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. NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dryair mole fraction, X CO 2 , in the Earth's atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and X CO 2 from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TC-CON). The OCO-2 X CO 2 retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 X CO 2 data quality throughout its mission.

The Cl-atom initiated oxidation mechanisms of both cyclopentane and cyclohexane have been studied as a function of temperature using an environmental chamber/FTIR technique. The oxidation of cyclohexane leads to the formation of the cyclohexoxy radical, the chemistry of which is characterized by a competition between ring-opening (R5) and reaction with O2 (R6) to form cyclohexanone. The yield of cyclohexanone is shown to increase with decreasing temperature, and a rate coefficient ratio k 6/k 5 = (1.3 ± 0.3) × 10-27 exp(5550 ± 1100/T) cm3 molecule-1 is obtained. The energy barrier to ring-opening is estimated to be 11.5 ± 2.2 kcal/mol. The dominant fate of the cyclopentoxy radical, formed in the Cl-atom initiated oxidation of cyclopentane, is ring-opening under all conditions studied here (230−300 K, 50−500 Torr O2), with only a minor contribution from the O2 reaction at the lowest temperatures studied. The barrier to ring-opening for the cyclopentoxy radical is probably less than 10 kcal/mol.

The Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission was recommended by the National Research Council's (NRC's) Earth Science Decadal Survey to measure tropospheric trace gases and aerosols and coastal ocean phytoplankton, water quality, and biogeochemistry from geostationary orbit, providing continuous observations within the field of view. To fulfill the mandate and address the challenge put forth by the NRC, two GEO-CAPE Science Working Groups (SWGs), representing the atmospheric composition and ocean color disciplines, have developed realistic science objectives using input drawn from several community workshops. The GEO-CAPE mission will take advantage of this revolutionary advance in temporal frequency for both of these disciplines. Multiple observations per day are required to explore the physical, chemical, and dynamical processes that determine tropospheric composition and air quality over spatial scales ranging from urban to continental, and over temporal scales ranging from diurnal to seasonal. Likewise, high-frequency satellite observations are critical to studying and quantifying biological, chemical, and physical processes within the coastal ocean. These observations are to be achieved from a vantage point near 95°–100°W, providing a complete view of North America as well as the adjacent oceans. The SWGs have also endorsed the concept of phased implementation using commercial satellites to reduce mission risk and cost. GEO-CAPE will join the global constellation of geostationary atmospheric chemistry and coastal ocean color sensors planned to be in orbit in the 2020 time frame.

We present a 20 year time series of in situ free tropospheric ozone observations above western North America during springtime and interpret results using hindcast simulations (1980–2014) conducted with the Geophysical Fluid Dynamics Laboratory global chemistry‐climate model (GFDL AM3). Revisiting the analysis of Cooper et al. (), we show that sampling biases can substantially influence calculated trends. AM3 cosampled in space and time with observations reproduces the observed ozone trend (0.65 ± 0.32 ppbv yr−1) over 1995–2008 (in simulations either with or without time‐varying emissions), whereas AM3 “true median” with continuous temporal and spatial sampling indicates an insignificant trend (0.25 ± 0.32 ppbv yr−1). Extending this analysis to 1995–2014, we find a weaker ozone trend of 0.31 ± 0.21 ppbv yr−1 from observations and 0.36 ± 0.18 ppbv yr−1 from AM3 “true median.” Rising Asian emissions and global methane contribute to this increase. While interannual variability complicates the attribution of ozone trends, multidecadal hindcasts can aid in the estimation of robust confidence limits for trends based on sparse observational records.