Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach

monitoring the spatio-temporal variations of surface chlorophyll-a concentration (chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. these two decades of satellite observations are however still too short to provide a comprehensive description of chl variations at decadal to multi-decadal timescales. this paper investigates the ability of a machine learning approach (a non-linear statistical approach based on support vector regression, hereafter svr) to reconstruct global spatio-temporal chl variations from selected surface oceanic and atmospheric physical parameters. with a limited training period (13 years), we first demonstrate that chl variability from a 32-years global physical-biogeochemical simulation can generally be skillfully reproduced with a svr using the model surface variables as input parameters. we then apply the svr to reconstruct satellite chl observations using the physical predictors from the above numerical model and show that the chl reconstructed by this svr more accurately reproduces some aspects of observed chl variability and trends compared to the model simulation. this svr is able to reproduce the main modes of interannual chl variations depicted by satellite observations in most regions, including el niño signature in the tropical pacific and indian oceans. in stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of chl trends estimated by satellite data, with a chl increase in most extratropical regions and a chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. results from our svr reconstruction over the entire period (1979–2010) also suggest that the interdecadal pacific oscillation drives a significant part of decadal chl variations in both the tropical pacific and indian oceans. overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate chl decadal variability.

监测叶绿素a浓度（叶绿素a是浮游生物生物量的指标）的时空变化，自1997年以来得益于连续且全球性的海洋颜色卫星测量的可用性而获得了极大的益处。然而，这两个十年的卫星观测数据仍然过于短暂，无法全面描述叶绿素a在十年至多十年时间尺度上的变化。本文探讨了基于支持向量回归（以下简称SVR）的非线性统计方法在重建全球时空叶绿素a变化方面的能力，该方法以选定的海洋表面和大气物理参数作为输入参数。在有限的训练期（13年）内，我们首先证明，使用SVR可以巧妙地重现32年全球物理生物地球化学模拟中的叶绿素变化。随后，我们将SVR应用于利用上述数值模型中的物理预测因子重建卫星叶绿素观测数据，并表明通过此SVR重建的叶绿素比模型模拟更准确地再现了观测到的叶绿素变化的一些方面和趋势。该SVR能够在大多数地区再现卫星观测描绘的年际叶绿素变化的主要模式，包括热带太平洋和印度洋中的厄尔尼诺特征。与生物地球化学模型模拟的趋势形成鲜明对比的是，它还准确地捕捉了卫星数据估计的叶绿素趋势的空间模式，大多数副热带地区叶绿素增加，而副热带环流中心叶绿素减少，尽管这些趋势的幅度低估了半数。我们SVR在整个时期（1979-2010）的重建结果还表明，跨十年太平洋振荡驱动了热带太平洋和印度洋中部分十年叶绿素变化的显著部分。总的来说，这项研究证明了非线性统计重建可以作为重建和调查叶绿素十年变化的有益补充工具，与现场和卫星观测以及传统的物理生物地球化学数值模拟相辅相成。