Global Latitudinal-Asymmetric Vegetation Growth Trends and Their Driving Mechanisms: 1982–2009

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2013-03, Vol.5 (3), p.1484-1497
• Main Authors: Mao, Jiafu, Shi, Xiaoying, Thornton, Peter, Hoffman, Forrest, Zhu, Zaichun, Myneni, Ranga
• Format: Article
• Language: English
• Subjects: Carbon dioxide; Climate change; CLM4; detection and attribution study; evaluation; factorial simulation; global vegetation growth trend; LAI; Land use; Remote sensing; Surface temperature; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs5031484

Using a recent Leaf Area Index (LAI) dataset and the Community Land Model version 4 (CLM4), we investigated percent changes and controlling factors of global vegetation growth for the period 1982 to 2009. Over that 28-year period, both the remote-sensing estimate and model simulation show a significant increasing trend in annual vegetation growth. Latitudinal asymmetry appeared in both products, with small increases in the Southern Hemisphere (SH) and larger increases at high latitudes in the Northern Hemisphere (NH). The south-to-north asymmetric land surface warming was assessed to be the principal driver of this latitudinal asymmetry of LAI trend. Heterogeneous precipitation functioned to decrease this latitudinal LAI gradient, and considerably regulated the local LAI change. A series of factorial experiments were specially-designed to isolate and quantify contributions to LAI trend from different external forcings such as climate variation, CO2, nitrogen deposition and land use and land cover change. The climate-only simulation confirms that climate change, particularly the asymmetry of land temperature variation, can explain the latitudinal pattern of LAI change. CO2 fertilization during the last three decades was simulated to be the dominant cause for the enhanced vegetation growth. Our study, though limited by observational and modeling uncertainties, adds further insight into vegetation growth trends and environmental correlations. These validation exercises also provide new quantitative and objective metrics for evaluation of land ecosystem process models at multiple spatio-temporal scales.