Reviews and syntheses: An empirical spatiotemporal description of the global surface–atmosphere carbon fluxes: opportunities and data limitations
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 c...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-08-01
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| Series: | Biogeosciences |
| Online Access: | https://www.biogeosciences.net/14/3685/2017/bg-14-3685-2017.pdf |
| Summary: | 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 (CO<sub>2</sub>) 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 CO<sub>2</sub> 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 CO<sub>2</sub> 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
CO<sub>2</sub> 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<sup>−1</sup>,
positive numbers are sources to the atmosphere), Russia
(0.1 ± 0.4 PgC yr<sup>−1</sup>), East Asia
(1.6 ± 0.3 PgC yr<sup>−1</sup>), South Asia
(0.3 ± 0.1 PgC yr<sup>−1</sup>), Australia
(0.2 ± 0.3 PgC yr<sup>−1</sup>), and most of the Ocean regions. Our NCE
estimates give a likely too large CO<sub>2</sub> sink in tropical areas such as the
Amazon, Congo, and Indonesia. Overall, and because of the overestimated
CO<sub>2</sub> uptake in tropical lands, our global bottom-up NCE amounts to a net
sink of −5.4 ± 2.0 PgC yr<sup>−1</sup>. By contrast, the accurately
measured mean atmospheric growth rate of CO<sub>2</sub> over 2001–2010 indicates
that the true value of NCE is a net CO<sub>2</sub> source of
4.3 ± 0.1 PgC yr<sup>−1</sup>. This mismatch of nearly 10 PgC yr<sup>−1</sup>
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
<i>p</i>CO<sub>2</sub> 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 CO<sub>2</sub> fluxes in the
land use, land use change, and forestry sector, upon which future emission
reductions are proposed. |
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| ISSN: | 1726-4170 1726-4189 |