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A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science
http://online.liebertpub.com/doi/full/10.1089/big.2014.0026[font face=Serif][font size=5]A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science[/font]
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[font size=4]ABSTRACT[/font]
[font size=3]Global climate change and its impact on human life has become one of our era's greatest challenges. Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data. This is a stark contrast from other fields such as advertising or electronic commerce where big data has been a great success story. This discrepancy stems from the complex nature of climate data as well as the scientific questions climate science brings forth. This article introduces a data science audience to the challenges and opportunities to mine large climate datasets, with an emphasis on the nuanced difference between mining climate data and traditional big data approaches. We focus on data, methods, and application challenges that must be addressed in order for big data to fulfill their promise with regard to climate science applications. More importantly, we highlight research showing that solely relying on traditional big data techniques results in dubious findings, and we instead propose a theory-guided data science paradigm that uses scientific theory to constrain both the big data techniques as well as the results-interpretation process to extract accurate insight from large climate data.[/font]
[font size=4]Introduction[/font]
[font size=3]Over the past few decades, the Internet has democratized the creation, access, and analysis of large datasets. As data continue to grow in size and complexity, new algorithms have been developed to learn from eclectic data sources—algorithms that autonomously identify patterns with minimal human input. These developments have led the field of data science to firmly enter the mainstream. In the midst of data science's democratization, global climate change has quickly become one of our era's most pressing issues. A lot remains to be understood about our planet and the physical processes that govern it to effectively answer questions about global climate change and its societal impacts. It has been proposed that given the abundance of climate data from model simulations, Earth-orbiting satellites, and in situ observations, we may close some of these knowledge gaps by directly learning from these large climate science datasets. However, Earth is a complex dynamical system like none we have studied before. As a result, big data–induced progress within climate science has been slower compared with big data's success in other fields such as biology or advertising. The slow progress has been vexing given that climate science has become one of the most data-rich domains in terms of data volume, velocity, and variety.[small]1[/small]
This article discusses some of the major big data challenges researchers face when mining climate data and how being mindful of such intricacies can have a significant impact on a socially relevant and commercially viable domain. We will use examples from existing research in climate and data science to demonstrate and discuss key concepts, with the goal of preparing a new generation of data scientists with the tools and processes for data science to have the highest impact on momentous challenges facing our society due to climate change.
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[font size=4]ABSTRACT[/font]
[font size=3]Global climate change and its impact on human life has become one of our era's greatest challenges. Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data. This is a stark contrast from other fields such as advertising or electronic commerce where big data has been a great success story. This discrepancy stems from the complex nature of climate data as well as the scientific questions climate science brings forth. This article introduces a data science audience to the challenges and opportunities to mine large climate datasets, with an emphasis on the nuanced difference between mining climate data and traditional big data approaches. We focus on data, methods, and application challenges that must be addressed in order for big data to fulfill their promise with regard to climate science applications. More importantly, we highlight research showing that solely relying on traditional big data techniques results in dubious findings, and we instead propose a theory-guided data science paradigm that uses scientific theory to constrain both the big data techniques as well as the results-interpretation process to extract accurate insight from large climate data.[/font]
[font size=4]Introduction[/font]
[font size=3]Over the past few decades, the Internet has democratized the creation, access, and analysis of large datasets. As data continue to grow in size and complexity, new algorithms have been developed to learn from eclectic data sources—algorithms that autonomously identify patterns with minimal human input. These developments have led the field of data science to firmly enter the mainstream. In the midst of data science's democratization, global climate change has quickly become one of our era's most pressing issues. A lot remains to be understood about our planet and the physical processes that govern it to effectively answer questions about global climate change and its societal impacts. It has been proposed that given the abundance of climate data from model simulations, Earth-orbiting satellites, and in situ observations, we may close some of these knowledge gaps by directly learning from these large climate science datasets. However, Earth is a complex dynamical system like none we have studied before. As a result, big data–induced progress within climate science has been slower compared with big data's success in other fields such as biology or advertising. The slow progress has been vexing given that climate science has become one of the most data-rich domains in terms of data volume, velocity, and variety.[small]1[/small]
This article discusses some of the major big data challenges researchers face when mining climate data and how being mindful of such intricacies can have a significant impact on a socially relevant and commercially viable domain. We will use examples from existing research in climate and data science to demonstrate and discuss key concepts, with the goal of preparing a new generation of data scientists with the tools and processes for data science to have the highest impact on momentous challenges facing our society due to climate change.
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