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Abstract. Plant and microbial nitrogen (N) dynamics and N availability regulate the photosynthetic capacity and capture, allocation, and turnover of carbon (C) in terrestrial ecosystems. Studies have shown that a wide divergence in representations of N dynamics in land surface models leads to large uncertainties in the biogeochemical cycle of terrestrial ecosystems and then in climate simulations as well as the projections of future trajectories. In this study, a plant C–N interface coupling framework is developed and implemented in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0). The main concept and structure of this plant C–N framework and its coupling strategy are presented in this study. This framework takes more plant N-related processes into account. The dynamic C/N ratio (CNR) for each plant functional type (PFT) is introduced to consider plant resistance and adaptation to N availability to better evaluate the plant response to N limitation. Furthermore, when available N is less than plant N demand, plant growth is restricted by a lower maximum carboxylation capacity of RuBisCO (Vc,max), reducing gross primary productivity (GPP). In addition, a module for plant respiration rates is introduced by adjusting the respiration with different rates for different plant components at the same N concentration. Since insufficient N can potentially give rise to lags in plant phenology, the phenological scheme is also adjusted in response to N availability. All these considerations ensure a more comprehensive incorporation of N regulations to plant growth and C cycling. This new approach has been tested systematically to assess the effects of this coupling framework and N limitation on the terrestrial carbon cycle. Long-term measurements from flux tower sites with different PFTs and global satellite-derived products are employed as references to assess these effects. The results show a general improvement with the new plant C–N coupling framework, with more consistent emergent properties, such as GPP and leaf area index (LAI), compared to the observations. The main improvements occur in tropical Africa and boreal regions, accompanied by a decrease in the bias in global GPP and LAI by 16.3 % and 27.1 %, respectively. 

Abstract. Changes in the nitrogen (N) status of forest ecosystems can directly and indirectly influence their carbon (C) sequestration potential by altering soil organic matter (SOM) decomposition, soil enzyme activity, and plant–soil interactions. However, model representations of linked C–N cycles and SOM decay are not well validated against experimental data. Here, we use extensive data from the Fernow Experimental Forest long-term whole-watershed N fertilization study to compare the response to N perturbations of two soil models that represent decomposition dynamics differently (first-order decay versus microbially explicit reverse Michaelis–Menten kinetics). These two soil models were coupled to a common vegetation model which provided identical input data. Key responses to N additions measured at the study site included a shift in plant allocation to favor woody biomass over belowground carbon inputs, reductions in soil respiration, accumulation of particulate organic matter (POM), and an increase in soil C:N ratios. The vegetation model did not capture the often-observed shift in plant C allocation with N additions, which resulted in poor predictions of the soil responses. We modified the parameterization of the plant C allocation scheme to favor wood production over fine-root production with N additions, which significantly improved the vegetation and soil respiration responses. Additionally, to elicit an increase in the soil C stocks and C:N ratios with N additions, as observed, we modified the decay rates of the POM in the soil models. With these modifications, both models captured negative soil respiration and positive soil C stock responses in line with observations, but only the microbially explicit model captured an increase in soil C:N. Our results highlight the need for further model development to accurately represent plant–soil interactions, such as rhizosphere priming, and their responses to environmental change. 

Abstract Soil organic matter decomposition and its interactions with climate depend on whether the organic matter is associated with soil minerals. However, data limitations have hindered global-scale analyses of mineral-associated and particulate soil organic carbon pools and their benchmarking in Earth system models used to estimate carbon cycle–climate feedbacks. Here we analyse observationally derived global estimates of soil carbon pools to quantify their relative proportions and compute their climatological temperature sensitivities as the decline in carbon with increasing temperature. We find that the climatological temperature sensitivity of particulate carbon is on average 28% higher than that of mineral-associated carbon, and up to 53% higher in cool climates. Moreover, the distribution of carbon between these underlying soil carbon pools drives the emergent climatological temperature sensitivity of bulk soil carbon stocks. However, global models vary widely in their predictions of soil carbon pool distributions. We show that the global proportion of model pools that are conceptually similar to mineral-protected carbon ranges from 16 to 85% across Earth system models from the Coupled Model Intercomparison Project Phase 6 and offline land models, with implications for bulk soil carbon ages and ecosystem responsiveness. To improve projections of carbon cycle–climate feedbacks, it is imperative to assess underlying soil carbon pools to accurately predict the distribution and vulnerability of soil carbon. 

Abstract The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationshipsa priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions. 

Abstract. Data collected from research networks presentopportunities to test theories and develop models about factors responsiblefor the long-term persistence and vulnerability of soil organic matter(SOM). Synthesizing datasets collected by different research networkspresents opportunities to expand the ecological gradients and scientificbreadth of information available for inquiry. Synthesizing these data ischallenging, especially considering the legacy of soil data that havealready been collected and an expansion of new network science initiatives.To facilitate this effort, here we present the SOils DAta Harmonizationdatabase (SoDaH; https://lter.github.io/som-website, last access: 22 December 2020), a flexible database designed to harmonize diverse SOM datasets frommultiple research networks. SoDaH is built on several network scienceefforts in the United States, but the tools built for SoDaH aim to providean open-access resource to facilitate synthesis of soil carbon data.Moreover, SoDaH allows for individual locations to contribute results fromexperimental manipulations, repeated measurements from long-term studies,and local- to regional-scale gradients across ecosystems or landscapes.Finally, we also provide data visualization and analysis tools that can beused to query and analyze the aggregated database. The SoDaH v1.0 dataset isarchived and availableat https://doi.org/10.6073/pasta/9733f6b6d2ffd12bf126dc36a763e0b4 (Wieder et al., 2020). 

This SOils DAta Harmonization (SoDaH) database is designed to bring together soil carbon data from diverse research networks into a harmonized dataset that can be used for synthesis activities and model development. The research network sources for SoDaH span different biomes and climates, encompass multiple ecosystem types, and have collected data across a range of spatial, temporal, and depth gradients. The rich data sets assembled in SoDaH consist of observations from monitoring efforts and long-term ecological experiments. The SoDaH database also incorporates related environmental covariate data pertaining to climate, vegetation, soil chemistry, and soil physical properties. The data are harmonized and aggregated using open-source code that enables a scripted, repeatable approach for soil data synthesis.