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Volume 7, issue 11
Biogeosciences, 7, 3707–3729, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 7, 3707–3729, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  18 Nov 2010

18 Nov 2010

Deciphering the components of regional net ecosystem fluxes following a bottom-up approach for the Iberian Peninsula

N. Carvalhais1,2, M. Reichstein2, G. J. Collatz3, M. D. Mahecha2, M. Migliavacca4, C. S. R. Neigh5, E. Tomelleri2, A. A. Benali1, D. Papale6, and J. Seixas1 N. Carvalhais et al.
  • 1Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
  • 2Max-Planck-Institut für Biogeochemie, P.O. Box 10 01 64, 07701 Jena, Germany
  • 3NASA Goddard Space Flight Center, Greenbelt, Code 614.4, Greenbelt, MD 20771, USA
  • 4European Commission – Directorate General Joint Research Centre, Institute for Environment and Sustainability, Climate Change Unit, 21027 Ispra (VA), Italy
  • 5NASA Postdoctoral Program Fellow, Goddard Space Flight Center, Greenbelt MD 20771, USA
  • 6Dipartimento di Scienze dell'Ambiente Forestale e delle sue Risorse, DISAFRI, Universitá degli Studi della Tuscia, Via Camillo de Lellis, snc-01100, Viterbo, Italy

Abstract. Quantification of ecosystem carbon pools is a fundamental requirement for estimating carbon fluxes and for addressing the dynamics and responses of the terrestrial carbon cycle to environmental drivers. The initial estimates of carbon pools in terrestrial carbon cycle models often rely on the ecosystem steady state assumption, leading to initial equilibrium conditions. In this study, we investigate how trends and inter-annual variability of net ecosystem fluxes are affected by initial non-steady state conditions. Further, we examine how modeled ecosystem responses induced exclusively by the model drivers can be separated from the initial conditions. For this, the Carnegie-Ames-Stanford Approach (CASA) model is optimized at set of European eddy covariance sites, which support the parameterization of regional simulations of ecosystem fluxes for the Iberian Peninsula, between 1982 and 2006.

The presented analysis stands on a credible model performance for a set of sites, that represent generally well the plant functional types and selected descriptors of climate and phenology present in the Iberian region – except for a limited Northwestern area. The effects of initial conditions on inter-annual variability and on trends, results mostly from the recovery of pools to equilibrium conditions; which control most of the inter-annual variability (IAV) and both the magnitude and sign of most of the trends. However, by removing the time series of pure model recovery from the time series of the overall fluxes, we are able to retrieve estimates of inter-annual variability and trends in net ecosystem fluxes that are quasi-independent from the initial conditions. This approach reduced the sensitivity of the net fluxes to initial conditions from 47% and 174% to −3% and 7%, for strong initial sink and source conditions, respectively.

With the aim to identify and improve understanding of the component fluxes that drive the observed trends, the net ecosystem production (NEP) trends are decomposed into net primary production (NPP) and heterotrophic respiration (RH) trends. The majority (~97%) of the positive trends in NEP is observed in regions where both NPP and RH fluxes show significant increases, although the magnitude of NPP trends is higher. Analogously, ~83% of the negative trends in NEP are also associated with negative trends in NPP. The spatial patterns of NPP trends are mainly explained by the trends in fAPAR (r=0.79) and are only marginally explained by trends in temperature and water stress scalars (r=0.10 and r=0.25, respectively). Further, we observe the significant role of substrate availability (r=0.25) and temperature (r=0.23) in explaining the spatial patterns of trends in RH. These results highlight the role of primary production in driving ecosystem fluxes.

Overall, our study illustrates an approach for removing the confounding effects of initial conditions and emphasizes the need to decompose the ecosystem fluxes into its components and drivers for more mechanistic interpretations of modeling results. We expect that our results are not only specific for the CASA model since it incorporates concepts of ecosystem functioning and modeling assumptions common to biogeochemical models. A direct implication of these results is the ability of this approach to detect climate and phenology induced trends regardless of the initial conditions.

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