Here, we report preliminary estimates of the column averaged carbon dioxide (CO₂) dry air mole fraction, X_CO2, 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_CO2 field, including its variation with latitude and season. However, low yields of retrieved X_CO2 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_CO2 retrievals with X_CO2 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_CO2 bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global X_CO2 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 O₂ A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT X_CO2 retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O₂ and CO₂ 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_CO2 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. (2011). 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 Observeratory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit

Abstract. The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the columnaveraged carbon dioxide (CO 2 ) dry air mole fraction (X CO 2 ) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols, i.e., contamination, within the instrument's field of view. Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) X CO 2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 µm O 2 A band, neglecting scattering by clouds and aerosols, which introduce photon path-length differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO 2 and H 2 O column abundances using observations taken at 1.61 µm (weak CO 2 band) and 2.06 µm (strong CO 2 band), while neglecting atmospheric scattering. The CO 2 and H 2 O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which are sensitive to different features in the spectra, provides the basis for cloud screening of the OCO-2 data set.To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning of algorithmic threshold parameters that allows for processing of 20-25 % of all OCO-2 soundings, agreement between the OCO-2 and MODIS cloud screening methods is found to be 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April-May) for OCO-2 nadir-land, glint-land and glint-water observations.No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near Published by Copernicus Publications on behalf of the Europ...

Abstract. The seasonal cycle accounts for a dominant mode of total column CO 2 (XCO 2 ) annual variability and is connected to CO 2 uptake and release; it thus represents an important quantity to test the accuracy of the measurements from space. We quantitatively evaluate the XCO 2 seasonal cycle of the Greenhouse Gases Observing Satellite (GOSAT) observations from the Atmospheric CO 2 Observations from Space (ACOS) retrieval system and compare average regional seasonal cycle features to those directly measured by the Total Carbon Column Observing Network (TCCON). We analyse the mean seasonal cycle amplitude, dates of maximum and minimum XCO 2 , as well as the regional growth rates in XCO 2 through the fitted trend over several years. We find that GOSAT/ACOS captures the seasonal cycle amplitude within 1.0 ppm accuracy compared to TCCON, except in Europe, where the difference exceeds 1.0 ppm at two sites, and the amplitude captured by GOSAT/ACOS is generally shallower compared to TCCON. This bias over Europe is not as large for the other GOSAT retrieval algorithms (NIES v02.21, RemoTeC v2.35, UoL v5.1, and NIES PPDF-S v.02.11), although they have significant biases at other sites. We find that the ACOS bias correction partially explains the shallow amplitude over Europe. The impact of the co-location method and aerosol changes in the ACOS algorithm were also tested and found to be few tenths of a ppm and mostly non-systematic. We find generally good agreement in the date of minimum XCO 2 between ACOS and TCCON, but ACOS generally infers a date of maximum XCO 2 2-3 weeks later than TCCON. We further analyse the latitudinal dependence of the seasonal cycle amplitude throughout the Northern Hemisphere and compare the dependence to that predicted by current optimized models that assimilate in situ measurements of CO 2 . In the zonal averages, models are consistent with the GOSAT amplitude to within 1.4 ppm, depending on the model and latitude. We also show that the seasonal cycle of XCO 2 depends on longitude especially at the mid-latitudes: the amplitude of GOSAT XCO 2 doubles from western USA to East Asia at 45-50 • N, which is only partially shown by the models. In general, we find that model-tomodel differences can be larger than GOSAT-to-model difPublished by Copernicus Publications on behalf of the European Geosciences Union. ferences. These results suggest that GOSAT/ACOS retrievals of the XCO 2 seasonal cycle may be sufficiently accurate to evaluate land surface models in regions with significant discrepancies between the models.

[1] The Tropospheric Emission Spectrometer (TES) is an infrared, high-resolution Fourier transform spectrometer which was launched onboard NASA's Aura satellite in 2004 and is providing global, vertically resolved measurements of ozone in the troposphere. TES version 2 (V002) data profiles have been validated in the troposphere and lower stratosphere by way of comparison to ozonesondes and aircraft measurements. TES measurements also have sensitivity throughout the stratosphere, and therefore TES ozone profiles can be integrated to determine the total and stratospheric column in addition to the tropospheric column ozone values. In this work we compare the ozone in the stratosphere measured by TES to observations from the Microwave Limb Sounder (MLS) instrument in order to show the quality of the TES measurements in the stratosphere. We also compare the determination of a total column value for ozone based on the TES profiles to the column measured by the Ozone Monitoring Instrument (OMI). The TES tropospheric ozone column value is also calculated from the TES profiles and compared with column values determined from ozonesonde data. Column measurements are useful because the errors are markedly reduced from errors at the profile levels and can be used to assess both biases and quality of the TES ozone retrievals. TES observations of total or partial column ozone compare well with the other instruments but tend toward higher values than the other measurements. Specifically, TES is higher than OMI by $10 Dobson units (DU) for the total ozone column. TES measures higher values in the stratosphere (above 100 hPa) by $3 DU and measures higher ozone column values ($4 DU) in the troposphere than ozonesondes. While the strength of the TES nadir ozone product is the vertical resolution it provides in the troposphere, a tropospheric column value derived from TES have utility in analyses using or validating tropospheric ozone residual products.

[1] We present vertical distributions of ozone from the Tropospheric Emission Spectrometer (TES) over the tropical Atlantic Ocean during January 2005. Between 10N and 20S, TES ozone retrievals have Degrees of Freedom for signal (DOF) around 0.7 -0.8 each for tropospheric altitudes above and below 500 hPa. As a result, TES is able to capture for the first time from space a distribution characterized by two maxima: one in the lower troposphere north of the ITCZ and one in the middle and upper troposphere south of the ITCZ. We focus our analysis on the north tropical Atlantic Ocean, where most of previous satellite observations showed discrepancies with in-situ ozone observations and models. Trajectory analyses and a sensitivity study using the GEOS-Chem model confirm the influence of northern Africa biomass burning on the elevated ozone mixing ratios observed by TES over this region.