the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Sep 2026
How models represent target systems in the world, and how we (need to) communicate this
Abstract. Humans are a representing species: we construct conceptual, numerical and material models that represent the world in certain respects. However, how scientists communicate about how their models represent the world, sometimes reflects that it is unclear by what virtue models can represent and can be used to make claims about the world. This becomes most apparent and problematic when models are used to make claims about a target system in communications between scientists, the public, and consultants.
After introducing four example models of different kinds from one geoscientific discipline, recent accounts of model representation from philosophy of science will be summarized and exemplified. Some of these centralize the representational relation between models and their target phenomena, whereas others centralize the role of the modellers in the process of using the model as representation for the target. The latter group of accounts convincingly demonstrate the role of the scientist, their skills and their background knowledge in the representation. This works in both ways: the modellers and other experts put trust in their models, not because the models are perfect, but because they have background knowledge and experience. However, the receiver, with different expertise, does not have this.
Following examples from the geoscientific literature, suggestions emerge how more nuanced claims can be made with models, and which background assumptions on both the modelling and the target systems, and the role of required skills, need to be explicated. While scientists may worry that putting a scientist in the picture could reduce the trust in their model, for effective communication the target audiences need understanding at a level that links to their background and training to be able to evaluate the model results and integrate these with their own experience and knowledge.
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Status: open (until 26 Nov 2026)
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EC1: 'Ed query about egusphere-2026-4944', David Crookall, 04 Sep 2026
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CEC1: 'Reply on EC1', John K. Hillier, 16 Sep 2026
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Publisher’s note: the content of this comment was removed on 30 September 2026 since the comment was posted by mistake.
Citation: https://doi.org/10.5194/egusphere-2026-4944-CEC1
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CEC1: 'Reply on EC1', John K. Hillier, 16 Sep 2026
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RC1: 'Comment on egusphere-2026-4944', Anonymous Referee #1, 08 Oct 2026
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Review of
“How models represent target systems in the world, and how we (need to) communicate this”
by M. G. Kleinhans.
Submitted to Geoscience Communication.
Summary
The author argues that geoscientists communicate about models as if correspondence between model and target (dynamic similarity or isomorphism) suffices to justify inferences about the target. Drawing on agent-centered accounts of scientific representation (DDI, DEKI), he argues that the modeler’s purpose, skill, and background knowledge are part of the representational relation. Four estuary models illustrate the accounts. An informal survey of 52 papers in EGU, AGU, and Copernicus journals is offered as evidence that modeler agency is rarely communicated, and two anecdotes from science-policy settings illustrate the consequences. The paper concludes that modelers should state the respects and degrees in which a model represents its target and should make their background knowledge explicit, both within science and when communicating with other audiences.
The topic is important and within the scope of Geoscience Communication. Introducing the philosophy of scientific representation to geoscientists in accessible form is worthwhile, and the author’s willingness to critique his own papers is commendable. The writing is lively.
There are four main problems. First, the empirical claim of Section 4.1 rests on an informal, single-coder survey with undefined criteria, and the supplementary table does not consistently support the text. Second, the recommendations are too general to act on. Third, relevant literature is missing, notably from climate modeling, where modeler judgment in tuning has been documented explicitly. Fourth, several conceptual statements are inconsistent or inaccurate.
In view of these problems, I recommend major revisions.
Major comments
1. The survey of 52 papers (Section 4.1 and supplement)
This is the only systematic evidence offered for the central claim that modeler agency is rarely communicated, so it needs to be sound.
Sampling. The selection rules differ by journal: GMD “highlight articles” excluding a digital twin and scenario construction; JAMES “most recently published”; keyword searches elsewhere; in ESurf, excluding repeat authors and the author’s own papers. Publication years range from 2006 to 2026, and Climate of the Past contributes two papers, from 2008 and 2014. None of this is described in the manuscript. The protocol belongs in the text.
Coding. The column headings (“model reality”, “explicit key in represent”, “benchmark / data”, “modeller agency”) are not defined, and there is one coder with no reliability check.
Text and table disagree. Line 299 says all papers address shortcomings; the table codes 37 of 52. Lines 305–306 say all numerical papers use isomorphism and all experimental papers use similarity, but Eltner (a cellular automaton) and Gonzalez-Munos (analog experiments) are coded for neither. Lines 308–309 say direct application to the target is more frequent in experiments; the table gives 7 of 13 (54%) versus 17 of 40 (43%), a weak difference at n = 13.
Errors in the table. Six papers have the value 2 in the benchmark column, so the summary fraction in row 4 (0.77) overstates the share of papers with benchmarks (34 of 52, or 0.65). The link for Ma et al. points to the JAMES home page rather than the article.
