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Interpreting seasonal changes of low-tropospheric CO2 over China based on SCIAMACHY observations during 2003–2011
Atmospheric Environment 103 (2015) 180e187
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Interpreting seasonal changes of low-tropospheric CO2 over China based on SCIAMACHY observations during 2003e2011 Wang Xi, Zhang Xingying*, Zhang Liyang, Gao Ling, Tian Lin National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
h i g h l i g h t s Satellite XCO2 data from SCIAMACHY was validated by in-situ data in China. Seasonal variation and long-term trend of low-tropospheric CO2 were discussed over China by satellite observation. The nature and anthropogenic influencing factors to the sources and sinks of CO2 over China have been discussed.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 September 2014 Received in revised form 2 December 2014 Accepted 20 December 2014 Available online 20 December 2014
The atmospheric carbon dioxide (CO2) concentration exhibits a strong seasonal variation. Analyzing the regional seasonal cycle could help to improve the interpretation of the sources and sinks of CO2 over certain areas. Based on a long-term (2003e2011) retrieved dataset from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), the seasonal cycle and inter-annual variations of column-averaged dry air mole fraction of atmospheric carbon dioxide (XCO2) over China have been analyzed. The result shows that XCO2 over China increases by about 4.2% from 2003 to 2011, but the seasonal fluctuation keeps the similar pattern with the average peak-to-peak amplitude of 9.35 ppm. The highest concentration appears in spring, and the lowest value always occurs in summer. Based on the multi-year averages, it can be discerned that the seasonal signal of XCO2 increases during colder seasons with a drop during the period from December to February of the following year. The potential affecting factors are also discussed in this manuscript, including Normalized Difference Vegetation Index (NDVI), air temperature, and industrial productions in Thermal Power Generation (TPG) and cement that are relative main contributors for the anthropogenic CO2 of China. The seasonal variations of CO2 are highly connected with the changes of NDVI and air temperature. While the increase of the anthropogenic CO2 emission over China since 2003 is probably caused by the rapid growth of coal combustion and cement manufacture. © 2014 Published by Elsevier Ltd.
Keywords: Carbon dioxide Seasonal variation SCIAMACHY China
1. Introduction Carbon dioxide (CO2) is a prominent anthropogenic greenhouse gas in the atmosphere (Forster et al., 2007). Due to human activities, the global atmospheric concentration of CO2 has exceeded the pre-industrial levels by about 40% in 2011 (Stocker et al., 2013). Further increase of CO2 is expected to result in a warmer climate with adverse consequences, such as rising sea levels and an increase of extreme weather conditions (Schneising et al., 2011). Historically, the United States (US) has long been the largest emitter
* Corresponding author. E-mail address: [email protected] (Z. Xingying). http://dx.doi.org/10.1016/j.atmosenv.2014.12.053 1352-2310/© 2014 Published by Elsevier Ltd.
of CO2 from fossil fuel combustion and cement production (Gregg et al., 2008). However, with the great economic growth, China has now become the largest national source of CO2 emissions in the world since 2006 (Auffhammer and Carson, 2008; Guan et al., 2009). In order to make a reliable prediction of climate, it is significant to have an accurate understanding for the variations of CO2. Researchers have found that the amount of CO2 in the atmosphere varies over the course of a year. Global CO2 concentrations show seasonal oscillations that are most heavily influenced by the growing season in the Northern Hemisphere (Randerson et al., 1999; Suni et al., 2003). However, much uncertainty remains in the seasonal cycles and the inter-annual variability of the atmospheric CO2 over China, which have been of particular concern and critical on interpreting the sources and sinks of regional carbon
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dioxide. The data from the traditional in situ CO2 monitoring network have been crucial to detect the temporal and spatial variability of atmospheric CO2 concentration. However, the sparse spatial coverage and absent observations over oceans and polar regions make it impossible to fully understand the mechanisms that result in the inter-annual variations and the global distributions of CO2 (Butz et al., 2009; Feng et al., 2009; Nevison et al., 2008; O'Dell et al., 2012). With wide global coverage and high measurement density, satellite observation is an effective approach to improve the monitoring of global greenhouse gases (Baker et al., 2010). Sensitive to different altitude, satellite observations of atmospheric CO2 can be achieved by the reflected solar radiation in the near-infrared/shortwave-infrared (NIR/SWIR) regions and emissions in the thermal infrared (TIR) bands (Schneising et al., 2011). The TIR observations are sensitive to CO2 in the middle to upper troposphere such as Atmospheric InfraRed Sounder (AIRS) onboard the Aqua satellite (Aumann et al., 2005; Chahine et al., 2006; Hungershoefer et al., 2010), whereas the SWIR observations are also sensitive to CO2 abundances near the surface such as SCIAMACHY onboard the European ENVIronmental SATellite (ENVISAT) (Bovensmann et al., 1999; Noel et al., 1999) and Thermal And Near infrared Sensor for carbon Observation (TANSO) onboard the Greenhouse gases Observing SATellite (GOSAT) (Hamazaki et al., 2004; Yokota et al., 2009; Yoshida et al., 2011). As a pioneering instrument for monitoring greenhouse gases, SCIAMACHY was launched in 2002 and provides a large number of atmospheric data products including the column amounts of CO2 until 2011. In this study, the dataset of column-averaged dry air mole fraction of atmospheric carbon dioxide (XCO2) derived by University of Bremen is applied for analyzing the seasonal variation and inter-annual changes of CO2 over China. The potential factors affecting the spatial and temporal distribution of CO2 over China are also discussed based on the long-term time series data of SCIAMACHY. 2. Data 2.1. Satellite data SCIAMACHY is a grating spectrometer that measures reflected, backscattered and transmitted solar radiation with the wavelength range between 240 and 2380 nm at moderate spectral resolution (0.2e1.5 nm) (Burrows et al., 1995). The instrument was launched on board ENVISAT which was operational from March 2002 to April 2012. The primary scientific objective of SCIAMACHY is the global measurement of various trace gases in the troposphere and stratosphere (Bovensmann et al., 1999). In order to obtain XCO2, several retrieval algorithms have been developed based on the € sch et al., 2006; Barkley et al., measurements from SCIAMACHY (Bo 2006; Buchwitz et al., 2006; Reuter et al., 2010; Schneising et al., 2008). One of them is the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) retrieval algorithm (Buchwitz et al., 2000). It was developed at the University of Bremen and has been improved to generate a global XCO2 dataset. The latest version 3.8 (WFMDv3.8) XCO2 level 3 product contains the gridded dataset (0.5 0.5 ) at monthly resolution from October 2002 to April 2012. The single measurement retrieval precision derived from the method of averaging daily standard deviations of the retrieved XCO2 at different locations distributed around the globe provides a consistent estimate of about 2.5 ppm (Schneising et al., 2011). Here, the WFMDv3.8 XCO2 level 3 data are applied for a long-term analysis (2003e2011) over China. More detailed information about the algorithm and product has been previously described (Heymann et al., 2012b, 2012a; Schneising et al., 2008) and given on the website of the University of Bremen
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(http://www.iup.uni-bremen.de/). The NDVI_sfc product from AVHRR Pathfinder Atmospheres e Extended (PATMOS -X; http://cimss.ssec.wisc.edu/patmosx/) (Heidinger et al., 2010) is selected for the analysis of CO2 concentration from January 2003 through December 2011, when AVHRR/3 instruments were onboard the polar orbiting NOAA-16 and NOAA18 satellites. The dataset for daily NDVI with a spatial resolution of 0.1 by 0.1 is provided by the University of WisconsineMadison's Cooperative Institute for Meteorological Satellite Studies (CIMSS).
