Articles | Volume 23, issue 11
https://doi.org/10.5194/acp-23-6285-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-6285-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jun 2023
Research article |
| 09 Jun 2023
| 09 Jun 2023
Uncertainty in parameterized convection remains a key obstacle for estimating surface fluxes of carbon dioxide
Andrew E. Schuh
CORRESPONDING AUTHOR
Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University, Fort Collins, CO, USA
Andrew R. Jacobson
CIRES, University of Colorado, Boulder, CO, USA
NOAA Global Monitoring Laboratory, Boulder, CO, USA
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Cited articles
Barnes, E. A., Parazoo, N., Orbe, C., and Denning, A. S.: Isentropic transport
and the seasonal cycle amplitude of CO2, J. Geophys. Res.-Atmos., 121, 8106–8124, https://doi.org/10.1002/2016JD025109, 2016. a
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore,
A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global
modeling of tropospheric chemistry with assimilated meteorology: Model
description and evaluation, J. Geophys. Res.-Atmos.,
106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. a
Bosilovich, M. G.: Technical Report Series on Global Modeling and Data
Assimilation, Volume 43 MERRA-2: Initial Evaluation of the Climate, Tech.
rep., NASA-GMAO, https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich803.pdf (last access: 12 May 2023), 2015. a
Craine, J. M., Elmore, A. J., Wang, L., Aranibar, J., Bauters, M., Boeckx, P.,
Crowley, B. E., Dawes, M. A., Delzon, S., Fajardo, A., Fang, Y., Fujiyoshi,
L., Gray, A., Guerrieri, R., Gundale, M. J., Hawke, D. J., Hietz, P., Jonard,
M., Kearsley, E., Kenzo, T., Makarov, M., Marañón-Jiménez,
S., McGlynn, T. P., McNeil, B. E., Mosher, S. G., Nelson, D. M., Peri, P. L.,
Roggy, J. C., Sanders-DeMott, R., Song, M., Szpak, P., Templer, P. H., der
Colff, D. V., Werner, C., Xu, X., Yang, Y., Yu, G., and
Zmudczyńska-Skarbek, K.: Isotopic evidence for oligotrophication of
terrestrial ecosystems, Nat. Ecol. Evol., 2, 1735–1744,
https://doi.org/10.1038/s41559-018-0694-0, 2018. a
Crowell, S., Baker, D., Schuh, A., Basu, S., Jacobson, A. R., Chevallier, F., Liu, J., Deng, F., Feng, L., McKain, K., Chatterjee, A., Miller, J. B., Stephens, B. B., Eldering, A., Crisp, D., Schimel, D., Nassar, R., O'Dell, C. W., Oda, T., Sweeney, C., Palmer, P. I., and Jones, D. B. A.: The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network, Atmos. Chem. Phys., 19, 9797–9831, https://doi.org/10.5194/acp-19-9797-2019, 2019. a
D'Arrigo, R., Jacoby, G. C., and Fung, I. Y.: Boreal forests
and atmosphere – biosphere exchange of carbon dioxide, Nature, 329,
321–323, https://doi.org/10.1038/329321a0, 1987. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and
Vitart, F.: The ERA-Interim reanalysis: configuration and performance of
the data assimilation system, Q. J. Roy. Meteorol.
Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a, b, c
Denning, A. S., Holzer, M., Gurney, K. R., Heimann, M., Law, R. M., Rayner,
P. J., Fung, I. Y., Fan, S.-M., Taguchi, S., Friedlingstein, P., Balkanski,
Y., Taylor, J., Maiss, M., and Levin, I.: Three-dimensional transport and
concentration of SF6. A model intercomparison study (TransCom 2),
Tellus, 51B, 266–297, 1999a. a
Denning, A. S., Takahashi, T., and Friedlingstein, P.: Can a strong atmospheric
CO2 rectifier effect by reconciled with a “reasonable” carbon budget?,
Tellus, 51B, 249–253, 1999b. a
Dentener, F.: Interannual variability and trend of CH4lifetime as a measure
for OH changes in the 1979–1993 time period, J.
