Parameter calibration in global soil carbon models using surrogate-based optimization

Xu, Haoyu; Zhang, Tao; Luo, Yiqi; Huang, Xin; Xue, Wei

doi:https://doi.org/10.5194/gmd-11-3027-2018

Articles | Volume 11, issue 7

https://doi.org/10.5194/gmd-11-3027-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-11-3027-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 11, issue 7

Development and technical paper

|

27 Jul 2018

Development and technical paper |

| 27 Jul 2018

Parameter calibration in global soil carbon models using surrogate-based optimization

Haoyu Xu, Tao Zhang, Yiqi Luo, Xin Huang, and Wei Xue

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Final revised paper (published on 27 Jul 2018)
Supplement to the final revised paper
Preprint (discussion started on 05 Apr 2017)
Supplement to the preprint

Interactive discussion

Status: closed

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

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RC1: 'Review of Parameter Calibration in Global Land Carbon Models Using Surrogate-based Optimization Xu et al.', Anonymous Referee #1, 11 Jul 2017
- AC1: 'Reply to RC1, Anonymous Referee #1', Haoyu Xu, 03 Nov 2017
- AC3: 'Reply to RC1, Anonymous Referee #1 (Version 2)', Haoyu Xu, 04 Dec 2017
RC2: 'Review of a “Parameter Calibration in Global Land Carbon Models Using Surrogate-based Optimization” by Xu et al.', Maarten Braakhekke, 21 Aug 2017
- AC2: 'Reply to RC2', Haoyu Xu, 03 Nov 2017
- AC4: 'Reply to RC2, Referee #2 (Version 2)', Haoyu Xu, 04 Dec 2017

Peer-review completion

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

AR by Haoyu Xu on behalf of the Authors (05 Feb 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (06 Feb 2018) by Christoph Müller

RR by Anonymous Referee #1 (04 Mar 2018)

Suggestions for revision or reasons for rejection

Second review of Xu et al. “Parameter calibration in global soil carbon models using surrogate-based optimization”.

This paper tests the performance of surrogate modeling approach for estimating parameters in three soil carbon models relative to selection of commonly used direct optimization approaches and an MCMC approach. The results suggest their surrogate modeling approach is amongst the most effective and is the most efficient (by their measure at least).

The description of the surrogate modeling and optimization approaches are relatively complete, but details about the models themselves, and how they have been used, are incomplete and confusing. This distracts from the main aspects of the work detailing the optimizations. This confusion is not helped by the authors repeatedly making claims about working with Earth system models and global land surface models, when in reality they working with a matrix approximation of a sub-component of such complex models, or models that are not part of such systems at all. Whilst it is exciting to see such approaches being applied in this way, the usability and applicability with fully functionally ESMs must not be overstated.

It is unclear how the three soil C models were actually run. What are the inputs? What is the climate forcing? Where do they come from? Are they a time series, or mean values used to estimate a steady state solution? Rather than describing the model equations – which could be moved to appendices – a more holistic description of the modeling approach including details of domain, forcing, NPP inputs would be beneficial.

Related to this is confusion as to how the CLM-CASA’ C-only model is being used. Section 2.1 talks about the matrix approximation in general terms – but not specifically about its use in this case. Whilst acknowledging there are references describing this, some detail is required directly in this manuscript. It is important to have clarity here, as one of the main arguments you make is how beneficial surrogate models are when working with computationally expensive, highly complex Earth system models. Yet, in reality the surrogate model is being built for the matrix approximation, which in itself was developed to address many of the issues related to parameterization of ESMs. Often in the manuscript you described the days to weeks of wall clock time associated with MCMC model runs in comparison to the efficiency of the surrogate. But is this really the case with the matrix model? Similarly, the 2- and 4-pool models are not highly complex (relative to an ESM), and it is unclear if there are real world benefits from developing a surrogate for such models (that is to say, is the efficiency gain really worth the expense in developing the surrogate for a model of this complexity?)

The results themselves are encouraging, and what might be expected, with the exception of the relatively poor performance of the MCMC scheme in the case of the 2- and 4-pool models. The authors suggest this “may be mainly due to the different targets of the parameter selection”. This needs further elaboration because I don’t really understand what that means, and it really is a surprising result.

Figure 2 needs some clarification. Why is the data so “blocky” and covers such a limited extent of the land mass? Is that the domain over which the model was actually run?

It seems like much of the appendix replicates Section 3.3 – but is actually written better and is clearer. I would suggest merging them into Section 3.3 alone.

The quality of the English is very variable across different sections of the manuscript. The abstract and introduction are very poor with numerous language errors – far too many for this reviewer to list individually and any resubmission will require a thorough proof reading.

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RR by Maarten Braakhekke (05 Mar 2018)

ED: Publish subject to minor revisions (review by editor) (13 Mar 2018) by Christoph Müller

AR by Haoyu Xu on behalf of the Authors (02 May 2018) Author's response Manuscript

ED: Publish as is (09 May 2018) by Christoph Müller

Short summary

This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of soil organic carbon. Experiments on three popular global soil carbon cycle models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. This research would help develop and improve the parameterization schemes of Earth climate systems.