scholarly journals Three-dimensional variations of atmospheric CO<sub>2</sub>: aircraft measurements and multi-transport model simulations

2011 ◽  
Vol 11 (24) ◽  
pp. 13359-13375 ◽  
Author(s):  
Y. Niwa ◽  
P. K. Patra ◽  
Y. Sawa ◽  
T. Machida ◽  
H. Matsueda ◽  
...  
Keyword(s):  
Transport Model ◽  
Three Dimensional ◽  
Co2 Flux ◽  
Surface Flux ◽  
Co2 Concentration ◽  
Surface Fluxes ◽  
Latitudinal Gradients ◽  
Dimensional Structure ◽  
Free Troposphere ◽  
Model Simulations

Abstract. Numerical simulation and validation of three-dimensional structure of atmospheric carbon dioxide (CO2) is necessary for quantification of transport model uncertainty and its role on surface flux estimation by inverse modeling. Simulations of atmospheric CO2 were performed using four transport models and two sets of surface fluxes compared with an aircraft measurement dataset of Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL), covering various latitudes, longitudes, and heights. Under this transport model intercomparison project, spatiotemporal variations of CO2 concentration for 2006–2007 were analyzed with a three-dimensional perspective. Results show that the models reasonably simulated vertical profiles and seasonal variations not only over northern latitude areas but also over the tropics and southern latitudes. From CONTRAIL measurements and model simulations, intrusion of northern CO2 in to the Southern Hemisphere, through the upper troposphere, was confirmed. Furthermore, models well simulated the vertical propagation of seasonal variation in the northern free troposphere. However, significant model-observation discrepancies were found in Asian regions, which are attributable to uncertainty of the surface CO2 flux data. In summer season, differences in latitudinal gradients by the fluxes are comparable to or greater than model-model differences even in the free troposphere. This result suggests that active summer vertical transport sufficiently ventilates flux signals up to the free troposphere and the models could use those for inferring surface CO2 fluxes.

scholarly journals Three-dimensional variations of atmospheric CO<sub>2</sub>: aircraft measurements and multi-transport model simulations

2011 ◽  
Vol 11 (4) ◽  
pp. 12805-12848 ◽  
Author(s):  
Y. Niwa ◽  
P. K. Patra ◽  
Y. Sawa ◽  
T. Machida ◽  
H. Matsueda ◽  
...  
Keyword(s):  
Marine Boundary Layer ◽  
Transport Model ◽  
Three Dimensional ◽  
Co2 Flux ◽  
Surface Fluxes ◽  
Dimensional Structure ◽  
Boreal Summer ◽  
Free Troposphere ◽  
Model Simulations ◽  
The North

Abstract. Numerical simulation and validation of three-dimensional structure of atmospheric carbon dioxide (CO2) is necessary for quantification of transport model uncertainty and its role on surface flux estimation by inverse modeling. Simulations of atmospheric CO2 were performed using four transport models and two sets of surface fluxes compared with an aircraft measurement dataset of Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL), covering various latitudes, longitudes, and heights. Under this transport model intercomparison project, spatiotemporal variations of CO2 concentration for 2006–2007 were analyzed with a three-dimensional perspective. Results show that the models reasonably simulated vertical profiles and seasonal variations not only over northern latitude areas but also over the tropics and southern latitudes. From CONTRAIL measurements and model simulations, intrusion of northern CO2 in to the Southern Hemisphere, through the upper troposphere, was confirmed. Furthermore, models well simulated the vertical propagation of seasonal variation in the northern free-troposphere. However, significant model–observation discrepancies were found in Asian regions, which are attributable to uncertainty of the surface CO2 flux data. The models consistently underestimated the north-tropics mean gradient of CO2 both in the free-troposphere and marine boundary layer during boreal summer. This result suggests that the north-tropics contrast of annual mean net non-fossil CO2 flux should be greater than 2.7 Pg C yr−1 for 2007.

scholarly journals Estimating surface CO<sub>2</sub> fluxes from space-borne CO<sub>2</sub> dry air mole fraction observations using an ensemble Kalman Filter

2009 ◽  
Vol 9 (8) ◽  
pp. 2619-2633 ◽  
Author(s):  
L. Feng ◽  
P. I. Palmer ◽  
H. Bösch ◽  
S. Dance
Keyword(s):  
Kalman Filter ◽  
Mole Fraction ◽  
Ensemble Kalman Filter ◽  
Transport Model ◽  
Co2 Flux ◽  
Surface Flux ◽  
Surface Fluxes ◽  
Co2 Fluxes ◽  
Dry Air ◽  
Geographical Regions

Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day lag window, recognising that XCO2 measurements include surface flux information from prior time windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths <0.3; and b) scene-dependent averaging kernels that relate the CO2 profiles to XCO2, accounting for differences between nadir and glint measurements, and the associated scene-dependent observation errors. We show that OCO XCO2 measurements significantly reduce the uncertainties of surface CO2 flux estimates. Glint measurements are generally better at constraining ocean CO2 flux estimates. Nadir XCO2 measurements over the terrestrial tropics are sparse throughout the year because of either clouds or smoke. Glint measurements provide the most effective constraint for estimating tropical terrestrial CO2 fluxes by accurately sampling fresh continental outflow over neighbouring oceans. We also present results from sensitivity experiments that investigate how flux estimates change with 1) bias and unbiased errors, 2) alternative duty cycles, 3) measurement density and correlations, 4) the spatial resolution of estimated flux estimates, and 5) reducing the length of the lag window and the size of the ensemble. At the revision stage of this manuscript, the OCO instrument failed to reach its orbit after it was launched on 24 February 2009. The EnKF formulation presented here is also applicable to GOSAT measurements of CO2 and CH4.

scholarly journals Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO<sub>2</sub> surface flux inversions

2015 ◽  
Vol 15 (6) ◽  
pp. 8883-8932 ◽  
Author(s):  
A. Babenhauserheide ◽  
S. Basu ◽  
S. Houweling ◽  
W. Peters ◽  
A. Butz
Keyword(s):  
Data Assimilation ◽  
Co2 Flux ◽  
Surface Flux ◽  
Co2 Concentration ◽  
Surface Fluxes ◽  
Filter Method ◽  
Operational Parameters ◽  
Atmospheric Concentration ◽  
The North ◽  
Concentration Measurements

Abstract. Data assimilation systems allow for estimating surface fluxes of greenhouse gases from atmospheric concentration measurements. Good knowledge about fluxes is essential to understand how climate change affects ecosystems and to characterize feedback mechanisms. Based on assimilation of more than one year of atmospheric in-situ concentration measurements, we compare the performance of two established data assimilation models, CarbonTracker and TM5-4DVar, for CO2 flux estimation. CarbonTracker uses an Ensemble Kalman Filter method to optimize fluxes on ecoregions. TM5-4DVar employs a 4-D variational method and optimizes fluxes on a 6° × 4° longitude/latitude grid. Harmonizing the input data allows analyzing the strengths and weaknesses of the two approaches by direct comparison of the modelled concentrations and the estimated fluxes. We further assess the sensitivity of the two approaches to the density of observations and operational parameters such as temporal and spatial correlation lengths. Our results show that both models provide optimized CO2 concentration fields of similar quality. In Antarctica CarbonTracker underestimates the wintertime CO2 concentrations, since its 5-week assimilation window does not allow for adjusting the far-away surface fluxes in response to the detected concentration mismatch. Flux estimates by CarbonTracker and TM5-4DVar are consistent and robust for regions with good observation coverage, regions with low observation coverage reveal significant differences. In South America, the fluxes estimated by TM5-4DVar suffer from limited representativeness of the few observations. For the North American continent, mimicking the historical increase of measurement network density shows improving agreement between CarbonTracker and TM5-4DVar flux estimates for increasing observation density.

scholarly journals Improved simulation of regional CO<sub>2</sub> surface concentrations using GEOS-Chem and fluxes from VEGAS

2013 ◽  
Vol 13 (15) ◽  
pp. 7607-7618 ◽  
Author(s):  
Z. H. Chen ◽  
J. Zhu ◽  
N. Zeng
Keyword(s):  
North American ◽  
Large Scale ◽  
Transport Model ◽  
Co2 Concentration ◽  
Regional Model ◽  
Surface Fluxes ◽  
Persistent Problem ◽  
Model Simulations ◽  
Co2 Concentrations ◽  
Single Station

