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

2008 ◽  
Vol 8 (6) ◽  
pp. 19917-19955 ◽  
Author(s):  
L. Feng ◽  
P. I. Palmer ◽  
H. Bösch ◽  
S. Dance
Keyword(s):  
Kalman Filter ◽  
Mole Fraction ◽  
Ensemble Kalman Filter ◽  
Surface Fluxes ◽  
Dry Air

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

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 Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

2013 ◽  
Vol 13 (23) ◽  
pp. 11643-11660 ◽  
Author(s):  
A. Chatterjee ◽  
A. M. Michalak
Keyword(s):  
Kalman Filter ◽  
Inverse Modeling ◽  
Ensemble Kalman Filter ◽  
Co2 Flux ◽  
Estimation Problem ◽  
Square Root ◽  
Flux Estimation ◽  
Source Sink ◽  
Operational Constraints ◽  
Square Root Filter

Abstract. Data assimilation (DA) approaches, including variational and the ensemble Kalman filter methods, provide a computationally efficient framework for solving the CO2 source–sink estimation problem. Unlike DA applications for weather prediction and constituent assimilation, however, the advantages and disadvantages of DA approaches for CO2 flux estimation have not been extensively explored. In this study, we compare and assess estimates from two advanced DA approaches (an ensemble square root filter and a variational technique) using a batch inverse modeling setup as a benchmark, within the context of a simple one-dimensional advection–diffusion prototypical inverse problem that has been designed to capture the nuances of a real CO2 flux estimation problem. Experiments are designed to identify the impact of the observational density, heterogeneity, and uncertainty, as well as operational constraints (i.e., ensemble size, number of descent iterations) on the DA estimates relative to the estimates from a batch inverse modeling scheme. No dynamical model is explicitly specified for the DA approaches to keep the problem setup analogous to a typical real CO2 flux estimation problem. Results demonstrate that the performance of the DA approaches depends on a complex interplay between the measurement network and the operational constraints. Overall, the variational approach (contingent on the availability of an adjoint transport model) more reliably captures the large-scale source–sink patterns. Conversely, the ensemble square root filter provides more realistic uncertainty estimates. Selection of one approach over the other must therefore be guided by the carbon science questions being asked and the operational constraints under which the approaches are being applied.

scholarly journals Influence of CO<sub>2</sub> observations on the optimized CO<sub>2</sub> flux in an ensemble Kalman filter

2014 ◽  
Vol 14 (24) ◽  
pp. 13515-13530 ◽  
Author(s):  
J. Kim ◽  
H. M. Kim ◽  
C.-H. Cho
Keyword(s):  
Kalman Filter ◽  
North America ◽  
Information Content ◽  
Ensemble Kalman Filter ◽  
Co2 Flux ◽  
Experimental Period ◽  
Diagonal Element ◽  
Cumulative Impact ◽  
Influence Matrix ◽  
The Tropics

Abstract. In this study, the effect of CO2 observations on an analysis of surface CO2 flux was calculated using an influence matrix in the CarbonTracker, which is an inverse modeling system for estimating surface CO2 flux based on an ensemble Kalman filter. The influence matrix represents a sensitivity of the analysis to observations. The experimental period was from January 2000 to December 2009. The diagonal element of the influence matrix (i.e., analysis sensitivity) is globally 4.8% on average, which implies that the analysis extracts 4.8% of the information from the observations and 95.2% from the background each assimilation cycle. Because the surface CO2 flux in each week is optimized by 5 weeks of observations, the cumulative impact over 5 weeks is 19.1%, much greater than 4.8%. The analysis sensitivity is inversely proportional to the number of observations used in the assimilation, which is distinctly apparent in continuous observation categories with a sufficient number of observations. The time series of the globally averaged analysis sensitivities shows seasonal variations, with greater sensitivities in summer and lower sensitivities in winter, which is attributed to the surface CO2 flux uncertainty. The time-averaged analysis sensitivities in the Northern Hemisphere are greater than those in the tropics and the Southern Hemisphere. The trace of the influence matrix (i.e., information content) is a measure of the total information extracted from the observations. The information content indicates an imbalance between the observation coverage in North America and that in other regions. Approximately half of the total observational information is provided by continuous observations, mainly from North America, which indicates that continuous observations are the most informative and that comprehensive coverage of additional observations in other regions is necessary to estimate the surface CO2 flux in these areas as accurately as in North America.

