the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Nov 2024
Atmospheric moisture tracking with WAM2layers v3
Abstract. This manuscript documents the atmospheric moisture tracking model WAM2layers v3 (Water Accounting Model – 2 layers – version 3). WAM2layers may be used to gain understanding of atmospheric dynamics and to study rainfall patterns and extremes by mapping their sources or sinks, often in the context of climate and land-use changes. To this end, WAM2layers solves a prognostic equation for tagged moisture in gridded atmospheric datasets such as reanalysis data or climate model output. WAM2layers can be used in forward mode to determine where evaporated water eventually precipitates, or in backward mode to determine where precipitation originally evaporated.
WAM2layers v3 represents a complete rewrite of the WAM2layers model originally introduced in 2010 and subsequently used in more than sixty academic studies. This latest version incorporates performance optimisations to cope with the increased resolution of input data, and introduces various best practices aimed at improved user-friendliness and software sustainability. Since an increasing number of researchers is using the code, this manuscript is intended as an updated description and reference in the academic literature. After describing the history, model formulation, and numerical implementation, we present and evaluate two example cases to illustrate the use and skill of WAM2layers v3. We then discuss best practices, some important assumptions, and directions for future development.
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RC1: 'Comment on egusphere-2024-3401', Anonymous Referee #1, 14 Dec 2024
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Review of Atmospheric moisture tracking with WAM2layers v3 by Peter Kalverla , Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
This study describes version3 of the WAM2layers model which is a Eulerian offline moisture tracking model. WAM2layers v3 is rewritten in Python from previous model code and shared following FAIR principles. This manuscript describes the model code and setup in detail, discusses model limitations and potential future developments and provides two use cases.
The manuscript gives a detailed and well-structured overview of the model principles and how it is implemented in python. Further, the efforts to share the code in a user-friendly way and to reach out to the community are a good example of code sharing and development. While the new model version is a good role model of maintaining research software, the description of model limitations and potential shortcomings is sometimes only vaguely formulated. Further, the readability of the introduction should be improved and the use cases’ outcomes better explained. As this study provides a substantial contribution to modelling science, and especially the moisture tracking community where previous versions of the model have been used in many studies, I recommend it for publication in GMD after addressing the following line-by-line comments:
Figure 1: Can you add a representation of the tagged moisture to the conceptual figure? A visualisation of the different moisture fluxes and/or moisture budgets could help to follow the equations.
27-51: The introduction contains a long description of different types of moisture tracking models. This part is difficult to read and not very relevant for this study. As these types are also summarised in the referenced studies, the detailed discussion could be removed from this manuscript.
65-83: This list does not contain much information. Could this information be provided in a more informative way? E.g in a table, where the references are listed?
97: “This facilitates…” What does “this” refer to?
229-230: “This distributes cloud and rain/snow water across all levels, which is not perfect, but better than not counting it at all.” Water is not distributed equally in the vertical column, with the majority residing in the lower troposphere. This is neglected if the cloud, rain and snow water is distributed equally across all levels. Does it make a difference for moisture tracking if the cloud and rain/snow water distribution is weighted by the relative water content of each layer? Further, "better than not counting it at all" is very vague. Can you be more precise in your statement?
267: “WAM2layers v3.1” I think this is the first time that you mention v3.1. Is there a difference to v3?
282: “the data is first interpolated to a finer time step” What is the new temporal resolution with “a finer time step”?
291: “ Sijt represents the total column water” But not Qtc, correct? Can you make it clearer that this is a “different” total column water than Qtc?
318: “ the vertical transport terms are directed from the upper to the lower layer, i.e., positive downward” This statement is repeated many times. Can you check if it is really needed each time?
358: “boundary of the domain”: It was not immediately clear to me, which domain this is – model domain or the tagging domain? Further, what do you consider as a significant boundary transport (compare also comment on Fig. 6)?
Figure 6: “By then, 42 % of moisture was tracked to its source; 3.4 % of was still in the domain’s atmosphere, and 54 % was associated with transport across the domain boundaries.” 42% tracked to the sources seems like a low number. Does this use case represent the typical performance of WAM2layers, and is this the expected tracking efficiency? Having 54% of moisture that is transported across the domain boundaries, what do you consider a significant transport across the boundaries (compare lines 358-359)?
372-374: “The spatial pattern of the sources corresponds to the sources determined by Insua-Costa et al. (2022) and Staal and Koren (2023).” How do the WAM2layer sources compare in terms of tracked moisture? You mention that a model intercomparison study is currently done, and I acknowledge that such a question can be investigated in more detail in an intercomparison study. But as the high loss/diffusion (?) of moisture is striking in this use case, this question comes immediately to my mind when you compare the WAM2layers patterns to other studies.
Figure 7: “Of the tagged moisture 7.8 % recycled within the source region.” How is recycling defined in this forward tracking mode?
465: What are “quick” regional moisture recycling calculations?
Minor comments:
43: “or something in between”: Can you be more precise?
161: “...starting isobaric coordinates to yields…” something is missing here
164 “ the horizontal transport in terms in (2) can be written” delete in terms
204: water → humidity?
242: “mask of values between 0 and 1” Do you mean mask of values of 0 or 1?
302: “where subscript t denotes the column totals” This should be introduces when t is used for the first time.
349: remove “a such that“
350: “These limits are not 100 % watertight“ Colloquial expression
369: “the, Ourthe” remove comma
379: “similar moisture source patterns” do you mean “moisture sink patterns”?
402: “ WAM2layers ships with “ Colloquial expression
430: “get a better grip on it” Colloquial expression → it is not clear what you exactly mean with “a better grip”
435: “ we should be careful not to throw the baby out with the bathwater.” Colloquial expression → can you spell this out, or reconsider if this statement is needed?Citation: https://doi.org/10.5194/egusphere-2024-3401-RC1 -
CC1: 'Reply on RC1', Peter C. Kalverla, 20 Dec 2024
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Dear reviewer,
Thank you for the constructive review! These comments will surely help to improve the manuscript. We will follow up with a more detailed response in due time.
Citation: https://doi.org/10.5194/egusphere-2024-3401-CC1
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CC1: 'Reply on RC1', Peter C. Kalverla, 20 Dec 2024
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Data sets
ERA5 data West Europe 2021 July for WAM2layers I. Benedict and C. Weijenborg https://doi.org/10.4121/f9572240-f179-4338-9e1b-82c5598529e2.v1
ERA5 data West Africa 1998 July and August for WAM2layers T. Gaasbeek and R. J. Van der Ent https://doi.org/10.4121/bbe10a2a-39dc-4098-a69f-0f677d06ecdd.v2
Model code and software
WAM2layers R. J. Van der Ent et al. https://doi.org/10.5281/zenodo.7010594
Interactive computing environment
WAM2layers R. J. Van der Ent et al. https://github.com/WAM2layers/WAM2layers
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