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Frontal boundaries have been shown to cause large changes in CO2 mole‐fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO2 dry air mole‐fraction (XCO2) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO2 frontal contrasts (ΔXCO2, i.e., warm minus cold sector average of XCO2). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO2 during four seasonal field campaigns of the Atmospheric Carbon and Transport‐America (ACT‐America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting‐—Chemistry (WRF‐Chem) showed that (a) all products had a similar sign of ΔXCO2 though with different levels of agreement in ΔXCO2 magnitudes among seasons; (b) ΔXCO2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO2 frontal contrasts. A linear regression analyses between ΔXCO2 for MFLL versus GMAO, and MFLL versus WRF‐Chem for summer‐2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO2 variability among four seasons provide guidance to the spatial structures of XCO2 transport errors in models and satellite measurements of XCO2 in synoptically‐active weather systems. Key Points: First airborne observations of column average CO2 dry‐air mole‐fraction (XCO2) changes across fronts observed during ACT‐A are reportedXCO2 frontal structures compare reasonably well with Weather Research and Forecasting—Chemistry and an in situ data driven assimilation systemResults reveal that the differences across models and data were generally much smaller than the magnitude of XCO2 frontal contrasts [ABSTRACT FROM AUTHOR]

To check the accuracy of column-average dry air CO2 mole fractions ("XCO2 ") retrieved from Orbiting Carbon Overvatory (OCO-2) data, a similar quantity has been measured from the Multi-functional Fiber Laser Lidar (MFLL) aboard aircraft flying underneath OCO-2 as part of the Atmospheric Carbon and Transport (ACT)-America flight campaigns. Here we do a lagged correlation analysis of these MFLL-OCO-2 column CO2 differences and find that their correlation spectrum falls off rapidly at along-track separation distances of under 10 km, with a correlation length scale of about 10 km, and less rapidly at longer separation distances, with a correlation length scale of about 20 km. The OCO-2 satellite takes many CO2 measurements with small (~3 km²) fields of view (FOVs) in a thin (<10 km wide) swath running parallel to its orbit: up to 24 separate FOVs may be obtained per second (across a ~6.75 km distance on the ground), though clouds, aerosols, and other factors cause considerable data dropout. Errors in the CO2 retrieval method have long been thought to be correlated at these fine scales, and methods to account for these when assimilating these data into top-down atmospheric CO2 flux inversions have been developed. A common approach has been to average the data at coarser scales (e.g., in 10-second-long bins) along-track, then assign an uncertainty to the averaged value that accounts for the error correlations. Here we outline the methods used up to now for computing these 10-second averages and their uncertainties, including the constant-correlation-with-distance error model currently being used to summarize the OCO-2 version 9 XCO2 retrievals as part of the OCO-2 flux inversion model intercomparison project. We then derive a new one-dimensional error model using correlations that decay exponentially with separation distance, apply this model to the OCO-2 data using the correlation length scales derived from the MFLL-OCO-2 differences, and compare the results (for both the average and its uncertainty) to those given by the current constant-correlation error model. To implement this new model, the data are averaged first across 2-second spans, to collapse the cross-track distribution of the real data onto the 1-D path assumed by the new model. A small percentage of the data that cause non-physical negative averaging weights in the model are thrown out. The correlation lengths over the ocean, which the land-based MFLL data do not clarify, are assumed to be twice those over the land. The new correlation model gives 10-second XCO2 averages that are only a few tenths of a ppm different from the constant-correlation model. Over land, the uncertainties in the mean are also similar, suggesting that the +0.3 constant correlation coefficient currently used in the model there is accurate. Over the oceans, the twice-the-land correlation lengths that we assume here result in a significantly lower uncertainty on the mean than the +0.6 constant correlation currently gives - measurements similar to the MFLL ones are needed over the oceans to do better. Finally, we show how our 1-D exponential error correlation model may be used to account for correlations in those inversion methods that choose to assimilate each X??푂2 retrieval individually, and to account for correlations between separate 10-second averages when these are assimilated instead. [ABSTRACT FROM AUTHOR]

We present an evaluation of airborne intensity‐modulated continuous‐wave (IM‐CW) lidar measurements of atmospheric column CO2 mole fractions during the Atmospheric Carbon and Transport–America (ACT‐America) project. This lidar system transmits online and offline wavelengths simultaneously on the 1.57111‐μm CO2 absorption line, with each modulated wavelength using orthogonal swept frequency waveforms. After the spectral characteristics of this system were calibrated through short‐path measurements, we used the HITRAN spectroscopic database to calculate the average‐column CO2 mole fraction (XCO2) from the lidar‐measured optical depths. Using in situ measurements of meteorological parameters and CO2 concentrations for calibration data, we demonstrate that our lidar CO2 measurements were consistent from season to season and had an absolute calibration error (standard deviation) of 0.80 ppm when compared to XCO2 values calculated from in situ measurements. By using a 10‐s or longer moving average, a precision of 1 ppm or better was obtained. The estimated CO2 measurement precision for 0.1‐, 1‐, 10‐, and 60‐s averages was determined to be 3.4, 1.2, 0.43, and 0.26 ppm, respectively. These correspond to measurement signal‐to‐noise ratios of 120, 330, 950, and 1,600, respectively. The drift in XCO2 over 1‐hr of flight time was found to be below 0.1 ppm. These analyses demonstrate that the measurement stability, precision, and accuracy are all well below the thresholds needed to study synoptic‐scale variations in atmospheric XCO2. Key Points: Demonstrate the MFLL laser spectrometer for use in column CO2 retrievalsDemonstrate the viability of this instrument for use in ACT America project goalsProvide a methodology for doing CO2 or perhaps other types of retrievals using lidar [ABSTRACT FROM AUTHOR]

NASA Langley Research Center is working on a continuous wave (cw) laser-based remote sensing scheme for the detection of CO2 and O2 from space-based platforms suitable for an active sensing of CO2 emissions over nights, days, and seasons (ASCENDS) mission. ASCENDS is a future space-based mission to determine the global distribution of sources and sinks of atmospheric carbon dioxide (CO2). A unique, multifrequency, intensity modulated cw laser absorption spectrometer operating at 1.57 μm for CO2 sensing has been developed. Effective aerosol and cloud discrimination techniques are being investigated in order to determine concentration values with accuracies less than 0.3%. In this paper, we discuss the demonstration of a pseudonoise code-based technique for cloud and aerosol discrimination applications. The possibility of using maximum length sequences for range and absorption measurements is investigated. A simple model for accomplishing this objective is formulated. Proof-of-concept experiments carried out using a sonar-based LIDAR simulator that was built using simple audio hardware provided promising results for extension into optical wavelengths. [ABSTRACT FROM AUTHOR]