Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., and Samek, W.: Layer-wise relevance propagation for neural networks with local renormalization layers, in: Proceedings, Part II 25, Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, 6–9 September 2016, Barcelona, Spain, Springer, 63–71,
https://doi.org/10.1007/978-3-319-44781-0_8, 2016.
a
Chien, C.-T., Durgadoo, J. V., Ehlert, D., Frenger, I., Keller, D. P., Koeve, W., Kriest, I., Landolfi, A., Patara, L., Wahl, S., and Oschlies, A.: FOCI-MOPS v1 – integration of marine biogeochemistry within the Flexible Ocean and Climate Infrastructure version 1 (FOCI 1) Earth system model, Geosci. Model Dev., 15, 5987–6024,
https://doi.org/10.5194/gmd-15-5987-2022, 2022.
a
Couespel, D., Tjiputra, J., Johannsen, K., Vaittinada Ayar, P., and Jensen, B.: Machine learning reveals regime shifts in future ocean carbon dioxide fluxes inter-annual variability, Commun. Earth Environ., 5, 99
https://doi.org/10.1038/s43247-024-01257-2, 2024.
a,
b,
c,
d,
e,
f,
g
DeVries, T., Yamamoto, K., Wanninkhof, R., Gruber, N., Hauck, J., Müller, J. D., Bopp, L., Carroll, D., Carter, B., Chau, T. T., and Doney, S. C.: Magnitude, trends, and variability of the global ocean carbon sink from 1985 to 2018, Global Biogeochem. Cy., 37, e2023GB007780,
https://doi.org/10.1029/2023GB007780, 2023.
a,
b,
c,
d,
e,
f
Fay, A. R. and McKinley, G. A.: Global open-ocean biomes: mean and temporal variability, Earth Syst. Sci. Data, 6, 273–284,
https://doi.org/10.5194/essd-6-273-2014, 2014.
a,
b,
c,
d,
e
Feely, R., Takahashi, T., Wanninkhof, R., McPhaden, M., Cosca, C., Sutherland, S., and Carr, M.-E.: Decadal variability of the air-sea CO
2 fluxes in the equatorial Pacific Ocean, J. Geophys. Res.-Oceans, 111, C08S90,
https://doi.org/10.1029/2005JC003129, 2006.
a
Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Bakker, D. C. E., Hauck, J., Landschützer, P., Le Quéré, C., Luijkx, I. T., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Barbero, L., Bates, N. R., Becker, M., Bellouin, N., Decharme, B., Bopp, L., Brasika, I. B. M., Cadule, P., Chamberlain, M. A., Chandra, N., Chau, T.-T.-T., Chevallier, F., Chini, L. P., Cronin, M., Dou, X., Enyo, K., Evans, W., Falk, S., Feely, R. A., Feng, L., Ford, D. J., Gasser, T., Ghattas, J., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Heinke, J., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Jacobson, A. R., Jain, A., Jarníková, T., Jersild, A., Jiang, F., Jin, Z., Joos, F., Kato, E., Keeling, R. F., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Körtzinger, A., Lan, X., Lefèvre, N., Li, H., Liu, J., Liu, Z., Ma, L., Marland, G., Mayot, N., McGuire, P. C., McKinley, G. A., Meyer, G., Morgan, E. J., Munro, D. R., Nakaoka, S.-I., Niwa, Y., O'Brien, K. M., Olsen, A., Omar, A. M., Ono, T., Paulsen, M., Pierrot, D., Pocock, K., Poulter, B., Powis, C. M., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Rosan, T. M., Schwinger, J., Séférian, R., Smallman, T. L., Smith, S. M., Sospedra-Alfonso, R., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tans, P. P., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G. R., van Ooijen, E., Wanninkhof, R., Watanabe, M., Wimart-Rousseau, C., Yang, D., Yang, X., Yuan, W., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2023, Earth Syst. Sci. Data, 15, 5301–5369,
https://doi.org/10.5194/essd-15-5301-2023, 2023.
a
Garcia, H. E., Weathers, K. W., Paver, C. R., Smolyar, I., Boyer, T. P., Locarnini, M. M., Zweng, M. M., Mishonov, A. V., Baranova, O. K., Seidov, D., and Reagan, J. R.: World Ocean Atlas 2018, in: Vol. 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Dissolved Oxygen Saturation,
https://www.ncei.noaa.gov/data/oceans/woa/WOA18/DOC/woa18_vol3.pdf (last access: 17 February 2025), 2019. a
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, J. Geophys. Res.-Oceans, 118, 6704–6716, 2013.
a,
b,
c,
d,
e
Goodfellow, I., Bengio, Y., and Courville, A.: Deep learning, MIT Press, ISBN 9780262337373, 2016.
