A dynamical/statistical approach to predict multidecadal AMOC variability and related North

Atlantic SST anomaliesMojib Latif

GEOMAR Helmholtz Centre for Ocean Research Kiel and University of Kiel

Thanks to M. Klöwer, H. Ding, R. Greatbatch, W. Park

monthly AMO index 1948 - 2012

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

1. force the Kiel Climate Model by NAO-related heat flux anomalies2. use the KCM‘s AMOC as predictor to statistically predict observed

North Atlantic SST

observed NA SST

Hindcast of the AMOC 1900-2010Kiel 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

The Meridional Overturning Circulation (AMOC) and observed North Atlantic SST

AMOC leads SST by 21 years

Klöwer et al. 2013

AMOC lags SST by 10 years

Statistical link between the model‘s AMOC and the observed NA SST

Explained variances by the two CCA modes

AMOC lags SST by 10 years AMOC leads SST by 21 years

suggests a rather high decadal predictability potential of North Atlantic SST

Klöwer et al. 2013

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.308299NACLIM www.naclim.eu