An improved empirical dynamic control system model of global mean sea level rise and surface temperature change

Abstract

Having great impacts on human lives, global warming and associated sea level rise are believed to be strongly linked to anthropogenic causes. Statistical approach offers a simple and yet conceptually verifiable combination of remotely connected climate variables and indices, including sea level and surface temperature. We propose an improved statistical reconstruction model based on the empirical dynamic control system by taking into account the climate variability and deriving parameters from Monte Carlo cross-validation random experiments. For the historic data from 1880 to 2001, we yielded higher correlation results compared to those from other dynamic empirical models. The averaged root mean square errors are reduced in both reconstructed fields, namely, the global mean surface temperature (by 24–37%) and the global mean sea level (by 5–25%). Our model is also more robust as it notably diminished the unstable problem associated with varying initial values. Such results suggest that the model not only enhances significantly the global mean reconstructions of temperature and sea level but also may have a potential to improve future projections.