Constraining parameters in marine pelagic ecosystem models - is it actually feasible with typical observations of standing stocks?
Constraining parameters in marine pelagic ecosystem models - is it actually feasible with typical observations of standing stocks?
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DOI：
10.5194/os-11-573-2015
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发表时间：
2015-01-01
期刊：
影响因子：
3.2
通讯作者：
Dietze, H.
中科院分区：
文献类型：
--
作者：
Loeptien, U.;Dietze, H.
在不断变化的气候中，海洋中上层生物地球化学可能会调节大气中与气候相关的物种（如CO2和N2O）的浓度。迄今为止，预测依赖于地球系统模型，将简单的远洋生物地球化学模型成分嵌入到三维海洋环流模型中。这些生物地球化学模型的大部分成分依赖于双曲Michaelis-Menten （MM）公式，该公式规定了光和营养物质对自养浮游植物碳同化的限制作用。三维耦合生物地球化学海洋环流模式的MM常数和其他模式参数通常都是经过调整的；改变参数，直到达到与观察到的存量“合理”相似为止。在这里，我们用双实验（或合成“观测”）探索对观测的要求，允许对模型参数进行更客观的估计。我们从基于“完美”（合成）观测的参数检索实验开始，我们通过低频噪声一步一步地扭曲这些观测，以接近现实条件。最后，我们用现实世界的观察证实了我们的发现。总之，我们发现MM常数特别难以约束，因为即使是观测固有的适度噪声（10%）也可能阻碍参数检索。这是值得关注的，因为MM参数是模型对外部条件预期变化的敏感性的关键。此外，我们还说明了当参数估计是基于稀疏的树木观测时，由高阶参数依赖引起的问题。有些与直觉相反，我们发现更多的观测数据有时会降低约束某些参数的能力。
In a changing climate, marine pelagic biogeochemistry may modulate the atmospheric concentrations of climate-relevant species such as CO2 and N2O. To date, projections rely on earth system models, featuring simple pelagic biogeochemical model components, embedded into 3-D ocean circulation models. Most of these biogeochemical model components rely on the hyperbolic Michaelis-Menten (MM) formulation which specifies the limiting effect of light and nutrients on carbon assimilation by autotrophic phytoplankton. The respective MM constants, along with other model parameters, of 3-D coupled biogeochemical ocean-circulation models are usually tuned; the parameters are changed until a "reasonable" similarity to observed standing stocks is achieved.Here, we explore with twin experiments (or synthetic "observations") the demands on observations that allow for a more objective estimation of model parameters. We start with parameter retrieval experiments based on "perfect" (synthetic) observations which we distort, step by step, by low-frequency noise to approach realistic conditions. Finally, we confirm our findings with real-world observations. In summary, we find that MM constants are especially hard to constrain because even modest noise (10 %) inherent to observations may hinder the parameter retrieval already. This is of concern since the MM parameters are key to the model's sensitivity to anticipated changes in the external conditions. Furthermore, we illustrate problems caused by high-order parameter dependencies when parameter estimation is based on sparse observations of standing stocks. Somewhat counter to intuition, we find that more observational data can sometimes degrade the ability to constrain certain parameters.