2.2. In-situ data The background CO2 monthly mean data are available from the World Data Centre for Greenhouse Gases (WDCGG) website (http:// ds.data.jma.go.jp/gmd/wdcgg/). The WDCGG archives measurement data for greenhouse and related gases in the atmosphere and the ocean. The data are classified into six categories according to the observation platforms or methods used (more information is described in WDCGG guide). The in-situ CO2 data applied here are achieved at stationary platforms and contributed by the Earth System Research Laboratory of National Oceanic and Atmospheric Administration (NOAA/ESRL). The sampling type is ‘fl’, which represents analysis of data sample in flasks. The data of monthly mean air temperature are provided by the National Climate Center of China (http://ncc.cma.gov.cn/cn/ member.php?Login¼1). The dataset is available as an ASCII file, which contains the monthly mean temperatures of 160 stations over China since January 1951. All values of the dataset are integers with the unit of 0.1 centigrade. The industrial data that are relative primary contributors for the anthropogenic CO2 are collected by China Statistical Yearbook 2013 (CSY2013) from the website of National Bureau of Statistics of China (http://data.stats.gov.cn/). The dataset archives monthly industrial productions over China since January 1983. The data series of Thermal Power Generation (TPG) and cement manufacture as the main sources for the anthropogenic carbon emissions are applied here for the analysis of CO2 variations.
Fig. 1. Comparison of monthly mean XCO2 from SCIAMACHY and Waliguan station (36.28 N, 100.9 E) during January 2003 to December 2011. The average absolute difference between the monthly means is given as the Bias (2.22 ppm), the standard deviation of monthly means by the Std (2.25 ppm), and the correlation coefficient of monthly means by R (0.93).
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3. Results and discussions 3.1. Comparison of the SCIAMACHY CO2 product in China To assess the accuracy of SCIAMACHY XCO2 products in China, the observations from WDCGG at Waliguan (WLG) station (36.28 N, 100.9 E) of China are selected for the comparisons with the monthly mean XCO2 retrievals. Fig. 1 illustrates the comparisons from January 2003 to December 2011. The result reveals that the data from SCIAMACHY are consistent with the in-situ measurements. The estimated regional-scale (radius of 300 km) correlation coefficient of monthly averages gets to 0.93 with the standard deviation of 2.25 ppm. 3.2. Trend of CO2 over China Dominated by rapid economic growth, China has now become the world leader in CO2 emissions since 2006. The top panel of Fig. 2 shows the monthly variation of XCO2 from SCIAMACHY over China with the corresponding linear-fitting trend. The result displays the similar seasonal variation every year with an overall increasing trend from 2003 to 2011. The annual mean CO2 concentration over China increased from 375.96 ppm to 391.73 ppm during these 9 years, with the annual growth rate of 1.75 ppm/a. In order to analysis the seasonal cycle of CO2 concentration over China, we extracted the seasonal signals by subtracting the linearfitting value of the growth trend. The time series of seasonal signals (denoted as DXCO2) from January 2003 to December 2011 are shown in the bottom panel of Fig. 2. The details describing the variation of seasonal signals and related analysis are presented in Section 3.4. 3.3. Seasonal variation of CO2 over China Based on the monthly mean XCO2 products of SCIAMACHY, the significant seasonal distribution (spring: March, April, and May; summer: June, July, and August; autumn: September, October, and November; winter: January, February, and December) of CO2 over China during 2003e2011 is shown in Fig. 3 aed. The CO2 concentration over China is higher in spring and lower in summer and autumn. The respiration of plants and soil are very strong but photosynthesis is relatively weak, which cause the high concentration of CO2 in spring. Meanwhile, the figure also show the more high concentration of CO2 located in the Northeast Plain, since
there is a dense population, high industrial and agricultural activities, extensive fossil fuel burning, maybe enhance the CO2 accumulation. Additionally, the low concentration of CO2 in summer primarily occurs in the southwest and northeast areas of China especially over Yunnan and Fujian province. The strong photosynthesis by large vegetation coverage over these areas is probably the reasons for the low concentration of CO2. The changes of CO2 concentration between seasons are also given in Fig. 3eeh, which represent the differences of CO2 between the following season and the previous one (bea, ceb, dec, and aed, respectively). It is clearly observed that the atmospheric CO2 concentration declines sharply in most parts of China from spring to summer, except the northwest arid regions (Fig. 3e). Then, when it gets to autumn, much of vegetation begins to die and decay from the north part of China due to the lower temperature and less solar radiation. Quantities of carbon dioxide are released back to the atmosphere, raising the atmospheric CO2 content over the north regions of China (Fig. 3f). Compared with the north region, the atmospheric CO2 concentration over south China shows a noticeable increase till winter due to the longer growing period of vegetation (Fig. 3g). From winter to spring, the concentration of CO2 over China accumulates to a higher level, especially for the northeast parts due to the extensive fossil fuel burning for domestic heating systems (Fig. 3h). Fig. 4 displays the monthly average XCO2 over China derived from SCIAMACHY WFMDv3.8 dataset and the measurements from Waliguan station for 2003e2011. The seasonal cycle of XCO2 for SCIAMACHY is highly consistent with the variation for the Waliguan station. The average peak-to-peak amplitude of seasonal cycle is 9.35 ppm over China based on the data from SCIAMACHY, which is 0.49 ppm less than the amplitude for the Waliguan station. The result reveals that both of the observations reach to the highest value in April and decrease to the lowest value in August. During the primary growing season in summer, photosynthesis outweighs respiration. This can lead to noticeably lower CO2 concentrations in the atmosphere. When it gets to winter, dominated respiration results in the accumulation of atmospheric CO2. Meanwhile, the energy consumption for heating increased greatly over China in winter. These both lead to the high CO2 levels in spring.
