Implications of overestimated anthropogenic CO2 emissions on East Asian and global land CO2 flux inversion
© The Author(s) 2017
Received: 19 September 2016
Accepted: 3 April 2017
Published: 16 May 2017
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.
KeywordsEast Asian carbon budget Fossil fuel emission Terrestrial biospheric uptake Emission monitoring and verification
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.
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.
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.
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 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.
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.
TS and PKP designed the model experiments, TS performed CO2 inversions and major part of the analyses, PKP and TS wrote the paper. Both authors read and approved the final manuscript
This work was first presented at the 13th Asia Oceania Geoscience Society (AOGS) annual meeting, Beijing, 2016. Authors thank Rona Thompson, Josep Canadell, Kazuhito Ichii, Masayuki Kondo for useful discussions while developing some of the concepts. The CARBONES and CARBONES-IEA fossil fuel CO2 emission maps were reformatted for this study by Ingrid van der Laan-Luijkx. We thank three anonymous reviewers for providing us with insightful comments. The revised manuscript has also benefitted from discussions with Frederic Chevallier, Benjamin Poulter and Eri Saikawa.
The authors declare that they have no competing interests.
Availability of data and materials
CO2 measurements are available for scientific use at http://ds.data.jma.go.jp/gmd/wdcgg; http://www.esrl.noaa.gov/gmd/dv/ftpdata.html; http://caos.sakura.ne.jp/tgr/. All the model results and analysis tools are available unconditionally from the ACTM group by contacting the lead authors.
This work is supported by the Environment Research and Technology Development Fund (2-1401) of the Ministry of the Environment, Japan, and Asia Pacific Network (APN) project ARCP2013-01CMY-Patra/Canadell. All model simulations are performed on JAMSTEC’s supercomputing facility in Yokohama.
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