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Patterns and Mechanisms of Decadal Climate Variability

Patterns and Mechanisms of Decadal Climate Variability. Is PDO really predictable? Background Linear Inverse Modeling Model and Data Results Discussion Summary. Seshadri (Shey) Rajagopal Interannual to Decadal Predictability of Tropical and North Pacific Sea Surface Temperatures

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Patterns and Mechanisms of Decadal Climate Variability

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  1. Patterns and Mechanisms of Decadal Climate Variability • Is PDO really predictable? • Background • Linear Inverse Modeling • Model and Data • Results • Discussion • Summary Seshadri (Shey) Rajagopal Interannual to Decadal Predictability of Tropical and North Pacific Sea Surface Temperatures Matthew Newman, Journal of Climate, 2007

  2. Background Huybers and Curry, Nature 441 (2006) doi:10.1038/nature04745 • Just a reminder! • Predictability comes primarily from the OCEAN • Other sources of predictability? • Proxy data • Modeling studies • Unresolved question • How much North Pacific SST predictability affected by Tropics? 2

  3. Focus • Need for Multivariate Analysis • Representation of tropical and North Pacific SST anomalies as individual single patterns not ideal • There is interaction • Propose to apply Linear Inverse Modeling (LIM) • Empirically derive linear dynamic system 3

  4. Some Basics • Approaches to modeling Physical System (Tarantola, 2004) • Parameterization of the System • Forward Modeling • Inverse Modeling Source: HWR 521 Systems Approach to Hydrologic Modeling 4

  5. Linear Inverse Modeling • Basic Equation • Where does the inverse part come in? State evolution in time Stable and predictable linear dynamics Unpredictable white noise 5

  6. Data • HadISST 1900-2002 • EOF computed 1950-2002 Newman M., J. of Climate, 2007 6

  7. Results – Is the LIM good enough? Does the LIM reproduce the main features of the observed power spectra for Tropical Pacific? Newman M., J. of Climate, 2007 7

  8. Results – Is the LIM good enough Does the LIM reproduce the main features of the observed power spectra for North Pacific? Newman M., J. of Climate, 2007 8

  9. Results - Forecast Skill Why does the forecast skill reduce from 1yr to 2yr forecast? Newman M., J. of Climate, 2007 9

  10. Results – Tropical North Pacific Coupling Newman M., J. of Climate, 2007 10

  11. Results – Comparison to coupled GCM’s Newman M., J. of Climate, 2007 11

  12. Discussion and open Questions • Pros of LIM • Simple (assumes stable linear dynamics) • Current observations of state (SST) sufficient • Empirically derive modes • Allows predictability up to 1yr lead times • Cons of LIM • Assumes stable linear dynamics • Are we getting the right answers for the right reasons • How can a system exhibit decadal variability and yet have skillful predictability for perhaps only 1 year lead times? 12

  13. Real world application 13 http://www.cdc.noaa.gov/forecast1/PDO.html

  14. Summary • LIM can explain observed power spectra of annual mean Tropical and North Pacific SST on interannual and interdecadal timescales. • Interaction between North Pacific and tropics appears poorly represented in coupled GCMs. • LIM can be used to constrain GCMs • Decadal SST predictability does not equal decadal atmospheric predictability or predictability over land 14

  15. References • Newman, M., 2007, Interannual to decadal predictability of tropical and north pacific sea surface temperatures, Journal of Climate • Penland, C., 1989, Random forcing and forecasting using principal oscillation pattern analysis, Monthly Weather Review, 117 • Penland, C., Magorian, T., 1993, Prediction of Nino 3 sea surface temperatures using linear inverse modeling, American Meteorological Society. • Inverse Problem Theory, Albert Tarantola, 2004, SIAM • http://www.cdc.noaa.gov/forecast1/PDO.html 15

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