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Hodson D., R. Sutton & J.Syktus Department of Meteorology, University of Reading, Reading, UK

The Influence of the Ocean on the North Atlantic Climate Variability 1871-2003 in C20C simulations with CSRIO AGCM. Hodson D., R. Sutton & J.Syktus Department of Meteorology, University of Reading, Reading, UK Department of Natural Resources, Mines and Energy, Brisbane, Australia.

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Hodson D., R. Sutton & J.Syktus Department of Meteorology, University of Reading, Reading, UK

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  1. The Influence of the Ocean on the North Atlantic Climate Variability 1871-2003 in C20C simulations with CSRIO AGCM Hodson D., R. Sutton & J.Syktus Department of Meteorology, University of Reading, Reading, UK Department of Natural Resources, Mines and Energy, Brisbane, Australia

  2. Variability of the North Atlantic Oscillation and the Tropical Atlantic dominate the climate of the North Atlantic sector and surrounding continents on interannual and decadal timescales. (Marshall et al., I.J.Clim., 21, 2001)

  3. Regression of NH MSPL onto first PC of MSLP anomalies of NA sector

  4. J.Climate v.10, 929-948 1997

  5. Some observations from this study • NAO index in response to imposed freshening was of negative sign • The response in low latitudes lagged that of polar region by more than decade • Response in ocean SST in North Atlantic was dipole like structure, cooling in sinking region and warming south of it. • There is a link between NAO, TAC and MOC – a coupled system

  6. CSIRO Mk3 AGCM Model • Atmosphere Grid: T63 (1.88o x 1.88o) 18 levels - hybrid ,p Semi-Lagrangian moisture transport UKMO convection (Gregory & Rowntree) Liquid water clouds (Rotstayn) • Land surface Soil model - 6 levels Temperature, water, ice 9 soil types 13 land surface and/or vegetation types Snow-cover model - 3 layers

  7. Experiments & Analysis Ensemble of 5 simulations for 1871-2003 each • SST (HadISST 1.1) only • SST and solar (monthly, Lean) • SST, solar and CO2 • Optimal detection – an objective method to identify which aspects of ocean variability have most influenced the atmosphere (extension of Sutton & Hodson 2002 J.Climate paper). Analysis of MSLP for North Atlantic region 0-80N, 90W-30E. • Timescale separation • Evaluation of the relative role of SST and additional radiative forcing factors • HadAm3 SST 1871-1999 comparison

  8. Sutton & Hodson, 2003 results • Ocean influence on multidecadal timescales is dominated by a single mode that is associated with changes in North Atlantic SST, has strong projection on the NAO in wintertime, and may be response to THC fluctuations. • On interannual timescales the climate of the Atlantic region is influenced by the Pacific ENSO and also by SST anomalies in the Atlantic (mainly tropical North Atlantic).

  9. Optimal detection method and further analysis • The resultant patterns represent the dominant modes of forced variability. • Unbiased estimate of of the dominant modes (EOF pattern) of forced variability + timeseries (PC) which describe the time evolution of the forced response. • We regress the SST forcing field onto the PC timeseries of MSLP to locate potential oceanic forcing regions. • Notes: PC timeseries standardized to have unit variance, • EOF plots are in hPa/stdev • SST regression- contours are regression coef in hPa/stdev of associated timeseries. Shading show the fraction of the variance in SST explained by the timeseries/PC x the sign of the regression coef.

  10. Influence of the Oceans on North Atlantic Climate Variability

  11. Leading modes of SST-forced MSLP variability in winter (DJF) - SST

  12. There is seasonality to the dominant mode of forced variability DJF MAM JJA SON

  13. Leading mode 1 for DJF x 4 Positive trend in the NAO during 1950-2003

  14. The SST+S+GHG case show strengthened meridional pressure gradient between the two centers of action of the NAO and NE shift Similar to results reported by Hu & Wu (COLA, CRT 127, 2002; The intensification and shift of the NAO in a global warming scenario simulation)

  15. Leading mode 2 for DJF x 4

  16. The second leading mode for SST+S case show strengthened meridional pressure gradient between the two centers of action of the NAO, however when CO2 forcing added there is a significant decrease in the meridional pressure gradient

  17. Key points • Dominant mode is a N-S dipole in MSLP • Strongly related to ENSO (SST regression) • Associated timeseries display multidecadal variability • Positive trend simulated in the NAO during the recent decades, in agreement with observation. • HadAM3 slightly different, CSIRO mode 1 strongest link to ENSO region.

  18. Low Pass Filter – Decadal Signal (DJF) x 4

  19. Leading mode of SST-forced low frequency variability • Associated timeseries display multidecadal variability. Low frequency signal show also similar Atlantic dipole in SST, similar to patterns seen in THC experiments • Had model different PC, data to 1994 only • Timescales and SST pattern suggest THC is likely driver • NAO response in future - a product of decadal and climate change – need to reconcile climate change projections and current climate state

  20. Linear trends in MSLP 1961-2003 DJF

  21. Linear trends in 2m Temperature 1961-2003 DJF

  22. Linear trends in Precipitation 1961-2003 DJF

  23. Preliminary conclusions • All the models & experiments indicate a significant influence of SST variability on North Atlantic climate • The dominant winter time mode of SST-forced variability in MSLP is an NAO-like dipole pattern, linked to SST variability in the tropical North Atlantic region strongly related to ENSO • All the models simulate a positive trend in the NAO during the recent decades, as was observed. At the same there was a cooling trend in the N.Atlantic and a warming in the Indian Ocean • Associated time series display multi-decadal variability. Low frequency signal show also similar Atlantic dipole in SST, similar to patterns seen in THC experiments. This suggests that this pattern may be due to multi-decadal variation in THC. • Variability in SST has a significant impact on the climate of North Atlantic and the THC driven changes may influence global climate.

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