Abstract. CO2 measurements have been combined with simulated CO2 distributions from a transport model in order to produce the optimal estimates of CO2 surface fluxes in inverse modeling. However, one persistent problem in using model–observation comparisons for this goal relates to the issue of compatibility. Observations at a single station reflect all underlying processes of various scales. These processes usually cannot be fully resolved by model simulations at the grid points nearest the station due to lack of spatial or temporal resolution or missing processes in the model. In this study the stations in one region were grouped based on the amplitude and phase of the seasonal cycle at each station. The regionally averaged CO2 at all stations in one region represents the regional CO2 concentration of this region. The regional CO2 concentrations from model simulations and observations were used to evaluate the regional model results. The difference of the regional CO2 concentration between observation and modeled results reflects the uncertainty of the large-scale flux in the region where the grouped stations are. We compared the regional CO2 concentrations between model results with biospheric fluxes from the Carnegie-Ames-Stanford Approach (CASA) and VEgetation-Global-Atmosphere-Soil (VEGAS) models, and used observations from GLOBALVIEW-CO2 to evaluate the regional model results. The results show the largest difference of the regionally averaged values between simulations with fluxes from VEGAS and observations is less than 5 ppm for North American boreal, North American temperate, Eurasian boreal, Eurasian temperate and Europe, which is smaller than the largest difference between CASA simulations and observations (more than 5 ppm). There is still a large difference between two model results and observations for the regional CO2 concentration in the North Atlantic, Indian Ocean, and South Pacific tropics. The regionally averaged CO2 concentrations will be helpful for comparing CO2 concentrations from modeled results and observations and evaluating regional surface fluxes from different methods.

scholarly journals Biases in regional carbon budgets from covariation of surface fluxes and weather in transport model inversions

2014 ◽  
Vol 14 (3) ◽  
pp. 1571-1585 ◽  
Author(s):  
I. N. Williams ◽  
W. J. Riley ◽  
M. S. Torn ◽  
S. C. Biraud ◽  
M. L. Fischer
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Interannual Variability ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Surface Flux ◽  
Co2 Concentration ◽  
Surface Fluxes ◽  
Carbon Uptake ◽  
Atmospheric Transport Model

Abstract. Recent advances in atmospheric transport model inversions could significantly reduce uncertainties in land carbon uptake through the assimilation of CO2 concentration measurements at weekly and shorter timescales. The potential of these measurements for reducing biases in estimated land carbon sinks depends on the strength of covariation between surface fluxes and atmospheric transport at these timescales and how well transport models represent this covariation. Daily to seasonal covariation of surface fluxes and atmospheric transport was estimated in observations at the US Southern Great Plains Atmospheric Radiation Measurement Climate Research Facility, and compared to an atmospheric transport model inversion (CarbonTracker). Covariation of transport and surface fluxes was stronger in CarbonTracker than in observations on synoptic (daily to weekly) timescales, with a wet year (2007) having significant covariation compared to a dry year (2006). Differences between observed and CarbonTracker synoptic covariation resulted in a 0.3 ppm CO2 enhancement in boundary layer concentrations during the growing season, and a corresponding enhancement in carbon uptake by 13% of the seasonal cycle amplitude in 2007, as estimated by an offline simplified transport model. This synoptic rectification of surface flux variability was of similar magnitude to the interannual variability in carbon sinks alone, and indicates that interannual variability in the inversions can be affected by biases in simulated synoptic rectifier effects. The most significant covariation of surface fluxes and transport had periodicities of 10 days and greater, suggesting that surface flux inversions would benefit from improved simulations of the effects of soil moisture on boundary layer heights and surface CO2 fluxes. Soil moisture remote sensing could be used along with CO2 concentration measurements to further constrain atmospheric transport model inversions.

scholarly journals Improved simulation of group averaged CO<sub>2</sub> surface concentrations using GEOS-Chem and fluxes from VEGAS

2013 ◽  
Vol 13 (1) ◽  
pp. 2243-2271
Author(s):  
Z. H. Chen ◽  
J. Zhu ◽  
N. Zeng
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Surface Characteristics ◽  
Co2 Concentration ◽  
Regional Model ◽  
Surface Fluxes ◽  
Persistent Problem ◽  
Model Simulations ◽  
The Difference ◽  
Grid Points

Abstract. CO2 measurements have been combined with simulated CO2 distributions from a transport model in order to produce the optimal estimates of CO2 surface fluxes in inverse modeling. However one persistent problem in using model-observation comparisons for this goal relates to the issue of compatibility. Observations at a single site reflect all underlying processes of various scales that usually cannot be fully resolved by model simulations at the grid points nearest the site due to lack of spatial or temporal resolution or missing processes in models. In this article we group site observations of multiple stations according to atmospheric mixing regimes and surface characteristics. The group averaged values of CO2 concentration from model simulations and observations are used to evaluate the regional model results. Using the group averaged measurements of CO2 reduces the noise of individual stations. The difference of group averaged values between observation and modeled results reflects the uncertainties of the large scale flux in the region where the grouped stations are. We compared the group averaged values between model results with two biospheric fluxes from the model Carnegie-Ames-Stanford-Approach (CASA) and VEgetation-Global-Atmosphere-Soil (VEGAS) and observations to evaluate the regional model results. Results show that the modeling group averaged values of CO2 concentrations in all regions with fluxes from VEGAS have significant improvements for most regions. There is still large difference between two model results and observations for grouped average values in North Atlantic, Indian Ocean, and South Pacific Tropics. This implies possible large uncertainties in the fluxes there.

