Attribution of seasonal leaf area index trends in the northern latitudes with "optimally" integrated ecosystem models

Abstract

Significant increases in remotely sensed vegetation indices in the northern latitudes since the 1980s have been detected and attributed at annual and growing season scales. However, we presently lack a systematic understanding of how vegetation responds to asymmetric seasonal environmental changes. In this study, we first investigated trends in the seasonal mean leaf area index (LAI) at northern latitudes (north of 30°N) between 1982 and 2009 using three remotely sensed long-term LAI data sets. The most significant LAI increases occurred in summer (0.009 m² m^-2 year^-1 , p < .01), followed by autumn (0.005 m² m^-2 year^-1 , p < .01) and spring (0.003 m² m^-2 year^-1 , p < .01). We then quantified the contribution of elevating atmospheric CO₂ concentration (eCO₂ ), climate change, nitrogen deposition, and land cover change to seasonal LAI increases based on factorial simulations from 10 state-of-the-art ecosystem models. Unlike previous studies that used multimodel ensemble mean (MME), we used the Bayesian model averaging (BMA) to optimize the integration of model ensemble. The optimally integrated ensemble LAI changes are significantly closer to the observed seasonal LAI changes than the traditional MME results. The BMA factorial simulations suggest that eCO₂ provides the greatest contribution to increasing LAI trends in all seasons (0.003-0.007 m² m^-2 year^-1 ), and is the main factor driving asymmetric seasonal LAI trends. Climate change controls the spatial pattern of seasonal LAI trends and dominates the increase in seasonal LAI in the northern high latitudes. The effects of nitrogen deposition and land use change are relatively small in all seasons (around 0.0002 m² m^-2 year^-1 and 0.0001-0.001 m² m^-2 year^-1 , respectively). Our analysis of the seasonal LAI responses to the interactions between seasonal changes in environmental factors offers a new perspective on the response of global vegetation to environmental changes.

Keywords: Bayesian model averaging; attribution; climate change; remote sensing; seasonal change; vegetation greening.