Abstract
Abstract. FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles (R2>0.94 at 1∘ spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr−1 with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R2∼0.75) than with GPP from DGVMs (mean global pixel-wise R2∼0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R2>0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions (R2=0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.
Highlights
Upscaling local eddy covariance (EC) measurements (Baldocchi et al, 2001) from tower footprint to global wall-towall maps uses globally available predictor variables such as satellite remote sensing and meteorological data (Jung et al, 2011)
Our results suggest a high degree of cross-product consistency of global mean gross primary productivity (GPP) patterns (Fig. 2)
The FLUXCOM initiative generated a large ensemble of global carbon flux products for two defined setups that differ in the set of predictor variables and spatial–temporal resolution
Summary
Upscaling local eddy covariance (EC) measurements (Baldocchi et al, 2001) from tower footprint to global wall-towall maps uses globally available predictor variables such as satellite remote sensing and meteorological data (Jung et al, 2011) These forcing data are first used to establish empirical models for fluxes of interest at the site level and to estimate gridded fluxes by applying these models across all vegetated grid cells. Previous FLUXNET upscaling efforts using machine learning techniques (Beer et al, 2010; Jung et al, 2009, 2011) yielded global products that present a data-driven “bottom-up” perspective on carbon fluxes between the biosphere and the atmosphere These bottom-up products are complementary to process-based model simulations and “top-down” atmospheric inversions. These setups systematically vary machine learning and flux partitioning methods as well as forcing data sets to separate measured net ecosystem exchange (NEE) into gross primary productivity (GPP) and terrestrial ecosystem respiration (TER) (Jung et al, 2019; Tramontana et al, 2016)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.