Abstract

Carbon use efficiency (CUE) refers to the allocation of photosynthesized products by plants and is commonly used to measure primary production in terrestrial ecosystems. Despite the fact that the effects of climate variation on vegetation CUE have been studied at various spatial scales, there is still a lack of consensus as to how vegetation CUE responds to climate factors. The objective of this study was to evaluate the spatiotemporal dynamics of terrestrial CUE based on the latest versions of Moderate Resolution Imaging Spectroradiometer (MODIS) products during 2000–2018, and to reveal the dominant climate factor that controls vegetation CUE in different regions by using path analysis (PA). The results showed that global vegetation CUE increased significantly due to a more rapid increase in net primary productivity (NPP) than in gross primary productivity (GPP) over this period. Central Africa and South America are the two areas that experienced clear increases in CUE, and Amazon and northeastern India experienced decreases in vegetation CUE due to reduced precipitation. CUE increased significantly in the north frigid, the torrid, and the south temperate zones over this period. The largest areas showing vegetation CUE increases and decreases occurred in the north temperate zone. Rising precipitation lowered the vegetation CUE in the north frigid zone, but promoted CUE increase in the torrid and south temperate zones. Higher temperatures led to higher CUE in the north frigid and south temperate zones. Reduced CUE due to higher temperature mostly occurred in the arid zones. CUE in the cold/boreal zone remained relatively stable due to the synchronized variation of GPP and NPP. The divergent responses of terrestrial CUE to climate variations call for flexible ecosystem management to adapt to the future warming climate in different regions. The results also highlighted that a dynamic parameterization scheme of vegetation carbon allocation should be considered in simulating the terrestrial C cycle in Earth System Models.

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