Geophys. Res., 108, 4442, https://doi.org/10.1029/2002JD002916, 2003. a
Donner, L. J., Horowitz, L. W., Fiore, A. M., Seman, C. J., Blake, D. R., and
Blake, N. J.: Transport of radon-222 and methyl iodide by deep convection in
the GFDL Global Atmospheric Model AM2, J. Geophys. Res.,
112, D17303, https://doi.org/10.1029/2006JD007548, 2007. a
Folkins, I., Bernath, P., Boone, C., Donner, L. J., Eldering, A., Lesins, G.,
Martin, R. V., Sinnhuber, B.-M., and Walker, K.: Testing convective
parameterizations with tropical measurements of
HNOsub3/sub, CO,
Hsub2/subO, and
Osub3/sub: Implications for the water
vapor budget, J. Geophys. Res.-Atmos., 111, D23304,
https://doi.org/10.1029/2006JD007325, 2006. a
Graven, H. D., Keeling, R. F., Piper, S. C., Patra, P. K., Stephens, B. B.,
Wofsy, S. C., Welp, L. R., Sweeney, C., Tans, P. P., Kelley, J. J., Daube,
B. C., Kort, E. A., Santoni, G. W., and Bent, J. D.: Enhanced Seasonal
Exchange of CO2 by Northern Ecosystems Since 1960, Science, 341,
1085–1089, https://doi.org/10.1126/science.1239207, 2013. a, b
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Baker, D., Bousquet,
P., Bruhwiler, L., Chen, Y.-H., Ciais, P., Fan, S., Fung, I. Y., Gloor, M.,
Heimann, M., Higuchi, K., John, J., Maki, T., Maksyutov, S., Masarie, K.,
Peylin, P., Prather, M., Pak, B., Randerson, J., Sarmiento, J. L., Taguchi,
S., Takahashi, T., Tans, P., and Yuen, C.-W.: Towards robust regional
estimates of CO2 sources and sinks using atmospheric transport models,
Nature, 415, 626–630, https://doi.org/10.1038/415626a, 2002. a
Holtslag, A. A. M. and Boville, B. A.: Local Versus Nonlocal Boundary-Layer
Diffusion in a Global Climate Model, J. Climate, 6, 1825–1842,
https://doi.org/10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2, 1993. a
Holtslag, A. A. M. and Moeng, C. H.: Eddy Diffusivity and Countergradient
Transport in the Convective Atmospheric Boundary-Layer, J.
Atmos. Sci., 48, 1690–1698, 1991. a
Houweling, S., Dentener, F., and Lelieveld, J.: The impact of nonmethane
hydrocarbon compounds on tropospheric photochemistry, J. Geophys.
Res.-Atmos., 103, 10673–10696, https://doi.org/10.1029/97JD03582, 1998. a
Jöckel, P., von Kuhlmann, R., Lawrence, M. G., Steil, B., Brenninkmeijer, C.
A. M., Crutzen, P. J., Rasch, P. J., and Eaton, B.: On a fundamental problem
in implementing flux-form advection schemes for tracer transport in
3-dimensional general circulation and chemistry transport models, Q.
J. Roy. Meteorol. Soc., 127, 1035–1052,
https://doi.org/10.1002/qj.49712757318, 2001. a
Keeling, C., Bacastow, R., Carter, A., Piper, S., Whorf, T., Heimann, M., Mook,
W., and Roeloffzen, H.: A three-dimensional model of atmospheric CO2
transport based on observed winds: 1. Analysis of observational data, Aspects of climate variability in the Pacific and the Western Americas, Geophys. Monogr. Ser., 55, 165–236,
1989a. a
Keeling, C., Piper, S., and Heimann, M.: A three-dimensional model of
atmospheric CO2 transport based on observed winds: 4. Mean annual
gradients and interannual variations, in: Aspects of Climate Variability in the Pacific and the Western Americas, Vol 55, edited by: Peterson, D. H., 305–363, Washington, DC: Am. Geophys. Union, 1989b. a
Krol, M., Houweling, S., Bregman, B., van den Broek, M., Segers, A., van Velthoven, P., Peters, W., Dentener, F., and Bergamaschi, P.: The two-way nested global chemistry-transport zoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417–432, https://doi.org/10.5194/acp-5-417-2005, 2005. a, b
