Implications of overestimated anthropogenic CO₂ emissions on East Asian and global land CO₂ flux inversion

Abstract

Measurement and modelling of regional or country-level carbon dioxide (CO₂) fluxes are becoming critical for verification of the greenhouse gases emission control. One of the commonly adopted approaches is inverse modelling, where CO₂ fluxes (emission: positive flux, sink: negative flux) from the terrestrial ecosystems are estimated by combining atmospheric CO₂ measurements with atmospheric transport models. The inverse models assume anthropogenic emissions are known, and thus the uncertainties in the emissions introduce systematic bias in estimation of the terrestrial (residual) fluxes by inverse modelling. Here we show that the CO₂ sink increase, estimated by the inverse model, over East Asia (China, Japan, Korea and Mongolia), by about 0.26 PgC year⁻¹ (1 Pg = 10¹² g) during 2001–2010, is likely to be an artifact of the anthropogenic CO₂ emissions increasing too quickly in China by 1.41 PgC year⁻¹. Independent results from methane (CH₄) inversion suggested about 41% lower rate of East Asian CH₄ emission increase during 2002–2012. We apply a scaling factor of 0.59, based on CH₄ inversion, to the rate of anthropogenic CO₂ emission increase since the anthropogenic emissions of both CO₂ and CH₄ increase linearly in the emission inventory. We find no systematic increase in land CO₂ uptake over East Asia during 1993–2010 or 2000–2009 when scaled anthropogenic CO₂ emissions are used, and that there is a need of higher emission increase rate for 2010–2012 compared to those calculated by the inventory methods. High bias in anthropogenic CO₂ emissions leads to stronger land sinks in global land–ocean flux partitioning in our inverse model. The corrected anthropogenic CO₂ emissions also produce measurable reductions in the rate of global land CO₂ sink increase post-2002, leading to a better agreement with the terrestrial biospheric model simulations that include CO₂-fertilization and climate effects.

Background

In order to combat global and regional climate change, efforts are being continued by the United Nations Framework Convention on Climate Change (UNFCCC) since the Kyoto Protocol (1996). The Paris Agreement (2015) during the 21st conference of parties (COP21; www.un.org/sustainabledevelopment/cop21), called for Intended Nationally Determined Contributions (INDCs) to greenhouse gases emission reduction to limit global warming below 2 °C and as close to 1.5 °C as possible. However, the measure of INDCs varies by country, e.g. China, the largest emitter of anthropogenic CO₂ by fossil fuel consumption and cement production (FFC), targets to achieve ambitious 33.8% lower CO₂ emissions per unit of gross domestic product (GDP) in 2014 than the 2005 level, and the CO₂ emissions per GDP will be at 60% of the 2005 level by 2030 (http://unfccc.int/focus/indc_portal/items/8766.php). On the other hand, developed nations declare straightforward reduction targets in total greenhouse gases (GHGs) emissions, e.g. the United States commits to a reduction of total GHG emission of 26–28% in 2025 compared to the 2005 levels, and Japan aims to reduce total GHG emissions by about 26% by 2030 compared to a base level of 2005–2013. To the best of our knowledge, no independent emission tracking system with sufficient accuracy exists today that can be employed for monitoring, reporting and verification (MRV) of the claimed emission reductions.

These committed emission reduction targets and their reference years demand for country-specific MRV systems that is supported by the national statistics and independent methods. The independent methods, such as the inverse modelling, suffer from lack of spatially and temporally uniform measurements, and from uncertainties in transport models (Gurney et al. 2002; Patra et al. 2006; Peylin et al. 2013). The other issue in inverse modelling is the separation of sources and sinks into component fluxes. Because the inversion system is ill-constrained by observations, only one component of the anthropogenic or terrestrial CO₂ fluxes can be optimized in state-of-the-art inverse models. The inverse models traditionally assume the anthropogenic CO₂ emission as a known quantity, which is calculated from better known industrial indicators, e.g. the GDP, FFC and energy intensity—energy consumed per unit of GDP. The terrestrial CO₂ exchange is lesser known compared to emissions due to FFC because of sparse inventories from forestry, unpredictable human intervention on land-use change, poor knowledge of environmental impacts on biospheric health, etc. If inverse modelling is adopted as one of the MRV systems for evaluating the progress of the Paris Agreement and INDCs, no assumption on the uncertainties in anthropogenic CO₂ emissions would be permitted.

