Juliues et.al. have provided an excellent analysis of the performance of the N₂O isotope analyzer, Picarro G5131-I, and the methods needed to accurately measure isotope data for N₂O at ambient levels. They correctly account for some of the more common use-cases for this type of analysis and the associated challenges for the relevant environments, e.g. changes in N₂O, O₂ and CH₄. The paper is expanding on the instrument analysis done in the mentioned paper Harris et.al 2020, showing that long-term drift and performance of the instrument necessitate a diligent calibration protocol.

Finally they provide access to a free MATLAB code written to incorporate the found corrections and guide users on what variables to measure and determine for their instruments before they can account for the instrument and matrix effects. This is a valuable addition to the community as it allows for standardization within the field and will help informing people of the potential pitfalls using the instrument-reported values uncritically.

I support the publication of this paper, though I have comments that I should like addressed to improve clarity and impact of the paper.

General comments:

Regarding the determining of the effect of changes in N₂O concentration, I am rather surprised at the scale and direction of these changes in comparison to the calculated values. It stands in contrast to the levels previously measured by CRDS-III (as shown in table 4), which while not correct are on the same order of the calculated offset. What is your best hypothesis for why the three instruments behave so differently, and have discussed potential explanations with the manufacturer? Because unless there has been relevant software updates for CRDS-III since Harris. 2020, I am under the assumption that the built-in post-correction for non-linearity should be close to the same, and if not it would appear as if the updated correction performs worse?

A final question in relation to this topic is; whether you also tried diluting below the reported detection limit, because the non-linear behavior of the calculated values first appear pronounced at 1/N₂O >0.003.

While access to MATLAB is common within the field, it would have been great and more inclusive if the developed MATLAB code functions were available in a free software such as R or python. I understand that it is quite the endeavor to change programing language, but I would be hopeful if it is something that you would consider within outlook and future work. Because as is, I unfortunately cannot test the code as I would otherwise have liked to do.

The uncertainty calculations in the appendix appear to be correct, so I believe it would improve the impact of the paper if you could provide an example of the scale of error one could expect. A suggestion would be correcting for the matrix effect of Cal 2.1 _330ppb, where the value and error could nicely be compared to the known value?

Supplement comments:

In the tables S8-10 you have calculated the mixing ratio of oxygen, though for table S9 and S10 I believe that you have incorrectly copied the concentration from CO₂ from the previous set of tables. A further correction to the correct oxygen values of table S8, is that the matrix of the Cal1 and Cal2 include oxygen, which means that you have a base concentration of oxygen at about 770 ppm rather than 0 for N₂O 0.33 ppm and double and triple that for 0.66 and 0.99 ppm N₂O. I understand that those can be considered negligible levels for the relevant purpose, but I would still prefer the correct values.

I appreciate the inclusion of experiment 6 and find it to be a worthwhile aspect to investigate, but I find the description of it difficult to follow. In part because the text refers to gas mixes not shown in the figure S6 or described in table S13, leaving me unsure how they are introduced and diluted. Additionally the many gasses with very similar names led me to mix up which gas was being discussed. I would suggest perhaps expanding on table S13 to show the dilution scheme for all discussed dilutions. And maybe update the figure to better indicate channel 1 and 2.  

Minor comments:

Line 165: You have written ¹⁴N¹⁶O¹⁷O and ¹⁴N¹⁶O¹⁸O, where I believe you meant to write ¹⁴N¹⁴N ¹⁷O and ¹⁴N¹⁴N ¹⁸O

Line 462: There is a misplaced 6 following the (990 ppb)

Line 523: I harbor suspicions of the non-linearity measurement of the 10/4-2025, as it shows a significantly different behavior for both CRDS II & III.  

Line 574: Did you conduct validation measurements for the inclusion of CO₂ removal to compare the found values to your validation experiment?

Minor supplement comments:

Line 17:  Missing lower case 4 for methane.

Table S1: You have in the accompanying text written that the change was in the range of 0.33-1.20 ppm, but the lowest concentration I see in the figure is 0.35 ppm.

Dear Editor,

This manuscript will help the scientific community use laser instruments for the determination of N2O and better understand their limitations. Importantly, the manuscript offers ways to move forward and help the scientific community improve data quality and reproducibility. I believe this manuscript will be well received and used by scientists across fields that rely on laser-based data. Below I include some detailed comments to help clarify a few minor issues before publication.

L20, gives the impression that 3 brands will be tested, but they are all Picarro.

L24, if only Picarro’s were tested, is it fair to say “any” instrument must be routinely characterized? Would another instrument brand be more reliable?

L25-26, The earlier Stephen Harris paper seems to suggest that some instruments have additive effects and that these corrections are possible. Are these non-additive effects specific to Picarro?

L28, the MATLAB code is certainly a nice addition. Of course not necessary, but it would be wonderful if there was an R version available so that everyone can access it.

L31, consider reversing the words to state “case study”.

L35, insert N2O right after “nitrous oxide”

L39, Consider using “N2O” consistently.

L465-466, when it is stated that the data fits well with a linear model, how is that assessed?

Fig 1. Please include more tick labels in the X and Y axis of figure 1. Unclear what is meant by “sliced” data and it would be helpful to include that in the figure caption.

Fig 5. There might be value in adding the R2 of the relationships to better understand the linear relationships (see above comment).

Fig 10, the caption refers to solid gray lines, but I can’t find gray lines in the figure other than the dashed ones.