Biophysical vegetation responses to elevated atmospheric carbon dioxide (CO2) affect regional hydroclimate through two competing mechanisms. Higher CO2 increases leaf area (LAI), thereby increasing transpiration and water losses. Simultaneously, elevated CO2 reduces stomatal conductance and transpiration, thereby increasing rootzone soil moisture. Which mechanism dominates in the future is highly uncertain, partly because these two processes are difficult to explicitly separate within dynamic vegetation models. We address this challenge by using the GISS ModelE global climate model to conduct a novel set of idealized 2×CO2 sensitivity experiments to: evaluate the total vegetation biophysical contribution to regional climate change under high CO2; and quantify the separate contributions of enhanced LAI and reduced stomatal conductance to regional hydroclimate responses. We find that increased LAI exacerbates soil moisture deficits across the sub-tropics and more water-limited regions, but also attenuates warming by ∼0.5–1°C in the US Southwest, Central Asia, Southeast Asia, and northern South America. Reduced stomatal conductance effects contribute ∼1°C of summertime warming. For some regions, enhanced LAI and reduced stomatal conductance produce nonlinear and either competing or mutually amplifying hydroclimate responses. In northeastern Australia, these effects combine to exacerbate radiation-forced warming and contribute to year-round water limitation. Conversely, at higher latitudes these combined effects result in less warming than would otherwise be predicted due to nonlinear responses. These results highlight substantial regional variation in CO2-driven vegetation responses and the importance of improving model representations of these processes to better quantify regional hydroclimate impacts.
Bibliographical noteFunding Information:
B.I. Cook, R. Seager, and A.P. Williams were all supported for this work by the NASA Modeling, Analysis, and Prediction program (NASA #80NSSC17K0265). J.S. Mankin was supported by the NOAA Modeling, Analysis, Predictions and Projections program (NOAA MAPP NA20OAR4310414). M.D.K. acknowledge support from Australian Research Council (ARC) Centre of Excellence for Climate Extremes (CE170100023), the ARC Discovery Grants (DP190101823, DP180101253) and the NSW Research Attraction and Acceleration Program. Resources supporting this work were provided by the NASA High‐End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. The GISS ModelE code used here resides within the ModelE development repository and is available upon request from the corresponding author. Resources supporting this work were provided by the NASA High‐End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center.
© 2021. The Authors.