Atmospheric CO2 Concentration Data Research Articles

An Assessment of Changes in the Thermal Environment during the COVID-19 Lockdown: Case Studies from the Greenland and Norwegian Seas

The COVID-19 lockdown had a significant impact on human activities, reducing anthropogenic heat and CO2 emissions. To effectively assess the impact of the lockdown on the thermal environment, we used the sliding paired t-test, which we improved from the traditional sliding t-test by introducing the paired t-test for sliding statistical tests, to test the abrupt change in the thermal environment. Furthermore, an additive decomposition model and wavelet analysis method were used to analyze the characteristics of trend and irregular change, coherence, and phase difference of the time series data with respect to the thermal environment. We chose the Greenland Sea and the Norwegian Sea, regions highly sensitive to changes in climate and ocean circulation, as case studies and used remote sensing data of the sea surface temperature (SST) and the atmospheric CO2 concentration data obtained from the Goddard Earth Sciences Data and Information Services Center from January 2015 to December 2021 for the analysis. The results show that although the annual spatial mean SST in 2020 is lower than the mean of all 7 years in most areas of the two seas, there is no evidence of a significant mutation in the decrease in the SST during the lockdown in 2020 compared with the temperatures before, according to the sliding paired t-test. The analysis of the irregular components of the monthly mean SST decomposed by an additive decomposition model also does not show the anomalously low SST during the lockdown in 2020. In addition, the lockdown had almost no impact on the increasing trend of CO2 concentration. The wavelet analysis also shows that there is no obvious anomaly in coherence or phase difference between the periodic variation of the SST and the CO2 concentrations in 2020 compared with other years. These results suggest that the direct effect of the COVID-19 lockdown on the thermal environment of the study area could be negligible.

An Assessment of Changes in the Thermal Environment during the COVID-19 Lockdown: Case Studies from the Greenland and Norwegian Seas

The COVID-19 lockdown had a significant impact on human activities, reducing anthropogenic heat and CO2 emissions. To effectively assess the impact of the lockdown on the thermal environment, we used the sliding paired t-test, which we improved from the traditional sliding t-test by introducing the paired t-test for sliding statistical tests, to test the abrupt change in the thermal environment. Furthermore, an additive decomposition model and wavelet analysis method were used to analyze the characteristics of trend and irregular change, coherence, and phase difference of the time series data with respect to the thermal environment. We chose the Greenland Sea and the Norwegian Sea, regions highly sensitive to changes in climate and ocean circulation, as case studies and used remote sensing data of the sea surface temperature (SST) and the atmospheric CO2 concentration data obtained from the Goddard Earth Sciences Data and Information Services Center from January 2015 to December 2021 for the analysis. The results show that although the annual spatial mean SST in 2020 is lower than the mean of all 7 years in most areas of the two seas, there is no evidence of a significant mutation in the decrease in the SST during the lockdown in 2020 compared with the temperatures before, according to the sliding paired t-test. The analysis of the irregular components of the monthly mean SST decomposed by an additive decomposition model also does not show the anomalously low SST during the lockdown in 2020. In addition, the lockdown had almost no impact on the increasing trend of CO2 concentration. The wavelet analysis also shows that there is no obvious anomaly in coherence or phase difference between the periodic variation of the SST and the CO2 concentrations in 2020 compared with other years. These results suggest that the direct effect of the COVID-19 lockdown on the thermal environment of the study area could be negligible.

If Some Critical Regions Achieve Carbon Neutrality, How Will the Global Atmospheric CO2 Concentration Change?

