静力学与自回归神经网络对全球海洋中物理预测变量驱动的浮游植物生物量动态的模拟
Static and auto-regressive neural emulation of phytoplankton biomass dynamics from physical predictors in the global ocean
AI总结：
本文提出使用UNet和自回归UNet模型，结合海洋物理预测变量，实现对全球海洋浮游植物生物量动态的重建与短期预测，以提高生物地球化学模型的准确性。
摘要：
浮游植物是海洋食物网的基础，驱动着生态过程和全球生物地球化学循环。尽管其生态和气候意义重大，但准确模拟浮游植物动态仍然是生物地球化学数值模型的主要挑战，原因包括参数化有限、观测数据稀疏以及海洋过程的复杂性。在此，我们探讨了深度学习模型如何用于解决这些限制，通过卫星观测和环境条件预测全球海洋中浮游植物生物量的时空分布。首先，我们研究了几种深度学习架构。在测试的模型中，UNet架构因其能更准确地重现浮游植物生物量的季节性和年代际模式而脱颖而出，优于其他模型如CNNs、ConvLSTM和4CastNet。当使用一到两个月的环境数据作为输入时，UNet表现更好，尽管它倾向于低估浮游植物生物量低频变化的幅度。因此，为了提高长期预测的性能，也测试了UNet的自回归版本，其中模型使用其自身的先前预测来预测未来条件。这种方法在短期预测（最多五个月）中表现良好，但其性能在更长的时间尺度上下降。总体而言，我们的研究显示，结合海洋物理预测变量与深度学习可以实现浮游植物动态的重建和短期预测。这些模型可能成为监测海洋健康和支持海洋生态系统管理的强大工具，特别是在气候变化背景下。
英文摘要：
Phytoplankton is the basis of marine food webs, driving both ecological processes and global biogeochemical cycles. Despite their ecological and climatic significance, accurately simulating phytoplankton dynamics remains a major challenge for biogeochemical numerical models due to limited parameterizations, sparse observational data, and the complexity of oceanic processes. Here, we explore how deep learning models can be used to address these limitations predicting the spatio-temporal distribution of phytoplankton biomass in the global ocean based on satellite observations and environmental conditions. First, we investigate several deep learning architectures. Among the tested models, the UNet architecture stands out for its ability to reproduce the seasonal and interannual patterns of phytoplankton biomass more accurately than other models like CNNs, ConvLSTM, and 4CastNet. When using one to two months of environmental data as input, UNet performs better, although it tends to underestimate the amplitude of low-frequency changes in phytoplankton biomass. Thus, to improve predictions over time, an auto-regressive version of UNet was also tested, where the model uses its own previous predictions to forecast future conditions. This approach works well for short-term forecasts (up to five months), though its performance decreases for longer time scales. Overall, our study shows that combining ocean physical predictors with deep learning allows for reconstruction and short-term prediction of phytoplankton dynamics. These models could become powerful tools for monitoring ocean health and supporting marine ecosystem management, especially in the context of climate change.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
