This file contains the datasets used in a manuscript published in JGR Biogeosciences (Nieberding, F., Wille, C., Ma, Y., Wang, Y., Maurischat, P., Lehnert, L., and Sachs, T.: Winter daytime warming and shift in summer monsoon increase plant cover and net CO2 uptake in a central Tibetan alpine steppe ecosystem, Journal of Geophysical Research: Biogeosciences, 126, e2021JG006441, doi:10.1029/2021JG006441, 2021.). The manuscript contains all the details on how the data was generated and processed and the corresponding code was published in the supplementary material.
Felix Nieberding, Felix Nieberding, 马耀明, Christian Wille, Christian Wille, Lukas Lehnert, Yuyang Wang, Philipp Maurischat, Weiqiang Ma, Torsten Sachs, Torsten Sachs
1) Data content (including elements and meanings): Gridded daily average air temperature of the Tibetan Plateau during 1980-2014 at 1-km resolution 2) Data source and processing method: Developed by integrating 8 types of reanalysis data (i.e., NNRP-2, 20CRV2c, JRA-55, ERA-Interim, MERRA2, CFSR, GLDAS and ERA5) downscaled with MODIS-estimated temperature lapse rates based on machine learing 3) Data quality description: According to leave-one-out validation based on stations, the average RMSE at China Adimistration Stations is about 1.7 ℃ and that at high-elevation field stations is about 1.9 ℃ 4) Data application results and prospects: This dataset can be used as air temperature input for driving long-term hydrologial modelling or evaluated for use in climate analysis
ZHANG Fan, ZHANG Hongbo
The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau.The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5)Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange.(6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling(NEE_fsd) as a measure for spatial and temporal variation.
Felix Nieberding, Felix Nieberding, 马耀明, Christian Wille, Christian Wille, Gerardo Fratini, Magnus Ole Asmussen, 王玉阳, 马伟强, Torsten Sachs, Torsten Sachs
The data include daily precipitation (Precip) amount and daily mean near-surface air temperature (T2M) over the Pan Third Pole region. The data is downscaled by using the Weather Research and Forecasting (WRF) model (3.7.1). The boundary and initial condition come from the fifth-generation global reanalysis product by the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5. The seasonal cycle and summer mean of precipitation over Tibet is well reproduced in comparison to the in situ observations.
Tinghai Ou
联系方式
中国科学院西北生态环境资源研究院
0931-4967287
poles@itpcas.ac.cn
关注我们
时空三极环境大数据平台 © 2018-2020
陇ICP备19003365号-4 | All Rights Reserved
｜ 
京公网安备11010502040845号