Fig. 2. The monthly variation (top) and seasonal signals (bottom) of XCO2 from SCIAMACHY over China during January 2003 to December 2011. The black solid line (top) represents the corresponding linear-fitting trend derived using the monthly means of XCO2 over China.
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Fig. 3. Seasonal averages of XCO2 from SCIAMACHY over China during 2003e2011. (a) Spring (MAM): March, April, and May; (b) summer (JJA): June, July, and August; (c) autumn (SON): September, October, and November; (d) winter (DJF): January, February, and December. Differences of XCO2 between seasons: (e) JJA e MAM; (f) SON e JJA; (g) DJF e SON; (h) MAM e DJF.
Fig. 4. Multi-year monthly average XCO2 over China derived from SCIAMACHY WFMDv3.8 dataset and the measurements from Waliguan station for 2003e2011.
3.4. Influencing factor of source and sink of CO2 over China 3.4.1. Nature factor The time series of seasonal signals of XCO2 over China from 2003 to 2011 are presented in Fig. 5a. The result displays that the seasonal signal of XCO2 over China decreases to the minimum in August every year and then gradually accumulates during October to April of the next year. Fig. 5b shows the multi-year monthly averages of DXCO2 for 2003e2011. It can be discerned that the seasonal signal of XCO2 increases during colder seasons with a drop during the period from December to February of the following year. To interpret the seasonal variation of XCO2, the time series of monthly means of NDVI over China were calculated from 2003 to 2011 (Fig. 5c) based on the observations from AVHRR. The maximum of NDVI mainly appears in August, when the value of DXCO2 reaches to the minimum. Meanwhile, when the value of NDVI decreases to the lowest value mostly in March, the maximum of DXCO2 is delayed by a month. Fig. 5d shows the multi-year monthly averages of NDVI for 2003e2011. The result reveals that the drop of seasonal signal of XCO2 from December to February is highly consistent with the smaller peak of NDVI. Studies have shown that the value of NDVI is a measure of vegetation abundance and directly related to the photosynthetic capacity (Myneni et al.,
1995; Sellers, 1985). Since the vegetation is one of the main sink of CO2, so the CO2 concentration shows opposition to the NDVI value. Air temperature follows the seasonal changes in solar radiation, which drives the functioning of marine and terrestrial ecosystems. It has been long recognized that photosynthesis and respiration are temperature sensitive (Wager, 1941). The short-term variation in temperature can affect the carbon dioxide concentration. For further explanations, here we used the near-surface air temperatures from 160 in-situ stations over China to analysis the seasonal cycle of CO2 concentration. The specific locations of the groundbased stations are shown in Fig. 6. Fig. 7 illustrates the time series of seasonal signals of XCO2 derived from SCIAMACHY WFMDv3.8 dataset (a) and monthly averages of air temperature over China derived from the observations of 160 in-situ stations (c). The seasonal signal of XCO2 decreases to the lowest value one month after the temperature gets to the peak value. The comparison of multi-year monthly averages of DXCO2 (Fig. 7b) and NDVI (Fig. 7d) for 2003e2011 over China shows that the drop of seasonal signal of XCO2 from December to February is noticeably linked to the lowest temperature in January. At the ecosystem level, the balance between respiration and photosynthesis determines whether an ecosystem is a net source or sinks of CO2 into the atmosphere (Atkin and Tjoelker, 2003). The rate of respiration by plants increases substantially with short-term increases in temperature (BUNCE et al., 1996). During the growing season, higher temperature causes increased photosynthesis and respiration, but photosynthesis is greater. This leads to the lower concentration of atmospheric CO2 during the warming period. Low temperatures can result in poor growth. Photosynthesis is slowed down at low temperatures. Respiration also gets weak, but dominates during the colder months of the year, resulting in higher CO2 levels in the atmosphere during those months. In January, the extreme low temperature leads to the weak respiration which is the primary source for CO2 in terrestrial biospheres. This may be one of the reasons for the drop of the seasonal signals of XCO2.
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Fig. 5. Comparison between NDVI and seasonal signals of XCO2 over China. (a) Time series of monthly means of DXCO2 derived from SCIAMACHY WFMDv3.8 dataset; (b) multi-year monthly averages of DXCO2 for 2003e2011; (c) time series of monthly means of NDVI derived from NDVI_sfc product from AVHRR; (d) multi-year monthly averages of NDVI for 2003e2011.
Fig. 6. Locations of the ground-based stations for air temperature measurements over China.