Due to anthropogenic emissions, the global CO2 concentration increases at a rate of approximately 2 ppm per year. With over 130 countries and regions committing to carbon neutrality goals and continuously reducing anthropogenic CO2 emissions, understanding how atmospheric CO2 concentrations will change globally and in other regions has become an intriguing question. Examining different regions’ efforts to reduce anthropogenic CO2 emissions through atmospheric CO2 observations is also meaningful. We used prior and posterior fluxes to drive the TM5 model. The posterior fluxes were based on the China Carbon Monitoring, Verification and Support System for Global (CCMVS-G), which assimilated the atmospheric CO2 concentration data from ground-based observation and satellite observation. We found that the CO2 concentration obtained using the posterior fluxes was more in line with the actual situation. Then, we presented some experiments to estimate how global and regional CO2 concentrations would change if certain key regions and the whole world achieved net zero emissions of anthropogenic CO2. After removing carbon fluxes from China, North America, and Europe, global CO2 concentrations decreased by around 0.58 ppm, 0.22 ppm, and 0.10 ppm, respectively. The most significant decrease occurred in the regions where fluxes were removed, followed by other areas at the same latitude affected by westerly winds. This indicates that fossil fuel flux is the main factor affecting CO2 concentrations, and that meteorological-driven transportation also significantly impacts CO2 concentrations. Most importantly, using this method, it is possible to quantitatively estimate the impact of achieving carbon neutrality in one region on CO2 concentrations in local regions as well as globally.

High-spatiotemporal resolution mapping of spatiotemporally continuous atmospheric CO2 concentrations over the global continent

Carbon dioxide (CO2) in the atmosphere is an important variable that connects the atmosphere and terrestrial ecosystems. However, satellite-observed atmospheric CO2 concentrations are always spatially discrete, and spatiotemporally continuous atmospheric CO2 concentration maps with fine resolution are scarce at the global scale. Therefore, a spatiotemporally continuous CO2 concentration dataset with high-spatiotemporal resolution was generated based on satellite CO2 observations and environmental factors in this study. First, the environmental factors affecting atmospheric CO2 concentrations were selected, and by integrating the atmospheric CO2 concentration data observed by the OCO-2 satellite, a sample dataset was created. The extreme random tree (ERT) model was then trained to determine the relationship between environmental factors and atmospheric CO2 concentrations. Finally, spatiotemporally continuous atmospheric CO2 concentration data (0.01° and 8-day resolution) over the global continent were generated based on the established ERT model and environmental data. Validation results based on ground atmospheric CO2 concentration observations indicated that the estimated atmospheric CO2 concentration achieved satisfactory performance with coefficients of determination and root mean square errors of 0.83 and 1.79 ppm, respectively. The spatiotemporal analysis found that the distribution of atmospheric CO2 concentrations had obvious seasonal patterns and spatial heterogeneity across different climatic zones. The generated atmospheric CO2 concentration data have the potential for subsequent studies of global climate change and carbon cycling.

Modeling the Carbon Cycle Dynamics and the Greenhouse Effect

A carbon cycle model is proposed that predicts changes in the atmospheric CO2 concentration, and provides a global temperature estimate from an empirical correlation of the two variables. The model is validated by simulating the anthropogenic carbon emissions and deforestation since the industrial revolution, and comparing the predicted and measured atmospheric CO2 concentration and global temperature data. The temperature data are also compared with those predicted by a greenhouse effect model based on the effective emission temperature hypothesis. The result suggests that radiative forcing by CO2 alone can account for only about half of the measured global warming. The model requires further elaboration for the other half, in order to be applicable to simulation of potential climate control.

Understanding the impacts of ‘Grain for Green’ land management practice on land greening dynamics over the Loess Plateau of China

