Coupling ecological concepts with an ocean-colour model: Phytoplankton size structure
Coupling ecological concepts with an ocean-colour model: Phytoplankton size structure
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DOI：
10.1016/j.rse.2022.113415
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发表时间：
2023-02
影响因子：
13.5
通讯作者：
Xuerong Sun;R. Brewin;S. Sathyendranath;G. Dall’Olmo;R. Airs;R. Barlow;A. Bracher;V. Brotas;M. Kheireddine;T. Lamont;E. Marañón;X. Morán;D. Raitsos;Fang Shen;G. Tilstone
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作者：
Xuerong Sun;R. Brewin;S. Sathyendranath;G. Dall’Olmo;R. Airs;R. Barlow;A. Bracher;V. Brotas;M. Kheireddine;T. Lamont;E. Marañón;X. Morán;D. Raitsos;Fang Shen;G. Tilstone
浮游植物在重要元素和化合物的行星循环中起着核心作用。因此，了解浮游植物如何应对气候变化是地球科学的一个主要问题。监测浮游植物是回答这个问题的关键。海洋颜色的卫星遥感是我们在高时间和大空间尺度上监测整个海洋表面浮游植物的唯一手段，连续的海洋颜色数据记录现在正在接近适合解决气候变化问题的长度，至少在某些地区是这样。然而，开发用于气候变化研究的海洋颜色算法需要解决海洋颜色信号中的模糊性问题。例如，对于相同的浮游植物叶绿素-a浓度（Chl-a），海洋的颜色可以根据浮游植物的类型而有所不同。解决这一模糊问题的一个途径是用海表温度（SST）的信息丰富海洋颜色数据，海表温度是三种浮游植物大小类别（PSCs）的变化的良好代表，与浮游植物生物量的总Chl-a变化无关。利用HPLC（高效液相色谱）色素的全球表面位置数据集、分级过滤数据和1991年至2021年同步卫星海温，我们重新调整、验证并推进了一个依赖海温的三组分模型，该模型量化了三种PSCs （pico-、nano-和microplankton）相关的总Chl-a和Chl-a之间的关系。与之前的研究类似，我们发现模型参数与海表温度之间存在显著的相关性，这可以显著提高模型的性能。将这些关系应用到40年的卫星海温月度复合资料中，在全球范围内观测到模式参数对气候变暖的显著变化趋势。这些参数的变化突出了使用标准经验算法估计海洋颜色浮游植物生物量（Chl-a）长期趋势的问题，这些算法隐含地假设了三种大小类别的总Chl-a和Chl-a之间的固定关系。提出的生态模型将成为新的海洋颜色建模框架的中心，该框架旨在研究浮游植物对气候变化的反应，本系列论文的后续部分将对此进行描述。
Phytoplankton play a central role in the planetary cycling of important elements and compounds. Understanding how phytoplankton are responding to climate change is consequently a major question in Earth Sciences. Monitoring phytoplankton is key to answering this question. Satellite remote sensing of ocean colour is our only means of monitoring phytoplankton in the entire surface ocean at high temporal and large spatial scales, and the continuous ocean-colour data record is now approaching a length suitable for addressing questions around climate change, at least in some regions. Yet, developing ocean-colour algorithms for climate change studies requires addressing issues of ambiguity in the ocean-colour signal. For example, for the same chlorophyll-a concentration (Chl-a) of phytoplankton, the colour of the ocean can be different depending on the type of phytoplankton present. One route to tackle the issue of ambiguity is by enriching the ocean-colour data with information on sea surface temperature (SST), a good proxy of changes in three phytoplankton size classes (PSCs) independent of changes in total Chl-a, a measure of phytoplankton biomass. Using a global surfacein-situdataset of HPLC (high performance liquid chromatography) pigments, size-fractionated filtration data, and concurrent satellite SST spanning from 1991 to 2021, we re-tuned, validated and advanced an SST-dependent three-component model that quantifies the relationship between total Chl-a and Chl-a associated with the three PSCs (pico-, nano- and microplankton). Similar to previous studies, striking dependencies between model parameters and SST were captured, which were found to improve model performance significantly. These relationships were applied to 40 years of monthly composites of satellite SST, and significant trends in model parameters were observed globally, in response to climate warming. Changes in these parameters highlight issues in estimating long-term trends in phytoplankton biomass (Chl-a) from ocean colour using standard empirical algorithms, which implicitly assume a fixed relationship between total Chl-a and Chl-a of the three size classes. The proposed ecological model will be at the centre of a new ocean-colour modelling framework, designed for investigating the response of phytoplankton to climate change, described in subsequent parts of this series of papers.