Fig. 7. Comparison between air temperature and seasonal signals of XCO2 over China. (a) Time series of monthly means of DXCO2 derived from SCIAMACHY WFMDv3.8 dataset; (b) multi-year monthly averages of DXCO2 for 2003e2011; (c) time series of monthly means of air temperature derived from the observations of 160 in-situ stations over China; (d) multi-year monthly averages of air temperature for 2003e2011.
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3.4.2. Anthropogenic factor Beside nature factors, human activities are also primary sources for greenhouse gases (Wei et al., 2011). The anthropogenic CO2 source is derived from a number of different processes including combustion of coal, oil, and natural gas, as well as oxidation of organic solvents and cement manufacture (Erickson et al., 2008). Thermal Power Generation (TPG) as the significant industrial process for coal combustion is strongly correlated with the release of CO2. Since 2000, China has been the world's second producer in electricity generation (behind the US) (Gregg et al., 2008). Release of CO2 from cement manufacture is another primary anthropogenic source over China. These two industrial productions that are relative primary contributors for the anthropogenic CO2 over China are selected for the analysis. Spatially, The TPG and cement productions from China are concentrated in the provinces around Beijing and along the east coast of China (Fig. 8), particularly in Guangdong province which has been the beneficiary of international investment and manufacturing (Gregg et al., 2008). To examine the regional differences in the seasonal pattern influenced by energy consumption, the mainland of China (not including Taiwan, Hong Kong, and Macao) has been divided into six regions including the northeast (NE), northwest (NW), southwest (SW), north (NC), south (SC), and east (EC) parts of China (Fig. 8). Fig. 9 shows the seasonal variations in XCO2, TPG, and cement productions for these six regions. The result reveals that the seasonal patterns are similar over all these regions. Additionally, the region with a larger amount of industrial productions always has a higher CO2 concentration except for the northwest area of China with high level of CO2 concentration due to its arid climate and sparse vegetation. Fig. 10 shows the time series of monthly means of XCO2, TPG, and cement production over China from 2003 to 2011. The data series for TPG and cement production each reveal a consistent seasonal pattern. For TPG, there is a significant peak in December with a precipitous drop in February. Additionally, a slight peak appears in late summer. The cement production shows a different seasonal pattern with two peaks in late spring and autumn. There is also a distinct decrease in February. It is clear that the rapid growth of coal combustion and cement manufacture is a significant reason for the rise of the atmospheric CO2 concentration since 2003. However, the annual cycle of industrial productions shows less
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correlation with the seasonal pattern of CO2 concentration. Nonetheless, the drop of anthropogenic emissions in late winter is probably one of the reasons for the variations of XCO2 from December to February of the following year. Five-year averages of monthly XCO2 for 2003 to 2007 and 2007 to 2011 are given in Fig. 11. It is clear that the CO2 cycle follows the same seasonal pattern. The increment from the early five years to recent period (Fig. 11) reveals that the value of XCO2 is increasing more rapidly during colder months probably due to the large energy consumption for heating over the north regions of China.
4. Conclusions In this study, the seasonal cycle and inter-annual variations of XCO2 over China based on a long-term (2003e2011) retrieved dataset from SCIAMACHY onboard ENVISAT have been analyzed. The main results of this study can be summarized as follows. (1) The atmospheric CO2 concentration exhibits a strong seasonal variation, the highest concentration of CO2 over China in spring, and the lowest value always in summer. (2) XCO2 over China increases by about 4.2% from 2003 to 2011. The average peak-to-peak amplitude of seasonal cycle from 2003 to 2011 was 9.35 ppm over China. (3) The regions with high concentration of CO2 were mostly distributed in East of China. While, the southwest areas of China, especially over Yunnan province, were the main regions showing low concentration. The arid areas over northwest China showed little changes during the season transitions, probably due to the lack of vegetation. (4) The seasonal variations of XCO2 were highly connected with the changes of NDVI and air temperature, which were primary factors indicating the intensity of photosynthesis and respiration. The drop of seasonal signals of XCO2 during colder seasons from December to February of next year were probably due to the shallow growth of NDVI and extreme low temperature during this period. (5) The annual cycle of industrial productions showed less correlation with the seasonal pattern of XCO2. However, the rapid growth of coal combustion and cement manufacture
Fig. 8. Spatial distributions of monthly mean TPG and cement productions over China from 2003 to 2011. The six regions with black solid lines as boundaries represent the northeast (NE), northwest (NW), southwest (SW), north (NC), south (SC), and east (EC) parts of China.
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Fig. 9. Seasonal variations of XCO2, TPG, and cement productions for the six regions displayed in Fig. 8. The seasonal averages of XCO2 are shown in black dots. The seasonal average productions of TPG and cement are shown in blue and red bars. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 10. Time series of monthly means of XCO2, TPG, and cement production over China from 2003 to 2011.
patmosx/). We also thank the WDCGG and the contributors for the CO2 data of the stations (obtained from http://ds.data.jma.go.jp/ gmd/wdcgg/). The study was supported by the National High Technology Research and Development Program (“863” Program) of China (Grant No. 2011AA12A104-3), the European Commission's Seventh Framework Programme “PANDA” (Grant No. FP7-SPACE2013-1), the Public industry-specific fund for meteorology (Grant No. GYHY201106045) and the Climate Change Science Funding from Chinese Meterological Administration (Grant No. CCSF201351). Fig. 11. Five-year averages of monthly XCO2 for 2003 to 2007 and 2007 to 2011 over China. The increment from 2003 to 2007 to 2007e2011 is shown in bars.
shows significant correlation with the atmospheric CO2 concentration over China since 2003.
