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The quizzes have been postponed!

The quizzes have been postponed!. New dates for Quiz #2 and Quiz #3 are now set respectively at the 12th of November, and the 5th of December. There will be some homework assignments starting from next class. Climate modeling: Successes and Challenges. A little history about climate modeling

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The quizzes have been postponed!

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  1. The quizzes have been postponed! • New dates for Quiz #2 and Quiz #3 are now set respectively at the 12th of November, and the 5th of December. • There will be some homework assignments starting from next class.

  2. Climate modeling: Successes and Challenges • A little history about climate modeling • Some basic concepts in the numerical modeling of climate • Successes • Challenges • The future of climate modeling

  3. A little history about climate modeling • Swedish scientist Svante Arrhenius (Year:1896)

  4. Manabe’s 3D GCM Syukuro Manabe A little history about climate modeling Geophysical Fluid Dynamics Laboratory (GFDL)

  5. Some basic concepts • The Newton’s law applied to the atmosphere and ocean; the conservation of mass, heat, water, etc…. • Represent the atmosphere, the ocean, and other components in the climate system by a set of boxes--the concept of grid and the issue of spatial resolution • The time step for integration • Numerical instability--constraints on the time step from the spatial resolution • Parameterization of sub-grid scale phenomena

  6. Successes and challenges • Simulation of the surface air temperature

  7. Successes and challenges • Simulation of the precipitation

  8. Successes and challenges Simulation of the ocean temperature

  9. Successes and challenges • Simulation of the sea-ice

  10. Successes and challenges • Simulation of the snow extent

  11. Successes and challenges • Variability--power spectrum

  12. Successes and challenges • Vertical structure of temperature

  13. Successes and challenges • Simulation of El Nino warming

  14. Successes and challenges Forecast of the 1997-98 El Nino

  15. The future of climate modeling • High spatial and temporal resolution • A better parameterization of sub-scale processes • Better observations--improved spatial and temporal coverage of the observational network • Improved understanding of the interactions/feedback loops among the components of the climate system • A better understanding of the model world-model diagnostics

  16. Model Diagnostics: Evaluating Key Climate Feedbacks

  17. Model Diagnostics: Evaluating Key Climate Feedbacks • Water vapor feedback • Cloud feedbacks • Atmospheric circulation feedbacks • Ocean feedbacks • Land surface feedbacks • Biosphere feedbacks • Societal feedbacks

  18. Model Diagnostics: Evaluating cloud feedbacks

  19. Model Diagnostics: Evaluating Cloud Feedbacks

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