What Uncertainties Exist in Tropical SST Reconstructions Derived From Tropical Precipitation Records? Jason C. Furtado Advisor: E. Di Lorenzo School of Earth & Atmospheric Sciences Georgia Institute of Technology EAS Graduate Student Symposium 2 November 2007

Previous Work Single Proxy Record Multiple Proxy Records Cobb et al Evans et al Palmyra Coral Reconstructed Leading SST Mode

Aims of the Study Compare two popular climate field reconstruction methods. Examine the uncertainties associated with each method. Evaluate the performance of a paleo-precipitation proxy network. Use tropical precipitation records to reconstruct tropical SSTs.

Data & Methods Precipitation CMAP (Xie and Arkin 1997) Output from International Center for Theoretical Physics (ICTP) AGCM (Molteni 2003) ERA-40 SSTs - NOAA ER SSTs (Smith and Reynolds 2003) Annual-mean anomalies used Spatially smoothed and detrended Reconstructions are done from

Reconstruction Methods EOF METHOD Premise: SSTs and precipitation are dynamically (and statistically) linked in the tropics. Regression Coefficient 1) EOFs are time invariant 2) 3)

Reconstruction Methods MULTIPLE REGRESSION Premise: SSTs and precipitation are dynamically (and statistically) linked in the tropics. Least-squares fitting (obtain optimal linear estimator E). Only retain first few covariability modes. Cross-validation method to test for robustness. EOF METHOD Regression Coefficient 1) EOFs are time invariant 2) 3)

How Good Are The Reconstructions? RMS Error: Skill: Spatial Correlation: Averaged over all 22 reconstructions

Evaluation - EOF Method RMS ERRORSKILL CMAP ICTP MODEL OUTPUT ERA-40

Evaluation -Multiple Regression RMS ERRORSKILL CMAP ICTP MODEL OUTPUT ERA-40

Spatial Correlations Correlation EOF Method Multi-Regression Mean r = 0.73 ICTP EOF Method Multi-Regression Mean r = 0.75 CMAP ERA-40 EOF Method Multi-Regression Mean r = 0.76 Mean r = 0.45 Mean r = 0.52 Mean r = 0.45

Why is Multiple Regression Better? 1st Left Singular Vector 2nd Left Singular Vector 1st Right Singular Vector 2nd Right Singular Vector Dynamical Response to ENSO Dipole (Tripole) in Precipitation SST Precip.

Proxy Network Tree Rings Corals Marine Sediments Lake Sediments Speleothem Ice Cores Use multiple regression method with CMAP data from only these points for SST reconstructions

Evaluation - Proxy Network ~20% decrease in skill in the tropical Pacific and ~50% in the Indian Ocean Designing an Ideal Paleo-Precipitation Network Use the adjoint (E T ) for sensitivity study.

But What About Stationarity? SST RSV-2 (Precip) LSV-2 (SST) Out-of-phase relationship b/t Indian and E Pacific ( ) In-phase relationship b/t Indian and E Pacific ( )

Conclusions Multiproxy tropical precipitation records effectively reconstruct tropical SSTs. The multiple regression method outperforms the EOF method, with a 20-30% improvement in skill in the tropical Pacific and much more in the Indian Ocean. The paleo-precipitation proxy network recovers almost 50% of the observed variance in tropical SSTs and 80% of the skill vs. the full tropical precipitation field. Is there a reconstruction technique that can account for the nonstationarity in the ENSO statistics / covariability modes?

Thank You! Questions?

Error Propagation Analysis Add an error term to the precipitation in the linearized relationship: Define: ; sn = signal-to-noise ratio

Error Propagation Analysis sn = 10sn = 2 Nonzero n p everywhere n p = 0 in Eastern Hemisphere n p = 0 in Western Hemisphere