Acknowledgment Thank for the SCIAMACHY CO2 data product from the University of Bremen (obtained from http://www.iup.uni-bremen.de/ sciamachy/NIR_NADIR_WFM_DOAS/products/) and the AVHRR data from Cooperative Institute for Meteorological Satellite Studies of SSEC/UW-Madison (obtained from http://cimss.ssec.wisc.edu/
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Interpreting seasonal changes of low-tropospheric CO2 over China based on SCIAMACHY observations during 2003e2011 Wang Xi, Zhang Xingying*, Zhang Liyang, Gao Ling, Tian Lin National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
h i g h l i g h t s Satellite XCO2 data from SCIAMACHY was validated by in-situ data in China. Seasonal variation and long-term trend of low-tropospheric CO2 were discussed over China by satellite observation. The nature and anthropogenic influencing factors to the sources and sinks of CO2 over China have been discussed.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 September 2014 Received in revised form 2 December 2014 Accepted 20 December 2014 Available online 20 December 2014
The atmospheric carbon dioxide (CO2) concentration exhibits a strong seasonal variation. Analyzing the regional seasonal cycle could help to improve the interpretation of the sources and sinks of CO2 over certain areas. Based on a long-term (2003e2011) retrieved dataset from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), the seasonal cycle and inter-annual variations of column-averaged dry air mole fraction of atmospheric carbon dioxide (XCO2) over China have been analyzed. The result shows that XCO2 over China increases by about 4.2% from 2003 to 2011, but the seasonal fluctuation keeps the similar pattern with the average peak-to-peak amplitude of 9.35 ppm. The highest concentration appears in spring, and the lowest value always occurs in summer. Based on the multi-year averages, it can be discerned that the seasonal signal of XCO2 increases during colder seasons with a drop during the period from December to February of the following year. The potential affecting factors are also discussed in this manuscript, including Normalized Difference Vegetation Index (NDVI), air temperature, and industrial productions in Thermal Power Generation (TPG) and cement that are relative main contributors for the anthropogenic CO2 of China. The seasonal variations of CO2 are highly connected with the changes of NDVI and air temperature. While the increase of the anthropogenic CO2 emission over China since 2003 is probably caused by the rapid growth of coal combustion and cement manufacture. © 2014 Published by Elsevier Ltd.
Keywords: Carbon dioxide Seasonal variation SCIAMACHY China
1. Introduction Carbon dioxide (CO2) is a prominent anthropogenic greenhouse gas in the atmosphere (Forster et al., 2007). Due to human activities, the global atmospheric concentration of CO2 has exceeded the pre-industrial levels by about 40% in 2011 (Stocker et al., 2013). Further increase of CO2 is expected to result in a warmer climate with adverse consequences, such as rising sea levels and an increase of extreme weather conditions (Schneising et al., 2011). Historically, the United States (US) has long been the largest emitter
* Corresponding author. E-mail address: [email protected] (Z. Xingying). http://dx.doi.org/10.1016/j.atmosenv.2014.12.053 1352-2310/© 2014 Published by Elsevier Ltd.
of CO2 from fossil fuel combustion and cement production (Gregg et al., 2008). However, with the great economic growth, China has now become the largest national source of CO2 emissions in the world since 2006 (Auffhammer and Carson, 2008; Guan et al., 2009). In order to make a reliable prediction of climate, it is significant to have an accurate understanding for the variations of CO2. Researchers have found that the amount of CO2 in the atmosphere varies over the course of a year. Global CO2 concentrations show seasonal oscillations that are most heavily influenced by the growing season in the Northern Hemisphere (Randerson et al., 1999; Suni et al., 2003). However, much uncertainty remains in the seasonal cycles and the inter-annual variability of the atmospheric CO2 over China, which have been of particular concern and critical on interpreting the sources and sinks of regional carbon
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dioxide. The data from the traditional in situ CO2 monitoring network have been crucial to detect the temporal and spatial variability of atmospheric CO2 concentration. However, the sparse spatial coverage and absent observations over oceans and polar regions make it impossible to fully understand the mechanisms that result in the inter-annual variations and the global distributions of CO2 (Butz et al., 2009; Feng et al., 2009; Nevison et al., 2008; O'Dell et al., 2012). With wide global coverage and high measurement density, satellite observation is an effective approach to improve the monitoring of global greenhouse gases (Baker et al., 2010). Sensitive to different altitude, satellite observations of atmospheric CO2 can be achieved by the reflected solar radiation in the near-infrared/shortwave-infrared (NIR/SWIR) regions and emissions in the thermal infrared (TIR) bands (Schneising et al., 2011). The TIR observations are sensitive to CO2 in the middle to upper troposphere such as Atmospheric InfraRed Sounder (AIRS) onboard the Aqua satellite (Aumann et al., 2005; Chahine et al., 2006; Hungershoefer et al., 2010), whereas the SWIR observations are also sensitive to CO2 abundances near the surface such as SCIAMACHY onboard the European ENVIronmental SATellite (ENVISAT) (Bovensmann et al., 1999; Noel et al., 1999) and Thermal And Near infrared Sensor for carbon Observation (TANSO) onboard the Greenhouse gases Observing SATellite (GOSAT) (Hamazaki et al., 2004; Yokota et al., 2009; Yoshida et al., 2011). As a pioneering instrument for monitoring greenhouse gases, SCIAMACHY was launched in 2002 and provides a large number of atmospheric data products including the column amounts of CO2 until 2011. In this study, the dataset of column-averaged dry air mole fraction of atmospheric carbon dioxide (XCO2) derived by University of Bremen is applied for analyzing the seasonal variation and inter-annual changes of CO2 over China. The potential factors affecting the spatial and temporal distribution of CO2 over China are also discussed based on the long-term time series data of SCIAMACHY. 2. Data 2.1. Satellite data SCIAMACHY is a grating spectrometer that measures reflected, backscattered and transmitted solar radiation with the wavelength range between 240 and 2380 nm at moderate spectral resolution (0.2e1.5 nm) (Burrows et al., 1995). The instrument was launched on board ENVISAT which was operational from March 2002 to April 2012. The primary scientific objective of SCIAMACHY is the global measurement of various trace gases in the troposphere and stratosphere (Bovensmann et al., 1999). In order to obtain XCO2, several retrieval algorithms have been developed based on the € sch et al., 2006; Barkley et al., measurements from SCIAMACHY (Bo 2006; Buchwitz et al., 2006; Reuter et al., 2010; Schneising et al., 2008). One of them is the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) retrieval algorithm (Buchwitz et al., 2000). It was developed at the University of Bremen and has been improved to generate a global XCO2 dataset. The latest version 3.8 (WFMDv3.8) XCO2 level 3 product contains the gridded dataset (0.5 0.5 ) at monthly resolution from October 2002 to April 2012. The single measurement retrieval precision derived from the method of averaging daily standard deviations of the retrieved XCO2 at different locations distributed around the globe provides a consistent estimate of about 2.5 ppm (Schneising et al., 2011). Here, the WFMDv3.8 XCO2 level 3 data are applied for a long-term analysis (2003e2011) over China. More detailed information about the algorithm and product has been previously described (Heymann et al., 2012b, 2012a; Schneising et al., 2008) and given on the website of the University of Bremen
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(http://www.iup.uni-bremen.de/). The NDVI_sfc product from AVHRR Pathfinder Atmospheres e Extended (PATMOS -X; http://cimss.ssec.wisc.edu/patmosx/) (Heidinger et al., 2010) is selected for the analysis of CO2 concentration from January 2003 through December 2011, when AVHRR/3 instruments were onboard the polar orbiting NOAA-16 and NOAA18 satellites. The dataset for daily NDVI with a spatial resolution of 0.1 by 0.1 is provided by the University of WisconsineMadison's Cooperative Institute for Meteorological Satellite Studies (CIMSS).