The agency coding appears too strict, and the quotations in the table contradict it. Sockness et al. write that “the model was not designed to be a scale model of those areas. Instead, it was designed to be a process model…”. That is precisely the statement of purpose and of representational respects that the Conclusions (lines 373–375) recommend, yet it is coded as showing neither agency nor an explicit key. Other examples: “We selected C = 2 to achieve sufficient stabilization” (Gärtner); “we found it necessary to use a line search algorithm” (Popov); “we assume no infiltration” (Gauvain); “their reliability in extrapolation… has not yet been convincingly demonstrated” (Coucheney). These are first-person statements of modeler choices and limits. If the author means something narrower, such as disclosure of the background knowledge on which trust rests, he should define it and recode. Otherwise the statement that “only two papers showed minor signs of communicated author agency” (line 313) is not supported.
Some entries are not models by the paper’s own definition (lines 72–73 and 140–152: a model permits intervention). The table itself notes “[not really a model to intervene on]” for Tarling; Pantaleo is a “sketch (not to scale)”; de Nardis is a structural interpretation of tomography. Exclude or justify.
Unattributed quotations (lines 251–253, “coastal modelling papers best not referenced”). Readers cannot check these, which sits awkwardly in a paper about transparency. Use quotations from the documented sample instead.
The author should choose between two options: present the survey as illustrative only and remove the quantitative statements, or turn it into a content analysis with a stated sampling procedure, a codebook, a second coder, and reported agreement. The second option would make the paper considerably stronger.
2. Missing literature
The DDI account originates with Hughes (1997), who is not cited. Figure 2 and Section 3.2 should cite him. For DEKI, Frigg and Nguyen (2020) is the fuller source.
Climate modeling offers direct evidence on the paper’s question. Mauritsen et al. (2012), Hourdin et al. (2017), and Schmidt et al. (2017) document how modelers tune global models, which targets they tune to, and why, and argue that this should be disclosed. These papers show that at least one modeling community does communicate modeler agency explicitly, and they show what that communication looks like. They also discuss the risk, noted in the abstract, that disclosure can be used to undermine trust. The survey seems to have missed this body of work.
Parker (2020) and Baumberger et al. (2017) on adequacy for purpose and confidence building are directly relevant to “in which respects and degrees”.
The sociology of trust in models complicates the paper’s premise that modelers know the limitations and outsiders lack the background (lines 16–18 and 247). MacKenzie’s “certainty trough” (1990), applied to climate modelers by Lahsen (2005), found that modelers themselves sometimes conflate model and world, while users at an intermediate distance are often the most confident. This deserves discussion.
Knowledge in modeling is distributed across communities and institutions (Edwards, 2010; Winsberg, 2010). Trust is built through intercomparison projects, shared code, documentation, and observational benchmarks, not only through individual expertise. The Ems-Dollard example supports this: trust was built “over considerable time by a large community” (lines 364–365). The claim that knowledge is carried “by the scientists” (line 350) should be broadened accordingly.
Model hierarchies (Held, 2005; Jeevanjee et al., 2017) are a widely used way of making explicit which respects of a target a model is meant to capture. The four estuary cases form a hierarchy of this kind, and the connection would help the reader.
On communicating model limits to non-specialists: Thompson and Smith (2019) and Saltelli et al. (2020).
Box is quoted but not cited (Box, 1976, 1979). The von Neumann quotation needs a source. Sterrett (2002) is cited but missing from the reference list. Suárez (2003) and Frigg and Hartmann (2020) are listed but not cited.
3. The recommendations
The Conclusions advise indicating “precisely in which respects and degrees a model represents a target” and communicating background knowledge. A reader of Geoscience Communication will want to know how. I suggest a short checklist or table that a modeler could apply in a paper or a briefing: the purpose of the model; whether the target is a specific system or a kind; which model properties are claimed to carry over to the target, and by what key; what was calibrated or tuned, and against what; what background knowledge (field data, geological reconstructions, earlier experiments) supports the inference; and known misrepresentations. Applying it to one of the four cases, with a revised version of one of the quoted claims, would show the reader what better practice looks like.
The abstract raises the concern that showing the modeler’s role may reduce trust, but the paper does not discuss this. Akerlof et al. (2012) and the tuning literature above suggest this concern is real in politicized settings. Weisberg et al. (2021) addresses it only in part. The trade-off should be discussed.
4. Conceptual points
Definition of a model. Lines 140–152 state that figures, paintings, and maps are not models because they do not permit intervention. Yet Figure 1 presents image data (panel a) and a cave painting (panel b) as “model representations of lions”, and lines 34–35 call the cave paintings “masterful modelling”. A causal loop diagram (de Haas et al., 2018) permits intervention only in thought, as would a map. The boundary should be drawn consistently, or the intervention criterion dropped.
