Abstract
Abstract. Physical and biogeochemical processes regulate soil carbon dynamics and CO2 flux to and from the atmosphere, influencing global climate changes. Integration of these processes into Earth system models (e.g., community land models (CLMs)), however, currently faces three major challenges: (1) extensive efforts are required to modify modeling structures and to rewrite computer programs to incorporate new or updated processes as new knowledge is being generated, (2) computational cost is prohibitively expensive to simulate biogeochemical processes in land models due to large variations in the rates of biogeochemical processes, and (3) various mathematical representations of biogeochemical processes exist to incorporate different aspects of fundamental mechanisms, but systematic evaluation of the different mathematical representations is difficult, if not impossible. To address these challenges, we propose a new computational framework to easily incorporate physical and biogeochemical processes into land models. The new framework consists of a new biogeochemical module, Next Generation BioGeoChemical Module (NGBGC), version 1.0, with a generic algorithm and reaction database so that new and updated processes can be incorporated into land models without the need to manually set up the ordinary differential equations to be solved numerically. The reaction database consists of processes of nutrient flow through the terrestrial ecosystems in plants, litter, and soil. This framework facilitates effective comparison studies of biogeochemical cycles in an ecosystem using different conceptual models under the same land modeling framework. The approach was first implemented in CLM and benchmarked against simulations from the original CLM-CN code. A case study was then provided to demonstrate the advantages of using the new approach to incorporate a phosphorus cycle into CLM. To our knowledge, the phosphorus-incorporated CLM is a new model that can be used to simulate phosphorus limitation on the productivity of terrestrial ecosystems. The method presented here could in theory be applied to simulate biogeochemical cycles in other Earth system models.
Highlights
Terrestrial ecosystems store almost three times as much carbon as the atmosphere; changes in the terrestrial carbon budgets have important implications for the future climate through carbon cycle feedbacks
A number of biogeochemistry modules have been developed and used in Earth system models (ESMs) to simulate the fluxes of carbon, nitrogen, energy, and water into and out of an ecosystem (e.g., CLM-CN, which originated from Biome-BGC (Thornton et al, 2007), CENTURY/DAYCENT (Parton et al, 2001, 1988), and other terrestrial biosphere models that participated in the Carbon Land Model Intercomparison Project (CLAMP) (Randerson et al, 2009), the Published by Copernicus Publications on behalf of the European Geosciences Union
Biogeochemical processes are represented by a set of ordinary differential equations (ODEs), and each of the equations is a sum of the contributions from various individual biogeochemical processes
Summary
Terrestrial ecosystems store almost three times as much carbon as the atmosphere; changes in the terrestrial carbon budgets have important implications for the future climate through carbon cycle feedbacks. International Land Model Benchmarking Project (ILAMB) (Luo et al, 2012), etc.) In these previous modules, biogeochemical processes are represented by a set of ordinary differential equations (ODEs), and each of the equations is a sum of the contributions from various individual biogeochemical processes. Many land surface models such as the current CLM only include C and N cycling, even though P cycling has been shown to be important in regulating terrestrial biogeochemical processes (Buendia et al, 2010; Goll et al, 2012; Wang et al, 2010; Yang et al, 2013). Anthropogenic sulfur (S), which is not included in the current CLM, can disturb the biogeochemical cycling in terrestrial ecosystems through competition for labile forms of organic carbon between nitrate-reducing and sulfate-reducing bacteria (Bünemann and Condron, 2007; Gu et al, 2012). P models and S models have been developed in the literature (Bünemann and Condron, 2007; Goll et al, 2012; Mitchell and Fuller, 1988; Wang et al, 2010), including them in CLM in its current code structure requires a nontrivial amount of work
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