The aim of this paper by Sebestyén and colleagues is to provide an overview of the interrelationship between data science and climate studies, as well as describes how sustainability climate issues can be managed using the Big Data tools.
Climate-related Big Data articles are analyzed and categorized, which revealed the increasing number of applications of data-driven solutions in specific areas, however, broad integrative analyses are gaining less of a focus.
The major objective of this study is to highlight the potential in the System of Systems (SoS) theorem, as the synergies between diverse disciplines and research ideas must be explored to gain a comprehensive overview of the issue.
The overview is guided through three closely related notions, namely, (1) data science as a novel interdisciplinary field connected to (2) machine learning that is a tool for improving automatic prediction or decision processes, and (3) Big Data which foster processing and connecting large amount of heterogeneous data.
Research questions formulated three aspects, which answering kept in focus through the whole paper:
• How and when Big Data appears in climate-related studies?
• What researches have been made in regard with Big Data applications in climate studies, and how they are structured?
• How to integrate the knowledge accumulated in diverse specific researches?
Data and systems science enables a large amount of heterogeneous data to be integrated and simulation models developed, while considering socio-environmental interrelations in parallel. The improved knowledge integration offered by the System of Systems thinking or climate computing has been demonstrated by analysing the possible inter-linkages of the latest Big Data application papers.
The analysis highlights how data and models focusing on the specific areas of sustainability can be bridged to study the complex problems of climate change.
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