Isomorphism in numerical models (lines 177–190). The statement that a model represents the target “by having an object for each object in the target” does not hold for most geoscience models. Discretization and parameterization of unresolved processes break any one-to-one correspondence: a parameterization represents the statistical effect of many unresolved objects. Partial isomorphism or homomorphism (da Costa and French, 2003) is closer. This point strengthens the author’s thesis, because parameterization is where much modeler judgment enters. Also, “there is nothing essentially physical about these models” (line 179) overstates the case; the equations encode conservation laws.
Scaling of mantle flow (lines 163–165). Analog models of mantle convection are not scaled by the Reynolds number. Inertia is negligible (Re ≪ 1), and the governing parameter is the Rayleigh number, with a very large Prandtl number. Please revise.
Relation between the two groups of accounts. Lines 236–238 note that agent-centered accounts still require the modeler to have good reasons to think the key captures relevant similarity or isomorphism. This is an important point and should come earlier. The abstract and Section 3 present the two groups as alternatives; it would be clearer to say that agent-centered accounts add to correspondence rather than replace it.
Box (lines 27–33). The assertion that Box’s statement is “wrong in both parts” is rhetorical, and the supporting argument restates Box’s own point. Box (1976) wrote that “since all models are wrong the scientist must be alert to what is importantly wrong”, which is close to the author’s message. The real target is the use of the aphorism as a platitude that deflects scrutiny of the respects in which a model is wrong. Also, the claim that all models are useful because building them is a learning experience conflates usefulness to the modeler with usefulness for inference about the target.
Passive voice (lines 258–261). The term is “passive voice”, not “passive tense”. Of the two quoted examples, only the first is passive; “The simulations show…” is active voice with a nonhuman subject. The issue is agentless phrasing, not voice. The hypothesis is plausible but untested, and readers will expect evidence or a citation. The manuscript itself alternates between “we” (lines 128 and 192) and “I”; consistent first person would model the advice.
Statues (lines 335–338). The analogy is tangential, and the claim about how Greeks and Romans regarded cult statues is contested among classicists. Some readers may also find the religious comparison off-putting. I suggest removing it.
Digital twins (lines 147–152). As usually defined (e.g., Bauer et al., 2021), a digital twin of the Earth system contains a model with data assimilation, so the remark targets a straw version. Calling the term an oxymoron adds little. Shorten.
5. Figures
Figure 1: panel (f) is labeled a numerical model but shows LEGO figures. The text (lines 51–52) calls it a “visualization of a numerical model”; explain what model is visualized. The description of panel (e) at lines 50–51 (“here representing linear stability analysis of periodic pattern formation in their initialization stage”) is obscure. Panel (e) is adapted from Pieri et al. (2015), a Wiley journal; please confirm reuse permission. Panel (a) has no credit.
The lion figure is engaging, but it does not show the reader how the four estuary cases map onto the DDI and DEKI elements. A table or figure that does this (target, denotation, what is demonstrated, the key, what is imputed, and where modeler judgment enters) would make the argument concrete.
Figure 2: cite Hughes (1997) in the caption.
6. Disclosure
The disclosure at line 263 of involvement in two of the four cases is welcome. The author’s role as “intermediator” in the Ems-Dollard program (line 368) should also be noted in the Competing interests section.
Minor comments
L29. Give a URL or citation for the GMD aims and scope.
L76–77. “on which models are only partly be based”: delete “be”.
L83–86. The point-source delta example is unclear. A point source is a river mouth; “sediment does not rain out of the sky” does not explain what is unrealistic. Presumably it is radial progradation in an unconfined basin from a fixed source.
L89–90. This sentence is garbled; please rewrite.
L113. “The models is”: “The model is”.
L121–122. “added” appears twice.
L128. “Here we employ”; elsewhere “I”. Be consistent.
L132. “correspondence relation between the target system in the world”: insert “the model and”.
L143. Baar et al. (2022) should be 2023.
L157–158. The cat and lion bone example needs a sentence of explanation (allometric scaling).
L162. Name the Shields number.
L167–168. Name the classic example (Froude versus Reynolds similarity).
L173–175. “dogmatism”: support with a citation or soften.
L175–176. Cite a source for analog modeling being on an equal footing with numerical modeling in tectonics.
L226. “between the mud flats and saltmarshes in estuaries”: “between the model materials and the mudflats and saltmarshes”.
L236. “model representation… do not”: “does not”.
L277. “Haas et al.”: “de Haas et al.”.
L281. “Kleinhans et al. (2022) goes”: “go”.
L285. “the reconstructions is”: “are”.
L302. “Soil science”: “SOIL”.
L305. “Formulations how”: “Formulations of how”.
L339. Comma after “on the other hand”.
L385. “pertained communication”: “pertained to communication”; “communicating model”: “communicating models”.
