Projections of leaf area index in earth system models
- 1Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA
- 2Department of Geography, University of California at Santa Barbara, Santa Barbara, CA 93106, USA
- 3Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80307, USA
- 4Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY 14853, USA
Abstract. The area of leaves in the plant canopy, measured as leaf area index (LAI), modulates key land–atmosphere interactions, including the exchange of energy, moisture, carbon dioxide (CO2), and other trace gases and aerosols, and is therefore an essential variable in predicting terrestrial carbon, water, and energy fluxes. Here our goal is to characterize the LAI projections from the latest generation of earth system models (ESMs) for the Representative Concentration Pathway (RCP) 8.5 and RCP4.5 scenarios. On average, the models project increases in LAI in both RCP8.5 and RCP4.5 over most of the globe, but also show decreases in some parts of the tropics. Because of projected increases in variability, there are also more frequent periods of low LAI across broad regions of the tropics. Projections of LAI changes varied greatly among models: some models project very modest changes, while others project large changes, usually increases. Modeled LAI typically increases with modeled warming in the high latitudes, but often decreases with increasing local warming in the tropics. The models with the most skill in simulating current LAI in the tropics relative to satellite observations tend to project smaller increases in LAI in the tropics in the future compared to the average of all the models. Using LAI projections to identify regions that may be vulnerable to climate change presents a slightly different picture than using precipitation projections, suggesting LAI may be an additional useful tool for understanding climate change impacts. Going forward, users of LAI projections from the CMIP5 ESMs evaluated here should be aware that model outputs do not exhibit clear-cut relationships to vegetation carbon and precipitation. Our findings underscore the need for more attention to LAI projections, in terms of understanding the drivers of projected changes and improvements to model skill.