资源环境科技发展态势分析平台

Changes in land and ocean carbon storage in response to elevated atmospheric carbon dioxide concentrations and associated climate change, known as the concentration-carbon and climate-carbon feedbacks, are principal controls on the response of the climate system to anthropogenic greenhouse gas emissions. Such feedbacks have typically been quantified in the context of natural ecosystems, but land management activities are also responsive to future atmospheric carbon and climate changes. Here we show that inclusion of such human-driven responses within an Earth system model shifts both the terrestrial concentration-carbon and climate-carbon feedbacks toward increased carbon storage. We introduce a conceptual framework for decomposing these changes into separate concentration-land cover, climate-land cover, and land cover-carbon effects, providing a parsimonious means to diagnose sources of variation across numerical models capable of estimating such feedbacks.

Plain Language Summary Estimating future changes to the Earth's climate requires an understanding of how carbon stored in vegetation and soils will respond to higher carbon dioxide in the atmosphere and changes in climate such as warmer temperatures and changes in precipitation. For instance, if plants and soils release more carbon, this will accelerate human-driven climate change, which is known as a positive feedback. Because climate change and higher atmospheric carbon dioxide will affect crop and forestry yields, we expect humans to alter their land management activities in the future, leading to greater or lesser storage of carbon in soils and vegetation. Higher crop yields could lead to less crop area globally and greater storage of carbon in forests and other natural vegetation. In this study, we introduce a method for quantifying such human influences on carbon storage, combining a model of land management with a model of atmospheric, land, and ecosystem processes. We find that both higher atmospheric carbon dioxide and climate change tend to reduce the footprint of human agriculture and therefore increase carbon storage on the land. Our method for quantifying such feedbacks provides a simple means to compare across models and identify areas of agreement or disagreement.