Phytoplankton response to climate change (PRIME)

浮游植物是地球系统的核心，将光与海洋中的生命联系在一起。它们被认为是地球上最具活力的初级生产者，每两到五天再生一次，对环境的变化反应迅速，调节主要元素和化合物的行星循环，并为海洋食物网提供能量。了解浮游植物对气候变化的反应对于预测地球系统未来的变化至关重要。近25年的连续数据采集，以及在几天内观察整个地球的无与伦比的能力，卫星遥感海洋颜色是我们在全球范围内监测浮游植物丰度的主要手段，通过估计叶绿素-a色素浓度，衡量浮游植物丰度。然而，利用卫星遥感海洋颜色监测浮游植物丰度的趋势存在两个主要挑战。首先，将海洋颜色信号转换为浮游植物丰度的估计需要考虑浮游植物的类型和水中其他光学活性成分。各空间机构使用的标准浮游植物丰度算法假定这些成分共变，这不适用于探测长期趋势，因为气候变化可能以不同的方式改变这些成分。其次，卫星反演的信号仅代表海洋表层（最多40 m）。在许多地区，在表层（40-200米）以下，存在着一个浮游植物森林，卫星看不到。目前，使用标准算法得出的表层浮游植物丰度的海洋颜色趋势是不可信的，我们也不能确定，如果存在这样的趋势，它们是否代表了隐藏在下面的森林的变化。在这个项目中，我的团队将开发第一个专门用于监测气候变化的海洋颜色算法，该算法使用理论和生态框架将信号从水成分中分离出来，并利用复合遥感（热和可见光卫星遥感）的原理。它将在气候质量海洋卫星数据记录的基础上运行，这些数据最近可通过欧洲航天局获得，以产生地表浮游植物丰度的数十年记录。此外，通过从过去30多年的海洋采样和新兴的自主海洋机器人中挖掘浮游植物丰度的垂直剖面，我的团队还将产生一个全球的，几十年的地下浮游植物丰度记录。把这些数据记录结合在一起，我们将量化人为气候变化对浮游植物丰度的影响，至少在记录足够长的海洋部分。这些数据记录将作为比较和改进气候模型的基准，模拟海洋碳循环、海洋生物多样性和鱼类资源的未来预测。这些数据记录将用于研究气候变化对海洋初级生产、浮游植物碳和海洋生物地理学的影响。

Phytoplankton are at the heart of the Earth system, connecting light to life in the ocean. Considered the most dynamic primary producer on Earth, regenerating every two to five days, they respond rapidly to changes in their environment, modulate the planetary cycling of major elements and compounds, and supply energy to the marine food-web. Understanding the response of phytoplankton to climate change is critical to predicting future changes in the Earth system. Approaching 25 years of continuous data acquisition, and with an unparalleled capability to view the entire planet within a few days, satellite remote sensing of ocean colour is our principal means of monitoring phytoplankton abundance at global scale, through estimates of the chlorophyll-a pigment concentration, a measure of phytoplankton abundance. However, there are two major challenges to using satellite remote sensing of ocean colour for monitoring trends in phytoplankton abundance. Firstly, converting the ocean-colour signal into estimates of phytoplankton abundance demands consideration of the phytoplankton type present and other optically active constituents in the water. Standard phytoplankton abundance algorithms in use by space agencies assume these constituents co-vary, which is unsuitable for detecting long-term trends as climate change may alter these constituents in different ways. Secondly, the signal retrieved by satellite is only representative of the surface layer of the ocean (top 40 m at most). For many regions, below the surface layer (40-200 m), there exists a forest of phytoplankton hidden from the eyes of the satellite. At present, ocean-colour trends in surface phytoplankton abundance using standard algorithms cannot be trusted, and we cannot be sure that, if any such trends exist, they are representative of changes in the forest hidden below. In this project, my team will develop the first ocean-colour algorithm designed specifically for monitoring climate change, that separates signals from water constituents using a theoretical and ecological framework, that harnesses the principles of compound remote sensing (thermal and visible satellite remote sensing). It will be run on climate-quality ocean satellite data records, recently available through the European Space Agency, to produce a multi-decadal record of surface phytoplankton abundance. Furthermore, by mining vertical profiles of phytoplankton abundance from the past 30+ years of oceanographic sampling and emerging autonomous ocean robotics, my team will also produce a global, multi-decadal record of subsurface phytoplankton abundance. Bringing these data records together, we will quantify the effect of anthropogenic climate change on phytoplankton abundance, at least in parts of the ocean where the record is long-enough. These data records will serve as a benchmark to compare and improve climate models, that simulate future predictions of the marine carbon cycle, marine biodiversity and fish stocks. The data records will be used as input to study the impact of climate change on oceanic primary production, phytoplankton carbon and ocean biogeography.