“ CO 2 makes up only a tiny portion of the atmosphere (%) and constitutes only % of the greenhouse effect . The atmospheric content of CO 2 has increased only % since emissions began to soar after 1945. Such a tiny increment of increase in CO 2 cannot cause the 10°F increase in temperature predicted by CO 2 advocates . Computer climate modelers build into their models a high water vapor component, which they claim is due to increased atmospheric water vapor caused by very small warming from CO 2 , and since water vapor makes up 90–95% of the greenhouse effect, they claim the result will be warming. The problem is that atmospheric water vapor has actually declined since 1948, not increased as demanded by climate models. If CO 2 causes global warming, then CO 2 should always precede warming when the Earth’s climate warms up after an ice age. However, in all cases, CO 2 lags warming by ∼ 800 years. Shorter time spans show the same thing—warming always precedes an increase in CO 2 and therefore it cannot be the cause of the warming .”
For nonexperimental data, causal direction can often be inferred if information about time is available. This is because (according to many, though not all, theories) causes must precede their effects temporally. This can be determined by statistical time series models, for instance, or with a statistical test based on the idea of Granger causality , or by direct experimental manipulation. The use of temporal data can permit statistical tests of a pre-existing theory of causal direction. For instance, our degree of confidence in the direction and nature of causality is much greater when supported by cross-correlations , ARIMA models, or cross-spectral analysis using vector time series data than by cross-sectional data .