Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application
Abstract
We use a variational method to assimilate multiple data streams into the terrestrial ecosystem carbon cycle model DALECv2 (Data Assimilation Linked Ecosystem Carbon). Ecological and dynamical constraints have recently been introduced to constrain unresolved components of this otherwise ill-posed problem. We recast these constraints as a multivariate Gaussian distribution to incorporate them into the variational framework and we demonstrate their advantage through a linear analysis. By using an adjoint method we study a linear approximation of the inverse problem: firstly we perform a sensitivity analysis of the different outputs under consideration, and secondly we use the concept of resolution matrices to diagnose the nature of the ill-posedness and evaluate regularisation strategies. We then study the non-linear problem with an application to real data. Finally, we propose a modification to the model: introducing a spin-up period provides us with a built-in formulation of some ecological constraints which facilitates the variational approach.
- Authors:
-
- Univ. of Surrey, Guildford (United Kingdom). Dept. of Mathematics
- Univ. of Reading, (United Kingdom). Dept. of Mathematics
- Publication Date:
- Research Org.:
- Univ. of Indiana, Bloomington, IN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1392991
- Grant/Contract Number:
- FG02-07ER64371
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Geoscientific Model Development (Online)
- Additional Journal Information:
- Journal Name: Geoscientific Model Development (Online); Journal Volume: 10; Journal Issue: 7; Journal ID: ISSN 1991-9603
- Publisher:
- European Geosciences Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES
Citation Formats
Delahaies, Sylvain, Roulstone, Ian, and Nichols, Nancy. Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application. United States: N. p., 2017.
Web. doi:10.5194/gmd-10-2635-2017.
Delahaies, Sylvain, Roulstone, Ian, & Nichols, Nancy. Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application. United States. https://doi.org/10.5194/gmd-10-2635-2017
Delahaies, Sylvain, Roulstone, Ian, and Nichols, Nancy. Mon .
"Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application". United States. https://doi.org/10.5194/gmd-10-2635-2017. https://www.osti.gov/servlets/purl/1392991.
@article{osti_1392991,
title = {Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application},
author = {Delahaies, Sylvain and Roulstone, Ian and Nichols, Nancy},
abstractNote = {We use a variational method to assimilate multiple data streams into the terrestrial ecosystem carbon cycle model DALECv2 (Data Assimilation Linked Ecosystem Carbon). Ecological and dynamical constraints have recently been introduced to constrain unresolved components of this otherwise ill-posed problem. We recast these constraints as a multivariate Gaussian distribution to incorporate them into the variational framework and we demonstrate their advantage through a linear analysis. By using an adjoint method we study a linear approximation of the inverse problem: firstly we perform a sensitivity analysis of the different outputs under consideration, and secondly we use the concept of resolution matrices to diagnose the nature of the ill-posedness and evaluate regularisation strategies. We then study the non-linear problem with an application to real data. Finally, we propose a modification to the model: introducing a spin-up period provides us with a built-in formulation of some ecological constraints which facilitates the variational approach.},
doi = {10.5194/gmd-10-2635-2017},
journal = {Geoscientific Model Development (Online)},
number = 7,
volume = 10,
place = {United States},
year = {Mon Jul 10 00:00:00 EDT 2017},
month = {Mon Jul 10 00:00:00 EDT 2017}
}
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