Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate-carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2exchange with the atmosphere across biomes and continents are lacking. Here we present ...
The global changes are marked by alteration on the normal patterns of important biochemical and biophysical processes of the Earth. However, the real effects as well as the feedbacks of the global changes over vegetation are still unclear. Part of this uncertainty can be attributed to the inattention of stakeholders and scientists towards vegetation and its complex interrelations with the environment, which dri...
Anthropogenic changes to land cover (LCC) remain common, but continuing land scarcity promotes the widespread intensification of land management changes (LMC) to better satisfy societal demand for food, fibre, fuel and shelter1. The biophysical effects of LCC on surface climate are largely understood2,3,4,5, particularly for the boreal6 and tropical zones7, but fewer studies have investigated the biophysical co...
Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate-carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2exchange with the atmosphere across biomes and continents are lacking. Here we present ...
Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate–carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2 exchange with the atmosphere across biomes and continents are lacking. Here we present...
We analyze the impacts of the steady state assumption on inverse model parameter retrieval from biogeochemical models. An inverse model parameterization study using eddy covariance CO2 flux data was performed with the Carnegie Ames Stanford Approach (CASA) model under conditions of strict and relaxed carbon cycle steady state assumption (CCSSA) in order to evaluate both the robustness of the model’s structure f...