Abstract
It has been well recognized that, for most climatic records, their current states are influenced by both past conditions and current dynamical excitations. However, how to properly use this idea to improve the climate predictive skills, is still an open question. In this study, we evaluated the decadal hindcast experiments of 11 models (participating in phase 5 of the Coupled Model Intercomparison Project, CMIP5) in simulating the effects of past conditions (memory part, M(t)) and the current dynamical excitations (non-memory part, varepsilon (t)). Poor skills in simulating the memory part of surface air temperatures (SAT) are found in all the considered models. Over most regions of China, the CMIP5 models significantly overestimated the long-term memory (LTM) of SAT. While in the southwest, the LTM was significantly underestimated. After removing the biased memory part from the simulations using fractional integral statistical model (FISM), the remaining non-memory part, however, was found reasonably simulated in the multi-model means. On annual scale, there were high correlations between the simulated and the observed varepsilon (t) over most regions of the country, and for most cases they had the same sign. These findings indicated that the current errors of dynamical models may be partly due to the unrealistic simulations of the impacts from the past. To improve predictive skills, a new strategy was thus suggested. As FISM is capable of extracting M(t) quantitatively, by combining FISM with dynamical models (which may produce reasonable estimations of varepsilon (t)), improved climate predictions with the effects of past conditions properly considered may become possible.
Highlights
Long-term memory (LTM) has become a wellknown concept in climate community
In order to evaluate the simulated memory part of the surface air temperatures over China, we employed the DFA2 to check whether the decadal hindcast simulations can reproduce the observed long-term memory (LTM)
It has been well recognized that most the surface air temperatures (SAT) records over different regions of China are characterized by LTM (Yuan et al 2010)
Summary
Long-term memory (LTM) has become a wellknown concept in climate community. LTM measures the connections of climate states observed at different time points. For a system with LTM, its current climate conditions can have long-lasting influences on the conditions in future (Kantelhardt et al 2001). This phenomenon is considered as a kind of “climate inertia”, and the ocean in climate system, with huge heat capacity, may be a main contributor (Yuan et al 2013)
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