Quantifying and reducing uncertainty in global carbon cycle predictions: lessons and perspectives from 15 years of data assimilation studies with the ORCHIDEE Terrestrial Biosphere Model - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Global Biogeochemical Cycles Année : 2022

Quantifying and reducing uncertainty in global carbon cycle predictions: lessons and perspectives from 15 years of data assimilation studies with the ORCHIDEE Terrestrial Biosphere Model

N. Macbean
S. Kuppel

Résumé

Predicting terrestrial carbon, C, budgets and carbon-climate feedbacks strongly relies on our ability to accurately model interactions between vegetation, C and water cycles, and the atmosphere. However, C fluxes simulated by global, process-based terrestrial biosphere models (TBMs) remain subject to large uncertainties, partly due to unknown or poorly calibrated parameters. This is because TBMs have not routinely been confronted against C cycle related datasets within a statistical data assimilation (DA) system. In this review, we present 15 years' development of a C cycle DA system for optimizing C cycle parameters of the ORCHIDEE TBM. We analyze the impact of assimilating multiple different C cycle related datasets on regional to global-scale gross and net CO2 fluxes. We find that assimilating atmospheric CO2 data is crucial for improving (increasing) ORCHIDEE predictions of the terrestrial land C sink. The improvement is predominantly due to the global-scale constraint these data provide for optimizing initial soil C stocks, which are likely in error due to inaccurate assumptions about steady state spin-up and incomplete knowledge of land use change histories. When comparing the data-constrained ORCHIDEE land C sink estimates to the CAMS atmospheric inversion, we show that while the two approaches agree on the global C sink magnitude, they continue to differ in how the global C sink is partitioned between the northern hemisphere and tropics. We also discuss technical challenges faced in our C cycle DA studies, in particular the difficulty in characterizing the error covariance matrix due to unknown observation biases and/or model-data inconsistencies. We offer our perspectives on how to tackle these challenges that we hope can serve as a roadmap for other TBM groups wishing to develop C cycle DA systems.
Fichier principal
Vignette du fichier
Global Biogeochemical Cycles - 2022 - MacBean - Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions .pdf (1.7 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03693760 , version 1 (30-08-2022)

Identifiants

Citer

N. Macbean, C. Bacour, N. Raoult, V. Bastrikov, E. Koffi, et al.. Quantifying and reducing uncertainty in global carbon cycle predictions: lessons and perspectives from 15 years of data assimilation studies with the ORCHIDEE Terrestrial Biosphere Model. Global Biogeochemical Cycles, 2022, 36 (7), pp.e2021GB007177. ⟨10.1029/2021GB007177⟩. ⟨hal-03693760⟩
93 Consultations
112 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More