Articles | Volume 22, issue 19
https://doi.org/10.5194/bg-22-5463-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-5463-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Refining marine net primary production estimates: advanced uncertainty quantification through probability prediction models
Jie Niu
College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guiyang 550025, China
Mengyu Xie
Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, China
Yanqun Lu
CORRESPONDING AUTHOR
Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, China
Liwei Sun
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
Na Liu
College of Life Science and Technology, Jinan University, Guangzhou 510632, China
Department of Sustainable Earth Systems Sciences, University of Texas, Dallas, Richardson, TX 75080, USA
Dongdong Liu
College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guiyang 550025, China
Chuanhao Wu
Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210024, China
Pan Wu
CORRESPONDING AUTHOR
College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guiyang 550025, China
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Cited articles
Al-Gabalawy, M., Hosny, N. S., and Adly, A. R.: Probabilistic forecasting for energy time series considering uncertainties based on deep learning algorithms, Electr. Pow. Syst. Res., 196, 107216, https://doi.org/10.1016/j.epsr.2021.107216, 2021.
Behrenfeld, M. J. and Falkowski, P. G.: Photosynthetic rates derived from satellite-based chlorophyll concentration, Limnol. Oceanogr., 42, 1–20, https://doi.org/10.4319/lo.1997.42.1.0001, 1997.
Behrenfeld, M. J., Boss, E., Siegel, D. A., and Shea, D. M.: Carbon-based ocean productivity and phytoplankton physiology from space, Global Biogeochem. Cy., 19, GB1006, https://doi.org/10.1029/2004GB002299, 2005.
BIPM, I., IFCC, I., ISO, I., and IUPAP, O.: Evaluation of measurement data–an introduction to the “Guide to the expression of uncertainty in measurement” and related documents, JCGM, 104, 1–104, 2008.
Braarud, T.: Salinity as an ecological factor in marine phytoplankton, Physiol. Plantarum, 4, 28–34, https://doi.org/10.1111/j.1399-3054.1951.tb07512.x, 1951.
Cael, B. B.: Variability-based constraint on ocean primary production models, Limnol. Oceanogr. Lett., 6, 262–269, https://doi.org/10.1002/lol2.10196, 2021.
Campbell, J., Antoine, D., Armstrong, R., Arrigo, K., Balch, W., Barber, R., Behrenfeld, M., Bidigare, R., Bishop, J., Carr, M., Esaias, W., Falkowski, P., Hoepffner, N., Iverson, R., Kiefer, D., Lohrenz, S., Marra, J., Morel, A., Ryan, J., Vedernikov, V., Waters, K., Yentsch, C., and Yoder, J.: Comparison of algorithms for estimating ocean primary production from surface chlorophyll, temperature, and irradiance, Global Biogeochem. Cy., 16, 9-1–9-15, https://doi.org/10.1029/2001GB001444, 2002.
Dave, A. C. and Lozier, M. S.: Examining the global record of interannual variability in stratification and marine productivity in the low-latitude and mid-latitude ocean, J. Geophys. Res.-Ocean., 118, 3114–3127, https://doi.org/10.1002/jgrc.20224, 2013.
Ding, Q. X. and Chen, W. Z.: Spatial and Temporal Variations in Net Primary Productivity in the China Seas Based on VGPM, Mar. Dev. Manage., 8, 31–35, 2016.
Dürr, O., Sick, B., and Murina, E.: Probabilistic deep learning: With python, keras and tensorflow probability, Manning Publications, 296 pp., https://ieeexplore.ieee.org/servlet/opac?bknumber=1028040 (last access: 28 June 2025), 2020.
Falkowski, P. G., Barber, R. T., and Smetacek, V.: Biogeochemical Controls and Feedbacks on Ocean Primary Production, Chem. Biol. Ocean., 281, 200–206, https://doi.org/10.1126/science.281.5374.200, 1998.
Gneiting, T. and Katzfuss, M.: Probabilistic forecasting, Ann. Rev. Stat. Appl., 1, 125–151, https://doi.org/10.1146/annurev-statistics-062713-085831, 2014.
Hersbach, H.: Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather Forecast., 15, 559–570, https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000.
He J. and Huang Z.: The distribution of corals in weizhou island, guangxi, Ocean Dev. Manage., 1, 57–62, 2019.
