Predicting Atlantic meridional overturning circulation (AMOC) variations using subsurface and surface fingerprints

Abstract

Recent studies have suggested that the leading modes of North Atlantic subsurface temperature ( T sub ) and sea surface height (SSH) anomalies are induced by Atlantic meridional overturning circulation (AMOC) variations and can be used as fingerprints of AMOC variability. Based on these fingerprints of the AMOC in the GFDL CM2.1 coupled climate model, a linear statistical predictive model of observed fingerprints of AMOC variability is developed in this study. The statistical model predicts a weakening of AMOC strength in a few years after its peak around 2005. Here, we show that in the GFDL coupled climate model assimilated with observed subsurface temperature data, including recent Argo network data (2003–2008), the leading mode of the North Atlantic T sub anomalies is similar to that found with the objectively analyzed T sub data and highly correlated with the leading mode of altimetry SSH anomalies for the period 1993–2008. A statistical auto-regressive (AR) model is fit to the time-series of the leading mode of objectively analyzed detrended North Atlantic T sub anomalies (1955–2003) and is applied to assimilated T sub and altimetry SSH anomalies to make predictions. A similar statistical AR model, fit to the time-series of the leading mode of modeled T sub anomalies from the 1000-year GFDL CM2.1 control simulation, is applied to predict modeled T sub , SSH, and AMOC anomalies. The two AR models show comparable skills in predicting observed T sub and modeled T sub , SSH and AMOC variations.