Understanding drivers and biases of simulated CO emissions from the INFERNO fire model over South America

Abstract

Integrating fire representation into climate models improves our understanding of ecosystem-fire-climate interactions by including connections between the carbon cycle and atmospheric composition. The Interactive Fires and Emissions algorithm for Natural Environments (INFERNO) is a new component of the UK Earth System Model (UKESM). Here, we evaluate carbon monoxide (CO) emissions from fires in South America as modelled by the INFERNO fire emissions model, which is coupled to the Joint UK Land Environment Simulator (JULES) in an offline configuration. Different satellite-based inventories were used for comparisons. To identify key factors driving simulated CO emissions and model-inventory biases, we use sensitivity experiments and a machine-learning approach. The findings indicate that INFERNO accurately capture the Arc of Deforestation in the southern Amazon as a primary source region of fire CO emissions, but it tends to overestimate these emissions by about 72 %. The simulated emission patterns in this region are largely determined by drought conditions and Plant Functional Type (PFT), particularly tree fractions. Aligned, the experiments show a 100 % increase in CO emissions in the southern Amazon region when using a drier meteorology dataset compared to the ERA5-based control run. In southern South America, INFERNO emissions, and in particular their seasonal cycle is affected by the tree PFT misrepresentation. The machine learning model explains 67 % of the model-inventory biases using only model inputs, highlighting room for improvement and the need to consider additional factors. The machine learning model identified soil moisture and tree PFT as major contributors to the model bias. Future model development should focus on improving the representation of fuel moisture, fuel load, and human activities (e.g., agriculture and deforestation) in the fire model.