Bayesian inversions of a dynamic vegetation model at four European grassland sites

Minet, J.; Laloy, E.; Tychon, B.; François, L.

doi:https://doi.org/10.5194/bg-12-2809-2015

Articles | Volume 12, issue 9

https://doi.org/10.5194/bg-12-2809-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/bg-12-2809-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 12, issue 9

Research article

|

13 May 2015

Research article |

| 13 May 2015

Bayesian inversions of a dynamic vegetation model at four European grassland sites

J. Minet, E. Laloy, B. Tychon, and L. François

Download

Final revised paper (published on 13 May 2015)
Preprint (discussion started on 29 Jan 2015)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC C271: 'Well written methodology, lacks overall focus', Anonymous Referee #1, 22 Feb 2015
- AC C1122: 'Response to reviewer #1', Julien Minet, 08 Apr 2015
RC C282: 'Reviewing comments on Bayesian inversions of a dynamic vegetation model in four European grassland sites by Minet et al.:', Anonymous Referee #2, 23 Feb 2015
- AC C1123: 'Response to reviewer #2', Julien Minet, 08 Apr 2015

Peer-review completion

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

AR by Julien Minet on behalf of the Authors (09 Apr 2015) Author's response Manuscript

ED: Publish as is (16 Apr 2015) by Trevor Keenan

AR by Julien Minet on behalf of the Authors (21 Apr 2015)

Short summary

We probabilistically invert the CARAIB dynamic vegetation model using a Markov chain Monte Carlo sampler, considering both homoscedastic and heteroscedastic eddy covariance residual errors with variances either fixed a priori or jointly inferred with the model parameters. A model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors result in more robust posterior parameter distributions.