ERA-interim reanalysis debiased at FLUXNET sites@en

(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）陆地表面与大气之间的碳、水和能量交换，在生态系统尺度上通过涡度协方差技术（eddy covariance technique）进行监测。目前，FLUXNET数据库已注册站点超过500个，其中多达250个站点共享其数据（免费合理使用数据集（Free Fair Use dataset））。当前诸多建模团队常使用FLUXNET数据集评估生态系统模型的性能，但该数据集要求作为输入的气象变量需具备连续时间序列。然而原始原位观测数据往往存在数据缺失，缺失时长从数小时到数月不等。为此，我们开发了一种全新且鲁棒的方法，用于填补站点尺度下观测的气象数据中的缺失值。本方法的优势在于可利用全球覆盖的连续气象数据（ERA-interim），该数据时间分辨率较高，覆盖时段为1989年至今。但此类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（LaThuile collection）的四级（L4）数据，该数据集需经注册后，可依据合理使用政策免费获取。所开发方法的性能因站点而异，同时也随气象变量的不同而变化。针对所有站点的平均情况而言，偏差校正可消除原位观测数据与ERA-interim数据之间初始偏差的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的ERA-interim数据之间的均方根误差（root mean square error, RMSE）仍然相对较大：在所有站点的平均水平下，该误差为原位观测数据标准差的27%至76%，具体比例同样取决于所考虑的气象变量。该方法在风速场的表现相对较差，尤其是在保留与FLUXNET站点实测值相近的标准差方面，其能力有待提升。