Land greening in China is regarded as contributing a great deal to greening of the Earth. The phenomenon is mainly attributed to climate change, arising atmospheric CO2 and ‘Grain for Green’ (GFG) land management policies. However, limited knowledge is known how much land greening is from contributions of the GFG practice. Therefore, the study took the typical region of the GFG practice, the Loess Plateau, as the study area, and used 1982–2015 satellite-observed GIMMS3g normalized difference vegetation index (NDVI) data, ERA-Interim climatic variables (precipitation, temperature and solar radiation) and atmospheric CO2 concentration data with the help of a developed TPRC-based NDVI model to derive GFG-induced NDVI after 1999. Furthermore, this study tracked the spatial-temporal dynamics of GFG-induced NDVI and assessed contributions of the GFG practice to regional vegetation changes. Results showed that satellite-observed NDVI and TPRC-based NDVI both exhibited an increasing spatial pattern from the northwestern to southeastern Loess Plateau, but their greening trends were separately 0.0022 and 0.0009 per year in 1982–2015 (p < 0.05). Note that the satellite-observed greening trend was much steeper with a slope of 0.0056 per year after 2006 (p < 0.05). The subsequent analyses documented that GFG-induced land greening were largely responsible for the steep trend. In space, evident greening patterns began to be observed in the central Loess Plateau from 2006 to 2008, afterwards expanded towards eastern and southwestern Loess Plateau. In 2011–2015, the increase magnitude of GFG-induced land greening in the Loess Plateau averagely accounted for 8.5 % in comparison to estimated TPRC-based NDVI, but in six natural zones were various, ranging from 3.2%–15.7%. In some regions of central Loess Plateau, GFG-induced NDVI contributed even more than 20 % to vegetation increase. This study highlights that land use management contributes more to land greening dynamics over the Loess Plateau compared to climate change and arising atmospheric CO2 concentration. These findings likely provide some valuable information for curbing or enhancing specific-location vegetation changes in future regional land management and planning.

Compiled records of atmospheric CO2 concentrations and stable carbon isotopes to reconstruct climate and derive plant ecophysiological indices from tree rings

The stable carbon isotopic composition (δ13C) measured in tree rings is a standard proxy for paleoclimate reconstructions and is increasingly being used as a paleophysiological proxy. To fully exploit the potential of tree ring δ13C proxy, atmospheric CO2 concentration and δ13C (δ13CO2) data are required to correct tree ring δ13C from the declining trend of δ13CO2 due to fossil fuel burning since 1850 CE, and to derive physiological parameters using biochemical models that link photosynthesis to δ13C. These atmospheric data are available from direct measurements or can be inferred from indirect proxies such as ice cores covering the Common Era (CE) at variable temporal resolutions. For almost two decades, tree-ring researchers have relied on a dataset derived from fitted linear regressions of ice core measurements available through the seminal McCarroll and Loader (2004) article for the 1850−2003 CE period. However, new calibrations and compilations of ice core data and direct measurements are now available as part of Earth System Modelling efforts which remain overlooked by the tree ring research community.Here, we present an overview of the new and freely available datasets and provide recommendations for their use in ecophysiology and paleoclimate research, that we expect will stimulate cross-disciplinary collaborations.

On the carbon cycle impact of combustion of harvested plant biomass vs. fossil carbon resources

This work examines the comparative impact on the carbon cycle, of harvested plant biomass combustion vs. fossil carbon combustion. A common carbon cycle model is shown to possess limitations and inconsistencies that lead to physically unrealistic predictions. The model is modified, so as to incorporate harvested biomass combustion as an energy resource, and the dependence of the photosynthesis rate on leaf area, rather than vegetated land area. The newly proposed model is validated with fossil fuel usage and atmospheric CO2 concentration data, since the beginning of the industrial revolution. Although, biomass is generally considered to be a green energy source, it is shown in this work that biomass burning may result in a higher, long term, CO2 atmospheric concentration than fossil carbon burning, depending on the rate of burning and reforestation intensity. A close examination of the characterization of harvested biomass as a renewable energy source is warranted.

Temporal and spatial distribution of the mid-tropospheric CO2 concentrations in Malaysia

Satellite observations of CO2 offer a unique opportunities to improve our understanding of the carbon sources and sinks. Due to the lack of studies of carbon dioxide (CO2) concentration in this region, we first confirmed the reliability of the mid-tropospheric Atmospheric Infrared Sounder (AIRS) CO2 data using atmospheric CO2 concentration data from the only available Global Atmospheric Watch (GAW) ground-based station observation in Malaysia. In this study, the spatial and temporal distribution of mid-troposphere CO2 in Malaysia from January 2009 to December 2012 was analyzed based on AIRS satellite product. The results show that the average CO2 concentrations were high in the eastern part of the study area and lower in the west. From January 2009 to December 2012, the mid-tropospheric CO2 concentrations increased gradually with annual growth rate about 1.293 ppmv/a. There was a significant seasonal CO2 variation with peak concentration was observed during the North-East monsoon (NEM) and the lowest was during South-West monsoon (SWM). The temporal distribution of CO2 concentrations was mainly affected by the amount of sunlight and precipitation received during both monsoons. The study suggested that mid-tropospheric AIRS CO2 data product was able to help in understanding the variations of atmospheric CO2 concentrations comprehensively.