Krol, M., de Bruine, M., Killaars, L., Ouwersloot, H., Pozzer, A., Yin, Y., Chevallier, F., Bousquet, P., Patra, P., Belikov, D., Maksyutov, S., Dhomse, S., Feng, W., and Chipperfield, M. P.: Age of air as a diagnostic for transport timescales in global models, Geosci. Model Dev., 11, 3109–3130, https://doi.org/10.5194/gmd-11-3109-2018, 2018. a
Lee, M. and Weidner, R.: JPL Publication 16-4: Surface Pressure Dependencies in
the GEOS-Chem Adjoint System and the Impact of the GEOS-5 Surface Pressure on
CO2 Model Forecast, Tech. rep., Jet Propulsion Laboratory California
Institute of Technology Pasadena, California, https://ntrs.nasa.gov/api/citations/20160009374/downloads/20160009374.pdf (last access: 12 May 2023), 2016. a
Lin, J.-T. and McElroy, M. B.: Impacts of boundary layer mixing on pollutant
vertical profiles in the lower troposphere: Implications to satellite remote
sensing, Atmos. Environ., 44, 1726–1739,
https://doi.org/10.1016/J.ATMOSENV.2010.02.009, 2010. a
Lin, S.-J. and Rood, R. B.: Multidimensional Flux-Form Semi-Lagrangian
Transport Schemes, Mon. Weather Rev., 124, 2046–2070, 1996. a
Lindqvist, H., O'Dell, C. W., Basu, S., Boesch, H., Chevallier, F., Deutscher, N., Feng, L., Fisher, B., Hase, F., Inoue, M., Kivi, R., Morino, I., Palmer, P. I., Parker, R., Schneider, M., Sussmann, R., and Yoshida, Y.: Does GOSAT capture the true seasonal cycle of carbon dioxide?, Atmos. Chem. Phys., 15, 13023–13040, https://doi.org/10.5194/acp-15-13023-2015, 2015. a
Liu, J., Wennberg, P. O., Parazoo, N. C., Yin, Y., and Frankenberg, C.:
Observational Constraints on the Response of High-Latitude Northern Forests
to Warming, AGU Advances, 1, e2020AV000228, https://doi.org/10.1029/2020AV000228,
2020. a
Lloyd, J. and Farquhar, G. D.: Effects of rising temperatures and [CO2] on
the physiology of tropical forest trees, Phil. Trans. R. Soc. B, 363, 1811–1817,
https://doi.org/10.1098/rstb.2007.0032, 2008. a
Malhi, Y., BALDOCCHI, D. D., and JARVIS, P. G.: The carbon balance of tropical,
temperate and boreal forests, Plant. Cell Environ., 22, 715–740,
https://doi.org/10.1046/j.1365-3040.1999.00453.x, 1999. a
Martin, R. V., Eastham, S. D., Bindle, L., Lundgren, E. W., Clune, T. L., Keller, C. A., Downs, W., Zhang, D., Lucchesi, R. A., Sulprizio, M. P., Yantosca, R. M., Li, Y., Estrada, L., Putman, W. M., Auer, B. M., Trayanov, A. L., Pawson, S., and Jacob, D. J.: Improved advection, resolution, performance, and community access in the new generation (version 13) of the high-performance GEOS-Chem global atmospheric chemistry model (GCHP), Geosci. Model Dev., 15, 8731–8748, https://doi.org/10.5194/gmd-15-8731-2022, 2022. a, b
Molod, A., Takas, L., Suarez, M., Bacmeister, J., Song, I., and Eichman, A.:
The GEOS-5 Atmospheric General Circulation Model: Mean Climate and
Development from MERRA to Fortuna, Tech. Rep. NASA/TM–2012-104606/Vol
28, National Aeronautics and Space Administration Goddard Space Flight
Center, Greenbelt, Maryland, https://ntrs.nasa.gov/api/citations/20120011790/downloads/20120011790.pdf (last access: 12 May 2023), 2012. a
Moorthi, S. and Suarez, M. J.: Relaxed Arakawa-Schubert. A Parameterization of
Moist Convection for General Circulation Models, Mon. Weather Rev., 120,
978–1002, https://doi.org/10.1175/1520-0493(1992)120<0978:RASAPO>2.0.CO;2, 1992. a, b
Naud, C. M., Booth, J. F., and Genio, A. D. D.: Evaluation of ERA-Interim and
MERRA Cloudiness in the Southern Ocean, J. Climate, 27, 2109–2124,
https://doi.org/10.1175/JCLI-D-13-00432.1, 2014. a
Norby, R. J. and Zak, D. R.: Ecological Lessons from Free-Air CO2 Enrichment
