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Decadal predictability without ocean dynamics.

Abhishekh Srivastava1, Timothy DelSole2,3

  • 1Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030; asrivas3@gmu.edu.

Proceedings of the National Academy of Sciences of the United States of America
|February 15, 2017
PubMed
Summary

Predictable climate variability components in coupled atmosphere-ocean models mirror those in simpler models. A linear regression model skillfully predicts observed anomalies, suggesting interactive ocean circulation isn't essential for multiyear climate predictability.

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CMIPaverage predictability timedecadal predictabilitydecadal prediction

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Area of Science:

  • Climate modeling
  • Atmosphere-ocean dynamics
  • Predictability studies

Background:

  • Coupled atmosphere-ocean models are crucial for understanding climate variability.
  • Previous studies identified multiyear predictability in these models and observations.
  • The role of interactive ocean circulation in this predictability remains debated.

Purpose of the Study:

  • To compare internal variability predictability in coupled versus non-interactive ocean models.
  • To assess the skill of a dynamical model-derived linear regression in predicting observed climate anomalies.
  • To determine the necessity of interactive ocean circulation for multiyear predictability.

Main Methods:

  • Analysis of predictable components in coupled atmosphere-ocean models.
  • Comparison with predictable components in climate models featuring a slab ocean mixed layer.
  • Development and application of a linear regression model using dynamical model output.
  • Validation against observed climate anomalies.

Main Results:

  • The most predictable components of internal variability are similar across coupled and slab ocean models.
  • A linear regression model accurately predicts observed anomalies at least 1-2 years in advance.
  • Model-derived components and their linear dynamics demonstrate realism.

Conclusions:

  • Interactive ocean circulation is not essential for multiyear predictability in climate models.
  • Simpler climate models capturing key dynamics can reproduce essential predictability features.
  • Dynamical model components offer a realistic basis for predicting climate variability.