Using a Tree Ring δ^(13)C Annual Series to Reconstruct Atmospheric CO_2 Concentration over the Past 300 Years

The annual series of δ13C were measured in tree rings of three Cryptomeria fortunei disks （OF-1, OF-2, and OF- 3） collected from West Tianmu Mountain, Zhejiang Province, China, according to cross-dating tree ring ages. There was no obvious decreasing trend of the δ13C annual time series of CF-2 before 1835. However, from 1835 to 1982 the three tree ring δ13C annual series exhibited similar decreasing trends that were significantly （P ≤ 0.001） correlated. The distribution characteristics of a scatter diagram between estimated δ13C series of CF-2 from modeling and the atmospheric CO2 concentration extracted from the Law Dome ice core from 1840 to 1978 were analyzed and a curvilinear regression equation for reconstructing atmospheric CO2 concentration was established with R2 = 0.98. Also, a test of independent samples indicated that between 1685 and 1839 the reconstructed atmospheric CO2 concentration .using the δ13C series of CF-2 had a close relationship with the Law Dome and Siple ice cores, with a standard deviation of 1.98. The general increasing trend of the reconstructed atmospheric CO2 concentration closely reflected the 10ng-term variation of atmospheric CO2 concentration recorded both before and after the Industrial Revolution. Between 1685 and 1840 the evaluated atmospheric CO2 concentration was stable, but after 1840 it exhibited a rapid increase. Given a longer δ13C annual time series of tree rings, it was feasible to rebuild a representative time series to describe the atmospheric CO2 concentration for an earlier period and for years that were not in the ice core record.

Spatial Inhomogeneity of Atmospheric CO_(2) Concentration and Its Uncertainty in CMIP6 Earth System Models

This paper provides a systematic evaluation of the ability of 12 Earth System Models(ESMs)participating in the Coupled Model Intercomparison Project Phase 6(CMIP6)to simulate the spatial inhomogeneity of the atmospheric carbon dioxide(CO_(2))concentration.The multi-model ensemble mean(MME)can reasonably simulate the increasing trend of CO_(2) concentration from 1850 to 2014,compared with the observation data from the Scripps CO_(2) Program and CMIP6 prescribed data,and improves upon the CMIP5 MME CO_(2) concentration(which is overestimated after 1950).The growth rate of CO_(2) concentration in the northern hemisphere(NH)is higher than that in the southern hemisphere(SH),with the highest growth rate in the mid-latitudes of the NH.The MME can also reasonably simulate the seasonal amplitude of CO_(2) concentration,which is larger in the NH than in the SH and grows in amplitude after the 1950s(especially in the NH).Although the results of the MME are reasonable,there is a large spread among ESMs,and the difference between the ESMs increases with time.The MME results show that regions with relatively large CO_(2) concentrations(such as northern Russia,eastern China,Southeast Asia,the eastern United States,northern South America,and southern Africa)have greater seasonal variability and also exhibit a larger inter-model spread.Compared with CMIP5,the CMIP6 MME simulates an average spatial distribution of CO_(2) concentration that is much closer to the site observations,but the CMIP6-inter-model spread is larger.The inter-model differences of the annual means and seasonal cycles of atmospheric CO_(2) concentration are both attributed to the differences in natural sources and sinks of CO_(2) between the simulations.

Atmospheric NO_2 Concentration Measurements Using Differential Absorption Lidar Technique

Using the Differential Absorption Lidar (DIAL) technique, two types of approaches, namely, reflection from retroreflector / topographic target and backscatter from atmosphere, are available for studying remotely the atmospheric NO, concentration. The Argon ion lidar system at the Indian Institute of Tropical Meteorology (IITM), Pune, India has been used for the measurements by following both the path-averaged and range-resolved approaches. For the former, a topographic target (hill) is used for determining path-averaged surface concentration. In the latter, spectral properties of atmospheric attenuation is used for making range-resolved measurements in the surface layer. The results of the observations collected by following both approaches are presented. The average surface NO2 concentration was found to vary between 0.01 and 0.105 ppm and the range-resolved measurements exhibited higher values suggesting treatment of the lidar data for scattering and extinction effects due to atmospheric aerosols and air molecules, and atmospheric turbulence. Certain modifications that are suggested to the experimental set-up, data acquisition and analysis to improve the measurements are briefly described.

