Abstract. This work describes the NASA Atmospheric CO 2 Observations from Space (ACOS) X CO 2 retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ∼0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small.

This work describes the NASA Atmospheric CO₂ Observations from Space (ACOS) X_CO2 retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to imperfect meteorology, surface reflectance, and radiative transfer assumptions are small

Weakly bonded pairs of water molecules (H2O)2, or water dimers (WDs), may play an important role in photochemistry and climate, but the overlap of most of its spectral features with the water monomer (WM) has made detection difficult. We report on WD absorption measurements by means of atmospheric long-path (18.34 kilometers) differential optical absorption spectroscopy of the near-infrared OH stretching mode 0>f 4>b overtone transition predicted to be located near 746 nanometers. Our observation is in reasonable agreement with the known thermochemistry, calculated and measured structure, and spectroscopy (band strength, shape, and width) of the WD. The observation implies that the WD 0>f 4>b band is located at 749.5 nanometers, with a full width at half maximum of approximately 19.4 wave numbers, and that its band strength ranges between 1.23 x 10(-22) and 5.25 x 10(-22) centimeters per molecule.

Abstract.We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO 2 from space-borne CO 2 dry-air mole fraction observations (X CO 2 ) 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 CO 2 surface fluxes over 144 geographical regions. We use a 12×8-day lag window, recognising that X CO 2 measurements include surface flux information from prior time windows. The observation operator that relates surface CO 2 fluxes to atmospheric distributions of X CO 2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO 2 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 CO 2 profiles to X CO 2 , accounting for differences between nadir and glint measurements, and the associated scene-dependent observation errors. We show that OCO X CO 2 measurements significantly reduce the uncertainties of surface CO 2 flux estimates. Glint measurements are generally better at constraining ocean CO 2 flux estimates. Nadir X CO 2 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 CO 2 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 Correspondence to: L. Feng (lfeng@staffmail.ed.ac.uk) 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 CO 2 and CH 4 .

[1] Space-based measurements of reflected sunlight in the near-infrared (NIR) region promise to yield accurate and precise observations of the global distribution of atmospheric CO 2 . The Orbiting Carbon Observatory (OCO) is a future NASA mission, which will use this technique to measure the column-averaged dry air mole fraction of CO 2 (X CO 2 ) with the precision and accuracy needed to quantify CO 2 sources and sinks on regional scales ($1000 Â 1000 km 2 ) and to characterize their variability on seasonal timescales. Here, we have used the OCO retrieval algorithm to retrieve X CO 2 and surface pressure from space-based Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) measurements and from coincident ground-based Fourier transform spectrometer (FTS) measurements of the O 2 A band at 0.76 mm and the 1.58 mm CO 2 band for Park Falls, Wisconsin. Even after accounting for a systematic error in our representation of the O 2 absorption cross sections, we still obtained a positive bias between SCIAMACHY and FTS X CO 2 retrievals of $3.5%. Additionally, the retrieved surface pressures from SCIAMACHY systematically underestimate measurements of a calibrated pressure sensor at the FTS site. These findings lead us to speculate about inadequacies in the forward model of our retrieval algorithm. By assuming a 1% intensity offset in the O 2 A band region for the SCIAMACHY X CO 2 retrieval, we significantly improved the spectral fit and achieved better consistency between SCIAMACHY and FTS X CO 2 retrievals. We compared the seasonal cycle of X CO 2 at Park Falls from SCIAMACHY and FTS retrievals with calculations of the Model of Atmospheric Transport and Chemistry/Carnegie-AmesStanford Approach (MATCH/CASA) and found a good qualitative agreement but with MATCH/CASA underestimating the measured seasonal amplitude. Furthermore, since SCIAMACHY observations are similar in viewing geometry and spectral range to those of OCO, this study represents an important test of the OCO retrieval algorithm and validation concept using NIR spectra measured from space. Finally, we argue that significant improvements in precision and accuracy could be obtained from a dedicated CO 2 instrument such as OCO, which has much higher spectral and spatial resolutions than SCIAMACHY. These measurements would then provide critical data for improving our understanding of the carbon cycle and carbon sources and sinks.

Abstract. Here, we report preliminary estimates of the column averaged carbon dioxide (CO 2 ) dry air mole fraction, X CO 2 , retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global X CO 2 field, including its variation with latitude and season. However, low yields of retrieved X CO 2 over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT X CO 2 retrievals with X CO 2 estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2 % (7-8 parts per million, ppm, with respect to dry air) X CO 2 bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global X CO 2 bias is associated with a systematic, 1 % overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O 2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT X CO 2 retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O 2 and CO 2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in X CO 2 on global scales as well as a ∼30 % reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011b). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observatory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit.

Abstract. We apply a continental-scale inverse modeling system for North America based on the GEOS-Chem model to optimize California methane emissions at 1/2 • × 2/3 • horizontal resolution using atmospheric observations from the CalNex aircraft campaign (May-June 2010) and from satellites. Inversion of the CalNex data yields a best estimate for total California methane emissions of 2.86 ± 0.21 Tg a −1 , compared with 1.92 Tg a −1 in the EDGAR v4.2 emission inventory used as a priori and 1.51 Tg a −1 in the California Air Resources Board (CARB) inventory used for state regulations of greenhouse gas emissions. These results are consistent with a previous Lagrangian inversion of the CalNex data. Our inversion provides 12 independent pieces of information to constrain the geographical distribution of emissions within California. Attribution to individual source types indicates dominant contributions to emissions from landfills/wastewater (1.1 Tg a −1 ), livestock (0.87 Tg a −1 ), and gas/oil (0.64 Tg a −1 ). EDGAR v4.2 underestimates emissions from livestock, while CARB underestimates emissions from landfills/wastewater and gas/oil. Current satellite observations from GOSAT can constrain methane emissions in the Los Angeles Basin but are too sparse to constrain emissions quantitatively elsewhere in California (they can still be qualitatively useful to diagnose inventory biases). Los Angeles Basin emissions derived from CalNex and GOSAT inversions are 0.42 ± 0.08 and 0.31 ± 0.08 Tg a −1 that the future TROPOMI satellite instrument (2015 launch) will be able to constrain California methane emissions at a detail comparable to the CalNex aircraft campaign. Geostationary satellite observations offer even greater potential for constraining methane emissions in the future.

In the legend of Fig. 2 of this Article, owing to an error during the production process, panels a and b were inadvertently labelled 'northeast China (within 38-54° N, 120-135° E)' and panels c and d were inadvertently labelled 'southwest China (within 18-30° N, 95-110° E)', rather than the other way around. The figure was correct. This error has been corrected online.