Andrew McRobert, Lund University
Improving the LPJ-GUESS modelled carbon balance with a marginal particle filter data assimilation technique
Coauthors
Marko Scholze, Ben SmithAbstract:
The recent increases in anthropogenic carbon dioxide (CO2) emissions have disrupted the equilibrium in the global carbon cycle pools with the ocean and terrestrial pools increasing their respective storage's to accommodate roughly half of the anthropogenic increase. Terrestrial ecosystem models (TEM) have been developed to quantify the modern carbon cycle changes. In this study, a marginal particle filter data assimilation technique has been used to calibrate the process parameters in the TEM model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator). The LPJ-GUESS model simulates individual plant function types (pft) as a competitive balance within high resolution forest patches. For each particle, the model output of NPP (Net Primary Productivity) is compared to eddy flux measurements from ICOS flux towers to minimize the cost function. A high-resolution regional carbon balance has been simulated for central Sweden using a network of several ICOS flux towers.