Prediction of SO_2 Concentration in Urban Atmosphere Based on B-P Neural Network

Base on the principle and method of B-P neural network,the prediction model of SO2 concentration in urban atmosphere was established by using the statistical data of a city in southwest China from 1991 to 2009,so as to forecast atmospheric SO2 concentration in a city of southwest China.The results showed that B-P neural network applied in the prediction of SO2 concentration in urban atmosphere was reasonable and efficient with high accuracy and wide adaptability,so it was worthy to be popularized.

Real-data assimilation experiment with a joint data assimilation system: assimilating carbon dioxide mole fraction measurements from the Greenhouse gases Observing Satellite

The performance of a joint data assimilation system（Tan-Tracker）,which is based on the PODEn4 Dvar assimilation method,in assimilating Greenhouse gases Observing SATellite（GOSAT） carbon dioxide（CO2） data,was evaluated.Atmospheric 3D CO2 concentrations and CO2 surface fluxes（CFs） from2010 were simulated using a global chemistry transport model（GEOS-Chem）.TheTan-Tracker system used the simulated CO2 concentrations and fluxes as a background field and assimilated the GOSAT column average dry-air mole fraction of CO2（X（CO2）） data to optimize CO2 concentrations and CFs in the same assimilation window.Monthly simulated X（CO2）（X（CO2）Sim）） and assimilated X（CO2）（X（CO2）,TT） data retrieved at different satellite scan positions were compared with GOSAT-observed X（CO2）（X（CO2）,obs）data.The average RMSE between the monthly X（CO2）,TT and X（CO2）,Obs data was significantly（30%） lower than the average RMSE between X（CO2）,Sim and X（CO2）,Obs）.Specifically,reductions in error were found for the positions of northern Africa（the Sahara）,the Indian peninsula,southern Africa,southern North America,and western Australia.The difference between the correlation coefficients of the X（CO2）,Sim）and X（CO2）,Obs and those of the X（CO2）Π）,TT and X（CO2）,Obs was only small.In general,the Tan-Tracker system performed very well after assimilating the GOSAT data.

Suspended sediment concentration mapping based on the MODIS satellite imagery in the East China inland, estuarine, and coastal waters

The purpose of this research is to improve the retrieval accuracy for the suspended sediment concentration(SSC) from in situ and satellite remote sensing measurements in turbid East China estuarine and coastal waters. For this aim, three important tasks are formulated and solved: 1) an estimation of remote-sensing reflectance spectra R_(rs)(λ) after atmospheric correction; 2) an estimation of R_(rs)(λ) from the radiometric signals above the air-water surface; and 3) an estimation of SSC from R_(rs)(λ). Six different models for radiometric R_(rs)(λ) determination and 28 models for SSC versus R_(rs)(λ) are analyzed based on the field observations made in the Changjiang River estuary and its adjacent coastal area. The SSC images based on the above-mentioned analysis are generated for the area.

The Observed Low CO_2 Concentration in the Rongbuk Valley on the Northern Slope of Mt. Everest

In the summers of 2006 and 2007, the atmospheric CO2 concentration and the wind speed in the Rongbuk Valley on the northern slope of Mr. Everest were measured by an ultrasonic anemometer with an Li-7500 CO2/H2O gas analyzer. The average CO2 concentration was 370.23±0.59 and 367.45±1.91 ppm in June of 2006 and 2007, respectively. The values are much lower than those at sites with similar latitudes and altitudes worldwide. The observed atmospheric CO2 concentration in Rongbuk Valley can be affected by the transportation of prevailing down-valley winds from the up-valley direction to the observation site. Our results suggest that the Mt. Everest region could be ideal for background atmospheric and environmental studies.

Impact of Spatial Inhomogeneity in Atmospheric CO_(2) Concentration on Surface Air Temperature Variations

