TAO Zui, MA Sheng, YANG Xiaofeng, WANG Yan. Assessing the uncertainties of phytoplankton absorption-based model estimates of marine net primary productivity[J]. Acta Oceanologica Sinica, 2017, 36(6): 112-121. doi: 10.1007/s13131-017-1047-8
Citation: TAO Zui, MA Sheng, YANG Xiaofeng, WANG Yan. Assessing the uncertainties of phytoplankton absorption-based model estimates of marine net primary productivity[J]. Acta Oceanologica Sinica, 2017, 36(6): 112-121. doi: 10.1007/s13131-017-1047-8

Assessing the uncertainties of phytoplankton absorption-based model estimates of marine net primary productivity

doi: 10.1007/s13131-017-1047-8
  • Received Date: 2016-04-08
  • Rev Recd Date: 2016-07-28
  • Satellite-derived phytoplankton pigment absorption (aph) has been used as a key predictor of phytoplankton photosynthetic efficiency to estimate global ocean net primary production (NPP). In this study, an aph-based NPP model (AbPM) with four input parameters including the photosynthetically available radiation (PAR), diffuse attenuation at 490 nm (Kd(490)), euphotic zone depth (Zeu) and the phytoplankton pigment absorption coefficient (aph) is compared with the chlorophyll-based model and carbon-based model. It is found that the AbPM has significant advantages on the ocean NPP estimation compared with the chlorophyll-based model and carbon-based model. For example, AbPM greatly outperformed the other two models at most monitoring sites and had the best accuracy, including the smallest values of RMSD and bias for the NPP estimate, and the best correlation between the observations and the modeled NPPs. In order to ensure the robustness of the model, the uncertainty in NPP estimates of the AbPM was assessed using a Monte Carlo simulation. At first, the frequency histograms of simple difference (δ), and logarithmic difference (δLOG) between model estimates and in situ data confirm that the two input parameters (Zeu and PAR) approximate the Normal Distribution, and another two input parameters (aph and Kd(490)) approximate the logarithmic Normal Distribution. Second, the uncertainty in NPP estimates in the AbPM was assessed by using the Monte Carlo simulation. Here both the PB (percentage bias), defined as the ratio of △NPP to the retrieved NPP, and the CV (coefficient of variation), defined as the ratio of the standard deviation to the mean are used to indicate the uncertainty in the NPP brought by input parameter to AbPM model. The uncertainty related to magnitude is denoted by PB and the uncertainty related to scatter range is denoted by CV. Our investigations demonstrate that PB of NPP uncertainty brought by all parameters with an annual mean of 5.5% covered a range of -5%-15% for the global ocean. The PB uncertainty of AbPM model was mainly caused by aph; the PB of NPP uncertainty brought by aph had an annual mean of 4.1% for the global ocean. The CV brought by all the parameters with an annual mean of 105% covered a range of 98%-134% for global ocean. For the coastal zone of Antarctica with higher productivity, the PB and CV of NPP uncertainty brought by all parameters had annual means of 7.1% and 121%, respectively, which are significantly larger than those obtained in the global ocean. This study suggests that the NPPs estimated by AbPM model are more accurate than others, but the magnitude and scatter range of NPP errors brought by input parameter to AbPM model could not be neglected, especially in the coastal area with high productivity. So the improving accuracy of satellite retrieval of input parameters should be necessary. The investigation also confirmed that the SST related correction is effective for improving the model accuracy in low temperature condition.
