Abstract
Abstract. Flux towers measure ecosystem-scale surface–atmosphere exchanges of energy, carbon dioxide and water vapour. The network of flux towers now encompasses ∼ 900 sites, spread across every continent. Consequently, these data have become an essential benchmarking tool for land surface models (LSMs). However, these data as released are not immediately usable for driving, evaluating and benchmarking LSMs. Flux tower data must first be transformed into a LSM-readable file format, a process which involves changing units, screening missing data and varying degrees of additional gap-filling. All of this often leads to an under-utilisation of these data in model benchmarking. To resolve some of these issues, and to help make flux tower measurements more widely used, we present a reproducible, open-source R package that transforms the FLUXNET2015 and La Thuile data releases into community standard NetCDF files that are directly usable by LSMs. We note that these data would also be useful for any other user or community seeking to independently quality control, gap-fill or use the FLUXNET data.
Highlights
Land surface models (LSMs) provide the lower boundary condition for climate and weather forecast models, simulating the exchange of carbon, water and energy fluxes between the soil, vegetation and the atmosphere (Pitman, 2003)
Flux towers are useful for modelling applications as they provide simultaneous observations of the meteorological data needed for forcing offline models as well as the key ecosystem variables against which models may be evaluated at time intervals similar to those used by LSMs, often over multiple years
In an effort to resolve some of these problems and to connect the flux tower researchers with the LSM researchers more strongly, we present the R package “FluxnetLSM” to facilitate the processing of FLUXNET datasets for use in LSMs
Summary
Land surface models (LSMs) provide the lower boundary condition for climate and weather forecast models, simulating the exchange of carbon, water and energy fluxes between the soil, vegetation and the atmosphere (Pitman, 2003). As LSMs must be provided with continuous meteorological forcing data, flux tower datasets require varying degrees of gap-filling of missing time steps This poses challenges for using these data for model evaluation and benchmarking. The available data overcome some of the limitations of raw eddy covariance measurements through significant post-processing and gap-filling These datasets cannot be employed directly by LSMs. Critically, not all FLUXNET data releases are provided with temporally continuous observations of all essential meteorological variables (e.g. precipitation and wind speed) for forcing LSMs. For example, across 155 FLUXNET2015 “FULLSET” open data policy (Tier 1) sites reporting half-hourly observations, most sites include gaps in rainfall and 77 % of the sites have missing air temperature observations with up to 61 % (median 5 %) of the time series missing despite this variable being nominally gap-filled. We describe the different functionalities of the package
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