Granger causality from changes in level of atmospheric CO&lt;sub&gt;2&lt;/sub&gt; to global surface temperature and the El Niño–Southern Oscillation, and a candidate mechanism in global photosynthesis

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Abstract

Abstract. A significant difference, now of some 16 years' duration, has been shown to exist between the observed global surface temperature trend and that expected from the majority of climate simulations. For its own sake, and to enable better climate prediction for policy use, the reasons behind this mismatch need to be better understood. While an increasing number of possible causes have been proposed, the candidate causes have not yet converged. With this background, this paper reinvestigates the relationship between change in the level of CO2 and two of the major climate variables, atmospheric temperature and the El Niño–Southern Oscillation (ENSO). Using time-series analysis in the form of dynamic regression modelling with autocorrelation correction, it is shown that first-difference CO2 leads temperature and that there is a highly statistically significant correlation between first-difference CO2 and temperature. Further, a correlation is found for second-difference CO2 with the Southern Oscillation Index, the atmospheric-pressure component of ENSO. This paper also shows that both these correlations display Granger causality. It is shown that the first-difference CO2 and temperature model shows no trend mismatch in recent years. These results may contribute to the prediction of future trends for global temperature and ENSO. Interannual variability in the growth rate of atmospheric CO2 is standardly attributed to variability in the carbon sink capacity of the terrestrial biosphere. The terrestrial biosphere carbon sink is created by the difference between photosynthesis and respiration (net primary productivity): a major way of measuring global terrestrial photosynthesis is by means of satellite measurements of vegetation reflectance, such as the Normalized Difference Vegetation Index (NDVI). In a preliminary analysis, this study finds a close correlation between an increasing NDVI and the increasing climate model/temperature mismatch (as quantified by the difference between the trend in the level of CO2 and the trend in temperature).