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Using Generative Art to Convey Past and Future Climate Transitions
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Long-term probabilistic temperature projections for all locations.

Xin Chen1, Adrian E Raftery1, David S Battisti2

  • 1Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, USA.

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Summary
This summary is machine-generated.

This study introduces a new method for probabilistic spatial forecasts of local temperature changes due to climate change. It provides a probability distribution for future temperatures globally, improving upon existing models.

Keywords:
Carbon emissionsCoupled model intercomparison projectIntergovernmental panel on climate changeNatural variabilityPattern scaling methodProbabilistic population projections

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Area of Science:

  • Climate Science
  • Environmental Science
  • Statistical Modeling

Background:

  • Current climate change projections lack full probabilistic interpretation.
  • Existing global temperature forecasts do not provide regional specificity.

Purpose of the Study:

  • To develop a method for probabilistic long-term spatial forecasts of local average annual temperature change.
  • To provide a probability distribution for future temperatures at any location and year.

Main Methods:

  • Combining a probabilistic global method with a pattern scaling approach.
  • Utilizing out-of-sample predictive validation for calibration.

Main Results:

  • High latitudes, continents, and the Northern Hemisphere warm more than low latitudes, oceans, and the Southern Hemisphere, respectively.
  • A 5% chance of 16°C warming in the Arctic; 95% chance of at least 2°C warming in North Africa, West Asia, and most of Europe.
  • Uncertainty sources shift from natural variability to model and emissions uncertainty by 2100.

Conclusions:

  • The developed method provides well-calibrated, probabilistic spatial forecasts for local temperature change.
  • Findings align with established patterns of differential warming across latitudes and surfaces.
  • The study quantifies regional temperature change probabilities and clarifies uncertainty drivers over time.