Assessing the uncertainties of phytoplankton absorption-based model estimates of marine net primary productivity
-
摘要: 基于卫星数据反演获取的浮游植物吸收系数(aph)被认为是反映浮游植物光合作用效率的关键指标,常用于全球海洋初级生产力的定量反演。本文选择以光合有效辐射、490nm水体漫衰减系数、真光层厚度和浮游植物吸收系数这四种遥感反演产品为输入参数的光吸收模型(AbPM)为研究对象,与叶绿素基模型及碳基模型进行了对比分析。相对其他两种模型,AbPM模型有着其自身的优势。对比其他两个模型,AbPM模型反演的初级生产力具有更高的精度,包括在大多数实测站点取得了最小的RMSD和bias值,与实测数据相关性也更好。为了验证模型的鲁棒性,蒙特卡洛方法被用于模型的不确定性分析。首先,利用四种参数对应的现场实测数据,计算各参数误差,根据其误差分布选择最合适的误差表达方式。结果显示,真光层深度(Zeu)与光合有效辐射(PAR)数据的遥感反演误差的最优表达方式为线性绝对误差,而浮游植物吸收系数(aph)和水体490nm漫衰减系数(Kd(490))最有误差表达方式为对数误差。接着,利用蒙特卡洛方法模拟计算AbPM模型在反演NPP中的不确定性。这里,百分比偏差(PB)和变异系数(CV)用于评估模型输入参数给模型带来的不确定性。其中,PB用于描述不确定性的量级,CV用于描述不确定性的发散程度。根据我们的研究,在全球初级生产力的反演结果中,四种输入参数带来的PB范围在-5%~15%之间,年平均PB为5.5%;CV范围则在98%~134%之间,年平均CV为105%。而对于南极圈附近高生产力海域来说,年平均PB和年平均CV分别为7.1%和121%,显著高于全球的平均水平。本文研究表明,AbPM模型反演初级生产力具有较高的精度。但输入参数的不确定性给模型带来的影响不可忽略,尤其是在近海和高生产力海域。因此提升输入参数产品的卫星遥感反演精度是必不可少的。另外,研究还确认了对模型进行海温修正有助于提升模型在低温海域的精度。Abstract: 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.
-
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
点击查看大图
计量
- 文章访问数: 680
- HTML全文浏览量: 48
- PDF下载量: 360
- 被引次数: 0