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.

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.

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.

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.

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.