An atmosphereocean time series model of global climate change
Time series models of global climate change tend to estimate a low climate-sensitivity (equilibrium effect on global temperature of doubling carbon dioxide concentrations) and a fast adjustment rate to equilibrium. These results may be biased by omission of a key variableheat stored in the ocean. A time series model of the atmosphereocean climate system is developed, in which surface temperature (atmospheric temperature over land and sea surface temperature) moves towards a long-run equilibrium with both radiative forcing and ocean heat content, while ocean heat content accumulates the deviations from atmospheric equilibrium. This model is closely related to Granger and Lee's multicointegration model. As there are only 55 years of observations on ocean heat content, the Kalman filter is used to estimate heat content as a latent state variable, which is constrained by the available observations. This method could be applied to other climate change problems where there are only limited observations on key variables. The final model adopted relates surface temperature to the heat content of the upper 300m of the ocean. The resulting parameter estimates are closer to theoretically expected values than those of previous time series models and the estimated climate sensitivity to a doubling of carbon dioxide is 4.4K.
global climate change multicointegration kalman filter time series analysis