Atmospheric CO_(2) concentration is characterized by spatial inhomogeneity and seasonal variability.The response of surface air temperature(SAT)to the inhomogeneity in CO_(2) concentration globally and regionally remains elusive.In this study,the BCC-CSM2-MR climate model was used to investigate the differences in global SAT in response to the spatially inhomogeneous distribution of atmospheric CO_(2) concentration.The analysis was based on three historical experiments(Hist_1dCO_(2),Hist_2dCO_(2),and Hist_3dCO_(2))conducted separately under the forcing of globally homogeneous,zonally homogeneous,and wholly spatially inhomogeneous CO_(2) concentrations from 1850 to 2014,derived from 12 Earth System Models of the Coupled Model Intercomparison Project Phase 6.The simulation results revealed similar trends of evolution in the global mean SATs in the 20th century under the three CO_(2) concentration distributions,and showed that the simulated historical SATs considering the meridional inhomogeneity of CO_(2) concentration in Hist_2dCO_(2) and the wholly spatial inhomogeneity in Hist_3dCO_(2) were more consistent with the observations.Compared with the results of Hist_1dCO_(2),the SATs in Hist_2dCO_(2) were warmer over land in the mid–high latitudes of the Northern Hemisphere(NH)than over other land areas.Further consideration of the zonally inhomogeneous CO_(2) concentration in Hist_3dCO_(2) revealed generally colder SATs over the NH mid–high-latitude ocean than over land at the same latitudes,and even the zonal mean SATs in the NH were slightly colder than those in Hist_2dCO_(2).These differences are ascribed to the uneven distribution of CO_(2) concentration along the same latitude in the NH in Hist_3dCO_(2),which leads to strong large-scale fluctuations in the atmospheric circulation.Eurasia is the region with the highest concentration of atmospheric CO_(2),which leads to remarkable regional SAT warming owing to enhanced downward longwave radiation.Warmer SATs in Eurasia in winter will further strengthen the northwesterly winds over eastern Asia,resulting in an increase in sea ice and strengthened cold SAT anomalies over the northern North Pacific.The simulated varied responses of the atmospheric circulation and SAT to inhomogeneous CO_(2) forcing highlight the imperative need for refined representation of the inhomogeneity of the atmospheric CO_(2) distribution in climate models for more accurate assessment of climate change.

A comparison of atmospheric CO_2 concentration GOSAT-based observations and model simulations

Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmospheric CO2 concentrations in a continuous space and time,which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO2 in a global scale.Satellite observations and model simulations of CO2 offer us two different approaches to understand the atmospheric CO2.However,the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO2.In this study,we compared and assessed the spatio-temporal variation of atmospheric CO2 using two datasets of the column-averaged dry air mole fractions of atmospheric CO2(XCO2)in a year from June 2009 to May 2010,respectively from GOSAT retrievals(V02.xx)and from Goddard Earth Observing System-Chemistry(GEOS-Chem),which is a global 3-D chemistry transport model.In addition to the global comparison,we further compared and analyzed the difference of CO2 between the China land region and the United States(US)land region from two datasets,and demonstrated the reasonability and uncertainty of satellite observations and model simulations.The results show that the XCO2 retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average,which is close to the validation conclusion for GOSAT by ground measures.This difference of XCO2 between the two datasets,however,changes with the different regions.In China land region,the difference is large,from 0.6 to 5.6 ppm,whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region.The goodness of fit test between the two datasets is 0.81 in the US land region,which is higher than that in the global land region(0.67)and China land region(0.68).The analysis results further indicate that the inconsistency of CO2concentration between satellite observations and model simulations in China is larger than that in the US and the globe.This inconsistency is related to the GOSAT retrieval error of CO2 caused by the interference among input parameters of satellite retrieval algorithm,and the uncertainty of driving parameters in GEOS-Chem model.

Measurements of atmospheric N_2O concentration and its emission fluxes from soil in China

Air samples in China were collected and determined for the concentration of N_2O. The production rates of N_2O from agricultural soils were measured with the chamber method.The results in- dicate that the background concentration of N_2O averages(308±5)×10^(-9)in 1989,which is close to that ob- tained at the other background stations abroad.However,at both urban and rural areas,atmospheric N_2O concentrations are higher.N_2O emission fluxes from several farmlands are different and dependent on the fac- tors of agricultural practice and climate.But their magnitudes are at the same order.Preliminary calculation shows that the released N_2O-N from agricultural sources(cultivated soil and N-fertilizers)amounts to 122 Gg/a in China in 1990.

