Partitioning the Indian Ocean based on surface fields of physical and biological properties

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Comprehensively sampling the ocean in situ remains a challenge, even in the current era of rapid technological development. In less than a decade, the deployment of thousands of autonomous profiling floats increased the number of ocean temperature profiles by an order of magnitude compared to ship-based sampling in the past. But expendable floats cannot sample all the physical and biogeochemical regimes in the global ocean. A promising avenue that could guide in situ sampling is to partition oceans based on selected properties in order to identify “homogeneous” areas. This approach greatly reduces the number of measurements needed to represent the state of the ocean. However, homogeneous areas can be partitioned in many ways: depending on whether a single or several properties are considered; and on whether the definition of boundaries is left to expert knowledge or derived from objective analysis techniques. Here, we use a clustering method to map and partition many surface variables, and we further examine how this partitioning is affected by various ways of averaging or normalizing the input data. We performed this study using 15 different surface fields of physical and biological properties derived from satellite remote sensing observations and from global model outputs at a monthly resolution. The area of study is the Indian Ocean - one of the least-sampled oceans - which is the focus of a global research effort under the auspices of the second International Indian Ocean Expedition (IIOE-2). We show a strong effect of the average absolute values of the data, which can be removed to better examine the phenology of the properties. However, normalization is not mandatory; the technique selected should depend on the scientific questions at hand. Our clusters did generally did not match closely the regions identified by Longhurst in his seminal work on ocean provinces.