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Abstract Large across‐model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across‐model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO 2 levels and five temperature levels. We developed a matrix‐based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter‐model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter‐model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO 2 (eCO 2 ) concentrations in the measurements, simulated NEP responded positively to eCO 2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across‐model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables. 

Increases in carbon (C) inputs to soil can replenish soil organic C (SOC) through various mechanisms. However, recent studies have suggested that the increased C input can also stimulate the decomposition of old SOC via priming. Whether the loss of old SOC by priming can override C replenishment has not been rigorously examined. Here we show, through data–model synthesis, that the magnitude of replenishment is greater than that of priming, resulting in a net increase in SOC by a mean of 32% of the added new C. The magnitude of the net increase in SOC is positively correlated with the nitrogen-to-C ratio of the added substrates. Additionally, model evaluation indicates that a two-pool interactive model is a parsimonious model to represent the SOC decomposition with priming and replenishment. Our findings suggest that increasing C input to soils likely promote SOC accumulation despite the enhanced decomposition of old C via priming.

 

How climate warming interacts with atmospheric nitrogen (N) deposition to affect carbon (C) release from soils remains largely elusive, posing a major challenge in projecting climate change‒terrestrial C feedback.

As part of a 5‐year (2006–2010) field manipulative experiment, this study was designed to examine the effects of 24‐hr continuous warming and N addition on soil respiration and explore the underlying mechanisms in a semi‐arid grassland on the Mongolian Plateau, China.

Across the 5 years and all plots, soil respiration was not changed under the continuous warming, but was decreased by 3.7% under the N addition. The suppression of soil respiration by N addition in the third year and later could be mainly due to the reductions in the forb‐to‐grass biomass ratios. Moreover, there were interactive effects between continuous warming and N addition on soil respiration. Continuous warming increased soil respiration by 5.8% in the ambient N plots, but reduced it by 6.3% in the enriched N plots. Soil respiration was unaffected by N addition in the ambient temperature plots yet decreased by 9.4% in the elevated temperature plots. Changes of soil moisture and the proportion of legume biomass in the community might be primarily responsible for the non‐additive effects of continuous warming and N addition on soil respiration.

This study provides empirical evidence for the positive climate warming‒soil C feedback in the ambient N condition. However, N deposition reverses the positive warming‒soil C feedback into a negative feedback, leading to decreased C loss from soils under a warming climate. Incorporating our findings into C‐cycling models could reduce the uncertainties of model projections for land C sink and global C cycling under multifactorial global change scenarios.

A freePlain Language Summarycan be found within the Supporting Information of this article.

 

The terrestrial carbon (C) cycle has been commonly represented by a series of C balance equations to track C influxes into and effluxes out of individual pools in earth system models (ESMs). This representation matches our understanding of C cycle processes well but makes it difficult to track model behaviors. It is also computationally expensive, limiting the ability to conduct comprehensive parametric sensitivity analyses. To overcome these challenges, we have developed a matrix approach, which reorganizes the C balance equations in the originalESMinto one matrix equation without changing any modeled C cycle processes and mechanisms. We applied the matrix approach to the Community Land Model (CLM4.5) with vertically‐resolved biogeochemistry. The matrix equation exactly reproduces litter and soil organic carbon (SOC) dynamics of the standardCLM4.5 across different spatial‐temporal scales. The matrix approach enables effective diagnosis of system properties such as C residence time and attribution of global change impacts to relevant processes. We illustrated, for example, the impacts ofCO₂fertilization on litter andSOCdynamics can be easily decomposed into the relative contributions from C input, allocation of external C into different C pools, nitrogen regulation, altered soil environmental conditions, and vertical mixing along the soil profile. In addition, the matrix tool can accelerate model spin‐up, permit thorough parametric sensitivity tests, enable pool‐based data assimilation, and facilitate tracking and benchmarking of model behaviors. Overall, the matrix approach can make a broad range of future modeling activities more efficient and effective.