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

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).

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

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-12-30989-2012 ◽

2012 ◽

Vol 12 (12) ◽

pp. 30989-31030 ◽

Cited By ~ 1

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 ◽

Spatial Coverage ◽

Methane Fluxes ◽

Surface Measurements ◽

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 &pm; 25 Tg yr−1. We find larger differences between regional prior and posterior fluxes, with the largest changes (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 5% of true values, with the exception of South Africa and Tropical South America where, due to a large seasonal cycle in the number of measurements because of clouds and aerosols, fluxes are within 17% and 19% of true fluxes, respectively. We evaluate our posterior methane fluxes by incorporating them into GEOS-Chem and sampling the model at the location and time of independent 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, 0.23) and the biases decreasing by a mean of 0.4 ppb (range: −8.9, 8.4 ppb).

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Estimating surface CO2 fluxes from space-borne CO2 dry air mole fraction observations using an ensemble Kalman Filter

Atmospheric Chemistry and Physics ◽

10.5194/acp-9-2619-2009 ◽

2009 ◽

Vol 9 (8) ◽

pp. 2619-2633 ◽

Cited By ~ 106

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.

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Error correlation between CO2 and CO as constraint for CO2 flux inversions using satellite data

Atmospheric Chemistry and Physics ◽

10.5194/acp-9-7313-2009 ◽

2009 ◽

Vol 9 (19) ◽

pp. 7313-7323 ◽

Cited By ~ 25

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 ◽

Correlation Coefficients ◽

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 chemical transport model (CTM) to relate the fluxes to the observed column concentrations. CTM transport error is a major source of uncertainty. We show that its effect can be reduced by using CO satellite observations as additional constraint in a joint CO2-CO inversion. CO is measured from space with high precision, is strongly correlated with CO2, and is more sensitive than CO2 to CTM transport errors on synoptic and smaller scales. Exploiting this constraint requires statistics for the CTM transport error correlation between CO2 and CO, which is significantly different from the correlation between the concentrations themselves. We estimate the error correlation globally and for different seasons by a paired-model method (comparing GEOS-Chem 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 GEOS-5 CTM forecasts of CO2 and CO columns for the same forecast time). We find strong error correlations (r2>0.5) between CO2 and CO columns over much of the extra-tropical Northern Hemisphere 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. We present a simple inverse modeling application to demonstrate 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.

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Towards better error statistics for atmospheric inversions of methane surface fluxes

Atmospheric Chemistry and Physics ◽

10.5194/acp-13-7115-2013 ◽

2013 ◽

Vol 13 (14) ◽

pp. 7115-7132 ◽

Cited By ~ 25

Author(s):

A. Berchet ◽

I. Pison ◽

F. Chevallier ◽

P. Bousquet ◽

S. Conil ◽

...

Keyword(s):

Expert Knowledge ◽

Transport Model ◽

Vertical Mixing ◽

Real Data ◽

Surface Fluxes ◽

Error Statistics ◽

The North ◽

Error Covariance ◽

Methane Fluxes ◽

General Statistical

Abstract. We adapt general statistical methods to estimate the optimal error covariance matrices in a regional inversion system inferring methane surface emissions from atmospheric concentrations. Using a minimal set of physical hypotheses on the patterns of errors, we compute a guess of the error statistics that is optimal in regard to objective statistical criteria for the specific inversion system. With this very general approach applied to a real-data case, we recover sources of errors in the observations and in the prior state of the system that are consistent with expert knowledge while inferred from objective criteria and with affordable computation costs. By not assuming any specific error patterns, our results depict the variability and the inter-dependency of errors induced by complex factors such as the misrepresentation of the observations in the transport model or the inability of the model to reproduce well the situations of steep gradients of concentrations. Situations with probable significant biases (e.g., during the night when vertical mixing is ill-represented by the transport model) can also be diagnosed by our methods in order to point at necessary improvement in a model. By additionally analysing the sensitivity of the inversion to each observation, guidelines to enhance data selection in regional inversions are also proposed. We applied our method to a recent significant accidental methane release from an offshore platform in the North Sea and found methane fluxes of the same magnitude than what was officially declared.

