High Predictive Skill of Global Surface Temperature a Year Ahead
Abstract
We discuss the high skill of real-time forecasts of global surface temperature a year ahead issued by the UK Met Office, and their scientific background. Although this is a forecasting and not a formal attribution study, we show that the main instrumental global annual surface temperature data sets since 1891 are structured consistently with a set of five physical forcing factors except during and just after the second World War. Reconstructions use a multiple application of cross validated linear regression to minimise artificial skill allowing time-varying uncertainties in the contribution of each forcing factor to global temperature to be assessed. Mean cross validated reconstructions for the data sets have total correlations in the range 0.93-0.95,interannual correlations in the range 0.72-0.75 and root mean squared errors near 0.06oC, consistent with observational uncertainties.Three transient runs of the HadCM3 coupled model for 1888-2002 demonstrate quite similar reconstruction skill from similar forcing factors defined appropriately for the model, showing that skilful use of our technique is not confined to observations. The observed reconstructions show that the Atlantic Multidecadal Oscillation (AMO) likely contributed to the re-commencement of global warming between 1976 and 2010 and to global cooling observed immediately beforehand in 1965-1976. The slowing of global warming in the last decade is likely to be largely due to a phase-delayed response to the downturn in the solar cycle since 2001-2, with no net ENSO contribution. The much reduced trend in 2001-10 is similar in size to other weak decadal temperature trends observed since global warming resumed in the 1970s. The causes of variations in decadal trends can be mostly explained by variations in the strength of the forcing factors. Eleven real-time forecasts of global mean surface temperature for the year ahead for 2000-2010, based on broadly similar methods, provide an independent test of the ideas of this study. They had the high correlation and root mean square error skill levels compared to observations of 0.74 and 0.07oC respectively. Pseudo-forecasts for the same period reconstructed from somewhat improved forcing data used for this study had the slightly better correlation of 0.80 and root mean squared error of 0.05oC. Finally we compare the statistical forecasts with dynamical hindcasts and forecasts of global surface temperature a year ahead made by the Met Office DePreSys coupled model. The statistical and dynamical forecasts of global surface temperature for 2011 will be compared with preliminary verification data.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2011
- Bibcode:
- 2011AGUFM.A32B..01F
- Keywords:
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- 1610 GLOBAL CHANGE / Atmosphere;
- 1616 GLOBAL CHANGE / Climate variability;
- 1650 GLOBAL CHANGE / Solar variability;
- 1627 GLOBAL CHANGE / Coupled models of the climate system