Atmospheric Carbon Dioxide Measurement from Aircraft and Comparison with OCO-2 and CarbonTracker Model Data

. Accurate monitoring of the atmospheric carbon dioxide (CO 2 ) and its distribution is of great significance for 10 studying the carbon cycle and predicting the future climate change. Compared to the ground observational sites, the airborne observations cover a wider area, and simultaneously observe a variety of surface types, which help in effectively monitoring the distribution of CO 2 sources and sinks. In this work, an airborne experiment was carried out in March 2019 over Shanhaiguan area, China (39-41N,119-121E). An Integrated Path Differential Absorption (IPDA) Light Detection and Ranging (LIDAR) system and a commercial instrument, the Ultraportable Greenhouse Gas Analyzer (UGGA), were used installed on an aircraft 15 to observe the CO 2 distribution over various surface types. The Pulse Integration Method (PIM) algorithm was used to calculate the Differential Absorption Optical Depth (DAOD) from the LIDAR data. The CO 2 column-averaged dry-air mixing ratio (XCO 2 ) was calculated over different types of surfaces including mountain, ocean and urban areas. The concentrations of the XCO 2 calculated from LIDAR measurements over ocean, mountain, and urban areas were 421.11, 427.67, and 430 ppm, respectively. Moreover, through the detailed analysis of the data obtained from the UGGA, the influence of pollution levels 20 on the CO 2 concentration was also studied. During the whole flight campaign, March 18 was heavily polluted with an Air Quality Index (AQI) of 175 and PM 2.5 of 131. The Aerosol Optical Depth (AOD) reported by a sun photometer installed at the Funning ground station was 1.28. Compared to the other days, the CO 2 concentration measured by UGGA at different heights was the largest on March 18 with an average value of 422.59 ppm, that was about 10 ppm higher than the measurements recorded on March 16. Moreover, the vertical profiles of Orbiting Carbon observatory-2 (OCO-2) and CarbonTracker were 25 also compared with the aircraft measurements. All the datasets showed a similar variation trend with some differences in their CO 2 concentrations, which proved the existence of a good agreement among them. airborne IPDA LIDAR on March 14 was processed and analysed. The results showed that the XCO 2 over the ocean surface was the smallest, with an 280 average value of 421.11ppm, and that was the largest over residential area with an average value of 430 ppm. The average XCO 2 value over the mountainous area was 427.67 ppm. Moreover, the dry-air mole fraction of CO 2 measured by UGGA was also analysed for several days and the results showed that the CO 2 concentration was the largest on 18 March, that was the most polluted day during the entire flight campaign. The UGGA CO 2 concentration was compared with the XCO 2 calculated using the IPDA LIDAR measurements, and both of the datasets showed a good agreement by exhibiting a similar variation 285 trend. In addition, the vertical profiles of CO 2 were also measured using UGGA and compared with OCO-2 and the Carbon Tracker CO 2 datasets. All the datasets showed a similar variation result with some differences in their concentrations. The CO 2 concentration from the Carbon Tracker was relatively larger than the dry-air mole fraction of CO 2 measured using the UGGA. The atmospheric CO 2 concentration was the highest near the ground and it decreased gradually with the progression in the altitude.

China significantly contributes to the global CO2 emission mainly due to the strong anthropogenic activities (Mustafa et al., 2020). The northern China, in particular, Beijing-Tianjin-Hebei is the most populated region with the largest anthropogenic emissions in the world (Lei et al., 2017;Yang et al., 2019). Under the United Nations Framework Convention on Climate Change (UNFCCC) 2015 Paris Climate Agreement, China pledged to reduce the CO2 emission per unit gross domestic product 70 (GDP) by 60-65% compared to 2005 levels, and peak carbon emission overall, by 2030 (UNFCC, 2015). It is crucial to measure the atmospheric CO2 using precise and accurate instruments for monitoring of the CO2 reduction progress and evaluation of how well specific policies are working. In this study, an airborne campaign was carried out during March 2019 to measure the atmospheric CO2 using an IPDA LIDAR, and a commercial instrument Ultraportable Greenhouse Gas Analyzer (UGGA; model 915-0011; Los Gatos Research, San Jose, CA, USA) over northeast China. The primary objective of the study was to 75 evaluate the performance of a newly developed IPDA LIDAR instrument over different types of surfaces including water bodies, mountains and urban residential areas. In addition, the influence of pollution on the atmospheric CO2 concentration was also studied using the measurement obtained from the UGGA installed on the aircraft. The details about observational site, flight campaign, and instruments are provided in Section 2. The results including the IPDA LIDAR measurements, UGGA observations and their comparisons are discussed in Section 3. 80