References. The Li et al. (2026) URL is a university proxy; use https://doi.org/10.1029/2026MS005737. For Morgan and Morrison (1999), cite the chapter (Morrison and Morgan, “Models as mediating instruments”). Add DOIs where available (Gupta, Pieri, Sterrett, Suárez 2003, Weisberg et al. 2021).
Supplementary table
Add a legend that defines each column and the coding rules.
Add columns for publication year and model type (numerical, analog, conceptual, field or mesocosm experiment), since the text makes claims by type.
Explain or correct the values of 2 in the benchmark column, and recompute row 4. Label row 4.
Fix the Ma et al. link.
For each paper coded for agency (and for borderline cases such as Sockness), give the passage that justifies the code.
Recommendation
Major revision. The paper makes a worthwhile argument, but the evidence in Section 4.1 must be put on a firmer footing or presented as illustrative only, the recommendations should be made concrete, and the literature on climate model tuning and on trust in models should be engaged.
References cited in this review
Baumberger, C., Knutti, R., and Hirsch Hadorn, G.: Building confidence in climate model projections: an analysis of inferences from fit, WIREs Clim. Change, 8, e454, 2017.
Bauer, P., Stevens, B., and Hazeleger, W.: A digital twin of Earth for the green transition, Nat. Clim. Change, 11, 80–83, 2021.
Box, G. E. P.: Science and statistics, J. Am. Stat. Assoc., 71, 791–799, 1976.
Box, G. E. P.: Robustness in the strategy of scientific model building, in: Robustness in Statistics, edited by Launer, R. L. and Wilkinson, G. N., Academic Press, 201–236, 1979.
da Costa, N. C. A. and French, S.: Science and Partial Truth, Oxford University Press, 2003.
Edwards, P. N.: A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming, MIT Press, 2010.
Frigg, R. and Nguyen, J.: Modelling Nature: An Opinionated Introduction to Scientific Representation, Springer, 2020.
Held, I. M.: The gap between simulation and understanding in climate modeling, Bull. Am. Meteorol. Soc., 86, 1609–1614, 2005.
Hourdin, F., et al.: The art and science of climate model tuning, Bull. Am. Meteorol. Soc., 98, 589–602, 2017.
Hughes, R. I. G.: Models and representation, Philos. Sci., 64, S325–S336, 1997.
Jeevanjee, N., Hassanzadeh, P., Hill, S., and Sheshadri, A.: A perspective on climate model hierarchies, J. Adv. Model. Earth Syst., 9, 1760–1771, 2017.
Lahsen, M.: Seductive simulations? Uncertainty distribution around climate models, Soc. Stud. Sci., 35, 895–922, 2005.
MacKenzie, D.: Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance, MIT Press, 1990.
Mauritsen, T., et al.: Tuning the climate of a global model, J. Adv. Model. Earth Syst., 4, M00A01, 2012.
Parker, W. S.: Model evaluation: an adequacy-for-purpose view, Philos. Sci., 87, 457–477, 2020.
Saltelli, A., et al.: Five ways to ensure that models serve society: a manifesto, Nature, 582, 482–484, 2020.
Schmidt, G. A., et al.: Practice and philosophy of climate model tuning across six US modeling centers, Geosci. Model Dev., 10, 3207–3223, 2017.
Thompson, E. L. and Smith, L. A.: Escape from model-land, Economics, 13, 20190040, 2019.
Winsberg, E.: Science in the Age of Computer Simulation, University of Chicago Press, 2010.Citation: https://doi.org/10.5194/egusphere-2026-4944-RC1 -
EC2: 'Thank you for RC1', David Crookall, 08 Oct 2026
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Thank you for an excellent and thorough review. Much appreciated.
Citation: https://doi.org/10.5194/egusphere-2026-4944-EC2 -
AC1: 'Reply on EC2', Maarten Kleinhans, 08 Oct 2026
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Yes completely agree. Thank you very much for an inspiring and thorough review! I will respond in detail later.
Citation: https://doi.org/10.5194/egusphere-2026-4944-AC1
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AC1: 'Reply on EC2', Maarten Kleinhans, 08 Oct 2026
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AC2: 'Reply on RC1', Maarten Kleinhans, 10 Oct 2026
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I thank the reviewer RC1 for a very helpful and thorough review. In this reply I only address the main comments and the four problems identified by RC1. The minor comments will be dealt with in the revision.
