Detectability of CO2 flux signals by a space‐based lidar mission

Satellite observations of carbon dioxide (CO2) offer novel and distinctive opportunities for improving our quantitative understanding of the carbon cycle. Prospective observations include those from space‐based lidar such as the active sensing of CO2 emissions over nights, days, and seasons (ASCENDS...

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Published in:Journal of Geophysical Research: Atmospheres
Main Authors: Hammerling, Dorit M., Kawa, S. Randolph, Schaefer, Kevin, Doney, Scott, Michalak, Anna M.
Format: Article in Journal/Newspaper
Language:unknown
Published: Natl. Oceanic and Atmos. Admin. 2015
Subjects:
fossil fuel emissions
CO2 fluxes
space‐based lidar
Southern Ocean
signal detection
permafrost thawing
Atmospheric and Oceanic Sciences
Science
Arctic
permafrost
Online Access:https://hdl.handle.net/2027.42/110893
https://doi.org/10.1002/2014JD022483
id ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/110893
record_format openpolar
institution Open Polar
collection University of Michigan: Deep Blue
op_collection_id ftumdeepblue
language unknown
topic fossil fuel emissions
CO2 fluxes
space‐based lidar
Southern Ocean
signal detection
permafrost thawing
Atmospheric and Oceanic Sciences
Science
spellingShingle fossil fuel emissions
CO2 fluxes
space‐based lidar
Southern Ocean
signal detection
permafrost thawing
Atmospheric and Oceanic Sciences
Science
Hammerling, Dorit M.
Kawa, S. Randolph
Schaefer, Kevin
Doney, Scott
Michalak, Anna M.
Detectability of CO2 flux signals by a space‐based lidar mission
topic_facet fossil fuel emissions
CO2 fluxes
space‐based lidar
Southern Ocean
signal detection
permafrost thawing
Atmospheric and Oceanic Sciences
Science
description Satellite observations of carbon dioxide (CO2) offer novel and distinctive opportunities for improving our quantitative understanding of the carbon cycle. Prospective observations include those from space‐based lidar such as the active sensing of CO2 emissions over nights, days, and seasons (ASCENDS) mission. Here we explore the ability of such a mission to detect regional changes in CO2 fluxes. We investigate these using three prototypical case studies, namely, the thawing of permafrost in the northern high latitudes, the shifting of fossil fuel emissions from Europe to China, and changes in the source/sink characteristics of the Southern Ocean. These three scenarios were used to design signal detection studies to investigate the ability to detect the unfolding of these scenarios compared to a baseline scenario. Results indicate that the ASCENDS mission could detect the types of signals investigated in this study, with the caveat that the study is based on some simplifying assumptions. The permafrost thawing flux perturbation is readily detectable at a high level of significance. The fossil fuel emission detectability is directly related to the strength of the signal and the level of measurement noise. For a nominal (lower) fossil fuel emission signal, only the idealized noise‐free instrument test case produces a clearly detectable signal, while experiments with more realistic noise levels capture the signal only in the higher (exaggerated) signal case. For the Southern Ocean scenario, differences due to the natural variability in the El Niño–Southern Oscillation climatic mode are primarily detectable as a zonal increase.Key PointsDetectability of regional changes in CO2 fluxes by space‐based lidarPermafrost thawing flux perturbation readily detectable by ASCENDS‐like missionSouthern Ocean ENSO‐related flux variability detectable as zonal change Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/110893/1/jgrd51945.pdf
format Article in Journal/Newspaper
author Hammerling, Dorit M.
Kawa, S. Randolph
Schaefer, Kevin
Doney, Scott
Michalak, Anna M.
author_facet Hammerling, Dorit M.
