In the first part of this paper series (Sun et al., 2023), we developed an ecological model that partitions the total chlorophyll-a concentration (Chl-a) into three phytoplankton size classes (PSCs), pico-, nano-, and microplankton. The parameters of this model are controlled by sea surface temperature (SST), intended to capture shifts in phytoplankton size structure independently of variations in total Chl-a. In this second part of the series, we present an Ocean Colour Modelling Framework (OCMF), building on the classical Case-1 assumption, that explicitly incorporates our ecological model. The OCMF assumes the presence of the three PSCs and the existence of an independent background of non-algal particles. The framework assumes each phytoplankton group resides in a distinct optical environment, assigning chlorophyll-specific inherent optical properties to each group, both directly (phytoplankton) and indirectly (non-algal particulate and dissolved substances). The OCMF is parameterised, validated, and assessed using a large global dataset of inherent and apparent optical properties. We use the OCMF to explore the influence of variations in temperature and Chl-a on phytoplankton size structure and its resulting effects on ocean colour. We also discuss applications of the OCMF, such as its potential for inverse modelling and phytoplankton climate trend detection, which will be explored further in subsequent papers.

中文翻译：

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将生态概念与海洋色彩模型相结合：参数化和正向建模


在本论文系列的第一部分（Sun et al.， 2023）中，我们开发了一个生态模型，将总叶绿素-a 浓度 （Chl-a） 分为三个浮游植物大小等级 （PSC），即皮级、纳米级和微浮游生物。该模型的参数由海面温度 （SST） 控制，旨在捕捉浮游植物大小结构的变化，而不受总 Chl-a 的变化影响。在本系列的第二部分中，我们提出了一个海洋色彩建模框架 （OCMF），该框架建立在经典的 Case-1 假设之上，明确结合了我们的生态模型。OCMF 假设存在三个 PSC 并且存在非藻类颗粒的独立背景。该框架假设每个浮游植物组都存在于不同的光学环境中，直接（浮游植物）和间接（非藻类颗粒和溶解物质）为每个组分配叶绿素特异性的固有光学特性。OCMF 使用包含固有和表观光学特性的大型全球数据集进行参数化、验证和评估。我们使用 OCMF 来探索温度和 Chl-a 变化对浮游植物大小结构的影响及其对海洋颜色的影响。我们还讨论了 OCMF 的应用，例如它在逆向建模和浮游植物气候趋势检测方面的潜力，这将在后续论文中进一步探讨。