2.2. In-situ data The background CO2 monthly mean data are available from the World Data Centre for Greenhouse Gases (WDCGG) website (http:// ds.data.jma.go.jp/gmd/wdcgg/). The WDCGG archives measurement data for greenhouse and related gases in the atmosphere and the ocean. The data are classified into six categories according to the observation platforms or methods used (more information is described in WDCGG guide). The in-situ CO2 data applied here are achieved at stationary platforms and contributed by the Earth System Research Laboratory of National Oceanic and Atmospheric Administration (NOAA/ESRL). The sampling type is ‘fl’, which represents analysis of data sample in flasks. The data of monthly mean air temperature are provided by the National Climate Center of China (http://ncc.cma.gov.cn/cn/ member.php?Login¼1). The dataset is available as an ASCII file, which contains the monthly mean temperatures of 160 stations over China since January 1951. All values of the dataset are integers with the unit of 0.1 centigrade. The industrial data that are relative primary contributors for the anthropogenic CO2 are collected by China Statistical Yearbook 2013 (CSY2013) from the website of National Bureau of Statistics of China (http://data.stats.gov.cn/). The dataset archives monthly industrial productions over China since January 1983. The data series of Thermal Power Generation (TPG) and cement manufacture as the main sources for the anthropogenic carbon emissions are applied here for the analysis of CO2 variations.
Fig. 1. Comparison of monthly mean XCO2 from SCIAMACHY and Waliguan station (36.28 N, 100.9 E) during January 2003 to December 2011. The average absolute difference between the monthly means is given as the Bias (2.22 ppm), the standard deviation of monthly means by the Std (2.25 ppm), and the correlation coefficient of monthly means by R (0.93).
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3. Results and discussions 3.1. Comparison of the SCIAMACHY CO2 product in China To assess the accuracy of SCIAMACHY XCO2 products in China, the observations from WDCGG at Waliguan (WLG) station (36.28 N, 100.9 E) of China are selected for the comparisons with the monthly mean XCO2 retrievals. Fig. 1 illustrates the comparisons from January 2003 to December 2011. The result reveals that the data from SCIAMACHY are consistent with the in-situ measurements. The estimated regional-scale (radius of 300 km) correlation coefficient of monthly averages gets to 0.93 with the standard deviation of 2.25 ppm. 3.2. Trend of CO2 over China Dominated by rapid economic growth, China has now become the world leader in CO2 emissions since 2006. The top panel of Fig. 2 shows the monthly variation of XCO2 from SCIAMACHY over China with the corresponding linear-fitting trend. The result displays the similar seasonal variation every year with an overall increasing trend from 2003 to 2011. The annual mean CO2 concentration over China increased from 375.96 ppm to 391.73 ppm during these 9 years, with the annual growth rate of 1.75 ppm/a. In order to analysis the seasonal cycle of CO2 concentration over China, we extracted the seasonal signals by subtracting the linearfitting value of the growth trend. The time series of seasonal signals (denoted as DXCO2) from January 2003 to December 2011 are shown in the bottom panel of Fig. 2. The details describing the variation of seasonal signals and related analysis are presented in Section 3.4. 3.3. Seasonal variation of CO2 over China Based on the monthly mean XCO2 products of SCIAMACHY, the significant seasonal distribution (spring: March, April, and May; summer: June, July, and August; autumn: September, October, and November; winter: January, February, and December) of CO2 over China during 2003e2011 is shown in Fig. 3 aed. The CO2 concentration over China is higher in spring and lower in summer and autumn. The respiration of plants and soil are very strong but photosynthesis is relatively weak, which cause the high concentration of CO2 in spring. Meanwhile, the figure also show the more high concentration of CO2 located in the Northeast Plain, since
there is a dense population, high industrial and agricultural activities, extensive fossil fuel burning, maybe enhance the CO2 accumulation. Additionally, the low concentration of CO2 in summer primarily occurs in the southwest and northeast areas of China especially over Yunnan and Fujian province. The strong photosynthesis by large vegetation coverage over these areas is probably the reasons for the low concentration of CO2. The changes of CO2 concentration between seasons are also given in Fig. 3eeh, which represent the differences of CO2 between the following season and the previous one (bea, ceb, dec, and aed, respectively). It is clearly observed that the atmospheric CO2 concentration declines sharply in most parts of China from spring to summer, except the northwest arid regions (Fig. 3e). Then, when it gets to autumn, much of vegetation begins to die and decay from the north part of China due to the lower temperature and less solar radiation. Quantities of carbon dioxide are released back to the atmosphere, raising the atmospheric CO2 content over the north regions of China (Fig. 3f). Compared with the north region, the atmospheric CO2 concentration over south China shows a noticeable increase till winter due to the longer growing period of vegetation (Fig. 3g). From winter to spring, the concentration of CO2 over China accumulates to a higher level, especially for the northeast parts due to the extensive fossil fuel burning for domestic heating systems (Fig. 3h). Fig. 4 displays the monthly average XCO2 over China derived from SCIAMACHY WFMDv3.8 dataset and the measurements from Waliguan station for 2003e2011. The seasonal cycle of XCO2 for SCIAMACHY is highly consistent with the variation for the Waliguan station. The average peak-to-peak amplitude of seasonal cycle is 9.35 ppm over China based on the data from SCIAMACHY, which is 0.49 ppm less than the amplitude for the Waliguan station. The result reveals that both of the observations reach to the highest value in April and decrease to the lowest value in August. During the primary growing season in summer, photosynthesis outweighs respiration. This can lead to noticeably lower CO2 concentrations in the atmosphere. When it gets to winter, dominated respiration results in the accumulation of atmospheric CO2. Meanwhile, the energy consumption for heating increased greatly over China in winter. These both lead to the high CO2 levels in spring.
