New study proposing gap-filling ensemble for robust GHG budgets based on eddy flux data

In a new study by Antje Lucas-Moffat and co-authors a generic method for gap filling eddy-covariance data is presented. Eddy flux measurements come with unavoidable gaps in their time series as some basic assumptions are not always fulfilled, for example in low-turbulence nights. To be able to determine longer term budgets of certain compounds, these gaps must be filled. There are a number of different methods, particularly for CO₂. However, every method inherently comes with an uncertainty and so far there is no consensus about a method that is supposed to be the best one to use.

The here presented approach claims that using an ensemble of all commonly used methods outscores the performance of solely using one specific method. This was tested by creating artificial gaps in valid measured time series by comparing the gap-filled with the original data.

As probably not every user has the full set of methods available at any time, an online tool will be posted in the Carbon Portal of ICOS. The orignal code of the tool is already available, a user-friendly interface is soon to come.

Two further extremely useful features of the tool:

• Provision of uncertainties (both random uncertainty from the measurements and systematic uncertainty from the gap filling)
• No limitation to CO₂; the tool can be used for a variety of GHG and air pollutants; some examples are given in the paper for CH₄, NH₃, and total N_r

Link to the article: https://www.sciencedirect.com/science/article/pii/S016819232200301X

Link to the web tool: https://meta.icos-cp.eu/objects/sgrzQkYlaKjOA2SgGsKDJj9h