- Literature survey: RC1 proposes two options: present the survey as illustrative only, or turn it into a content analysis. While RC1 prefers the second option, I will likely go for the first. I agree with the reviewer that at present the literature survey is not thorough but at best illustrative. The difficulty I am facing with this paper is that I am seducing geoscientists to read something about philosophy of science (PoS) that I think is highly relevant for practice, while very little of all the useful work in PoS arrives in the geosciences. That means the paper needs to be very accessible and readable, and therefore concise. I am glad that the reviewer agrees this is worthwhile and appreciates the lively writing. I agree that most points of the reviewer will improve precision in such a way that it does not come at the cost of clarity and accessibility for this readership.
- Missing literature: I am aware of, and read many of, the bodies of literature in PoS and that related to climate modelling. The present selection of references was made for its accessibility with the intended audience of this paper in mind. I will carefully study the papers and books that RC1 suggests and include more of them. I think I prefer to write a bit more about the sociology of trust in models and the communication of model limitations to non-specialists. I submitted to this journal Geoscience Communication because I have the experience that a better understanding of the philosophical issues around model representation will help both the doing of geoscience and the communication in the context of societal issues.
- Recommendations: I thank RC1 for the excellent suggestion to make these concrete with a checklist applied to the four main cases.
- I agree with all conceptual points made by RC1 and will clarify these mostly as proposed. I will expand on the Box statement, while maintaining the rhetorical assertions. I must do so because Box is so frequently cited and because I experienced in many interactions that geoscientists actually believed in it, or did not continue to think about model representation, after citing Box.
- It is a good idea to make a table or figure to map how the four estuary cases map onto the DDI and DEKI elements and I will probably expand figure 2 to do this.
Citation: https://doi.org/10.5194/egusphere-2026-4944-AC2
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EC2: 'Thank you for RC1', David Crookall, 08 Oct 2026
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CC1: 'Comment on egusphere-2026-4944', Niklas Hohmann, 19 Oct 2026
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This manuscript discusses different approaches to model representation and how this is communicated both within the academic community and in science communication in general. As a modeler in the geosciences, the paper is an enlightening read with pleasant and appropriate positionality. The examples are well chosen (both the Lion example and the four case studies) and described in sufficient depth for non-specialists to easily understand them and how the discussed philosophical concepts relate to them.
At parts there is abundant signposting in the text, which I found not necessary given this is a short text. However, this might be simply disciplinary differences or a matter of taste.
Below are some general comments where I feel the focus of the manuscript could be clarified or improved, followed by some technical comments:
Usage of isomorphism
Isomorphism is used both in a narrow, mathematical-technical sense (lines 180 ff) on the “passive voice” side modeling lingo (3.1), but also re-appears later in the “active voice” part (3.2, line 238). Given that the isomorphism concept is featured prominently in 3.1, it is confusing to have it re-appear in an opposing chapter later. From what I understand the major difference between the two approaches is that in 3.1, the isomorphism exists impersonally, while in 3.2, it is established actively by the modeler.
In that sense, the key in the DEKI model nicely corresponds to the mathematical notion of an isomorphism as a structure-preserving map (between model and target). Based on that, it might be useful to build the contrast between 3.1 and 3.2 along the lines of impersonal (“passive voice”) vs personal (“active voice”) establishment of the representational relation instead of starting with similarity and isomorphism. However, it is well possible that this is at odds with the underlying philosophical literature – in this case I think it is better to stick with the literature.Representation and representational relation
Throughout, the usage of “representation” (as a standalone concept, e.g. lines 140 ff and potentially the model) and representational relation (as a relation between two or more objects, e.g., the relation between model and target) is sometimes blurry, and it would be useful for the reader to use them consistently. For example in line 143: “grounds the representation in the modeler” – from my reading this would be a representational relation established by the modeler. For this, I like the notion of the “representational process” (line 203), as it highlights the role of the modeler more in establishing the relation, potentially over a longer interval of time. It might be worth to keep this distinction throughout (or, blatantly stealing from Searle’s speech act theory and being more explicit about agency, introduce a “representational act” that establishes that relation ;).Carrying theory through the examples
I found the theory developed in 3.1 and 3.2 a very useful mental model. Unfortunately, it is not carried through the examples and rest of the text explicitly, and I think it would be nice to have the examples grounded more in the established theory to see where they fall on the spectrum. The philosophical framework as presented is largely descriptive, but given the target is “Geoscience Communication” it wouldn’t be a stretch to use it prescriptively for communication about scientific models here (given some references from science communication).Recommendation for communication
The manuscript takes a clear (and justified) position on writing in active tense (line 261). Reading the abstract and this section, I was expecting a similar statement regarding the communication about models in the concluding section (see also my point above on descriptive vs. prescriptive), but this is currently mostly implicitly. I think a clear list of recommendations for communicating about model representation or a hypothesis on how this interacts with science communication in the conclusion section would help the uptake of the points made and keep the momentum of the message. In this line, the last example (lines 355 ff) is currently undersold as a case study how communication about models can jeopardize projects (and how this could have been avoided) and should be sharpened to really drive this key point home.Technical comments:
Line 9: “sometimes reflects that it is unclear by what virtue models can represent and can be used to make claims about the world” - clarify structure: ”by what virtue models can represent and [how they] can be used to make claims about the world”
Line 18: What is “this”? Ambiguous reference
Line 21: Last sentence is hard to follow, consider splitting
Line 28: Sentence structure: and in -> , which can also be found in the aims …
Line 37: skip “more” as there was no explicit question raised in the first paragraph
Line 43: Good point about the platitudes. Potentially link back to the Box quote for clarity
Line 56/57: Which implications do follow? Give a brief outlook or skip.
