{'fraction_non_alphanumeric': 0.022964509394572025, 'fraction_numerical': 0.019485038274182326, 'mean_word_length': 5.1191489361702125, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Reply to comments from reviewer #2

7 Despite the great success of machine learning, its application in climate dynamics has not been 8 well developed. One concern might be how well the trained neural networks could learn a dynamical 9 system and what can be the potential application of this kind of learning. In this paper, three 10 machine learning methods are used: reservoir computer (RC), back propagation based artificial 11 neural network (BP), and long short-term memory neural network (LSTM). It shows that the 12 coupling relations or dynamics among variables in linear or nonlinear systems can be well learnt by 13 RC and LSTM, which can be further applied to reconstruct one time series from the other. 14 Specifically, we analyzed the climatic toy models to address two questions: (i) what factors 15 significantly influence machine learning reconstruction; and (ii) how to select suitable explanatory 16 variables for machine learning reconstruction. The results reveal that both linear and nonlinear 17 coupling relations between variables do influence the reconstruction quality of machine learning. If 18 there is a strong linear coupling between two variables, then the reconstruction can be bi-directional, 19 and both of these two variables can be an explanatory variable for reconstructing the other. When the 20 linear coupling among variables is absent, but with the significant nonlinear coupling, the machine 21