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Decadal Climate Predictability and AMO Fluctuations

Explore decadal climate predictability and Atlantic Multidecadal Oscillation (AMO) fluctuations, their impact on climate systems, and the predictability experiments. Investigate AMO's influence on atmospheric circulation and its predictability.

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Decadal Climate Predictability and AMO Fluctuations

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  1. Decadal Predictability and Predictions Thomas DelworthGFDL/NOAA Collaborators: Keith Dixon, Shaoqing Zhang, Tony Rosati, Matt Harrison, Rong Zhang, Fanrong Zeng, Hyun-Chul Lee Initial Value Problem Weekly-Seasonal Decadal Climate Variability And Change Boundary Value Problem Multidecadal to Centennial Climate Change

  2. Decadal predictability and predictions • Predictability arising from knowledge of future changes in radiative forcing agents, and climate system response to those changes. • Predictability arising from initial state of the system - “committed warming” - natural variability of the system

  3. Decadal predictability and predictions • On multi-year to decadal scales, is there any predictability associated with the initial state of the system? - What phenomena might give rise to such predictability? • Can we realize that predictability? - Dependence on models, observations, initialization • Are the benefits of realizing that predictability “worth the cost”?

  4. Outline • Describe one phenomenon that might give rise to decadal scale predictability – observational and modeling results • Provide preliminary results from predictability experiments • Briefly discuss observational and assimilation system requirements • Summary and outlook

  5. Atlantic Ocean Temperature (70oW-0oW,0oN-60oN)

  6. Reconstruction of Atlantic Multidecadal Oscillation (AMO) Gray et al., 2004

  7. Atlantic meridional overturning circulation

  8. SST Change: 1940-1960 minus 1971-1990

  9. Evidence (instrumental, paleo, modeling) that something like the Atlantic Multidecadal Oscillation exists • Lack adequate theoretical understanding • AMO remains a viable hypothesis for some of the observed Atlantic changes over the last century KEY QUESTIONS: Does the AMO impact large-scale atmospheric climate? Can we predict AMO fluctuations?

  10. Hybrid coupled model- based on GFDL CM2.1 Global Atmosphere/Land System Heat Water Momentum Heat Heat Water Mom. Pacific Dynamic Ocean Atlantic Slab Ocean Indian Dynamic Ocean GFDL CM2.1 2o atm 1/3 to 1o ocn nomads.gfdl.noaa.gov/CM2.X Constant Flux Adjustment Time varying heating to induce AMO-like SST variations

  11. Regression of modeled LF JJAS Rainfall Anomaly on modeled AMO Index Modeled AMO Index Regression of observed LF JJAS Rainfall Anomaly (CRU data) on observed AMO Index Observed AMO Index

  12. Regression of LF ASO vertical shear of zonal wind (m/s) on AMO index (1958-2000) ECMWF 40-yr Reanalysis MODEL (10-member ensemble mean)

  13. Simulated multidecadal JJAS surface air temperature difference (K) (1931-1960) –(1961-1990)

  14. Summary so far … • AMO fluctuations • Generate multidecadal fluctuations in Sahel and Indian summer rainfall • Modulate the vertical shear of the zonal wind over the main development region for Atlantic hurricanes • Influence summer temperature over North America and Europe • Crucial issues: • How much of AMO-like behavior is internal variability versus forced climate change? • To what extent are AMO fluctuations generated internally in the Atlantic versus forced from other parts of the globe?

  15. SST anomalies associated with interdecadal MOC fluctuations Small Tropical Amplitude

  16. Anomalous poleward heat transport in Atlantic/Arctic associated with MOC maximum Atlantic Heat Transport (1014 Watts) MOC maximum MOC increasing MOC weakening

  17. JJA Precipitation Anomalies Associated with Maximum MOC Units: cm/day

  18. JJA: Change in Vertical Shear of zonal wind (850mb-300mb) Associated with Maximum MOC

  19. Key Issues • What sets the timescale? (spectral peak around 20 yrs) • How robust are these fluctuations? • Are these related to the observed AMO? • “Should” there be a larger tropical signal associated with these? • NEXT: Predictability of these fluctuations. • LATER: Issues of initialization for prediction.

  20. The N. Atl. MOC in the 1860 Control

  21. Ensemble starting at year 1101

  22. Ensemble starting at year 1201

  23. Air temperature Histogram, 30N-90N

  24. Looking at 21st Century SimulationsProjected Atlantic SST Change (relative to 1991-2004 mean) Areal average 70oW-0oW 0oN-60oN Results from GFDL CM2.1 Global Climate Model (SRES A1B) Observed Trend from 1950-2004

  25. Key Issues: • How much impact is there for continental climate? Results to date are mixed, even in perfect predictability experiments. • Does this translate into predictability of atmospheric circulation of climatic relevance (ie, tropical conditions relevant to hurricanes; Pacific SST patterns of relevance for North American drought). • Are our current models a fair evaluation of the actual predictability in the system? - Are our models good enough? - Do model atmospheres interact with the ocean realistically? - Are we missing inherent types of oceanic variability? (d) Are observing and assimilation systems up to the challenge?

  26. Impact of observational network on “observation” of MOC CONCLUSION: ARGO network plus atmospheric assimilation allows accurate “observation” of MOC in perfect model context. (S. Zhang, personal communication)

  27. Summary and Discussion • Decadal prediction is a mixture of boundary forced and initial value problem • Changing radiative forcing will be a key ingredient, particularly aerosols that can change more rapidly • On multi-year timescales there is some basis for predictability, probably originating in ocean • Substantial challenge for models, observations, and assimilation systems • Unclear what the cost/benefit is – does this add much to the radiatively forced component? • Some of predictability will arise from unrealized climate change already in the system

  28. Directions and needed activities • AMO is one potentially predictable phenomenon … others? • Predictability experiments of various sorts to quantify what can be predictable (given current capabilities) • Improved models • Sustained observation systems – ARGO looks quite promising • Theoretical work on dynamical underpinning of phenomena that may give rise to decadal predictability (AMO and others) • Challenge: If conditions were ripe for another “Dust Bowl” or “mega-drought”, would we know it?

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