The Ocean Carbon States Database: multivariate application of cluster analysis on the ocean carbon cycle
Abstract
Advanced pattern recognition and data mining techniques are becoming exceedingly popular in Climate and Earth Sciences as means of decomposing big data into its most significant features. This is particularly important for studies of the global carbon cycle, where ample data is available yet unexplored because of its size and complexity. We need to study these data sets because a lack of understanding confounds our ability to accurately describe, understand, and predict CO2 concentrations and their changes in the major planetary carbon reservoirs.
Here we describe the implementation of multivariate k-means clustering on pCO2 (Landschuetzer product) and temperature at 10m depth (ARGO Coriolis product) in the North Atlantic basin for 2000-2015. As the observation-based data is organized into various regimes, which we will call "ocean carbon states", we gain insight into the physical and/or biogeochemical processes controlling the ocean carbon cycle. We show that k-means effectively produces dynamic states which demonstrate complex interannual and spatial variability. Using various correlational methods, we can also parameterize the ocean carbon states by relevant climate indices (ENSO, AO, NAO) and other physical fields like salinity and chlorophyll. Since many useful data mining tools are inaccessible because of prerequisites for in-depth understanding of data science, we seek to standardize our methodology for all Earth Science applications. In parallel to this study, we present a clustering toolbox which allows users to load and prepare their own data, run k-means cluster analysis, and most importantly, interpret the results in a straightforward way.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMIN41D0874L
- Keywords:
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- 1855 Remote sensing;
- HYDROLOGYDE: 1908 Cyberinfrastructure;
- INFORMATICSDE: 1914 Data mining;
- INFORMATICSDE: 1942 Machine learning;
- INFORMATICS