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

Salil Mahajan; Rong Zhang; Thomas L. Delworth; Shaoqing Zhang; Anthony J. Rosati; You Soon Chang

doi:10.1016/j.dsr2.2010.10.067

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

Salil Mahajan, Rong Zhang, Thomas L. Delworth, Shaoqing Zhang, Anthony J. Rosati, You Soon Chang

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26 Scopus citations

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.

Original language	English
Pages (from-to)	1895-1903
Number of pages	9
Journal	Deep-Sea Research Part II: Topical Studies in Oceanography
Volume	58
Issue number	17-18
DOIs	https://doi.org/10.1016/j.dsr2.2010.10.067
State	Published - Sep 2011
Externally published	Yes

Funding

Mahajan S. is supported by the Visiting Scientist Program jointly sponsored by Princeton University and GFDL/NOAA. Chang Y.-S. is supported by the GFDL/NOAA Visiting Scientist Program administered by UCAR. The altimeter products were produced by SSALTO/DUACS and distributed by AVISO with support from CNES.

Funders	Funder number
GFDL
National Oceanic and Atmospheric Administration
University Corporation for Atmospheric Research
Princeton University

Keywords

AMOC fingerprints
AMOC prediction
AMOC variability
GFDL CM2.1

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10.1016/j.dsr2.2010.10.067

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title = "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 (Tsub) 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 Tsub anomalies is similar to that found with the objectively analyzed Tsub 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 Tsub anomalies (1955-2003) and is applied to assimilated Tsub and altimetry SSH anomalies to make predictions. A similar statistical AR model, fit to the time-series of the leading mode of modeled Tsub anomalies from the 1000-year GFDL CM2.1 control simulation, is applied to predict modeled Tsub, SSH, and AMOC anomalies. The two AR models show comparable skills in predicting observed Tsub and modeled Tsub, SSH and AMOC variations.",

keywords = "AMOC fingerprints, AMOC prediction, AMOC variability, GFDL CM2.1",

author = "Salil Mahajan and Rong Zhang and Delworth, {Thomas L.} and Shaoqing Zhang and Rosati, {Anthony J.} and Chang, {You Soon}",

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AU - Rosati, Anthony J.

AU - Chang, You Soon

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AB - Recent studies have suggested that the leading modes of North Atlantic subsurface temperature (Tsub) 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 Tsub anomalies is similar to that found with the objectively analyzed Tsub 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 Tsub anomalies (1955-2003) and is applied to assimilated Tsub and altimetry SSH anomalies to make predictions. A similar statistical AR model, fit to the time-series of the leading mode of modeled Tsub anomalies from the 1000-year GFDL CM2.1 control simulation, is applied to predict modeled Tsub, SSH, and AMOC anomalies. The two AR models show comparable skills in predicting observed Tsub and modeled Tsub, SSH and AMOC variations.

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

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Keywords

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