On the impact of transport model errors for the estimation of CO2 surface fluxes from GOSAT observations

A series of observing system simulation experiments is presented in which column averaged dry air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing SATellite (GOSAT) are made consistent or not with the transport model embedded in a flux inversion system. The GOSAT observations improve the random errors of the surface carbon budget despite the inconsistency. However, we find biases in the inferred surface CO2 budget of a few hundred MtC/a at the subcontinental scale, that are caused by differences of only a few tenths of a ppm between the simulations of the individual XCO2 soundings. The accuracy and precision of the inverted fluxes are little sensitive to an 8-fold reduction in the data density. This issue is critical for any future satellite constellation to monitor XCO2 and should be pragmatically addressed by explicitly accounting for transport errors in flux inversion systems

The added value of satellite observations of methane for understanding the contemporary methane budget

Surface observations have recorded large and incompletely understood changes to atmospheric methane (CH(4)) this century. However, their ability to reveal the responsible surface sources and sinks is limited by their geographical distribution, which is biased towards the northern midlatitudes. Data from Earth-orbiting satellites designed specifically to measure atmospheric CH(4) have been available since 2009 with the launch of the Japanese Greenhouse gases Observing SATellite (GOSAT). We assess the added value of GOSAT to data collected by the US National Oceanic and Atmospheric Administration (NOAA), which have been the lynchpin for knowledge about atmospheric CH(4) since the 1980s. To achieve that we use the GEOS-Chem atmospheric chemistry transport model and an inverse method to infer a posteriori flux estimates from the NOAA and GOSAT data using common a priori emission inventories. We find the main benefit of GOSAT data is from its additional coverage over the tropics where we report large increases since the 2014/2016 El Niño, driven by biomass burning, biogenic emissions and energy production. We use data from the European TROPOspheric Monitoring Instrument to show how better spatial coverage and resolution measurements allow us to quantify previously unattainable diffuse sources of CH(4), thereby opening up a new research frontier. This article is part of a discussion meeting issue ‘Rising methane: is warming feeding warming? (part 1)’

Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal

Tropical ecosystems are large carbon stores that are vulnerable to climate change. The sparseness of ground-based measurements has precluded verification of these ecosystems being a net annual source (+ve) or sink (-ve) of atmospheric carbon. We show that two independent satellite data sets of atmospheric carbon dioxide (CO2), interpreted using independent models, are consistent with the land tropics being a net annual carbon emission of [Formula: see text] [Formula: see text] and [Formula: see text] petagrams (PgC) in 2015 and 2016, respectively. These pan-tropical estimates reflect unexpectedly large net emissions from tropical Africa of [Formula: see text] PgC in 2015 and [Formula: see text] PgC in 2016. The largest carbon uptake is over the Congo basin, and the two loci of carbon emissions are over western Ethiopia and western tropical Africa, where there are large soil organic carbon stores and where there has been substantial land use change. These signals are present in the space-borne CO2 record from 2009 onwards

Large Chinese land carbon sink estimated from atmospheric carbon dioxide data

Limiting the rise in global mean temperatures relies on reducing carbon dioxide (CO2) emissions and on the removal of CO2 by land carbon sinks. China is currently the single largest emitter of CO2, responsible for approximately 27 per cent (2.67 petagrams of carbon per year) of global fossil fuel emissions in 20171. Understanding of Chinese land biosphere fluxes has been hampered by sparse data coverage2–4, which has resulted in a wide range of a posteriori estimates of flux. Here we present recently available data on the atmospheric mole fraction of CO2, measured from six sites across China during 2009 to 2016. Using these data, we estimate a mean Chinese land biosphere sink of −1.11 ± 0.38 petagrams of carbon per year during 2010 to 2016, equivalent to about 45 per cent of our estimate of annual Chinese anthropogenic emissions over that period. Our estimate reflects a previously underestimated land carbon sink over southwest China (Yunnan, Guizhou and Guangxi provinces) throughout the year, and over northeast China (especially Heilongjiang and Jilin provinces) during summer months. These provinces have established a pattern of rapid afforestation of progressively larger regions5,6, with provincial forest areas increasing by between 0.04 million and 0.44 million hectares per year over the past 10 to 15 years. These large-scale changes reflect the expansion of fast-growing plantation forests that contribute to timber exports and the domestic production of paper7. Space-borne observations of vegetation greenness show a large increase with time over this study period, supporting the timing and increase in the land carbon sink over these afforestation regions

Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003-2016

The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. Annual mean CO2 growth rates have been determined from satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2, for the years 2003 to 2016. The XCO2 growth rates agree with National Oceanic and Atmospheric Administration (NOAA) growth rates from CO2 surface observations within the uncertainty of the satellite-derived growth rates (mean difference +/- standard deviation: 0.0 +/- 0.3 ppm year(-1);R: 0.82). This new and independent data set confirms record-large growth rates of around 3 ppm year(-1) in 2015 and 2016, which are attributed to the 2015-2016 El Nino. Based on a comparison of the satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Nino Southern Oscillation (ENSO) indices, we estimate by how much the impact of ENSO dominates the impact of fossil-fuel-burning-related emissions in explaining the variance of the atmospheric CO2 growth rate. Our analysis shows that the ENSO impact on CO2 growth rate variations dominates that of human emissions throughout the period 2003-2016 but in particular during the period 2010-2016 due to strong La Nina and El Nino events. Using the derived growth rates and their uncertainties, we estimate the probability that the impact of ENSO on the variability is larger than the impact of human emissions to be 63 % for the time period 2003-2016. If the time period is restricted to 2010-2016, this probability increases to 94%

Consistent regional fluxes of CH4 and CO2 inferred from GOSAT proxy XCH4 : XCO2 retrievals, 2010–2014

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 (CH4) and carbon dioxide (CO2) directly from GOSAT retrievals of XCH4:XCO2, using sparse ground-based CH4 and CO2 mole fraction data to anchor the ratio. This work builds on previously reported theory that takes advantage that: (1) these ratios are less prone to systematic error than either the full physics data products or the proxy CH4 data products; and (2) the resulting CH4 and CO2 fluxes are self-consistent. We show that a posteriori fluxes inferred from the GOSAT data generally outperform the fluxes inferred only from in situ data, as expected. GOSAT CH4 and CO2 fluxes are consistent with global growth rates for CO2 and CH4 reported by NOAA, and with a range of independent data including in particular new profile measurements (0–7 km) over the Amazon basin that were collected specifically to help validate GOSAT over this geographical region. We find that large-scale multi-year annual a posteriori CO2 fluxes inferred from GOSAT data are similar to those inferred from the in situ surface data but with smaller uncertainties, particularly over the tropics. GOSAT data are consistent with smaller peak-to-peak seasonal amplitudes of CO2 than either a priori or the in situ inversion, particularly over the tropics and the southern extra-tropics. Over the northern extra-tropics, GOSAT data show larger uptake than the a priori but less than the in situ inversion, resulting in small net emissions over the year. We also find evidence that the carbon balance of tropical South America was perturbed following the droughts of 2010 and 2012 with net annual fluxes not returning to an approximate annual balance until 2013. In contrast, GOSAT data significantly changed the a priori spatial distribution of CH4 emission with a 40 % increase over tropical South America and tropical Asia and smaller decrease over Eurasia and temperate South America. We find no evidence from GOSAT that tropical South American CH4 fluxes were dramatically affected by the two large-scale Amazon droughts. However, we find that GOSAT data are consistent with double seasonal peaks in fluxes that are reproduced over the five years we studied: a small peak in January to April and a larger peak in June to October, which is likely due to superimposed emissions from different geographical regions

The Significance of Fast Radiative Transfer for Hyperspectral SWIR XCO2 Retrievals

Fast radiative transfer (RT) methods are commonplace in most algorithms which retrieve the column-averaged dry-mole fraction of carbon dioxide (XCO2) in the Earth’s atmosphere. These methods are required to keep the computational effort at a manageable level and to allow for operational processing of tens of thousands of measurements per day. Without utilizing any fast RT method, the involved computation times would be one to two orders of magnitude larger. In this study, we investigate three established methods within the same retrieval algorithm, and for the first time, analyze the impact of the fast RT method while keeping every other aspect of the algorithm the same. We perform XCO2 retrievals on measurements from the OCO-2 instrument and apply quality filters and parametric bias correction. We find that the central 50% of scene-by-scene differences in XCO2 between retrieval sets, after threshold filtering and bias correction, that use different fast RT methods, are less than 0.40 ppm for land scenes, and less than 0.11 ppm for ocean scenes. Significant regional differences larger than 0.3 ppm are observed and further studies with larger samples and regional-scale subsets need to be undertaken to fully understand the impact on applications that utilize space-based XCO2