Abstract
Oceans play a major role in the global carbon budget, absorbing approximately 27% of anthropogenic carbon dioxide (CO2). As the degree to which an ocean can serve as a carbon sink is determined by the partial pressure of CO2 in the surface water, it is critical to obtain an accurate estimate of the spatial distributions of CO2 and its temporal variation on a global scale. However, this is extremely challenging due to insufficient measurements, large seasonal variability, and short spatial de-correlation scales. This paper presents an open source software package that implements a feed-forward neural network and a back-propagation training algorithm to solve a problem with one output variable and a large number of training patterns. We discuss the employment of the neural network for global ocean CO2 mapping.
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Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quéré, C., Myneni, R.B., Piao, S., Thornton, P.: Carbon and Other Biogeochemical Cycles. In: Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (eds.) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge (2013)
Quéré, C., Le, G.P., Peters, R.J., Andres, R.M., Andrew, T.A., Boden, P., Ciais, P., Friedlingstein, R.A., Houghton, G., Marland, R., Moriarty, S., Sitch, P., Tans, A., Arneth, A., Arvanitis, D.C.E., Bakker, L., Bopp, J.G., Canadell, L.P., Chini, S.C., Doney, A., Harper, I., Harris, J.I., House, A.K., Jain, S.D., Jones, E., Kato, R.F., Keeling, K., Klein Goldewijk, A., Körtzinger, C., Koven, N., Lefèvre, F., Maignan, A., Omar, T., Ono, G.-H., Park, B., Pfeil, B., Poulter, M.R., Raupach, P., Regnier, C., Rödenbeck, S., Saito, J., Schwinger, J., Segschneider, B.D., Stocker, T., Takahashi, B., Tilbrook, S., van Heuven, N., Viovy, R., Wanninkhof, A.: Global Carbon Budget 2013. Earth System Science Data 6, 235–263, doi:10.5194/essd-6-235-2014
Wanninkhof, R.: Relationship between Wind-Speed and Gas-Exchange over the Ocean. J. Geophys. Res.-Oceans 97, 7373–7382 (1992)
Bakker, D.C.E., Pfeil, B., Smith, K., Hankin, S., Olsen, A., Alin, S.R., Cosca, C., Harasawa, S., Kozyr, A., Nojiri, Y., O’Brien, K.M., Schuster, U., Telszewski, M., Tilbrook, B., Wada, C., Akl, J., Barbero, L., Bates, N.R., Boutin, J., Bozec, Y., Cai, W.-J., Castle, R.D., Chavez, F.P., Chen, L., Chierici, M., Currie, K., de Baar, H.J.W., Evans, W., Feely, R.A., Fransson, A., Gao, Z., Hales, B., Hardman-Mountford, N.J., Hoppema, M., Huang, W.-J., Hunt, C.W., Huss, B., Ichikawa, T., Johannessen, T., Jones, E.M., Jones, S.D., Jutterström, S., Kitidis, V., Körtzinger, A., Landschützer, P., Lauvset, S.K., Lefèvre, N., Manke, A.B., Mathis, J.T., Merlivat, L., Metzl, N., Murata, A., Newberger, T., Omar, A.M., Ono, T., Park, G.-H., Paterson, K., Pierrot, D., Ríos, A.F., Sabine, C.L., Saito, S., Salisbury, J., Sarma, V.V.S.S., Schlitzer, R., Sieger, R., Skjelvan, I., Steinhoff, T., Sullivan, K.F., Sun, H., Sutton, A.J., Suzuki, T., Sweeney, C., Takahashi, T., Tjiputra, J., Tsurushima, N., van Heuven, S.M.A.C., Vandemark, D., Vlahos, P., Wallace, D.W.R., Wanninkhof, R., Watson, A.J.: An update to the Surface Ocean CO2 Atlas (SOCAT version 2). Earth System Science Data Discussions 6, 465–512 (2013), doi:10.5194/essdd-6-465-2013.
