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Quantifying Uncertainty in Climate Projections including Biogeochemical Feedbacks (ClimB)
Date du début: 1 mai 2011, Date de fin: 30 avr. 2013 PROJET  TERMINÉ 

State-of-the-art earth system models used for long-term climate projections are becoming ever more complex in terms of not only spatial resolution but also the number of processes included in the models. Biogeochemical processes are just beginning to be incorporated into these complex models due to a difficulty in resolving heterogeneous biogeochemical processes. The motivation of this project is to quantify how climate projections are influenced by additional biogeochemical feedbacks that have not yet been taken into account in contemporary climate models. The researcher proposes an approach to look into coupled carbon-nitrogen-phosphorus-sulfur (C-N-P-S) cycle processes together with first-order physical processes. Biogeochemical studies show that C-N-P-S cycles are intimately interlinked via biosphere, resulting in an amplification of carbon cycle feedbacks. The proposed study will treat rivers and the global coastal zone explicitly, which are known to be essential for global biogeochemical cycle but are not represented in today’s earth system models. Uncertain parameters in the model will be constrained by an inverse estimation approach using various geophysical observations during the past millennium. The project addresses the following two questions: - Do coupled C-N-P-S biogeochemical cycles reduce the uncertainty in the past and present global carbon budget? - How much do coupled C-N-P-S cycle feedbacks contribute to the future global warming? How do they affect uncertainties? The interdisciplinary study will not only have a profound implication for future climate projections but also provide a new insight into outstanding research questions that cannot be answered in individual fields alone. Our research will serve as a pilot study to inform the climate research community as to whether coupled C-N-P-S cycle processes are important to be included in next-generation climate models.

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