PowerPoint-Prsentation

The AMOC in the Kiel Climate Model

WP 3.1 Suitability of the ocean observation system components for initialization PI: Mojib LatifWith contribution from: Wonsun Park, Thomas Martin, Fritz Krger, Jin BaNACLIM Kickoff Meeting 5- 9 November, 2012

MotivationModes of variability / time scales and forcingDynamics of the multi-decadal modePredictability and initializationObservation

AMOC: RAPID array (26.5N)

Transport timeseries obtained from the first 3.5 years of observations at 26.5N. 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 = 106m3s-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 -16.6 3.2Sv (transport from mooring densities).http://www.noc.soton.ac.uk

Natural variability in Kiel Climate Model(4200 year control simulation)

Park and Latif 2012

Atlantic Meridional Overturning Circulation in Kiel Climate ModelPark and Latif 2008

Three time scales: MCV (300-400a), 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 2010Principal Oscillation Pattern: -> Preal -> -Pimag -> -Preal -> Pimag ->

Atlantic Multidecadal Variability (~60a) (regression patterns)

SSTSLPSSHPark and Latif 2010

Atlantic Multidecadal Variability (~60a) (regression patterns)

SSTSLPSSH

Park and Latif 2010

AMV and AMOC

Ba et al. submitted

60yrSalinity leads the AMOC

Ba et al. submitted

Restored Salinity: variability goes down

Ba et al. submitted

State-of-the-art ocean observing system

http://www.argo.ucsd.edu/About_Argo.html

Satellite data

SMOS

SSH: Regional trends Derived from multi-missions Ssalto/Duacs Period:1992-2010 ICDC, ZMAW; GermanySSH -Trend

Scientific work planPerfect model approachInitialization: sampling according to existing ocean observing system componentsHindcasts with reduced set of initial conditionsQuantification contribution of different components of ocean observing systemInvestigate potential observational needs to enhance decadal predictionComparison with other models (e.g. KCM with MPI-ESM)

Temperature and Salinity patterns

TemperatureSalinityBa et al. submittedEOF 1 (expl var 17%)Natural variability is important at least till 40 yrs22 ensemble runs with 1%/yr CO2 increase

Latif and Park 2012WP3.1 Suitability of the ocean observing system components for initializationObjectivesTo investigate the benefit of the different ocean observing system components for the initialization of decadal climate prediction systemsTo quantify the impact of the different observing system components in terms of decadal hindcast skillTo identify the necessary enhancements and potential reductions of the present observing systemsInteractions with other WPsWP1.1WP1.2WP2.1WP2.2WP2.3WP3.2

WP 3.1: Working ProgramBenefits of ocean observing systems:ARGO drifting profiling floatsRAPID AMOC observing system at 26.5NAsses the usefulness of satellite data

Model setup and control runHindcast experiments and skill assessmentBenefits of different ocean regionsAMOC and NAO

Abb. 3: Korrelation des Nordatlantischen Oszillation Index und der AMOC Strke bei 30 N als Funktion der Zeitverschiebung (Jahre) in den CMIP3 (Coupled Model Intercomparison Project 3) Modellen.

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