The study ‘Multiscale statistical analysis of thermal and non-thermal components of seawater pCO2 in the Western English Channel: scaling, time-reversibility, and dependence’ statistically analyzes the pCO2 data from the ASTAN cardinal buoy (Brittany, west coast of France) with at 30-minute intervals. The total data is available for 5 years. The author applies various statistical approaches to quantify the relationship between the ocean state variables influencing the thermal and non-thermal components of the pCO2 variability. The paper is overall well written but is statistically too heavy. But depreciating the scientific explanations or essence for performing these statistical analyses. In general, any statistical analysis is performed to explain an underlying problem. This study lacks these explanations and seems like a statistical methodology that was simply applied to a set of variables, for the sake of applying. However, I believe the manuscript could be an important contribution if the authors are able to enhance the explanation highlighting the need for this hefty statistical analysis to understand the physics-based connection between different variables. My major and minor comments are as follows. 

Major comments 

Although the authors claim to have analyzed 5 years of data, Table 1 clearly shows that the primary variable, i.e., pCO2 of this study, have >60% gap. This raises a serious question on the significance of the statistics performed in this study. The authors should explain these limitations and how does it impact the overall outcome of this study. 

Table 1 also highlights that the data gap varies for different variables. The authors perform PDF quotient which is based on joint probability. The variable data availability across the different variables may have a significant impact on such statistical analysis. The authors should make equal data length and then perform such PDF quotient. This may or may not change the outcome of this study, but such analysis is quint essential to show. Also, please provide figures showing the available data count for different time periods. This will give an idea how the distribution of data may impact the seasonality or the interannual or higher order variabilities.  

Please explain the meaning of the time-reversal symmetry and asymmetry. Also, explain why such analysis is important. What scientific problem is addressed or highlighted, with such analysis? Especially keeping in mind variables such as pCO2. The return period analysis is generally performed for storms, cyclones, sea level rise, etc. These suggest the time required for reoccurrence of such events that may impact the coastal population/society. 

Between lines 145-150 ‘This provides information that complements what is obtained from the correlation. While the correlation is a single numerical...’ authors present some key conclusions. However, it is important to highlight why is it important to know which values are in (x,y) plane exhibit weak or strong relationship. 

Lines 159-160 ‘The CV of pCO2.....’ this could be because of the high missing values in pCO2. 

Minor comments 

Provide full form of ASTAN and WEC on its first occurrence in the manuscript. 

Improve Fig. 2, why is it required?  

Line 161 ‘.. X sat time series still exhibits significant fluctuations..’ How does the author decide that the fluctuations are significant? 

Elongate Fig. 3 along X-axis for clarity of image. 

Line 171 ‘In general, this indicates that the non-thermal component...’ Explain how does the author reach this conclusion? 

Lines 175-177 ‘On the contrary..’ The results reported should be visible from Fig. 3 

Line 178 should be Figs 4a and 4b 

Lines 179-180 ‘More...’ The conclusion presented here is subjected to the amount of data availability and could change if more data is available. Authors should consider this. 

Line 184 says that for scaling data below daily was discarded, more explanation is required for this statement. Especially, when the authors have very high-resolution data. 

Line 192, please explain ‘small-scale intermittency’ in detail. What is the physical relevance of the same? 

Lines 217-218 ‘As for the Fourier spectra...’ Explain the physical relevance to obtain information at such small scales. Also, since pCO2 = f(SST) isn’t the observation reported in this line but obvious? 

Line 229, please remove the ‘biological pump’ reference. This data is only at surface, and a detailed analysis of the sub-surface data is required to confirm the role of the ‘biological pump’.