Studying the impact of environmental change on soil C cycling has a long and established history. Soils are heterogeneous, requiring a multidisciplinary approach integrating biogeochemistry, physics, microbial diversity, and other drivers. Although we can increasingly integrate multiple processes into soil C cycling models, challenges remain in determining the appropriate level of complexity or abstraction, calibrating parameters, and selecting ecologically relevant variables. Meanwhile, growing data from (long-term) experiments can support model design, calibration, and validation, alongside advances in hybrid modelling and machine learning.
However, integrating climatic and biotic interactions into biogeochemical process models for heterogeneous soils remains limited by methodological challenges. Long-term datasets are scarce, especially those designed to test model structures, parameterisation strategies, and process representations. Modelling studies may also fail to incorporate the latest experimental insights because model development and evaluation is time consuming or required data are unavailable. Conversely, experiments are not always designed with modelling applications in mind, limiting their use for model testing and generalisation across sites and scales. This session brings together experimental and modelling scientists to discuss advances and challenges in soil C cycling. We particularly welcome contributions demonstrating successful integration of experimental and modelling approaches, identifying critical knowledge gaps, and exploring pathways toward more robust predictions of soil carbon dynamics under global change.
We invite contributions linked to:
• Soil C and nutrient cycling and microbial diversity experiments, especially those designed to support model development and/or containing long time series.
• (Hybrid) modelling at various spatial scales, including data-model integration, mechanistic process representations, parameterisation and calibration, model evaluation, uncertainty analysis, and machine learning.
• Studies linking climatic and biological drivers of soil C cycling.
• Research integrating experiments and modelling, including data assimilation, model validation, long-term datasets, and identification of key processes controlling soil C dynamics under environmental change.
• Studies addressing transferability and scaling of soil C processes from laboratory and field experiments to ecosystem, landscape, and global scales