An event-based approach to understanding decadal AMOC fluctuations and their impacts

1. An event-based approach to understanding decadal AMOC fluctuations and their impacts Lesley Allison, Ed Hawkins, Tim Woollings and Laura Jackson NCAS-Climate, University of Reading Hadley Centre, UK Met Office • Detecting AMOC events; meridional coherence • Asymmetry in climate response to positive & negative events • European temperature response? • Comparison with mid-1990s North Atlantic warming event

2. Unforced rapid changes in AMOC strength Composite of decadal surface air temperature change Hawkins & Sutton (2008) Add something about regression analysis etc?

3. North Atlantic rapid warming in mid-1990s SST anomaly (°C) Robson et. al. (2012)

4. Questions Do models exhibit common signatures of decadal AMOC events? How important is the latitude at which AMOC events are detected? Are the impacts of positive events equal and opposite to the impacts of negative events? What controls the European temperature response? Are the events similar to observed decadal changes in the North Atlantic?

5. Models HadCM3 5000-year control simulation GFDL CM2.1 500-year control simulation

6. The 10 largest AMOC events of each sign

7. Defining climate field anomalies Sv x x lag t1 t2 Years • Normalised by AMOC change • Averaged over 10 events (N=10) • Lags (of both sign) relative to event end • Annual mean data

8. Meridional coherence of events GFDL CM2.1 Events detected at 50°N Events detected at 26°N -1 Sv/Sv 1

9. Meridional coherence of events HadCM3 Events detected at 50°N Events detected at 26°N -1 Sv/Sv 1

10. SST changes: HadCM3 26°N events 1.5 1 0.5 0 -0.5 -1 -1.5 K/Sv

11. SST changes: GFDL CM2.1 26°N events 1.5 1 0.5 0 -0.5 -1 -1.5 K/Sv

12. GIN Seas asymmetry Air temperature SST 1.5 1 0.5 0 -0.5 -1 -1.5 K/Sv

13. GIN Seas asymmetry: Sea ice concentration 8 Maximum 4 0 Minimum -4 -8 % cover % cover change / Sv

14. Subdued response in European air temperature 1.5 1 0.5 0 -0.5 -1 -1.5 K/Sv

15. Near-surface winds Long-term mean

16. Near-surface winds & sea level pressure 1.5 1 0.5 0 -0.5 -1 -1.5 hPa/Sv

17. Cloudiness?

18. Comparison with mid-90s North Atlantic warming event: SST Stages during AMOC increase [Normalised event length] End Start 1997 1986

19. Comparison with mid-90s North Atlantic warming event: SST 5 years later Event end (AMOC 26°N max) 1998 2000 2001 2002 1997 1999

20. Summary Explore the response in individual seasons

21. Summary (1) • Examining unforced rapid (~decadal) AMOC fluctuation events in AOGCMs (HadCM3 & GFDL CM2.1). • The models have different characteristics of AMOC variability (eg, different dominant time scales). The AMOC events in GFDL CM2.1 are generally stronger than those in HadCM3. • Rapid events are generally a recovery from a previously anomalous state (i.e., they are part of the natural variability, rather than an abrupt deviation from the mean).

22. Summary (2) • AMOC changes tend to occur at higher latitudes before the events are detected at 26°N • There are patterns of SST change associated with stages of the events. These are relatively robust across events within each model, but the spatial pattern differs between the models. • There are impacts on surface air temperature that are robust across events in HadCM3 (less so in GFDL CM2.1). • Preliminary work suggests there might be some similarity between the fingerprints of the model events and the observed North Atlantic warming event in the mid 1990s.

23. Summary (1) • Studying unforced rapid (~decadal) MOC fluctuation events in AOGCMs (HadCM3 & GFDL CM2.1). • The models have different characteristics of MOC variability: the MOC index in GFDL CM2.1 has a strong variability on a ~20 year timescale (the time series consists of lots of “rapid events”). The MOC events in GFDL CM2.1 are generally stronger than those in HadCM3. • Rapid events are generally a recovery from a previously anomalous state (i.e., they are part of the natural variability, rather than an abrupt deviation from the mean).

24. Summary (2) • In both models there is a ~linear relationship between the magnitude of an MOC event and North Atlantic SSTs, although the spatial pattern of the SST change differs between the models. • The relationship between event magnitude and SST change appears to be stronger for positive events, and stronger for GFDL CM2.1 than for HadCM3. • Within each model, the largest events share common characteristics in the spatial pattern of SST response, although there is considerable variation between events, especially in HadCM3 (indicating that other processes may influence the signal).