Juban, J., Siebert, N., and Kariniotakis, G. N.: Probabilistic short-term wind power forecasting for the optimal management of wind generation, in: 2007 IEEE Lausanne Power Tech, Lausanne, Switzerland, 1–5 July 2007, 683–688, https://doi.org/10.1109/PCT.2007.4538398, 2007.
Kaplan, D.: On the Quantification of Model Uncertainty: A Bayesian Perspective, Psychometrika, 86, 215–238, https://doi.org/10.1007/s11336-021-09754-5, 2021.
Lee, Z., Marra, J., Perry, M. J., and Kahru, M.: Estimating Oceanic Primary Productivity from Ocean Color Remote Sensing: A Strategic Assessment, J. Mar. Syst., 149, 50–59, https://doi.org/10.1016/j.jmarsys.2014.11.015, 2015.
Li, C. and Wang, F.: Holocene volcanic effusion in Weizhou Island and its geological significance, J. Mineral. Petrol., 24, 28–34, https://doi.org/10.3969/j.issn.1001-6872.2004.04.005, 2004.
Li, W., Tiwari, S. P., El-Askary, H. M., Qurban, Amiridis, V., and ManiKandan, K. P.: Synergistic use of remote sensing and modeling for estimating net primary productivity in the red Sea with VGPM, eppley-VGPM, and CbPM models intercomparison, IEEE Trans. Geosci. Remote Sens., 58, 8717–8734, https://doi.org/10.1109/TGRS.2020.2990373, 2020.
Matheson, J. E. and Winkler, R. L.: Scoring rules for continuous probability distributions, Manag. Sci., 22, 1087–1096, https://doi.org/10.1287/mnsc.22.10.1087, 1976.
Milutinović, S. and Bertino, L.: Assessment and propagation of uncertainties in input terms through an ocean-color-based model of primary productivity, Remote Sens. Environ., 115, 1906–1917, https://doi.org/10.1016/j.rse.2011.03.013, 2011.
Pan, X., Wong, G. T., Shiah, F. K., and Ho, T. Y.: Enhancement of biological productivity by internal waves: observations in the summertime in the northern South China Sea, J. Oceanogr., 68, 427–437, https://doi.org/10.1007/s10872-012-0107-y, 2012.
Perfors, A., Tenenbaum, J. B., Griffiths, T. L., and Xu, F.: A tutorial introduction to Bayesian models of cognitive development, Cognition, 120, 302–321, https://doi.org/10.1016/j.cognition.2010.11.015, 2011.
Pic, R., Dombry, C., Naveau, P., and Taillardat, M.: Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk, Int. J. Forecast., 39, 1564–1572, https://doi.org/10.1016/j.ijforecast.2022.11.001, 2023.
Platt, T. and Sathyendranath, S.: Oceanic primary production: estimation by remote sensing at local and regional scales, Science, 241, 1613–1620, https://doi.org/10.1126/science.241.4873.1613, 1988.
Platt, T., Caverhill, C., and Sathyendranath, S.: Basin-scale estimates of oceanic primary production by remote sensing: The North Atlantic, J. Geophys. Res.-Ocean., 96, 15147–15159, https://doi.org/10.1029/91JC01118, 1991.
Ryther, J. H.: Photosynthesis in the Ocean as a Function of Light Intensity 1, Limnol. Oceanogr., 1, 61–70, https://doi.org/10.4319/lo.1956.1.1.0061, 1956.
Ryther, J. H. and Yentsch, C. S.: The estimation of phytoplankton production in the ocean from chlorophyll and light data 1, Limnol. Oceanogr., 2, 281–286, https://doi.org/10.1002/lno.1957.2.3.0281, 1957.
Saba, V. S., Friedrichs, M. A., Antoine, D., Armstrong, R. A., Asanuma, I., Behrenfeld, M. J., Ciotti, A.M., Dowell, M., Hoepffner, N., Hyde, K.J. W., Ishizaka, J., Kameda, T., Marra, J., Mélin, F., Morel, A., O'Reilly, J., Scardi, M., Smith Jr, W. O., Smyth, T. J., Tang, S., Uitz, J., Waters, K., and Westberry, T. K.: An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe, Biogeosciences, 8, 489–503, https://doi.org/10.5194/bg-8-489-201, 2011.
Sathyendranath, S., Longhurst, A., Caverhill, C. M., and Platt, T.: Regionally and seasonally differentiated primary production in the North Atlantic, Deep-Sea Res. Pt. I, 42, 1773–1802, https://doi.org/10.1016/0967-0637(95)00059-F, 1995.
