Multi-scale CO2
Abstract Five time series of estimated atmospheric CO 2 with sampling intervals ranging from 0.5 million years to the relatively high frequency of one week are analysed. The yearly series shows a clear increasing trend since the beginning of the first Industrial Revolution around 1760. The weekly series shows a clear increasing trend and also seasonal variation. In both cases, the trend is fitted by a conceptual model that consists of a baseline value with an exponential trend superimposed. For the weekly series, the seasonal variation is modelled as an exponential of a sum of sine and cosine terms. The deviations from these deterministic models are treated as detrended and deseasonalised time series.Then,threesub-categoriesof autoregressive integrated moving average (ARIMA) models are fitted to the five time series: ARMA models which are stationary; FARIMA models which are stationary but have long memory and are fractal processes, and ARIMA models which are variations on a random walk and so non-stationary in the variance.The FARIMA and ARIMA models provide better fits to the data than the corresponding ARMA models. All the fitted models are close to the boundary of stability, and are consistent with claims that climate change due to an increase in atmospheric CO 2 may not quickly be reversed even if CO 2 emissions are stopped.