# Variability in the North Pacific Physical Mechanisms - PowerPoint PPT Presentation

Variability in the North Pacific Physical Mechanisms

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Variability in the North Pacific Physical Mechanisms

## Variability in the North Pacific Physical Mechanisms

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1. Variability in the North PacificPhysical Mechanisms Christopher L. Castro AT 750

2. Plausible Atmospheric Mechanisms of North Pacific Variability The Null Hypothesis of Stochastic Variability (Deser et al. 2002) Interannual fluctuations in North Pacific SSTs arise solely as a result of stochastic (random) forcing from the atmosphere, so there are no peaks in the SST power spectra which exceed the confidence interval for a red noise process. When a re-emergence mechanism is accounted for, this may explain SST variability for a period of several years. The Atmospheric Bridge (Alexander et al. 2002) North Pacific SSTs are linked to tropical SSTs via atmospheric teleconnection patterns, which affect the ocean via latent and sensible heat exchanges and wind stress. These change SSTs, mixed layer depth, and ocean currents. Internal ocean variability is ignored in this discussion, but, as suggested for the Atlantic before, may also play a role in long term Pacific variability

3. The Stochastic Null Hypothesis: Original Frankignoul and Hasselmann Model Frankignoul and Hasselmann (1977) conceived a very simple model for the time rate of change of SSTs: SST anomaly ρ Density of seawater White noise atmospheric forcing cp Heat capacity of seawater λ Fixed thermal damping parameter H Fixed ocean mixed layer depth

4. Stochastic Null Hypothesis:Original FH Model Using this simple model the lag autocorrelation (r) per time lag (τ) is: Strength Provides a simple and reasonable hypothesis for SST variability anywhere which potentially more physically interesting possibilities may be tested against. Weakness Yields an E-folding time of three to six months for extratropical SSTA with realistically prescribed damping and mixed layer depth, so no memory on annual or longer timescale.

5. The Re-Emergence Mechanism • What is it? • In nature, the mixed layer depth is not fixed, but varies seasonally. • Vigorous air-sea exchanges in winter create perturbations in mixed layer • temperature which can extend 100-200 m in depth. • The winter anomalies are then sequestered underneath the thermocline • in spring as the mixed layer shallows to a depth of 20-30 m. • When the mixed layer deepens the following winter, a portion of the • previous winter’s thermal anomalies may become re-entrained in the • mixed layer and affect SSTAs. • Originally described by Alexander and Deser (1995) and lead to a reconsideration of the stochastic null hypothesis. • Deser’s hypothesis: Incorporating the re-emergence mechanism in a modified FH model should extend the persistence in SSTA beyond a time scale of three to six months.

6. Data SST (1948-1997) Comprehensive Ocean Atmosphere Dataset (COADS) Global Sea-Ice Sea Surface Temperature (GISST) in which missing SSTs are filled in using and EOF-based technique Subsurface Temperature (1960-92, Scripps) Temperatures at 11 levels to 400 m depth, collected by bathymographs and Nansen bottle casts. Subjected to optimal interpolation. Climatological Mixed Layer Depth (Levitus) Model results Integrations on the order of thousands of years for stochastic ocean models.

7. Modified to FH Model to account for Re-emergence Using original FH model, replace the fixed mixed layer depth H with an effective mixed layer depth (Heff) defined by the maximum depth of the mixed layer in the winter season. Heat content anomaly of the ocean to the maximum winter depth of the thermocline (Heff) is substituted for SSTA. This will then account for “sequestered” water from the previous winter underneath the shallow summer thermocline.

