Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-26T18:00:58.740Z Has data issue: false hasContentIssue false

8 - Data Assimilation and Inverse Modelling of Atmospheric Trace Constituents

from Part II - ‘Fluid’ Earth Applications: From the Surface to the Space

Published online by Cambridge University Press:  20 June 2023

Alik Ismail-Zadeh
Affiliation:
Karlsruhe Institute of Technology, Germany
Fabio Castelli
Affiliation:
Università degli Studi, Florence
Dylan Jones
Affiliation:
University of Toronto
Sabrina Sanchez
Affiliation:
Max Planck Institute for Solar System Research, Germany
Get access

Summary

Abstract: During the past two decades, there have been significant efforts to better quantify emissions of environmentally important trace gases along with their trends. In particular, there has been a clear need for robust estimates of emissions on policy-relevant scales of trace gases that impact air quality and climate. This need has driven the expansion of the observing network to better monitor the changing composition of the atmosphere. This chapter will discuss the use of various data assimilation and inverse modelling approaches to quantify these emissions, with a focus on the use of satellite observations. It will discuss the inverse problem of retrieving the atmospheric trace gas information from the satellite measurements, and the subsequent use of these satellite data for quantifying sources and sinks of the trace gases.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. L. (2001). An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review, 129, 2884–903.Google Scholar
Arellano, A. F., Jr., Kasibhatla, P. S., Giglio, L. et al. (2006). Time dependent inversion estimates of global biomass-burning CO emissions using measurement of pollution in the troposphere (MOPITT) measurements. Journal of Geophysical Research, 111. https://doi.org/10.1029/2005JD006613.Google Scholar
Arellano, A. F., Jr., Raeder, K., Anderson, J. L. et al. (2007). Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission. Atmospheric Chemistry and Physics, 7, 5695–710.Google Scholar
Barkley, M. P., Smedt, I. D., Roozendael, M. V. et al. (2013). Top-down isoprene emissions over tropical South America inferred from SCIAMACHY and OMI formaldehyde columns. Journal of Geophysical Research: Atmospheres, 118(12), 6849–68. https://doi.org/10.1002/jgrd.50552.Google Scholar
Barré, J., Gaubert, B., Arellano, A. F. J. et al. (2015). Assessing the impacts of assimilating IASI and MOPITT CO retrievals using CESMCAM-chem and DART. Journal of Geophysical Research, 120, 10501–29. https://doi.org/10.1002/2015JD023467.Google Scholar
Basu, S., Guerlet, S., Butz, A. et al. (2013). Global CO2 fluxes estimated from GOSAT retrievals of total column CO2. Atmospheric Chemistry and Physics, 13, 8695–717. https://doi.org/10.5194/acp-13-8695-2013.Google Scholar
Bauwens, M., Stavrakou, T., Muller, J.-F. et al. (2016). Nine years of global hydrocarbon emissions based on source inversion of OMI formaldehyde observations. Atmospheric Chemistry and Physics, 16, 10133–58. https://doi.org/10.5194/acp-16-10133-2016.Google Scholar
Byrne, B., Jones, D. B. A., Strong, K. et al. (2017). Sensitivity of CO2 surface flux constraints to observational coverage. Journal of Geophysical Research, 122, 6672–94. https://doi.org/10.1002/2016JD026164.Google Scholar
Crawford, J. H., Heald, C. L., Fuelberg, H. E. et al. (2004). Relationship between Measurements of Pollution in the Troposphere (MOPITT) and in situ observations of CO based on a large-scale feature sampled during TRACE-P. Journal of Geophysical Research, 109, D15S04. https://doi.org/10.1029/2003JD004308.CrossRefGoogle Scholar
Crowell, S., Baker, D., Schuh, A. et al. (2019). The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network. Atmospheric Chemistry and Physics, 19, 9797–831. https://doi.org/10.5194/acp-19-9797-2019.Google Scholar
