves in the southeastern Atlantic and in the southern Pacific reveal a much smaller seasonality. The calculation of temperature normalized pCO 2 for several latitudes in different oceanic basins confirms that the seasonality in shelf pCO 2 cannot solely be explained by temperature-induced changes in solubility but are also the result of seasonal changes in circulation, mixing and biological productivity. Our results also reveal that the amplitudes of both thermal and nonthermal seasonal variations in pCO 2 are significantly larger at high latitudes. Finally, because this product's spatial extent includes parts of the open ocean as well, it can be readily merged with existing global open-ocean products to produce a true global perspective of the spatial and temporal variability of surface ocean pCO 2 .

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Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean <i>p</i>CO<sub>2</sub> Mapping intercomparison (SOCOM)

Cited by 210 publications

References 64 publications

Uncertainty in the global oceanic CO<sub>2</sub> uptake induced by wind forcing: quantification and spatial analysis

Uncertainty in the global oceanic CO<sub>2</sub> uptake induced by wind forcing: quantification and spatial analysis

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Global high-resolution monthly <i>p</i>CO<sub>2</sub> climatology for the coastal ocean derived from neural network interpolation

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Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean &lt;i&gt;p&lt;/i&gt;CO&lt;sub&gt;2&lt;/sub&gt; Mapping intercomparison (SOCOM)

Cited by 210 publications

References 64 publications

Uncertainty in the global oceanic CO&lt;sub&gt;2&lt;/sub&gt; uptake induced by wind forcing: quantification and spatial analysis

Uncertainty in the global oceanic CO&lt;sub&gt;2&lt;/sub&gt; uptake induced by wind forcing: quantification and spatial analysis

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Global high-resolution monthly &lt;i&gt;p&lt;/i&gt;CO&lt;sub&gt;2&lt;/sub&gt; climatology for the coastal ocean derived from neural network interpolation

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Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean <i>p</i>CO<sub>2</sub> Mapping intercomparison (SOCOM)

Uncertainty in the global oceanic CO<sub>2</sub> uptake induced by wind forcing: quantification and spatial analysis

Uncertainty in the global oceanic CO<sub>2</sub> uptake induced by wind forcing: quantification and spatial analysis

Global high-resolution monthly <i>p</i>CO<sub>2</sub> climatology for the coastal ocean derived from neural network interpolation