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We proposed the implementation of the multiple regression to create a statistical model for description of the climate change under the influence of specified climate-impacting factors. This model provides not only estimates of the temporal evolution of global temperature, but also a set of corresponding confidence intervals with a given level of statistical significance (probability). The elimination of the linear trend of climatic temperature series (CRUTEM) and atmospheric CO2 concentration allows objectively and quantitatively assess the impact of natural climate change factors. The global CRUTEM temperature responds quasi-synchronously to fluctuations in the average surface temperature of the North Atlantic (AMO index), but with a delay of about 15 years – on changes in solar activity (Wolf numbers). The linear trend of increasing CO2 concentrations in the atmosphere explains almost all the interannual variability and reflects the linear trend of global temperature, but it covers a part of its interannual variability.

Quantitative estimates of the impact of the most important factors on global climate change over the past 150 years