Over the years, we have found it difficult to quantify uncertainties in FFC CO₂ emissions (Andres et al. 2012; Olivier et al. 2014), and the inverse modelling community is well informed about some of the interference on flux inversions (Gurney et al. 2005; Peylin et al. 2011). Clearly the maximum uncertainty in FFC CO₂ emissions is found for China (Guan et al. 2012; Liu et al. 2015; Korsbakken et al. 2016). A biased higher or lower FFC CO₂ emission will lead to artificially stronger (weaker) biospheric CO₂ sink over a given land region. In the recent times, the inverse model results are post-processed by applying a FFC CO₂ emission correction term (Peylin et al. 2013; Thompson et al. 2016). This method is a good approximation when biases in assumed FFC CO₂ emission only influence land CO₂ flux of the same region, but this is probably not the case because our regional fluxes are poorly constrained by observational data (Saeki et al. 2017; Thompson et al. 2016). However, the effect of high/low bias in the rate of FFC CO₂ emission increase on the decadal variations of inversion estimated terrestrial CO₂ uptake has not been discussed in detail (Patra et al. 2016a). Because the model transport biases do not vary significantly from year to year, our inversion systems can better constrain the interannual/decadal variation in land fluxes compared to their magnitude, provided an accurate estimation of FFC CO₂ emissions is available.

Any wrong assumption on the growth rate of a priori FFC CO₂ emission is compensated by the land source or sink in such a way that detection of bias in a priori emission remained thus far elusive by validation approaches using independent aircraft CO₂ observations. This situation is different for species with predominantly terrestrial source, e.g. CH₄, because the total emission can be well estimated by inverse model (atmospheric sink is parameterized separately and do not affect regional source inversion for the species with lifetime of several years). Patra et al. 2016b have used aircraft measurements over Sendai, Japan, to validate CH₄ emissions and emission increase rates for the East Asia region, and suggested that the rate of anthropogenic CH₄ emission increase should be only at 59% of that is estimated for 2002–2010 by the Emission Database for Global Atmospheric Research (EDGAR42FT; Olivier et al. 2014). About 75% of FFC CO₂ emissions and up to 40% of anthropogenic CH₄ emissions are caused due to the coal/oil industry (mining and burning), which have produced 82 and 72% of the increase in their emissions, respectively, in the period 2002–2010 (EDGAR42FT; http://edgar.jrc.ec.europa.eu/overview.php?v=42FT2010). Since the increase rate of CH₄ emissions over China is closely related to that of CO₂ emissions, we apply the CH₄ emission increase rate for correcting CO₂ emission increase rate.

In this study, we have used results from three inversion cases, simulated using varied FFC CO₂ emissions, to illustrate the impacts of assumptions on a priori emissions on the estimated land CO₂ fluxes. The rate of a priori FFC CO₂ emission increase is then scaled by the reduced emission increase rate determined by CH₄ inversion. We show that the application of revised rate of FFC CO₂ emission increase leads to (1) no significant increase in CO₂ sink over the East Asia region after 2002, and (2) global land CO₂ sink increase agree better with that simulated by the global dynamic vegetation models (DGVMs). Implications of correct magnitude of FFC CO₂ emissions on land–ocean partitioning of global CO₂ sinks are also discussed.

Methods

We have used FFC CO₂ emission maps from the (1) CDIAC: emissions of top-20 countries from CDIAC (Boden et al. 2016) distributed using EDGAR4 emission maps (2010 emission maps repeated for the latter years), (2) CARBONES: a project of the European Union (I. van der Laan-Luijkx, personal communication, 2015; as in Thompson et al. 2016; see also www.carbones.eu/wcmqs/project/ccdas/#Fossil%20Fuel), and (3) CARBONES emission maps, but scaled with the IEA (International Energy Agency) emissions for South Asia, East Asia, Southeast Asia and rest of the world (as in Thompson et al. 2016; referred to as IEA). The global total FFC CO₂ emissions were 6.9, 6.8 and 6.6 PgC year⁻¹ in 2002, and 9.4, 9.1 and 8.5 PgC year⁻¹ in 2011, respectively, for the CDIAC, CARBONES and IEA inventories. CO₂ inversions are performed for 2001–2012 to optimize fluxes from 84 regions of the globe using the JAMSTEC’s atmospheric chemistry-transport model (ACTM) and CO₂ observations from 66 sites taken from GLOBALVIEW-CO₂ (2013) data products (Ref. Saeki et al. 2017 for more details; Thompson et al. 2016). The region divisions of 84-region inverse model and location of CO₂ measurement sites are depicted in Fig. 1. Apart from the FFC CO₂ emissions, the net ecosystem exchange (NEE) of terrestrial biosphere is taken from Carnegie-Ames-Stanford Approach (CASA) terrestrial biosphere model (Randerson et al. 1997), and oceanic exchanges are taken from Takahashi et al. (2009). ACTM inversion results for 2002–2011 are presented here after discarding the first and last year of inversions as spin-up and spin-down, respectively.