Sathyendranath, S., Platt, T., Kovač, Ž., Dingle, J., Jackson, T., Brewin, R. J. W., Franks, P., Marañón, E., Kulk, G., and Bouman, H.A.: Reconciling models of primary production and photoacclimation, Appl. Optics, 59, C100–C114, https://doi.org/10.1364/AO.386252, 2020.
Schepen, A., Zhao, T., Wang, Q. J., and Robertson, D. E.: A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments, Hydrol. Earth Syst. Sci., 22, 1615–1628, https://doi.org/10.5194/hess-22-1615-2018, 2018.
Schwanenberg, D., Fan, F. M., Naumann, S., Kuwajima, J. I., Montero, R. A., and Assis dos Reis, A.: Short-term reservoir optimization for flood mitigation under meteorological and hydrological forecast uncertainty, Water Resour. Manag., 29, 1635–1651, https://doi.org/10.1007/s11269-014-0899-1, 2015.
Silsbe, G. M., Behrenfeld, M. J., Halsey, K. H., Milligan, A. J., and Westberry, T. K.: The CAFE model: A net production model for global ocean phytoplankton, Global Biogeochem. Cy., 30, 1756–1777, https://doi.org/10.1002/2016GB005521, 2016.
Tan, S. C. and Shi, G. Y.: Satellite Remote Sensing of Marine Primary Productivity, Adv. Earth Sci., 20, 863–870, https://doi.org/10.11867/j.issn.1001-8166.2005.08.0863, 2005.
Westberry, T., Behrenfeld, M. J., Siegel, D. A., and Boss, E.: Carbon-based primary productivity modeling with vertically resolved photoacclimation, Global Biogeochem. Cy., 22, GB2024, https://doi.org/10.1029/2007GB003078, 2008.
Westberry, T. K., Silsbe, G. M., and Behrenfeld, M. J.: Gross and net primary production in the global ocean: An ocean color remote sensing perspective, Earth-Sci. Rev., 237, 104322, https://doi.org/10.1016/j.earscirev.2023.104322, 2023.
Xie, M.: wzd_code, Zenodo [code], https://doi.org/10.5281/zenodo.17169615, 2025a. Xie, M.: wzd_data1, Zenodo [data set], https://doi.org/10.5281/zenodo.17169703, 2025b.
Yang, B.: Seasonal relationship between net primary and net community production in the subtropical gyres: Insights from satellite and Argo profiling float measurements, Geophys. Res. Lett., 48, e2021GL093837, https://doi.org/10.1029/2021GL093837, 2021.
Yang, B., Fox, J., Behrenfeld, M. J., Boss, E. S., Haëntjens, N., Halsey, K. H., Emerson, S. R., and Doney, S. C.: In situ estimates of net primary production in the western North Atlantic with Argo profiling floats, J. Geophys. Res.-Biogeo., 126, e2020JG006116, https://doi.org/10.1029/2020JG006116, 2021.
Yu, W., Wang, W., Yu, K., Wang, Y., Huang, X., Huang, R., Liao, Z., Xu, S., and Chen, X.: Rapid decline of a relatively high latitude coral assemblage at weizhou island, northern south China Sea, Biodivers. Conserv., 28, 3925–3949, https://doi.org/10.1007/s10531-019-01858-w, 2019.
Zamo, M. and Naveau, P.: Estimation of the continuous ranked probability score with limited information and applications to ensemble weather forecasts, Math. Geosci., 50, 209–234, https://doi.org/10.1007/s11004-017-9709-7, 2018.
Zhao, T., Wang, Q. J., Bennett, J. C., Robertson, D. E., Shao, Q., and Zhao, J.: Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model, J. Hydrol., 528, 329–340, https://doi.org/10.1016/j.jhydrol.2015.06.043, 2015.
Zou, Q. and Wen, J: Battery state-of-health estimation incorporating model uncertainty based on Bayesian model averaging, Energy, 308, 132884, https://doi.org/10.1016/j.energy.2024.132884, 2024.
Short summary
This study employs two probabilistic methods – the Bayesian model and a deep-learning-based neural network – to estimate net primary production (NPP) and quantify its uncertainties. Results indicate that both models effectively capture NPP dynamics, with the neural network model outperforming the Bayesian approach in predictive accuracy. Furthermore, these models successfully predict interannual trends in NPP variation across the study area.
This study employs two probabilistic methods – the Bayesian model and a deep-learning-based...
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