Deep Sea Research Part II: Topical Studies in Oceanography
Predicting Atlantic meridional overturning circulation (AMOC) variations using subsurface and surface fingerprints
Introduction
Recent studies have demonstrated tele-connections between the North Atlantic and regional climate variability at multidecadal timescales (e.g. Enfield et al., 2001, Knight et al., 2006, Zhang and Delworth, 2006). Low-frequency variability in the North Atlantic is often thought to be linked to Atlantic meridional overturning circulation (AMOC) variability (Delworth and Mann, 2000, Knight et al., 2005, Zhang, 2008). Griffies and Bryan (1997) have shown that AMOC variations provide decadal predictability of simulated North Atlantic variations. However, estimating AMOC variations has been a major challenge. Instantaneous surveys across 25°N suggest a long-term slowdown of the AMOC (Bryden et al., 2005), but these snapshots could be aliased by large seasonal variations (Cunningham et al., 2007). To reconstruct the past AMOC variations when no direct observations are available, as well as to evaluate future AMOC impacts, it would be very useful to develop fingerprints for AMOC variations. The fingerprints need to be quantities that can be derived from both climate models and observations. The identification of AMOC fingerprints would link the ocean circulation with well-observed variables and contribute to the interpretation of AMOC variations, allowing improved assessments of the impacts of AMOC variations on global climate change.
Previous studies have suggested that basin averaged North Atlantic sea surface temperature (SST) anomalies could be taken as a fingerprint of the multidecadal AMOC variations (Latif et al., 2004, Knight et al., 2005). The anti-correlated relationship between the tropical North Atlantic SST and subsurface temperature anomalies has also been shown as a signature of the AMOC variability (Zhang, 2007). The North Atlantic SST anomalies might be influenced by high frequency synoptic atmospheric variability and changes in the radiative forcing (Mann and Emanuel, 2006), thus their linkage to the AMOC variability is highly debated. A recent study (Zhang, 2008) found that the leading mode of altimeter SSH data is highly correlated with that of instrumental subsurface ocean temperature data in the North Atlantic, and both show opposite anomalies in the subpolar gyre and the Gulf Stream path. A millennial control simulation using a coupled ocean–atmosphere model (GFDL CM2.1) suggests that such a dipole pattern is likely to be a distinctive fingerprint of AMOC variations. The fingerprint using modeled and observed SSH/subsurface temperature data suggests that the recent slowdown of the subpolar gyre is a part of a multidecadal variation and linked to a strengthening of the AMOC. Note that the relationship between the subpolar gyre and AMOC has not been universally established in all models, and one ocean-only modeling study suggests a contrary in-phase relationship between the two (Böning et al., 2006). Nonetheless, with recent advancement in measurement of subsurface oceans by the Argo network and satellite altimetry, it may be possible to monitor AMOC variations using this fingerprint.
In this paper, we extend the analysis of Zhang (2008) to include more recent measurements and highlight the link between these new measurements and the capability of estimating AMOC variations. In particular, we take into account the observed ocean subsurface temperature from the Argo network to establish a new framework for monitoring and predicting AMOC variations using the observed subsurface temperature fingerprint. We apply the Argo data through the GFDL coupled data assimilation (CDA) product (Zhang et al., 2007b). Furthermore, we make predictions of AMOC variations using a statistical auto-regressive (AR) model fit to the time-series of the observed fingerprints of the AMOC. Schneider and Griffies (1999) apply discriminant analysis to North Atlantic decadal variability of SSH and conclude that the predictive power of AR models, as applied here, is comparable to that of climate models. Applying the AR model to the assimilated subsurface temperature and altimetry SSH anomalies predicts a decline of the AMOC strength in the coming decade. A similar statistical AR model, fit to the time-series of the leading mode of modeled subsurface temperature anomalies from a 1000-year control simulation of the fully coupled ocean–atmosphere model (GFDL CM2.1, Delworth et al., 2006), is applied to modeled subsurface temperature, SSH, and AMOC index anomalies to make predictions. The two AR models show comparable skills in predicting observed subsurface temperature and modeled subsurface temperature, SSH and AMOC index variations. As a caveat, the simulated AMOC varies considerably in different climate models in terms of mean intensity, time-scale and amplitude (Stouffer et al., 2006). Hence, the results from the GFDL CM2.1 model are likely to be model dependent. However, the AR2 model used to make future predictions in the real world is independently computed from the observed time-series of the AMOC fingerprints, and thus is not dependent on the GFDL CM2.1 model time series.
Section snippets
Description of data and models
In this study, the observed North Atlantic ocean subsurface temperature data are derived from the publicly available yearly averaged dataset of objectively analyzed ocean temperature anomalies (Levitus et al., 2005) based on instrumental data for the period of 1955–2003. Following (Zhang, 2008), we use subsurface temperature anomalies at a depth of 400 m in our analysis. A quadratic monotonic function is fit to the time-series of the basin averaged subsurface temperature anomaly in the North
AMOC fingerprints
The spatial pattern of the leading empirical orthogonal function (EOF1) of detrended North Atlantic subsurface temperature anomalies at a depth of 400 m (Tsub) displays a dipole pattern (Fig. 1A), i.e. warming in the subpolar gyre and cooling near the Gulf Stream path; the principal component of the leading mode (PC1) of the Tsub is strongly correlated with that of the altimetry SSH for the period 1993–2003 (r=0.95, Fig. 1D), as discussed in Zhang (2008). Fig. 1B shows the spatial pattern of the
Predicting AMOC variations using subsurface temperature and SSH fingerprints
We now take a step further by forecasting AMOC variations in the near future using linear statistical models. The two identified indices of AMOC variations, namely, SSH and Tsub PC1s, respectively, provide slightly different initial conditions for conducting forecasts. Satellite altimetry and Argo data provide extensive observations for the recent past, but are too short to reconstruct AMOC variations in the past several decades. Our approach here is to construct a single AR model for AMOC
Summary and discussion
The potential impacts of AMOC on global and regional climate, including hemispheric scale surface temperature variations (Zhang et al., 2007a), Atlantic hurricane activities, Sahel and Indian summer monsoons (Knight et al., 2006, Zhang and Delworth, 2006), North American and West European precipitation (Enfield et al., 2001, Sutton and Hodson, 2005), make it crucial to accurately monitor and predict AMOC variations to improve global and regional climate predictions. Recent modeling and
Acknowledgments
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.
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Introduction to: Atlantic Meridional Overturning Circulation (AMOC)
2011, Deep-Sea Research Part II: Topical Studies in Oceanography