Fig. 2. The monthly variation (top) and seasonal signals (bottom) of XCO2 from SCIAMACHY over China during January 2003 to December 2011. The black solid line (top) represents the corresponding linear-fitting trend derived using the monthly means of XCO2 over China.
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Fig. 3. Seasonal averages of XCO2 from SCIAMACHY over China during 2003e2011. (a) Spring (MAM): March, April, and May; (b) summer (JJA): June, July, and August; (c) autumn (SON): September, October, and November; (d) winter (DJF): January, February, and December. Differences of XCO2 between seasons: (e) JJA e MAM; (f) SON e JJA; (g) DJF e SON; (h) MAM e DJF.
Fig. 4. Multi-year monthly average XCO2 over China derived from SCIAMACHY WFMDv3.8 dataset and the measurements from Waliguan station for 2003e2011.
3.4. Influencing factor of source and sink of CO2 over China 3.4.1. Nature factor The time series of seasonal signals of XCO2 over China from 2003 to 2011 are presented in Fig. 5a. The result displays that the seasonal signal of XCO2 over China decreases to the minimum in August every year and then gradually accumulates during October to April of the next year. Fig. 5b shows the multi-year monthly averages of DXCO2 for 2003e2011. It can be discerned that the seasonal signal of XCO2 increases during colder seasons with a drop during the period from December to February of the following year. To interpret the seasonal variation of XCO2, the time series of monthly means of NDVI over China were calculated from 2003 to 2011 (Fig. 5c) based on the observations from AVHRR. The maximum of NDVI mainly appears in August, when the value of DXCO2 reaches to the minimum. Meanwhile, when the value of NDVI decreases to the lowest value mostly in March, the maximum of DXCO2 is delayed by a month. Fig. 5d shows the multi-year monthly averages of NDVI for 2003e2011. The result reveals that the drop of seasonal signal of XCO2 from December to February is highly consistent with the smaller peak of NDVI. Studies have shown that the value of NDVI is a measure of vegetation abundance and directly related to the photosynthetic capacity (Myneni et al.,
1995; Sellers, 1985). Since the vegetation is one of the main sink of CO2, so the CO2 concentration shows opposition to the NDVI value. Air temperature follows the seasonal changes in solar radiation, which drives the functioning of marine and terrestrial ecosystems. It has been long recognized that photosynthesis and respiration are temperature sensitive (Wager, 1941). The short-term variation in temperature can affect the carbon dioxide concentration. For further explanations, here we used the near-surface air temperatures from 160 in-situ stations over China to analysis the seasonal cycle of CO2 concentration. The specific locations of the groundbased stations are shown in Fig. 6. Fig. 7 illustrates the time series of seasonal signals of XCO2 derived from SCIAMACHY WFMDv3.8 dataset (a) and monthly averages of air temperature over China derived from the observations of 160 in-situ stations (c). The seasonal signal of XCO2 decreases to the lowest value one month after the temperature gets to the peak value. The comparison of multi-year monthly averages of DXCO2 (Fig. 7b) and NDVI (Fig. 7d) for 2003e2011 over China shows that the drop of seasonal signal of XCO2 from December to February is noticeably linked to the lowest temperature in January. At the ecosystem level, the balance between respiration and photosynthesis determines whether an ecosystem is a net source or sinks of CO2 into the atmosphere (Atkin and Tjoelker, 2003). The rate of respiration by plants increases substantially with short-term increases in temperature (BUNCE et al., 1996). During the growing season, higher temperature causes increased photosynthesis and respiration, but photosynthesis is greater. This leads to the lower concentration of atmospheric CO2 during the warming period. Low temperatures can result in poor growth. Photosynthesis is slowed down at low temperatures. Respiration also gets weak, but dominates during the colder months of the year, resulting in higher CO2 levels in the atmosphere during those months. In January, the extreme low temperature leads to the weak respiration which is the primary source for CO2 in terrestrial biospheres. This may be one of the reasons for the drop of the seasonal signals of XCO2.
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Fig. 5. Comparison between NDVI and seasonal signals of XCO2 over China. (a) Time series of monthly means of DXCO2 derived from SCIAMACHY WFMDv3.8 dataset; (b) multi-year monthly averages of DXCO2 for 2003e2011; (c) time series of monthly means of NDVI derived from NDVI_sfc product from AVHRR; (d) multi-year monthly averages of NDVI for 2003e2011.
Fig. 6. Locations of the ground-based stations for air temperature measurements over China.
