Evidence for the Atlantic Multidecadal Oscillation as an internal climate mode

1. Evidence for the Atlantic Multidecadal Oscillation as an internal climate mode Jeff Knight*, Rob Allan*, Chris Folland*, Michael Vellinga*, Peili Wu* and Mike Mann† * Hadley Centre, Exeter, UK † Pennsylvania State University, USA Multidecadal to Centennial Global Climate Workshop, Honolulu, Hawaii Friday, 17th November 2006

2. AMO in observations

3. AMO in observations Large scale SST patterns (after Folland et al., 1999) HadISST Low-pass (> 13.3y) EOFs • 1911-2002 • 40ºS - 70ºN • Projections 1870-2002

4. AMO in observations Mean North Atlantic SST • ‘AMO index’ • Low-pass (> 13.3y) filtered detrended HadISST • Larger than trend or interannual Surface temperature anomaly • Regression 1870-1999 • HadCRUTv blended SST/air temp • 90% confidence interval accounting for autocorrelation Palaeoclimate – AMO back to C16-17th? • Tree rings (Gray et al., 2004) • Multiproxy (Delworth and Mann, 2000) Models show some THC-SST links • e.g. Delworth and Mann, 2000 Is the AMO long-lived/periodic? Forced or internal?

5. AR4 20th Century Forcings Coupled Ensembles

6. AR4 Ensembles Single model North Atlantic mean SST (NASST) • Example of an AR4 20c3m ensemble with few members • High inter-member variability leads to very broad uncertainty in the ensemble mean • Not easy to distinguish the observations from the possible model estimates of the forced response Grey = annual means for 3 ensemble members Red = ensemble mean Black = 90% limits of estimated ens mean Blue = Observed SST from HadSST2 All data relative to 1900-99 average

7. AR4 Ensembles Multi-model ensemble • Super-ensemble (34) using data from 11 models with natural + anthro forcings and available SST • Narrower uncertainty on ensemble mean • Range is now a function of both internal climate variability and model and forcing differences • Represents a ‘best estimate’ of the forced response • Atlantic SST is inconsistent with the forced response for much of the last 150 years Red = super-ensemble mean (34 members) Black = 90% limits of estimated ens mean Blue = Observed SST from HadSST2 All data relative to 1900-99 average

8. AR4 Ensembles North Atlantic SST trends Linear change = Trend (K/year) x Period (year) OBS minus AR4 OBS • Obs show clear multidecadal trend oscillations • Model trends are present but relatively weak • Difference therefore resembles obs • Obs trends almost always significantly different from the forced signal AR4 AVG

9. AR4 Ensembles A better AMO index? Removing a model-based estimate of the historical forced response as an improvement on • linear detrending • subtracting ‘background’ estimates based on global mean temperature Black = 90% limits of estimated ens mean Blue = Observed SST from HadSST2 All data relative to 1900-99 average

10. AR4 Ensembles Implications The inconsistency between observed North Atlantic SST and the ensemble estimate of the forced response suggests several possibilities: • The AMO is an internal mode • Models are inadequate to represent the effects of known forcings on climate* • The forcings used are incorrect or incomplete* *In the latter 2 cases, the errors would have to be specific to the Atlantic as the models perform well for the global mean

11. HadCM3 Control Simulation

12. Control Simulation 1400 Year Coupled Model Representation of the AMO 70-180 Year band Observed AMO Pattern 0° 60° 120° 180° Similar pattern and time scale to observed AMO fluctuations. Similar magnitude – North Atlantic low frequency (>45 year) standard deviation is 0.10K, 0.14K in observations. Observed AMO likely to be long-lived climate mode related to modern-day THC variability.

13. Control Simulation 1400 Year HadCM3 control simulation Maximum overturning streamfunction at 30°N Persistent band of variability between 70-120 years Compares with observed period of ~65 years (instrumental) and 40-130 years (palaeo – Gray et al. 2004).

14. Mechanism

15. Mechanism Density anomalies related to the THC Regress 0-800m averaged density onto THC At THC peak, high densities in the mid-latitude and sub-polar ocean Low densities in sub-tropical ocean Density anomalies at 60°N mostly result from the contribution of salinity anomalies, rather than thermal anomalies.

