[1] The stratospheric climate and variability from simulations of sixteen chemistryclimate models is evaluated. On average the polar night jet is well reproduced though its variability is less well reproduced with a large spread between models. Polar temperature biases are less than 5 K except in the Southern Hemisphere (SH) lower stratosphere in spring. The accumulated area of low temperatures responsible for polar stratospheric cloud formation is accurately reproduced for the Antarctic but underestimated for the Arctic. The shape and position of the polar vortex is well simulated, as is the tropical upwelling in the lower stratosphere. There is a wide model spread in the frequency of major sudden stratospheric warnings (SSWs), late biases in the breakup of the SH vortex, and a weak annual cycle in the zonal wind in the tropical upper stratosphere. Quantitatively, "metrics" indicate a wide spread in model performance for most diagnostics with systematic biases in many, and poorer performance in the SH than in the Northern Hemisphere (NH). Correlations were found in the SH between errors in the final warming, polar temperatures, the leading mode of variability, and jet strength, and in the NH between errors in polar temperatures, frequency of major SSWs, and jet strength. Models with a stronger QBO have stronger tropical upwelling and a colder NH vortex. Both the qualitative and quantitative analysis indicate a number of common and long-standing model problems, particularly related to the simulation of the SH and stratospheric variability.

A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how accurately models can predict the NAO. Most dynamical models, however, have limited skill in seasonal forecasts of the winter NAO. A new empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill than current dynamical models. The empirical model provides robust and skilful prediction of the December-January-February (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions of sea-ice concentration, stratospheric circulation, and sea-surface temperature. The predictability is, for the most part, derived from the relatively long persistence of sea ice in the autumn. The lower stratospheric circulation and sea-surface temperature appear to play more indirect roles through a series of feedbacks among systems driving NAO evolution. This MLR model also provides skilful seasonal outlooks of winter surface temperature and precipitation over many regions of Eurasia and eastern North America.

The present work identifies two types of La Niña based on the spatial distribution of sea surface temperature (SST) anomaly. In contrast to the eastern Pacific (EP) La Niña event, a new type of La Niña (central Pacific, or CP La Niña) is featured by the SST cooling center over the CP. These two types of La Niña exhibit a fundamental difference in SST anomaly evolution: the EP La Niña shows a westward propagation feature while the CP La Niña exhibits a standing feature over the CP. The two types of La Niña can give rise to a significantly different teleconnection around the globe. As a response to the EP La Niña, the North Atlantic (NA)-Western European (WE) region experiences the atmospheric anomaly resembling a negative North Atlantic Oscillation (NAO) pattern accompanied by a weakening Atlantic jet. It leads to a cooler and drier than normal winter over Western Europe. However, the CP La Niña has a roughly opposing impact on the NA-WE climate. A positive NAO-like climate anomaly is observed with a strengthening Atlantic jet, and there appears a warmer and wetter than normal winter over Western Europe. Modeling experiments indicate that the above contrasting atmospheric anomalies are mainly attributed to the different SST cooling patterns for the two types of La Niña. Mixing up their signals would lead to difficulty in seasonal prediction of regional climate. Since the La Niña-related SST anomaly is clearly observed during the developing autumn, the associated winter climate anomalies over Western Europe could be predicted a season in advance.

This paper presents a rolling bearing fault diagnosis approach by integrating wavelet packet decomposition (WPD) with multi-scale permutation entropy (MPE). The approach uses MPE values of the sub-frequency band signals to identify faults appearing in rolling bearings. Specifically, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of sub-frequency band signals by means of the WPD method. Then, each sub-frequency band signal is divided into a series of subsequences, and MPEs of all subsequences in corresponding sub-frequency band signal are calculated. After that, the average MPE value of all subsequences about each sub-frequency band is calculated, and is considered as the fault feature of the corresponding sub-frequency band. Subsequently, MPE values of all sub-frequency bands are considered as input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of the rolling bearing. Experimental study on a data set from the Case Western Reserve University bearing data center has shown that the presented approach can accurately identify faults in rolling bearings.