Kawa, S. Randolph
Schaefer, Kevin
Doney, Scott
Michalak, Anna M.
author_sort Hammerling, Dorit M.
title Detectability of CO2 flux signals by a space‐based lidar mission
title_short Detectability of CO2 flux signals by a space‐based lidar mission
title_full Detectability of CO2 flux signals by a space‐based lidar mission
title_fullStr Detectability of CO2 flux signals by a space‐based lidar mission
title_full_unstemmed Detectability of CO2 flux signals by a space‐based lidar mission
title_sort detectability of co2 flux signals by a space‐based lidar mission
publisher Natl. Oceanic and Atmos. Admin.
publishDate 2015
url https://hdl.handle.net/2027.42/110893
https://doi.org/10.1002/2014JD022483
geographic Southern Ocean
geographic_facet Southern Ocean
genre Arctic
permafrost
Southern Ocean
genre_facet Arctic
permafrost
Southern Ocean
op_relation Hammerling, Dorit M.; Kawa, S. Randolph; Schaefer, Kevin; Doney, Scott; Michalak, Anna M. (2015). "Detectability of CO2 flux signals by a space‐based lidar mission." Journal of Geophysical Research: Atmospheres 120(5): 1794-1807.
2169-897X
2169-8996
https://hdl.handle.net/2027.42/110893
doi:10.1002/2014JD022483
Journal of Geophysical Research: Atmospheres
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Bian, H., S. R. Kawa, M. Chin, S. Pawson, Z. Zhu, P. Rasch, and S. Wu ( 2006 ), A test of sensitivity to convective transport in a global atmospheric CO 2 simulation, Tellus B, 58, 463 – 475, doi:10.1111/j.1600-0889.2006.00212.x.
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spelling ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/110893 2023-08-20T04:03:12+02:00 Detectability of CO2 flux signals by a space‐based lidar mission Hammerling, Dorit M. Kawa, S. Randolph Schaefer, Kevin Doney, Scott Michalak, Anna M. 2015-03-16 application/pdf https://hdl.handle.net/2027.42/110893 https://doi.org/10.1002/2014JD022483 unknown Natl. Oceanic and Atmos. Admin. Wiley Periodicals, Inc. Hammerling, Dorit M.; Kawa, S. Randolph; Schaefer, Kevin; Doney, Scott; Michalak, Anna M. (2015). "Detectability of CO2 flux signals by a space‐based lidar mission." Journal of Geophysical Research: Atmospheres 120(5): 1794-1807. 2169-897X 2169-8996 https://hdl.handle.net/2027.42/110893 doi:10.1002/2014JD022483 Journal of Geophysical Research: Atmospheres Parazoo, N. C., A. S. Denning, S. R. Kawa, K. D. Corbin, R. S. Lokupitiya, and I. T. Baker ( 2008 ), Mechanisms for synoptic variations of atmospheric CO 2 in North America, South America and Europe, Atmos. Chem. Phys., 8, 7239 – 7254. Kuze, A., H. Suto, M. Nakajima, and T. Hamazaki ( 2009 ), Thermal and near infrared sensor for carbon observation Fourier‐transform spectrometer on the greenhouse gases observing satellite for greenhouse gases monitoring, Appl. Opt., 48, 6716 – 6733, doi:10.1364/AO.48.006716. Law, R. M., et al. ( 2008a ), TransCom model simulations of hourly atmospheric CO 2: Experimental overview and diurnal cycle results for 2002, Global Biogeochem. Cycles, 22, GB3009, doi:10.1029/2007GB003050. Law, R. M., R. J. Matear, and R. J. Francey ( 2008b ), Comment on saturation of the Southern ocean CO 2 sink due to recent climate change, Science, 319, 570. Le Quéré, C., M. R. Raupach, J. G. Canadell, and E. A. Marland ( 2009 ), Trends in the sources and sinks of carbon dioxide, Nat. Geosci., 2, 831 – 836. Lemke, P., et al. ( 2007 ), Changes in snow, ice and frozen ground, in Climate Change 2007: The Physical Science Basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., Cambridge Univ. Press, Cambridge, U. K. Mao, J., and S. R. Kawa ( 2004 ), Sensitivity studies for space‐based measurement of atmospheric total column carbon dioxide by reflected sunlight, Appl. Opt., 43, 914 – 927. Meredith, M. P., A. C. N. Garabato, A. M. Hogg, and R. Farneti ( 2012 ), Sensitivity of the overturning circulation