Aircraft Instrumentation
The aircraft used in this experiment was a Yun-8, which was equipped with four turboprop engines. The cruise and the maximum speeds of the aircraft were 550 and 660 km h−1, respectively. The Atmospheric Carbon Dioxide LIDAR (ACDL) conducted its first flight experiment during March 2019 over Shanhaiguan, China. The working wavelengths of the ACDL 85 were 532, 1064, and 1572 nm, respectively. The 1572 nm channel was used for IPDA technique to measure the atmospheric CO2, while 532 and 1064 nm channels were used to detect the aerosols and clouds. The aerosol and cloud optical parameters, such as the extinction coefficient, backscatter coefficient, LIDAR ratio and the Aerosol Optical Depth (AOD) are helpful in providing accurate inversion of CO2 column concentration (Crisp et al., 2012;O'Dell et al., 2012). More detail about the ACDL is described in our previous article (Zhu et al., 2020). The ACDL system used for the atmospheric CO2 measurement 90 is shown in Figure 1, and more detail about the main components of the system is provided in Table 1. The ACDL consisted of a laser transmitter, an instrument control, an environmental control, and a LIDAR transceiver subsystem. Figure 1(a) shows the transceiver system. It mainly included a laser, a telescope, a receiving system and an APD 95 detector, which were mounted in a pod outside the aircraft. Figure 1(b) shows the laser frequency monitoring and control system, electronic control system and the data acquisition system of the equipment. These systems were installed inside the aircraft and armoured optical fibres and cables were used to transmit the information to the instruments in the pod. An Inertial Navigation System (INS) was also installed to record the attitude information of the aircraft during the flight. The real-time altitude and position information of aircraft were acquired using a Global Positioning System (GPS) system. Figure 1(c) shows 100 the Aircraft Integrated Meteorological Measurement System (AIMMS). The AIMMS was installed to measure the atmospheric temperature, pressure, relative humidity and other meteorological parameters during the flight. Figure 1(d) shows a commercial instrument UGGA, that was installed in an unsealed cabin of the aircraft and a 1/4-inch Teflon pipe was used to connect it with the external atmosphere. The UGGA used a laser absorption technology known as the off-axis Integrated Cavity Output Spectroscopy (ICOS) to measure trace gas concentration in dry mole fraction with a high precision of <0.30 ppm for CO2 and 105 <2 ppb for CH4 (UGGA user manual; model 915-0011; Los Gatos Research, San Jose, CA, USA). More details about the UGGA and ICOS spectroscopy are given in previous studies (Baer et al., 2002;Paul et al., 2001;Sun et al., 2020). Before the flight experiment, the UGGA was calibrated against the standard gas, and the uncertainty was within 0.1 ppm.

Experimental Site
The airborne campaign was conducted from 11 -19 March 2019. More detail about the flights is given in Table 2. Figure 2  110 shows the geolocation of the experimental site and path of the flight carried out on 14 March. In order to detect the changing trend of atmospheric CO2 concentration over various types of surfaces, the path of the flight was designed to observe the ocean, urban residential and the mountain areas. The starting point of the flight was A, and the ending point was B. The flight path covered a variety of surface types, including the ocean, the mountain, and the urban residential areas. The distribution of the carbon sources and sinks in the study area can be more accurately distinguished through the detection of various surface types. 115 Figure 3 shows the flight altitude and the corresponding surface elevation information during the level flight period. The altitude of the aircraft was measured by the GPS system. The height and the ground elevation were measured using the airborne IPDA LIDAR. The altitude of the horizontal flight of the plane on March 14 was about 6.8 km. Moreover, the altitude information about various types of surfaces is also shown in Figure 3.

Aircraft Data
A variety of data were measured using the aircraft and incorporated in this study. The aircraft data included the ACDL data, 125 in-situ data and the auxiliary data. The in-situ CO2 dry-air mole fraction data was measured using the UUGA which was installed in an unsealed cabin of the aircraft. The auxiliary data included the inertial navigational and meteorological data. The inertial navigational data was measured using the INS, and the meteorological data was measured using the AIMMS, which was installed on the aircraft shell. In addition, a colour Complementary Metal Oxide Semiconductor (CMOS) camera (model: IDS ui-3360cp-c-hq Rev.2) with a resolution of 2048x1088 pixels was also installed next to the lidar telescope to observe 130 various types of surfaces. The image sampling rate was 1 Hz. Each picture incorporated the shooting time, and it provided a convenience to find the types of surfaces at different times. The photo name included the camera date and time, which was synchronized with the other instruments installed on the aircraft.