Anthropogenic CH4 Emissions in the Yangtze River Delta Based on A “Top-Down” Method

There remains significant uncertainty in the estimation of anthropogenic CH4 emissions at local and regional scales. We used atmospheric CH4 and CO2 concentration data to constrain the anthropogenic CH4 emission in the Yangtze River Delta one of the most populated and economically important regions in China. The observation of atmospheric CH4 and CO2 concentration was carried out from May 2012 to April 2017 at a rural site. A tracer correlation method was used to estimate the anthropogenic CH4 emission in this region, and compared this “top-down” estimate with that obtained with the IPCC inventory method. The annual growth rates of the atmospheric CO2 and CH4 mole fractions are 2.5 ± 0.7 ppm year−1 and 9.5 ± 4.7 ppb year−1, respectively, which are 9% and 53% higher than the values obtained at Waliguan (WLG) station. The average annual anthropogenic CH4 emission is 4.37 (± 0.61) × 109 kg in the YRD (excluding rice cultivation). This “top-down” estimate is 20–70% greater than the estimate based on the IPCC method. We suggest that possible sources for the discrepancy include low biases in the IPCC calculation of emission from landfills, ruminants and the transport sector.

A Time Series Correlation Analysis Using the Keeling Curve as an Alternative Evaluation Method for Carbon Emission Modeling

This study uses atmospheric CO2 concentration data (the Keeling Curve) as an alternative measurement of anthropogenic carbon emissions to test the relationship between environmental pressure and economic development. Using verified detrending procedures, no significant relationship is observed between global population growth and increases in atmospheric carbon concentration. Changes in world GDP, however, have a significant effect on CO2 concentration in the atmosphere. GDP per capita is also a strong indicator of the Keeling Curve. The use of the affluence level, GDP per capita, correlates to the environmental impact when the environmental pressure is altered to atmospheric CO2 concentration, even though population does not correlate with the Keeling Curve.

An attempt to introduce atmospheric CO2 concentration data to estimate the gross primary production by the terrestrial biosphere and analyze its effects

Quantitative estimations of the GPP (gross primary production) and its variations at spatial scales are important issues with future significance due to the increasing atmospheric CO2 levels. However, the effects of the spatiotemporal variability in the atmospheric CO2 concentrations on GPP estimations are challenging with respect to the terrestrial ecosystem due to land cover component characteristics and difficulties associated with measuring CO2 concentrations over large spatial areas. The development of remote sensing offer a means to routinely monitor CO2 concentrations both spatially and temporally from space. To introduce continuous spatial CO2 data as an indicator for the estimation of the terrestrial biosphere GPP, we used the decoupling coefficients to evaluate the canopy CO2 concentrations, photosynthetic biochemical models to calculate the photosynthetic rate, and Big-leaf model to scale up to a global scale. The GPPs estimated by this method are relatively consistent with the GPP derived from Flux tower sites. Thus, the method proposed in this study utilizing continuous spatial CO2 data to estimate the GPP is practicable and feasible. Finally, we compared the GPPs under different atmospheric CO2 concentrations conditions between 2000 and 2014 and analyzed the effects of the spatiotemporal variability in the atmospheric CO2 concentrations on the GPP estimates. The results show that, in general, the terrestrial GPP increases as atmospheric CO2 concentrations increase, and the increases in the lower latitudes are more significant than those in the middle and high latitudes; by comparing the annual GPP estimates in 2000 with those in 2014, it was observed that the increases in forest GPP is greater than that of other functional types. The effects of the variations in the spatial distribution of atmospheric CO2 concentrations on the terrestrial GPP distribution vary based on time and location. Regarding the annual GPP estimates, without considering the CO2 spatial distribution, the estimates overestimate the GPP in the lower latitudes and underestimate those in the middle and high latitudes. Regarding the monthly GPP estimates, using the annual averages caused the GPP estimates of the Northern Hemisphere to be overestimated during the first half of the year, while those during the second half of the year were underestimated; the GPP estimates for the Southern Hemisphere were underestimated each month. However, using monthly averages caused the GPP estimates for the Northern Hemisphere to be overestimated in summer and underestimated in spring and autumn, which are opposite to the estimates for the Southern Hemisphere.