a,
b
Gruber, N., Bakker, D. C., DeVries, T., Gregor, L., Hauck, J., Landschützer, P., McKinley, G. A., and Müller, J. D.: Trends and variability in the ocean carbon sink, Nat. Rev. Earth Environ., 4, 119–134, 2023.
a,
b,
c
Han, J., Kamber, M., and Mining, D.: Concepts and techniques, Morgan Kaufmann, 3rd edn., Elsevier Science, 443–496, ISBN 9780123814807, 2011. a
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., and Saynisch-Wagner, J.: Towards neural Earth system modelling by integrating artificial intelligence in Earth system science, Nature Mach. Intel., 3, 667–674, 2021.
a,
b
Jones, D. and Ito, T.: Gaussian mixture modeling describes the geography of the surface ocean carbon budget, University Corporation for Atmospheric Research (UCAR),
https://nora.nerc.ac.uk/id/eprint/526396 (last access: 17 February 2025), 2019.
a,
b,
c,
d,
e,
f
Key, R. M., Olsen, A., van Heuven, S., Lauvset, S. K., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., and Suzuki, T.: Global Ocean Data Analysis Project, Version 2 (GLODAPv2), ORNL/CDIAC-162, NDP-093. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee,
https://doi.org/10.3334/CDIAC/OTG.NDP093_GLODAPv2, 2015.
a,
b
Krasting, J. P., De Palma, M., Sonnewald, M., Dunne, J. P., and John, J. G.: Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning, Commun. Earth Environ., 3, 91,
https://doi.org/10.1038/s43247-022-00419-4, 2022.
a,
b,
c,
d,
e
Kriest, I. and Oschlies, A.: MOPS-1.0: towards a model for the regulation of the global oceanic nitrogen budget by marine biogeochemical processes, Geosci. Model Dev., 8, 2929–2957,
https://doi.org/10.5194/gmd-8-2929-2015, 2015.
a
Landschützer, P., Gruber, N., Bakker, D. C. E., Schuster, U., Nakaoka, S., Payne, M. R., Sasse, T. P., and Zeng, J.: A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink, Biogeosciences, 10, 7793–7815,
https://doi.org/10.5194/bg-10-7793-2013, 2013.
a,
b,
c
Landschützer, P., Gruber, N., and Bakker, D. C.: Decadal variations and trends of the global ocean carbon sink, Global Biogeochem. Cy., 30, 1396–1417, 2016.
a,
b
Lauderdale, J. M., Dutkiewicz, S., Williams, R. G., and Follows, M. J.: Quantifying the drivers of ocean-atmosphere CO
2 fluxes, Global Biogeochem. Cy., 30, 983–999, 2016. a
Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the
1° × °×1° GLODAP version 2, Earth Syst. Sci. Data, 8, 325–340,
https://doi.org/10.5194/essd-8-325-2016, 2016.
a,
b
Levitus, S., Boyer, T. P., Conkright, M. E., O’Brien, T., Antonov, J., Stephens, C., Stathoplos, L., Johnson, D., and Gelfeld, R.: World ocean database 1998. Volume 1, Introduction,
https://repository.library.noaa.gov/view/noaa/49345 (last access: 17 February 2025), 1998. a
Lin, F., Bai, B., Bai, K., Ren, Y., Zhao, P., and Xu, Z.: Contrastive Multi-view Hyperbolic Hierarchical Clustering, in: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, edited by: Raedt, L. D., International Joint Conferences on Artificial Intelligence Organization, Messe Wien, Vienna, Austria, 23–29 July 2022, 3250–3256, arXiv [preprint],
https://doi.org/10.48550/arXiv.2205.02618, 2022.
a
Longhurst, A.: Seasonal cycles of pelagic production and consumption, Prog. Oceanogr., 36, 77–167, 1995.
a,
b
Lovenduski, N. S., Gruber, N., Doney, S. C., and Lima, I. D.: Enhanced CO
2 outgassing in the Southern Ocean from a positive phase of the Southern Annular Mode, Global Biogeochem. Cy., 21, GB2026,
https://doi.org/10.1029/2006GB002900, 2007.
a
Madec, G.: NEMO ocean engine, Note du Pôle modélisation, Inst. Pierre-Simon Laplace, p. 406,
https://www.nemo-ocean.eu/doc/ (last access: 17 February 2025), 2016. a
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116,
https://doi.org/10.5194/gmd-10-2057-2017, 2017.
a
Mikaloff Fletcher, S., Gruber, N., Jacobson, A. R., Gloor, M., Doney, S., Dutkiewicz, S., Gerber, M., Follows, M., Joos, F., Lindsay, K., Menemenlis, D., Mouchet, A., Müller, S. A., and Sarmiento, J. L.: Inverse estimates of the oceanic sources and sinks of natural CO
2 and the implied oceanic carbon transport, Global Biogeochem. Cy., 21, GB1010,
https://doi.org/10.1029/2006GB002751, 2007.
a,
b
Mohanty, S.: Detection and Tracking of Carbon Biomes via Integrated Machine Learning, GitHub [code],
https://github.com/swemoh/Detection-and-Tracking-of-Carbon-Biomes, last access: 24 February 2025.