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Spatio-Temporal Consistency Evaluation of XCO2 Retrievals from GOSAT and OCO-2 Based on TCCON and Model Data for Joint Utilization in Carbon Cycle Research

Atmosphere ◽

10.3390/atmos10070354 ◽

2019 ◽

Vol 10 (7) ◽

pp. 354 ◽

Cited By ~ 5

Author(s):

Yawen Kong ◽

Baozhang Chen ◽

Simon Measho

Keyword(s):

Carbon Cycle ◽

Mole Fraction ◽

Transport Model ◽

Total Carbon ◽

Temporal Consistency ◽

Model Data ◽

Sources And Sinks ◽

Spatio Temporal ◽

The Impact

The global carbon cycle research requires precise and sufficient observations of the column-averaged dry-air mole fraction of CO 2 (XCO 2 ) in addition to conventional surface mole fraction observations. In addition, assessing the consistency of multi-satellite data are crucial for joint utilization to better infer information about CO 2 sources and sinks. In this work, we evaluate the consistency of long-term XCO 2 retrievals from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2) in comparison with Total Carbon Column Observing Network (TCCON) and the 3D model of CO 2 mole fractions data from CarbonTracker 2017 (CT2017). We create a consistent joint dataset and compare it with the long-term model data to assess their abilities to characterize the carbon cycle climate. The results show that, although slight increasing differences are found between the GOSAT and TCCON XCO 2 in the northern temperate latitudes, the GOSAT and OCO-2 XCO 2 retrievals agree well in general, with a mean bias ± standard deviation of differences of 0.21 ± 1.3 ppm. The differences are almost within ±2 ppm and are independent of time, indicating that they are well calibrated. The differences between OCO-2 and CT2017 XCO 2 are much larger than those between GOSAT and CT XCO 2 , which can be attributed to the significantly different spatial representatives of OCO-2 and the CT-transport model 5 (TM5). The time series of the combined OCO-2/GOSAT dataset and the modeled XCO 2 agree well, and both can characterize significantly increasing atmospheric CO 2 under the impact of a large El Niño during 2015 and 2016. The trend calculated from the dataset using the seasonal Kendall (S-K) method indicates that atmospheric CO 2 is increasing by 2–2.6 ppm per year.

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The impact of transport model differences on CO2 surface flux estimates from OCO-2 retrievals of column average CO2

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-7189-2018 ◽

2018 ◽

Vol 18 (10) ◽

pp. 7189-7215 ◽

Cited By ~ 24

Author(s):

Sourish Basu ◽

David F. Baker ◽

Frédéric Chevallier ◽

Prabir K. Patra ◽

Junjie Liu ◽

...

Keyword(s):

Transport Model ◽

Co2 Flux ◽

Surface Flux ◽

Transport Models ◽

Near Surface ◽

Tropical Regions ◽

Spatial Coverage ◽

Global Transport ◽

The Impact

Abstract. We estimate the uncertainty of CO2 flux estimates in atmospheric inversions stemming from differences between different global transport models. Using a set of observing system simulation experiments (OSSEs), we estimate this uncertainty as represented by the spread between five different state-of-the-art global transport models (ACTM, LMDZ, GEOS-Chem, PCTM and TM5), for both traditional in situ CO2 inversions and inversions of XCO2 estimates from the Orbiting Carbon Observatory 2 (OCO-2). We find that, in the absence of relative biases between in situ CO2 and OCO-2 XCO2, OCO-2 estimates of terrestrial flux for TRANSCOM-scale land regions can be more robust to transport model differences than corresponding in situ CO2 inversions. This is due to a combination of the increased spatial coverage of OCO-2 samples and the total column nature of OCO-2 estimates. We separate the two effects by constructing hypothetical in situ networks with the coverage of OCO-2 but with only near-surface samples. We also find that the transport-driven uncertainty in fluxes is comparable between well-sampled northern temperate regions and poorly sampled tropical regions. Furthermore, we find that spatiotemporal differences in sampling, such as between OCO-2 land and ocean soundings, coupled with imperfect transport, can produce differences in flux estimates that are larger than flux uncertainties due to transport model differences. This highlights the need for sampling with as complete a spatial and temporal coverage as possible (e.g., using both land and ocean retrievals together for OCO-2) to minimize the impact of selective sampling. Finally, our annual and monthly estimates of transport-driven uncertainties can be used to evaluate the robustness of conclusions drawn from real OCO-2 and in situ CO2 inversions.

