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Multidecadal Variability of the Atlantic Meridional Overturning Circulation in CFS

Explore the long-term fluctuations in the Atlantic Meridional Overturning Circulation (AMOC) and its connection to the Atlantic Multidecadal Oscillation (AMO). Investigate AMOC feedback loops, AMO index defined by Enfield et al., AMOC simulation in CFS with GFS atmospheric component, and MOM3 oceanic component. Understand potential mechanisms of AMOC variability and the significance of ocean-atmosphere interaction in AMOC oscillation. Detailed analysis of AMOC modes, climate feedback processes, and leading MSSA modes. Participate in scientific discussions on AMOC dynamics and CFS modeling system.

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Multidecadal Variability of the Atlantic Meridional Overturning Circulation in CFS

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  1. Long-term Variability of the Atlantic Meridional Overturning Circulation in the NCEP Climate Forecast System Bohua Huang Acknowledgment: Z. Hu, E. Schneider, Z. Wu, K. Pegion, J. Manganello, Y. Xue

  2. Hydrographic Cross Section W. Atlantic Evidence of Deep Flows in Atlantic North Atlantic Deep Water (NADW) Salty Moving southward within 2-4 km Antarctic Bottom Water (AABW) Fresher, colder Moving northward near bottom Po. Temp Salinity Oxygen From Tomczak and Godfrey, Regional Oceanography

  3. NADW is Coupled with Upper Ocean Circulation Atlantic Meridional Overturning Circulation(AMOC) From Latif et al. (2009), 1st US AMOC Meeting

  4. Multidecadal AMOC Oscillation is the leading candidate for the Atlantic Multidecadal Oscillation (AMO) AMO index defined by Enfield et al. (2001)

  5. Potential Mechanisms of AMOC Variability Most evidence points towards the “ocean-only” oscillator (Latif) From Latif et al. (2009), 1st US AMOC Meeting

  6. Scientific Questions • What role does the ocean-atmosphere interaction play in the multidecadal AMOC oscillation? • Does the tropical-subtropical Atlantic Ocean also play a part in the multidecadal AMOC oscillation?

  7. CFS Simulation • CFS-v1, operational system for seasonal to interannual prediction at NCEP since August 2004 • Atmospheric component: GFS (2003), T62 (~200 km), 64 sigma levels (prescribed sea ice cover affects surface flux into the ocean) • Oceanic component: MOM3, 1ox1o (1/3o lat within 10oS-10oN), 40 levels, non-polar (70oS-65oN) no transport between Atlantic and Arctic Ocean no sea-ice formation mechanism • Daily coupling over active ocean domain (without flux correction) • Initial condition: January 1, 1985, Atmosphere: NCEP Reanalysis 2; Ocean: GODAS • Integration is ongoing, 400-yrs done

  8. AMOC Index Year-to-Year Interannual Decadal Multidecadal Century

  9. AMOC Index Year-to-Year Interannual Decadal Multidecadal Multidecadal + Century

  10. Leading MSSA (EXEOF) Modes, 5-yr Running Mean 30-Year Lags MSSA1 -PC1 -EEMD C Century Mode MSSA 1(22.3%) -PC1 -EEMD M Multidecadal Mode MSSA2-3 (7.6%+6.4%) MSSA2-3

  11. Multi-Decadal Mode

  12. Multi-Decadal Mode

  13. Multi-Decadal Mode

  14. Multi-Decadal Mode

  15. Multi-Decadal Mode

  16. cm/s

  17. AMOC Feedback Loops Strong AMOC Warm HC, SST around 30o-45oN Strong NAO Downwelling Strong AMOC Weak Subtr. High Warm Subtr. SST Weak N.E. Trade Wind Delayed AMOC Weakening Cold Subtr.HC Weak STC Weak wind Curl Subtr.Upwelling

  18. AMOC leads AMOC lags

  19. 65% First EOF and spectrum of accompanying principal component of the annual mean AMOC stream function, SST and Northern Hemispheric 500-hPa geopotential as simulated by a 1000-yr integration of the ECHAM3-LSG model. The standard deviations of the corresponding principal components are 6.71 Sv, 1.67 K, and 114 gpm, respectively (From Grötzner et al., 1999, J. Climate).

  20. The pattern of AMOC EOF1 from CCSM3 and its time series with power spectrum and autocorrelation (from Danabasoglu, 2008, J. Climate).

  21. Summary • AMOC fluctuates on a wide range of time scales in CFS. • An intermittent multidecadal (30-yr) oscillation is generated by ocean-atmosphere feedback within the Atlantic sector. • Delayed response of the northern subtropical cell is crucial for the oscillation.

  22. Leading MSSA (EXEOF) Modes, Seasonal mean Lag 20 seasons (5-yrs) MSSA1-2 (3.7%+3.6%) EOF1 EOF2 68.2% 28.4% ENSO Mode PC1 COR 0.57 NINO3 lags 1 season —Normalized NINO3 index PC2 COR 0.52 NINO3 leads 5 seasons

  23. ENSO Mode

  24. ENSO Mode

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