in the southern ocean to decadal changes in wind forcing, J. Clim., 25, 99 – 110. National Research Council ( 2007 ), Earth Science and Applications From Space: National Imperatives for the Next Decade and Beyond, 456 pp., The National Acad. Press, Washington, D. C. 20001. Olivier, J., G. Janssens‐Maenhout, and J. Peters ( 2012 ), Trends in Global CO 2 Emissions; 2012 Report, PBL Netherlands Environmental Assessment Agency; Ispra: Joint Research Centre, The Hague, Netherlands. Olsen, S. C., and J. T. Randerson ( 2004 ), Differences between surface and column atmospheric CO 2 and implications for carbon cycle research, J. Geophys. Res., 109, D02301, doi:10.1029/2003JD003968. Peters, G. P., G. Marland, C. L. Quéré, T. Boden, J. G. Canadell, and M. R. Raupach ( 2011 ), Rapid growth in CO 2 emissions after the 2008–2009 global financial crisis, Nat. Clim. Change, 2, 2 – 4. Randerson, J. T., M. V. Thompson, C. M. Malmstrom, C. B. Field, and I. Y. Fung ( 1996 ), Substrate limitations for heterotrophs: Implications for models that estimate the seasonal cycle of atmospheric CO 2, Global Biogeochem. Cycles, 10 ( 4 ), 585 – 602, doi:10.1029/96GB01981. Rienecker, M. M., et al. ( 2011 ), MERRA: NASA's Modern‐Era retrospective analysis for research and applications, J. Clim., 24, 3624 – 3648. Schaaf, C. B., et al. ( 2002 ), First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ., 83 ( 1–2 ), 135 – 148, doi:10.1016/S0034-4257(02)00091-3. Schaefer, K., G. J. Collatz, P. Tans, A. S. Denning, I. Baker, J. Berry, L. Prihodko, N. Suits, and A. Philpott ( 2008 ), combined simple biosphere/Carnegie‐Ames‐Stanford approach terrestrial carbon cycle model, J. Geophys. Res., 113, G03034, doi:10.1029/2007JG000603. Schaefer, K., T. Zhang, L. Bruhwiler, and A. P. Barrett ( 2011 ), Amount and timing of permafrost carbon release in response to climate warming, Tellus B, 63, 165 – 180, doi:10.1111/j.1600-0889.2011.00527.x. Shiga, Y. P., A. M. Michalak, S. R. Kawa, and R. J. Engelen ( 2013 ), In‐situ CO 2 monitoring network evaluation and design: A criterion based on atmospheric CO 2 variability, J. Geophys. Res. Atmos., 118, 2007 – 2018, doi:10.1002/jgrd.50168. Spiers, G. D., R. T. Menzies, J. Jacob, L. E. Christensen, M. W. Phillips, Y. Choi, and E. V. Browell ( 2011 ), Atmospheric CO 2 measurements with a 2 µm airborne laser absorption spectrometer employing coherent detection, Appl. Opt., 50, 2098 – 2111, doi:10.1364/AO.50.002098. Takahashi, T., et al. ( 2002 ), Global sea‐air CO 2 flux based on climatological surface ocean pCO 2, and seasonal biological and temperature effects, Deep Sea Res., Part II, 49 ( 9–10 ), 1601 – 1622, doi:10.1016/S0967-0645(02)00003-6. Tarnocai, C., J. G. Canadell, E. A. G. Schuur, P. Kuhry, G. Mazhitova, and S. Zimov ( 2009 ), Soil organic carbon pools in the northern circumpolar permafrost region, Global Biogeochem. Cycles, 23, GB2023, doi:10.1029/2008GB003327. van der Werf, G. R., J. T. Randerson, L. Giglio, G. J. Collatz, M. Mu, P. S. Kasibhatla, D. C. Morton, R. S. DeFries, Y. Jin, and T. T. van Leeuwen ( 2010 ), Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10, 11,707 – 11,735, doi:10.5194/acp-10-11707-2010. Yokota, T., Y. Yoshida, N. Eguchi, Y. Ota, T. Tanaka, H. Watanabe, and S. Maksyutov ( 2009 ), Global concentrations of CO 2 and CH 4 retrieved from GOSAT: First preliminary results, SOLA, 5, 160 – 163, doi:10.2151/sola.2009-041. Zhang, T., R. G. Barry, K. Knowles, J. A. Heginbottom, and J. Brown ( 1999 ), Statistics and characteristics of permafrost and ground‐ice distribution in the Northern Hemisphere, Polar Geogr., 23 ( 2 ), 132 – 154. Zimov, S. A., E. A. G. Schuur, and F. S. Chapin III ( 2006 ), Permafrost and the Global Carbon Budget, Science, 312 ( 5780 ), 1612 – 1613, doi:10.1126/science.1128908, 16 June. Abshire, J. B., H. Riris, G. R. Allan, C. J. Weaver, J. Mao, X. Sun, W. E. Hasselbrack, S. R. Kawa, and