Carbon neutrality and mitigating contribution of terrestrial carbon sink on anthropogenic climate warming in China, the United States, Russia and Canada

CUI Yaoping; LI Nan; FU Yiming; CHEN Liangyu

doi:10.1007/s11442-021-1878-0

2021 , Vol. 31 >Issue 7: 925 - 937

DOI: https://doi.org/10.1007/s11442-021-1878-0

Research Articles

Carbon neutrality and mitigating contribution of terrestrial carbon sink on anthropogenic climate warming in China, the United States, Russia and Canada

CUI Yaoping ^,¹^,² ,
LI Nan ¹^,² ,
FU Yiming ² ,
CHEN Liangyu ²

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1. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, Henan, China
2. College of Geography and Environmental Science, Henan University, Kaifeng 475004, Henan, China

Cui Yaoping, PhD, E-mail: cuiyp@lreis.ac.cn

Received date: 2021-02-15

Accepted date: 2021-04-27

Online published: 2021-09-25

Supported by

National Natural Science Foundation of China(42071415)

National Natural Science Foundation of China(41671425)

Outstanding Youth Foundation of Henan Natural Science(202300410049)

National Key Research and Development Program of China(2021YFE0106700)

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Abstract

Carbon dioxide (CO₂) is a major climate forcing factor, closely related to human activities. Quantifying the contribution of CO₂ emissions to the global radiative forcing (RF) is therefore important to evaluate climate effects caused by anthropogenic and natural factors. China, the United States (USA), Russia and Canada are the largest countries by land area, at different levels of socio-economic development. In this study, we used data from the CarbonTracker CO₂ assimilation model (CT2017 data set) to analyze anthropogenic CO₂ emissions and terrestrial ecosystem carbon sinks from 2000 to 2016. We derived net RF contributions and showed that anthropogenic CO₂ emissions had increased significantly from 2000 to 2016, at a rate of 0.125 PgC yr^-1. Over the same period, carbon uptake by terrestrial ecosystems increased at a rate of 0.003 PgC yr^-1. Anthropogenic CO₂ emissions in China and USA accounted for 87.19% of the total, while Russian terrestrial ecosystems were the largest carbon sink and absorbed 14.69 PgC. The resulting cooling effect was -0.013 W m^-2 in 2016, representing an offset of -45.06% on climate warming induced by anthropogenic CO₂. This indicates that net climate warming would be significantly overestimated if terrestrial ecosystems were not included in RF budget analyses. In terms of cumulative effects, we analyzed RFs using reference atmospheres of 1750, at the start of the Industrial Revolution, and 2000, the initial year of this study. Anthropogenic CO₂ emissions in the study area contributed by + 0.42 W m^-2 and +0.32 W m^-2 to the global RF, relative to CO₂ levels of 1750 and 2000, respectively. We also evaluated correlations between global mean atmospheric temperature and net, anthropogenic and natural RFs. We found that the combined (net) RF caused by CO₂ emissions accounted for 30.3% of global mean temperature variations in 2000-2016.

Key words： greenhouse gas; radiative forcing; mitigating effect; terrestrial ecosystems; carbon emissions

Cite this article

CUI Yaoping , LI Nan , FU Yiming , CHEN Liangyu . Carbon neutrality and mitigating contribution of terrestrial carbon sink on anthropogenic climate warming in China, the United States, Russia and Canada[J]. Journal of Geographical Sciences, 2021 , 31(7) : 925 -937 . DOI: 10.1007/s11442-021-1878-0

1 Introduction

In recent decades, average global surface temperature has increased markedly because of combined effects of human activities (anthropogenic effects) and natural changes. This phenomenon is commonly known as “global warming” or “climate change”. It is now established that emissions of greenhouse gases (GHGs) by human activities have played a major role in global temperature increases, particularly since the 1950s. Successive assessment reports of the Intergovernmental Panel on Climate Change (IPCC) have evaluated the level of confidence of human responsibility as “very high” to “extremely high” (exceeding 90%- 99%) (IPCC, 2013). Carbon dioxide (CO₂) is the most abundant long-lived GHG (NOAA, 2016). Therefore, research on the impact of atmospheric CO₂ changes on temperature is important, on the one hand, to evaluate the respective roles of anthropogenic and natural factors in regulating the climate and, on the other hand, to provide the scientific basis for concerted international efforts towards emissions reduction and carbon neutrality.

