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.

Reduced cloudiness and enhanced downwelling radiation are associated with the unprecedented 2007 Arctic sea ice loss. Over the Western Arctic Ocean, total summertime cloud cover estimated from spaceborne radar and lidar data decreased by 16% from 2006 to 2007. The clearer skies led to downwelling shortwave (longwave) radiative fluxes increases of +32 Wm−2 (−4 Wm−2) from 2006 to 2007. Over three months, simple calculations show that these radiation differences alone could enhance surface ice melt by 0.3 m, or warm the surface ocean by 2.4 K, which enhances basal ice melt. Increased air temperatures and decreased relative humidity associated with an anti‐cyclonic atmospheric circulation pattern explain the reduced cloudiness. Longer‐term observations show that the 2007 cloudiness is anomalous in the recent past, but is not unprecedented. Thus, in a warmer world with thinner ice, natural summertime circulation and cloud variability is an increasingly important control on sea ice extent minima.

The Orbiting Carbon Observatory-2 (OCO-2), scheduled to launch in July 2014, is a NASA mission designed to measure atmospheric CO 2 . Its main purpose is to allow inversions of net flux estimates of CO 2 on regional to continental scales using the total column CO 2 retrieved using high-resolution spectra in the 0.76, 1.6, and 2.0 μm ranges. Recently, it was shown that solar-induced chlorophyll fluorescence (SIF), a proxy for gross primary production (GPP, carbon uptake through photosynthesis), can be accurately retrieved from space using high spectral resolution radiances in the 750 nm range from the Japanese GOSAT and European GOME-2 instruments. Here, we use real OCO-2 thermal vacuum test data as well as a full repeat cycle (16 days) of simulated OCO-2 spectra under realistic conditions to evaluate the potential of OCO-2 for retrievals of chlorophyll fluorescence and also its dependence on clouds and aerosols. We find that the single-measurement precision is 0.3-0.5 Wm (15-25% of typical peak values), better than current measurements from space but still difficult to interpret on a single-sounding basis. The most significant advancement will come from smaller ground-pixel sizes and increased measurement frequency, with a 100-fold increase compared to GOSAT (and about 8 times higher than GOME-2). This will largely decrease the need for coarse spatial and temporal averaging in data analysis and pave the way to accurate local studies. We also find that the lack of full global mapping from the OCO-2 only incurs small representativeness errors on regional averages. Eventually, the combination of net ecosystem exchange (NEE) derived from CO 2 source/sink inversions and SIF as proxy for GPP from the same satellite will provide a more process-based understanding of the global carbon cycle.

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.

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