Response to comment on "Multiyear prediction of monthly mean atlantic meridional overturning circulation at 26.5°N"

Science

Volumn 338, Issue 6107, 2012, Pages

  Matei, Daniela ^a   Baehr, Johanna ^b   Jungclaus, Johann H ^a   Haak, Helmuth ^a   Müller, Wolfgang A ^a   Marotzke, Jochem ^a  

^a MAX PLANCK INSTITUTE FOR METEOROLOGY   (Germany)

^b UNIVERSITY OF HAMBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CHANGE; GEOMETRY; NOTE; PREDICTION AND FORECASTING; PRIORITY JOURNAL; SAMPLE SIZE; TENSION;

EID: 84868231705     PISSN: 00368075     EISSN: 10959203     Source Type: Journal    
DOI: 10.1126/science.1223200     Document Type: Note

References (14)

1. D. Matei et al., Science 335, 76 (2012).
2. S. A. Cunningham et al., Science 317, 935 (2007).
3. T. Kanzow et al., Science 317, 938 (2007).
4. Because our model underwent some technical changes since the Coupled Model Intercomparison Project phase 3 (CMIP3), we constructed and used in (1) a different 20th-century realization from that in the CMIP3 archive (10).
5. G. A. Vecchi et al., Science 338, 604 (2012); www.sciencemag.org/cgi/ content/full/338/6107/604-c.
6. Vecchi et al. motivate their alternative reference forecast by arguing that the reference forecasts used in (1) ignored the issue of a repeating annual cycle. Although both reference forecasts used in (1) do include baseline knowledge of seasonality, we stand by our discussion in (1) that the limitation of observational record to half a decade does not allow a robust estimation of the seasonal cycle from the observations (11, 12); we therefore decided to keep the seasonal cycle in both modeled and observed AMOC time series and investigated the predictability of the full AMOC signal using monthly means. As already discussed in (1), the observed AMOC seasonal cycle with a maximum in October and a minimum in April has highly nontrivial causes (11, 12). The representation of the AMOC seasonal cycle in the model, and the improvement of monthly mean predictions of the AMOC strength through initialization, should therefore not be considered straightforward.
7. Figure 1 in (5), and the associated note 14, prompted us to revisit the computation of the significance level. In (1), we have computed the significance level using a single-sided t test taking into account the serial autocorrelation of both time series. However, because the determination of the effective number of degrees of freedom (df) in short time series is associated with uncertainty (13), we have repeated our significance test by constructing an empirical probability density function of correlations. We have calculated the correlations of each year of monthly means of the observed AMOC with the 350 years of uninitialized runs of our model. The 10% significance level now lies at a correlation value of 0.61, compared with 0.55 in the paper. In a similar way, we have reestimated the 10% significance level for the correlation skill of upper-mid-ocean transport and upper-ocean zonal density difference to be 0.65 and 0.67, instead of 0.6 used in the original paper. Hence, we should have been slightly more conservative, but this modification does not change the conclusions drawn from both figures 2 and 3 of (1).
8. K. E. Taylor, J. Geophys. Res. 106, (D7), 7183 (2001).
9. CLIMREF.
10. J. H. Jungclaus et al., J. Clim. 19, 3952 (2006).
11. T. Kanzow et al., J. Clim. 23, 5678 (2010).
12. M. P. Chidichimo, T. Kanzow, S. A. Cunningham, W. E. Johns, J. Marotzke, Ocean Sci. 6, 475 (2010).
13. H. von Storch, F. W. Zwiers, Statistical Analysis in Climate Research (Cambridge University Press, Cambridge, 1999), p. 484.
14. I. T. Jolliffe, D. B. Stephenson, Forecast Verification. A Practitioner's Guide in Atmospheric Science (Wiley and Sons, Chichester, UK, 2003), p. 240.