scholarly journals Estimating regional methane surface fluxes: the relative importance of surface and GOSAT mole fraction measurements

2013 ◽  
Vol 13 (11) ◽  
pp. 5697-5713 ◽  
Author(s):  
A. Fraser ◽  
P. I. Palmer ◽  
L. Feng ◽  
H. Boesch ◽  
A. Cogan ◽  
...  
Keyword(s):  
Mole Fraction ◽  
Transport Model ◽  
Correlation Coefficients ◽  
Methane Flux ◽  
Surface Fluxes ◽  
Total Carbon ◽  
Tropical Regions ◽  
Spatial Coverage ◽  
Methane Fluxes ◽  
Atmospheric Sampling

Abstract. We use an ensemble Kalman filter (EnKF), together with the GEOS-Chem chemistry transport model, to estimate regional monthly methane (CH4) fluxes for the period June 2009–December 2010 using proxy dry-air column-averaged mole fractions of methane (XCH4) from GOSAT (Greenhouse gases Observing SATellite) and/or NOAA ESRL (Earth System Research Laboratory) and CSIRO GASLAB (Global Atmospheric Sampling Laboratory) CH4 surface mole fraction measurements. Global posterior estimates using GOSAT and/or surface measurements are between 510–516 Tg yr−1, which is less than, though within the uncertainty of, the prior global flux of 529 ± 25 Tg yr−1. We find larger differences between regional prior and posterior fluxes, with the largest changes in monthly emissions (75 Tg yr−1) occurring in Temperate Eurasia. In non-boreal regions the error reductions for inversions using the GOSAT data are at least three times larger (up to 45%) than if only surface data are assimilated, a reflection of the greater spatial coverage of GOSAT, with the two exceptions of latitudes >60° associated with a data filter and over Europe where the surface network adequately describes fluxes on our model spatial and temporal grid. We use CarbonTracker and GEOS-Chem XCO2 model output to investigate model error on quantifying proxy GOSAT XCH4 (involving model XCO2) and inferring methane flux estimates from surface mole fraction data and show similar resulting fluxes, with differences reflecting initial differences in the proxy value. Using a series of observing system simulation experiments (OSSEs) we characterize the posterior flux error introduced by non-uniform atmospheric sampling by GOSAT. We show that clear-sky measurements can theoretically reproduce fluxes within 10% of true values, with the exception of tropical regions where, due to a large seasonal cycle in the number of measurements because of clouds and aerosols, fluxes are within 15% of true fluxes. We evaluate our posterior methane fluxes by incorporating them into GEOS-Chem and sampling the model at the location and time of surface CH4 measurements from the AGAGE (Advanced Global Atmospheric Gases Experiment) network and column XCH4 measurements from TCCON (Total Carbon Column Observing Network). The posterior fluxes modestly improve the model agreement with AGAGE and TCCON data relative to prior fluxes, with the correlation coefficients (r2) increasing by a mean of 0.04 (range: −0.17 to 0.23) and the biases decreasing by a mean of 0.4 ppb (range: −8.9 to 8.4 ppb).

scholarly journals Evaluating a 3-D transport model of atmospheric CO<sub>2</sub> using ground-based, aircraft, and space-borne data

2010 ◽  
Vol 10 (7) ◽  
pp. 18025-18061 ◽  
Author(s):  
L. Feng ◽  
P. I. Palmer ◽  
Y. Yang ◽  
R. M. Yantosca ◽  
S. R. Kawa ◽  
...  
Keyword(s):  
Boundary Layer ◽  
North America ◽  
Seasonal Cycle ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Fluxes ◽  
A Posteriori ◽  
Upper Troposphere ◽  
Co2 Concentrations ◽  
Geographical Regions

Abstract. We evaluate the GEOS-Chem atmospheric transport model (v8-02-01) of CO2 over 2003–2006, driven by GEOS-4 and GEOS-5 meteorology from the NASA Goddard Global Modelling and Assimilation Office, using surface, aircraft and space-borne concentration measurements of CO2. We use an established ensemble Kalman filter to estimate a posteriori biospheric+biomass burning (BS+BB) and oceanic (OC) CO2 fluxes from 22 geographical regions, following the TransCom 3 protocol, using boundary layer CO2 data from a subset of GLOBALVIEW surface sites. Global annual net BS+BB+OC CO2 fluxes over 2004–2006 for GEOS-4 (GEOS-5) meteorology are −4.4±0.9 (−4.2±0.9), −3.9±0.9 (−4.5±0.9), and −5.2±0.9 (−4.9±0.9) Pg C yr−1 , respectively. The regional a posteriori fluxes are broadly consistent in the sign and magnitude of the TransCom-3 study for 1992–1996, but we find larger net sinks over northern and southern continents. We find large departures from our a priori over Europe during summer 2003, over temperate Eurasia during 2004, and over North America during 2005, reflecting an incomplete description of terrestrial carbon dynamics. We find GEOS-4 (GEOS-5) a posteriori CO2 concentrations reproduce the observed surface trend of 1.91–2.43 ppm yr−1, depending on latitude, within 0.15 ppm yr−1 (0.2 ppm yr−1) and the seasonal cycle within 0.2 ppm (0.2 ppm) at all latitudes. We find the a posteriori model reproduces the aircraft vertical profile measurements of CO2 over North America and Siberia generally within 1.5 ppm in the free and upper troposphere but can be biased by up to 4–5 ppm in the boundary layer at the start and end of the growing season. The model has a small negative bias in the free troposphere CO2 trend (1.95–2.19 ppm yr−1) compared to AIRS data which has a trend of 2.21–2.63 ppm yr−1 during 2004–2006, consistent with surface data. Model CO2 concentrations in the upper troposphere, evaluated using CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner) aircraft measurements, reproduce the magnitude and phase of the seasonal cycle of CO2 in both hemispheres. We generally find that the GEOS meteorology reproduces much of the observed tropospheric CO2 variability, suggesting that these meteorological fields will help make significant progress in understanding carbon fluxes as more data become available.

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 Quantifying the vertical transport of CHBr<sub>3</sub> and CH<sub>2</sub>Br<sub>2</sub> over the Western Pacific

2016 ◽  
Author(s):  
Robyn Butler ◽  
Paul I. Palmer ◽  
Liang Feng ◽  
Stephen J. Andrews ◽  
Elliot L. Atlas ◽  
...  
Keyword(s):  
Mole Fraction ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Open Ocean ◽  
Western Pacific ◽  
Study Region ◽  
Air Masses ◽  
Coastal Oceans ◽  
Geographical Regions ◽  
The Western Pacific

Abstract. We use the GEOS-Chem global 3-D atmospheric chemistry transport model to interpret atmospheric observations of bromoform (CHBr3) and dibromomethane (CH2Br2) collected during the CAST and CONTRAST aircraft measurement campaigns over the Western Pacific, January–February, 2014. We use a new linearised, tagged version of CHBr3 and CH2Br2, allowing us to study the influence of emissions from specific geographical regions on observed atmospheric variations. The model describes 32 %–37 % of CHBr3 observed variability and 15 %–45 % of CH2Br2 observed variability during CAST and CONTRAST, reflecting errors in vertical model transport. The model has a mean positive bias of 30 % that is larger near the surface reflecting errors in the poorly constrained prior emission estimates. We find using the model that observed variability of CHBr3 and CH2Br2 is driven by ocean emissions, particularly by the open ocean above which there is deep convection. We find that contributions from coastal oceans and terrestrial sources over the Western Pacific are significant above altitudes > 6 km, but is still dominated by the open ocean emissions and by air masses transported over longer time lines than the campaign period. In the absence of reliable ocean emission estimates, we use a new physical age of air simulation to determine the relative abundance of halogens delivered by CHBr3 and CH2Br2 to the tropical transition layer (TTL). We find that 6 % (47 %) of air masses with halogen released by the ocean reach the TTL within two (three) atmospheric e-folding lifetimes of CHBr3 and almost all of them reach the TTL within one e-folding lifetime of CH2Br2. We find these gases are delivered to the TTL by a small number of rapid convection events during the study period. Over the duration of CAST and CONTRAST and over our study region, oceans delivered a mean (range) CHBr3 and CH2Br2 mole fraction of 0.46 (0.13–0.72) and 0.88 (0.71–1.01) pptv, respectively, to the TTL, and a mean (range) Bry mole fraction of 3.14 (1.81–4.18) pptv to the upper troposphere. Open ocean emissions are responsible for 75 % of these values, with only 8 % from coastal oceans.

