Assessing the uncertainties of phytoplankton absorption-based model estimates of marine net primary productivity
Assessing the uncertainties of phytoplankton absorption-based model estimates of marine net primary productivity
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评估基于浮游植物吸收的海洋净初级生产力模型估计值的不确定性
DOI：
10.1007/s13131-017-1047-8
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发表时间：
2017-05
影响因子：
1.4
通讯作者：
WANG Yan
中科院分区：
文献类型：
--
作者：
TAO Zui;MA Sheng;YANG Xiaofeng;WANG Yan
卫星获取的浮游植物色素吸收（aph）已被用作浮游植物光合效率的关键预测因子，以估计全球海洋净初级生产力（NPP）。以光合有效辐射（PAR）、490 nm漫射衰减（Kd（490））、真光层深度（Zeu）和浮游植物色素吸收系数（aph）为输入参数，将基于光合作用的NPP模型（AbPM）与基于叶绿素的模型和基于碳的模型进行了比较。结果表明，与叶绿素基模式和碳基模式相比，ABPM模式在估算海洋NPP方面具有明显的优势。例如，ABPM在大多数监测点的表现远远优于其他两个模型，并具有最好的准确性，包括最小的RMSD值和NPP估计偏差，以及观测值和模拟NPP之间的最佳相关性。为了确保模型的鲁棒性，在NPP估计的AbPM的不确定性进行了评估，使用Monte Carlo模拟。首先，模型估计值与实测值的简单差（δ）和对数差（δ ε）的频率直方图证实了两个输入参数（Zeu和PAR）近似于正态分布，而另两个输入参数（apandKd（490））近似于对数正态分布。第二，在NPP估计在ABPM的不确定性进行了评估，通过使用Monte Carlo模拟。在此基础上，用偏差百分比（PB）和变异系数（CV）来表示输入参数对NPP的不确定性。PB是ΔNPP与NPP反演值的比值，CV是标准差与均值的比值。与震级相关的不确定度用PB表示，与散射范围相关的不确定度用CV表示。研究表明，全球海洋NPP不确定性的年平均值为5.5%，其PB范围为-5%-15%。ABPM模式的PB不确定性主要是由大气降水引起的，大气降水引起的NPP不确定性的全球年平均PB为4.1%。各参数的年平均变异系数为105%，覆盖了全球海洋的98%~ 134%。对于生产力较高的南极海岸带，各参数带来的NPP不确定性的年平均PB和CV分别为7.1%和121%，显著大于全球海洋的结果。研究表明，ABPM模型估算的NPP较其它模型精度高，但输入参数对NPP的影响不容忽视，尤其是在沿海高生产力地区。因此，提高卫星反演输入参数的精度是十分必要的。研究还证实了在低温条件下，SST相关订正对提高模式精度是有效的。
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, anaph-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 carbonbased 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 (Zeuand PAR) approximate the Normal Distribution, and another two input parameters (aphandKd(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 ΔNPP 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 byaph; the PB of NPP uncertainty brought byaphhad 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.
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