Abstract
The aim of this paper 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. Our major objective 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. 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.
Highlights
Climate change is a pressing issue of today, for which data-based models and decision support techniques offer a more comprehensive understanding of its complexity
The results indicated that “soil organic carbon (SOC) in a future scenario of climate change depends on average temperature of coldest quarter (41.9%), average temperature of warmest quarter (34.5%), annual precipitation (22.2%), and annual average temperature (1.3%).”
The article show that the emergence of Big Data and machine learning methods enables climate solution research to overcome generic recommendations and provide policy solutions at urban, street, building and household scale, adapted to specific contexts, but scalable to global mitigation potentials
Summary
Climate change is a pressing issue of today, for which data-based models and decision support techniques offer a more comprehensive understanding of its complexity. The aim of this paper is to reveal data-based techniques and their applicability in terms of climate researches. How can Big Data, through data science answer sustainability climate issues and be applicable in scientific researches and decision sciences in an integrated manner. 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. The focus point of this research is the interconnectedness of the complex climate-related systems, for which exploration Big Data provides an efficient toolbox.
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