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Abstract

This paper presents global land carbon fluxes for the period 1982–2010 (gross primary production, GPP, and net ecosystem exchange, NEE) estimated with the Max Planck Institute – Carbon Cycle Data Assimilation System (MPI-CCDAS v1). The primary aim of this work is to analyze the performance of the MPI-CCDAS when it is confronted with three different time periods for data assimilation (DA), and thereby to assess its prognostic capability. To this extend we assimilated nearly three decades (1982–2010) of space borne measurements of the fraction of absorbed photosynthetic active radiation (FAPAR) and atmospheric CO2 concentrations from the global network of flask and in situ measurements. Both data sets were incorporated with different assimilation windows covering the periods 1982–1990, 1990–2000 and 1982–2010. The assimilation results show a considerable improvement in the long-term trend and seasonality of FAPAR in the Northern Hemisphere, as well as in the long term trend and seasonal amplitude of the atmospheric CO2 concentrations when compared to the observations in sites globally distributed. After the assimilation, the global net land-atmosphere CO2 exchange (NEE) was −1.2PgCyr−1, in agreement with independent estimates, while gross primary production (GPP; 92.5PgCyr−1) was somewhat below the magnitude of independent estimates. The NEE in boreal eastern regions (Northeast Asia) increased on average by −0.13PgCyr−1, which translated into an intensification of the carbon uptake in those regions by nearly 30% than the contribution to the global annual average in the model before the assimilation.

Our results demonstrate that using information only over a decade already yielded a large fraction of the overall model improvement, in particular for the simulation of phenological seasonality, its interannual variability (IAV) and long-term trend. Adding longer than decadal data did only lead to very moderate improvements in the long-term trend of the FAPAR simulated by the model, which may be attributed to the small model-data mismatch at the long timescales compared to the significantly larger observational signal and model-data mismatch error at seasonal cycle time scale. Decadal data also significantly improved the seasonality, IAV and long-term simulated trend in atmospheric CO2. Importantly, when running the MPI-CCDAS v1 with 30 years of data, the results remained in line with observations throughout this period, suggesting that the model can represent land uptake to a sufficient degree to make it compatible with the atmospheric CO2 record. Using data from 1982 to 1990 in the assimilation yielded only a difference to the observations of 2±1.3ppm for the period 15 to 19 years after the end of the assimilation. This suggests that despite imperfections in the representation of IAV, model-data fusion can increase the prognostic capacity of land carbon cycle models at relevant time-scales.