Mohanty, S., Kazempour, D., Patara, L., and Kröger, P.: Detection and Tracking of Dynamic Ocean Carbon Uptake Regimes Built Upon Spatial Target-Driver Relationships via Adaptive Hierarchical Clustering, in: 2023 IEEE 19th International Conference on e-Science, Limassol, Cyprus, 9–13 October 2023, 1–10,
https://doi.org/10.1109/e-Science58273.2023.10254820, 2023a.
a,
b,
c,
d,
e,
f,
g
Mohanty, S., Kazempour, D., Patara, L., and Kröger, P.: Interactive Detection and Visualization of Ocean Carbon Regimes, in: Proceedings of the 18th International Symposium on Spatial and Temporal Data, Calgary, Alberta, Canada, 23–25 August 2023, 171–174,
https://doi.org/10.1145/3609956.3609973, 2023b.
a
Mohanty, S., Patara, L., Kazempour, D., and Kroeger, P.: Supplementary data to Mohanty et al. (2024): Detection and Tracking of Carbon Biomes via Integrated Machine Learning Methods, GEOMAR Helmholtz Centre for Ocean Research Kiel [data set],
https://data.geomar.de/downloads/20.500.12085/6a915912-270a-401f-99fc-78ef91598045/ (last access: 17 February 2025), 2024. a
Orr, J. C., Najjar, R. G., Aumont, O., Bopp, L., Bullister, J. L., Danabasoglu, G., Doney, S. C., Dunne, J. P., Dutay, J.-C., Graven, H., Griffies, S. M., John, J. G., Joos, F., Levin, I., Lindsay, K., Matear, R. J., McKinley, G. A., Mouchet, A., Oschlies, A., Romanou, A., Schlitzer, R., Tagliabue, A., Tanhua, T., and Yool, A.: Biogeochemical protocols and diagnostics for the CMIP6 Ocean Model Intercomparison Project (OMIP), Geosci. Model Dev., 10, 2169–2199,
https://doi.org/10.5194/gmd-10-2169-2017, 2017.
a
Patara, L., Visbeck, M., Masina, S., Krahmann, G., and Vichi, M.: Marine biogeochemical responses to the North Atlantic Oscillation in a coupled climate model, J. Geophys. Res.-Oceans, 116, C07023,
https://doi.org/10.1029/2010JC006785, 011.
a
Pérez, F. F., Mercier, H., Vázquez-Rodríguez, M., Lherminier, P., Velo, A., Pardo, P. C., Rosón, G., and Ríos, A. F.: Atlantic Ocean CO
2 uptake reduced by weakening of the meridional overturning circulation, Nat. Geosci., 6, 146–152, 2013. a
Pfeil, B., Olsen, A., Bakker, D. C. E., Hankin, S., Koyuk, H., Kozyr, A., Malczyk, J., Manke, A., Metzl, N., Sabine, C. L., Akl, J., Alin, S. R., Bates, N., Bellerby, R. G. J., Borges, A., Boutin, J., Brown, P. J., Cai, W.-J., Chavez, F. P., Chen, A., Cosca, C., Fassbender, A. J., Feely, R. A., González-Dávila, M., Goyet, C., Hales, B., Hardman-Mountford, N., Heinze, C., Hood, M., Hoppema, M., Hunt, C. W., Hydes, D., Ishii, M., Johannessen, T., Jones, S. D., Key, R. M., Körtzinger, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lenton, A., Lourantou, A., Merlivat, L., Midorikawa, T., Mintrop, L., Miyazaki, C., Murata, A., Nakadate, A., Nakano, Y., Nakaoka, S., Nojiri, Y., Omar, A. M., Padin, X. A., Park, G.-H., Paterson, K., Perez, F. F., Pierrot, D., Poisson, A., Ríos, A. F., Santana-Casiano, J. M., Salisbury, J., Sarma, V. V. S. S., Schlitzer, R., Schneider, B., Schuster, U., Sieger, R., Skjelvan, I., Steinhoff, T., Suzuki, T., Takahashi, T., Tedesco, K., Telszewski, M., Thomas, H., Tilbrook, B., Tjiputra, J., Vandemark, D., Veness, T., Wanninkhof, R., Watson, A. J., Weiss, R., Wong, C. S., and Yoshikawa-Inoue, H.: A uniform, quality controlled Surface Ocean CO
2 Atlas (SOCAT), Earth Syst. Sci. Data, 5, 125–143,
https://doi.org/10.5194/essd-5-125-2013, 2013.