The Temporal and Spatial Distributions of the Near-Surface CO2 Concentrations in Central Asia and Analysis of Their Controlling Factors

As the main anthropogenic greenhouse gas that contributes most to global warming, CO2 plays an important role in climate changes in Central Asia. Due to the lack of studies of near-surface CO2 in this region, we first confirmed the applicability of the near-surface Greenhouse Gases Observing Satellite (GOSAT) CO2 data in Central Asia using atmospheric CO2 concentration data from nine ground-based station observations. We then analyzed the temporal and spatial distributions of the near-surface CO2 concentrations in Central Asia and their controlling factors using statistical analysis methods. The results show that the near-surface CO2 concentrations are high in the western part of this region and low in the east. From June 2009 to May 2013, the near-surface CO2 concentrations increased gradually, with the highest value being in spring and the lowest in autumn. The temporal distribution of CO2 concentrations is mainly affected by photosynthesis, respiration, and heating. The combined effect of terrestrial ecosystems and CO2 diffusion by wind is responsible for the higher near-surface CO2 concentration in the northern, western, and southwestern areas of the five Central Asian countries compared to the central, eastern, and southern areas, and energy consumption and wind are the major factors that affect the heterogeneity of the spatial distribution of the CO2 concentrations in Xinjiang.

Spatiotemporal dynamic simulation of grassland carbon storage in China

Based on the Terrestrial Ecosystem Model (TEM 5.0), together with the data of climate (temperature, precipitation and solar radiation) and environment (grassland vegetation types, soil texture, altitude, longitude and latitude, and atmospheric CO2 concentration data), the spatiotemporal variations of carbon storage and density, and their controlling factors were discussed in this paper. The results indicated that: (1) the total carbon storage of China’s grasslands with a total area of 394.93×104 km2 was 59.47 Pg C. Among them, there were 3.15 Pg C in vegetation and 56.32 Pg C in soil carbon. China’s grasslands covering 7.0–11.3% of the total world’s grassland area had 1.3–11.3% of the vegetation carbon and 9.7–22.5% of the soil carbon in the world grasslands. The total carbon storage increased from 59.13 to 60.16 Pg C during 1961–2013 with an increasing rate of 19.4 Tg C yr−1. (2) The grasslands in the Qinghai-Tibetan Plateau contributed most to the total carbon storage during 1961–2013, accounting for 63.2% of the total grassland carbon storage, followed by Xinjiang grasslands (15.8%) and Inner Mongolia grasslands (11.1%). (3) The vegetation carbon storage showed an increasing trend, with the average annual growth rate of 9.62 Tg C yr−1 during 1961–2013, and temperature was the main determinant factor, explaining approximately 85% of its variation. The vegetation carbon storage showed an increasing trend in most grassland regions, however, a decreasing trend in the central grassland in the southern China, the western and central parts of the Inner Mongolian grasslands as well as some parts on the Qinghai-Tibetan Plateau. The soil carbon storage showed a significantly increasing trend with a rate of 7.96 Tg C yr−1, which resulted from the interaction of more precipitation and low temperature in the 1980s and 1990s. Among them, precipitation was the main determinant factor of increasing soil carbon increases of China’s grasslands.