OCO-2 Dataset
The Orbiting Carbon observatory-2 (OCO-2), developed by NASA is the second satellite after the Greenhous gases Observing 135 SATellite (GOSAT) to monitoring the CO2 in the atmosphere to get a better understanding of the carbon cycle (Crisp, 2015;Crisp et al., 2008). The main objectives of the mission included measuring the atmospheric CO2 with sufficient precision, accuracy and spatiotemporal resolution required to quantify the CO2 sources and sinks at the regional and global scales. The sun-synchronous near-polar satellite included three high-resolution spectrometers making coincident measurements of the reflected sunlight in the near-infrared CO2 at 1.61 and 2.06 μm and oxygen at 0.76 μm (Wunch et al., 2017), In this study, 140 OCO-2 XCO2 version 10r Level 2 Lite product was used.

CarbonTracker Dataset
CarbonTracker is an inverse model framework developed by (Peters et al., 2005). It combines the two-way nested transfer model 5 (TM5) with offline Atmospheric Tracer transfer model and updates the atmospheric CO2 distribution and surface fluxes every year (Krol et al., 2004). It supports high-resolution data at regional level and coarse-resolution data at global scale. 145 The Carbon Tracker provides the global CO2 distribution at 25 pressure levels with a spatial grid resolution of 3°×2° https://doi.org/10.5194/amt-2021-92 Preprint. Discussion started: 10 June 2021 c Author(s) 2021. CC BY 4.0 License.
(Longitude/Latitude) and a temporal resolution of 3 hours (Babenhauserheide et al., 2015). The data product CTNRT2020 was used in this study (Jacobson et al., 2020).

IPDA Theory
The ACDL system developed for this study was based on two different wavelengths referred as the online and the offline 150 wavelengths. The laser pulse of the online wavelength was strongly attenuated because it was absorbed by the trace gas molecules while propagating through the atmosphere. In contrast, the offline pulse was only weakly attenuated . The online and offline wavelengths selected in this study were not affected by other molecules except CO2. Because the online and the offline wavelengths were very close, the difference of scattering and absorption caused by the aerosols and the gas molecules in the atmosphere could be ignored. Therefore, the difference between the two wavelength echo signals was 155 mainly caused by atmospheric CO2. The airborne IPDA lidar equation (Ehret et al., 2008;Refaat et al., 2016) is given in the following: Where, is the echo power, is the wavelength, is the receiving optical efficiency, is the overlap factor, is the area of the telescope, is the height of the hard target above sea level, is the altitude of the aircraft platform, is the emission 160 energy of the laser, Δt is the effective pulse width of the echo pulse, * is the target reflectivity, 2 is the two-way integral optical depth caused by the CO2, and is the atmospheric transmission efficiency. The detection signals of online and offline pulses are defined as 0 ( ) and 0 ( ), respectively. The echo signals of the online and offline pulses are ( , ), and ( , ), respectively. The IPDA single-pass Differential Absorption Optical Depth (DAOD) of the CO2 can be expressed as (Refaat et al., 2015): 165 Where, Δ 2 is the differential absorption cross section of the online and offline wavelengths, 2 is the molecular density of the CO2, is the height of the hard target above sea level, and is the altitude of the aircraft platform. and are pressure and temperature profiles. When the APD detector receives the signal, it can convert the power into voltage using equation 3 (Zhu et al., 2020): 170 Where, ℜ represents the voltage response rate of the APD detector. Within the linear response range of the detector, the voltage response rate is a fixed value ℜ which the indicates signal power. Therefore, equation 2 can also be expressed as: Where 0 ( ) and 0 ( ) are the detection signals voltage of online and offline pulses. ( , ) and ( , ) are the 175 echo signals voltage of the online and offline pulses. For the airborne experiment, the vertical path XCO2 (in ppm) can be calculated the following equations: Where, is the Avogadro's constant, is the gas constant, ( ) and ( ) are the pressure and temperature profiles, 180 respectively. 2 is the dry-air ratio of water vapor, represents the integral weight function. can be calculated using the temperature, pressure and humidity profiles obtained by the AIMMS and the High-resolution Transmission Molecular Absorption (HITRAN) database (Gordon et al., 2017).