  • loading
  • Antoine D, Morel A. 1996. Oceanic primary production:1. Adaptation of a spectral light-photosynthesis model in view of application to satellite chlorophyll observations. Global Biogeochemical Cycles, 10(1):43-55
    Austin R W, Petzold T J. 1986. Spectral dependence of the diffuse attenuation coefficient of light in ocean waters. Optical Engineering, 25(3):253471
    Behrenfeld M J, Falkowski P G. 1997. A consumer's guide to phytoplankton primary productivity models. Limnology and Oceanography, 42(7):1479-1491
    Behrenfeld M J, Randerson J T, McClain C R, et al. 2001. Biospheric primary production during an ENSO transition. Science, 291(5513):2594-2597
    Bianchi A A, Bianucci L, Piola A R, et al. 2005. Vertical stratification and air-sea CO2 fluxes in the Patagonian shelf. Journal of Geophysical Research, 110(C7), doi: 10.1029/2004JC002488
    Carr M E, Friedrichs M A M, Schmeltz M, et al. 2006. A comparison of global estimates of marine primary production from ocean color. Deep Sea Research Part Ⅱ—Topical Studies in Oceanography, 53(5-7):741-770
    Eppley R W. 1972. Temperature and phytoplankton growth in the sea. Fishery Bulletin, 70:1063-1086
    Falkowski P G, Barber R T, Smetacek V. 1998. Biogeochemical controls and feedbacks on ocean primary production. Science, 281(5374):200-206
    Friedrichs M A M, Carr M E, Barber R T, et al. 2009. Assessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean. Journal of Marine Systems, 76(1-2):113-133
    Garcia H E, Locarnini R A, Boyer T P, et al. 2010. World ocean atlas 2009, volume 4:nutrients (phosphate, nitrate, silicate). In:Levitus S, ed. NOAA Atlas NESDIS 71. Washington, DC:US Government Printing Office, 398
    Gordon H R, Clark D K. 1980. Remote sensing optical properties of a stratified ocean:an improved interpretation. Applied Optics, 19(20):3428-3430
    Hirawake T, Takao S, Horimoto N, et al. 2011. A phytoplankton absorption-based primary productivity model for remote sensing in the Southern Ocean. Polar Biology, 34(2):291-302
    Hiscock M R, Lance V P, Apprill A M, et al. 2008. Photosynthetic maximum quantum yield increases are an essential component of the Southern Ocean phytoplankton response to iron. Proceedings of the National Academy of Sciences of the United States of America, 105(12):4775-4780
    Kiefer D A, Cullen J J. 1991. Phytoplankton growth and light absorption as regulated by light, temperature, and nutrients. Polar Research, 10(1):163-172
    Kishi M J, Kashiwai M, Ware D M, et al. 2007. NEMURO—a lower trophic level model for the North Pacific marine ecosystem. Ecological Modelling, 202(1-2):12-25
    Lee Z, Lance V P, Shang Shaoling, et al. 2011. An assessment of optical properties and primary production derived from remote sensing in the Southern Ocean (SO GasEx). Journal of Geophysical Research, 116(C4):doi: 10.1029/2010JC006747
    Lee Z P, Carder K L. 2004. Absorption spectrum of phytoplankton pigments derived from hyperspectral remote-sensing reflectance. Remote Sensing of Environment, 89(3):361-368
    Lee Z P, Carder K L, Marra J, et al. 1996. Estimating primary production at depth from remote sensing. Applied Optics, 35(3):463-474
    Longhurst A, Sathyendranath S, Platt T, et al. 1995. An estimate of global primary production in the ocean from satellite radiometer data. Journal of Plankton Research, 17(6):1245-1271
    Ma Sheng, Tao Zui, Yang Xiaofeng, et al. 2014. Estimation of marine primary productivity from satellite-derived phytoplankton absorption data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(7):3084-3092
    Marañón E, Cermeño P, Latasa M, et al. 2012. Temperature, resources, and phytoplankton size structure in the ocean. Limnology and Oceanography, 57(5):1266-1278
