Hiroshi Tanimoto, Director of the Earth System Division at the National Institute for Environmental Studies (NIES), Japan, and Astrid Müller with their international research team, have developed a new method to assess satellite observations of XCO2 at- over open ocean areas, which are currently inaccessible through established validation network sites. In the new approach, a baseline CO2 data set is formulated by combining observations of freighters and passenger planes that have been carried out in cooperation with private sector operators.

(Background)

After the entry into force of the Paris Agreement, the commitment to reduce greenhouse gas emissions is accelerated. CO2 is the most important greenhouse gas of anthropogenic origin. Emissions from fossil fuel combustion and cement production have caused an accelerated increase in atmospheric CO2 to over 410 ppm in 2020 since the 1950s (Dlugokencky and Tans, 2021). High-quality, high-density measurements are needed to estimate changes in anthropogenic and natural emissions with a view to implementing the Paris Agreement and achieving the goal of zero net greenhouse gas emissions. tight. Increased research and development activities have been carried out to monitor CO2 from more than 200 locations on the Earth’s surface (in 1958 there were only 2 sites), and by a growing fleet of space satellites with global coverage. Among these satellites, the greenhouse gas observation satellite (GOSAT) and the orbiting carbon observatory 2 (OCO-2) were launched in 2009 and 2014. The advantage of space observations is their spatial coverage and high temporal, even on inaccessible sites. regions of the globe, but with less accuracy compared to in situ measurements. Ocean surfaces are one of those hard-to-reach areas. They cover 70% of the Earth and play the most reliable role in removing (~ 2.5 PgC / yr) anthropogenic CO2 emitted (~ 10 PgC / yr) into the atmosphere (Friedlingstein et al., 2019) .

XCO2 satellite data products require validation, which is typically performed against terrestrial XCO2 data products from the Total Carbon Column Observing Network (TCCON) (Wunch et al., 2011), a network of Fourier transform infrared spectrometers in the United States. ground. However, validation sites observing the atmosphere over the ocean are limited to certain coastal and island sites. Therefore, the accuracy of the XCO2 satellite data products over the ocean cannot be fully verified using TCCON.

Recent studies have pointed out that there is apparently a greater bias in satellite observations over the oceans than over land surfaces. TCCON observations of XCO2 at Burgos in the Philippines indicated an XCO2 satellite bias of -0.8 ppm near the tropical Pacific (Velazco et al., 2017). A bias of -0.7 ppm was also observed when observing vertical CO2 profiles using airplanes (Kulawik et al., 2019). However, these aerial campaigns are limited and expensive. To improve satellite data on the oceans, until now there has been no effective method to systematically assess ocean biases over long time periods and large spatial areas.

In cooperation with the private sector, the National Institute for Environmental Studies carried out long-range atmospheric observations by freighters operating between Japan and North America, Australia and Southeast Asia, and passenger planes flying from Japan to various parts of the world. Taking advantage of regular and cost-effective observations with wide geographic coverage, the new approach is an efficient method for evaluating satellite observations over oceans where no baseline data was available. In this study, we applied the method to CO2 in the western Pacific Ocean.

(Method)

In our study, we combine the CO2 observations of cargo ships (Ship Of Opportunity – SOOP) and airliners (Comprehensive Observation Network for Trace gas by Airliner – CONTRAIL) and, using model calculations, we built CO2 profiles from which we obtained the data of the “average mixing ratios on a CO2 column” (obs. XCO2) over the Pacific Ocean. We analyzed the consistency of obs. XCO2 dataset with satellite estimates from GOSAT (greenhouse gas observation satellite: NIES v02.75, National Institute for Environmental Studies; ACOS v7.3, Atmospheric CO2 Observation from Space) and OCO-2 (Orbiting Carbon Observatory, v9r).

(Results)

Our analysis revealed that the new dataset accurately captures seasonal and interannual variations in CO2 in the western Pacific Ocean. In the comparison of the XCO2 satellite of GOSAT and OCO-2 with the obs. XCO2, we found a negative bias of about 1 ppm in the northern mid-latitudes. This bias has been considerably reduced for newer satellite products (ACOS v9, OCO-2 v10). The differences between the obs. The XCO2 and the XCO2 satellite could be attributed to the remaining uncertainties in the satellite data, introduced by the limitations of the recovery algorithms due to the lack of validation data over open oceans. With our new approach, these uncertainties can be identified.

