ESCIMO.spread (v2): parameterization of a spreadsheet-based  energy balance snow model for inside-canopy conditions

Marke, T.; Mair, E.; Förster, K.; Hanzer, F.; Garvelmann, J.; Pohl, S.; Warscher, M.; Strasser, U.

doi:https://doi.org/10.5194/gmd-9-633-2016

Articles | Volume 9, issue 2

https://doi.org/10.5194/gmd-9-633-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-9-633-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 2

Model description paper

|

16 Feb 2016

Model description paper |

| 16 Feb 2016

ESCIMO.spread (v2): parameterization of a spreadsheet-based energy balance snow model for inside-canopy conditions

T. Marke, E. Mair, K. Förster, F. Hanzer, J. Garvelmann, S. Pohl, M. Warscher, and U. Strasser

Download

Final revised paper (published on 16 Feb 2016)
Preprint (discussion started on 25 Sep 2015)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC C2503: 'Review of a modified verson of ESCIMO.spread', Anonymous Referee #1, 15 Oct 2015
- AC C3536: 'Answers to the comments by Referee #1', Thomas Marke, 31 Dec 2015
RC C2603: 'Review of ESCIMO.spread (v2) by Marke et al. 2015', Wolfgang Gurgiser, 23 Oct 2015
- AC C3538: 'Answers to the comments by Referee #2', Thomas Marke, 31 Dec 2015

Peer-review completion

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

AR by Thomas Marke on behalf of the Authors (31 Dec 2015) Author's response Manuscript

ED: Referee Nomination & Report Request started (06 Jan 2016) by Jeffrey Neal

RR by Wolfgang Gurgiser (18 Jan 2016)

Suggestions for revision or reasons for rejection

In the updated version the authors have improved their manuscript notably and the (at least from my point of view) fundamental problem of missing energy conservation in an “energy balance model” approach seems to be adequately solved. I further thank the authors for extending their evaluation in a way that now allows comparing the skill in predicting inside canopy conditions with the model / unmodified outside canopy measurements (Table 1 & Table 2). I’m not totally sure that for all shown model evaluation measurements at one spot for one winter season only with sensors which obviously suffer from biases/errors (e.g. Fig 8), those errors potentially varying from sensor to sensor, are sufficient. For temperature and humidity it seems that the outside canopy measurements are similar good predictors for inside canopy conditions (Table 1 and Table 2) as the model output which raises the principle question about the gain of increased model complexity and the validity of the added concept, respectively. Of course, the reason for the missing increase in model skill despite resolving more processes might be too poor input and evaluation data quality which raises kind of a (common) chicken-egg problem (trust the measurements or the model output). Overall, I emphasize the updates in the revised manuscript and the value of the model as an “easy to use” (educational) tool. Before developing the model (parameters) further, I would be curious to see further, detailed evaluation experiments.

Some final, minor suggestions:
Fig. 12: I’ve read the motivation to have the same y-axis in Fig. 11 and Fig. 12 but as the ESCIMO.spread.v1 is far of the measurements I would not keep the wide range of the y-axis in Fig. 12.

Fig. 10 & Fig. 13: I would add the 45° lines in the scatter plots.

Hide

ED: Publish subject to minor revisions (Editor review) (18 Jan 2016) by Jeffrey Neal

AR by Thomas Marke on behalf of the Authors (19 Jan 2016) Author's response Manuscript

ED: Publish as is (20 Jan 2016) by Jeffrey Neal

AR by Thomas Marke on behalf of the Authors (28 Jan 2016) Manuscript

Short summary

This article describes the extension of the ESCIMO.spread spreadsheet-based point energy balance snow model by (i) an advanced approach for precipitation phase detection, (ii) a concept for cold and liquid water storage consideration and (iii) a canopy sub-model that allows one to quantify the effect of a forest canopy on the meteorological conditions inside the forest as well as the simulation of snow accumulation and ablation inside a forest stand.