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

Sylvain Delahaies,Ian Roulstone,Nancy Nichols

doi:10.5194/gmd-10-2635-2017

Sylvain Delahaies, Ian Roulstone + Show 1 more

Open Access

https://doi.org/10.5194/gmd-10-2635-2017

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Abstract

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.

Highlights

Carbon is a fundamental constituent of life and understanding its global cycle is a key challenge for the modelling of the Earth system
Inter-comparison experiments (Fox et al, 2009; Hill et al, 2012) have demonstrated the relative merit of various inverse modelling strategies using net ecosystem exchange (NEE) and MODIS leaf area index observations: most results agreed on the fact that parameters and initial stocks directly related to fast processes were best estimated with narrow confidence intervals, whereas those related to slow processes were poorly estimated with very large uncertainties
It represents the basic processes at the heart of more sophisticated models of the carbon cycle, and, besides its large modelling skills, its simplicity allows for close mathematical scrutiny

Summary

Introduction

Carbon is a fundamental constituent of life and understanding its global cycle is a key challenge for the modelling of the Earth system. Our knowledge of the biogeochemical processes of ecosystems and an ever-growing amount of Earth observation systems can be combined using inverse modelling strategies to improve model predictions and uncertainty quantification. The work of Williams et al (2005) established the benefit of using DALEC together with net ecosystem exchange (NEE) of CO2 measurements in a Bayesian framework to estimate initial carbon stocks and model parameters, to improve flux predictions for ecosystem models and to quantify uncertainties. Inter-comparison experiments (Fox et al, 2009; Hill et al, 2012) have demonstrated the relative merit of various inverse modelling strategies using NEE and MODIS leaf area index observations: most results agreed on the fact that parameters and initial stocks directly related to fast processes were best estimated with narrow confidence intervals, whereas those related to slow processes were poorly estimated with very large uncertainties. To date very few systematic analysis has been carried out to explain the large differences among results

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