    Marra J, Ho C, Trees C C. 2003. An alternative algorithm for the calculation of primary productivity from remote sensing data. LDEO Technical Report # LDEO-2003-1. Grant:National Aeronautics and Space Administration
    Marra J, Trees C C, O'Reilly J E. 2007. Phytoplankton pigment absorption:a strong predictor of primary productivity in the surface ocean. Deep Sea Research Part I—Oceanographic Research Papers, 54(2):155-163
    McClain C R, Cleave M L, Feldman G C, et al. 1998. Science quality Sea WiFS data for global biosphere research. Sea Technology, 39(9):10-15
    Medina-Gómez I, Herrera-Silveira J A. 2006. Primary production dynamics in a pristine groundwater influenced coastal lagoon of the Yucatan Peninsula. Continental Shelf Research, 26(8):971-986
    Milutinović S, Behrenfeld M J, Johannessen J A, et al. 2009. Sensitivity of remote sensing-derived phytoplankton productivity to mixed layer depth:lessons from the carbon-based productivity model. Global Biogeochemical Cycles, 23(4):doi:10.1029/2008 GB003431
    Milutinović S, Bertino L. 2011. Assessment and propagation of uncertainties in input terms through an ocean-color-based model of primary productivity. Remote Sensing of Environment, 115(8):1906-1917
    Morel A, Maritorena S. 2001. Bio-optical properties of oceanic waters:a reappraisal. Journal of Geophysical Research, 106(C4):7163-7180
    Muller-Karger F, Varela R, Thunell R, et al. 2001. Annual cycle of primary production in the Cariaco Basin:response to upwelling and implications for vertical export. Journal of Geophysical Research, 106(C3):4527-4542
    Ondrusek M E, Bidigare R R, Waters K, et al. 2001. A predictive model for estimating rates of primary productio牮?物敮洠潴瑨敥?獳敵湢獴楲湯杰?慣灡灬氠楎捯慲瑴楨漠湐獡??佦捩散愠湏潣汥潡杮椮愠???????????????ch Part Ⅱ—Topical Studies in Oceanography, 48(8-9):1837-1863
    Platt T, Sathyendranath S. 1993. Estimators of primary production for interpretation of remotely sensed data on ocean color. Journal of Geophysical Research, 98(C8):14561-14576
    Reay D S, Priddle J, Nedwell D B, et al. 2001. Regulation by low temperature of phytoplankton growth and nutrient uptake in the Southern Ocean. Marine Ecology Progress Series, 219:51-64
    Ricchiazzi P, Yang Shiren, Gautier C, et al. 1998. SBDART:a research and teaching software tool for plane-parallel radiative transfer in the Earth's atmosphere. Bulletin of the American Meteorological Society, 79(10):2101-2114
    Saba V S, Friedrichs M A M, Antoine D, et al. 2011. An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe. Biogeosciences, 8(2):489-503
    Saba V S, Friedrichs M A M, Carr M E, et al. 2010. Challenges of modeling depth-integrated marine primary productivity over multiple decades:a case study at BATS and HOT. Global Biogeochemical Cycles, 24(3):doi: 10.1029/2009GB003655
    Siegel D A, Westberry T K, O'Brien M C, et al. 2001. Bio-optical modeling of primary production on regional scales:the Bermuda BioOptics project. Deep Sea Research Part Ⅱ—Topical Studies in Oceanography, 48(8-9):1865-1896
    Sunda W G, Huntsman S A. 1997. Interrelated influence of iron, light and cell size on marine phytoplankton growth. Nature, 390(6658):389-392
    Uitz J, Huot Y, Bruyant F, et al. 2008. Relating phytoplankton photophysiological properties to community structure on large scales. Limnology and Oceanography, 53(2):614-630
    Westberry T, Behrenfeld M J, Siegel D A, et al. 2008. Carbon-based primary productivity modeling with vertically resolved photoacclimation. Global Biogeochemical Cycles, 22(2):doi: 10.1029/2007GB003078
    Woźniak B, Ficek D, Ostrowska M, et al. 2007. Quantum yield of photosynthesis in the Baltic:a new mathematical expression fo
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (594) PDF downloads(360) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint