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Biogeosciences An interactive open-access journal of the European Geosciences Union
Biogeosciences, 14, 3685-3703, 2017
https://doi.org/10.5194/bg-14-3685-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Reviews and syntheses
09 Aug 2017
Reviews and syntheses: An empirical spatiotemporal description of the global surface–atmosphere carbon fluxes: opportunities and data limitations
Jakob Zscheischler1,2, Miguel D. Mahecha2,3,4, Valerio Avitabile5, Leonardo Calle6, Nuno Carvalhais2,7, Philippe Ciais8, Fabian Gans2, Nicolas Gruber9, Jens Hartmann10, Martin Herold5, Kazuhito Ichii11,12, Martin Jung2, Peter Landschützer9,13, Goulven G. Laruelle14, Ronny Lauerwald14,15, Dario Papale16, Philippe Peylin7, Benjamin Poulter6,17, Deepak Ray18, Pierre Regnier14, Christian Rödenbeck2, Rosa M. Roman-Cuesta5, Christopher Schwalm19, Gianluca Tramontana16, Alexandra Tyukavina20, Riccardo Valentini21, Guido van der Werf22, Tristram O. West23, Julie E. Wolf23, and Markus Reichstein2,3,4 1Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
2Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745 Jena, Germany
3German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany
4Michael Stifel Center Jena for Data-Driven and Simulation Science, 07743 Jena, Germany
5Wageningen University & Research, Laboratory of Geo-Information Science and Remote Sensing, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
6Institute on Ecosystems and Department of Ecology, Montana State University, Bozeman, MT 59717, USA
7CENSE, Departamento de Ciências e Engenharia do Ambiente, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal
8Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, 91191, Gif sur Yvette, France
9Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zurich, Switzerland
10Institute for Geology, CEN – Center for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany 55, 20146 Hamburg, Germany
11Department of Environmental Geochemical Cycle Research, Agency for Marine-Earth Science and Technology, Yokohama, Japan
12Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan
13Max Planck Institute for Meteorology, Bundesstr. 53, Hamburg, Germany
14Dept. Geoscience, Environment & Society (DGES), CP160/02, Université Libre de Bruxelles, 1050 Brussels, Belgium
15College of Engineering, Mathematics and Physical Sciences, University of Exeter, EX4 4QE Exeter, Devon, UK
16Department for Innovation in Biological, Agro-food and Forest systems (DIBAF), University of Tuscia, Viterbo, 01100, Italy
17NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD 20771, USA
18Institute on the Environment (IonE), University of Minnesota, Saint Paul, MN 55108, USA
19Woods Hole Research Center, Falmouth MA 02540, USA
20Department of Geographical Sciences, University of Maryland, College Park, MD, USA
21CMCC, Via A. Imperatore, 16, 73100, Lecce, Italy
22Faculty of Earth and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
23Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA
Abstract. Understanding the global carbon (C) cycle is of crucial importance to map current and future climate dynamics relative to global environmental change. A full characterization of C cycling requires detailed information on spatiotemporal patterns of surface–atmosphere fluxes. However, relevant C cycle observations are highly variable in their coverage and reporting standards. Especially problematic is the lack of integration of the carbon dioxide (CO2) exchange of the ocean, inland freshwaters and the land surface with the atmosphere. Here we adopt a data-driven approach to synthesize a wide range of observation-based spatially explicit surface–atmosphere CO2 fluxes from 2001 to 2010, to identify the state of today's observational opportunities and data limitations. The considered fluxes include net exchange of open oceans, continental shelves, estuaries, rivers, and lakes, as well as CO2 fluxes related to net ecosystem productivity, fire emissions, loss of tropical aboveground C, harvested wood and crops, as well as fossil fuel and cement emissions. Spatially explicit CO2 fluxes are obtained through geostatistical and/or remote-sensing-based upscaling, thereby minimizing biophysical or biogeochemical assumptions encoded in process-based models. We estimate a bottom-up net C exchange (NCE) between the surface (land, ocean, and coastal areas) and the atmosphere. Though we provide also global estimates, the primary goal of this study is to identify key uncertainties and observational shortcomings that need to be prioritized in the expansion of in situ observatories. Uncertainties for NCE and its components are derived using resampling. In many regions, our NCE estimates agree well with independent estimates from other sources such as process-based models and atmospheric inversions. This holds for Europe (mean ± 1 SD: 0.8 ± 0.1 PgC yr−1, positive numbers are sources to the atmosphere), Russia (0.1 ± 0.4 PgC yr−1), East Asia (1.6 ± 0.3 PgC yr−1), South Asia (0.3 ± 0.1 PgC yr−1), Australia (0.2 ± 0.3 PgC yr−1), and most of the Ocean regions. Our NCE estimates give a likely too large CO2 sink in tropical areas such as the Amazon, Congo, and Indonesia. Overall, and because of the overestimated CO2 uptake in tropical lands, our global bottom-up NCE amounts to a net sink of −5.4 ± 2.0 PgC yr−1. By contrast, the accurately measured mean atmospheric growth rate of CO2 over 2001–2010 indicates that the true value of NCE is a net CO2 source of 4.3 ± 0.1 PgC yr−1. This mismatch of nearly 10 PgC yr−1 highlights observational gaps and limitations of data-driven models in tropical lands, but also in North America. Our uncertainty assessment provides the basis for setting priority regions where to increase carbon observations in the future. High on the priority list are tropical land regions, which suffer from a lack of in situ observations. Second, extensive pCO2 data are missing in the Southern Ocean. Third, we lack observations that could enable seasonal estimates of shelf, estuary, and inland water–atmosphere C exchange. Our consistent derivation of data uncertainties could serve as prior knowledge in multicriteria optimization such as the Carbon Cycle Data Assimilation System (CCDAS) and atmospheric inversions, without over- or under-stating bottom-up data credibility. In the future, NCE estimates of carbon sinks could be aggregated at national scale to compare with the official national inventories of CO2 fluxes in the land use, land use change, and forestry sector, upon which future emission reductions are proposed.

Citation: Zscheischler, J., Mahecha, M. D., Avitabile, V., Calle, L., Carvalhais, N., Ciais, P., Gans, F., Gruber, N., Hartmann, J., Herold, M., Ichii, K., Jung, M., Landschützer, P., Laruelle, G. G., Lauerwald, R., Papale, D., Peylin, P., Poulter, B., Ray, D., Regnier, P., Rödenbeck, C., Roman-Cuesta, R. M., Schwalm, C., Tramontana, G., Tyukavina, A., Valentini, R., van der Werf, G., West, T. O., Wolf, J. E., and Reichstein, M.: Reviews and syntheses: An empirical spatiotemporal description of the global surface–atmosphere carbon fluxes: opportunities and data limitations, Biogeosciences, 14, 3685-3703, https://doi.org/10.5194/bg-14-3685-2017, 2017.
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Here we synthesize a wide range of global spatiotemporal observational data on carbon exchanges between the Earth surface and the atmosphere. A key challenge was to consistently combining observational products of terrestrial and aquatic surfaces. Our primary goal is to identify today’s key uncertainties and observational shortcomings that would need to be addressed in future measurement campaigns or expansions of in situ observatories.
Here we synthesize a wide range of global spatiotemporal observational data on carbon exchanges...
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