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Depth-Integrated and Depth-Resolved Models of Kara Sea Primary Production

机译:卡拉海初级生产的深度综合和深度分解模型

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Primary production (PP) models of the Kara Sea are developed based on data collected on fall expeditions (September-October 1993, 2007, and 2011) and their precision assessment utilizes the dataset collected in September 2013. The algorithms for different model types (depth-integrated and depth-resolved) are compared. The depth-resolved model performs slightly better than the depth-integrated one (the root-mean-square-difference (RMSD) are 0.29 and 0.31, respectively). These algorithms utilize the daily assimilation number (DAN) and photosynthetic efficiency (psi) as the model coefficients, and surface chlorophyll a (chl a) and photosynthetically active radiation (PAR) as input variables. These algorithms perform better than the models that use chl a alone. Our results suggest that an increase in the performance of the Kara Sea PP models depends on the input of the photophysiological characteristics of phytoplankton (DAN and psi) and PAR. To a lesser extent, this concerns the advantages of the depth-resolved model over the depth-integrated one. The constructed region-specific Kara Sea PP models combined with satellite-derived chl a and PAR can be used to estimate annual values and long-term variation of PP in hydrologically and hydrochemically similar waters of the Arctic Ocean.
机译:卡拉海的初级生产(PP)模型是基于秋季考察(1993年9月至10月,2011年和2011年)收集的数据开发的,其精度评估使用了2013年9月收集的数据集。不同模型类型(深度的算法) -集成和深度解析)进行比较。深度解析模型的性能比深度积分模型好一些(均方根差(RMSD)分别为0.29和0.31)。这些算法将每日同化数(DAN)和光合效率(psi)作为模型系数,并将表面叶绿素a(chla)和光合有效辐射(PAR)作为输入变量。这些算法的性能优于单独使用chl的模型。我们的结果表明,卡拉海PP模式性能的提高取决于浮游植物(DAN和psi)和PAR的光生理特性的输入。在较小程度上,这涉及深度解析模型相对于深度积分模型的优势。结合卫星衍生的chl a和PAR构建的特定地区的Kara Sea PP模型可用于估计北冰洋在水文和水化学上相似的水域中PP的年值和长期变化。

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