16. Mechanism Salinity leading density anomalies 0-800m salinity contribution to density regressed onto zonal mean density at 60°N First signs of positive salinity anomalies in subtropics 6 decades (half a period) before a THC peak

17. Mechanism Salinity budget analyses 0-800m mean salinity driven density tendencies regressed onto the THC In tropics (0-35°N) density increases ~ 6 decades before the peak THC, induced by surface flux forcing and removed by transport In mid-latitudes (35-48°N) density increases ~ 4 decades before, caused by transport and removed by surface flux forcing Sub-polar (48-65°N) density increases ~ 2 decades before by transport

18. Climate Impacts

19. Climate Impacts North Atlantic-European circulation response to the AMO • No winter NAO signal at any lead/lag • Anomalies typically smaller than observed multidecadal NAO change DJF MAM JJA SON Simulated precipitation regression with AMO index Simulated MSLP regression with AMO index • Summer/Autumn signal in Europe • Little sign of US summer signal (Sutton & Hodson,2005) Broadest signal in summer and autumn

20. Climate Impacts Atlantic Hurricanes – obs model comparisons HadCM3 decadal AMO-shear correlation Goldenberg main development area highlighted NCEP/NCAR reanalysis 200-850 hPa shear August-October (ASO) (1951-60)-(1971-80) Correlation of simulated main development area shear with SST HadCM3 AMO index (red), versus mean Goldenberg area shear (black) Model supports an AMO relationship with hurricane development shear, but also shows an IPO relationship. AMO and IPO are uncorrelated (0.06).

21. Conclusions

22. Conclusions • The AMO is inconsistent with an estimate of the response of Atlantic SST to natural and anthropogenic forcings from the AR4 models • Either the AMO is internal or the models or their forcings are wrong • This analysis shows an increasing AMO in recent decades • A 1400 year HadCM3 control simulation suggests the AMO is a long-lived coupled mode of climate variability associated with modern-day variations in the strength of the THC • Diagnosis of the simulated mechanism reveals a delayed salinity feedback via displacements of ITCZ rainfall caused by THC-related temperature anomalies • The simulation confirms AMO links with a range of important regional climate phenomena such as NE Brazil and Sahel rainfall, Atlantic Hurricane formation and European circulation.

23. Questions & Answers

24. Climate Impacts

25. Climate Impacts North East Brazil Rainfall NE Brazil has large multidecadal wet season (MAM) rainfall variability Simulated ITCZ shifts north and away when N Atlantic warm (AMO+)  drier NE Brazil Simulated rainfall changes similar in size to observations

26. Climate Impacts Sahel Rainfall African Sahel has large multidecadal rainfall variability JJA simulated ITCZ shifts north when N Atlantic warm (AMO+) wetter Sahel Simulated changes about one-third of those observed. Compare ITCZ shifts with Caribbean palaeo salinity variations (Schmidt et al., 2004).

27. Climate Impacts Atlantic Hurricanes – the observed relationship Major Hurricanes Goldenberg et al. (2001) claim a link between the frequency of major Atlantic hurricane formation and AMO variations in North Atlantic SST. Suggest AMO affects vertical shear in the hurricane formation region via circulation changes 1998 1944 Emanuel (2005) suggests a more direct link between SST and the integrated intensity of storms.

28. Reconstruction and Forecast of the THC

29. Reconstruction and forecast of the THC Decadal Northern North Atlantic SST as a statistical predictor  Use HadCM3 simulation to make a statistical model between SST-THC Use SST from HadISST dataset to reconstruct running decadal THC 1870-2002 

30. Reconstruction and forecast of the THC THC Predictability • Look for points in the control simulation where the THC index rises through present day (decade 1997-2003) reconstructed value (0.63 Sv) • Track the subsequent THC evolution for each of these ‘analogues’ for 6 decades. • Use these to represent the next ~35 years (observed period shorter than in model). • Natural downturn in THC in next decade, to levels of 1960s before 2030 (on average -0.70 Sv)

31. Control Simulation Coupled Model Representation of the AMO 70-180 Year band 25-125 Year band 0° 60° 120° 180°

32. Temperature (°C) Year Trend (°C decade-1) Centre year of 30-year trend AR4 Ensembles North Atlantic mean temperature North Atlantic (0°-80°W, 10°-70°N) Annual mean SST 4 member ensemble with HadCM3 (black) Solar+Volc+Anthro. Stott et al. (2000) Observed SST data from HadISST (blue) Anomalies difficult without bias 30 year trends Uncertainty in ensemble mean trend 90% limits (shaded) Inconsistent (1900-1930) to (1925-1955) Also (1945-1975) to (1965-1995) Uncertainty still large with 4 members

33. Control Simulation THC-Mean temperature cross-correlations Northern Hemisphere Southern Hemisphere Global 0.09°C Sv-1 (0.55) 0.01°C Sv-1 (0.13) 0.05°C Sv-1 (0.59) Suggests potential predictability of climate for several decades into the future

34. Mechanism Transport time scale 100 year run with a unit tracer at the surface between 0-15°N Follow tracer concentrations averaged over 0-800m Slow buildup in sub-polar ocean

35. Mechanism Coupled ocean-atmosphere interactions Precipitation change associated with an ITCZ shift caused by SST anomalies supplies the tropical fresh water flux forcing Coupled mechanism involving a delayed oceanic salinity feedback.