Both anthropogenic GHG emissions and carbon cycle of terrestrial ecosystems are influenced by human activities and natural factors. Without mitigation measures, global warming caused by unprecedented anthropogenic GHG increases could continue indefinitely (Eby et al., 2013; Clark et al., 2016). The terrestrial ecosystem balance between carbon sources and sinks (CO₂ release or capture sites) is an important regulating mechanism for anthropogenic CO₂ (Fang et al., 2018). At the beginning of the 21st century, annual global CO₂ emissions from fossil fuels represented about 9.0 PgC yr^-1 (Le Quéré et al., 2018), while the terrestrial ecosystem carbon sink had increased from 1.1±0.9 PgC yr^-1 in the 1990s to 1.5±0.9 PgC yr^-1 (Heimann and Markus, 2008; IPCC, 2013). However, countries worldwide are in different stages of development, with different intensities of human activities and terrestrial ecosystems, which will exert very different impacts on global climate.

Radiative Forcing (RF) is a concept used to quantify how different factors influence the Earth’s energy balance (difference between absorbed solar irradiation and outgoing radiation at the tropopause). It is used, notably in the IPCC reports, to evaluate the relative contribution of natural and anthropogenic factors to climate change (Tang et al., 2020). Because CO₂ accounts for about 65% of the total radiative forcing (NOAA, 2016), understanding atmospheric CO₂ changes is critical to explain past climate or to make predictions. Numerous long-term RF simulation studies have been carried out (Archer et al., 2009; Anderson et al., 2012; Kirschbaum et al., 2013; Landry and Matthews, 2016). For example, Joos et al. (2013) confirmed that CO₂ impact on atmospheric temperature is the largest in the 20 years following emission. Other studies have shown that the total CO₂ radiative forcing since the start of the Industrial Revolution (around 1750) was 1.82±0.19 W m^-2, but that between 2000-2010 only, it increased by as much as 0.2 W m^-2 (IPCC, 2013; Feldman et al., 2015). This shows that short timescale correspondence analysis needs to be strengthened, especially the 21st century, for which more detailed data are available.

Four countries: China, the United States (USA), Russia and Canada, which account for a significant share of worldwide anthropogenic CO₂ emissions, are also the largest by land area. In 2014, they produced nearly half of the global CO₂ emissions from fossil fuel combustion (Boden et al., 2017). Historically, USA was the world’s largest CO₂ emitter. China’s CO₂ annual emissions exceeded that of USA for the first time in 2006 (Anselm, 2017). From 2001 to 2006, 54% of the world’s cumulative CO₂ emissions originated in China (Gregg et al., 2008), but CO₂ emissions values of per capita remain lower in China than in the other countries (Rüstemoğlu and Andrés, 2016). Studying CO₂ emissions from major emitters and their impact on atmospheric temperature is of great significance to countries to further decrease their emissions and helps to achieve international long-term climate change mitigation goals.

This study focuses on the impact of atmospheric CO₂ concentration changes on climate and their attribution to anthropogenic and natural factors in China, USA, Russia and Canada from 2000 to 2016. The spatiotemporal distribution of anthropogenic carbon emissions and their RFs are derived from a CO₂ assimilation dataset. The compensating effect of the terrestrial biosphere, acting as a carbon sink to offset anthropogenic sources, is also analyzed.

2 Materials and methods

2.1 Study area

For this study, we select the four largest countries in the world: Russia, Canada, China, and USA, in order of decreasing land area. They represent 31.2% of the global land area. They are at different stages of economic development: in the World Bank’s “Global Economic Prospects” report (Word Bank, 2018), USA and Canada (in North America) are “advanced economies”, while Russia and China (in Europe and Asia) are classified as “emerging market and developing economies”. Since 2000, their economic development rate was, in decreasing order of annual values, China, then Russia, Canada and finally USA (World Bank, 2017).

2.2 Data

CO₂ data used in this study were extracted from the 2017 version of the “CarbonTracker” data set (CT2017) provided by the Earth System Research Laboratory of the National Oceanic and Atmospheric Administration (NOAA) of USA (Peter et al., 2007). Carbon Tracker is a global atmospheric CO₂ measurement and modeling system that tracks CO₂ removal from the atmosphere (sinks) and release from the Earth's surface (sources). Observation data are assimilated and processed to obtain annual CO₂ flux estimates. Spatial resolution of the initial data is 1°×1° and temporal resolution is one month. The data are processed into annual CO₂ flux. Flux estimates include the net ecosystem exchange (NEE) of the terrestrial biosphere (excluding wildfires), wildfire combustion, and fossil fuel CO₂ emissions. We define the anthropogenic carbon flux (AF) as CO₂ released from fossil fuel combustion, which reflects human activities and socioeconomic status, and the natural carbon flux (NF) as the CO₂ flux from terrestrial ecosystems and wildfires. We combine AF and NF to analyze the corresponding RFs.

Global monthly average temperature data are derived from the surface temperature analysis of the Goddard Institute for Space Studies (GISS) of the National Aeronautics and Space Administration (NASA) of USA, for the period 2000-2016. For the analysis, we also calculated annual mean temperatures.

2.3 Methods

Results from single climate models are inevitably affected by uncertainty. Discrepancies between models are often large. In addition, feedbacks and correlations between climate-influencing natural factors, combined with climate models complexity, prevent from assessing separately the climate sensitivity caused by changes in atmospheric CO₂. To quantify the respective impact of natural and anthropogenic factors on climate from 2000 to 2016 in the four countries, we derive RFs from the NF (terrestrial ecosystems and wildfires) and the AF (fossil fuel combustion), as well as the net total RF. We calculate 1) the annual CO₂ concentration change, and two types of RFs: 2) an annual RF (ARF) for each year of our study period, to evaluate the offsetting effect of natural factors on anthropogenic emissions; and 3) the cumulative RF (CRF) that takes into account historical variations of CO₂ to estimate the warming contribution of anthropogenic emissions.

1) The annual CO₂ flux (A, in mol m^-2) can be converted to atmospheric CO₂ concentration (e(t) in ppm) by:

(1)$e(t)\text{=}\frac{A}{0.1765\times {{10}^{15}}}\times S$

where A is the annual CO₂ flux, the denominator on the right side (in mol ppm^-1) is a conversion factor (Prather, 2007), and S is the land surface considered (global study area or one country).

2) We use the parameterized method proposed by Myhre et al. (1998) to calculate ARFs of anthropogenic or natural CO₂ sources:

(2)$\Delta ARF\left( t \right)\text{=}5.35\text{ln}\left( \text{1+}\frac{e(t)}{{{C}_{0}}} \right)$

where ΔC(t) is calculated as in Equation 1, and C₀ is the background CO₂ concentration.

If the NEE (e(t)) is negative, then its RF is also negative and Equation 2 becomes:

(3)$\Delta ARF\left( t \right)\text{=}-5.35\text{ln}\left( \text{1+}\frac{\text{abs}(e(t))}{{{C}_{0}}} \right)$

3) Cumulative radiative forcing (CRF). Unlike most atmospheric trace constituents, CO₂ is not chemically active in the atmosphere and does not deposit on the surface. Instead, it is alternately trapped or released, at very different time scales, by reservoirs such as the atmosphere, oceans and terrestrial ecosystems. This redistribution defines the “global carbon cycle” and implies that climate response is conditioned not only by current CO₂ concentration changes but also by past emissions and variations, so that CO₂ continues to affect climate thousands of years after emission (Lee et al., 2010).

We consider a single CO₂ emission event as a pulse that will be partially removed by the carbon cycle. This slow removal can be described by an impulse response function (IRF) (Joos et al., 2013):

(4)$IR{{F}_{\text{C}{{\text{O}}_{\text{2}}}}}(t)={{\alpha }_{0}}+\sum\limits_{i=1}^{3}{{{\alpha }_{i}}\cdot \exp \left( \frac{-t}{{{\tau }_{i}}} \right)}, for t ≥ 0$

where τ_i is the characteristic time scale of removal into reservoir i (for example, oceans), α₀ is the fraction of emitted CO₂ remaining in the atmosphere, α_i the fraction removed into res-ervoir i, and the sum of all a coefficients is normalized to 1. $IR{{F}_{\text{C}{{\text{O}}_{\text{2}}}}}(t)$ thus represents the fraction of the initial emission still present in the atmosphere at time t.

