Quantifying the model structural error in carbon cycle data assimilation systems

Sylvain Kuppel*, F. Chevallier, P. Peylin

*Corresponding author for this work

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

This study explores the impact of the structural error of biosphere models when assimilating net ecosystem exchange (NEE) measurements or CO2 concentration measurements to optimise uncertain model parameters within carbon cycle data assimilation systems (CCDASs). This error has been proven difficult to identify and is often neglected in the total uncertainty budget. We propose a simple method which is derived from the model-minus-observation mismatch statistics. This diagnosis is applied to a state-of-the-art biogeochemical model using measurements of the net surface CO2 flux at twelve sites located in temperate, deciduous, broadleaf forests. We find that the structural model error in the NEE space has a standard deviation of 1.5 to 1.7 gC m -2 d-1, without a significant correlation structure beyond the lag of a few days, and a large spatial structure that can be approximated with an exponential decay of e-folding length of 500 km. In the space of concentrations, its characteristics are commensurate with the transport errors, both for surface air sample measurements and total column measurements. The inferred characteristics are confirmed by complementary optimality diagnostics performed after site-scale parameter optimisations.

Original languageEnglish
Pages (from-to)45-55
Number of pages11
JournalGeoscientific Model Development
Volume6
Issue number1
DOIs
Publication statusPublished - 11 Jan 2013

Fingerprint

Data Assimilation
Structural Model
carbon cycle
data assimilation
Carbon
Cycle
net ecosystem exchange
Ecosystem
Ecosystems
Model Error
Correlation Structure
Spatial Structure
Parameter Optimization
Scale Parameter
Exponential Decay
Folding
deciduous forest
Model
Standard deviation
biosphere

Keywords

  • Carbon cycle data
  • Assimilation Systems
  • Biosphere models

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Modelling and Simulation

Cite this

Quantifying the model structural error in carbon cycle data assimilation systems. / Kuppel, Sylvain; Chevallier, F.; Peylin, P.

In: Geoscientific Model Development, Vol. 6, No. 1, 11.01.2013, p. 45-55.

Research output: Contribution to journalArticle

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