To get a deep understand of the carbon cycle of Tibetan lakes, the CO₂ flux over Ngoring Lake (NL), a large freshwater lake in the source region of the Yellow River, was monitored from 2011 to 2013 using an eddy covariance system. Based on the monthly mean CO₂ flux, NL was a significant sink of atmospheric CO₂ in 2012, with the greatest negative (downward) CO₂ flux of −0.83 (±0.27) g C m⁻² d⁻¹ occurring in October. While in 2011 and 2013, NL was in quasi‐equilibrium with atmospheric CO₂. The process controlling the daily variation in the CO₂ flux was investigated using statistical analysis. The daily mean of the surface wind speed was significantly correlated with the observed daily CO₂ flux. Under the assumption that the vertical gradient in the CO₂ concentration near the water surface varied slowly, this correlation confirms the rationality of using the surface wind to express the gas transfer velocity. Following this consideration, because the thermal contrast between NL and the surrounding grassland was also significantly correlated with the daily CO₂ flux, a new parameterization scheme of k₆₀₀ is suggested in this study. Potential mechanisms for the coupling process between the surface wind and the thermal contrast of the lake are discussed. Based on the surface energy balance analysis, the heat storage rate of the lake played different roles in the CO₂ fluxes during the three years, depending on the CO₂ saturation of the lake water. The process controlling the monthly mean CO₂ flux was also investigated.

The planetary boundary layer height (PBLH) is a crucial indicator reflecting the region of the atmosphere characterized by continuous turbulence. Here, we use radiosonde and surface meteorological observations (4–7 times per day, year‐round measurements) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to derive the PBLH (PBLH_MOSAiC), and further evaluate the PBLH from the ERA5 reanalysis (PBLH_ERA5). Comparisons between PBLH_MOSAiC and PBLH_ERA5 from different perspectives reveal that: (a) The overestimation of PBLH_ERA5 when the sea ice concentration is >90% is significant with the centered root mean squared error reaching up to 201 m; (b) The difference between the two products is notably pronounced in cold seasons, while it is comparatively diminished in warm seasons; (c) In neutral boundary layers, differences in PBLH_ERA5 are larger compared with stable and convective boundary layers. In addition, the analysis of error sources indicates that the bias of PBLH_ERA5 is sensitive to the bias of vertical thermal structure and wind speed profiles in ERA5 data sets in all conditions. Finally, we find a Random Forest model effectively reduces the bias of PBLH_ERA5 with the index of agreement reaching up to 0.71 in the test data set, while a multiple linear regression demonstrates comparable performance to the Random Forest model.

Turbulent motions in the Arctic stable boundary layer are characterized by intermittency, but they are rarely investigated due to limited observations, in particular over the sea‐ice surface. In the present study, we explore the characteristics of turbulent intermittency over the Arctic sea‐ice surface using data collected during the Multidisciplinary drifting Observation for the Study of Arctic Climate expedition from October 2019 to September 2020. We first develop a new algorithm, which performs well in identifying the spectral gap over the Arctic sea‐ice surface. Then the characteristics of intermittency are investigated. It is found that the strength of intermittency increases under the conditions of light surface wind speed, small surface wind speed gradient, and strong surface air temperature gradient. The momentum flux, sensible heat flux, and latent heat flux calculated by raw eddy‐covariance fluctuations are overestimated by 3%, 10%, and 24%, respectively, because submesoscale motions are included. Furthermore, the characteristics of the atmospheric boundary layer structure under various intermittency conditions reveal that strong low‐level jets are favorable to surface turbulent motions that result in weak intermittency, while strong temperature inversions above the surface layer suppress surface turbulent motions and lead to strong intermittency.