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Alexander Gershunov Climate Research Division Scripps Institution of Oceanography

VASCILLATIONS OF THE TROPICAL AND NORTH PACIFIC CO-RELATION DIAGNOSED IN OBSERVATIONS, PROXY RECONSTRUCTION, AND IN COUPLED MODELS. Alexander Gershunov Climate Research Division Scripps Institution of Oceanography Michael Evans and Malcolm Hughes Laboratory of Tree-Ring Research

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Alexander Gershunov Climate Research Division Scripps Institution of Oceanography

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  1. VASCILLATIONS OF THE TROPICAL AND NORTH PACIFIC CO-RELATION DIAGNOSED IN OBSERVATIONS, PROXY RECONSTRUCTION, AND IN COUPLED MODELS Alexander Gershunov Climate Research Division Scripps Institution of Oceanography Michael Evans and Malcolm Hughes Laboratory of Tree-Ring Research University of Arizona Hervé Douville Météo-France Centre National de Recherches Météorologiques Past PDO Group 1

  2. Part of the Motivation • ENSO/NPO interaction is important for North American climate variability and prediction • Relationship appears to have been weakening lately • Lets try to figure out why

  3. ENSO-PDO relationship (EPR)

  4. EPR

  5. Part 1 Past EPR estimated from Pacific SST proxy reconstruction Historical context provided for the observed EPR Part 2 Several possibilities for future EPR evolution provided by CGCMs Theoretically, this provides a climate change context for the observed EPR Outline

  6. Part ISea Surface Temperature Pattern Reconstruction Atmospheric Circulation Precipitation and temperature patterns Forcing Patterns Trees Growing Rings Statistical Reconstruction Model SST

  7. Statistical Methodology • Predictor: Proxy chronologies spanning a large geographic region • Predictand: Observed monthly SST over a relevant geographic area • Statistical model: • Predictor and Predictand fields are pre-filtered with p Principal Components (PCs) • Patterns of variability in the Predictor and Predictand fields represented by their p respective PCs are related to each other via k canonical correlates derived from Canonical Correlation Analysis (CCA). k  p « T, where T is the number of temporal observations available for model training • The optimal statistical model is defined by considering cross-validated measures of skill for all reasonable combinations of p and k displayed on the Skill Optimization Surface (SOS) • Reconstruction:Patterns in pre-instrumental Proxy record are used with the optimal statistical model defined on the observational overlap to reconstruct pre-instrumental annual SST fields.

  8. SST – Proxy coupling: An example of climate patterns used in reconstruction CC1: the Pacific global warming signature Top: Coupled CCA mode 1 (CC1) ofApril-March annual average SST (colors) related with Proxy data (circles) Right: Temporal evolution of leading coupled mode

  9. SST – Proxy coupling: An example of climate patterns used in reconstruction CC2: ENSO and PDO Top: Coupled CCA mode 2 (CC2) ofApril-March annual average SST (colors) related with Proxy data (circles) Right: Temporal evolution of second coupled mode

  10. SST – Proxy coupling: An example of climate patterns used in reconstruction CC3: Pacific Decadal Variability Top: Coupled CCA mode 3 (CC3) ofApril-March annual average SST (colors) related with Proxy data (circles) Right: Temporal evolution of third coupled mode

  11. SST – Proxy coupling: An example of climate patterns used in reconstruction CC4: ENSO Top: Coupled CCA mode 4 (CC4) ofApril-March annual average SST (colors) related with Proxy data (circles) Right: Temporal evolution of fourth coupled mode

  12. Model Diagnostics I: Optimal Model Complexity and Reconstruction Skill

  13. Model Diagnostics II: Space-Time Structure

  14. Model Diagnostics II: Space-Time Structure

  15. Model Diagnostics III: Autoregressive Order

  16. Model Diagnostics III: AR(1) Structure

  17. Model Diagnostics III: AR(2) Structure

  18. Model Diagnostics III: AR(3) Structure

  19. Pacific SST Reconstruction: Time-Space Structure

  20. Pacific SST Reconstruction: Time-Space Structure

  21. ENSO reconstructed and observed

  22. PDO Reconstructed and Observed Leading PC of SST north of 20N

  23. PDO Reconstructed and Observed

  24. PDO Reconstructed and Observed

  25. ENSO-PDO Relationship

  26. Reconstruction Conclusions • Reconstruction provides a context for interpreting the observed EPR: • The recent observed weakening EPR does seem to be unprecedented but … • Reconstructed EPR may be unrealistically stable because reconstruction model was trained on a stable EPR period (1900-1980)

  27. Part IICGCMs • Are CGCMs relevant tools for looking at the ENSO – PDO relationship? • Three coupled models: SRES B2 • CNRM: 1950 - 2099 • CCCMA: 1900 - 2100 • CCSR: 1890 - 2100

  28. CNRM

  29. CCCMA

  30. CCSR

  31. CCSR

  32. All Coupled models

  33. SRES B2 NINO3.4 NINO3.4 in CNRM NINO3.4 in CCCMA NINO3.4 in CCSR

  34. NINO3.4 spectra

  35. SRES B2 PDO PDO in CNRM PDO in CCCMA PDO (PC1) in CCSR PC2 (PDO) in CCSR

  36. CGCM conclusions • Although the three models considered here appear to be consistent with each other in their portrayal of EPR, • They are inconsistent with each other in their simulations of ENSO and the PDO • They are all, in their own special ways, inconsistent with the observations. PDO and ENSO characteristics are not well reproduced in any of the three models. • In the models, EPR is weaker, while its variability is stronger than observed. It is not influenced by global warming. • This is consistent with the interpretation that EPR variability is due mainly to sampling variability, at least in the models

  37. Overall Conclusions • Observed weakening of the EPR is highly significant compared to the reconstructed past. However, • Reconstruction of EPR may be flawed because the model was trained on strong EPR period and ENSO + PDO is one of the most prominent “reconstructed” modes • CGCMs cannot provide insight into the causes for the recent observed weakening of EPR The fact is that ENSO-PDO relationship, like all such relationships, is variable

  38. BETTER ASK SIMPLER QUESTIONS

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