Deeter, M. N., Worden, H. M., Edwards, D. P., Gille, J. C., and Andrews, A. E. (2012). Evaluation of MOPITT retrievals of lower-tropospheric carbon monoxide over the United States. Journal of Geophysical Research, 117 (D13306). https://doi.org/10.1029/2012JD017553.Google Scholar
Deng, F., Jones, D., O’Dell, C. W., Nassar, R., and Parazoo, N. C. (2016). Combining GOSAT XCO2 observations over land and ocean to improve regional CO2 flux estimates. Journal of Geophysical Research, 121, 1896–913. https://doi.org/10.1002/2015JD024157.Google Scholar
Derber, J. C. (1989). A variational continuous assimilation technique. Monthly Weather Review, 117, 2437–46. https://doi.org/10.1175/1520-0493(1989)117<2437:AVCAT>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar
Elbern, H., and Schmidt, H. (2001). Ozone episode analysis by four-dimensional variational chemistry data assimilation. Journal of Geophysical Research, 106 (D4), 3569–90.Google Scholar
Elguindi, N., Granier, C., Stavrakou, T. et al. (2020). Intercomparison of magnitudes and trends in anthropogenic surface emissions from bottom-up inventories, top-down estimates, and emission scenarios. Earth’s Future, 8 (e2020EF001520). https://doi.org/10.1029/2020EF001520.Google Scholar
Enting, I. G., Trudinger, C. M., and Francey, R. J. (1995). A synthesis inversion of the concentration and δ13C of atmospheric CO2. Tellus, 47B, 3552.Google Scholar
Feng, L., Palmer, P. I., Parker, R. J. et al. (2016). Estimates of European uptake of CO2 inferred from GOSAT XCO2 retrievals: Sensitivity to measurement bias inside and outside Europe. Atmospheric Chemistry and Physics, 16, 1289–302. https://doi.org/10.5194/acp-16-1289-2016.Google Scholar
Feng, L., Palmer, P. I., Yang, Y. et al. (2011). Evaluating a 3-D transport model of atmospheric CO2 using ground-based, aircraft, and space-borne data. Atmospheric Chemistry and Physics, 11, 2789–803. https://doi.org/10.5194/acp-11-2789-.Google Scholar
Gaubert, B., Emmons, L. K., Raeder, K. et al. (2020). Correcting model biases of CO in East Asia: Impact on oxidant distributions during KORUS-AQ. Atmospheric Chemistry and Physics, 20, 14617–47. https://doi.org/10.5194/acp20-.Google Scholar
GBD 2019 Risk Factors Collaborators. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the global burden of disease study 2019. Lancet, 396, 1223–49. https://doi.org/10.1016/S0140-6736(20)30752-2.Google Scholar
Hamill, T. M., Whitaker, J. S., and Snyder, C. (2001). Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Monthly Weather Review, 129, 2776–90.Google Scholar
Heald, C. L., Jacob, D. J., Jones, D. B. A. et al. (2004). Comparative inverse analysis of satellite (MOPITT) and aircraft (TRACE-P) observations to estimate Asian sources of carbon monoxide. Journal of Geophysical Research, 109 (D23306). https://doi.org/10.1029/2004JD005185.Google Scholar
Hooghiemstra, P. B., Krol, M. C., Bergamaschi, P. et al. (2012). Comparing optimized CO emission estimates using MOPITT or NOAA surface network observations. Journal of Geophysical Research, 117 (D06309). https://doi.org/https://doi.org/10.1029/2011JD017043.Google Scholar
Hunt, B. R., Kostelich, E. J., and Szunyogh, I. (2007). Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112–26.Google Scholar
Jacobson, A. R., Schuldt, K. N., Miller, J. B. et al. (2020). CarbonTracker CT2019B. https://doi.org/10.25925/20201008.CrossRefGoogle Scholar
Jiang, Z., Jones, D. B. A., Worden, H. M., and Henze, D. K. (2015). Sensitivity of top-down CO source estimates to the modeled vertical structure in atmospheric CO. Atmospheric Chemistry and Physics, 15, 1521–37. https://doi.org/10.5194/acp-15-1521-2015.Google Scholar
Jiang, Z., Worden, J. R., Worden, H. et al. (2017). A 15-year record of CO emissions constrained by MOPITT CO observations. Atmospheric Chemistry and Physics, 17, 4565–83. https://doi.org/10.5194/acp-17-4565-2017.Google Scholar
Jiang, Z., Zhu, R., Miyazaki, K. et al. (2022). Decadal variabilities in tropospheric nitrogen oxides over United States, Europe, and China. Journal of Geophysical Research: Atmospheres, 127. https://doi.org/10.1029/2021JD035872.Google Scholar
