Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> This study introduces an improved method of the Globally Resolved Energy Balance Model(GREB) by the Bayes network. Starting from the climate elements relationship included in the GREB model, we reconstruct the model by the Bayes network to solve the problem of low model accuracy due to over-reliance on boundary conditions and initial conditions and the inability to use observed data for dynamic correction of model parameters. The improved model is applied to the simulation of surface average temperature and atmospheric average temperature based on the 3.75°×3.75° global data sets by Environmental Prediction (NCEP)/ National Center for Atmospheric Research(NCAR) from 1985 to 2014. The results illustrate that the improved model has higher average accuracy and lower spatial differentiation than the original GREB model. And the improved method provides a strong support for other dynamic model improvements.
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