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

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. The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO 2 ) with the accuracy, resolution, and coverage needed to quantify CO 2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO 2 dry air mole fraction, X CO 2 , that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of X CO 2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes X CO 2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north-south X CO 2 gradient is small. Enhanced X CO 2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north-south X CO 2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in X CO 2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO 2 . As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart X CO 2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

[1] There are notable differences in the joint histograms of cloud top height and optical depth being produced from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectro-Radiometer (MISR) and by the International Satellite Cloud Climatology Project (ISCCP). These differences have their roots in the different retrieval approaches used by the three projects and are driven largely by responses of the retrievals to (1) stratocumulus (or more broadly low-level clouds under temperature inversions), (2) small (subpixel) or broken low-level clouds, and (3) multilayer clouds. Because each data set has different strengths and weakness, the combination tells us more about the observed cloud fields than any of the three by itself. In particular, the MISR stereo height retrieval provides a calibration insensitive approach to determining cloud height that is especially valuable in combination with ISCCP or MODIS because the combination provides a means to estimate the amount of multilayer cloud, where the upper cloud is optically thin. In this article we present a review of the three data sets using case studies and comparisons of annually averaged joint histograms on global and regional scales. Recommendations for using these data in climate model evaluations are provided.