Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation

Viskari, Toni; Laine, Maisa; Kulmala, Liisa; Mäkelä, Jarmo; Fer, Istem; Liski, Jari

doi:https://doi.org/10.5194/gmd-13-5959-2020

Articles | Volume 13, issue 12

https://doi.org/10.5194/gmd-13-5959-2020

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

https://doi.org/10.5194/gmd-13-5959-2020

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

Articles | Volume 13, issue 12

Methods for assessment of models

|

01 Dec 2020

Methods for assessment of models |

| 01 Dec 2020

Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation

Toni Viskari, Maisa Laine, Liisa Kulmala, Jarmo Mäkelä, Istem Fer, and Jari Liski

Download

Final revised paper (published on 01 Dec 2020)
Supplement to the final revised paper
Preprint (discussion started on 02 Jul 2020)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC1: 'review', Anonymous Referee #1, 16 Jul 2020
RC2: 'Comments by Referee #2', Anonymous Referee #2, 26 Aug 2020
AC1: 'Response to reviewer comments', Toni Viskari, 22 Sep 2020

Peer-review completion

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

AR by Toni Viskari on behalf of the Authors (22 Sep 2020)

ED: Publish subject to technical corrections (09 Oct 2020) by Carlos Sierra

AR by Toni Viskari on behalf of the Authors (16 Oct 2020) Author's response Manuscript

Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Article (686 KB)
Full-text XML

Short summary

The research here established whether a Bayesian statistical method called state data assimilation could be used to improve soil organic carbon (SOC) forecasts. Our test case was a fallow experiment where SOC content was measured over several decades from a plot where all vegetation was removed. Our results showed that state data assimilation improved projections and allowed for the detailed model state be updated with coarse total carbon measurements.