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Was Australia a sink or source of carbon dioxide in 2015? Data assimilation using OCO-2 satellite data

10.5194/egusphere-egu2020-6432 ◽

2020 ◽

Author(s):

Yohanna Villalobos ◽

Peter Rayner ◽

Steven Thomas ◽

Jeremy Silver

Keyword(s):

Satellite Data ◽

Transport Model ◽

Carbon Sink ◽

In Situ Measurements ◽

Surface Fluxes ◽

Total Carbon ◽

Cereal Crops ◽

Continental Scale ◽

In Situ Data

Estimates of the net CO2 flux at a continental scale are essential to building up confidence in the global carbon budget. In this study, we present the assimilation of the satellite data from the Orbiting Carbon Observatory-2 (OCO-2) (land nadir and glint data) to estimate the Australian CO2 surface fluxes for 2015. We used the Community Multiscale Air Quality (CMAQ) model and a four-dimensional variational scheme. Our preliminary results suggest that Australia was a slight carbon sink during 2015 of -0.15 +- 0.11 PgC y-1 compared to the prior estimate of 0.13 +- 0.55 PgC y-1. The monthly seasonal cycle shows there was not a good agreement between the prior and posterior fluxes in 2015. Our monthly posterior estimates suggest that from May to August, Australia was a sink of CO2 and that from October to December, it was a source of CO2 compared to the prior estimates, which showed an opposite sign. To understand these results more deeply, we aggregated the CO2 surface fluxes into six categories using Land Cover Type Product the Moderate Resolution Imaging Spectroradiometer (MODIS) and divided them into two areas (north and south). Our posterior fluxes aggregated in the southern and northern Australia indicates that most of the uptake of CO2 is driven by grasses and cereal crops. Grasses and cereal crops in these two regions represent -0.11 +- 0.027 and -0.06 +- 0.05 PgC/y respectively. In the southern region, the monthly time series of this category shows that this uptake occurs mainly from June to September, whereas in the north, it occurs from January to March. We evaluate our posterior CO2 concentration against The Total Carbon Column Observing Network (TCCON) and in-situ measurements.&#160; We use the TCCON stations from Darwin, Wollongong, and Lauder (in New Zealand). Amongst the in-situ measurements, we considered stations located at Gunn Point (near Darwin), Cape Grim (in Tasmania) and Iron Bark and Burncluith (in Queensland). Analysis of the monthly biases indicates that CO2 concentration simulated by posterior fluxes are in better agreement with TCCON data compared to in-situ measurements. In general, monthly mean biases in TCCON Darwin are improved by almost 70 per cent. Lauder and Wollongong stations are strongly affected by ocean fluxes which have small prior uncertainty in this inversion. Biases are hence not much improved here. We verify this by relating bias to wind direction. If the winds come from the ocean, fluxes over Australia are less constrained by OCO-2 data. Biases against in situ data are generally not improved by assimilation, suggesting either problems with the transport model or an inability for OCO-2 data to constrain fluxes at scales relevant to these measurements.

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The seasonal cycle of atmospheric CO2 in the southern hemisphere over the last ten years seen by GOSAT

10.5194/egusphere-egu21-2044 ◽

2021 ◽

Author(s):

Eva-Marie Schömann ◽

Sourish Basu ◽

Sanam N. Vardag ◽

Markus Haun ◽

Lena Schreiner ◽

...

Keyword(s):

Carbon Cycle ◽

Southern Hemisphere ◽

Transport Model ◽

Seasonal Pattern ◽

Inverse Model ◽

Surface Fluxes ◽

Inverse Models ◽

Spatial Coverage ◽

Sensitivity Studies ◽

Clear Seasonal Pattern

In the southern hemisphere, the sparse coverage of in-situ CO2 measurements prevents a robust determination of regional carbon fluxes and leads to large uncertainties in inverse model results. Therefore, the extensive spatial coverage afforded by satellite CO2 measurements is especially valuable there. By analyzing satellite measurements, new insights on the carbon cycle can be derived and carbon cycle models can be validated for the southern hemisphere.Here, we present a comparison of atmospheric CO2 data in Australia provided by the Greenhouse gases Observing SATellite (GOSAT) and the CarbonTracker (CT2019) inverse model from 2009 to 2018. We find that the seasonality of GOSAT CO2 is different from that of CarbonTracker across much of the southern hemisphere. This discrepancy follows a clear seasonal pattern with the largest difference of ~2ppm between October and December. We investigate the origin of the discrepancy by utilizing the CO2 components provided by CarbonTracker and different fire CO2 emission databases. Further, we conduct several sensitivity studies by assimilating GOSAT CO2 in the TM5-4DVar data assimilation system, and by transporting different surface fluxes through the TM5 transport model. Our results suggest that the underestimation of local and transported wildfire CO2 emissions could cause the observed discrepancy in the seasonality of column CO2 between GOSAT and inverse models such as CarbonTracker in the southern hemisphere.

