ORCID Profile
0000-0002-4792-1259
Current Organisation
E O Lawrence Berkeley National Laboratory
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Publisher: American Geophysical Union (AGU)
Date: 09-2020
DOI: 10.1029/2019MS001766
Abstract: This paper documents the biogeochemistry configuration of the Energy Exascale Earth System Model (E3SM), E3SMv1.1‐BGC. The model simulates historical carbon cycle dynamics, including carbon losses predicted in response to land use and land cover change, and the responses of the carbon cycle to changes in climate. In addition, we introduce several innovations in the treatment of soil nutrient limitation mechanisms, including explicit dependence on phosphorus availability. The suite of simulations described here includes E3SM contributions to the Coupled Climate‐Carbon Cycle Model Intercomparison Project and other projects, as well as simulations to explore the impacts of structural uncertainty in representations of nitrogen and phosphorus limitation. We describe the model spin‐up and evaluation procedures, provide an overview of results from the simulation c aign, and highlight key features of the simulations. Cumulative warming over the twentieth century is similar to observations, with a midcentury cold bias offset by stronger warming in recent decades. Ocean biomass production and carbon uptake are underpredicted, likely due to biases in ocean transport leading to widespread anoxia and undersupply of nutrients to surface waters. The inclusion of nutrient limitations in the land biogeochemistry results in weaker carbon fertilization and carbon‐climate feedbacks than exhibited by other Earth System Models that exclude those limitations. Finally, we compare with an alternative representation of terrestrial biogeochemistry, which differs in structure and in initialization of soil phosphorus. While both configurations agree well with observational benchmarks, they differ significantly in their distribution of carbon among different pools and in the strength of nutrient limitations.
Publisher: American Geophysical Union (AGU)
Date: 07-2019
DOI: 10.1029/2018MS001603
Publisher: Copernicus GmbH
Date: 10-07-2014
Abstract: Abstract. Accurate representation of soil organic matter (SOM) dynamics in Earth system models is critical for future climate prediction, yet large uncertainties exist regarding how, and to what extent, the suite of proposed relevant mechanisms should be included. To investigate how various mechanisms interact to influence SOM storage and dynamics, we developed an SOM reaction network integrated in a one-dimensional, multi-phase, and multi-component reactive transport solver. The model includes representations of bacterial and fungal activity, multiple archetypal polymeric and monomeric carbon substrate groups, aqueous chemistry, aqueous advection and diffusion, gaseous diffusion, and adsorption (and protection) and desorption from the soil mineral phase. The model predictions reasonably matched observed depth-resolved SOM and dissolved organic matter (DOM) stocks and fluxes, lignin content, and fungi to aerobic bacteria ratios. We performed a suite of sensitivity analyses under equilibrium and dynamic conditions to examine the role of dynamic sorption, microbial assimilation rates, and carbon inputs. To our knowledge, observations do not exist to fully test such a complicated model structure or to test the hypotheses used to explain observations of substantial storage of very old SOM below the rooting depth. Nevertheless, we demonstrated that a reasonable combination of sorption parameters, microbial biomass and necromass dynamics, and advective transport can match observations without resorting to an arbitrary depth-dependent decline in SOM turnover rates, as is often done. We conclude that, contrary to assertions derived from existing turnover time based model formulations, observed carbon content and Δ14C vertical profiles are consistent with a representation of SOM consisting of carbon compounds with relatively fast reaction rates, vertical aqueous transport, and dynamic protection on mineral surfaces.
Publisher: Springer Science and Business Media LLC
Date: 15-04-2020
DOI: 10.1038/S41477-020-0625-3
Abstract: Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.
Publisher: American Geophysical Union (AGU)
Date: 12-2019
DOI: 10.1029/2018MS001583
Location: United States of America
No related grants have been discovered for Jinyun Tang.