Spring phytoplankton blooms in the southern North Sea substantially contribute to annual primary production and largely influence food web dynamics. Studying long-term changes in spring bloom dynamics is therefore crucial for understanding future climate responses and predicting implications on the marine ecosystem. This paper aims to study long term changes in spring bloom dynamics in the Dutch coastal waters, using historical coastal in-situ data and satellite observations as well as projected future solar radiation and air temperature trajectories from regional climate models as driving forces covering the 21st century. The main objective is to derive long-term trends and quantify climate induced uncertainties in future coastal phytoplankton phenology. The three main methodological steps to achieve this goal include (1) developing a data fusion model to interlace coastal in-situ measurements and satellite chlorophyll-a observations into a single multi-decadal signal; (2) applying a Bayesian structural time series model to produce long-term projections of chlorophyll-a concentrations over the 21st century; and (3) developing a feature extraction method to derive the cardinal dates (beginning, peak, end) of the spring bloom to track the historical and the projected changes in its dynamics. The data fusion model produced an enhanced chlorophyll-a time series with improved accuracy by correcting the satellite observed signal with in-situ observations. The applied structural time series model proved to have sufficient goodness-of-fit to produce long term chlorophyll-a projections, and the feature extraction method was found to be robust in detecting cardinal dates when spring blooms were present. The main research findings indicate that at the study site location the spring bloom characteristics are impacted by the changing climatic conditions. Our results suggest that towards the end of the 21st century spring blooms will steadily shift earlier, resulting in longer spring bloom duration. Spring bloom magnitudes are also projected to increase with 0.4 %/year trend. Based on the ensemble simulation the largest uncertainty lies in the timing of spring bloom beginning and -end timing, while the peak timing has less variation. Further studies would be required to link the findings of this paper and ecosystem behaviour to better understand possible consequences to the ecosystem.

中文翻译：

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气候变化引起的浮游植物春季水华动态的趋势和不确定性

北海南部春季浮游植物大量繁殖，极大地促进了年度初级生产，并在很大程度上影响了食物网动态。因此，研究春季水华动态的长期变化对于了解未来的气候响应和预测对海洋生态系统的影响至关重要。本文旨在研究荷兰沿海水域春季水华动态的长期变化，利用历史沿海原位数据和卫星观测以及区域气候模型预测的未来太阳辐射和气温轨迹作为覆盖 21 世纪的驱动力. 主要目标是得出长期趋势并量化未来沿海浮游植物物候学中气候引起的不确定性。实现这一目标的三个主要方法步骤包括 (1) 开发数据融合模型，将海岸原位测量和卫星叶绿素-a 观测交织成一个单一的多年代际信号；(2) 应用贝叶斯结构时间序列模型生成 21 世纪叶绿素 a 浓度的长期预测；(3) 开发一种特征提取方法来推导出春季盛开的基本日期（开始、高峰、结束），以跟踪其动态的历史和预测变化。数据融合模型通过用原位观测校正卫星观测信号，产生了增强的叶绿素-a 时间序列，具有更高的精度。应用的结构时间序列模型证明具有足够的拟合优度以产生长期叶绿素 a 预测，并且发现特征提取方法在检测春季盛开时的基本日期方面具有鲁棒性。主要研究结果表明，在研究地点，春季水华特征受到气候条件变化的影响。我们的研究结果表明，到 21 世纪末，春季开花将稳步提前，从而导致春季开花持续时间更长。预计春季水华规模也将以每年 0.4% 的趋势增加。基于集合模拟，最大的不确定性在于春季开花开始和结束的时间，而高峰时间变化较小。需要进一步研究将本文的发现与生态系统行为联系起来，以更好地了解对生态系统可能造​​成的后果。