Theory of three-pattern decomposition of global atmospheric circulation

This paper reviews the three-pattern decomposition of global atmospheric circulation(3P-DGAC)developed in recent years,including the decomposition model and the dynamical equations of global horizontal,meridional,and zonal circulations.Compared with the traditional two-dimensional(2D)circulation decomposition method,the 3P-DGAC can effectively decompose the actual vertical vorticity into two components that are caused by the horizontal circulation and convergent/divergent movement(associated with the meridional and zonal circulations).It also decomposes the vertical velocity into the components of the meridional vertical circulation and the zonal vertical circulation,thus providing a new method to study the dynamical influences of convergent/divergent motions on the evolution of actual vertical vorticity and an accurate description of local vertical circulations.The 3P-DGAC is a three-dimensional(3D)circulation decomposition method based on the main characteristics of the actual atmospheric movements.The horizontal,meridional,and zonal circulations after the 3P-DGAC are the global generalization of Rossby waves in the middle-high latitudes and Hadley and Walker circulations in low latitudes.Therefore,the three-pattern decomposition model and its dynamical equations provide novel theoretical tools for studying complex interactions between middle-high and low latitude circulations as well as the physical mechanisms of the abnormal evolution of large-scale atmospheric circulations under the background of global warming.

A New Hybrid Machine Learning Model for Short-Term Climate Prediction by Performing Classification Prediction and Regression Prediction Simultaneously

Machine learning methods are effective tools for improving short-term climate prediction.However,commonly used methods often carry out classification and regression prediction modeling separately and independently.Such a single modeling approach may obtain inconsistent prediction results in classification and regression and thus may not meet the needs of practical applications well.To address this issue,this study proposes a selective Naive Bayes ensemble model(SENB-EM)by introducing causal effect and voting strategy on Naive Bayes.The new model can not only screen effective predictors but also perform classification and regression prediction simultaneously.After being applied to the area prediction of summer western North Pacific subtropical high(WNPSH)from 2008 to 2021,it is found that the accuracy classification score(a metric to assess the overall classification prediction accuracy)and the time correlation coefficient(TCC)of SENB-EM can reach 1.0 and 0.81,respectively.After integrating the results of different models[including multiple linear regression ensemble model(MLR-EM),SENB-EM,and Chinese Multimodel Ensemble Prediction System(CMME)used by National Climate Center(NCC)]for 2017-2021,the TCC of the ensemble results of SENB-EM and CMME can reach 0.92(the highest result among them).This indicates that the prediction results of the summer WNPSH area provided by SENB-EM have a high reference value for the real-time prediction.It is worth noting that,except for the numerical prediction results,the SENB-EM model can also give the range of numerical prediction intervals and predictions for anomalous degrees of the WNPSH area,thus providing more reference information for meteorological forecasters.Overall,as a new hybrid machine learning model,the SENB-EM has a good prediction ability;the approach of performing classification prediction and regression prediction simultaneously through integration is informative to short-term climate prediction.