Fig. 1

Maps of CO₂ inversion regions (54 land and 30 ocean; shaded) and atmospheric observation network (circle) with mostly surface sites, except for the Japan AirLines (JAL) flight track between Tokyo and Sydney/Brisbane. The snow-covered (zero flux) Antarctica and Greenland are not optimized by the inversion system. The CH₄ inverse model solves for emissions from 53 land only regions (1 region less in the Southeast Asia; ref. Patra et al. 2016b)

Similarly, CH₄ inversions are performed for 53-land regions only, using ACTM forward simulations and atmospheric data from 37 sites (see Patra et al. 2016b for details). The regional CH₄ emissions for the East Asia region are taken as the mean of their five ensemble cases, which have passed the post-inversion validation test using independent aircraft measurements by Tohoku Univ (Umezawa et al. 2014). The inversion ensemble members are based on different a priori emission scenarios. The EDGAR42FT inventory, used as a priori, suggested an increase of 23.4 Tg-CH₄ emissions from East Asia, which is contributed entirely by the anthropogenic emission increase rate in China. The validation exercise using independent aircraft measurement over Sendai by Tohoku University permitted an increase of about 16.1 Tg-CH₄ during the period of 2002–2012. For a robust estimation of a posteriori to a priori emissions scaling factor, we applied linear fits to the data (Fig. 2a). Here we will apply a scaling factor of 0.59 (=1.53/2.61; slopes of the linear fits for a posteriori and a priori emissions in Fig. 2a) to FFC CO₂ emission “increase rate” for the period 2003–2014, relative to the emissions for 2002 from CDIAC inventory, using the slopes of the fitted lines to the East Asian CH₄ emission time series. A comparison of East Asian total CH₄ emissions with the anthropogenic CH₄ emissions from China suggests the dominating role of China for recent emission increases over the East Asia region. The application of this CH₄-inversion-derived scaling factor to the CO₂ emission increase rate assumes constant CH₄/CO₂ emission ratio over the periods of our analyses, and is deemed valid as per the linearity maintained in anthropogenic emission inventories of CO₂ and CH₄ over the period of 1970–2012 (EDGAR4; Fig. 2b).

Fig. 2

a Comparisons of CH₄ inversion results (black: a priori; blue: a posteriori) for the East Asia (EA) region with the EDGAR42FT estimated anthropogenic emissions for China. The linear fits to the annual mean values are shown as lines, with slopes the fits marked. b Linear relationship of anthropogenic CO₂ and CH₄ emissions for China over the period of 1970–2012 (Olivier et al. 2014). The inter-decadal values are marked by text and red circles

We have used DGVM model simulated CO₂ fluxes from the Trends and drivers of the regional-scale sources and sinks of carbon dioxide (TRENDY) project, covering the period of 1990–2012. The DGVMs account for the effect of CO₂ fertilization and climate variations (simulation case S2; Sitch et al. 2015). Inverse model estimated ensemble mean CO₂ fluxes for East Asia for the period of 1993–2012 are taken from Thompson et al. (2016).

Results and discussion

This topic of discussion is most relevant for the East Asia region because the global FFC CO₂ emission increase rate in the 2000s is mainly driven by the industrial CO₂ emission from China (about 65% of 2.17 PgC year⁻¹ increase in global emission during 2001–2010). Because our inverse model does not well constrain CO₂ fluxes from China alone, due to lack of measurement sites within the source regions (Fig. 1), we aggregated six inverse model regions to discuss the change in fluxes over East Asia in this study over the past 2 decades.

