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Abstract This paper presents a novel application of convolutional neural network (CNN) models for filtering the intraseasonal variability of the tropical atmosphere. In this deep learning filter, two convolutional layers are applied sequentially in a supervised machine learning framework to extract the intraseasonal signal from the total daily anomalies. The CNN-based filter can be tailored for each field similarly to fast Fourier transform filtering methods. When applied to two different fields (zonal wind stress and outgoing longwave radiation), the index of agreement between the filtered signal obtained using the CNN-based filter and a conventional weight-based filter is between 95% and 99%. The advantage of the CNN-based filter over the conventional filters is its applicability to time series with the length comparable to the period of the signal being extracted. Significance StatementThis study proposes a new method for discovering hidden connections in data representative of tropical atmosphere variability. The method makes use of an artificial intelligence (AI) algorithm that combines a mathematical operation known as convolution with a mathematical model built to reflect the behavior of the human brain known as artificial neural network. Our results show that the filtered data produced by the AI-based method are consistent with the results obtained using conventional mathematical algorithms. The advantage of the AI-based method is that it can be applied to cases for which the conventional methods have limitations, such as forecast (hindcast) data or real-time monitoring of tropical variability in the 20–100-day range. 

Abstract This study evaluates the ability of state-of-the-art subseasonal to seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden Julian Oscillations and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostics developed to track teleconnections along their preferred pathways in the troposphere and stratosphere, and to measure the global and regional responses induced by teleconnections across both the Northern and Southern Hemispheres. Results of this study will help the modeling community understand to what extent the potential to predict the weather on S2S time scales is achieved by the current generation of forecasting systems, while informing where to focus further development efforts. The findings of this study will also provide impact modelers and decision makers with a better understanding of the potential of S2S predictions related to MJO teleconnections. 

We propose a set of MJO teleconnection diagnostics that enables an objective evaluation of model simulations, a fair model-to-model comparison, and a consistent tracking of model improvement. Various skill metrics are derived from teleconnection diagnostics including five performance-based metrics that characterize the pattern, amplitude, east–west position, persistence, and consistency of MJO teleconnections and additional two process-oriented metrics that are designed to characterize the location and intensity of the anomalous Rossby wave source (RWS). The proposed teleconnection skill metrics are used to compare the characteristics of boreal winter MJO teleconnections (500-hPa geopotential height anomaly) over the Pacific–North America (PNA) region in 29 global climate models (GCMs). The results show that current GCMs generally produce MJO teleconnections that are stronger, more persistent, and extend too far to the east when compared to those observed in reanalysis. In general, models simulate more realistic teleconnection patterns when the MJO is in phases 2–3 or phases 7–8, which are characterized by a dipole convection pattern over the Indian Ocean and western to central Pacific. The higher model skill for phases 2, 7, and 8 may be due to these phases producing more consistent teleconnection patterns between individual MJO events than other phases, although the consistency is lower in most models than observed. Models that simulate realistic RWS patterns better reproduce MJO teleconnection patterns. 

In an assessment of 29 global climate models (GCMs), Part I of this study identified biases in boreal winter MJO teleconnections in anomalous 500-hPa geopotential height over the Pacific–North America (PNA) region that are common to many models: an eastward shift, a longer persistence, and a larger amplitude. In Part II, we explore the relationships of the teleconnection metrics developed in Part I with several existing and newly developed MJO and basic state (the mean subtropical westerly jet) metrics. The MJO and basic state diagnostics indicate that the MJO is generally weaker and less coherent and propagates faster in models compared to observations. The mean subtropical jet also exhibits notable biases such as too strong amplitude, excessive eastward extension, or southward shift. The following relationships are found to be robust among the models: 1) models with a faster MJO propagation tend to produce weaker teleconnections; 2) models with a less coherent eastward MJO propagation tend to simulate more persistent MJO teleconnections; 3) models with a stronger westerly jet produce stronger and eastward shifted MJO teleconnections; 4) models with an eastward extended jet produce an eastward shift in MJO teleconnections; and 5) models with a southward shifted jet produce stronger MJO teleconnections. The results are supported by linear baroclinic model experiments. Our results suggest that the larger amplitude and eastward shift biases in GCM MJO teleconnections can be attributed to the biases in the westerly jet, and that the longer persistence bias is likely due to the lack of coherent eastward MJO propagation.