ERA-interim reanalysis debiased at FLUXNET sites

(preliminary) Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 sites registered and up to 250 of them sharing data (Free Fair Use dataset). Many modelling groups use the FLUXNET dataset for evaluating ecosystem model's performances but it requires uninterrupted time series for the meteorological variables used as input. Because original in-situ data often contain gaps, from very short (few hours) up to relatively long (some months), we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-interim) and high temporal resolution spanning from 1989 to today. These data are however not measured at site level and for this reason a method to downscale and correct the ERA-interim data is needed. We apply this method on the level 4 data (L4) from the LaThuile collection, freely available after registration under a Fair-Use policy. The performances of the developed method vary across sites and are also function of the meteorological variable. On average overall sites, the bias correction leads to cancel from 10% to 36% of the initial mismatch between in-situ and ERA-interim data, depending of the meteorological variable considered. In comparison to the internal variability of the in-situ data, the root mean square error (RMSE) between the in-situ data and the un-biased ERA-I data remains relatively large (on average overall sites, from 27% to 76% of the standard deviation of in-situ data, depending of the meteorological variable considered). The performance of the method remains low for the Wind Speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.（初步研究阶段）陆面与大气之间的碳、水和能量交换，在生态系统尺度上通过涡度协方差（eddy covariance）技术进行监测。目前，FLUXNET数据库已收录超过500个注册站点，其中多达250个站点共享其观测数据（免费合理使用数据集）。诸多建模团队依托FLUXNET数据集开展生态系统模型性能评估工作，但该类研究要求作为模型输入的气象变量需具备连续时间序列。由于原始原位观测（in-situ）数据往往存在数据缺失，缺失时长从数小时到数月不等，因此本研究开发了一种全新且鲁棒性强的方法，用于填补站点尺度下气象观测数据的缺失值。本方法的优势在于可利用全球连续的ERA-interim再分析数据，该数据集的时间分辨率较高，时间跨度为1989年至今。但该数据并非站点尺度的原位观测数据，因此需要开发一套方法对ERA-interim数据进行降尺度与校正。我们将该方法应用于LaThuile数据集集中的第四级（Level 4, L4）数据，该数据集需完成注册后，可依据合理使用政策免费获取。所提方法的性能随站点不同存在差异，同时也取决于所涉及的气象变量类型。针对所有站点的平均情况而言，偏差校正可使原位观测数据与ERA-interim数据之间的初始偏差抵消10%至36%，具体比例取决于所考虑的气象变量。相较于原位观测数据的内部变率，原位观测数据与经无偏校正后的ERA-I数据之间的均方根误差（root mean square error, RMSE）仍然相对较大——针对所有站点的平均情况而言，该误差范围为原位观测数据标准差的27%至76%，具体比例取决于所考虑的气象变量。该方法在风速场的校正性能仍有待提升，尤其是在保留与FLUXNET站点实测值相近的标准差方面表现欠佳。