Influence of HITRAN Database Updates on Retrievals of Atmospheric CO_2 from Near-Infrared Spectra

The line-transition parameters of the High Resolution Transmission （HITRAN） 2008 database have been updated relative to previous editions. The transmission spectra and sensitivity to changes in CO2 concentrations using line parameters from the HITRAN 2004 and HITRAN 2008 databases are compared to evaluate the effect of the database updates on retrievals of carbon dioxide vertical columns from nearinfrared reflected sunlight. This comparison is done in three spectral regions covering the 2.06-, 1.61-, and 1.58-μm CO2 bands used by the Greenhouse Gases Observatory Satellite （GOSAT） instrument and the planned successor to the Orbiting Carbon Observatory （OCO）. The updates to the HITRAN database have the largest effects on the transmittance and the off-line to on-line transmittance ratio in the 2.06-μm region and the smallest effects on these parameters in the 1.58-μm region. The influence of the updates to the HITRAN database on the off-line to on-line ratio calculation in the narrow spectral region 4855-4880 cm^-1 could be equivalent to a change in CO2 of more than 50 ppmv. Use of the HITRAN 2004 database will lead to an underestimate of the column CO2 abundance in the 2.06- and 1.61-pro spectral regions, whereas it will lead to an overestimate of the column CO2 abundance in the 1.58-μm spectral region.

Global land 1° mapping dataset of XCO_(2) from satellite observations of GOSAT and OCO-2 from 2009 to 2020

A global mapping data of atmospheric carbon dioxide(CO_(2))concen-trations can help us to better understand the spatiotemporal varia-tions of CO_(2) and the driving factors of the variations to support the actions for emissions reduction and control.Greenhouse gases satel-lites that measure atmospheric CO_(2),such as the Greenhouse Gases Observing Satellite(GOSAT)and Orbiting Carbon Observatory(OCO-2),have been providing global observations of the column averaged dry-air mole fractions of CO_(2)(XCO_(2))since 2009.However,these XCO_(2) retrievals are irregular in space and time with many gaps.In this paper,we mapped a global spatiotemporally continuous XCO_(2) data-set(Mapping-XCO_(2))using the XCO_(2) retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps.The dataset covers a geographic range from 56°S to 65°N and 169°W to 180°E for a 1°grid interval in space and 3-day time interval.The uncer-tainties of the mapped XCO_(2) values are generally less than 1.5 parts per million(ppm).The spatiotemporal characteristics of global XCO_(2) that are revealed by the Mapping-XCO_(2) are similar to the model data obtained from CarbonTracker.Compared to the ground observa-tions,the overall standard bias is 1.13 ppm.The results indicate that this long-term Mapping-XCO_(2) dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO_(2) and can support studies related to the carbon cycle and anthropogenic CO_(2) emissions.The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.

Origin of high particle number concentrations reaching the St. Louis, Midwest Supersite

Ultrafine particles are associated with adverse health effects. Total Particle Number Concentration（TNC） of fine particles were measured during 2002 at the St. Louis — Midwest supersite. The time series showed overall low level with frequent large peaks. The time series was analyzed alongside criteria pollutant measurements and meteorological observations. Multiple regression analysis was used to identify further contributing factors and to determine the association of different pollutants with TNC levels. This showed the strong contribution of sulfur dioxide（SO2） and nitrogen oxides（NO x） to high TNC levels. The analysis also suggested that increased dispersion resulting from faster winds and higher mixing heights led to higher TNC levels. Overall, the results show that there were intense particle nucleation events in a SO2 rich plume reaching the site which contributed around 29% of TNC. A further 40% was associated with primary emissions from mobile sources. By separating the remaining TNC by time of day and clear sky conditions,we suggest that most likely 8% of TNC are due to regional nucleation events and 23% are associated with the general urban background.

Holocene initiation and expansion of the southern margins of northern peatlands triggered by the East Asian summer monsoon recession

Northern peatlands represent one of the largest biospheric carbon reservoirs in the world.Their southern margins act as new carbon reservoirs,which can greatly influence the global carbon dynamics.However,the Holocene initiation,expansion and climate sensitivity of these peatlands remain intensely debated.Here we used a compilation of basal peat ages across six isolated peatlands at the southern margins of northern peatlands to address these issues.We found that the earliest initiation event of these peatlands occurred after the Younger Dryas(YD,12,800–11,700 years ago)period.The second initiation event and rapid expansion occurred since 5 ka cal.BP.The recession of East Asian summer monsoon(EASM)during the YD period and at around 5 ka cal.BP likely played a major role in controlling the initiation and expansion of these peatlands.The rapid expansion of these peatlands possibly contributed to the significant increases in atmospheric methane concentrations during the late Holocene because of the minerotrophic fens status and rapid expansion of them.These ecological processes are different from northern peatlands,indicating the special carbon sink and source implications of these peatlands in the global carbon cycle.