Hydrological changes in the climate system from leaf responses to increasing CO2
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Vegetation is a major component of the climate system because of its controls on the energy and water balance over land. This functioning changes because of the physiological response of leaves to increased CO2. A climate model is used to compare these changes with the climate changes from radiative forcing by greenhouse gases. For this purpose, we use the Community Earth System Model coupled to a slab ocean. Ensemble integrations are done for current and doubled CO2. The consequent reduction of transpiration and net increase of surface radiative heating from reduction in cloudiness increases the temperature over land by a significant fraction of that directly from the radiative warming by CO2. Large-scale atmospheric circulation adjustments result. In particular, over the tropics, a low-level westerly wind anomaly develops associated with reduced geopotential height over land, enhancing moisture transport and convergence, and precipitation increases over the western Amazon, the Congo basin, South Africa, and Indonesia, while over mid-latitudes, land precipitation decreases from reduced evapotranspiration. On average, land precipitation is enhanced by 0.03 mm day−1 (about 19 % of the CO2 radiative forcing induced increase). This increase of land precipitation with decreased ET is an apparent negative feedback, i.e., less ET makes more precipitation. Global precipitation is slightly reduced. Runoff increases associated with both the increased land precipitation and reduced evapotranspiration. Examining the consistency of the variations among ensemble members shows that vegetation feedbacks on precipitation are more robust over the tropics and in mid to high latitudes than over the subtropics where vegetation is sparse and the internal climate variability has a larger influence.
KeywordsVegetation feedbacks Transpiration Precipitation Climate modeling Leaf physiology
This work is supported by grant DE-SC0002246 from the US Department of Energy. The Texas Advanced Computing Center (TACC) at The University of Texas at Austin is also acknowledged for providing the high performance computing resources for this work. Helpful comments from two anonymous reviewers improved the paper.
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