Previous studies have revealed some common biases in coupled general circulation model's simulations of the East Asian (EA) winter monsoon (EAWM), including colder surface air temperature and more winter precipitation over the EA region. In this study, we examined 41 fully coupled atmosphere-ocean models from fifth phase of the Coupled Model Intercomparison Project (CMIP5), which will be widely used in the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC), and address whether the current state-of-the-art CMIP5 models can characterise the climatology of the East Asian winter monsoon. We also compared the results with the models from third phase of CMIP, which was extensively used in the fourth assessment report of the IPCC. The results show that the cold surface air temperature (SAT) bias is lessened and the precipitation amount decreased with the current CMIP5 models. Moreover, the CMIP5 models performbetter at predicting surface winds and high-level jet streams than the CMIP3 models. Moreover, CMIP5 models show more model consistency in most EAWM parameters, and the interannual variability of the SAT is closer to the observations. We also examined the change in the radiation energy budget in the CMIP5 models and compared with CMIP3 models. Although the improvements are significant, deficiencies still exist in the simulation of the EAWM, e.g., the stronger EA major trough and the stronger zonal sea level pressure gradient.

The Eliassen–Palm flux (EPF) and Plumb’s wave activity flux (WAF) were computed, using ERA-Interim data, to analyze the influence of planetary wave 3 on a stratospheric sudden warming event from 17 February to 15 March 2005 (SSW05). It was found that 1) SSW05 consisted of three stages: a prior minor warming (MnW05), a late final warming (FW05), and a warming stagnation between MnW05 and FW05; 2) the wave 3 first decreased total upward EPFs by more than 30% at 100 hPa, resulting in the warming stagnation, and then increased upward EPFs by greater than 50%, leading to FW05; and 3) the anomalies of wave-3 activity fluxes were associated with the pattern of Atlantic blocking high in the latter two stages. The interactions between the wave 3 and wave 1 partitioned the zonal upward channel of total wave activity fluxes from one longitudinal region into two longitudinal regions and affected SSW05.

Differential braking and active steering have already been integrated to overcome their shortcomings. However, existing research mainly focuses on two-axle vehicles and controllers are mostly designed to use one control method to improve the other. Moreover, many experiments are needed to improve the robustness; therefore, these control methods are underutilized. This paper proposes an integrated control system specially designed for multi-axle vehicles, in which the desired lateral force and yaw moment of vehicles are determined by the sliding mode control algorithm. The output of the sliding mode control is distributed to the suitable wheels based on the abilities and potentials of the two control methods. Moreover, in this method, fewer experiments are needed, and the robustness and simultaneity are both guaranteed. To simplify the optimization system and to improve the computation speed, seven simple optimization subsystems are designed for the determination of control outputs on each wheel. The simulation results show that the proposed controller obviously enhances the stability of multi-axle trucks. The system improves 68% of the safe velocity, and its performance is much better than both differential braking and active steering. This research proposes an integrated control system that can simultaneously invoke differential braking and active steering of multi-axle vehicles to fully utilize the abilities and potentials of the two control methods. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.A) l i−1 − l v > 0, B bli ≥ 0, A bli < 0, δ i > 0 B) l i−1 − l v > 0, B bli > 0, A bli < 0, δ i = 0 C) l i−1 − l v = 0, δ i = 0 a4 B bri = 0, A bri = 0 (Cannot use this wheel to brake) B bli = 0, A bli = 0 (Cannot use this wheel to brake)

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