Apparent ecosystem carbon turnover time: uncertainties and robust features

Autor: N. Fan, S. Koirala, M. Reichstein, M. Thurner, V. Avitabile, M. Santoro, B. Ahrens, U. Weber, N. Carvalhais
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Earth System Science Data, Vol 12, Pp 2517-2536 (2020)
Druh dokumentu: article
ISSN: 1866-3508
1866-3516
DOI: 10.5194/essd-12-2517-2020
Popis: The turnover time of terrestrial ecosystem carbon is an emergent ecosystem property that quantifies the strength of land surface on the global carbon cycle–climate feedback. However, observation- and modeling-based estimates of carbon turnover and its response to climate are still characterized by large uncertainties. In this study, by assessing the apparent whole ecosystem carbon turnover times (τ) as the ratio between carbon stocks and fluxes, we provide an update of this ecosystem level diagnostic and its associated uncertainties in high spatial resolution (0.083∘) using multiple, state-of-the-art, observation-based datasets of soil organic carbon stock (Csoil), vegetation biomass (Cveg) and gross primary productivity (GPP). Using this new ensemble of data, we estimated the global median τ to be 43-7+7 yr (median-difference to percentile 25+difference to percentile 75) when the full soil is considered, in contrast to limiting it to 1 m depth. Only considering the top 1 m of soil carbon in circumpolar regions (assuming maximum active layer depth is up to 1 m) yields a global median τ of 37-6+3 yr, which is longer than the previous estimates of 23-4+7 yr (Carvalhais et al., 2014). We show that the difference is mostly attributed to changes in global Csoil estimates. Csoil accounts for approximately 84 % of the total uncertainty in global τ estimates; GPP also contributes significantly (15 %), whereas Cveg contributes only marginally (less than 1 %) to the total uncertainty. The high uncertainty in Csoil is reflected in the large range across state-of-the-art data products, in which full-depth Csoil spans between 3362 and 4792 PgC. The uncertainty is especially high in circumpolar regions with an uncertainty of 50 % and a low spatial correlation between the different datasets (0.2
Databáze: Directory of Open Access Journals
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