Abstract. Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100 000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25 %, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20 % over land and 40 % over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.

Abstract. We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO 2 (X CO 2 ) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO 2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in X CO 2 south of 25 • S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use Correspondence to: D. Wunch (dwunch@gps.caltech.edu) the observed correlation between free-tropospheric potential temperature and X CO 2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TC-CON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25 • S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.

The optical spectrograph and infrared imager system (OSIRIS) on board the Odin spacecraft is designed to retrieve altitude profiles of terrestrial atmospheric minor species by observing limb-radiance profiles. The grating optical spectrograph (OS) obtains spectra of scattered sunlight over the range 280-800 nm with a spectral resolution of approximately 1 nm. The Odin spacecraft performs a repetitive vertical limb scan to sweep the OS 1 km vertical field of view over selected altitude ranges from approximately 10 to 100 km. The terrestrial absorption features that are superimposed on the scattered solar spectrum are monitored to derive the minor species altitude profiles. The spectrograph also detects the airglow, which can be used to study the mesosphere and lower thermosphere. The other part of OSIRIS is a three-channel infrared imager (IRI) that uses linear array detectors to image the vertical limb radiance over an altitude range of approximately 100 km. The IRI observes both scattered sunlight and the airglow emissions from the oxygen infrared atmospheric band at 1.27 µm and the OH (3-1) Meinel band at 1.53 µm. A tomographic inversion technique is used with a series of these vertical images to derive the two-dimensional distribution of the emissions within the orbit plane.Résumé : Le système de spectrographie optique et d'imagerie infrarouge (OSIRIS) à bord du satellite Odin est conçu pour enregistrer les profils en altitude des éléments mineurs de l'atmosphère en observant les profils de radiance du limbe. Le spectrographe optique à réseau (OS) obtient les spectres de la lumière solaire diffusée sur le domaine entre 280-800 nm, avec une résolution spatiale approximative de 1 nm. Le satellite Odin balaye verticalement le limbe de façon répétée, de telle sorte que l'ouverture verticale de 1 km du OS parcoure les domaines voulus entre 10 et 100 km. Nous analysons les spectres solaires diffusés en superposition avec les caractéristiques terrestres d'absorption, afin de déterminer les profils en altitude des éléments mineurs de l'atmosphère. Le spectrographe détecte aussi la luminescence nocturne atmosphérique qui peut être utilisé pour étudier la mésosphère et la thermosphère. L'autre partie d'OSIRIS est un imageur infrarouge (IRI) à trois canaux qui utilise une banque linéaire de détecteurs pour imager la radiance du limbe sur un domaine d'altitude d'approximativement 100 km. L'IRI observe à la fois la lumière solaire diffusée et les émissions de luminescence nocturne atmospérique provenant de la bande infrarouge de l'oxygène atmosphérique à 1.27 µm et la bande de Meinel de l'OH (3-1) à 1.53 µm. Nous utilisons une technique d'inversion tomographique avec une série de ces images verticales pour obtenir la distribution bidimensionnelle des émissions à l'intérieur de l'orbite.[Traduit par la Rédaction] Can.

Abstract. We use aircraft observations of carbon monoxide (CO) from the NASA ARCTAS and NOAA ARCPAC campaigns in

[1] The goal of this study is to determine how H 2 O and HDO measurements in water vapor can be used to detect and diagnose biases in the representation of processes controlling tropospheric humidity in atmospheric general circulation models (GCMs). We analyze a large number of isotopic data sets (four satellite, sixteen ground-based remote-sensing, five surface in situ and three aircraft data sets) that are sensitive to different altitudes throughout the free troposphere. Despite significant differences between data sets, we identify some observed HDO/H 2 O characteristics that are robust across data sets and that can be used to evaluate models. We evaluate the isotopic GCM LMDZ, accounting for the effects of spatiotemporal sampling and instrument sensitivity. We find that LMDZ reproduces the spatial patterns in the lower and mid troposphere remarkably well. However, it underestimates the amplitude of seasonal variations in isotopic composition at all levels in the subtropics and in midlatitudes, and this bias is consistent across all data sets. LMDZ also underestimates the observed meridional isotopic gradient and the contrast between dry and convective tropical regions compared to satellite data sets. Comparison with six other isotope-enabled GCMs from the SWING2 project shows that biases exhibited by LMDZ are common to all models. The SWING2 GCMs show a very large spread in isotopic behavior that is not obviously related to that of humidity, suggesting water vapor isotopic measurements could be used to expose model shortcomings. In a companion paper, the isotopic differences between models are interpreted in terms of biases in the representation of processes controlling humidity.Citation: Risi, C., et al. (2012), Process-evaluation of tropospheric humidity simulated by general circulation models using water vapor isotopologues: 1. Comparison between models and observations,