To calculate the redistribution of anthropogenic carbon between the major carbon reservoirs and to simulate the climate response to CO₂ forcing, a “global change model” used to analyze the climate effect of carbon must include each reservoir in the carbon cycle simulations. Therefore, we use the IRF coefficients with the mean values of multi-model simulation results provided by Joos et al. (2013) (Table 1).

Table 1 IRF fitting coefficients

i	0	1	2	3
a_i	0.2173	0.2240	0.2824	0.2763
t_i	∞	394.4	36.54	4.304

The CRF is defined as the cumulative contribution of CO₂ emissions to the global atmospheric CO₂ concentration at time t and can be represented as the sum of historical CO₂ emissions e at time t' multiplied by the fraction remaining in the atmosphere after time (t - t') (Joos et al., 2013):

(5)$\Delta C(t)=\int_{t{}_{0}}^{t}{IR{{F}_{C{{O}_{2}}}}(t-{t}')}e({t}')d{t}'\text{+}e({{t}_{0}})$

We use two references: 278.00 ppm in 1750 and 369.18 ppm in 2000, as CO₂ background concentrations in our calculations. The 1750 value is often used as a reference in the literature. The 2000 value gives the reference to analyze RF by new CO₂ emissions relatively to CO₂ concentration levels at the start of the century. If the NEE is negative, we disregard the decay process and calculate CO₂ concentration for the first year only.

Finally, we derive the percent offset (D) induced by natural factors on anthropogenic RFs:

(6)$D\text{=}\frac{\left| R{{F}_{natural}} \right|}{R{{F}_{anthropogenic}}}\times 100\text{ }\!\!%\!\!\text{ }$

3 Results

3.1 Spatiotemporal variations of CO₂ emissions and flux in 2000-2016

During the study period, total anthropogenic carbon emissions in the study area were 63.49 PgC and uptake by terrestrial ecosystems (natural sinks) was 28.51 PgC (Figure 1). The highest emissions were found in China and USA with 33.01 PgC and 22.34 PgC, respectively, accounting for 87.19% of the total, with a corresponding terrestrial ecosystem offset of 22.92% and 20.19%, respectively. Anthropogenic emissions were 1.55 PgC and 6.59 PgC in Canada and Russia, respectively, while corresponding ecosystem sinks exceeded the emissions and reached 3.20 PgC and 13.23 PgC, respectively; Canada and Russia thus acted as a carbon sink in 2000-2016.

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Figure 1 Attribution of anthropogenic carbon emissions and natural carbon sinks in China, the United States, Russia and Canada from 2000 to 2016

We also analyzed emission trends during the study period. Total net carbon emissions (sources minus sinks) increased from 0.78 PgC in 2000 to 2.58 PgC in 2016 at an average rate of 0.124 PgC yr^-1 (Figure 2). Anthropogenic fossil fuel emissions increased at a rate of 0.125 PgC yr^-1 while a slight decrease is observed from 2014-2016. Despite interannual fluctuations, the terrestrial ecosystem uptake showed an overall downward trend (-0.003 PgC yr^-1), with a yearly averaged NEE of -1.67 PgC, indicating an increasing ability to absorb CO₂. The strongest net absorption occurred in 2009 (-2.10 PgC). CO₂ wildfire emissions were consistently small, with marked interannual fluctuations and a small increasing trend of 0.002 PgC yr^-1.

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Figure 2 Total annual carbon emissions and sinks from 2000 to 2016

Geographically, the largest AF increases (up to 33.81 mol m^-2 yr^-1) occurred in southeastern coastal and central regions of China while USA mostly showed a significant downward trend. In northern Canada, Russia, eastern China and Tibet, the anthropogenic emissions continued to increase but terrestrial ecosystems absorbed increasing amounts of CO₂, therefore there was no significant net emission increasing trend in these regions (Figures 3 and 4).