Modelling the effects of mixing processes on the composition of the free troposphere using a three-dimensional chemical transport model

2004 ◽  
Vol 19 (4) ◽  
pp. 391-399
Author(s):  
C. Giannakopoulos ◽  
M.P. Chipperfield ◽  
K.S. Law ◽  
D.E. Shallcross ◽  
K.-Y. Wang ◽  
...  
Keyword(s):  
Transport Model ◽  
Three Dimensional ◽  
Chemical Transport ◽  
Free Troposphere ◽  
Chemical Transport Model ◽  
Mixing Processes

scholarly journals Error correlation between CO<sub>2</sub> and CO as constraint for CO<sub>2</sub> flux inversions using satellite data

2009 ◽  
Vol 9 (3) ◽  
pp. 11783-11810
Author(s):  
H. Wang ◽  
D. J. Jacob ◽  
M. Kopacz ◽  
D. B. A. Jones ◽  
P. Suntharalingam ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Large Scale ◽  
Transport Model ◽  
Meteorological Data ◽  
Co2 Flux ◽  
Surface Fluxes ◽  
Satellite Observations ◽  
Carbon Surface ◽  
Chemical Transport Model ◽  
Error Correlation

Abstract. Inverse modeling of CO2 satellite observations to better quantify carbon surface fluxes requires a forward model such as a chemical transport model (CTM) to relate the fluxes to the observed column concentrations. Model transport error is an important source of observational error. We investigate the potential of using CO satellite observations as additional constraints in a joint CO2–CO inversion to improve CO2 flux estimates, by exploiting the CTM transport error correlations between CO2 and CO. We estimate the error correlation globally and for different seasons by a paired-model method (comparing CTM simulations of CO2 and CO columns using different assimilated meteorological data sets for the same meteorological year) and a paired-forecast method (comparing 48- vs. 24-h CTM forecasts of CO2 and CO columns for the same forecast time). We find strong positive and negative error correlations (r2>0.5) between CO2 and CO columns over much of the world throughout the year, and strong consistency between different methods to estimate the error correlation. Application of the averaging kernels used in the retrieval for thermal IR CO measurements weakens the correlation coefficients by 15% on average (mostly due to variability in the averaging kernels) but preserves the large-scale correlation structure. Results from a testbed inverse modeling application show that CO2–CO error correlations can indeed significantly reduce uncertainty on surface carbon fluxes in a joint CO2–CO inversion vs. a CO2–only inversion.

scholarly journals Carbon balance of China constrained by CONTRAIL aircraft CO<sub>2</sub> measurements

2014 ◽  
Vol 14 (6) ◽  
pp. 7683-7709
Author(s):  
F. Jiang ◽  
H. M. Wang ◽  
J. M. Chen ◽  
T. Machida ◽  
L. X. Zhou ◽  
...  
Keyword(s):  
Transport Model ◽  
Carbon Sink ◽  
Terrestrial Ecosystem ◽  
Co2 Flux ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Inversion Method ◽  
Climate Factors ◽  
Mean Climate ◽  
Atmospheric Co2 Concentrations

Abstract. Terrestrial CO2 flux estimates in China using atmospheric inversion method are beset with considerable uncertainties because very few atmospheric CO2 concentration measurements are available. In order to improve these estimates, nested atmospheric CO2 inversion during 2002–2008 is performed in this study using passenger aircraft-based CO2 measurements over Eurasia from the Comprehensive Observation Network for Trace gases by Airliner (CONTRAIL) project. The inversion system includes 43 regions with a focus on China, and is based on the Bayesian synthesis approach and the TM5 transport model. The terrestrial ecosystem carbon flux modeled by the BEPS model and the ocean exchange simulated by the OPA-PISCES-T model are considered as the prior fluxes. The impacts of CONTRAIL CO2 data on inverted China terrestrial carbon fluxes are quantified, the improvement of the inverted fluxes after adding CONTRAIL CO2 data are rationed against climate factors and evaluated by comparing the simulated atmospheric CO2 concentrations with three independent surface CO2 measurements in China. Results show that with the addition of CONTRAIL CO2 data, the inverted carbon sink in China increases while those in South and Southeast Asia decrease. Meanwhile, the posterior uncertainties over these regions are all reduced. CONTRAIL CO2 data also have a large effect on the inter-annual variation of carbon sinks in China, leading to a better correlation between the carbon sink and the annual mean climate factors. Evaluations against the CO2 measurements at three sites in China also show that the CONTRAIL CO2 measurements have improved the inversion results.

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