(FACE) Experiments, Annu. Rev. Ecol. Evol. S., 42, 181–203,
https://doi.org/10.1146/annurev-ecolsys-102209-144647, 2011. a, b
Oda, T., Maksyutov, S., and Andres, R. J.: The Open-source Data Inventory for Anthropogenic CO2, version 2016 (ODIAC2016): a global monthly fossil fuel CO2 gridded emissions data product for tracer transport simulations and surface flux inversions, Earth Syst. Sci. Data, 10, 87–107, https://doi.org/10.5194/essd-10-87-2018, 2018. a
Orbe, C., Oman, L. D., Strahan, S. E., Waugh, D. W., Pawson, S., Takacs, L. L.,
and Molod, A. M.: Large-Scale Atmospheric Transport in GEOS Replay
Simulations, J. Adv. Model. Earth Syst., 9, 2545–2560,
https://doi.org/10.1002/2017MS001053, 2017. a
Peiro, H., Crowell, S., Schuh, A., Baker, D. F., O'Dell, C., Jacobson, A. R., Chevallier, F., Liu, J., Eldering, A., Crisp, D., Deng, F., Weir, B., Basu, S., Johnson, M. S., Philip, S., and Baker, I.: Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7, Atmos. Chem. Phys., 22, 1097–1130, https://doi.org/10.5194/acp-22-1097-2022, 2022. a, b
Penuelas, J., Fernández-Martínez, M., Vallicrosa, H., Maspons, J.,
Zuccarini, P., Carnicer, J., Sanders, T. G. M., Krüger, I., Obersteiner, M.,
Janssens, I. A., Ciais, P., and Sardans, J.: Increasing atmospheric CO2
concentrations correlate with declining nutritional status of European
forests, 3, 125, https://doi.org/10.1038/s42003-020-0839-y, 2020. a
Peters, W., Krol, M. C., Dlugokencky, E. J., Dentener, F. J., Bergamaschi, P.,
Dutton, G., von Velthoven, P., Miller, J. B., Bruhwiler, L., and Tans, P. P.:
Toward regional-scale modeling using the two-way nested global model TM5:
Characterization of transport using SF6, J. Geophys.
Res.-Atmos., 109, d19314, https://doi.org/10.1029/2004JD005020, 2004. a
Peters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway, T. J.,
Masarie, K., Miller, J. B., Bruhwiler, L. M. P., Petron, G., Hirsch, A. I.,
Worthy, D. E. J., van der Werf, G. R., Randerson, J. T., Wennberg, P. O.,
Krol, M. C., and Tans, P. P.: An atmospheric perspective on North
American carbon dioxide exchange: CarbonTracker, P.
Natl. Acad. Sci. USA, 104,
18925–18930, https://doi.org/10.1073/pnas.0708986104, 2007. a
Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki, T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van der Laan-Luijkx, I. T., and Zhang, X.: Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions, Biogeosciences, 10, 6699–6720, https://doi.org/10.5194/bg-10-6699-2013, 2013. a
Posselt, D. J., van den Heever, S., Stephens, G., and Igel, M. R.: Changes in
the Interaction between Tropical Convection, Radiation, and the Large-Scale
Circulation in a Warming Environment, J. Climate, 25, 557–571,
https://doi.org/10.1175/2011JCLI4167.1, 2012. a
Prather, M. J., Zhu, X., Strahan, S. E., Steenrod, S. D., and Rodriguez, J. M.:
Quantifying errors in trace species transport modeling, P.
Natl Acad. Sci. USA, 105, 19617–19621,
https://doi.org/10.1073/pnas.0806541106, 2008. a
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu,
E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S.,
Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster,
R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder,
C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and
Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for Research
and Applications, J. Climate, 24, 3624–3648,
https://doi.org/10.1175/JCLI-D-11-00015.1, 2011. a
Schimel, D., Stephens, B. B., and Fisher, J. B.: Effect of increasing CO2 on
the terrestrial carbon cycle, P. Natl. Acad.
