Sea surface temperature (SST) can influence the phytoplankton biomass, measured as sea surface chlorophyll concentration (SSCC), by affecting the physical and chemical properties of the seawater, living environment, and the consumption of zooplankton in a complex way. Yet, the quantitative assessment of the spatiotemporal variation of the inherent synchronous association between SSCC and SST at large spatial and temporal scales is still lacking. Accordingly, in the present study a synthetic approach was proposed that combines wavelet coherency analysis (WCA) with Bayesian maximum entropy (BME) modeling and hotspot analysis in order to evaluate the association between SSCC and SST globally during the period July 2002-February 2019. The WCA-based statistical results showed that SSCC has strong association with SST; particularly strong synchronous variations between SSCC and SST were found at the 1-year and the 5-year periods. During the 1-year period, cluster characteristics were explored in the BME-generated space–time maps of the association strength as well as in the corresponding hotspot maps. Geographically, high association strengths between SSCC and SST were detected in the mid-latitude regions of the Pacific Ocean, in the south and north of the tropical regions of the Atlantic Ocean, and in the southern part of the Indian Ocean. Temporally, most of the sub-regions exhibited a stable level of association strength during the entire study period (only a few sub-regions exhibited fluctuations or a slightly decreasing association strength trend). In conclusion, by assimilating the available knowledge bases at each grid point the proposed synthetic approach assessed quantitatively the strength of the periodic association between SSCC and SST globally; and the approach could be employed to map the space–time variation of the association strength between related natural attributes in the space–time-frequency domain.

海面温度 (SST) 可以通过复杂的方式影响海水的物理和化学性质、生活环境和浮游动物的消耗，从而影响浮游植物生物量，以海面叶绿素浓度 (SSCC) 来衡量。然而，仍然缺乏对大时空尺度上 SSCC 和 SST 之间固有同步关联的时空变化的定量评估。因此，在本研究中，提出了一种综合方法，将小波相干分析 (WCA) 与贝叶斯最大熵 (BME) 建模和热点分析相结合，以评估 2002 年 7 月至 2019 年 2 月期间全球范围内 SSCC 与 SST 之间的关联。基于 WCA 的统计结果表明 SSCC 与 SST 有很强的相关性；在 1 年和 5 年期间发现 SSCC 和 SST 之间特别强烈的同步变化。在 1 年期间，在 BME 生成的关联强度时空图中以及相应的热点图中探索了集群特征。在地理上，在太平洋中纬度地区、大西洋热带地区的南部和北部以及印度洋南部发现了 SSCC 和 SST 之间的高关联强度。从时间上看，大部分子区域在整个研究期间表现出稳定的关联强度水平（只有少数子区域表现出波动或略有下降的关联强度趋势）。综上所述，通过同化每个网格点的可用知识库，所提出的综合方法定量评估了全球 SSCC 和 SST 之间周期性关联的强度；该方法可用于映射时空域中相关自然属性之间关联强度的时空变化。