UC Berkeley

Evapotranspiration cools the land surface by consuming a large fraction of the net radiative energy at the surface. In forested regions, trees actively control the rate of transpiration by modulating stomatal conductance in response to environmental conditions, and species with different stomatal dynamics can affect the atmosphere in distinct ways. Using principal component analysis (PCA) and Markov chain Monte Carlo (MCMC) parameter estimation with direct, tree-level measurements of water use, we show that Douglas-firs (Pseudotsuga menziesii), a common evergreen needleleaf tree species in the Northern California Coast Range, decrease their transpiration sharply in the summer dry season in response to a dry root zone; and in contrast, broadleaf evergreen tree species, especially Pacific madrones (Arbutus menziesii), transpire maximally in the summer dry season because their transpiration is much less sensitive to a dry root zone and increases continually in response to increasing atmospheric evaporative demand. We scale up these tree-level observations to construct a bottom-up estimate of regional transpiration, and we use these regional estimates along with atmospheric models, one simple and one comprehensive, to quantify the potential impact of species transpiration differences on regional summertime climate. The atmospheric models suggest that these species differences in transpiration could affect the well-mixed atmospheric boundary layer temperature and humidity by 1-1.5 degrees C and 1 g/kg, respectively, and near-surface temperature and humidity by 1.5-2.5 degrees C and 2-3 g/kg, respectively. We further investigate the sensitivity of California climate to evapotranspiration by estimating the sensitivity of wind energy forecasts at a California wind farm to regional-scale perturbations in soil moisture using a regional atmospheric model. These tests show that forecasts at this particular farm are most sensitive to soil moisture in the Central Valley, and that changes in soil moisture on the order of previously-demonstrated errors in atmospheric reanalyses lead to wind energy forecast errors of 1-3 m/s, which can translate to differences in forecasted wind energy of 15-40% of a wind farm’s maximum rated power.