# _atmosphere_
## 2 Materials and Methods
### Data Collection and Processing  
Subsequent to raw high-frequency data collection, half-hour CO\({}_{2}\) fluxes were computed from spike removal, two-dimensional coordinate rotation, time-lag compensation, and the Webb-Pearman-Leuning (WPL) 'Burba correction' for density fluctuations because of the self-heating effect [30] using Eddypro 7.0.6 (Li-Cor, Lincoln, NE, USA). The calculated CO\({}_{2}\) fluxes were screened to improve data quality by removal of a quality flag with \"2\" [31] and outliers (beyond 4.5 standard deviations) in a 10-day window, and by filtering against lower turbulence (threshold nighttime friction velocity was 0.15 m s\({}^{-1}\)). The annual valid data coverage was -40%, with -70% and -20% in the daytime and nighttime, respectively, comparable to data reported for ChinaFLUX [32] and other alpine sites [22]. The CO\({}_{2}\) flux data were gap-filled by a machine learning algorithm of boosted regression trees (BRT) [33], which was conducted using a dataset with a valid flux subset and corresponding routine meteorological subset, including air temperature, atmospheric water vapor, net radiation, wind velocity, and topsoil temperature. Machine learning algorithms were found to outperform other conventional gap-filling techniques by incorporating all of the main environmental controls simultaneously [34; 35]. Because of the distinctly different processes governing CO\({}_{2}\) exchanges between growing season daytime (Growing: the simultaneous occurrence of plant photosynthesis and ecosystem respiration) and nongrowing season all-time and growing season nighttime (Nongrowing: only ecosystem respiration occurred), we filled data gaps separately. Firstly, boosted regression trees were fitted using the Growing valid dataset (BRT-growing) and the Nongrowing valid dataset (BRT-nongrowing), respectively. Secondly, the fitted BRT-growing and BRT-nongrowing, respectively, were used to fill data gaps with corresponding meteorological variables. After several comparisons, tree complexity, learning rate, and number of trees in BRT were fixed at 5, 0.005, and 5000, respectively.  
Figure 1: The seasonal variations in precipitation (**a**), air temperature (\(T_{\mathrm{A}}\)), (**b**), net radiation (\(R_{\mathrm{n}}\)), (**c**), topsoil temperature (\(T_{\mathrm{s}}\)), (**d**), atmospheric water vapor (Vapor), (**e**) and enhanced vegetation index (EVI), (**f**) from 2018 to 2019 at the two alpine wetlands.  
The growing season daytime ecosystem respiration was estimated from the nongrowing-BRT and extrapolated based on the daytime meteorological variables. Therefore, daily ecosystem respiration (RES) is the sum of daytime respiration and nocturnal respiration. Daily GPP equals the values that daily RES minus daily NEE.  
The enhanced vegetation index (EVI, MYD13Q1) was adopted as a metric for capturing the relationship between CO\({}_{2}\) fluxes and biotic variables in alpine grasslands [35; 36; 37]. The 8-day EVI was flux tower-centric at 500 \(\times\) 500 m spatial resolution and was obtained from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, [[https://modis.ornl.gov/globalsubset/](https://modis.ornl.gov/globalsubset/)]([https://modis.ornl.gov/globalsubset/](https://modis.ornl.gov/globalsubset/))). Half-hour values of CO\({}_{2}\) fluxes and meteorological variables were aggregated into an 8-day scale to match the temporal resolution of the EVI.