The authors' application of Jensen-Shannon complexity and Fisher-Shannon information plane to solar wind fluctuations yields interesting and relevant findings for space plasma physics.  The methodology is clearly and thoroughly explained, and the results and discussion sections of the manuscript are well-organized and effectively presented. The study is worthy of publication in NPG, with a minor correction.

The authors should cite previous studies that have investigated solar wind time series using entropy and nonlinear dynamics concepts, which have established the stochastic nature of solar wind. Please see my review comments below.

Introduction section 

Page 1, Line 20: Please remove the abbreviation 'e.g.' from the citation bracket. Additionally, ensure that all instances of 'e.g.' are removed throughout the entire manuscript.

Page 2, Lines 55-65: While you discuss previous studies that applied Jensen-Shannon complexity analysis to solar wind fluctuations, you omit relevant literature that utilized entropy measures and other nonlinear dynamics tools to investigate solar wind fluctuations. These studies have consistently reported that solar wind exhibits stochastic behavior. Please consider incorporating these references to provide a more comprehensive overview of the field. See the article below for example.

https://doi.org/10.1016/j.asr.2008.12.026

https://doi.org/10.5194/npg-28-257-2021

https://doi.org/10.1029/2018JA025318

https://doi.org/10.1007/s41614-022-00095-z

Page 3, Line 65: “The Jensen-Shannon complexity analysis is only one of a number of methods to investigate the nature of fluctuation”.  The Jensen-shannon complexity metric is not the only information theory tools that have been applied in space plasma physics. The statement can be corrected as “The Jensen-Shannon complexity metric is also one of the information theory techniques that is useful to investigate the nature of solar wind fluctuation”

Page 3, Line 75-80: Rephrase the statement “The key purpose of the analysis presented in this paper has been to investigate how different complexity measures compared for different solar wind types presented above.

                                     To

“The key purpose of this analysis is to examine how Jensen-Shannon complexity and Fisher-Shannon information plane capture the fluctuation signatures of distinct solar wind structures, including slow streams, fast streams, sheaths, and magnetic clouds.”

Data and Methods section

Page 3, line 85: It is better to use “The solar wind data used in this study”

This is an innovative and original study, contributing new findings to the existing knowledge of dynamical complexity in the solar-terrestrial system. The authors apply information theory measures to investigate the complex character of the dynamics of solar wind. The manuscript is well-written and deserves to be published in NPG following a minor revision. These are my remarks:

There are two recent review articles highlighting the importance of information theory for the study of coupling processes in the solar-terrestrial system (Balasis et al., 2023; McGranaghan, 2024). Please consider mention these pertinent review articles either in Introduction and / or Discussion. 

L. 22: The solar wind exhibits large-scale organisation,

I think that the word 'organisation' here could create some mild confusion to the reader, 

because the expression 'large-scale organisation' is (a) often used in the field of business and finance in a different context, and/or (b) could be linked by a reader to systems exhibiting self-organized criticality (SOC) etc. Maybe 'structure' instead of 'organisation' is a more suitable word here.

L. 34-35: ICME sheaths as compressed structures more resemble SIRs

Do you mean 'may resemble SIRs' or something else?

L. 56: The nature of solar wind fluctuations can be assessed using Jensen-Shannon complexity analysis

Please elaborate in the text on the following interconnected points:

1. What is the rationale of using complexity analysis for assessing the nature of solar wind fluctuations?

2. If you establish the reasoning for point 1, why then to use the specific analysis? Which is the benefit from performing time series analysis through this measure given the plethora of the available information theory techniques?

L. 65: magnetic clouds clearly exhibited the lowest entropies and highest complexities

I am not sure that lower entropy means higher complexity for a system. I would say the opposite: lower entropy means higher order and therefore less complexity for a system. Could you please explain this point?

L. 74: There are a few earlier as well as more recent applications of Fisher information in the context of geophysics / geomagnetism and space physics / space weather (see for instance, Balasis et al., 2016, 2023, respectively).

L. 86-87: Three intervals of data were considered for each solar wind type, each consisting of 12 hours of measurements.

Please consider changing this point a bit by adding the specific times of the three intervals and mentioning the number of solar wind type to make it more clear for the reader:

Three time intervals (1) from ... to ..., 2) from ... to ..., 3) from ... to ...) of data were considered for each of the four solar wind types, each consisting of 12 hours of measurements.

L. 295: overall, what is clearly absent/missing in the Discussion is the (expected) comparison to other/similar space physics studies using information theory measures. Please provide in this section such a useful comparison.

L. 347: The analysed magnetic clouds had more internal structure than the other solar wind data 

Could you please elaborate a bit on this point? Does more internal structure mean more order / lower entropy / lower complexity or vice versa in your perspective?

References

Balasis, G., S. M. Potirakis, and M. Mandea (2016), Investigating Dynamical Complexity of Geomagnetic Jerks using Various Entropy Measures, Front. Earth Sci., 4:71, doi:10.3389/feart.2016.00071.

Balasis, G.; Boutsi, A.Z.; Papadimitriou, C.; Potirakis, S.M.; Pitsis, V.; Daglis, I.A.; Anastasiadis, A.; Giannakis, O. Investigation of dynamical complexity in Swarm-derived geomagnetic activity indices using information theory. Atmosphere 2023, 14, 890. https://doi.org/10.3390/atmos14050890

Balasis, G., Balikhin, M.A., Chapman, S.C. et al. Complex Systems Methods Characterizing Nonlinear Processes in the Near-Earth Electromagnetic Environment: Recent Advances and Open Challenges. Space Sci Rev 219, 38 (2023). https://doi.org/10.1007/s11214-023-00979-7

McGranaghan, R.M. Complexity Heliophysics: A Lived and Living History of Systems and Complexity Science in Heliophysics. Space Sci. Rev. 2024, 220, 52.