Line 66: Is the Chauvet Cave image from Wikimedia from the public domain? If not, please clarify the license.
Line 72: Not necessarily always impossible, but potentially inconvenient or undesired (as in the Lion example)
Line 88ff: I like the selection of example models. They cover all important aspects needed for the discussion and are introduced in sufficient detail so that readers not familiar with the field can understand the arguments made based on them.
Line 128: Clarify within-sentence hierarchy: It is not obvious on first superficial reading whether the scientists or the philosophers produce a useful but under-used complementary science (I assume it’s the philosophers)
Line 161: For example,
Line 258: That’s a long sentence, consider splitting
Line 306: That’s a great observation! It might be worth a sentence speculating where this comes from – it is disciplinary culture/education? This will give some hints on how this passive communication style about models could be improved
Lines 313ff. : Consider providing direct quotes from the two papers to highlight this and demonstrate how author agency can be expressed in scientific publishing.
Line 331: “I illustrate the lack of trust due to not having the skills with an anecdote”: This is important and would profit form some unpacking: Who is lacking trust and what skill is missing? Being explicit here will highlight the problems with and the importance of model communication to broader audiences further below
Line 340: Confirmed in what sense? Add minimal context on how this is used as a technical term.
Line 343ff.: Which parameter? Is this the case for all of hydrological modeling? As is, the sentence reads as if the level of uncertainty used to depend on historic data and a parameter, but this is not necessarily the case now. Or is this a reference to that specific study?
Line 360ff: “…but was unable to understand why certain choices were made that led the model to reproduce past estuarine developments acceptably to the modeler” Move the modeler earlier in the sentence to distinguish the two positions (project lead vs.modeler)
Line 363f.: “Fortunately, in this setting of the hydrosociological setting” Too much setting?
Line 365: “and many other systems” if not needed remove to keep focus.
Lines 365 ff: Inconsistent tense: “Models are combined and new measurements are conducted” vs. “ultimately devolved into”, which makes it hard to follow what the timeline of the problem & project was/is.
Line 378: pointed -> point.
Line 385: modelS / model results
Line 385: pertained to
Line 387f.: Here it is unclear if the discussion focuses on establishing the background knowledge of the modeler to the audience, or creating said background knowledge in the audience itself (e.g., increase knowledge about the nature of science as in line 392).
Line 394: What does “this” refer to? Is it “model representation and its impact”?
Best regards,
Niklas HohmannCitation: https://doi.org/10.5194/egusphere-2026-4944-CC1 -
EC3: 'Thanks for CC1', David Crookall, 19 Oct 2026
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Thank you, Niklas, for an excellent review (even if technically a CC). Much appreciated.
Citation: https://doi.org/10.5194/egusphere-2026-4944-EC3 -
AC3: 'Reply on CC1', Maarten Kleinhans, 24 Oct 2026
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I thank Niklas Hohmann, reviewer CC1, for a very helpful and insightful review. In this reply I only address the main comments and one technical comment of CC1. The minor comments will be dealt with in the revision.
- Usage of isomorphism: this needs to be clarified in the manuscript. The philosophical account of isomorphism in section 3.1 is in the same family as similarity in that it does not consider the role of the modeller relevant. In section 3.2 the role of the modeller is considered relevant in the DDI and DEKI accounts (and others). However, that constitutes at least an addition to the similarity and isomorphism accounts, and at most a rejection of their adequateness for lack of the agency. I will clarify this in the revision and use the example models to show. For example, the representation in the fourth case (experiment) is built on similarity. I will consider the use of the passive/active voice if I can manage to make that help to clarify the philosophical ideas.
- Representation and representational relation: I will clarify the usage of these two terms. In short, representation is the process and the representational relation between model and target is the obtained result. I agree that it is an act, though the isomorphism and similarity accounts do not consider the role of the actor as important as the DDI and such accounts.
- Carrying theory through the examples: I agree and thank the reviewer for the suggestion. It is also in line with the fifth point of RC1 that a table or figure is needed to detail the steps in model representation in the DEKI account. This will turn the four model cases into examples of communicating about models, which, in turn, I can use to formulate the recommendations clearly.
