The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the climate system. It transports heat from the equatorial Atlantic to the higher latitudes of the North Atlantic, which contributes to the mild winters in Europe. Another component relevant for the weather and climate in Europe is the North Atlantic Oscillation (NAO). The NAO describes atmospheric pressure differences over the Atlantic which affect temperature and precipitation over Europe, especially in winter. Thus, understanding the interaction of the AMOC and the NAO is of interest for the political, economic, and agricultural sectors.
One problem though is that observational records for the AMOC are too short in time to learn more about its interaction with the NAO. Therefore, climate models have to be used to study this topic. Still, climate models are subject to uncertainties and systematic errors. In this project, we focus on the differences across a large number of climate models. We compare them in terms of their AMOC and NAO variability and interaction, to learn more about the effect of model uncertainties.
We conclude that the AMOC-NAO interaction of a model is very sensitive to its tendency to be, on average, rather warm and salty or cold and fresh in the subpolar region of the North Atlantic. Models that fall into the warm-salty category, show a stronger and longer-lasting response of the AMOC to the NAO compared to the cold-fresh models. This is linked to the models' sea ice cover and the stability of the water column in the Labrador Sea. Furthermore, it was found that the potential to make skillful decadal climate predictions, is higher for the models categorised as cold-fresh.
These results stress that climate model behaviour is very diverse suggesting that research findings related to North Atlantic climate should not rely only on a single model result or the multi-model mean. Moreover, this project's results identify key elements in the models that lead to uncertainty in the North Atlantic climate variability, which is highly valuable for future model improvement enabling also better climate predictions.
