Towards a new reanalysis with the IPSL climate model

1. Towards a new reanalysis with the IPSL climate model Juliette Mignot, Didier Swingedouw, Eric Guilyardi, Pablo Ortega

2. Motivation The large-scale ocean and the Arctic climate Quadfasel 2005 Zhang et al 2015

3. Ovide section Motivation Recent measurements: decadal variability or decreasing trend? Quadfasel 2005 Smeed et al. 2014 Mercier et al. (2015)

4. Motivation Longer reconstructions of the N. Atlocean circulation: oceanicreanalysis • Poorly consistent in terms of oceanic circulation Western subpolar gyre intensity Anomalous intensity of the AMOC circulation at 45°N from 6 reanalysis data sets Mean values (Sv) Karspeck et al 2015 Born et al 2015

5. Motivation Longer reconstructions of the N. Atlocean circulation: oceanicreanalysis Which data to constrain? • Correlation of the zonally averaged temperature between SODA and ORAS4 • Even on temperature, ocean reanalysis hardly agree below 300m Ray et al. 2014 How are reanalysis constructed? • Very complex (sometimes ad-hoc) systems. • May alter the physics of the model and of the reconstruction • Difficult to trace the origins of the biases Zhang et al 2010

6. Firstapproach to reconstruct the ocean circulation at IPSL • Constrain (nudge) towards SST only • (and only towards anomalies) 1963-Agung 1982-El Chichon 1991-Pinatubo Global SST nudged simulations (4 members) Free historical simulations (4 members) HadISST SODA 2.2.4 Ray et al. 2014 • Close agreement with observations and reanalysis (independent from the nudging data set) • Spread among members of nudged runs strongly reduced as compared to free historical runs

7. Firstapproach to reconstruct the ocean circulation at IPSL Convincing agreement with independent reconstructions Robson et al. 2014 Density in the Labrador Sea EN3, Smith and Murphy 2007 6 4 2 0 -2 -4 Density 1000-2500m (1013kg) Independent reconstructions Obs. (Huck et Huck et al 2008 Latif et al. 2004 IPSL climate model + SST nudging Swingedouw et al. 2013

8. Second stepto reconstruct the ocean circulation at IPSL: Improving the surface nudging AMOC maximum at 48N (No Ekman) • Perfect model framework • Nudging SST and SSS + imposing the wind stress • Reconstruction sensitive to the initial conditions and fails reproducing the extreme AMOC peak. Ortega et al. 2017 • To improve the representation of AMOC extremeevents • To test the sensitivity to the initial AMOC state

9. Second stepto reconstruct the ocean circulation at IPSL: Improving the surface nudging Causes of the AMOC extreme and its misrepresentation AMOC maximum at 48N (No Ekman) 1000-2000m density – South of Iceland The extreme is related to the formation of deep dense waters by convection Ortega et al. 2017

10. Second stepto reconstruct the ocean circulation at IPSL: Improving the surface nudging • Need to reconstruct the precise convection details • g0 = - 40 W/m2/K is equivalent to • a relaxation time of 6 weeks in a layer of h0=40m • However, convection happens down to 2000 m & lasts for 2-3 months • g0 = - 40 W/m2/K is too weak to enforce this • We introduce a variable gT, proportional to the mixed layer depth, but keeping the same relaxation time • gT = (g0 / h0)• mixed layer depth Ortega et al. 2017

11. Second stepto reconstruct the ocean circulation at IPSL: Improving the surface nudging AMOC48N in a new set of SurfaceTSWind experiments 15 yrs AMOC maximum Adverse initial conditions (15 yrs before peak) Constant g AMOC extreme Any initial state Ortega et al. 2017

12. Second stepto reconstruct the ocean circulation at IPSL: Improving the surface nudging AMOC48N in a new set of SurfaceTSWind experiments 15 yrs AMOC maximum Exact initial conditions (15 yrs before peak) Constant g AMOC extreme Any initial state Ortega et al. 2017

13. Second stepto reconstruct the ocean circulation at IPSL: Improving the surface nudging AMOC48N in a new set of SurfaceTSWind experiments 15 yrs AMOC maximum Adverse initial conditions (15 yrs before peak) Variable g AMOC extreme Any initial state Ortega et al. 2017

14. Second stepto reconstruct the ocean circulation at IPSL: Improving the surface nudging AMOC48N in a new set of SurfaceTSWind experiments 15 yrs AMOC maximum Exact initial conditions (15 yrs before peak) Variable g AMOC extreme Any initial state Ortega et al. 2017

15. Second stepto reconstruct the ocean circulation at IPSL: Improving the surface nudging AMOC48N in a new set of SurfaceTSWind experiments 25 yrs AMOC maximum Any initial conditions (25 yrs before peak) Variable g AMOC extreme Any initial state Ortega et al. 2017

16. Second stepto reconstruct the ocean circulation at IPSL: Improving the surface nudging The use of a relaxation term proportional to the mixed layer depth contributes decisively to characterize the extreme AMOC events, irrespective of the initial conditions considered.

17. Future stepsto reconstruct the ocean circulation at IPSL: BLUEACTION • Apply the « variable restoring » configuration in historical conditions • -> requires salinity data, still very uncertain Atlantic [30N-50N] Subpolar Gyre [56N-64N] SSS_ORAS4 SSS_ORAS4 SSS_SODA224 SSS_SODA224 SSS_Reverdin2009 SSS_Reverdin2010

18. Future steps to reconstruct the ocean circulation at IPSL: BLUEACTION • Apply the « variable restoring » configuration in historical conditions • -> requires salinity data, still very uncertain • Test constrains of the coupled model by atmospheric winds Global 2m-temperature HadCRUT historical free runs SST-nudged runs Wind-nudged runs SST+Wind nudged runs

19. Future steps to reconstruct the ocean circulation at IPSL: BLUEACTION • Apply the « variable restoring » configuration in historical conditions • -> requires salinity data, still very uncertain • Test constrains of the coupled model by atmospheric winds • Compare to reconstructions performedwith 3D-oceanic nudging on the ARGO period • -> necessarilymuchshorter. Relevance for Blue Action WP2: Develop and analyse the reanalyse WP3: Test the sensitivity of the reanalysis to additionnal Greenland Ice Sheet melting WP4: Use this reanalysis as initial conditions and/or validation data set for decadal predictions