Wind regimes and their drivers in mountainous forests: collaborative observations by Qingyuan Ker Towers

Abstract

The thermal-induced wind regime is an important feature of mountain meteorology, which affects energy and scalar transports over complex terrains. Understanding wind regimes allows us to better interpret eddy covariance flux measurements and its data quality control. Due to a high spatial heterogeneity in wind features over complex terrains, single site-based measurement limits understanding of wind regimes. Here, we demonstrated wind regimes and their drivers using the Qingyuan Ker Towers (three towers in a valley: T1, mixed broadleaved forest; T2, Mongolian oak forest; T3, larch plantation forest) in mountainous forests of Northeast China.

In the daytime, down-slope winds dominated above the canopy at T1 and T3, while up-slope winds dominated at T2; in the nighttime, T1, T2 and T3 were dominated by down-valley, up-slope and down-slope winds, respectively. Along vertical gradients, different degrees of wind direction shears were observed, indicating frequently decoupling wind directions between above- and below-canopy. The profiles of wind speed at T1 and T2 were similar, showing a monotonical increase as height increases, whereas T3 showed an “S”-shaped profile with a secondary maximum in the trunk space. In general, the wind regimes did not exhibit traits of a typical thermal-induced wind circulation during the peak growing season. The similarity analysis suggested that the wind regimes were influenced by the strong background wind. Additionally, frequent cloudy weather with weak solar radiation is unfavorable to thermal-induced circulation. The wind regimes, thermal gradient and pattern of CO₂ concentration and flux jointly suggested that strong shallow drainage flows may occur frequently at T3, probably leading to an underestimation of net ecosystem exchange of CO₂ (NEE) during the nighttime, whereas drainage flows are expected to be weaker at T1 and T2. These analyses improve our understanding when NEE is reliably measured and provide an insight for correcting the flux data.