A dynamical/statistical approach to predict multidecadal AMOC variability and related North Atlantic SST anomalies Mojib Latif GEOMAR Helmholtz Centre.

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Presentation transcript:

A dynamical/statistical approach to predict multidecadal AMOC variability and related North Atlantic SST anomalies Mojib Latif GEOMAR Helmholtz Centre for Ocean Research Kiel and University of Kiel monthly AMO index 1948 - 2012 Thanks to M. Klöwer, H. Ding, R. Greatbatch, W. Park

SAT trend 1980-2012 There is a marked inter-hemispheric asymmetry in the warming during the last decades, especially in the Atlantic

New analysis of North Atlantic surface heat fluxes since 1880 suggests that the ocean drives SST at decadal time scales

Can we predict the AMO given the large model biases? CMIP5 multi-model mean SST bias (39 models) model bias may prevent us from exploiting the decadal full predictability potential

The null hypothesis for multi-decadal AMOC variability NAO-related SAT pattern (°C), +1σ The NAO affects Labrador Sea convection which in turn drives AMOC (Delworth and Greatbatch, 2000; Eden and Jung, 2001)

The NAO drives convection in the North Atlantic, which in turn drives the AMOC Latif and Keenlyside 2011

Dynamical/statistical approach to predict North Atlantic SST observed NA SST force the Kiel Climate Model by NAO-related heat flux anomalies use the KCM‘s AMOC as predictor to statistically predict observed North Atlantic SST

Hindcast of the AMOC 1900-2010 Kiel Climate Model forced by NAO-related heat flux anomalies Klöwer et al. 2013

Link between the KCM‘s AMOC and observed North Atlantic SST Klöwer et al. 2013

Statistical link between the model‘s AMOC and the observed NA SST The Meridional Overturning Circulation (AMOC) and observed North Atlantic SST AMOC lags SST by 10 years AMOC leads SST by 21 years Klöwer et al. 2013 Statistical link between the model‘s AMOC and the observed NA SST

Explained variances by the two CCA modes Klöwer et al. 2013 AMOC lags SST by 10 years AMOC leads SST by 21 years suggests a rather high decadal predictability potential of North Atlantic SST

Forecast of North Atlantic SST until 2030 Klöwer et al. 2013 Dynamical/statistical prediction based on the North Atlantic Oscillation and the Kiel Climate Model’s AMOC

Take home message Climate models are useful tools to study the dynamics and predictability of climate variability and change. However, they suffer from large biases. Model improvement is a key issue during the next years. In the meantime, hybrid approaches may prove useful.

The research leading to these results has received funding from the European Union 7th Framework Programme (FP7 2007-2013), under grant agreement n.308299 NACLIM www.naclim.eu