Description and evaluation of the process-based forest model 4C v2.2 at four European forest sites

Lasch-Born, Petra; Suckow, Felicitas; Reyer, Christopher P. O.; Gutsch, Martin; Kollas, Chris; Badeck, Franz-Werner; Bugmann, Harald K. M.; Grote, Rüdiger; Fürstenau, Cornelia; Lindner, Marcus; Schaber, Jörg

doi:https://doi.org/10.5194/gmd-13-5311-2020

Articles | Volume 13, issue 11

https://doi.org/10.5194/gmd-13-5311-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-13-5311-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 11

Model evaluation paper

|

05 Nov 2020

Model evaluation paper |

| 05 Nov 2020

Description and evaluation of the process-based forest model 4C v2.2 at four European forest sites

Petra Lasch-Born, Felicitas Suckow, Christopher P. O. Reyer, Martin Gutsch, Chris Kollas, Franz-Werner Badeck, Harald K. M. Bugmann, Rüdiger Grote, Cornelia Fürstenau, Marcus Lindner, and Jörg Schaber

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Final revised paper (published on 05 Nov 2020)
Supplement to the final revised paper
Preprint (discussion started on 15 Jan 2019)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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SC1: 'Executive Editor comment on gmd-2019-2', Astrid Kerkweg, 30 Jan 2019
- SC2: 'DOI and revisions', Petra Lasch-Born, 20 Aug 2019
RC1: 'Comments on manuscript Lasch et al.', Anonymous Referee #1, 19 Feb 2019
- AC1: 'reply to referee 1', Petra Lasch-Born, 18 Sep 2019
RC2: 'Referee comments', Anonymous Referee #2, 03 Sep 2019
- AC2: 'reply to reviewer 2 comments', Petra Lasch-Born, 05 Nov 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Petra Lasch-Born on behalf of the Authors (05 Nov 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (12 Feb 2020) by Richard Neale

RR by Anonymous Referee #2 (04 Mar 2020)

Suggestions for revision or reasons for rejection

I was pleased to see that the authors made a considerable effort to revise the manuscript “Description and evaluation of the process-based forest model 1 4C v2.2 at four European forest sites”. The introduction (including the aim of the manuscript), and the methods (including the model description), are now clear and to the point. In my opinion, the results, discussion and conclusions still lack focus. When revising these sections it could help the authors to think of a target audience, for example, prospective model users and write for them, i.e., what does a prospective model user needs to know to choose for 4C for their research instead of another model? If prospective users are targeted the discussion should answer questions like for which forest, soils, climate regions, etc is 4C a good choice? For which applications can 4C be used? Which simulations should be carefully interpreted given known model deficiencies? Which models with similar capabilities (but different strengths and deficiencies) are available?

If the target audience are the 4C model developers and the study is seen as a benchmark then the authors should start from the objectives of the model (thus its reason of existence, why was this model developed?) and evaluate all process needed to demonstrate that 4C meets these objectives. This evaluation should be systematic and quantitative such that future versions of the model can be tested against the same data and the evolution in model performance can be monitored.

The figures are still plagued by inconsistent color coding (see previous review) making it difficult for the reader to understand the figures at a glance. Fig 8 for example, what is the difference between the black and red data? Simulations vs observations (as in fig 7)? If not (i.e. simply 25-75 quantile vs outliers) use a single color. Fig 9 has very little value from a benchmarking point of view. If a model uses climate drivers with prescribed seasonality (as is the case for 4C) the results are expected to show a strong seasonality as well (as shown in Fig 9). To become meaningful from a benchmarking point of view the residuals should be shown, hence, the observations should be subtracted from the simulations. Fig 10, is this the best figure for the message? A scatter graph would be better. Do you really need to show so many different soil depths? One figure + statistics for the other soil layers would be more informative.

The top half of the table with previous evaluation results still contains qualitative language. I.e., “good correspondence of simulated yield with experimental data”. Such assessment is useless from a benchmarking point of view because whether model performance is “good” or not depends on the assessor and the intended application of the model. Neither of these are reported.

The conclusion starts with an outlook that has not been discussed previously. Parameters are mentioned in general but should be specified to become informative (which parameters do the authors have in mind and many records of that parameter are available in TRY for the species parameterized in 4C?)

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RR by Anonymous Referee #3 (06 Jul 2020)

Suggestions for revision or reasons for rejection

I was invited as additional referee for the manuscript “Description and evaluation of the process-based forest model 4C v2.2 at four European forest sites”. The previous manuscript has been revised extensively, and, in my opinion, the authors have incorporated most of the referees' feedback satisfactorily, justifying publication of the manuscript in Geoscientific Model Development after another minor revision.

One issue that has already been pointed out by a different referee, and yet remains vague, is the novelty statement of the manuscript. A study that claims to improve our ability to model forest ecosystems should be very precise about its novelty. I would expect a table or, at least, longer paragraph comparing the new 4C version with similar models. For instance, you should answer the question: What can be simulated with this model that cannot or just to a limited extent be simulated by other models? Moreover, I wondered why there was no simulation and evaluation of species mixtures? Monospecific stands are relatively simple to simulate in comparison to mixed stands where different species traits need to interact in a meaningful way.

A few minor points:
L 64 omit one parenthesis
L 78 impact of biotic disturbance agents on what?
L 80 “partly” what parts?
L 123 isn’t this a weekly time step that is just divided by seven?
L 176-177 doesn’t read well. Further, citing the new TRY database paper (Kattge et al. 2020) would be more useful here
L 179 one or several studies?
L 332 parameters were derived
L 464-466 does that mean trees grow better if disturbance is disabled as more structural C is available? That wouldn’t make much sense.
Table 1: I agree with the previous referee that quantitative figures would greatly improve interpretation of the results column. We always under- or overestimate something to some degree. Maybe the authors can at least provide ranges where quantifications are still missing.
L 517 you mean “we did not re-calibrate any species parameter”?
L 600-602 and elsewhere: provide values for under- and overestimates and correlations in the main text even if they are shown in tables
L 645 wrong order!? Should read 0.887<ME<0.974
L 723 omit “).”

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ED: Publish subject to minor revisions (review by editor) (08 Jul 2020) by Richard Neale

AR by Petra Lasch-Born on behalf of the Authors (14 Aug 2020) Author's response Manuscript

ED: Publish as is (05 Sep 2020) by Richard Neale

AR by Petra Lasch-Born on behalf of the Authors (15 Sep 2020)

Short summary

The process-based model 4C has been developed to study climate impacts on forests and is now freely available as an open-source tool. This paper provides a comprehensive description of the 4C version (v2.2) for scientific users of the model and presents an evaluation of 4C. The evaluation focused on forest growth, carbon water, and heat fluxes. We conclude that 4C is widely applicable, reliable, and ready to be released to the scientific community to use and further develop the model.