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Figure 3 Spatial distribution of CO₂ flux trends in China, the United States, Russia and Canada in 2000-2016

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Figure 4 Statistical significance of the CO₂ flux trends in China, the United States, Russia and Canada

3.2 Changes in annual radiative forcing

Because of the characteristics of atmospheric CO₂ and because its distribution in the study area does not reflect the global CO₂ distribution, we used global concentration averages to calculate RFs. Figure 5 shows RFs induced by yearly CO₂ flux variations. In 2000-2016, the terrestrial ecosystem offsetting effect was (-12.72 ± 1.18) × 10^-3 W m^-2 yr^-1, while natural wildfires and anthropogenic fossil fuels induced RFs of (1.25 ± 0.37) × 10^-3 W m^-2 yr^-1 and (25.50 ± 4.27) × 10^-3 W m^-2 yr^-1, respectively. From 2000 to 2016, the average global ARF was (14.17 ± 4.38) × 10^-3 W m^-2 yr^-1 (Figure 5a).

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Figure 5 ARF averages and trends between 2000-2016 induced by natural and anthropogenic sources for China, the United States, Russia and Canada (a) and separately per country (b-e)

In 2000, the fossil fuel combustion ARF in USA was 9.28×10^-3 W m^-2, higher than that of China (6.21×10^-3 W m^-2, Figures 5b and 5c). By 2016, the ARF had decreased to 8.32×10^-3 W m^-2 in USA but increased to 18.36×10^-3 W m^-2 in China. From 2002-2016, the rate of decrease was -0.068×10^-3 W m^-2 yr^-1 in USA. Conversely, in China, the ARF increased rapidly until 2014, up to a maximum of 18.53×10^-3 W m^-2, then decreased slightly until 2016. ARFs due to natural factors were negative in China and in USA but could not compensate for the large positive ARFs caused by fossil fuel combustion. Therefore, net ARFs in China and in USA remained strongly positive, representing a net warming effect. Moreover, the net ARF rate was small in USA (-0.130×10^-3 W m^-2 yr^-1) but increased rapidly in China to its maximum value of 1.14×10^-3 W m^-2 yr^-1 in 2013.

Annual mean ARFs of terrestrial ecosystems in Russia and Canada were (-5.32±1.72)× 10^-3 W m^-2 and (-1.19 ± 1.07) ×10^-3 W m^-2, respectively (Figures 5d and 5e). Minimum values (i.e., highest CO₂ uptake by terrestrial ecosystems) occurred in 2002 (-9.11×10^-3 W m^-2) and 2011 (-3.54×10^-3 W m^-2) in Russia and Canada, respectively. ARFs caused by natural sources were higher in both countries than anthropogenic ARFs throughout the study period, indicating that natural sinks induced a cooling effect exceeding the warming caused by anthropogenic carbon emissions.

Our results also showed that total RF should have reached 0.030 W m^-2 in 2016 if only fossil fuel combustion was considered. With terrestrial ecosystems acting as a carbon sink, we calculated a net RF of 0.018 W m^-2, indicating a cooling offset of -0.012 W m^-2. Thus, the net offsetting effect of natural factors on fossil fuel combustion was -41.13%, including the cooling effect of terrestrial ecosystems (-0.013 W m^-2), with a contribution rate of -45.06%, and a slight warming effect attributed to wildfires, with a contribution rate of + 3.93% (Figure 5a).

3.3 Changes in cumulative radiative forcing

CRF calculations yielded very different results when the 1750 or 2000 atmospheric CO₂ background concentrations were used as the reference. Anthropogenic and net CRF values calculated with the 2000 background were consistently smaller than with the 1750 background. In 2016, the difference between anthropogenic and net CRF was 0.10 W m^-2 and 0.06 W m^-2 using 1750 and 2000, respectively, as the reference. Assuming no additional CO₂ emissions occurred after 2016, we obtained anthropogenic and net CRF values in 2040 of 0.23 W m^-2 and 0.13 W m^-2, respectively, for the 2000 reference. This is 27.43% and 28.50% lower than the 2016 values (reference in 2000), and 27.25% and 28.39% lower than the values calculated in 2040 for the 1750 reference, respectively. We further calculated that, by 2100, anthropogenic and net CRFs would represent 54%-56% of their 2016 levels. However, if only anthropogenic emissions were considered, the CO₂ warming effect was largely overestimated. With the 1750 background, calculated net CRFs were systematically lower than anthropogenic CRFs by 0.18 W m^-2 in 2016, and 0.11 W m^-2 in 2100 (Figure 6a).

