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Paleoclimate Reconstruction via Data Assimilation

Paleoclimate Reconstruction via Data Assimilation. Nathan Steiger University of Washington Department of Atmospheric Sciences Advisors: David Battisti , Greg Hakim, Gerard Roe. Data Assimilation: An Analogy. Ultimate Goal: A better climate reconstruction.

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Paleoclimate Reconstruction via Data Assimilation

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  1. Paleoclimate Reconstruction via Data Assimilation Nathan Steiger University of Washington Department of Atmospheric Sciences Advisors: David Battisti, Greg Hakim, Gerard Roe

  2. Data Assimilation: An Analogy

  3. Ultimate Goal: A better climate reconstruction gdargaud.net/Antarctica/Epica.html ncar.ucar.edu/

  4. Data Assimilation Update Step: A Picture Story Ensemble + Observation  Analysis Analysis Mean

  5. Data Assimilation: An intelligent blending of a model with observations • Ensemble Kalman Filter (EnKF) • Uses an ensemble of model states • Functions well with sparse observation networks • I use a variation on the EnKF called the “Ensemble Square Root Filter” (EnSRF) • Proxies are naturally time-averaged • The algorithm can incorporate time-averaged data

  6. Why Data Assimilation? • Climate reconstruction (via an optimized recipe for mixing data + model) • Fill in gaps and override observations if they are inconsistent with model physics • Doesn’t assume stationary statistics • Does the relationship between observations and patterns of climate variability stay the same over time? • Are there fixed patternsor modes of climate variability?

  7. Why an Ensemble? • Optimal proxy locations • Given that we have observations/proxies at x1,x2,x3,… where would be the best locations to choose next? • Optimal locations are not always where you would initially think • (This sort of process is already used in weather forecasting)

  8. Statistical Reconstruction:What has been done before? Mann et al. GRL 1999

  9. Statistical Reconstruction: Principle Components

  10. Statistical Reconstruction

  11. Statistical Reconstruction vs. Data Assimilation • Reduce the number of observations • Add error to the observations

  12. Data Assimilation: A Proof of concept PCA Method Cumulative Variance

  13. Data Assimilation: A Proof of concept PCA Method Data Assimilation

  14. Data Assimilation: A Proof of concept • PCA projects onto a fuzzy version of the global temperature changes when only a few PCs are utilized • Data assimilation seems to capture regional detail and variances that PCA does not

  15. Future Directions • Improved Climate Reconstruction • How do observation errors and observation density affect the reconstruction? • Do assimilating other climate fields (not temperature) give better performance? • Regional reconstructions: What does Greenland tell you about Europe?

  16. Future Directions • Optimal Proxy Network • Where and what number of locations are best? • Do current proxy locations tell us enough? • Decadal Variability • If local records happen to reflect local persistence, can DA spread around that information effectively?

  17. Acknowledgements • Greg, Gerard, & David • My fellow graduate students

  18. Corrected Hockey Stick After Huybers GRL 2005

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