Sci. USA, 112, 436–441,
https://doi.org/10.1073/pnas.1407302112, 2014. a, b
Schuh, A. E., Jacobson, A. R., Basu, S., Weir, B., Baker, D., Bowman, K.,
Chevallier, F., Crowell, S., Davis, K. J., Deng, F., Denning, S., Feng, L.,
Jones, D., Liu, J., and Palmer, P. I.: Quantifying the Impact of Atmospheric
Transport Uncertainty on CO2 Surface Flux Estimates, Global
Biogeochem. Cy., 33, 484–500,
https://doi.org/10.1029/2018GB006086, 2019. a, b, c, d, e, f, g, h
Schuh, A. E., Byrne, B., Jacobson, A. R., Crowell, S. M. R., Deng, F., Baker,
D. F., Johnson, M. S., Philip, S., and Weir, B.: On the role of atmospheric
model transport uncertainty in estimating the Chinese land carbon sink,
Nature, 603, E13–E14, https://doi.org/10.1038/s41586-021-04258-9,
2022. a, b
Stanevich, I., Jones, D. B. A., Strong, K., Keller, M., Henze, D. K., Parker, R. J., Boesch, H., Wunch, D., Notholt, J., Petri, C., Warneke, T., Sussmann, R., Schneider, M., Hase, F., Kivi, R., Deutscher, N. M., Velazco, V. A., Walker, K. A., and Deng, F.: Characterizing model errors in chemical transport modeling of methane: using GOSAT XCH4 data with weak-constraint four-dimensional variational data assimilation, Atmos. Chem. Phys., 21, 9545–9572, https://doi.org/10.5194/acp-21-9545-2021, 2021. a
Stephens, B. B., Gurney, K. R., Tans, P. P., Sweeney, C., Peters, W.,
Bruhwiler, L., Ciais, P., Ramonet, M., Bousquet, P., Nakazawa, T., Aoki, S.,
Machida, T., Inoue, G., Vinnichenko, N., Lloyd, J., Jordan, A., Heimann, M.,
Shibistova, O., Langenfelds, R. L., Steele, L. P., Francey, R. J., and
Denning, A. S.: Weak northern and strong tropical land carbon uptake from
vertical profiles of atmospheric CO2, Science, 316, 1732–1735, 2007. a, b
Taszarek, M., Allen, J. T., Marchio, M., and Brooks, H. E.: Global climatology
and trends in convective environments from ERA5 and rawinsonde data, npj
Clim. Atmos. Sci., 4, 35,
https://doi.org/10.1038/s41612-021-00190-x, 2021. a
Wang, J., Feng, L., Palmer, P. I., Liu, Y., Fang, S., Bösch, H., O'Dell,
C. W., Tang, X., Yang, D., Liu, L., and Xia, C.: Large Chinese land carbon
sink estimated from atmospheric carbon dioxide data, Nature, 586, 720–723,
https://doi.org/10.1038/s41586-020-2849-9, 2020. a
Wu, S., Mickley, L. J., Jacob, D. J., Logan, J. A., Yantosca, R. M., and Rind,
D.: Why are there large differences between models in global budgets of
tropospheric ozone?, J. Geophys. Res., 112, D05302,
https://doi.org/10.1029/2006JD007801, 2007.
a
Yu, K., Keller, C. A., Jacob, D. J., Molod, A. M., Eastham, S. D., and Long, M. S.: Errors and improvements in the use of archived meteorological data for chemical transport modeling: an analysis using GEOS-Chem v11-01 driven by GEOS-5 meteorology, Geosci. Model Dev., 11, 305–319, https://doi.org/10.5194/gmd-11-305-2018, 2018. a, b, c, d
Short summary
A comparison of atmospheric carbon dioxide concentrations resulting from two different atmospheric transport models showed large differences in predicted concentrations with significant space–time correlations. The vertical mixing of long-lived trace gases by convection was determined to be the main driver of these differences. The resulting uncertainty was deemed significant to the application of using atmospheric gradients of carbon dioxide to estimate surface fluxes of carbon dioxide.
A comparison of atmospheric carbon dioxide concentrations resulting from two different...
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