Articles | Volume 10, issue 7
Geosci. Model Dev., 10, 2635–2650, 2017
Geosci. Model Dev., 10, 2635–2650, 2017

Development and technical paper 10 Jul 2017

Development and technical paper | 10 Jul 2017

Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application

Sylvain Delahaies1, Ian Roulstone1, and Nancy Nichols2 Sylvain Delahaies et al.
  • 1Department of Mathematics, University of Surrey, Guildford, UK
  • 2Department of Mathematics, University of Reading, Reading, UK

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. Here 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. 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.

Short summary
Carbon is a fundamental constituent of life and understanding its global cycle is a key challenge for the modelling of the Earth system. We use a variational method to estimate parameters and initial conditions for the carbon cycle model DALECv2 using multiple sources of observations. We develop a methodology that helps understanding the nature of the inverse problem and evaluating solution strategies, then we demonstrate the efficiency of the variational method in an experiment using real data.