MACHINE LEARNING BASED BIAS CORRECTION FOR MODIS AEROSOL OPTICAL DEPTH IN BEIJING

Climate change worsens wildfires, but fire drivers are unclear and models run slowly. Simulating a 2023 Inner Mongolia grassland fire, we tested how weather and land cover affect simulation accuracy and found that wind controls burned area and spread distance, humidity drives spread rate and flame length, while fuel depth dominates burned area. In addition, computational optimizations cut runtime by 30%. These findings deepen understanding of fire mechanisms and improve model emergency response.

In this paper, we examined the inter-model differences among five Earth System Models in simulating the impact of aerosols on plant productivity. All models showed that the impact of human-made aerosols on global plant productivity was negative, but with the divergence in the amount of reduction. We found that the divergence was mostly caused by the parameterization of model in simulating canopy photosynthesis, which determines how strongly plants react to changes in climatic factors.

TanSat-2 is designed for global carbon monitoring, offering high-resolution dual-band observations of solar-induced chlorophyll fluorescence – a key indicator of photosynthesis. Simulations show its data processing can retrieve fluorescence with high accuracy. These results suggest TanSat-2 will enhance global tracking of the carbon cycle and vegetation health, providing valuable insights for climate change research.