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Figure 6 CRF trends between 2000-2016 induced by natural and anthropogenic sources for China, the United States, Russia and Canada (a) and separately per country (b-e). The decrease observed in 2016-2100 was derived from Equations 4 and 5, assuming no further CO₂ emissions occurred after 2016.

From 2000 to 2016, the anthropogenic and net CRFs, both in China and in USA, increased continuously (Figures 6b and 6c). In contrast, net CRFs in Russia and Canada showed large fluctuations. The anthropogenic CRF in Russia was consistently higher than in Canada, but the cooling effect caused by natural factors in Russia was also stronger than in Canada. In 2016, net CRFs in Russia calculated with the 1750 and 2000 references were 0.05×10^-3 W m^-2 and 0.04×10^-3 W m^-2, respectively, both smaller than the corresponding values in Canada (1.71×10^-3 W m^-2 and 1.28×10^-3 W m^-2, respectively, Figures 6d and 6e). Since 2016, assuming no additional CO₂ emissions, anthropogenic CRFs in these countries decreased first rapidly, then at a slower rate, down to about half of their 2016 values by 2100.

4 Discussion

Figure 7 shows the relationship between annual global mean temperature and the net, anthropogenic and natural CRFs induced by atmospheric CO₂. We calculated closer correlations between temperature and anthropogenic CRF caused by fossil fuel combustion (R = 0.54, P = 0.02) (Figure 7b) than between temperature and natural CRF (R = 0.26, P = 0.30) (Figure 7c). This result shows that natural forcings caused by terrestrial ecosystems are not sufficient to explain atmospheric temperature variations. This is confirmed by a higher correlation between global mean temperature and interannual net CRF variations in 2000-2016 (R = 0.55, P = 0.02) (Figure 7a), which shows that up to 30.3% of the temperature changes can be directly attributed to CO₂. Nevertheless, RFs caused by additional factors should also be evaluated: other GHGs, especially CH₄ and N₂O (Xu et al., 2010; Mendoza et al., 2015; Tian et al., 2011), and biogeophysical factors such as albedo, evapotranspiration rate and roughness length (Feddema et al., 2005; Montenegro et al., 2009; Davin and Noblet-Ducoudré, 2010; Kirschbaum et al., 2013; Schwaab et al., 2015). In addition, aerosol and ENSO effects on climate were not included in this study (Kondo et al., 2011; Clarke et al., 2017).

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Figure 7 Linear regression and correlations between global mean temperature and yearly net, anthropogenic and natural CRFs in China, the United States, Russia and Canada over the period 2000-2016

In climate change research, the RF generated by a doubled atmospheric CO₂ concentration is a standard indicator, often used to evaluate the impact of human activities on climate (Roe and Baker, 2007; Loehle, 2014; Rohling et al., 2018). In 1996, for the IPCC Second Assessment Report (SAR), a value of 4.37 W m^-2 was calculated for this indicator. After successive refinements, the latest RF parameterization scheme, recommended in the Fifth Assessment Report (AR5) of 2014, includes overlapping between different GHG absorption zones, regulation of stratospheric temperature and short-wave radiative forcing (Myhre et al., 1998; Li et al., 2021). The RF generated by a doubling of CO₂ was thus revised in AR5, with a value of 15% lower than that reported in the SAR. This illustrates large uncertainties that remain when estimating climate sensitivity to atmospheric CO₂ concentration changes.

