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| Gregor, Luke; Kok, Schalk; Monteiro, Pedro M. S.. |
| The Southern Ocean accounts for 40% of oceanic CO2 uptake, but the estimates are bound by large uncertainties due to a paucity in observations. Gap-filling empirical methods have been used to good effect to approximate pCO(2) from satellite observable variables in other parts of the ocean, but many of these methods are not in agreement in the Southern Ocean. In this study we propose two additional methods that perform well in the Southern Ocean: support vector regression (SVR) and random forest regression (RFR). The methods are used to estimate Delta pCO(2) in the Southern Ocean based on SOCAT v3, achieving similar trends to the SOM-FFN method by Landschitzer et al. (2014). Results show that the SOM-FFN and RFR approaches have RMSEs of similar magnitude... |
| Tipo: Text |
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| Ano: 2017 |
URL: https://archimer.ifremer.fr/doc/00662/77388/79018.pdf |
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| Bakker, Dorothee C. E.; Pfeil, Benjamin; Landa, Camilla S.; Metzl, Nicolas; O'Brien, Kevin M.; Olsen, Are; Smith, Karl; Cosca, Cathy; Harasawa, Sumiko; Jones, Stephen D.; Nakaoka, Shin-ichiro; Nojiri, Yukihiro; Schuster, Ute; Steinhoff, Tobias; Sweeney, Colm; Takahashi, Taro; Tilbrook, Bronte; Wada, Chisato; Wanninkhof, Rik; Alin, Simone R.; Balestrini, Carlos F.; Barbero, Leticia; Bates, Nicholas R.; Bianchi, Alejandro A.; Bonou, Frederic; Boutin, Jacqueline; Bozec, Yann; Burger, Eugene F.; Cai, Wei-jun; Castle, Robert D.; Chen, Liqi; Chierici, Melissa; Currie, Kim; Evans, Wiley; Featherstone, Charles; Feely, Richard A.; Fransson, Agneta; Goyet, Catherine; Greenwood, Naomi; Gregor, Luke; Hankin, Steven; Hardman-mountford, Nick J.; Harlay, Jerome; Hauck, Judith; Hoppema, Mario; Humphreys, Matthew P.; Hunt, Christopherw.; Huss, Betty; Ibanhez, J. Severino P.; Johannessen, Truls; Keeling, Ralph; Kitidis, Vassilis; Koertzinger, Arne; Kozyr, Alex; Krasakopoulou, Evangelia; Kuwata, Akira; Landschuetzer, Peter; Lauvset, Siv K.; Lefevre, Nathalie; Lo Monaco, Claire; Manke, Ansley; Mathis, Jeremy T.; Merlivat, Liliane; Millero, Frank J.; Monteiro, Pedro M. S.; Munro, David R.; Murata, Akihiko; Newberger, Timothy; Omar, Abdirahman M.; Ono, Tsuneo; Paterson, Kristina; Pearce, David; Pierrot, Denis; Robbins, Lisa L.; Saito, Shu; Salisbury, Joe; Schlitzer, Reiner; Schneider, Bernd; Schweitzer, Roland; Sieger, Rainer; Skjelvan, Ingunn; Sullivan, Kevin F.; Sutherland, Stewart C.; Sutton, Adrienne J.; Tadokoro, Kazuaki; Telszewski, Maciej; Tuma, Matthias; Van Heuven, Steven M. A. C. .; Vandemark, Doug; Ward, Brian; Watson, Andrew J.; Xu, Suqing. |
| The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO(2) (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO(2) values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO(2) values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO(2) values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and... |
| Tipo: Text |
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| Ano: 2016 |
URL: https://archimer.ifremer.fr/doc/00383/49405/49890.pdf |
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| Gregor, Luke; Lebehot, Alice D.; Kok, Schalk; Monteiro, Pedro M. Scheel. |
| Over the last decade, advanced statistical inference and machine learning have been used to fill the gaps in sparse surface ocean CO2 measurements (Rodenbeck et al., 2015). The estimates from these methods have been used to constrain seasonal, interannual and decadal variability in sea-air CO2 fluxes and the drivers of these changes (Landschutzer et al., 2015, 2016; Gregor et al., 2018). However, it is also becoming clear that these methods are converging towards a common bias and root mean square error (RMSE) boundary: "the wall", which suggests that pCO(2) estimates are now limited by both data gaps and scale-sensitive observations. Here, we analyse this problem by introducing a new gap-filling method, an ensemble average of six machine-learning models... |
| Tipo: Text |