Original Echo Signals 185
The performance of the ACDL system was evaluated by comparing the original echo signals over three different surface types, including the ocean, the mountain, and the urban residential areas. The original signals of the ACDL over the ocean, urban residential, and mountainous areas are shown in Figures 4, 5, and 6, respectively. The amplification signals from left to right are online monitor signal, online echo signal, offline monitor signal and offline echo signal. In each group of original echo signals, the monitoring signals are fixed at the same position but the echo signals appear in different positions due to the 190 different heights of the target. The original signals were filtered before using, and signals whose pulse peak values were not in the linear region of APD were discarded. The echo signals in the ocean area were significantly smaller than those over the residential and the mountain areas. This might be due to the low reflectivity of the ocean, which leads to the reduction of the signal noise ratio (SNR) over the ocean. Moreover, no significant difference was observed between the echo signal strengths of residential and mountain areas. 195 (Zhu et al., 2020) used the Matched Filter Algorithm (MFA) to extract the weak echo signals over the ocean in a previous research work. In addition, the differences between the Pulse Peak Method (PPM) and PIM were also compared while calculating the DAOD. The results showed that the SNR and accuracy of PIM were higher than those of the PPM. In this study, the PIM method was used to calculate the DAOD. The sum SNR was calculated using the following set of equations: 200

Data Processing and Inversion Results
Where, is the point number of the pulse, and represent the mean and standard deviation. is the value of each point on the pulse, and is the standard deviation of each point. We can increase the SNR of each pulse by accumulating the 205 number of points on the pulse. Figure 7a shows the online wavelength monitoring signal, and figure 7b shows the change of SNR related to the number of accumulated points taken on the pulse. Figure 8a and 9a show the typical echo signals over the land and the ocean area. Figure 8b and 9b show the change of SNR related to the number of accumulated points taken on the pulse over different surface types. For the residential and mountain areas, the SNR was the highest when 5 points were taken before the pulse peak and 9 points were taken after the peak. And for the weak echo signal in the ocean area, when 7 points 210 were taken before the pulse peak and 10 points were taken after the peak, the SNR was the largest.
The DAOD results calculated using the IPDA theory are shown in Figure 10. The DOAD values were smaller over the mountain area, however, no difference was found between the DAOD values of ocean and residential areas. The average DAOD values for mountain, ocean and residential areas were 0.44, 0.46, and 0.46, respectively. The results of the IWF and the XCO2 calculated using equations 5 and 6 are shown in figures 11 and 12. The average values of the IWF over ocean, 215 residential, and the mountainous areas are 1083.26, 1079.75, and 1037.05, respectively. In addition, the standard deviation of the IWF was the smallest for ocean surface and the largest for the mountainous area. The higher standard deviation for mountainous areas might be due to the fluctuations in height. Before retrieving the XCO2, the aircraft attitude angle and the doppler shift were corrected using the inertial navigation data. The XCO2 calculated from the ACDL measurements is shown in Figure 12. The XCO2 is the largest over residential areas and the smallest over ocean. The largest XCO2 over the urban 220 residential areas might be attributed to the strong anthropogenic emissions (Mustafa et al., 2020), and the water body is generally a sink of the CO2. The average values of XCO2 over urban, oceanic, and mountainous areas were 430, 427.67, and 421.11 ppm, respectively. The distribution of XCO2 on the flight trajectory and the surface photos captured using the installed coloured CMOS camera are shown in Figure 13.

In-Situ Measurement Results 225
Other data observed by airborne the ACDL are still being processed and analysed. In this study, the in-situ observations measured using the UGGA were also analysed for several days. The vertical profiles of the atmospheric CO2 were measured using the UGGA during spiral and the descent of the aircraft and the results are shown in figure 14. The data recorded below 0.5 km were discarded because it produced errors and sudden spikes due to slowing down of the aircraft and the sudden pressure changes. Figure 14 shows that the atmospheric CO2 concentration is the largest near the ground, and it decreases 230 gradually with the progression in the altitude. This might be due to the weak photosynthesis as the plants are in dormant stage during winter in the northeast China (Mustafa et al., 2021). Moreover, the northeast China is also a source of carbon due to heating and industrial activities, which also contributes significantly to the atmospheric CO2 (Shan et al., 1997). In addition, https://doi.org/10.5194/amt-2021-92 Preprint. Discussion started: 10 June 2021 c Author(s) 2021. CC BY 4.0 License.
the CO2 concentration at different altitudes were the highest on 18 March. This could be caused by the weather conditions and pollution levels. Table 3 shows the weather report released by the Qinhuangdao meteorological station on each day of the 235 flight. The AOD values measured using various instruments on each flight day are shown Figure Figure 17 shows the comparison of the XCO2 calculated from the ACDL measurements with the dry-air mole fraction of CO2 245 measured using the UGGA. Both of the datasets show a good agreement by exhibiting a similar variation trend. The results from the two datasets also show that the concentration of the atmospheric CO2 is the highest over the residential area and the lowest over ocean surface. The average value of XCO2 obtained by the ACDL calculations was 426.27 ppm, and the average value of CO2 mole fraction obtained by the UGGA measurements was 413.91 ppm. Moreover, the standard deviation of the UGGA observations was relatively smaller than that of the ACDL measurements, and this might be due to the different working 250 principles of the two instruments. The ACDL measures the weighted average concentrations at different altitudes. However, the UGGA measures the CO2 value at the aircraft location.