Zeng, J.Y., Nojiri, Y., Murphy, P.P., Wong, C.S., Fujinuma, Y.: A comparison of Delta pCO2 distributions in the northern North Pacific using results from a commercial vessel in 1995-1999. Deep-Sea Res. Part II-Top. Stud. Oceanogr 49, 5303–5315 (2002)
Lefevre, N., Watson, A.J., Watson, A.R.: A comparison of multiple regression and neural network techniques for mapping in situ pCO2 data. Tellus Ser. B-Chem. Phys. Meteorol. 57, 375–384 (2005)
Chierici, M., Fransson, A., Nojiri, Y.: Biogeochemical processes as drivers of surface fCO(2) in contrasting provinces in the subarctic North Pacific Ocean. Glob. Biogeochem. Cycle 20 (2006)
Sarma, V.V.S.S., Saino, T., Sasaoka, K., Nojiri, Y., Ono, T., Ishii, M., Inoue, H.Y., Matsumoto, K.: Basin-scale pCO2 distribution using satellite sea surface temperature, Chla, and climatological salinity in the North Pacific in spring and summer. Glob. Biogeochem. Cycle 20 (2006)
Jamet, C., Moulin, C., Lefevre, N.: Estimation of the oceanic pCO2 in the North Atlantic from VOS lines in-situ measurements: parameters needed to generate seasonally mean maps. Ann. Geophys. 25, 2247–2257 (2007)
Friedrich, T., Oschlies, A.: Neural network-based estimates of North Atlantic surface pCO(2) from satellite data: A methodological study. J. Geophys. Res.-Oceans 114 (2009)
Telszewski, M., et al.: Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network. Biogeosciences 6, 1405–1421 (2009)
Takamura, T.R., Inoue, H.Y., Midorikawa, T., Ishii, M., Nojiri, Y.: Seasonal and Inter-Annual Variations in pCO2sea and Air-Sea CO2 Fluxes in Mid-Latitudes of the Western and Eastern North Pacific during 1999-2006: Recent Results Utilizing Voluntary Observation Ships. Journal of the Meteorological Society of Japan 88, 883–898 (2010)
Landschützer, P., Gruber, N., Bakker, D.C.E., Schuster, U., Nakaoka, S., Payne, M.R., Sasse, T., Zeng, J.: A Neural Network-based Estimate of the Seasonal to Inter-annual Variability of the Atlantic Ocean Carbon Sink. Biogeosciences Discuss 10, 8799–8849 (2013)
Nakaoka, S., Telszewski, M., Nojiri, Y., Yasunaka, S., Miyazaki, C., Mukai, H., Usui, N.: Estimating temporal and spatial variation of ocean surface pCO2 in the North Pacific using a Self Organizing Map neural network technique. Biogeosciences 10, 6093–6106 (2013)
Li, Z., Adamec, D., Takahashi, T., Sutherland, S.C.: Global aurocorrelation scales of the partial pressure of oceanic CO2. J Geophys. Res. 110, C08002 (2005), doi:10.1029/2004JC002723.
Wanninkhof, R., Park, G.H., Takahashi, T., Sweeney, C., Feely, R., Nojiri, Y., Gruber, N., Doney, S.C., McKinley, G.A., Lenton, A., Le Quéré, C., Heinze, C., Schwinger, J., Graven, H., Khatiwala, S.: Global ocean carbon uptake: magnitude, variability and trends. Biogeosciences 10, 1983–2000 (2013)
Takahashi, T., Sutherland, S.C., Wanninkhof, R., Sweeney, C., Feely, R.A., Chipman, D.W., Hales, B., Friederich, G., Chavez, F., Sabine, C., Watson, A., Bakker, D.C.E., Schuster, U., Metzl, N., Inoue, H.Y., Ishii, M., Midorikawa, T., Nojiri, Y., Körtzinger, A., Steinhoff, T., Hoppema, M., Olafsson, J., Arnarson, T.S., Tilbrook, B., Johannessen, T., Olsen, A., Bellerby, R., Wong, C.S., Delille, B., Bates, N.R., de Baar, H.J.W.: Climatological mean and decadal change in surface ocean pCO2, and net sea-air CO2 flux over the global oceans (vol 56, pg 554, 2009). Deep-Sea Res. Part I-Oceanogr. Res. Pap. 56, 2075–2076 (2009)
Zeng, J., Nojiri, Y., Landschützer, P., Telszewski, M., Nakaoka, S.: A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network. Journalof Atmospheric and Oceanic Technology 31, 1838–1849 (2014)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back-Propagating Errors. Nature 323, 533–536 (1986)
LeCun, Y., Bottou, L., Orr, G.B., Muller, K.R.: Efficient backprop. Neural Networks: Tricks of the Trade 1524, 9–50 (1998)
Wilamowski, B.M., Yu, H.: Improved Computation for Levenberg-Marquardt Training. IEEE Transactions on Neural Networks 21, 930–937 (2010)
Blum, E.K., Li, L.K.: Approximation-Theory and Feedforward Networks. Neural Networks 4, 511–515 (1991)
Hornik, K.: Approximation Capabilities of Multilayer Feedforward Networks. Neural Networks 4, 251–257 (1991)
Svozil, D., Kvasnicka, V., Pospichal, J.: Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems 39, 43–62 (1997)
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Zeng, J., Nakajima, H., Nojiri, Y., Nakaoka, Si. (2015). Reconstructing the Carbon Dioxide Absorption Patterns of World Oceans Using a Feed-Forward Neural Network: Software Implementation and Employment Techniques. In: Denzer, R., Argent, R.M., Schimak, G., Hřebíček, J. (eds) Environmental Software Systems. Infrastructures, Services and Applications. ISESS 2015. IFIP Advances in Information and Communication Technology, vol 448. Springer, Cham. https://doi.org/10.1007/978-3-319-15994-2_42
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DOI: https://doi.org/10.1007/978-3-319-15994-2_42
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