Dataset Information

Global terrestrial carbon fluxes of 1999-2019 estimated by upscaling eddy covariance data with a random forest.

ABSTRACT: The terrestrial biosphere is a key player in slowing the accumulation of carbon dioxide in the atmosphere. While quantification of carbon fluxes at global land scale is important for mitigation policy related to climate and carbon, measurements are only available at sites scarcely distributed in the world. This leads to using various methods to upscale site measurements to the whole terrestrial biosphere. This article reports a product obtained by using a Random Forest to upscale terrestrial net ecosystem exchange, gross primary production, and ecosystem respiration from FLUXNET 2015. Our product covers land from -60°S to 80°N with a spatial resolution of 0.1°?×?0.1° every 10 days during the period 1999-2019. It was compared with four existing products. A distinguishable feature of our method is using three derived variables of leaf area index to represent plant functional type (PFT) so that measurements from different PFTs can be mixed better by the model. This product can be valuable for the carbon-cycle community to validate terrestrial biosphere models and cross check datasets.

SUBMITTER: Zeng J

PROVIDER: S-EPMC7518252 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Global terrestrial carbon fluxes of 1999-2019 estimated by upscaling eddy covariance data with a random forest.

Zeng Jiye J Matsunaga Tsuneo T Tan Zheng-Hong ZH Saigusa Nobuko N Shirai Tomoko T Tang Yanhong Y Peng Shushi S Fukuda Yoko Y

Scientific data 20200924 1

The terrestrial biosphere is a key player in slowing the accumulation of carbon dioxide in the atmosphere. While quantification of carbon fluxes at global land scale is important for mitigation policy related to climate and carbon, measurements are only available at sites scarcely distributed in the world. This leads to using various methods to upscale site measurements to the whole terrestrial biosphere. This article reports a product obtained by using a Random Forest to upscale terrestrial net ...[more]

PMID: 32973132

Similar Datasets

Project description:Wetlands represent the dominant natural source of methane (CH4) to the atmosphere. Thus, substantial effort has been spent examining the CH4 budgets of global wetlands via continuous ecosystem-scale measurements using the eddy covariance (EC) technique. Robust error characterization for such measurements, however, remains a major challenge. Here, we quantify systematic, random and gap-filling errors and the resulting uncertainty in CH4 fluxes using a 3.5 year time series of simultaneous open- and closed path CH4 flux measurements over a sub-boreal wetland. After correcting for high- and low frequency flux attenuation, the magnitude of systematic frequency response errors were negligible relative to other uncertainties. Based on three different random flux error estimations, we found that errors of the CH4 flux measurement systems were smaller in magnitude than errors associated with the turbulent transport and flux footprint heterogeneity. Errors on individual half-hourly CH4 fluxes were typically 6%-41%, but not normally distributed (leptokurtic), and thus need to be appropriately characterized when fluxes are compared to chamber-derived or modeled CH4 fluxes. Integrated annual fluxes were only moderately sensitive to gap-filling, based on an evaluation of 4 different methods. Calculated budgets agreed on average to within 7% (≤ 1.5 g - CH4 m-2 yr-1). Marginal distribution sampling using open source code was among the best-performing of all the evaluated gap-filling approaches and it is therefore recommended given its transparency and reproducibility. Overall, estimates of annual CH4 emissions for both EC systems were in excellent agreement (within 0.6 g - CH4 m-2 yr-1) and averaged 18 g - CH4 m-2 yr-1. Total uncertainties on the annual fluxes were larger than the uncertainty of the flux measurement systems and estimated between 7-17%. Identifying trends and differences among sites or site years requires that the observed variability exceeds these uncertainties.

Dataset Information

Global terrestrial carbon fluxes of 1999-2019 estimated by upscaling eddy covariance data with a random forest.

Publications

Global terrestrial carbon fluxes of 1999-2019 estimated by upscaling eddy covariance data with a random forest.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets