Improvement of global litter turnover rate predictions using a Bayesian MCMC approach

Abstract

Abstract. Global terrestrial carbon (C) cycle has a strong influence on atmospheric CO 2 concentrations and temperatures. Litter mass is relatively small in comparison to soil and plant pools but its turnover rate is fast. Litter dynamics is important part of the global terrestrial carbon cycle as it is a critical stage in the soil organic matter formation and nutrient mineralization. Litter turnover rates have been observed on site, regional, and global levels, however little effort has been put into validating and calibrating litter decay models against the observations. In this study, we used a Bayesian Markov Chain Monte Carlo data assimilation technique and globally observed leaf litter turnover rates to calibrate a first order litter decay model with different assumptions about litter quality limitations of decomposition. The first order decay model with original parameters and a commonly-used litter quality limitation function explained 15% of the spatial variation in the observed leaf litter turnover rates, and parameter calibration increased the explained variation in the observations to 44%. When litter quality limitation of decomposition was determined by litter lignin-to-nitrogen ratio rather than structural lignin content the performance of the calibrated first order decay model was further improved, explaining 62% of variation in the observations. Litter feedbacks to changing climate differed between the original and best-fitting models: original model predicted a 16% decrease in leaf litter pool after 95 years of climate change (2006-2100), whereas the bestfitting model predicted a 2% increase. Furthermore, assuming that litter quality decreased with increasing CO 2 concentrations resulted in a 28% decrease in leaf litter pool predicted by the original model, and a 15% increase predicted by the best-fitting model. Thus, assimilating observed leaf litter turnover rates into a first-order decay model improved model fit and reversed leaf litter feedbacks to changing climate.

Transient Traceability Analysis of Land Carbon Storage Dynamics: Procedures and Its Application to Two Forest Ecosystems

“…This framework using matrix representation will also enable data assimilation to be easily applied in ecosystem models or other complex models. Model projections of ecosystem models such as the TECO model (Shi et al, ) or complex models such as global land C models (Hararuk et al, ; Hararuk & Luo, ; He et al, ) have been substantially improved via data assimilation using matrix representation of these models. Data assimilation technique was recognized as the highest priority to improve predictions of soil C dynamics in ESMs (Luo et al, ).…”

Explicitly representing soil microbial processes in Earth system models

“…Long‐term soil manipulations [e.g., Lajtha et al , ; Melillo et al , ], accompanied by detailed measurements of microbial function, may be necessary to distinguish between alternative models of soil C response to changes in litter inputs or climate. Both first‐order and microbial‐explicit models could be improved and compared using such long‐term data sets and statistical approaches such as Bayesian data assimilation [ Hararuk and Luo , ; Tang and Zhuang , ; Wang et al , ; Xu et al , ]. The results of these analyses will be essential for improving soil C components of ESMs.…”

Towards Harmonizing Leaf Litter Decomposition Studies Using Standard Tea Bags—A Field Study and Model Application

“…Litterbags have been used extensively to examine the importance of different drivers and how they affect the nutrient and mineral fluxes from the plant litter to the soil (e.g., [5][6][7]). Based on a meta-analysis of 66 litterbag experiments involving 818 plant species, Cornwell et al [8] showed that within a climate region, plant species traits are important drivers of their litter decomposition.…”