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Mechanisms for synoptic transport of atmospheric CO2 in the midlatitudes and tropics

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-8-12197-2008 ◽

2008 ◽

Vol 8 (3) ◽

pp. 12197-12225

Author(s):

N. Parazoo ◽

A. Denning ◽

S. Kawa ◽

K. Corbin ◽

R. Lokupitiya ◽

...

Keyword(s):

Boundary Layer ◽

North America ◽

Transport Model ◽

Co2 Flux ◽

Convective Transport ◽

Surface Fluxes ◽

Moist Convection ◽

Chemical Transport Model ◽

Tropical Regions ◽

Budget Equation

Abstract. Synoptic variations of CO2 mixing ratio produced by interactions between weather and surface fluxes are investigated mechanistically and quantitatively in midlatitude and tropical regions using continuous in-situ CO2 observations in North America, South America and Europe and forward chemical transport model simulations with the Parameterized Chemistry Transport Model. Frontal CO2 climatologies show consistently strong, characteristic frontal CO2 signals throughout the midlatitudes of North America and Europe. Transitions between synoptically identifiable CO2 air masses or transient spikes along the frontal boundary typically characterize these signals. One case study of a summer cold front shows that CO2 gradients organize with deformational flow along weather fronts producing strong and spatially coherent variations. A boundary layer budget equation is constructed in order to determine contributions to boundary layer CO2 tendencies by horizontal and vertical advection, moist convection, and biological and anthropogenic surface fluxes. Analysis of this equation suggests that, in midlatitudes, advection is responsible for 50–90% of the amplitude of frontal variations in the summer, depending on upstream influences, and 50% of all day-to-day variations throughout the year. Simulations testing sensitivity to local cloud and surface fluxes further suggest that horizontal advection is a major source of CO2 variability in midlatitudes. In the tropics, coupling between convective transport and surface CO2 flux is most important. Due to the scarcity of tropical observations available at the time of this study, future work should extend such mechanistic analysis to additional tropical locations.

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Using airborne HIAPER Pole-to-Pole Observations (HIPPO) to evaluate model and remote sensing estimates of atmospheric carbon dioxide

10.5194/acp-2015-961 ◽

2016 ◽

Cited By ~ 1

Author(s):

C. Frankenberg ◽

S. S. Kulawik ◽

S. Wofsy ◽

F. Chevallier ◽

B. Daube ◽

...

Keyword(s):

Carbon Dioxide ◽

Atmospheric Carbon Dioxide ◽

Correlation Coefficients ◽

Added Value ◽

Total Carbon ◽

Accuracy And Precision ◽

Atmospheric Carbon ◽

Carbon Dioxide Co2 ◽

Global Carbon ◽

Comparable Quality

Abstract. In recent years, space-borne observations of atmospheric carbon-dioxide (CO2) have become increasingly used in global carbon-cycle studies. In order to obtain added value from space-borne measurements, they have to suffice stringent accuracy and precision requirements, with the latter being less crucial as it can be reduced by just enhanced sample size. Validation of CO2 column averaged dry air mole fractions (XCO2) heavily relies on measurements of the Total Carbon Column Observing Network TCCON. Owing to the sparseness of the network and the requirements imposed on space-based measurements, independent additional validation is highly valuable. Here, we use observations from the HIAPER Pole-to-Pole Observations (HIPPO) flights from January 2009 through September 2011 to validate CO2 measurements from satellites (GOSAT, TES, AIRS) and atmospheric inversion models (CarbonTracker CT2013B, MACC v13r1). We find that the atmospheric models capture the XCO2 variability observed in HIPPO flights very well, with correlation coefficients (r2) of 0.93 and 0.95 for CT2013B and MACC, respectively. Some larger discrepancies can be observed in profile comparisons at higher latitudes, esp. at 300 hPa during the peaks of either carbon uptake or release. These deviations can be up to 4 ppm and hint at misrepresentation of vertical transport. Comparisons with the GOSAT satellite are of comparable quality, with an r2 of 0.85, a mean bias μ of −0.06 ppm and a standard deviation σ of 0.45 ppm. TES exhibits an r2 of 0.75, μ of 0.34 ppm and σ of 1.13 ppm. For AIRS, we find an r2 of 0.37, μ of 1.11 ppm and σ of 1.46 ppm, with latitude-dependent biases. For these comparisons at least 6, 20 and 50 atmospheric soundings have been averaged for GOSAT, TES and AIRS, respectively. Overall, we find that GOSAT soundings over the remote pacific ocean mostly meet the stringent accuracy requirements of about 0.5 ppm for space-based CO2 observations.

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