Figure 3 shows the effect of different FFC CO₂ emission a priori on the estimation of residual land CO₂ fluxes estimated by the 84-region inverse model for the East Asia region. We find the FFC CO₂ emissions as per the CDIAC inventory method are always higher compared to the CARBONES and IEA inventories, varying from 0.49 PgC in 2002 to a maximum of 0.76 in 2009 for CDIAC–IEA. The CDIAC–CARBONES differences decrease from a maximum of 0.47 in 2003 to 0.12 PgC in 2011 (Fig. 3a). A fairly compensatory land CO₂ fluxes are estimated for the East Asia region with the interannual variations (IAVs) being opposite in phase. The mean (± ¹⁻ σ standard deviations for the IAVs) differences of 0.59 ± 0.08 and 0.37 ± 0.12 PgC in FFC CO₂ emissions lead to a mean uptake bias over the East Asia by −0.40 ± 0.10 and −0.21 ± 0.08 PgC, respectively, for the 2002–2011 period. This suggests about 60–67% of the FFC CO₂ emission bias is transferred to land uptake increase for the East Asia region, and the rest of 33–40% of FFC CO₂ bias affects inverse model results for other regions.

Fig. 3

Time series of differences in FFC CO₂ emission cases (CARBONES and IEA) relative to the CDIAC case (a), and the resulting differences in land CO₂ sinks estimated by 84-region inverse model (b) for East Asia (black line: CDIAC–CARBONES; red line: CDIAC–IEA)

It would have been ideal to validate the a posteriori land CO₂ fluxes using aircraft (independent) observations that are not used in the inversion system, as in the case of inverted CH₄ emissions using vertical profiles over Sendai, Japan (Patra et al. 2016b). Such validation has also been attempted in Thompson et al. (2016), but large model–model differences in annual mean land fluxes for the East Asia region could not be separated using aircraft observations over Korea and Japan. Both the ACTM and MACC models with East Asian land fluxes of −0.94 and −0.44 PgC year⁻¹, respectively, for 2008–2011 well-simulated the CO₂ concentration within the planetary boundary layer and vertical gradients between 1 and 4 km (Thompson et al. 2016; their Fig. 2). Unlike CH₄, which has sources predominantly on the Earth’s surface (a global surface sink of ~27 Tg-CH₄ year⁻¹, compared to ~550 Tg-CH₄ year⁻¹ of emission), CO₂ has very strong source-sink variations over the land regions (Fig. 4). The annual mean biases for FFC CO₂ emissions or land fluxes are much smaller than the seasonal variations in land CO₂ exchange. In addition, our inversion system optimizes land-CO₂ fluxes for a given FFC CO₂ emission, where the inverted land fluxes can compensate for biases in an a priori FFC CO₂ emission. Such a compensatory effect helps to simulate observed CO₂ concentrations from aircrafts for very different source (FFC) and sink (land biosphere) combinations.

Fig. 4

Monthly time variations of FFC CO₂ emissions and land CO₂ fluxes as simulated by the a priori model (CASA; annually balanced to no net fluxes) and that estimated by the inverse model for East Asia. Broken lines are for 12-month running mean CASA (green) and inversion (blue) fluxes

Figure 5 shows the quickest FFC CO₂ emission increase in China is seen during 2003–2006 coincides well with the worsening emission intensity (CO₂ emission per GDP produced), as used in preparation of EDGAR emission inventories (Olivier et al. 2014). The FFC CO₂ emissions increase from China alone explains about 62% (1.72 out of 2.62 PgC year⁻¹) global FFC emission increase in the period of 2002–2011 (Fig. 5a, b). In 2002–2003, the rate of FFC CO₂ emission increase (~20% year⁻¹) from China was twice its GDP growth rate (10% year⁻¹). The CO₂ emission increase is caused by a combined effect of fuel consumption and worsening emission intensity, from 216 kgC per thousand US$ in 2002 to 250 kgC per thousand US$ in 2005 (Fig. 5c). Note also that the emission intensity of China was about three and two times higher in 2002 and 2012, respectively, compared to those of India, Japan or USA. Given the available historical information, it is probably difficult to revise the inventory emissions retrospectively for the first half of the 2000s. Therefore we apply a CH₄ emission scaling factor of 0.59 to the rate of anthropogenic CO₂ emission increase for China (Ref. Fig. 2a and associated text). By talking into account the sequestered CO₂ in carbonating cement materials (Xi et al. 2016), the FFC CO₂ emission increase would be moderated by 0.09 PgC in the period 2002–2012, which is about 12% of the mismatch in FFC CO₂ emissions between CDIAC/GCP and this study in 2012 (Fig. 5).

