We use the GEOS-Chem global 3-D model of atmospheric chemistry and transport
and an ensemble Kalman filter to simultaneously infer regional fluxes of
methane (CH
Atmospheric growth of the two most abundant non-condensable greenhouse gases
(GHGs), carbon dioxide (CO
Inferring CO
Space-borne observations of short-wave IR (SWIR) that are sufficiently
precise to detect small changes in lower tropospheric CO
We build on previous work that developed a novel approach to estimate
simultaneously regional CO
In the next section, we describe the ensemble Kalman filter approach, the
observations we use to infer the CO
We develop an existing EnKF framework that has been used to estimate CO
In the ensemble Kalman filter framework, the prior flux error covariance
The Kalman gain matrix
Where possible, we use consistent emission inventories for CO
Top panel indicates the geographic basis functions used in our CO
We define the pulse-like basis functions (Eq. 1) guided by the TransCom-3 regions (Gurney et al., 2002), with each continental region further divided equally into four subregions. Figure 1 shows the 44 land regions and 11 ocean regions that we use in this study; in comparison, Fraser et al. (2014) used 11 land regions and 1 ocean region. We describe the inversion on these smaller geographic regions to help reduce aggregation errors associated with fluxes being estimated on a coarse spatial resolution (Patra et al., 2005).
We distinguish CO
We assume an a priori uncertainty of 60 % for the coefficients
corresponding to the natural CO
We assimilated GOSAT XCH
We also assimilate CO
To determine the importance of the ratio data, we run twin sets of experiments: (1) “ratio” experiments that include the GOSAT data and the in situ data sets, and (2) “in situ” experiments that use only the in situ surface data.
We use independent observations of atmospheric CO
TCCON is a global network of ground-based Fourier transform spectrometer (FTS) instruments that measure,
among other compounds, the total atmospheric columns of CO
We also use aircraft measurements from four projects to evaluate our a
posteriori model concentrations: (1) data collected during experiments 1–5
from the HIAPER pole-to-pole observations (HIPPO) that provide latitude–altitude cross sections of
tropospheric mole fractions of CO
Figure 2 shows that the in situ only and the ratio inversions result in
similar annual net CO
Annual mean (2010–2014, inclusive) regional net fluxes of (top)
CO
Figure 3 and Table 1 compare the time series of the prior and posterior
global net CO
A priori and a posterior estimates of the annual net CO
The net monthly CO
Figure 3 also shows that the monthly a posteriori flux estimates by the in
situ and ratio inversions are similar over the northern landmasses (Fig. 1), with the exception of the summer in 2014 when the ratio inversion shows
significantly smaller uptake. Over the tropical landmasses, a posteriori
fluxes from the ratio inversion show a much smaller seasonal cycle, with
exception of boreal summer months in 2014 when these fluxes have larger
uptake. In general, uncertainties for the monthly fluxes inferred by the
ratio inversion (GOSAT plus in situ data) are smaller (up to 30 %) than
using only the in situ data. This reflects the poor spatial coverage of the
current in situ observing network particularly over tropical ecosystems
(Fig. 1). Over the southern landmasses, the a posteriori fluxes for the two
inversions are similar and typically within their uncertainties. We find
that both inversions show a gradual reduction in the peak-to-trough
amplitude, which appears to support a similar downward trend in the a priori
estimates from about 9.0 GtC a
Figure 2 shows that a priori and the a posteriori global annual net CH
The same as Fig. 3 but for CH
Figure 4 shows that, at the global scale, the monthly a posteriori fluxes
inferred from the ratio and in situ inversions have larger seasonal
variations than the a priori: a typical seasonal minimum of about 450 Mt a
GOSAT XCH
In general, the ratio inversion shows the best agreement with independent
CH
The same as Table 1 but for CH
Here, we focus on tropical South America (Fig. 1) for three reasons.
First, in situ surface data are particularly sparse over this geographical
region, including two sites (Fig. 5) over which we use the observed
CO
Figure 6 shows that the a posteriori monthly CH
The same as Fig. 3 but for CO
Table 3 shows that the a posteriori annual fluxes inferred by the ratio inversion are significantly larger than the in situ inversion in 2010, 2011, and 2012 by about 0.7, 0.4, and 0.5 GtC, respectively. A posteriori fluxes from the ratio inversion show net emissions are smaller in 2013 and 2014 than in 2010 or 2012, which is due to larger uptake in the dry season and smaller emissions in the wet seasons (Fig. 6). This result reveals the continental-scale impact of the severe droughts in 2010 and 2012 over tropical Southern America. Our result for 2010 is consistent with recent studies based on regional-scale AMAZONICA aircraft observations (Gatti et al., 2014; van der Laan-Luijkx et al., 2015; Alden et al., 2016). The in situ inversion fails to reproduce this increase in net emissions during the 2010 dry season, instead showing a large uptake (Fig. 6).