Terrestrial ecosystems are critical for the survival and development of human societies, but also for the Earth’s climate. They can act as carbon sources and sinks. For example, in China, rice fields, natural wetlands and drylands contribute to global warming whereas forests, woodlands and grasslands play a mitigating role (Cui et al., 2019). Studies have shown that forest areas in China and North America are expanding (Houghton et al., 2012; Li et al., 2020). Moreover, Russian forests constitute the world’s largest contiguous forest belt, accounting for 17% of the global forest cover, thus they play a key role in global carbon cycle regulation (Loboda and Chen, 2016). Nevertheless, the efficiency of natural carbon sources and sinks is sensitive to climate change and to factors such as global mean temperature increases or deforestation (Cao and Li, 2000; Fang et al., 2018). Our results show that, despite the warming effect of wildfires, terrestrial ecosystems represent a net global CO₂ sink, increasingly efficient over the study period. This may be partly caused by the larger vegetation coverage (Dass et al., 2016; Cui et al., 2017; Zhang et al., 2017) and the enhancing effect of elevated CO₂ levels on photosynthesis (Lim et al., 2004). Additionally, climate effects in different countries depend on their CO₂ emission history. The USA was historically the first contributor to total carbon emissions. In the past ten years only, emissions in China have become larger than in USA (Gregg et al., 2008; Rüstemoğlu and Andrés, 2016; Anselm, 2017). Because our study period was 2000-2016, we did not include CO₂ emissions before 2000, which may influence climate warming through accumulated CO₂ fluxes (Tian et al., 2011).

Furthermore, we focused on the land area and calculated anthropogenic and natural RFs caused by CO₂ separately in each country. We did not consider carbon flow and CO₂ import and export trade between countries, and we did not include carbon exchange processes between atmosphere and ocean, and ocean possesses a very strong carbon sink function (Lovenduski et al., 2008; Landschützer et al., 2015). These should be addressed in future studies.

5 Conclusions

In this study, we analyzed spatiotemporal changes of anthropogenic and natural CO₂ fluxes in China, USA, Russia, and Canada from 2000 to 2016, using the CT2017 dataset of the CarbonTracker CO₂ assimilation model. We calculated associated RFs and discussed both the ARF and the cumulative effect of total CO₂ emissions at the end of the century (2100). We confirmed that net climate warming would be significantly overestimated if the terrestrial ecosystem carbon sink was omitted from the analyses. We estimated that RFs induced by CO₂ emissions in 2000-2016 would still represent more than half of the CRF at the end of the century. Finally, we found a significant correlation between the net CRF and atmospheric temperature variations over the study period.

Our results will be useful to better understand the impact of anthropogenic and natural factors on climate, especially the offsetting effect of terrestrial ecosystem carbon sinks on the warming caused by anthropogenic CO₂ emissions.

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Outlines

模态框（Modal）标题

Abstract

Cite this article

1 Introduction

2 Materials and methods

2.1 Study area

2.2 Data

2.3 Methods

Table 1 IRF fitting coefficients

3 Results

3.1 Spatiotemporal variations of CO2 emissions and flux in 2000-2016

Figure 1 Attribution of anthropogenic carbon emissions and natural carbon sinks in China, the United States, Russia and Canada from 2000 to 2016

Figure 2 Total annual carbon emissions and sinks from 2000 to 2016

Figure 3 Spatial distribution of CO2 flux trends in China, the United States, Russia and Canada in 2000-2016

Figure 4 Statistical significance of the CO2 flux trends in China, the United States, Russia and Canada

3.2 Changes in annual radiative forcing

Figure 5 ARF averages and trends between 2000-2016 induced by natural and anthropogenic sources for China, the United States, Russia and Canada (a) and separately per country (b-e)

3.3 Changes in cumulative radiative forcing

Figure 6 CRF trends between 2000-2016 induced by natural and anthropogenic sources for China, the United States, Russia and Canada (a) and separately per country (b-e). The decrease observed in 2016-2100 was derived from Equations 4 and 5, assuming no further CO2 emissions occurred after 2016.

4 Discussion

Figure 7 Linear regression and correlations between global mean temperature and yearly net, anthropogenic and natural CRFs in China, the United States, Russia and Canada over the period 2000-2016

5 Conclusions

References

3.1 Spatiotemporal variations of CO₂ emissions and flux in 2000-2016

Figure 3 Spatial distribution of CO₂ flux trends in China, the United States, Russia and Canada in 2000-2016

Figure 4 Statistical significance of the CO₂ flux trends in China, the United States, Russia and Canada

Figure 6 CRF trends between 2000-2016 induced by natural and anthropogenic sources for China, the United States, Russia and Canada (a) and separately per country (b-e). The decrease observed in 2016-2100 was derived from Equations 4 and 5, assuming no further CO₂ emissions occurred after 2016.