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| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00676/78797/81035.pdf |
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| Gregor, Luke; Gruber, Nicolas. |
| Ocean acidification has profoundly altered the ocean's carbonate chemistry since preindustrial times, with potentially serious consequences for marine life. Yet, no long-term, global observation-based data set exists that allows us to study changes in ocean acidification for all carbonate system parameters over the last few decades. Here, we fill this gap and present a methodologically consistent global data set of all relevant surface ocean parameters, i.e., dissolved inorganic carbon (DIC), total alkalinity (TA), partial pressure of CO2 (pCO2), pH, and the saturation state with respect to mineral CaCO3 (Ω) at a monthly resolution over the period 1985 through 2018 at a spatial resolution of 1∘×1∘. This data set, named OceanSODA-ETHZ, was created by... |
| Tipo: Text |
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| Ano: 2021 |
URL: https://archimer.ifremer.fr/doc/00683/79538/82214.pdf |
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| Friedlingstein, Pierre; O'Sullivan, Michael; Jones, Matthew W.; Andrew, Robbie M.; Hauck, Judith; Olsen, Are; Peters, Glen P.; Peters, Wouter; Pongratz, Julia; Sitch, Stephen; Le Quere, Corinne; Canadell, Josep G.; Ciais, Philippe; Jackson, Robert B.; Alin, Simone; Aragao, Luiz E. O. C.; Arneth, Almut; Arora, Vivek; Bates, Nicholas R.; Becker, Meike; Benoit-cattin, Alice; Bittig, Henry C.; Bopp, Laurent; Bultan, Selma; Chandra, Naveen; Chevallier, Frederic; Chini, Louise P.; Evans, Wiley; Florentie, Liesbeth; Forster, Piers M.; Gasser, Thomas; Gehlen, Marion; Gilfillan, Dennis; Gkritzalis, Thanos; Gregor, Luke; Gruber, Nicolas; Harris, Ian; Hartung, Kerstin; Haverd, Vanessa; Houghton, Richard A.; Ilyina, Tatiana; Jain, Atul K.; Joetzjer, Emilie; Kadono, Koji; Kato, Etsushi; Kitidis, Vassilis; Korsbakken, Jan Ivar; Landschutzer, Peter; Lefevre, Nathalie; Lenton, Andrew; Lienert, Sebastian; Liu, Zhu; Lombardozzi, Danica; Marland, Gregg; Metzl, Nicolas; Munro, David R.; Nabel, Julia E. M. S.; Nakaoka, Shin-ichiro; Niwa, Yosuke; O'Brien, Kevin; Ono, Tsuneo; Palmer, Paul I.; Pierrot, Denis; Poulter, Benjamin; Resplandy, Laure; Robertson, Eddy; Rodenbeck, Christian; Schwinger, Jorg; Seferian, Roland; Skjelvan, Ingunn; Smith, Adam J. P.; Sutton, Adrienne J.; Tanhua, Toste; Tans, Pieter P.; Tian, Hanqin; Tilbrook, Bronte; Van Der Werf, Guido; Vuichard, Nicolas; Walker, Anthony P.; Wanninkhof, Rik; Watson, Andrew J.; Willis, David; Wiltshire, Andrew J.; Yuan, Wenping; Yue, Xu; Zaehle, Sonke. |
| Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate - 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 and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions ( EFOS) are based on energy statistics and cement production data, while emissions from land-use change ( ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly... |
| Tipo: Text |
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| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00677/78860/81159.pdf |
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| Gregor, Luke; Kok, Schalk; Monteiro, Pedro M. S.. |
| Resolving and understanding the drivers of variability of CO2 in the Southern Ocean and its potential climate feedback is one of the major scientific challenges of the ocean-climate community. Here we use a regional approach on empirical estimates of pCO(2) to understand the role that seasonal variability has in long-term CO2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and location-restricted ship measurements of pCO(2). In this study we use an ensemble of three machine-learning products: support vector regression (SVR) and random forest regression (RFR) from Gregor et al. (2017), and the self-organising-map feed-forward neural network (SOM-FFN) method from Land-schutzer et al.... |
| Tipo: Text |
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| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00673/78492/80822.pdf |
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