A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

Forkel, Matthias; Dorigo, Wouter; Lasslop, Gitta; Teubner, Irene; Chuvieco, Emilio; Thonicke, Kirsten

doi:https://doi.org/10.5194/gmd-10-4443-2017

Articles | Volume 10, issue 12

https://doi.org/10.5194/gmd-10-4443-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-10-4443-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 10, issue 12

Model description paper

|

06 Dec 2017

Model description paper |

| 06 Dec 2017

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

Matthias Forkel, Wouter Dorigo, Gitta Lasslop, Irene Teubner, Emilio Chuvieco, and Kirsten Thonicke

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Final revised paper (published on 06 Dec 2017)
Preprint (discussion started on 14 Dec 2016)

Interactive discussion

Status: closed

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

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- Supplement

SC1: 'Executive Editor Comment on "Identifying required model structures to predict global fire activity from satellite and climate data"', Astrid Kerkweg, 03 Jan 2017
- AC1: 'Reply to the Short Comment by A. Kerkweg', Matthias Forkel, 04 Jan 2017
  - SC2: 'Reply to reply with respect to Executive Editor comment', Astrid Kerkweg, 09 Jan 2017
RC1: 'Reviewer comments', Anonymous Referee #1, 13 Jul 2017
- AC2: 'Response to Anonymous Referee #1', Matthias Forkel, 13 Jul 2017
  - RC2: 'Include that Figure 2 (parameter ranking for random forest) in the final MS', Anonymous Referee #1, 13 Jul 2017
RC3: 'Review on Forkel et al.', Anonymous Referee #2, 31 Aug 2017

Peer-review completion

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

AR by Matthias Forkel on behalf of the Authors (27 Sep 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (06 Oct 2017) by Gerd A. Folberth

RR by Gerd A. Folberth (20 Oct 2017)

Suggestions for revision or reasons for rejection

The manuscript discusses a novel approach to assessing the key factors that drive global fire activity. It is a data-driven model approach in which empirical relationships for a variety of predictor variables are applied. The skill of the set of SOFIA models is compared to a machine learning technique and a process oriented fire model in reproducing observed (using the GFED and CCI datasets) fire activity (burnt area serves as the chosen metric).

Being the topical editor I have followed the paper from the beginning. I have studied the first submission and the revised version of the manuscript and I have carefully studied the reviewers concerns. Unfortunately, the reviewers' opinion is divided. Taking into account the fact that it has taken an unusual amount of time to obtain the reviews I have decided to act as third reviewer. And as such I am satisfied that, on the one hand, the manuscript fits into the scope of GMD and fulfils the criteria required for publication (code availability etc.). The manuscript is well structured and well written, and the quality of the presentation has, in my opinion, increased in the revised version.

On the other hand, I think that this manuscript represents a timely contribution to an important contemporary field of research. It may not contribute a quantum leap in the science of global fire activity in natural environments but it does contribute a number of very useful insights and tools and outlines a new, useful approach that will help to promote knowledge in this important research field.

I have next to no comments with regards to changes to the manuscript (only one minor suggestion) and recommend publication in GMD.

SPECIFIC COMMENTS:

Figure R3: I suggest to reduce the horizontal scale (population density) or add a zoomed-in region to show the gradient in more detail. As it stands, the curve looks very much like a step function.

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ED: Publish subject to technical corrections (20 Oct 2017) by Gerd A. Folberth

Short summary

Wildfires affect infrastructures, vegetation, and the atmosphere. However, it is unclear how fires should be accurately represented in global vegetation models. We introduce here a new flexible data-driven fire modelling approach that allows us to explore sensitivities of burned areas to satellite and climate datasets. Our results suggest combining observations with data-driven and process-oriented fire models to better understand the role of fires in the Earth system.