OCO-2 Measurement Results
During this flight experiment, the OCO-2 passed over the flight area on March 16 and the observations over the study area are shown in Figure 18  When the altitude is more than 3 km, the CO2 concentration is almost constant. Thig might be due to the stability of the upper atmosphere.

Conclusions
In this study, a 1.57 μm double-pulse airborne IPDA LIDAR was developed for atmospheric CO2 monitoring. The airborne experiment using the newly developed instrument was carried out during 11 -19 March 2019 over Shanhaiguan, China. The 270 IPDA LIDAR was installed on a research aircraft with some other instrument including a commercial CO2 monitoring UGGA, an AIMMS, an INS, and a coloured CMOS camera. The flight path passed across various types of surfaces including the ocean, the mountain, and the residential areas. From the original signals obtained by the IPDA LIDAR, the echo signals over the ocean area were relatively smaller than those over the mountain and the residential areas. In order to process the echo signal with low SNR over the ocean, PIM method was used to calculate DAOD. By calculating the SNR of the detection signal of 275 online wavelength, we determined that when 6 points were taken before the pulse peak and 7 points after the peak, the SNR was the largest. For the residential and mountain areas, the SNR was the highest when 5 points were taken before the pulse peak and 9 points were taken after the peak. And for the weak echo signal over the ocean area, when 7 points were taken before the pulse peak and 10 points were taken after the peak, the SNR was the largest. The data obtained by airborne IPDA LIDAR on March 14 was processed and analysed. The results showed that the XCO2 over the ocean surface was the smallest, with an 280 average value of 421.11ppm, and that was the largest over residential area with an average value of 430 ppm. The average XCO2 value over the mountainous area was 427.67 ppm. Moreover, the dry-air mole fraction of CO2 measured by UGGA was also analysed for several days and the results showed that the CO2 concentration was the largest on 18 March, that was the most polluted day during the entire flight campaign. The UGGA CO2 concentration was compared with the XCO2 calculated using the IPDA LIDAR measurements, and both of the datasets showed a good agreement by exhibiting a similar variation 285 trend. In addition, the vertical profiles of CO2 were also measured using UGGA and compared with OCO-2 and the Carbon Tracker CO2 datasets. All the datasets showed a similar variation result with some differences in their concentrations. The CO2 concentration from the Carbon Tracker was relatively larger than the dry-air mole fraction of CO2 measured using the UGGA.  -14, doi:10.1117/1.2898457, 2008. 330 Crisp, D., Fisher, B. M., O'Dell, C., Frankenberg, C., Basilio, R., Bösch, H., Brown, L. R., Castano, R., Connor, B., Deutscher, N. M., Eldering, A., Griffith, D., Gunson, M., Kuze, A., Mandrake, L., McDuffie, J., Messerschmidt, J., Miller, C. E., Morino, I., Natraj, V., Notholt, J., O'Brien, D. M., Oyafuso, F., Polonsky, I., Robinson, J., Salawitch, R., Sherlock, V., Smyth, M., Suto, H., Taylor, T. E., Thompson, D. R., Wennberg, P. O., Wunch, D. andYung, Y. L.: The ACOS CO 2 retrieval algorithm -Part II: Global XCO2 data characterization, Atmos. Meas. Tech., 5(4), 687-707, doi:10.5194/amt-5-687-2012, 2012 Du, J., Zhu, Y., Li, S., Zhang, J., Sun, Y., Zang, H., Liu, D., Ma, X., Bi, D., Liu, J., Zhu, X. and Chen, W.: Double-pulse 157 μm integrated path differential absorption lidar ground validation for atmospheric carbon dioxide measurement, Appl. Opt., 340