The same as Table 1 but for CH
Monthly mean partial CO
The same as Fig. 7 but for comparison of the monthly mean partial
CH
A posteriori CH
Figures 7 and 8 show that a posteriori fluxes from the ratio inversion
generally decrease the mean model difference against independent AMAZONICA
and ACO aircraft observations of CO
Building on the previously reported theory, we simultaneously inferred regional
CO
We showed that a posteriori fluxes inferred from the GOSAT data generally
outperformed the fluxes inferred only from in situ data, as expected given
their greater measurement coverage. GOSAT CH
We found that large-scale multi-year annual a posteriori CO
We showed that GOSAT data results in significant changes with respect to a
priori spatial distribution of CH
While the sensitivity of our results to model error and to the temporal and
spatial resolution of fluxes requires further investigation, our analysis,
in the wider context of other studies, supports the adoption of using
space-borne observations of CO
The University of Leicester GOSAT Proxy XCH
We use independent observations to evaluate the a posteriori model concentrations that correspond to the flux estimates, acknowledging limitations associated with sparse observation coverage and atmospheric transport model errors (Chevallier et al., 2014). We sample the GEOS-Chem atmospheric chemistry transport at the time and location of each individual observation.
Differences between observed and
(Top) HIPPO-3 (Wofsy et al., 2011), May 2010, and a posteriori
model partial columns of (left) CO
Figures A1 and A2 show that the ratio inversion is marginally more
consistent with HIPPO XCO
The ratio and in situ inversions show similar spatial structure to HIPPO
XCH
Figure A2 shows that the two a posteriori models reproduce the hemispheric
CO
Monthly means CARIBIC and a posteriori model (left) CO
Figure A3 shows that the two a posteriori models reproduce the observed
annual trend of CO
Figure A4 shows that the two a posteriori models have a similar level of
agreement with 24 independent TCCON XCO
Mean multi-year statistics (2010–2014) of the differences between
TCCON (top) XCO
L. Feng and P. I. Palmer designed the experiments and wrote the paper; H. Bösch, R. J. Parker, and Alex Webb provided the GOSAT XCO
The authors declare that they have no conflict of interest.
Work at the University of Edinburgh was partly funded by the NERC National
Centre for Earth Observation (NCEO) and the European Space Agency Climate
Change Initiative (ESA-CCI). P. I. Palmer gratefully acknowledges funding
from the NCEO and his Royal Society Wolfson Research Merit Award. NCEO and
the European Space Agency Climate Change Initiative funded work at the
University of Leicester. H. Bösch and R. J. Parker are supported by the ESA Climate Change
Initiative (ESA-CCI). R. J. Parker was also funded by an ESA Living Planet
Fellowship. We thank NERC and FAPESP for their joint funding of the
Amazonian Carbon Observatory Project (NERC reference NE/J016284/1). M.
Gloor was financially supported by the NERC consortium grant AMAZONICA
(NE/F005806/1) which we also thank for providing access to additional
aircraft profiles. The TCCON network is supported by NASA's Carbon Cycle
Science Program through a grant to the California Institute of Technology.
The TCCON stations from Bialystok, Orleans, and Bremen are supported by the
EU projects InGOS, GAIA-CLIM, and ICOS-INWIRE, and by the Senate of Bremen.
The TCCON station at Sodankylä is supported by the EU project GAIA-CLIM.
N. Deutscher is supported by an Australian Research Council –
Discovery Early Career Researcher Award (DE140100178). TCCON measurements at
Eureka were made by the Canadian Network for Detection of Atmospheric
Composition Change (CANDAC) with additional support from the Canadian Space
Agency. TCCON operation at the Tsukuba and Rikubetsu sites is supported in part
by the GOSAT project. Works by J. Wang and Y. Liu are funded by
Helmholtz–CAS joint research groups (HCJRG-307). We also thank the HIPPO
team for their observations (
The authors would like to thank the two anonymous reviewers for their insightful comments, which helped to improve the manuscript significantly. Edited by: R. Müller Reviewed by: two anonymous referees