The AMOC in the Kiel Climate Model WP 3.1 Suitability of the ocean observation system components for initialization PI: Mojib Latif With contribution from: Wonsun Park, Thomas Martin, Fritz Krüger, Jin Ba NACLIM Kickoff Meeting 5- 9 November, 2012

Motivation Modes of variability / time scales and forcing Dynamics of the multi-decadal mode Predictability and initialization – Observation

AMOC: RAPID array (26.5°N) Transport timeseries obtained from the first 3.5 years of observations at 26.5°N. The different curves show the MOC (red line) and its constituents, i.e. the transport through the Florida Straits (blue line), the Ekman transport (black line), and the density driven transport obtained from the mooring data (pink line). The transport units are Sverdrups (Sv, 1Sv = 10 6 m 3 s -1 ). The mean and standard deviations for the different transports are 18.5 ±4.9Sv (MOC), 31.7 ±2.8Sv (Florida Straits), 3.5 ±3.4Sv (Ekman), and ±3.2Sv (transport from mooring densities).

Natural variability in Kiel Climate Model (4200 year control simulation ) Park and Latif 2012

Atlantic Meridional Overturning Circulation in Kiel Climate Model Park and Latif 2008

Three time scales: MCV ( a), QCV (~100a), MDV (~60a ) Park and Latif 2012 Singular Spectrum Analysis (SSA)

Atlantic Multidecadal Variability (~60a) SST: (POP1:42% PDV; POP2: 20% AMV) NH SST [°C] Park and Latif 2010 Principal Oscillation Pattern: … -> P real -> -P imag -> -P real -> P imag -> …

Atlantic Multidecadal Variability (~60a) (regression patterns) SST SLP SSH Park and Latif 2010

Atlantic Multidecadal Variability (~60a) (regression patterns) SST SLP SSH Park and Latif 2010

AMV and AMOC Ba et al. submitted 60yr

Salinity leads the AMOC Ba et al. submitted

Restored Salinity: variability goes down Ba et al. submitted

State-of-the-art ocean observing system

Satellite data SMOS SSH: Regional trends Derived from multi-missions Ssalto/Duacs Period: ICDC, ZMAW; Germany SSH -Trend

Scientific work plan Perfect model approach Initialization: sampling according to existing ocean observing system components Hindcasts with reduced set of initial conditions Quantification contribution of different components of ocean observing system Investigate potential observational needs to enhance decadal prediction Comparison with other models (e.g. KCM with MPI-ESM)

Temperature and Salinity patterns Temperature Salinity Ba et al. submitted EOF 1 (expl var 17%)

Natural variability is important at least till 40 yrs 22 ensemble runs with 1%/yr CO2 increase Latif and Park 2012

WP3.1 Suitability of the ocean observing system components for initialization Objectives – To investigate the benefit of the different ocean observing system components for the initialization of decadal climate prediction systems – To quantify the impact of the different observing system components in terms of decadal hindcast skill – To identify the necessary enhancements and potential reductions of the present observing systems

Interactions with other WPs WP1.1 WP1.2 WP2.1 WP2.2 WP2.3 WP3.2

WP 3.1: Working Program Benefits of ocean observing systems: ARGO – drifting profiling floats RAPID – AMOC observing system at 26.5°N Asses the usefulness of satellite data Model setup and control run Hindcast experiments and skill assessment Benefits of different ocean regions

AMOC and NAO