Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C-only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit vs. implicit representation of soil microbial activity, as represented in the MIcrobial-MIneral Carbon Stabilization (MIMICS) and Carnegie–Ames–Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS-CN model, which also increases the inferred SOM turnover time. Over the historical period, however, higher rates of N mineralization were fueled by warming-induced acceleration of SOM decomposition over high latitude ecosystems in the MIMICS-CN simulation reduce this N limitation, resulting in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS-CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios.

Plant and microbial nitrogen (N) dynamics and nitrogen availability regulate the photosynthetic capacity and capture, allocation, turnover of carbon (C) in terrestrial ecosystem. It is important to adequately represent plant N processes in land surface models. In this study, a plant C-N framework was developed by coupling a biophysical and dynamic land surface processes model, SSiB4/TRIFFID, with a soil organic matter cycling model, DayCent-SOM, to fully incorporate N regulations to investigate the impact of N on plant growth and C cycling. To incorporate the N limitation in the coupled system, the parameterization for dynamic C/N ratios for each plant functional type (PFT) was developed first. Then, after accounting for plant/soil N-cycling, when available N is less than demand, N would restrict the plant growth, reducing the net primary productivity (NPP), but also impact plant respiration rates and phenology. The improvements of the newly-developed model, the SSiB5/TRIFFID/DayCent-SOM, was preliminary verified at three flux tower sites with different PFTs. Furthermore, several offline global simulations were conducted from 1948 to 2007 to predict the long-term mean vegetation distribution and terrestrial C cycling, and the results are evaluated with satellite-derived observational data. The sensitivity of the terrestrial C cycle to N processes is also assessed. In general, new model can better reproduce observed emergent properties, including gross primary productivity (GPP), leaf area index (LAI), and respiration. The main improvements occur in tropical Africa and boreal regions, accompanied by a decrease of the bias in global GPP and LAI by 16.3% and 27.1%, respectively.

It is important to adequately represent plant nitrogen (N) biogeochemistry and its respective processes in land surface models. Thus far, various N representations in models lead to uncertainty in estimating model responses to global warming. Through plant and microbial N dynamics, nitrogen availability regulates the capture, allocation, turnover of carbon (C), and photosynthetic capacity. In this study, to fully incorporate these N regulations, we have developed a plant C-N framework by coupling a biophysical and dynamic land model, SSiB4/TRIFFID, with a soil organic matter cycling model, DayCent-SOM, to simulate the impact of nitrogen on the plant growth and C cycling. To incorporate the N limitation in the coupled system, we first developed the parameterization for the C/N ratios. Then, after accounting for daily plant/soil N-cycling, N will not only limit the plant growth when not sufficient, causing the net primary productivity (NPP) to be down-regulated, but will also impact plant respiration rates and phenology. Using this newly-developed model named SSiB5/TRIFFID/DayCent-SOM, we conduct several simulations from 1948 to 2007 to predict the global vegetation distribution and terrestrial C cycling, and the results are evaluated with satellite-derived observational data. The sensitivity of the terrestrial C cycle to N processes is also assessed. In general, the coupled model can better reproduce observed emergent properties, including gross primary productivity (GPP), NPP, leaf area index (LAI), and respiration. The main improvement occurs in tropical Africa and boreal regions, accompanied by a decrease of the bias in global GPP and LAI by 16.3% and 27.1%, respectively.

Sustainable agriculture and forestry is an important topic under climate change. This is a potential route for increasing long-term soil and biomass carbon storage, soil water retention capacity and for reducing water and wind erosion risks. This study uses the Styrian Raab and Enns catchment regions in Southeastern Austria as showcase regions for exploring sustainable whole-system options for climate change adaptation and mitigation under increased hot-dry conditions in agriculture and forestry. Based on dense data of the WegenerNet observing network and further hydrometeorological data, combined with hydrological modelling (WaSiM), the current hydrological disturbance potential in the Southeastern Austria focus regions is assessed. Furthermore, downscaled IPCC climate change scenarios are used for future projections and the results are evaluated for increasing heat and drought risks. This work provides the hydrological context for modelling the soil water and carbon storage enhancement options that farming, forestry and land-use practices might apply. A first key study aspect in this context is the sustainable potential of bioenergy crops. Using the local-scale WegenerNet data combined with Harmonized World Soil Database (HWSD) soil data, potential yields for bioenergy from lignocellulosic biomass (forest and Miscanthus, willow and poplar) is modelled using MiscanFor, SalixFor, PopFor and DAYCENT models for representative local areas in the showcase regions. Using DAYCENT biogeochemical modelling with different agricultural, forest management, and land use practices under climate change, sustainable system options under different future climate change scenarios are developed. These results will be used in turn to develop whole-system options, namely to jointly achieve increase of soil carbon and robustness of soil water retention capacity, increase of soil quality, reduction of soil erosion and degradation, reduced compaction, stabilisation of slopes, sustainability and resilience in the soil as well as the agricultural and forest production system. Sustainable whole-system options for farmers and forest managers are the major study focus and will supply data for local estimates in the chosen representative areas in the showcase regions as well as for upscaling to country level.