Reviews and syntheses: An empirical spatiotemporal description of the global surface-atmosphere carbon fluxes: opportunities and data limitations

Type Article
Date 2017-08
Language English
Author(s) Zscheischler JakobORCID1, 2, Mahecha Miguel D.2, 3, 4, Avitabile Valerio5, Calle LeonardoORCID6, 7, Carvalhais NunoORCID2, 8, Ciais Philippe9, Gans Fabian2, Gruber NicolasORCID10, Hartmann JensORCID11, Herold MartinORCID, Ichii KazuhitoORCID12, 13, Jung Martin2, Landschuetzer Peter10, 14, Laruelle Goulven G.15, Lauerwald RonnyORCID15, 16, Papale DarioORCID17, Peylin PhilippeORCID8, Poulter BenjaminORCID6, 7, 18, Ray DeepakORCID19, Regnier Pierre15, Roedenbeck Christian2, Roman-Cuesta Rosa M.5, Schwalm Christopher20, Tramontana Gianluca17, Tyukavina Alexandra21, Valentini Riccardo22, Van Der Werf Guido23, West Tristram O.24, Wolf Julie E.24, Reichstein MarkusORCID2, 3, 4
Affiliation(s) 1 : Swiss Fed Inst Technol, Inst Atmospher & Climate Sci, Univ Str 16, CH-8092 Zurich, Switzerland.
2 : Max Planck Inst Biogeochem, Hans Knoll Str 10, D-07745 Jena, Germany.
3 : German Ctr Integrat Biodivers Res iDiv, Deutsch Pl 5E, D-04103 Leipzig, Germany.
4 : Michael Stifel Ctr Jena Data Driven & Simulat Sci, D-07743 Jena, Germany.
5 : Wageningen Univ & Res, Lab Geo Informat Sci & Remote Sensing, Droevendaalsesteeg 3, NL-6708 PB Wageningen, Netherlands.
6 : Montana State Univ, Inst Ecosyst, Bozeman, MT 59717 USA.
7 : Montana State Univ, Dept Ecol, Bozeman, MT 59717 USA.
8 : Univ Nova Lisboa, Fac Cienc & Tecnol, Dept Cienc & Engn Ambiente, CENSE, Caparica, Portugal.
9 : CEA, CNRS, UVSQ, Lab Sci Climat & Environnement, F-91191 Gif Sur Yvette, France.
10 : Swiss Fed Inst Technol, Inst Biogeochem & Pollutant Dynam, Zurich, Switzerland.
11 : Univ Hamburg, CEN, Ctr Earth Syst Res & Sustainabil, Inst Geol, Germany 55, D-20146 Hamburg, Germany.
12 : Agcy Marine Earth Sci & Technol, Dept Environm Geochem Cycle Res, Yokohama, Kanagawa, Japan.
13 : Natl Inst Environm Studies, Ctr Global Environm Res, Tsukuba, Ibaraki, Japan.
14 : Max Planck Inst Meteorol, Bundesstr 53, Hamburg, Germany.
15 : Univ Libre Bruxelles, DGES, CP160-02, B-1050 Brussels, Belgium.
16 : Univ Exeter, Coll Engn Math & Phys Sci, Exeter EX4 4QE, Devon, England.
17 : Univ Tuscia, Dept Innovat Biol Agro Food & Forest Syst DIBAF, I-01100 Viterbo, Italy.
18 : Biospher Sci Lab, Goddard Space Flight Ctr, NASA, Greenbelt, MD 20771 USA.
19 : Univ Minnesota, Inst Environm IonE, St Paul, MN 55108 USA.
20 : Woods Hole Res Ctr, Falmouth, MA 02540 USA.
21 : Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA.
22 : CMCC, Via A Imperatore 16, I-73100 Lecce, Italy.
23 : Vrije Univ Amsterdam, Fac Earth & Life Sci, Amsterdam, Netherlands.
24 : Pacific Northwest Natl Lab, Joint Global Change Res Inst, College Pk, MD USA.
Source Biogeosciences (1726-4170) (Copernicus Gesellschaft Mbh), 2017-08 , Vol. 14 , N. 15 , P. 3685-3703
DOI 10.5194/bg-14-3685-2017
WOS© Times Cited 25
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 pCO(2) 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.

Full Text
File Pages Size Access
Publisher's official version 19 1 MB Open access
Supplement 7 500 KB Open access
Preprint 32 2 MB Open access
Supplement to the preprint 6 458 KB Open access
Top of the page

How to cite 

Zscheischler Jakob, Mahecha Miguel D., Avitabile Valerio, Calle Leonardo, Carvalhais Nuno, Ciais Philippe, Gans Fabian, Gruber Nicolas, Hartmann Jens, Herold Martin, Ichii Kazuhito, Jung Martin, Landschuetzer Peter, Laruelle Goulven G., Lauerwald Ronny, Papale Dario, Peylin Philippe, Poulter Benjamin, Ray Deepak, Regnier Pierre, Roedenbeck Christian, Roman-Cuesta Rosa M., Schwalm Christopher, Tramontana Gianluca, Tyukavina Alexandra, Valentini Riccardo, Van Der Werf Guido, West Tristram O., Wolf Julie E., Reichstein Markus (2017). Reviews and syntheses: An empirical spatiotemporal description of the global surface-atmosphere carbon fluxes: opportunities and data limitations. Biogeosciences, 14(15), 3685-3703. Publisher's official version : https://doi.org/10.5194/bg-14-3685-2017 , Open Access version : https://archimer.ifremer.fr/doc/00661/77322/