A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)

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. References: Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, https://doi.org/10.1111/j.1365-2486.2008.01606.x. Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731. Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020. Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, https://doi.org/10.5194/bg-11-1037-2014. Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x.本数据集涵盖了青藏高原中部高寒草原生态系统近15年的涡度协方差（eddy covariance）观测数据。所有数据均遵循标准化质量控制流程进行处理，以确保本数据集不同年度间以及与其他数据集之间的可比性。 为实现生态系统-大气交换通量的可靠估算，需严格执行以下校正流程与分析步骤： (1) 由于观测站点地处偏远，无法始终对涡度协方差（eddy covariance, EC）系统进行持续维护，因此传感器的清洁与校准工作只能不定期开展。此外，区域内裸土占比高且风速大，导致红外气体分析仪（infrared gas analyzer, IRGA）的测量光路中出现污垢堆积。传感器在该严苛环境下的安装，使得CO₂与H₂O气体密度观测产生了显著漂移。若未对该浓度偏差进行校正，将会扭曲碳吸收量的估算结果。我们采用了Fratini等人（2014）提出的改进型漂移校正流程：该方法不再依赖校准日期间的线性插值，而是以瓦里关大气本底台的CO₂浓度观测序列作为参考时间序列。 (2) 对计算得到的通量数据进行严格质量过滤，仅保留代表真实物理过程的通量值。 (3) 在长期观测期间，EC系统附近陆续修建了多座建筑物。我们评估了这些障碍物对湍流流场的影响，以识别出土地覆盖贡献存在不确定性的通量数据，并将其排除在后续计算之外。 (4) 参考Burba等人（2008）的方法，并结合Frank与Massman（2020）对原始方法的修订版本，针对低温环境下的仪器表面加热效应（以下简称传感器自热校正）计算了行业通用的校正值。 (5) 随后，我们采用Reichstein等人（2005）提出的经典且广泛应用的间隙填充流程，以完整呈现年度生态系统净CO₂交换量的变化特征。 (6) 我们通过两种方式估算通量不确定性：一是参考Finkelstein与Sims（2001）的方法计算随机通量误差（RE）；二是以间隙填充所用通量的标准差（NEE_fsd）作为时空变异的衡量指标。 参考文献： Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D. 与 Xu, L. (2008). 论仪器表面热交换对开路式气体分析仪CO₂通量观测的影响, 《全球变化生物学》, 14, 1854-1876, https://doi.org/10.1111/j.1365-2486.2008.01606.x. Finkelstein, P. L. 与 Sims, P. F. (2001). 涡度相关通量观测中的采样误差, 《地球物理研究杂志·大气卷》, 106, 3503–3509, doi:10.1029/2000JD900731. Frank, J. M. 与 Massman, W. J. (2020). 开路式红外气体分析仪自热校正的新视角, 《农业与森林气象学》, 290, 107986, doi:10.1016/j.agrformet.2020.107986. Fratini, G., McDermitt, D. K. 与 Papale, D. (2004). 气体浓度观测偏差导致的涡度协方差通量误差：成因、量化与校正, 《生物地球科学》, 11, 1037-1051, https://doi.org/10.5194/bg-11-1037-2014. Reichstein, M. 等 (2005). 生态系统净交换量分解为同化作用与生态系统呼吸的方法：综述与改进算法, 《全球变化生物学》, 11, 1424-1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x.