An approach for verifying biogenic greenhouse gas emissions inventories with atmospheric CO2 concentration data

Verifying national greenhouse gas (GHG) emissions inventories is a critical step to ensure that reported emissions data to the United Nations Framework Convention on Climate Change (UNFCCC) are accurate and representative of a country’s contribution to GHG concentrations in the atmosphere. Furthermore, verifying biogenic fluxes provides a check on estimated emissions associated with managing lands for carbon sequestration and other activities, which often have large uncertainties. We report here on the challenges and results associated with a case study using atmospheric measurements of CO2 concentrations and inverse modeling to verify nationally-reported biogenic CO2 emissions. The biogenic CO2 emissions inventory was compiled for the Mid-Continent region of United States based on methods and data used by the US government for reporting to the UNFCCC, along with additional sources and sinks to produce a full carbon balance. The biogenic emissions inventory produced an estimated flux of −408 ± 136 Tg CO2 for the entire study region, which was not statistically different from the biogenic flux of −478 ± 146 Tg CO2 that was estimated using the atmospheric CO2 concentration data. At sub-regional scales, the spatial density of atmospheric observations did not appear sufficient to verify emissions in general. However, a difference between the inventory and inversion results was found in one isolated area of West-central Wisconsin. This part of the region is dominated by forestlands, suggesting that further investigation may be warranted into the forest C stock or harvested wood product data from this portion of the study area. The results suggest that observations of atmospheric CO2 concentration data and inverse modeling could be used to verify biogenic emissions, and provide more confidence in biogenic GHG emissions reporting to the UNFCCC.

Correlation between solar activity and the local temperature of Antarctica during the past 11,000 years

The solar impact on the Earth's climate change is a long topic with intense debates. Based on the reconstructed data of solar sunspot number (SSN), the local temperature in Vostok (T), and the atmospheric CO2 concentration data of Dome Concordia, we investigate the periodicities of solar activity, the atmospheric CO2 and local temperature in the inland Antarctica as well as their correlations during the past 11,000 years before AD 1895. We find that the variations of SSN and T have some common periodicities, such as the 208 year (yr), 521yr, and ~1000yr cycles. The correlations between SSN and T are strong for some intermittent periodicities. However, the wavelet analysis demonstrates that the relative phase relations between them usually do not hold stable except for the millennium-cycle component. The millennial variation of SSN leads that of T by 30–40 years, and the anti-phase relation between them keeps stable nearly over the whole 11,000 years of the past. As a contrast, the correlations between CO2 and T are neither strong nor stable. These results indicate that solar activity might have potential influences on the long-term change of Vostok's local climate during the past 11,000 years before modern industry.

Thermal growing season and timing of biospheric carbon uptake across the Northern Hemisphere

Gridded daily temperature from 1950 to 2011 and atmospheric CO2concentration data from high‐latitude observing stations and the CarbonTracker assimilation system are used to examine recent spatiotemporal variability of the thermal growing season and its relationship with seasonal biospheric carbon uptake and release in the Northern Hemisphere. The thermal growing season has lengthened substantially since 1950 but most of the lengthening has occurred during the last three decades (2.9 days decade−1, p &lt; 0.01 for 1980–2011), with stronger rates of extension in Eurasia (4.0 days decade−1, p &lt; 0.01) than in North America (1.2 days decade−1, p &gt; 0.05). Unlike most previous studies, which had more limited data coverage over the past decade, we find that strong autumn warming of about 1°C during the second half of the 2000s has led to a significant shift toward later termination of the thermal growing season, resulting in the longest potential growing seasons since 1950. On average, the thermal growing season has extended symmetrically by about a week during this period, starting some 4.0 days earlier and ending about 4.3 days later. The earlier start of the thermal growing season is associated with earlier onset of the biospheric carbon uptake period at high northern latitudes. In contrast, later termination of the growing season is associated with earlier termination of biospheric carbon uptake, but this relationship appears to have decoupled since the beginning of the period of strong autumn warming during the second half of the 2000s. Therefore, owing to these contrasting biospheric responses at the margins of the growing season, the current extension in the thermal growing season length has not led to a concomitant extension of the period of biospheric carbon uptake.

