Abstract
According to the characteristics of forced and unforced components to climate change, sophisticated statistical models were used to fit and separate multiple scale variations in the global mean surface temperature (GMST) series. These include a combined model of the multiple linear regression and autoregressive integrated moving average models to separate the contribution of both the anthropogenic forcing (including anthropogenic factors (GHGs, aerosol, land use, Ozone, etc) and the natural forcing (volcanic eruption and solar activities)) from internal variability in the GMST change series since the last part of the 19th century (which explains about 91.6% of the total variances). The multiple scale changes (inter-annual variation, inter-decadal variation, and multi-decadal variation) are then assessed for their periodic features in the remaining residuals of the combined model (internal variability explains the rest 8.4% of the total variances) using the ensemble empirical mode decomposition method. Finally, the individual contributions of the anthropogenic factors are attributed using a partial least squares regression model.
Highlights
It is generally believed that long-term climate changes can be divided into two parts: the external forcing ‘signals’ and the internal variability of the climate system (Santer et al 2001, Hegerl et al 2007, Bindoff et al 2013)
An optimal fingerprint (OFP) is used to estimate the response coefficient of the climate change to external forcing. It can be implemented by generalized multiple regression (GMR), that is, observational climate changes are always regarded as the result of linear superposition of the climate change signals caused by external forcing, plus the internal variabilities of the climate system
Based on the current scientific understanding of the global mean surface temperature (GMST) change and its causes, this study developed a set of new statistical methods to quantify the contribution of external forcing to large-scale GMST change without relying on climate model simulations
Summary
Working Group for UN Environment Consultation, Zhuhai, People’s Republic of China ∗ Author to whom any correspondence should be addressed.
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