Improvements to Simulating the Carbon Cycle in Land Surface Models
thesisposted on 05.08.2019 by Beth A Drewniak
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
Earth System Models (ESMs) are the tools we use to experiment on the Earth, understand processes that drive climate, explore climate response to forcing, and project future climate. In order to use ESMs more effectively, we must identify the important components of biological systems that drive productivity and soil carbon storage that models currently lack. My research focuses on improving elements related to the land component of those models, or Land Surface Models (LSMs). By way of an introduction, I will begin with a review of some observed processes that models may have difficulty with: the non-additive effects of co-occurring stressors. I will discuss the effects of nitrogen deposition and drought on vegetation as they relate to productivity, particularly focusing on carbon uptake and partitioning. I will then highlight several areas of model development that may help the model capture vegetation response to increasing nitrogen deposition and drought. I applied two of those suggestions in the Energy Exascale Earth System Land Model (E3SM): dynamic roots and dynamic allocation. I integrated a dynamic root function in the E3SM Land Model (ELM) such that the vertical distribution of fine roots can respond to the water and nitrogen needs of the plant. Next, I addressed allocation partitioning by adding an economic-type production function to ELM to maximize NPP by distributing biomass between leaves and stems for optimum carbon and nitrogen uptake. Finally, I evaluate the impact of humans in another LSM, the Community Land Model, by investigating the impacts of management practices of cultivation on soil carbon stocks. Although broad ranging, each model development piece contributes to a model’s ability to simulate the carbon cycle. Furthermore, each development suggests additional model focus areas that can further improve the model.