Fig. 5

Time series of global total FFC CO₂ emissions (a) and FFC CO₂ emission scenarios for China (b). Three scenarios based on a scaling factor from CH₄ inversion results for East Asia and as per the economic growth are shown in comparison with that estimated by CDIAC and IEA emission inventories. The annual emission estimates of Liu et al. (2015) agree within ±4% with those estimated by IEA for individual years. c The emission intensity of China in comparison with India, Japan and USA

The FFC CO₂ emissions, before and after CH₄ emission scaling, are compared with a scenario when the emissions are assumed to increase as per the GDP increase rate of China. This suggest that until about 2009, the FFC CO₂ emission increase was faster than the GDP increase since 2003, while the scaled emission increase is still higher than that due to the GDP increase but until about 2006. The emission intensity probably improved at a faster rate leading to the Beijing Olympic in 2008 and the years that followed (ref. EDGAR42FT). Thus, the net FFC CO₂ emission increase during 2003–2014 at the rate of GDP increase is in apparent violation of the common development mechanism of industrialization (Kaya Identity; Kaya and Yokoburi 1997). Emissions per unit of GDP should decrease with time as the industries strive to improve productivity, and more energy is produced through green technology investments (Raupach et al. 2007). About 20.3% of total energy generated in China (4.994 PWh) has been by renewable energy in 2012 compared to that of 17.6% (of 1.654 PWh) in 2002 (IEA, International Energy Agency 2016; www.iea.org/statistics/statisticssearch/). About 79 and 83% of China’s energy is generated from coal and crude oil in 2002 and 2012, respectively, which constitute about 89 and 87% of its total CO₂ emissions (IEA 2016; Boden et al. 2016).

Choosing different time periods, we find that the rate of FFC CO₂ emission increase (1.04 PgC as per CDIAC) is about 75% of Chinese GDP increase during 2007–2014, and our revised emissions are about 64% of the economic growth for another time period of 2002−2011. As a sensitivity case, we have checked the calculation for an arbitrary scaling factor of 0.69, and that produced an FFC CO₂ emission of 2.19 PgC year⁻¹ in 2012 compared to the case presented here with 2.02 PgC year⁻¹. This maximum difference of 0.17 PgC year⁻¹ in East Asian CO₂ flux during the period of 2010–2012 (Fig. 8) does not affect most of the conclusions of this work, and the difference is much smaller than that derived using CDIAC FFC emissions (0.71 PgC year⁻¹). Our revised FFC CO₂ emissions show better agreement, compared to CDIAC, with a consumption-based emission inventory for China (Fig. 5b; IEA 2016). The difference between IEA and our revised FFC CO₂ emissions has developed during the period of 2003–2007, when the emission intensity worsened for China, and only a systematic bias is seen for the later 5 years.

This correction to Chinese FFC CO₂ emission increase after 2003 has large consequences for the global and regional CO₂ budgets. Figure 6 shows the 84-region inverse model estimated global CO₂ fluxes using three cases of FFC CO₂ emissions as described in “Methods”. The FFC CO₂ emissions are balanced well by the residual/inverted land sink estimation for simulating the global atmospheric burden increase, which is seen as the greatest and smallest global total sinks for CDIAC and IEA, respectively (further details in Saeki et al. 2017). More interesting is to note that the CO₂ land sink is greater by 1.18 PgC year⁻¹ for CDIAC case compared to IEA emission case, and that is at the expenses of a large reduction in oceanic CO₂ sink by 0.51 PgC year⁻¹. This is because the FFC CO₂ biases are located over the land region, and thus the compensatory sink biases in inverse model occur over the land regions when constrained by observations. Some leakage of FFC CO₂ is expected, e.g. 33–40% for the East Asia region, as most of the FFC CO₂ emission signals are not strongly constrained by measurements within the region. The bias in global total CO₂ fluxes for the CARBONES and IEA inversion cases arises from the land total alone, with the ocean total sinks being similar. Our results clearly suggest that the land–ocean partitioning of CO₂ sinks is affected greatly by the uncertainties in FFC CO₂ a priori emissions, which was more often linked to transport model uncertainties (Gurney et al. 2002; Peylin et al. 2013).

