Paleoclimate reconstruction via data assimilation
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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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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

Data Assimilation: An Analogy


Ultimate goal a better climate reconstruction

Ultimate Goal: A better climate reconstruction

gdargaud.net/Antarctica/Epica.html

ncar.ucar.edu/


Data assimilation update step a picture story

Data Assimilation Update Step: A Picture Story

Ensemble

+ Observation

 Analysis

Analysis Mean


Data assimilation an intelligent blending of a model with observations

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


Why data assimilation

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?


Why an ensemble

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)


Statistical reconstruction what has been done before

Statistical Reconstruction:What has been done before?

Mann et al. GRL 1999


Statistical reconstruction principle components

Statistical Reconstruction: Principle Components


Statistical reconstruction

Statistical Reconstruction


Statistical reconstruction vs data assimilation

Statistical Reconstruction vs. Data Assimilation

  • Reduce the number of observations

  • Add error to the observations


Data assimilation a proof of concept

Data Assimilation: A Proof of concept

PCA Method

Cumulative Variance


Data assimilation a proof of concept1

Data Assimilation: A Proof of concept

PCA Method

Data Assimilation


Paleoclimate reconstruction via data assimilation

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


Future directions

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?


Future directions1

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?


Acknowledgements

Acknowledgements

  • Greg, Gerard, & David

  • My fellow graduate students


Corrected hockey stick

Corrected Hockey Stick

After Huybers GRL 2005


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