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首页> 美国卫生研究院文献>Wiley-Blackwell Online Open >An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll‐a based models
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An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll‐a based models

机译：利用基于卫星海洋颜色/原位叶绿素a的模型评估北冰洋浮游植物的初级生产力

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We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll‐a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed‐layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite‐derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low‐productivity seasons as well as in sea ice‐covered/deep‐water regions. Depth‐resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption‐based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll‐a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic‐relevant parameters.
机译：通过评估在北冰洋重现综合NPP的技能，我们调查了32种净初级生产力（NPP）模型。这些模型提供了两种来源，分别是表面叶绿素a浓度（叶绿素），光合有效辐射（PAR），海面温度（SST）和混合层深度（MLD）。这些模型对表面叶绿素的不确定性最敏感，通常使用原位叶绿素要好于卫星衍生值。它们对PAR，SST和MLD中的不确定性不那么敏感，这可能是由于输入数据范围相对狭窄和/或输入数据源之间的差异相对较小。无论类型或复杂程度如何，大多数模型都无法完全再现原位NPP的可变性，而其中一些模型几乎没有偏差（即再现了原位NPP的平均值）。这些模型在低产季节以及海冰覆盖/深水区域中表现相对较好。与其他模型类型相比，深度解析模型与原位NPP的相关性更高，但有更高的趋势高估了平均NPP，而基于吸收的模型显示的最低偏差与相关性较弱相关。当没有亚表面最大叶绿素（SCM）时，模型表现更好。作为一个整体，这些模型高估了平均NPP，但是当存在SCM时，某些模型低估了NPP，这部分抵消了这一点。我们的研究表明，对于北冰洋，需要对NPP模型进行仔细调整，因为大多数性能相对较好的模型是使用与北极相关的参数的模型。

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