8. Heat Content vs. SSTA Heat content SSTA Heat content anomalies decay smoothly in time. SSTA lag autocorrelation decreases in summer and then rebounds the following winter. The leading EOF of heat content in the Pacific resembles the PDO as defined by Mantua et al. (1997)

9. NP Lag Autocorrelation Results FH Model with Heff Modification Observed SSTA Observed heat content anomaly SSTA, original FH model Heat content anomaly, modified FH model Heat content anomaly, modified FH model with λ =0 in summer *Similar figure for North Atlantic

10. Entrainment Modification to FH Model to Account for Re-emergence Deser et al. also considered a more complicated modification of the original FH model that accounts for entrainment. This time we consider the actual SSTA instead of the heat content anomaly. Temperature anomaly below mixed layer Seasonally varying thermal damping parameter λ we Entrainment velocity (=dH/dt) Seasonally varying ocean mixed layer depth H

11. NP Lag Autocorrelation Results FH Model with Entrainment Modification Observed SSTA SSTA, FH entraining model Heat content, FH entraining model (Step-curve) Heat content, FH model with Heff A stochastic model which accounts for re-emergence can explain persistence of NP SSTAs for several years. Get nearly identical results for a “full physics” AOGCM (not shown)

12. Observed Winter SST Autocorrelation vs. FH model with Entrainment The observed pattern of winter SST autocorrelation is very close to that predicted by the FH model with entrainment. Greatest discrepancy where the mixed layer depth is shallow, near coastlines.

13. Is stochastic forcing the whole story of Pacific SST Variability? Even for very large winter mixed layer depths such as occur in the far North Atlantic where the thermohaline circulation overturns, SSTs will never be persistent beyond a timescale of five years or so. Though the re-emergence mechanism is physically valid, it cannot enhance variability of SSTAs at the interdecadal timescale, and thus cannot account for the PDO. Far North Atlantic Mid-latitude North Pacific Variation of e-folding times with increase in Heff using modified FH model

14. The Atmospheric Bridge

15. How does the Atmosphere Force NP SSTA in a Canonical El Nino Event? El Nino is associated with positive phases of the PNA and NP patterns in winter which occur via a teleconnection response to tropical Pacific SST (e.g. Hoskins and Karoly 1981) Strengthened Aleutian Low Stronger northwesterly winds advect cold air over the central North Pacific. Stronger southerly winds advect warm air in the eastern Pacific extending to the west coast of North America The anomalous sensible and latent heat fluxes alone lead to the NP SST EOF, implying they are the dominant mechanisms. Wind stress (Ekman transport) enhances the anomaly in the central North Pacific, but actually damps it elsewhere

16. Evaluation of the Atmospheric Bridge in an AOGCM Framework The atmospheric bridge mechanism is evaluated through three sets of long-term AOGCM simulations with multiple ensembles. Alexander et al.’s methodology with GFDL model is typical Tropical Ocean Global Atmosphere (TOGA): Seasonally-evolving climatological SSTs specified everywhere except the tropical east Pacific, where SSTs follow observations. No interactive ocean. Mixed layer model (MLM): Couples the atmosphere with a grid of column ocean models at each model grid point outside the tropical Pacific. Effect of tropical Pacific + other tropical oceans on extratropical SSTs. North Pacific Mixed Layer Model (NP-MLM): Couples the atmosphere with ocean mixed layer model at grid points in the North Pacific only and specifies climatological SSTs everywhere else except tropical Pacific. Effect of tropical Pacific only on extratropical SSTs

17. Canonical El Nino – La Nina DifferencesSST and MSLP Reanalysis Observations MLM Experiment Tropical Pacific SSTs are the dominant player in forcing North Pacific SST variability, and the other oceans play minor roles.

18. Canonical El Nino – La Nina MLM Differences: Contributions to NP SST Anomaly Surface sensible and latent heat fluxes Entrainment Wind (Ekman) stress

19. Reconciling the Atmospheric Bridge and Re-emergence Mechanisms As in nature and in the simple stochastic climate model, the MLM captures the seasonal cycle of a deepening mixed layer which shoals in summer and re-emerges the following winter. The ENSO cycle appears to superimpose longer term variability on this process. Composite El Nino – La Nina ocean temperature differences and El Nino and La Nina mixed layer depth in the central North Pacific for MLM experiments.

20. Towards a Coherent Picture of the PDO Re-Emergence Mechanism Atmospheric Bridge Timescale of atmospheric forcing Days to week (“white noise”) Month to a whole season Atmospheric Forcing Mechanism to SST Baroclinic mid-latitude storms Equivalent barotropic teleconnection responses to tropical SSTs ?? Timescale of ocean response A few years A few decades?