A flux tower dataset tailored for land model evaluation

Eddy covariance flux towers measure the exchange of water, energy, and carbon fluxes between the land and atmosphere. They have become invaluable for theory development and evaluating land models. However, flux tower data as measured (even after site post-processing) are not directly suitable for land surface modelling due to data gaps in model forcing variables, inappropriate gap-filling, formatting, and varying data quality. Here we present a quality-control and data-formatting pipeline for tower data from FLUXNET2015, La Thuile, and OzFlux syntheses and the resultant 170-site globally distributed flux tower dataset specifically designed for use in land modelling. The dataset underpins the second phase of the Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER), an international model intercomparison project encompassing >20 land surface and biosphere models. The dataset is provided in the Assistance for Land-surface Modelling Activities (ALMA) NetCDF format and is CF-NetCDF compliant. For forcing land surface models, the dataset provides fully gap-filled meteorological data that have had periods of low data quality removed. Additional constraints required for land models, such as reference measurement heights, vegetation types, and satellite-based monthly leaf area index estimates, are also included. For model evaluation, the dataset provides estimates of key water, carbon, and energy variables, with the latent and sensible heat fluxes additionally corrected for energy balance closure. The dataset provides a total of 1040 site years covering the period 1992-2018, with individual sites spanning from 1 to 21 years. The dataset is available at http://doi.org/10.25914/5fdb0902607e1 (Ukkola et al., 2021).

Automated summary

This study presents a quality-control and data-formatting pipeline for flux tower data and provides a globally distributed dataset for land modeling. The dataset, including meteorological data, additional constraints for land models, and estimates of key variables, is available for model evaluation and benchmarking.