a
Prend, C. J., Hunt, J. M., Mazloff, M. R., Gille, S. T., and Talley, L. D.: Controls on the boundary between thermally and non-thermally driven
pCO
2 regimes in the South Pacific, Geophys. Res. Lett., 49, e2021GL095797,
https://doi.org/10.1029/2021gl095797, 2022.
a,
b,
c
Reygondeau, G., Cheung, W. W. L., Wabnitz, C. C. C., Lam, V. W. Y., Frölicher, T., and Maury, O.: Climate change-induced emergence of novel biogeochemical provinces, Front. Mar. Sci., 7, 657,
https://doi.org/10.3389/fmars.2020.00657, 2020.
a,
b
Ribeiro, M. T., Singh, S., and Guestrin, C.: “Why should i trust you?” Explaining the predictions of any classifier, in: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, California, USA, 13–17 August 2016, 1135–1144,
https://doi.org/10.1145/2939672.2939778, 2016.
a
Sallée, J.-B., Pellichero, V., Akhoudas, C., Pauthenet, E., Vignes, L., Schmidtko, S., Garabato, A. N., Sutherland, P., and Kuusela, M.: Summertime increases in upper-ocean stratification and mixed-layer depth, Nature, 591, 592–598, 2021.
a,
b,
c
Schmittner, A., Oschlies, A., Matthews, H. D., and Galbraith, E. D.: Future changes in climate, ocean circulation, ecosystems, and biogeochemical cycling simulated for a business-as-usual CO
2 emission scenario until year 4000 AD, Global Biogeochem. Cy., 22, GB1013,
https://doi.org/10.1029/2007GB002953, 2008.
a
Sonnewald, M., Dutkiewicz, S., Hill, C., and Forget, G.: Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces, Sci. Adv,, 6, eaay4740,
https://doi.org/10.1126/sciadv.aay4740, 2020.
a,
b,
c
Sutton, A. J., Williams, N. L., and Tilbrook, B.: Constraining Southern Ocean CO
2 flux uncertainty using uncrewed surface vehicle observations, Geophys. Res. Lett., 48, e2020GL091748,
https://doi.org/10.1029/2020GL091748, 2021.
a
Swart, N. C., Fyfe, J. C., Gillett, N., and Marshall, G. J.: Comparing trends in the southern annular mode and surface westerly jet, J. Climate, 28, 8840–8859, 2015.
a,
b
Takahashi, T., Olafsson, J., Goddard, J. G., Chipman, D. W., and Sutherland, S.: Seasonal variation of CO
2 and nutrients in the high-latitude surface oceans: A comparative study, Global Biogeochem. Cy., 7, 843–878, 1993. a
Takahashi, T., Sutherland, S. C., Sweeney, C., Poisson, A., Metzl, N., Tilbrook, B., Bates, N., Wanninkhof, R., Feely, R. A., Sabine, C., and Olafsson, J.: Global sea–air CO
2 flux based on climatological surface ocean
pCO
2, and seasonal biological and temperature effects, Deep-Sea Res. Pt. II, 49, 1601–1622, 2002.
a,
b,
c
Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A., Chipman, D. W., Hales, B., Friederich, G., Chavez, F., Sabine, C., and Watson, A.: Climatological mean and decadal change in surface ocean
pCO
2, and net sea–air CO
2 flux over the global oceans, Deep-Sea Res. Pt. II, 56, 554–577, 2009.
a,
b
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S. G., Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., and Böning, C. W.: JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do), Ocean Model., 130, 79–139, 2018. a
Vichi, M., Allen, J. I., Masina, S., and Hardman-Mountford, N. J.: The emergence of ocean biogeochemical provinces: A quantitative assessment and a diagnostic for model evaluation, Global Biogeochem. Cy., 25, 1046–1058,
https://doi.org/10.1029/2010GB003867, 2011.
a
Wanninkhof, R.: Relationship between wind speed and gas exchange over the ocean revisited, Limnol. Oceanogr.: Meth., 12, 351–362, 2014. a
Williams, R. G. and Follows, M. J.: Ocean dynamics and the carbon cycle: Principles and mechanisms, Cambridge University Press, ISBN 9780511977817, 2011.
a,
b,
c