Self-organization maps (SOM) in the definition of a “transfer function” for a diatoms-based climate proxy

This study utilizes self-organizing maps (SOM) —a particular type of artificial neural network (ANN) — as a nonlinear analysis tool for identifying species that can be used as proxies for climate variables. Instrumental, environmental, and biological time-series data from the Alfonso Basin (Gulf of California) were analyzed. The analysis comprised three stages: (1) standardization and selection of biological databases; (2) elimination of noise in the time series data; (3) definition of an ANN architecture and assessment of differences between models built using either noisy or noiseless time series. Noise reduction improved the SOM outputs. A global structuring index was estimated to define the associative linkage between diatoms and climate variables. The results showed that diatoms identified are the most suitable proxies for reconstructing the main climate variables including precipitation, atmospheric temperature, and sea surface temperature. The method developed has the potential to improve the hierarchization and selection of variables that can be potentially reconstructed without a significant loss of climate information.