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Hypothesis generation in climate research with interactive visual data exploration.

Johannes Kehrer1, Florian Ladstädter, Philipp Muigg

  • 1Department of Informatics, University of Bergen, Norway. johannes.kehrer@uib.no

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Summary
This summary is machine-generated.

This study identifies sensitive atmospheric regions for climate change detection. Interactive visualization of climate data helps pinpoint robust indicators and refine analysis parameters.

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Area of Science:

  • Climate Science
  • Atmospheric Science
  • Data Visualization

Background:

  • Climate change research necessitates identifying sensitive indicators.
  • Detecting and attributing climate change requires robust analytical methods.

Purpose of the Study:

  • To identify specific atmospheric regions as sensitive and robust indicators of climate change.
  • To demonstrate interactive visual data exploration for hypothesis generation in climate analysis.

Main Methods:

  • Interactive visual data exploration of multi-variate, time-dependent climate data.
  • Utilizing a coordinated multiple views framework with new visualization and interaction technology.
  • Deriving and interactively exploring higher-order information like linear trends and signal-to-noise ratios.

Main Results:

  • Identification of specific atmospheric height layers as sensitive indicators of climate change.
  • Efficient narrowing down of parameters for computational data analysis through interactive exploration.
  • Successful application to ECHAM5 climate model data and ERA-40 reanalysis data.

Conclusions:

  • Interactive visualization is effective for identifying climate change indicators.
  • The developed approach shows potential for generalization to other climate data analysis applications.