Optimization of an observing system for the North Atlantic Meridional Overturning Circulation!

Transcription

1 Optimization of an observing system for the North Atlantic Meridional Overturning Circulation! Johanna Baehr Institute of Oceanography University of Hamburg (Germany)

2 Optimization of an MOC observing array at 26N Detection of MOC changes

3 Depth [m] 26N: array design MOC = [mid-ocean + Ekman + Florida Current] mass bal corrected Depth [m] MOC [Sv] MOC [Sv] MOC Time [years] MOC reconstruction (FC+mid-ocean+Ekman) Baehr et al. (2004)

4 Optimal array design Root mean square error [Sv] N=n max Number of density profiles Baehr et al. (2008)

5 Location of profile 2 Optimal array design: two profiles Location of profile 1 Sv Baehr et al. (2008)

6 Optimal array design: two profiles Root mean square error [Sv] N=n max Number of density profiles Baehr et al. (2008)

7 Optimal array design: differential evolution Root mean square error [Sv] N=n max Number of density profiles Baehr et al. (2008)

8 Optimal array design: nine profiles Baehr et al. (2008)

9 Array design: optimal vs heuristic/ intuition-based Root mean square error [Sv] N=n max Number of density profiles Hirschi et al., (2003), Baehr et al. (2004), Baehr et al. (2008)

10 Array design: optimal vs heuristic/ intuition-based

11 Optimal array and heuristic array design provide similar results; solution needs to be tested for its robustness.

12 RAPID 26N array Kanzow et al. (2007)

13 Observed and simulated hydrography RAPID ECHAM5/ MPI-OM Depth [m] Depth [m] Western boundary Eastern boundary Baehr et al. (2008) Temperature Salinity Temperature Salinity

14 Observed and simulated MOC variability Mid-ocean Ekman MOC FC RAPID ECHAM5/MPI-OM RMS variability Baehr et al. (2008)

15 Observed and simulated hydrography Western boundary Eastern boundary Depth Depth Temperature Salinity Temperature Salinity Observed difference between 1957 and 2004 occupation Simulated regression of MOC vs T, scaled by -8 Sv Baehr et al. (2007)

16 Detection of MOC changes Detection variable d c for natural variability Spatial regression pattern between MOC and zonal density gradient d c = Ñ T Ψ c Detection variable Simulated zonal density gradient in control run Time [years] Baehr et al. (2007)

17 Detection of MOC changes Detection variable d c natural variability Spatial regression pattern between MOC and zonal density gradient d c = Ñ T Ψ c Detection variable Range of natural variability Simulated zonal density gradient in control run Time [years] Baehr et al. (2007)

18 Detection of MOC changes Observed zonal density gradient ( ) d c = Ñ T Ψ c Simulated zonal density gradient in control run Detection variable 01 April September 2006

19 Detection of MOC changes Observed zonal density gradient ( ) d = Ñ T Ψ Simulated zonal density gradient in climate change scenario Detection variable 01 April September 2006

20 Detection of MOC changes Observed zonal density gradient ( ) d = Ñ T Ψ RAPID array observations Detection variable Climate change scenario RAPID array observations 01 April September 2006

21 Observed and simulated hydrography Western boundary Eastern boundary RAPID ECHAM5/ MPI-OM ECCO/ GODAE Depth [m] Depth [m] Temperature Salinity Temperature Salinity

22 Observed and simulated MOC variability Mid-ocean Ekman MOC FC RAPID/MOCHA ECHAM5/MPI-OM ECCO-GODAE RMS variability Baehr et al. (2008)

23 Detection of MOC changes Observed zonal density gradient ( ) d = Ñ T Ψ ECCO-GODAE state estimate Detection variable Climate change scenario RAPID array observations ECCO-GODAE state estimate 01 April September 2006

24 Detection of MOC changes Observed zonal density gradient ( ) d = Ñ T Ψ ECCO-GODAE state estimate incl. RAPID data Detection variable Climate change scenario 01 April September 2006 RAPID array observations ECCO-GODAE state estimate ECCO-GODAE state estimate incl. RAPID data

25 Conclusions Optimal array and heuristic array design provide similar results; solution needs to be tested for its robustness. Deep density gradients can be a useful detection variable for MOC changes. Additional observations necessary to constrain both assimilations and establish natural variability.