Probabilistic Modeling of Long-term Peatland Carbon Dynamics

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Peatlands regulate atmospheric greenhouse gas concentrations and thus the global climate. They form one of the largest terrestrial C stores and current and projected long-term shifts in temperature, precipitation, and nitrogen deposition represent a potential threat to these functions. Dynamic peatland models (DPM) are needed to acquire a mechanistic understanding of process interactions, to predict long-term changes in C accumulation rates, and to synthesize contrasting results of individual studies. For about 40 years DPM have continuously been improved by including additional processes, temporal dynamics, and spatial heterogeneity. Sensitivity analyses of DPM have revealed that uncertainties are generally large yet crucial for a correct interpretation of process interactions. In other disciplines, the application of probabilistic models, uncertainty analysis, and uncertainty reduction via data assimilation has proven useful extensions of former deterministic models. However, uncertainty has barely been quantified and analyzed for probabilistic DPM. To make DPM more useful, we suggest to develop a probabilistic DPM and to quantify, analyze, and reduce uncertainties in its input data and parameters, using uncertainty analysis and data assimilation. We expect that data assimilation can reduce uncertainties especially for long-term decomposition rates if one synthesizes different existing data sources (peat core data and litter bag data) and the information provided by multiple peat properties at the same time (e.g. C and N content). With this framework, we aim to assess the impact uncertainties have for our understanding of the effects of temperature, precipitation, and nitrogen deposition on peatland C accumulation, identify conditions under which experiments yield contrasting results, and provide strategies for efficient future uncertainty reduction in DPM.