Fig. 6

Bar plot of global total, land and ocean sinks by inverse modelling for three different anthropogenic emission inventories, averaged over 2002–2011. The mean FFC CO₂ emissions were 8.23 ± 0.78, 7.96 ± 0.76 and 7.61 ± 0.59 PgC year⁻¹, respectively, for CDIAC, CARBONES and IEA inventories

Further, the land CO₂ sink bias due to uncertainties in FFC CO₂ emission should influence our understanding of the global and regional carbon budgets. Figure 7 shows sectorial CO₂ sources/sinks budget obtained from the Global Carbon Project (GCP; Le Quéré et al. 2015). The GCP estimated global land CO₂ sink depends on the FFC CO₂ inventory, model of land-use change emissions and atmospheric burden increase from measured CO₂ concentration. We corrected the global FFC CO₂ emission using scaled emissions for China only (broken red line; =Global − CDIAC China + CH₄-Scaled China), and correspondingly calculated the corrected land CO₂ sink (broken brown line; Fig. 7). Apparently, the recent release of CO₂ emission inventory by the British Petroleum (BP 2016) also suggested a slower emission increase during 2007–2014, compared to the CDIAC inventory, and is comparable to our corrected FFC CO₂ emission. However, the rate of BP emission increase is still slightly faster than CDIAC inventory and much faster than our corrected FFC emission for the problematic time period of 2002–2007. The higher emissions during the late 1990s in the latest BP emission inventory, compared to CDIAC, agree better with Francey et al. (2013), who suggested no stabilization of emissions during the period 1996–1999.

Fig. 7

Time series of CO₂ fluxes as estimated by the Global Carbon Project for fossil fuel and cement (FFC), land-use change (LUC), atmospheric burden increase (ABI), oceanic exchange (OCN), residual land biosphere (LND = FFC + LUC – ABI − OCN), ensemble mean land fluxes simulated by the global dynamic vegetation models (from TRENDY project). FFC corrected by CH₄ inversion scaling (corFFC) are also shown in comparison with recent update of British Petroleum (BP) emission inventory of global totals. Corrected land fluxes (corLND) is based on residuals calculated using corFFC emissions. Three horizontal dotted-lines at −2.62, 0 and 3.78 PgC are marked for easy reference to the flux variabilities

The corrected FFC CO₂ emissions may also help resolve one the mismatches in the GCP’s residual land CO₂ sink budget and ensemble mean of the TRENDY DGVM simulations. A comparison suggests the GCP land sink increased by 2.04 PgC during the period 2001–2014, while that increased by only 1.34 PgC in the case of the TRENDY S2 simulation. The revised GCP land sink, using corrected FFC CO₂ emissions, is calculated to be 1.27 PgC, which is in closer agreement with the TRENDY simulation (Fig. 7). The assessment of the CO₂ sink simulation, by the DGVMs, has large implications for future prediction of carbon–climate feedback.

The policymaking of emission mitigation or verifying the success of INDCs, towards reduction of global levels of GHGs concentration, rely on our ability to estimate the time evolution of regional sources and sinks. However, estimations of FFC CO₂ emissions and the land sink in China has been one of most discussed issues in recent times (Liu et al. 2015; Thompson et al. 2016; Jiang et al. 2016). Jiang et al. (2016) estimated a large sink in China’s terrestrial biosphere, in the range of 0.39–0.51 PgC year⁻¹ for the period 2006–2009, which agrees well with the ensemble mean CO₂ sink of 0.46 PgC year⁻¹ estimated by Thompson et al. (2016). Strong regional CO₂ sink over a short period of time is in no apparent contradiction with our state-of-the-art knowledge, when all the component fluxes have uncertainties. However, we are unable to propose a simple biospheric mechanism for increasing CO₂ uptake systematically by 0.26 PgC within a decade in East Asia, from a mean flux of −0.20 PgC year⁻¹ in 2000–2002 to −0.46 PgC year⁻¹ in 2009–2011. The CO₂ uptake increase of similar amount is also calculated over a longer time period, from 0.08 ± 0.14 (mean ± ¹⁻ σ for IAVs) PgC year⁻¹ during 1993–2002 to 0.36 ± 0.14 PgC year⁻¹ during 2003–2012. The corrected CO₂ uptakes for the same periods are calculated as 0.08 ± 0.14 and 0.03 ± 0.16 PgC year⁻¹, respectively, using the CH₄-scaled FFC CO₂ emissions.