Fig. 7. Comparison between air temperature and seasonal signals of XCO2 over China. (a) Time series of monthly means of DXCO2 derived from SCIAMACHY WFMDv3.8 dataset; (b) multi-year monthly averages of DXCO2 for 2003e2011; (c) time series of monthly means of air temperature derived from the observations of 160 in-situ stations over China; (d) multi-year monthly averages of air temperature for 2003e2011.
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3.4.2. Anthropogenic factor Beside nature factors, human activities are also primary sources for greenhouse gases (Wei et al., 2011). The anthropogenic CO2 source is derived from a number of different processes including combustion of coal, oil, and natural gas, as well as oxidation of organic solvents and cement manufacture (Erickson et al., 2008). Thermal Power Generation (TPG) as the significant industrial process for coal combustion is strongly correlated with the release of CO2. Since 2000, China has been the world's second producer in electricity generation (behind the US) (Gregg et al., 2008). Release of CO2 from cement manufacture is another primary anthropogenic source over China. These two industrial productions that are relative primary contributors for the anthropogenic CO2 over China are selected for the analysis. Spatially, The TPG and cement productions from China are concentrated in the provinces around Beijing and along the east coast of China (Fig. 8), particularly in Guangdong province which has been the beneficiary of international investment and manufacturing (Gregg et al., 2008). To examine the regional differences in the seasonal pattern influenced by energy consumption, the mainland of China (not including Taiwan, Hong Kong, and Macao) has been divided into six regions including the northeast (NE), northwest (NW), southwest (SW), north (NC), south (SC), and east (EC) parts of China (Fig. 8). Fig. 9 shows the seasonal variations in XCO2, TPG, and cement productions for these six regions. The result reveals that the seasonal patterns are similar over all these regions. Additionally, the region with a larger amount of industrial productions always has a higher CO2 concentration except for the northwest area of China with high level of CO2 concentration due to its arid climate and sparse vegetation. Fig. 10 shows the time series of monthly means of XCO2, TPG, and cement production over China from 2003 to 2011. The data series for TPG and cement production each reveal a consistent seasonal pattern. For TPG, there is a significant peak in December with a precipitous drop in February. Additionally, a slight peak appears in late summer. The cement production shows a different seasonal pattern with two peaks in late spring and autumn. There is also a distinct decrease in February. It is clear that the rapid growth of coal combustion and cement manufacture is a significant reason for the rise of the atmospheric CO2 concentration since 2003. However, the annual cycle of industrial productions shows less
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correlation with the seasonal pattern of CO2 concentration. Nonetheless, the drop of anthropogenic emissions in late winter is probably one of the reasons for the variations of XCO2 from December to February of the following year. Five-year averages of monthly XCO2 for 2003 to 2007 and 2007 to 2011 are given in Fig. 11. It is clear that the CO2 cycle follows the same seasonal pattern. The increment from the early five years to recent period (Fig. 11) reveals that the value of XCO2 is increasing more rapidly during colder months probably due to the large energy consumption for heating over the north regions of China.
4. Conclusions In this study, the seasonal cycle and inter-annual variations of XCO2 over China based on a long-term (2003e2011) retrieved dataset from SCIAMACHY onboard ENVISAT have been analyzed. The main results of this study can be summarized as follows. (1) The atmospheric CO2 concentration exhibits a strong seasonal variation, the highest concentration of CO2 over China in spring, and the lowest value always in summer. (2) XCO2 over China increases by about 4.2% from 2003 to 2011. The average peak-to-peak amplitude of seasonal cycle from 2003 to 2011 was 9.35 ppm over China. (3) The regions with high concentration of CO2 were mostly distributed in East of China. While, the southwest areas of China, especially over Yunnan province, were the main regions showing low concentration. The arid areas over northwest China showed little changes during the season transitions, probably due to the lack of vegetation. (4) The seasonal variations of XCO2 were highly connected with the changes of NDVI and air temperature, which were primary factors indicating the intensity of photosynthesis and respiration. The drop of seasonal signals of XCO2 during colder seasons from December to February of next year were probably due to the shallow growth of NDVI and extreme low temperature during this period. (5) The annual cycle of industrial productions showed less correlation with the seasonal pattern of XCO2. However, the rapid growth of coal combustion and cement manufacture
Fig. 8. Spatial distributions of monthly mean TPG and cement productions over China from 2003 to 2011. The six regions with black solid lines as boundaries represent the northeast (NE), northwest (NW), southwest (SW), north (NC), south (SC), and east (EC) parts of China.
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Fig. 9. Seasonal variations of XCO2, TPG, and cement productions for the six regions displayed in Fig. 8. The seasonal averages of XCO2 are shown in black dots. The seasonal average productions of TPG and cement are shown in blue and red bars. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 10. Time series of monthly means of XCO2, TPG, and cement production over China from 2003 to 2011.
patmosx/). We also thank the WDCGG and the contributors for the CO2 data of the stations (obtained from http://ds.data.jma.go.jp/ gmd/wdcgg/). The study was supported by the National High Technology Research and Development Program (“863” Program) of China (Grant No. 2011AA12A104-3), the European Commission's Seventh Framework Programme “PANDA” (Grant No. FP7-SPACE2013-1), the Public industry-specific fund for meteorology (Grant No. GYHY201106045) and the Climate Change Science Funding from Chinese Meterological Administration (Grant No. CCSF201351). Fig. 11. Five-year averages of monthly XCO2 for 2003 to 2007 and 2007 to 2011 over China. The increment from 2003 to 2007 to 2007e2011 is shown in bars.
shows significant correlation with the atmospheric CO2 concentration over China since 2003.
Acknowledgment Thank for the SCIAMACHY CO2 data product from the University of Bremen (obtained from http://www.iup.uni-bremen.de/ sciamachy/NIR_NADIR_WFM_DOAS/products/) and the AVHRR data from Cooperative Institute for Meteorological Satellite Studies of SSEC/UW-Madison (obtained from http://cimss.ssec.wisc.edu/
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