scholarly journals Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model

2014 ◽  
Vol 7 (1) ◽  
pp. 339-377 ◽  
Author(s):  
S. Skachko ◽  
Q. Errera ◽  
R. Ménard ◽  
Y. Christophe ◽  
S. Chabrillat
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Transport Model ◽  
Control Variable ◽  
Tracer Transport ◽  
Error Statistics ◽  
Proper Comparison ◽  
Antarctic Ozone ◽  
Two Parameters ◽  
Update Frequency

Abstract. The Ensemble Kalman filter (EnKF) assimilation method is applied to the tracer transport using the same stratospheric transport model as in the 4D-Var assimilation system BASCOE. This EnKF version of BASCOE was built primarily to avoid the large costs associated with the maintenance of an adjoint model. The EnKF developed in BASCOE accounts for two adjustable parameters: a parameter α controlling the model error term and a parameter r controlling the observational error. The EnKF system is shown to be markedly sensitive to these two parameters, which are adjusted based on the monitoring of a χ2-test measuring the misfit between the control variable and the observations. The performance of the EnKF and 4D-Var versions was estimated through the assimilation of Aura-MLS ozone observations during an 8 month period which includes the formation of the 2008 Antarctic ozone hole. To ensure a proper comparison, despite the fundamental differences between the two assimilation methods, both systems use identical and carefully calibrated input error statistics. We provide the detailed procedure for these calibrations, and compare the two sets of analyses with a focus on the lower and middle stratosphere where the ozone lifetime is much larger than the observational update frequency. Based on the Observation-minus-Forecast statistics, we show that the analyses provided by the two systems are markedly similar, with biases smaller than 5% and standard deviation errors smaller than 10% in most of the stratosphere. Since the biases are markedly similar, they have most probably the same causes: these can be deficiencies in the model and in the observation dataset, but not in the assimilation algorithm nor in the error calibration. The remarkably similar performance also shows that in the context of stratospheric transport, the choice of the assimilation method can be based on application-dependent factors, such as CPU cost or the ability to generate an ensemble of forecasts.

scholarly journals Modelling regional scale surface fluxes, meteorology and CO<sub>2</sub> mixing ratios for the Cabauw tower in the Netherlands

2009 ◽  
Vol 6 (10) ◽  
pp. 2265-2280 ◽  
Author(s):  
L. F. Tolk ◽  
W. Peters ◽  
A. G. C. A. Meesters ◽  
M. Groenendijk ◽  
A. T. Vermeulen ◽  
...  
Keyword(s):  
The Netherlands ◽  
Surface Energy ◽  
Co2 Flux ◽  
Surface Fluxes ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Mixing Ratio ◽  
Surface Energy Fluxes ◽  
Mixing Ratios ◽  
Flux Model

Abstract. We simulated meteorology and atmospheric CO2 transport over the Netherlands with the mesoscale model RAMS-Leaf3 coupled to the biospheric CO2 flux model 5PM. The results were compared with meteorological and CO2 observations, with emphasis on the tall tower of Cabauw. An analysis of the coupled exchange of energy, moisture and CO2 showed that the surface fluxes in the domain strongly influenced the atmospheric properties. The majority of the variability in the afternoon CO2 mixing ratio in the middle of the domain was determined by biospheric and fossil fuel CO2 fluxes in the limited area domain (640×640 km). Variation of the surface CO2 fluxes, reflecting the uncertainty of the parameters in the CO2 flux model 5PM, resulted in a range of simulated atmospheric CO2 mixing ratios of on average 11.7 ppm in the well-mixed boundary layer. Additionally, we found that observed surface energy fluxes and observed atmospheric temperature and moisture could not be reconciled with the simulations. Including this as an uncertainty in the simulation of surface energy fluxes changed simulated atmospheric vertical mixing and horizontal advection, leading to differences in simulated CO2 of on average 1.7 ppm. This is an important source of uncertainty and should be accounted for to avoid biased calculations of the CO2 mixing ratio, but it does not overwhelm the signal in the CO2 mixing ratio due to the uncertainty range of the surface CO2 fluxes.

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