- Recommendation for communication: See previous point: I hesitated to do so initially, assuming that the example would already be enough to help others reflect on communication, but a clear list of recommendations is on second thoughts a good idea (if only for hasty readers).
- Line 306: I stated: “All papers with numerical models appeared to use an isomorphic representational relation and all papers with experiments appeared to use similarity of physical or chemical dynamics.”. CC1 remarked: “That’s a great observation! It might be worth a sentence speculating where this comes from – it is disciplinary culture/education? This will give some hints on how this passive communication style about models could be improved”. Tentative answer: This is a great question and I think that isomorphic representation is impossible with material experiments because the isomorphism is in the mathematical and numerical relations. However, I need to read and think about the question whether a representation by similarity is possible with numerical models.
Citation: https://doi.org/10.5194/egusphere-2026-4944-AC3
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EC3: 'Thanks for CC1', David Crookall, 19 Oct 2026
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RC2: 'Comment on egusphere-2026-4944', Sebastián Granados, 20 Oct 2026
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Overall observations.
The manuscript offers an interesting philosophical review of numerical and conceptual models in the geosciences. However, it gives insufficient attention to how these models are used to interpret present processes, reconstruct past events, and predict future events. Although my expertise lies in hydraulic, hydrological, and geomorphic modelling rather than philosophy, I find that the discussion remains somewhat disconnected from the models currently used to understand real-world systems and inform decisions, such as flood zoning and geophysical exploration for mineral resources. Overall, I consider the manuscript publishable and worthy of consideration for publication. PS I would reconsider the figures, maybe using more geo examples.
Specific comments
Abstract
Line 1. “Humans are a representing species: we construct conceptual, numerical and material models that represent the world in certain respects.” I would also mention that we humans also create models not only of this planet, for example there is interesting models about astrobiology, extraterrestrial geomorphology etc. Even though this text is focused on Earth, it could be worth mentioning somewhere regarding these kinds of models that actually question the frontiers of numerical and conceptual modelling.
Introduction
I understand the philosophical point of view of the author regarding “ what is a model” which can vary from a lot perspectives, cultures, historical periods, materials and geographical areas. I think, one of the main aspects that is being obviated in the introduction is the mention of Cartography. Which is one of the most ancient and powerful ways of representing reality for decision making. Maps in general have played a key role in human history, power and continue to provide key information of society.
Line 50. I get a little lost seeing the pictographic representations of prehistoric animals and behaviors (please add a cite) are you referring to the caves in France, Spain etc ?. As mentioned before, I can’t find a strong connection with the lions and geoscience. I understand the point, but other readers might get confused since this an EGU publication. I think the introduction should conceptualize more geosciences models.
2 Kinds of models and some examples
Line 75 “A model is more than a theory made numerical: models can intermediate in many ways between theory and the world and also are a source of knowledge in their own right”
Line 80. In a certain way we could say that models in geosciences are intended to reconstruct past, predict future and monitor-understand current geomorphic processes (either endogenic or exogenic).
Lines 110 to 125. Here I would consider clarifying the difference between what is a hydrological, hydraulic and geomorphic model. For readers not familiar with modelling geomorphic processes I think this a key aspect to consider since you refer types of models. Also, other types of models could be considered such atmospheric models for example.
3 Model representation
Line 140. Why is 3D map not a model ? If it was created from LiDAR, photogrammetry etc. Please explain why these types of “ models” are not a “ model”. 3D modelling is had groundbreaking advances in the last two decades for understanding geomorphology for example and also engineering
Line 179. “There is nothing essentially physical about these models”
There has been a great amount of research regarding the validation of hydraulic-geomorphic numerical models with laboratory experiments (flume sediment transport for example). I don’t understand the idea that there is nothing physical, since the main of objective of modelling is to understands the physics (in these cases) of the reality of the Earth.
Figure 2. I see no relation with geosciences models. I understand that this paper has a strong philosophical overview, but as a numerical modeler myself I find some key elements missing such as data collection, pre processing, processing, post processing, validation, field verifications etc.
4 How we communicate about models
Line 255. We all agree that models do not represent with precision or exactitude reality. Either on going processes, past events or future scenarios of earth dynamics (or extraterrestrial). But most of us at least hydraulic-hydrological-geomorphic modelers agree that without numerical modelling it would be difficult to take informed decisions for human development and adaptation of critical problems (such as sea level rise, microplastics pollution, extinction of species etc…). A lot of the developed models (either open source or commercial) have had deep impacts on public policies and in general the world economy. For example, without geophysical models it would not be possible to locate critical fossil fuel sources that move the global economy. My point is that a great variety of models have been well communicated either in a public or private-corporate manner that allow human progress. The concept of “human progress” is highly debatable I agree, but models have been deeply linked with the concept of “ communication” since mostly these are done from multidisciplinary approach. The geologist models the stratigraphy of a fossil fuel deposit, the engineer decides where to drill or excavate, just mentioning one of the cases where models move the capitalist global economy. Without the communication of the way models work and how they help us how reality is being interpreted it would be difficult to achieve the advances humanity has achieved.
