A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science

Area of Science:

• Climate Science
• Data Science
• Environmental Science

Background:

• Global climate change presents significant challenges to human life.
• Despite abundant climate data, data science has had limited impact on climate understanding.
• Big data techniques have succeeded in other fields but face unique challenges in climate science.

Purpose of the Study:

• Introduce data science to climate data mining challenges and opportunities.
• Highlight differences between climate data mining and traditional big data approaches.
• Propose a theory-guided data science paradigm for climate research.

Main Methods:

• Focus on data, methods, and application challenges in climate data mining.
• Critique the limitations of solely relying on traditional big data techniques.
• Introduce a theory-guided data science paradigm.

Main Results:

• Traditional big data techniques yield dubious findings in climate science.
• A theory-guided approach is essential for accurate climate data analysis.
• Scientific theory can constrain big data techniques and interpretation.

Conclusions:

• Big data has immense potential for climate science if applied correctly.
• A theory-guided data science paradigm is crucial for reliable climate insights.
• Overcoming data and methodological challenges is key to leveraging big data for climate action.