Recent global CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; flux inferred from atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; observations and its regional analyses

Abstract. The net surface exchange of CO2 for the years 2002–2007 is inferred from 12 181 atmospheric CO2 concentration data with a time-dependent Bayesian synthesis inversion scheme. Monthly CO2 fluxes are optimized for 30 regions of the North America and 20 regions for the rest of the globe. Although there have been many previous multiyear inversion studies, the reliability of atmospheric inversion techniques has not yet been systematically evaluated for quantifying regional interannual variability in the carbon cycle. In this study, the global interannual variability of the CO2 flux is found to be dominated by terrestrial ecosystems, particularly by tropical land, and the variations of regional terrestrial carbon fluxes are closely related to climate variations. These interannual variations are mostly caused by abnormal meteorological conditions in a few months in the year or part of a growing season and cannot be well represented using annual means, suggesting that we should pay attention to finer temporal climate variations in ecosystem modeling. We find that, excluding fossil fuel and biomass burning emissions, terrestrial ecosystems and oceans absorb an average of 3.63 ± 0.49 and 1.94 ± 0.41 Pg C yr−1, respectively. The terrestrial uptake is mainly in northern land while the tropical and southern lands contribute 0.62 ± 0.47, and 0.67 ± 0.34 Pg C yr−1 to the sink, respectively. In North America, terrestrial ecosystems absorb 0.89 ± 0.18 Pg C yr−1 on average with a strong flux density found in the south-east of the continent.

The 158-Year Climate Experiment

An analysis of the impact of carbon dioxide on global warming was performed using global average temperature data covering the last 158 years, along with corresponding atmospheric CO2 concentration data. A statistically significant correlation between the two was developed and used directly to calculate values of the key CO2 climate sensitivity factors (CSFs). When CO2 was treated as a proxy for all possible radiative and feedback forcing agents, an upper bound for the temperature rise due to a doubling of CO2 concentration was found to be 1.92 K. When the impact of CO2 alone was separated out from that of other radiative forcing agents, its CSF was found to be 0.250 KW−1m2, and the 2xCO2 temperature rise is 0.96 K. These values are less, by more than a factor of 3, than those typically reported from use of general circulation models.

陸域生態系のＣＯ２吸収と地球環境

A five-year strategic project (S-1 project), which was organized to clarify the carbon dynamics of terrestrial ecosystems in East Asia was conducted from April 2002 to March 2007 with financial support from the Ministry of Environment, Japan. It had four themes. The first was a bottom-up approach using micrometeorological and ecological techniques to measure carbon dioxide fluxes at sixteen tower sites in Japan and East Asia. The second was a top-down approach using. an inverse model based on atmospheric CO2 concentration data finely measured at six western Siberian sites. The third was a modeling approach driven by data-based quality-controlled data from the first theme and artificial satellite data. The fourth and final theme synthesized the results of the three themes mentioned above following a systems approach, which led to a comprehensive conclusion.The main results obtained by the many principal investigators of the S-1 project are summarized below.1. Characteristic seasonal changes of Net Ecosystem Production (NEP) were obtained from ten tower flux sites including forest and grassland sites.2. Annual NEP values obtained by micrometeorological and ecological techniques showed almost satisfactory agreement at the six forest sites, although those obtained by the micrometeorological technique were usually higher than ones obtained by the ecological technique.3. A satisfactory agreement was obtained for seasonal changes of biomass at a paddy field site between results obtained by micrometeorological and ecological harvesting techniques.4. Seasonal changes of NEP in western Siberia estimated by an inverse model showed a similar pattern to those of an ecological process-based model, Sim-CYCLE, but there was a fairly large difference between the estimated annual values of the two models.5. Gross Primary Production (GPP) and Net Primary Production (MPP) maps of a spacially high resolution drawn by Sim-CYCLE revealed a characteristic pattern reflecting the diverse vegetation types in East Asia. This region was estimated to be a net carbon sink, with 57 Tg carbon on an annual average during the six years from 2000 to 2005, although annual values fluctuated greatly due to weather conditions.