4.2 Model representation in public affairs, public engagement and consulting
Overall in this section I think something key is missing. Addressing how some groups of society question models. Climate change projections are valid, true, possible, or a scam as come qualify it? Models have strong numerical backbones that try to represent reality and future scenarios. Nevertheless, even if they were developed by people that have spent their lives “perfecting” these kind of tools, they keep getting questioned and submitted to scrutiny by society and sometimes key decision makers such as politicians. In a paper of this type I think this would be important to address in a paragraph or two, since questioning models many times means questioning research, science and eventually leading how we are going to design public policies for future generations. I leave the comment for this section: Should we take into account climate change models into urban planning? ( an example)
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EC4: 'Thanks for RC2', David Crookall, 20 Oct 2026
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Many thanks, Sebastian, for your excellent review. Much appreciated.
Citation: https://doi.org/10.5194/egusphere-2026-4944-EC4 -
AC4: 'Reply on RC2', Maarten Kleinhans, 24 Oct 2026
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I thank Sebastián Granados, reviewer RC2, for a very helpful review showing where clarifications are needed. In this reply I address what I think are the main comments made by RC2. All comments and questions will be dealt with in the revision.
- Other modelling? “models about astrobiology, extraterrestrial geomorphology”: answer: Yes I intended to include all these (philosophers of science also claim that level of generality) and I will mention some other disciplines and model targets.
- Other representations? “one of the main aspects that is being obviated in the introduction is the mention of Cartography.” Related question: “Why is 3D map not a model?”: answer: I would like to pull this above the level of semantics in the revision. I will need to explain what kinds of representations exist, and be more precise about what I consider a model. This echoes a point made by RC1 about my statement that the models I focus on are those that can be intervened on, which is not clearly applicable to the conceptual model. I will consider including 3D maps, which I agree are very important, but my issue is that I do not want to extend the paper to all kinds of representations, including any figures containing, for example, data, because then I would have to expand the review on theories on representation far beyond the scope and length of the present paper.
- Twisting the lion’s tail? “can’t find a strong connection with the lions and geoscience”: answer: I will clarify this link better (I have used it so often that I forgot it is not clear prima vista). An urban myth has it that medieval philosopher Roger Bacon remarked that we would need to twist the lion’s tail to learn about the species. This inspired an apt analogy: if we wished to apply an experimental approach to earth science then we cause much danger to ourselves (as in fact we do in many ways), so we are better of twisting the tail of a model representation. Another reason to use the lions is that it does not favour a particular branch of the geosciences. For readability and credibility I had to focus on four cases from my own field, but I need to write the paper such that it is clear to the reader that, and how, the ideas are broadly applicable to model representation in the geosciences.
- Missing modelling activities? “as a numerical modeler myself I find some key elements missing such as data collection, preprocessing, processing, postprocessing, validation, field verifications etc.”: answer: This is where I need to draw a line. I completely agree that these key elements are important activities in model making and model use and there is a great body of philosophical literature about it. In the revision I will motivate my focus by asserting that modellers are generally fairly familiar with these important activities, about which there is much literature, and that I will focus on a much less obvious aspect of model use in geoscientific practices, namely the model representation.
- How to address model doubt in society? “a great variety of models have been well communicated either in a public or private-corporate manner that allow human progress … Without the communication of the way models work and how they help us how reality is being interpreted it would be difficult to achieve the advances humanity has achieved. … questioning models many times means questioning research, science and eventually leading how we are going to design public policies for future generations.”: answer: I believe we agree here and will improve my formulations to make that clearer. My two reasons to write this paper are that models are very important in science and in society, and that communicating clearly about models and what they can do in our impact activities is an important part of defending science against scepticism. The reviewer’s example (Should we take into account climate change models into urban planning?) echoes my example of the parliamentary committee’s statement (line 332). While others have already demonstrated that presenting models as rock-solid truth machines built on strong numerical backbones is not helping, and I will include some material from the climate modelling literature (see response to RC1), the issues of geoscience communication to decision makers and policymakers is much broader than what can be covered in a single paper. What I will attempt to recommend clearly in the revision is how to communicate about model representation.
Citation: https://doi.org/10.5194/egusphere-2026-4944-AC4
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EC4: 'Thanks for RC2', David Crookall, 20 Oct 2026
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Publisher’s note: the content of this comment was removed on 30 September 2026 since the comment was posted by mistake.