Jones, D. B. A., Bowman, K. W., Logan, J. A. et al. (2009). The zonal structure of tropical O3 and CO as observed by the Tropospheric Emission Spectrometer in 2004. Part 1: Inverse modeling of CO emissions. Atmospheric Chemistry and Physics, 9, 3547–62.Google Scholar
Khattatov, B. V., Gille, J. C., Lyjak, L. V. et al. (1999). Assimilation of photochemically active species and a case analysis of UARS data. Journal of Geophysical Research (D15), 18715–37.Google Scholar
Kopacz, M., Jacob, D. J., Fisher, J. A. et al. (2010). Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES). Atmospheric Chemistry and Physics, 10, 855–76.Google Scholar
Le Quéré, C., Andrew, R. M., Friedlingstein, P. et al. (2018). Global Carbon Budget 2017. Earth System Science Data, 405–48. https://doi.org/10.5194/essd10–405–2018.Google Scholar
Levelt, P. F., Khattatov, B. V., Gille, J. C. et al. (1998). Assimilation of MLS ozone measurements in the global three-dimensional chemistry transport model ROSE. Geophysical Research Letters, 25(24), 4493–96.Google Scholar
Liu, J., Bowman, K., Lee, M. et al. (2014). Carbon monitoring system flux estimation and attribution: Impact of ACOS-GOSAT XCO2 sampling on the inference of terrestrial biospheric sources and sinks. Tellus B, 66, 22486. https://doi.org/10.3402/tellusb.v66.22486.Google Scholar
Liu, J., Bowman, K. W., and Henze, D. K. (2015). Source-receptor relationships of column average CO2 and implications for the impact of observations on flux inversions. Journal of Geophysical Research, 120, 5214–36. https://doi.org/10.1002/2014JD022914.Google Scholar
Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P. et al. (2019). Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015. Atmospheric Chemistry and Physics, 19, 7859–81. https://doi.org/10.5194/acp-19-7859-2019.Google Scholar
Marais, E. A., Jacob, D. J., Kurosu, T. P. et al. (2012). Isoprene emissions in Africa inferred from OMI observations of formaldehyde columns. Atmospheric Chemistry and Physics, 12(14), 6219–35. https://doi.org/10.5194/acp-12–6219–2012Google Scholar
Millet, D. B., Jacob, D. J., Turquety, S. et al. (2006). Formaldehyde distribution over North America: Implications for satellite retrievals of formaldehyde columns and isoprene emission. Journal of Geophysical Research: Atmospheres, 111(D24). https://doi.org/10.1029/2005JD006853.Google Scholar
Miyazaki, K., Bowman, K., Sekiya, T. et al. (2020). Updated tropospheric chemistry reanalysis and emission estimates, TCR-2, for 2005–2018. Earth System Science Data, 12, 2223–59. https://doi.org/10.5194/essd-12-2223-2020.Google Scholar
Miyazaki, K., Eskes, H., Sudo, K. et al. (2017). Decadal changes in global surface NOx emissions from multi-constituent satellite data assimilation. Atmospheric Chemistry and Physics, 17, 807–37. https://doi.org/10.5194/acp-17-807-2017.Google Scholar
Miyazaki, K., Eskes, H. J., Sudo, K. (2012). Simultaneous assimilation of satellite NO2, O3, CO, and HNO3 data for the analysis of tropospheric chemical composition and emissions. Atmospheric Chemistry and Physics, 12(20), 9545–79. https://doi.org/10.5194/acp-12-9545-2012.Google Scholar
Müller, J.-F., and Stavrakou, T. (2005). Inversion of co and NOX emissions using the adjoint of the images model. Atmospheric Chemistry and Physics, 5(5), 1157–86. https://doi.org/10.5194/acp-5-1157-2005.Google Scholar
Palmer, P. I., Feng, L., Baker, D. et al. (2019). Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal. Nature Communications, 10, 3344. https://doi.org/10.1038/s41467-019-11097-w.Google Scholar
Palmer, P. I., Jacob, D. J., Fiore, A. M. eet al. (2003). Mapping isoprene emissions over North America using formaldehyde column observations from space. Journal of Geophysical Research: Atmospheres, 108 (D6). https://doi.org/10.1029/2002JD002153.Google Scholar
Parrington, M., Jones, D. B. A. Bowman, K. W. et al. (2008). Estimating the summertime tropospheric ozone distribution over North America through assimilation of observations from the tropospheric emission spectrometer. Journal of Geophysical Research, 113 (D18307). https://doi.org/10.1029/2007JD009341.CrossRefGoogle Scholar