S. Biraud ( 2010 ), Pulsed airborne lidar measurements of atmospheric CO 2 column absorption, Tellus B, 62, 770 – 783, doi:10.1111/j.1600-0889.2010.00502.x. Andres, R. J., T. A. Boden, and G. Marland ( 2009 ), Monthly Fossil‐Fuel CO 2 Emissions: Mass of Emissions Gridded by One Degree Latitude by One Degree Longitude, Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tenn., 37831‐6290. Andres, R. J., J. S. Gregg, L. Losey, G. Marland, and T. A. Boden ( 2011 ), Monthly, global emissions of carbon dioxide from fossil fuel consumption, Tellus B, 63, 309 – 327, doi:10.1111/j.1600-0889.2011.00530.x. ASCENDS Workshop Steering Committee ( 2008 ), Active Sensing of CO 2 Emissions over Nights, Days, and Seasons (ASCENDS) Mission NASA Science Definition and Planning Workshop Report, 1–78. Baker, D. F., H. Bösch, S. C. Doney, D. O'Brien, and D. S. Schimel ( 2010 ), Carbon source/sink information provided by column CO 2 measurements from the orbiting carbon observatory, Atmos. Chem. Phys., 10, 4145 – 4165, doi:10.5194/acp-10-4145-2010. Bian, H., S. R. Kawa, M. Chin, S. Pawson, Z. Zhu, P. Rasch, and S. 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IndexNoFollow fossil fuel emissions CO2 fluxes space‐based lidar Southern Ocean signal detection permafrost thawing Atmospheric and Oceanic Sciences Science Article 2015 ftumdeepblue https://doi.org/10.1002/2014JD02248310.1364/AO.48.00671610.1029/2007GB00305010.1029/2003JD00396810.1029/96GB0198110.1016/S0034-4257(02)00091-310.1029/2007JG00060310.1111/j.1600-0889.2011.00527.x10.1002/jgrd.5016810.1364/AO.50.00209810.1016/S0967-0645(02)0 2023-07-31T21:03:20Z Satellite observations of carbon dioxide (CO2) offer novel and distinctive opportunities for improving our quantitative understanding of the carbon cycle. Prospective observations include those from space‐based lidar such as the active sensing of CO2 emissions over nights, days, and seasons (ASCENDS) mission. Here we explore the ability of such a mission to detect regional changes in CO2 fluxes. We investigate these using three prototypical case studies, namely, the thawing of permafrost in the northern high latitudes, the shifting of fossil fuel emissions from Europe to China, and changes in the source/sink characteristics of the Southern Ocean. These three scenarios were used to design signal detection studies to investigate the ability to detect the unfolding of these scenarios compared to a baseline scenario. Results indicate that the ASCENDS mission could detect the types of signals investigated in this study, with the caveat that the study is based on some simplifying assumptions. The permafrost thawing flux perturbation is readily detectable at a high level of significance. The fossil fuel emission detectability is directly related to the strength of the signal and the level of measurement noise. For a nominal (lower) fossil fuel emission signal, only the idealized noise‐free instrument test case produces a clearly detectable signal, while experiments with more realistic noise levels capture the signal only in the higher (exaggerated) signal case. For the Southern Ocean scenario, differences due to the natural variability in the El Niño–Southern Oscillation climatic mode are primarily detectable as a zonal increase.Key PointsDetectability of regional changes in CO2 fluxes by space‐based lidarPermafrost thawing flux perturbation readily detectable by ASCENDS‐like missionSouthern Ocean ENSO‐related flux variability detectable as zonal change Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/110893/1/jgrd51945.pdf Article in Journal/Newspaper Arctic permafrost Southern Ocean University of Michigan: Deep Blue Southern Ocean Journal of Geophysical Research: Atmospheres 120 5 1794 1807

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