Nicholas G. Heavens Caltech K.-F. Li, M.-C. Liang, L.-C. Lin, K.-K. Tung, and Y.L. Yung

1. Assessing Low Frequency Variability in North Atlantic Ocean Sea Surface Temperatures in Global Climate Models   Nicholas G. Heavens Caltech K.-F. Li, M.-C. Liang, L.-C. Lin, K.-K. Tung, and Y.L. Yung 16 December 2009 AGU Fall Meeting Abstract # GC32A-08 NOAA NOAA/ESRL

2. AMO Should be Simulation Priority • Obscures or enhances global temperature trend attributed to anthropogenic forcings • Affects climate of North America, Europe, and West Africa Goldenberg et al., 2001 Janet Nye, NOAA NEFSC

3. Simulated AMOs are elusive Stoner et al. (2009) comparison of Climate of the 20th Century CMIP3 runs with ERA-40 and Kaplan SST First found by Delworth et al. (1993) before AMO identified (variability in Atlantic MOC) More recent work: 1. Atmosphere-ocean vs. ocean alone 2.Hierarchy of model complexity 3. Surveys of IPCC models Stoner et al. (2009)

4. This study • Stoner et al. (2009) concerned Climate of 20th century runs too short to assess multi-decadal variability like AMO • Paleoclimate records indicate AMO pre-dates 20th century (last millennium or more) • To what extent do global climate models simulate the Atlantic Multidecadal Oscillation (AMO) without secular forcing?

5. Procedure • Find longest pre-industrial run for 22 CMIP3 models with daily data (100-550 yr. runs) • Calculate AMO Index just like the real ocean • Correlate detrended, deseasonalized annual mean local time series with AMO Index to get spatial pattern • Power spectrum analysis. • Amplitude based on variance of ten year running mean • Compare with both modern instrumental and Gray et al. (2004) AMO reconstruction.

6. Results: Power Spectra HadISST Gray et al., 2004 (1567-1870) BCCR-BCM2.0 GFDL CM 2.1 ECHO-G (MIUB) GISS AOM PCM1 (NCAR) HadCM3 CGCM2.3.2 (MRI)

7. Results: Spatial Patterns

8. Summary • Only seven CMIP3 models simulate multi-decadal variability • ECHO-G has: (1) variability with spectrum similar to Gray et al. (2004); (2) reasonable amplitude; (3) qualitatively similar spatial pattern to modern (in-family with other models); (4) minimal global SST drift • Take-home: (1) decadal predictability in North Atlantic may prove difficult; (2) period matching of ECHO-G remarkable (given ENSO…)

9. Primary AMO-related change is intensity of sub-circulation controlling downwelling at 50°-60° N, AMO Relation to Atlantic MOC Streamfunction (m3s-1) solid line (+ correlation with AMO Index) dashed (-) • Explanations from previous modeling work, circulation is driven by positive salinity anomaly shut down by: • Atmospheric feedback with NAO produces weak evaporation in sinking regions (Timmermann et al., 1998): • salinity primarily produced in situ • 2. Feedback with eddy salinity transport • from south through sub-polar gyre water temperatures by atmospheric feedback/water accumulation (Dong and Sutton, 2005; Frankignoul and Msadek, 2008) (studies disagree about NAO role) • salinity produced elsewhere Mean The period sensitivity arises through timing of various processes: phase lag

10. Validation beyond SST? • Grain size sorting by bottom • currents: sub-circulation intensity • proxy? 2. Evaporation rates in Labrador Sea: Salt content in downwind ice cores related to winds blowing over open waters. Evaporation proxy? Boessenkool et al. (2007) Roethlisberger et al. (2009) Take-home: Collection of high-resolution proxies related to deep circulation or salt budget priority for validation of model AMOs

11. Questions?

12. References • Boessenkool, K. P., I. R. Hall, H. Elderfield, and I. Yashayaev (2007), North Atlantic climate and deep-ocean flow speed changes during the last 230 years, Geophys. Res. Lett., 34, L13614, doi:10.1029/2007GL030285. • Delworth T.L, Manabe S., Stouffer R.J. (1993) Interdecadal variations of the thermohaline circulation in a coupled ocean–atmosphere model. J.Climate, 6, 1993–2011. • Dong B. and R.T. Sutton (2005), Mechanism of interdecadal thermohaline circulation variability in a coupled ocean–atmosphere gcm, J. Climate, 18, 1117–1135. • Goldenberg, S. B., C. W. Landsea, A. M. Mestas-Nuñez, and W. M. Gray (2001), The recent increase in Atlantic hurricane activity: Causes and implications, Science, 293, 474–479. • Gray, S.T., L.J. Graumlich, J.L. Betancourt, and G.T. Pederson (2004), A tree-ring based reconstruction of the Atlantic Multidecadal Oscillation since 1567 A.D. Geophysical Research Letters, 31, L12205, doi:10.1029/2004GL019932. • Msadek, R. and C. Frankignoul (2008), Atlantic multidecadal oceanic variability and its influence on the atmosphere in a climate model, Climate Dyn., 33, 45-62. • Roethlisberger, R., X. Crosta, N.J. Abram, L. Armand, and E.W. Wolff, 2009, Potential and limitations of marine and ice core sea proxies: an example from the Indian Ocean sector • Stoner, A.M.K., K. Hayhoe, and D.J. Wuebbles (2009), Assessing General Circulation Model Simulations of Atmospheric Teleconnection Patterns. J. Climate, 22, 4348–4372. • Timmermann A, M. Latif, R. Voss, A. Grotzner (1998) Northern hemispheric interdecadal variability: a coupled air–sea mode, J. Climate, 11, 1906–1931