Pétron, G., Granier, C., Khattotov, B. et al. (2004). Monthly CO surface sources inventory based on the 2000–2001 MOPITT satellite data. Geophysical Research Letters, 31 (L21107). https://doi.org/10.1029/2004GL020560.Google Scholar
Qu, Z., Henze, D. K., Worden, H. M. et al. (2022). Sector-based top-down estimates of NOx, SO2, and CO emissions in East Asia. Geophysical Research Letters, 49 (e2021GL096009). https://doi.org/10.1029/2021GL096009.Google Scholar
Rabier, F., J¨arvinen, H., Klinker, E., Mahfouf, J.-F., and Simmons, A. (2000). The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Quarterly Journal of the Royal Meteorological Society, 126, 1143–70.CrossRefGoogle Scholar
Rigby, M., Montzka, S. A., Prine, R. G. et al. (2017). Role of atmospheric oxidation in recent methane growth. Proceedings of the National Academy of Sciences, 114(21), 5373–7. https://doi.org/10.1073/pnas.1616426114.Google Scholar
Riishøjgaard, L. P., Štajner, I., and Lou, G.-P. (2000). The GEOS ozone data assimilation system. Advances in Space Research, 25, 1063–72. https://doi.org/10.1016/S0273-1177(99)00443-.CrossRefGoogle Scholar
Rodgers, C. D. (2000). Inverse Methods for Atmospheric Sounding: Theory and Practice. Singapore: World Scientific Publishing.Google Scholar
Rodgers, C. D., and Connor, B. J. (2003). Intercomparison of remote sounding instruments. Journal of Geophysical Research, 108(D3), 4116. https://doi.org/10.1029/2002jd002299.Google Scholar
Sitch, S., Friedlingstein, P., Gruber, N. et al. (2015). Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences, 12, 653–79. https://doi.org/10.5194/bg-12-653-2015.Google Scholar
Stanevich, I., Jones, D. B. A., Strong, K. et al. (2021). Characterizing model errors in chemical transport modeling of methane: Using GOSAT XCH4 data with weak-constraint four-dimensional variational data assimilation. Atmospheric Chemistry and Physics, 21, 9545–72. https://doi.org/10.5194/acp-21-9545-2021.Google Scholar
Stavrakou, T., and Müller, J.-F. (2006). Grid-based versus big region approach for inverting CO emissions using measurement of pollution in the troposphere (MOPITT) data. Journal of Geophysical Research, 111(D15), 304. https://doi.org/10.1029/2005JD006896.Google Scholar
Trémolet, Y. (2006). Accounting for an imperfect model in 4d-var. Quarterly Journal of the Royal Meteorological Society, 132, 2483–504. https://doi.org/10.1256/qj.05.224.Google Scholar
Trémolet, Y. (2007). Model-error estimation in 4d-var. Quarterly Journal of the Royal Meteorological Society, 133, 1267–80. https://doi.org/10.1002/qj.94.Google Scholar
Turner, A. J., Frankenberg, C., and Kort, E. A. (2019). Interpreting contemporary trends in atmospheric methane. Proceedings of the National Academy of Sciences, 116(8), 2805–13. https://doi.org/10.1073/pnas.1814297116.Google Scholar
Worden, H. M., Deeter, M. N., Edwards, D. P. et al. (2010). Observations of near-surface carbon monoxide from space using MOPITT multispectral retrievals. Journal of Geophysical Research, 115,(D18), 314. https://doi.org/10.1029/2010JD014242.Google Scholar
Yoshida, Y., Ota, Y., Eguchi, N. et al. (2011). Retrieval algorithm for CO2 and CH4 column abundances from short-wavelength infrared spectral observations by the greenhouse gases observing satellite. Atmospheric Measurement Techniques, 4, 717–34. https://doi.org/10.5194/amt-4-717-2011.Google Scholar
Zhang, X., Jones, D. B. A., Keller, M. et al. (2019). Quantifying emissions of co and nox using observations from MOPITT, OMI, TES, and OSIRIS. Journal of Geophysical Research, 124 (1029). https://doi.org/11701193/2018JD028670.Google Scholar
Zheng, B., Chevallier, F., Yin, Y. et al. (2019). Global atmospheric carbon monoxide budget 2000–2017 inferred from multi-species atmospheric inversions. Earth System Science Data, 11, 1411–36. https://doi.org/10.5194/essd-11-1411-2019.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×