The TRENDY S2 simulation, including CO₂ fertilization and climate effects, produced CO₂ uptakes of 0.09 ± 0.11 and 0.13 ± 0.10, respectively, for the periods 1993–2002 and 2003–2012 (Fig. 8). The TRENDY simulations showed excellent agreement with inversion results for a slower rate of FFC CO₂ emission increase for both the interannual variations and sink magnitudes, except for magnitudes during 2010–2012. These CO₂ uptakes are also in good agreement with the average biomass carbon sink of 0.17 PgC year⁻¹ during 1999–2008, estimated based on forest stands inventory in China (Zhang et al. 2013), or the net carbon sink in the range of 0.19–0.26 PgC year⁻¹ using inventory, biogeochemical models and inverse models (Piao et al. 2009).

Fig. 8

Effect of FFC CO₂ emission increase rate on regional carbon budget of East Asia. The mean inversion flux of East Asia (from Thompson et al. 2016) is corrected a posteriori for revised China FFC CO₂ emissions using scaling from CH₄ inversion (This work, FFC revised) and that from the IEA (This work, FFC IEA). The mean CO₂ exchange simulated by TRENDY DGVMs (S2 simulation) are also shown

The anomalously high East Asian emission of ~0.2 PgC year⁻¹ during 2010–2012 is likely to arise from an underestimation of the rate of FFC CO₂ emission increase. Although the Chinese economic growth showed no sign of slowing down, the CDIAC estimated FFC CO₂ emission showed sharp decrease in the rate of emission increase since 2011 (Fig. 5). As per the Chinese economic growth, a proportional increase in FFC CO₂ emission would raise the emission by 0.3 PgC in the period 2009–2012. The proposed emission increase is consistent with one of the scenarios in Korsbakken et al. (2016), i.e. the National apparent consumption (pre-third National Economic Census, NEC) and that of the IEA. The a posteriori CH₄ emissions also show continued increase during the years 2010–2012 (Fig. 2a, blue line). The use of FFC CO₂ emissions from IEA show excellent agreement between corrected land sinks and the TRENDY simulation for the period 2010–2012, but produced a too strong sink increase during 2005–2009 (Fig. 8). Resolving the rate of increase in FFC CO₂ emission from China for different time segments is beyond the scope of this work. We believe large investment is required to develop independent MRV system involving atmospheric measurement and model for tracking CO₂ emissions from industrial and biospheric activities.

Conclusions

We have shown that the large land CO₂ uptake increase by 0.26 PgC year⁻¹ during 2001–2010, estimated for the East Asia by recent inverse modelling studies, is likely to be caused by a possible overestimation of increase rate in ‘assumed’ anthropogenic CO₂ emissions. Unfortunately, the FFC CO₂ emission and land sink dipole remained unresolved by validation experiments using independent CO₂ data. However, the CH₄ inversion results (predominantly source) showed an overestimation of anthropogenic emissions from East Asia, mainly China (Patra et al. 2016b). The strong correlation in anthropogenic emissions of CO₂ and CH₄ for the period 1970–2012 (Olivier et al. 2014) provides evidence for a reduction in the rate of FFC CO₂ emission increase by a factor of 0.59 for the period of 2002-2012. These results have large implications for carbon emission mitigation target, and development of MRV systems (e.g. for the INDCs). One fixed scaling factor of 0.59 for anthropogenic CO₂ emission for the period 2002–2012 is found to be inappropriate in this study. The flattening of FFC CO₂ emission after about 2009 in our revised case (Fig. 2a, black circle) is apparently inconsistent with the consumption-based emission inventories, and implies that CO₂ emission continued to increase at similar rates between 2009 and 2012, which is consistent with China’s national apparent consumption of coal and IEA emissions. The continued increase in anthropogenic emissions for 2010–2012 is also suggested by the CH₄ inversion results. We need to evaluate applications of any such factor using different chemical tracers, probably at every 3–5 year time intervals.

The accuracy of net terrestrial CO₂ uptake should be established in relation with the a priori FFC CO₂ emissions. Currently, the FFC CO₂ emission inventories are largely unconstrained by independent observations. We show that a significant bias in a priori FFC emissions leads to an abrupt carbon uptake increase in the regional and global terrestrial ecosystem mainly, and partly affects the oceanic carbon uptake estimations. High biased FFC CO₂ emissions produced stronger land sinks in global land–ocean flux partitioning because the emission bias occurs mostly over the land and affect the residual sink estimation within the same region. In particular, a reduced rate of FFC CO₂ emission increase is suggested to produce an overall consistency between the inverse model estimated East Asian and global land uptake increases with those simulated by the terrestrial vegetation models for the time periods after 2002.