Modelling global-scale potential grassland changes in spatio-temporal patterns to global climate change

Grassland is one of the most widespread vegetation types worldwide and plays a significant role in global carbon cycling. Understanding the sensitivity of grassland to climate change and the effect of climate changes on the grassland ecosystems is a key issue in global carbon cycling. One of the goals of this study was to evaluate the three net primary productivity (NPP)climate models, i.e. the Miami model, the Schuur model and the classification indices-based model. Results indicated that the classification indices-based model was the most effective model at estimating large-scale grassland NPP. In this research, changes in the spatial pattern of global potential grassland from recent past (19502000) to future (20012050) A2a scenario were analysed with the integrated orderly classification system of grassland (IOCSG) approach in a Geographic Information System (GIS) environment. NPP was evaluated with the classification indices-based model. Results indicate that under recent past climatic conditions, the main parts of global grassland are the savanna and tundra and alpine grassland and will be converted into the savanna, steppe and semi-desert grassland in A2a scenario. As a whole, areas of grassland will increase by 31.76 million hectares. The classification indices-based model estimated a 12.40% increase of total NPP in grassland from recent past to A2a scenario. It will impose a new issue for future grassland researches to support sustainable development and to provide action relevant knowledge to meet the challenge of climate change.