(Expectations for the future)

Advances in recovery algorithms are rapid, with almost a new release every year. To evaluate the improvements of these algorithms, our new approach is of great importance. With the help of the private sector, we can quickly extend the spatial and temporal coverage of the baseline data to complement the established validation networks. We hope to be able to contribute with the new dataset to further improvements in satellite data and, therefore, contribute to a better understanding of changes in the carbon cycle in response to climate change. In the future, our new method of combining observations from cargo ships and passenger planes will be spatially and temporally extended and applied to other important trace gases. Specifically, we plan to use the new dataset for the GOSAT GW assessment, which is slated to launch in 2023.

###

(The references)

Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, NOAA / GML, available at: http: // www.esrl.noaa.government/gmd / ccgg / trends /, last accessed: January 7, 2021.

Friedlingstein, P., Jones, MW, O’Sullivan, M., Andrew, RM, Hauck, J., Peters, GP, Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Bakker, DCE, Canadell, JG, Ciais, P., Jackson, RB, Anthoni, P., Barbero, L., Bastos, A., Bastrikov, V., Becker, M., Bopp, L., Buitenhuis, E .., Chandra, N., Chevallier, F., Chini, LP, Currie, KI, Feely, RA, Gehlen, M., Gilfillan, D., Gkritzalis, T., Goll, DS, Gruber, N., Gutekunst , S., Harris, I., Haverd, V., Houghton, RA, Hurtt, G., Ilyina, T., Jain, AK, Joetzjer, E., Kaplan, JO, Kato, E., Klein Goldewijk, K ., Korsbakken, JI, Landschützer, P., Lauvset, SK, Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D., Marland, G., McGuire, PC, Melton, JR, Metzl, N., Munro, DR, Nabel, JEMS, Nakaoka, S.-I., Neill, C., Omar, AM, Ono, T., Peregon, A., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Séférian, R., Schwinger, J., Smith, N., Tans, PP, Tian, ​​H., Tilbrook, B., Tubiello , FN, van der Werf, GR, Wiltshire, AJ, and Zaehle , S .: G Global Carbon Budget 2019, Earth Syst. Sci. Data, 11, 1783-1838, https: //do I.org /ten.5194 /essd-11- 1783-2019, 2019.

Kulawik, SS, Crowell, S., Baker, D., Liu, J., McKain, K., Sweeney, C., Biraud, SC, Wofsy, S., O’Dell, CW, Wennberg, PO, Wunch, D., Roehl, CM, Deutscher, NM, Kiel, M., Griffith, DWT, Velazco, VA, Notholt, J., Warneke, T., Petri, C., De Mazière, M., Sha, MK, Sussmann , R., Rettinger, M., Pollard, DF, Morino, I., Uchino, O., Hase, F., Feist, DG, Roche, S., Strong, K., Kivi, R., Iraci, L .., Shiomi, K., Dubey, MK, Sepulveda, E., Rodriguez, OEG, Té, Y., Jeseck, P., Heikkinen, P., Dlugokencky, EJ, Gunson, MR, Eldering, A., Crisp , D., Fisher, B., and Osterman, GB: Characterization of OCO-2 and ACOS-GOSAT biases and errors for CO2 flux estimates, Atmos. My. Technology. Discuss. [preprint], https: //do I.org /ten.5194 /amt-2019-257, 2019.

Velazco, VA, Morino, I., Uchino, O., Hori, A., Kiel, M., Bukosa, B., Deutscher, NM, Sakai, T., Nagai, T., Bagtasa, G., Izumi, T., Yoshida, Y. and Griffith, DWT: TCCON Philippines: first measurement results, satellite data and model comparisons in Southeast Asia, Remote Sens., 9, 1-18, https: //do I.org /ten.3390 /rs9121228, 2017.

Wunch, D., Toon, GC, Blavier, J.-FL, Washenfelder, RA, Notholt, J., Connor, BJ, Griffith, DWT, Sherlock, V., and Wennberg, PO: The Total Carbon Column Observing Network, Philos. TR Soc. A, 369, 2087-2112, https: //do I.org /ten.1098 /rsta.2010.0240, 2011.