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Enhancing Climate Models: Importance of Integration and Data Calibration

Achieving accurate climate change assessments requires data/model integration and robust error bars. This article discusses the challenges in retrodiction/prediction and the need for a probabilistic approach for under-constrained systems. It explores Bayesian calibration methods and the significance of accurate propagation of data uncertainties. The text emphasizes the bumpy phase and likelihood spaces and the use of stochastic methodologies in model calibration. Additionally, it touches upon future data collection, meltwater links, and interim results in climate modeling.

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Enhancing Climate Models: Importance of Integration and Data Calibration

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Presentation Transcript

  1. THE NEED FOR DATA/MODEL INTEGRATION Retrodiction/prediction is meaningless without meaningful error bars or probability distribution of model results Major challenge for climate change assessments Are PMIP/observation discrepancies due to faulty boundary conditions or to problems in the models? even dynamical process modelling needs constraints on boundary conditions for under-constrained systems need data/physics integration -> calibrate model against observational data

  2. Criteria for calibration methodology Complicated under-constrained non-linear system with threshold behavior effectively large number of poorly constrained model parameters Large set of diverse noisy constraint data Data and & model limitations -> need a fundamentally probabilistic approach bumpy phase and likelihood spaces (shown below) further rule out gradient-based approaches such as adjoint (eg. 4D var) methods -> stochastic methodology accurate propagation of data uncertainties -> Bayesian approach => Markov Chain Monte Carlo

  3. Bayesian calibration • Sample over posterior probability distribution for the ensemble parameters given fits to observational data using Markov Chain Monte Carlo (MCMC) methods • Other constraints: • Minimize margin forcing • LGM ice volume bounds • Hudson Bay glaciated at -25 kyr • Post MCMC scoring: • Marine Limits • Strandlines

  4. Large ensemble Bayesian calibration • Bayesian neural network integrates over weight space • Self-regularized • Can handle local minima

  5. It works

  6. Bumpy likelihood space

  7. Another data/model link: guide future data collection

  8. North American Climate and meltwater phasing -> meltwater/iceberg discharge is a critical link between cryosphere and climate system

  9. The meltwater link • Need more marine observations to corroborate/refute results • What happens to a meltwater plume in the Arctic Ocean? • Mixing dynamics in the GIN Seas?

  10. A couple of other interim results The calibration tends to favour an ice volume for North America that is too low to meet global LGM eustatic constraints -> can be addressed by strong H2/H1/mwp1-a events -> also starting to give consideration to larger marine components (especially given recent HOTRAX data)

  11. Where to: Completion of interim global calibrated deglacial ice/meltwater chronology EMIC/GCM recursion to get climatological self-consistency Calibration of glacial inception ice & climate with the glacial systems model coupled to a reduced AOGCM Ditto for deglaciation -> forward in time: P(future cryospheric evolution) eventual calibration of full glacial cycle ice & climate

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