Simulation of atmospheric N[subscript 2]O with GEOS-Chem and its adjoint: evaluation of observational constraints

We describe a new 4D-Var inversion framework for nitrous oxide (N[subscript 2]O) based on the GEOS-Chem chemical transport model and its adjoint, and apply it in a series of observing system simulation experiments to assess how well N[subscript 2]O sources and sinks can be constrained by the current...

Full description

Bibliographic Details
Main Authors: Wells, K. C., Millet, D. B., Bousserez, N., Henze, D. K., Chaliyakunnel, S., Griffis, T. J., Luan, Y., Dlugokencky, E. J., O'Doherty, Simon, Weiss, R. F., Dutton, G. S., Elkins, J. W., Krummel, P. B., Langenfelds, R. L., Steele, L. P., Kort, E. A., Wofsy, S. C., Umezawa, T., Prinn, Ronald G.
Other Authors: Massachusetts Institute of Technology. Center for Global Change Science
Format: Article
Language:en_US
Published: Copernicus GmbH 2015
Online Access:http://hdl.handle.net/1721.1/99661
https://orcid.org/0000-0001-5925-3801
_version_ 1826193523939999744
author Wells, K. C.
Millet, D. B.
Bousserez, N.
Henze, D. K.
Chaliyakunnel, S.
Griffis, T. J.
Luan, Y.
Dlugokencky, E. J.
O'Doherty, Simon
Weiss, R. F.
Dutton, G. S.
Elkins, J. W.
Krummel, P. B.
Langenfelds, R. L.
Steele, L. P.
Kort, E. A.
Wofsy, S. C.
Umezawa, T.
Prinn, Ronald G.
author2 Massachusetts Institute of Technology. Center for Global Change Science
author_facet Massachusetts Institute of Technology. Center for Global Change Science
Wells, K. C.
Millet, D. B.
Bousserez, N.
Henze, D. K.
Chaliyakunnel, S.
Griffis, T. J.
Luan, Y.
Dlugokencky, E. J.
O'Doherty, Simon
Weiss, R. F.
Dutton, G. S.
Elkins, J. W.
Krummel, P. B.
Langenfelds, R. L.
Steele, L. P.
Kort, E. A.
Wofsy, S. C.
Umezawa, T.
Prinn, Ronald G.
author_sort Wells, K. C.
collection MIT
description We describe a new 4D-Var inversion framework for nitrous oxide (N[subscript 2]O) based on the GEOS-Chem chemical transport model and its adjoint, and apply it in a series of observing system simulation experiments to assess how well N[subscript 2]O sources and sinks can be constrained by the current global observing network. The employed measurement ensemble includes approximately weekly and quasi-continuous N[subscript 2]O measurements (hourly averages used) from several long-term monitoring networks, N[subscript 2]O measurements collected from discrete air samples onboard a commercial aircraft (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container; CARIBIC), and quasi-continuous measurements from the airborne HIAPER Pole-to-Pole Observations (HIPPO) campaigns. For a 2-year inversion, we find that the surface and HIPPO observations can accurately resolve a uniform bias in emissions during the first year; CARIBIC data provide a somewhat weaker constraint. Variable emission errors are much more difficult to resolve given the long lifetime of N[subscript 2]O, and major parts of the world lack significant constraints on the seasonal cycle of fluxes. Current observations can largely correct a global bias in the stratospheric sink of N[subscript 2]O if emissions are known, but do not provide information on the temporal and spatial distribution of the sink. However, for the more realistic scenario where source and sink are both uncertain, we find that simultaneously optimizing both would require unrealistically small errors in model transport. Regardless, a bias in the magnitude of the N[subscript 2]O sink would not affect the a posteriori N[subscript 2]O emissions for the 2-year timescale used here, given realistic initial conditions, due to the timescale required for stratosphere–troposphere exchange (STE). The same does not apply to model errors in the rate of STE itself, which we show exerts a larger influence on the tropospheric burden of N[subscript 2]O than does the chemical loss rate over short (< 3 year) timescales. We use a stochastic estimate of the inverse Hessian for the inversion to evaluate the spatial resolution of emission constraints provided by the observations, and find that significant, spatially explicit constraints can be achieved in locations near and immediately upwind of surface measurements and the HIPPO flight tracks; however, these are mostly confined to North America, Europe, and Australia. None of the current observing networks are able to provide significant spatial information on tropical N[subscript 2]O emissions. There, averaging kernels (describing the sensitivity of the inversion to emissions in each grid square) are highly smeared spatially and extend even to the midlatitudes, so that tropical emissions risk being conflated with those elsewhere. For global inversions, therefore, the current lack of constraints on the tropics also places an important limit on our ability to understand extratropical emissions. Based on the error reduction statistics from the inverse Hessian, we characterize the atmospheric distribution of unconstrained N[subscript 2]O, and identify regions in and downwind of South America, central Africa, and Southeast Asia where new surface or profile measurements would have the most value for reducing present uncertainty in the global N[subscript 2]O budget.
first_indexed 2024-09-23T09:40:29Z
format Article
id mit-1721.1/99661
institution Massachusetts Institute of Technology
language en_US
last_indexed 2024-09-23T09:40:29Z
publishDate 2015
publisher Copernicus GmbH
record_format dspace
spelling mit-1721.1/996612022-09-26T12:58:39Z Simulation of atmospheric N[subscript 2]O with GEOS-Chem and its adjoint: evaluation of observational constraints Wells, K. C. Millet, D. B. Bousserez, N. Henze, D. K. Chaliyakunnel, S. Griffis, T. J. Luan, Y. Dlugokencky, E. J. O'Doherty, Simon Weiss, R. F. Dutton, G. S. Elkins, J. W. Krummel, P. B. Langenfelds, R. L. Steele, L. P. Kort, E. A. Wofsy, S. C. Umezawa, T. Prinn, Ronald G. Massachusetts Institute of Technology. Center for Global Change Science Prinn, Ronald G. We describe a new 4D-Var inversion framework for nitrous oxide (N[subscript 2]O) based on the GEOS-Chem chemical transport model and its adjoint, and apply it in a series of observing system simulation experiments to assess how well N[subscript 2]O sources and sinks can be constrained by the current global observing network. The employed measurement ensemble includes approximately weekly and quasi-continuous N[subscript 2]O measurements (hourly averages used) from several long-term monitoring networks, N[subscript 2]O measurements collected from discrete air samples onboard a commercial aircraft (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container; CARIBIC), and quasi-continuous measurements from the airborne HIAPER Pole-to-Pole Observations (HIPPO) campaigns. For a 2-year inversion, we find that the surface and HIPPO observations can accurately resolve a uniform bias in emissions during the first year; CARIBIC data provide a somewhat weaker constraint. Variable emission errors are much more difficult to resolve given the long lifetime of N[subscript 2]O, and major parts of the world lack significant constraints on the seasonal cycle of fluxes. Current observations can largely correct a global bias in the stratospheric sink of N[subscript 2]O if emissions are known, but do not provide information on the temporal and spatial distribution of the sink. However, for the more realistic scenario where source and sink are both uncertain, we find that simultaneously optimizing both would require unrealistically small errors in model transport. Regardless, a bias in the magnitude of the N[subscript 2]O sink would not affect the a posteriori N[subscript 2]O emissions for the 2-year timescale used here, given realistic initial conditions, due to the timescale required for stratosphere–troposphere exchange (STE). The same does not apply to model errors in the rate of STE itself, which we show exerts a larger influence on the tropospheric burden of N[subscript 2]O than does the chemical loss rate over short (< 3 year) timescales. We use a stochastic estimate of the inverse Hessian for the inversion to evaluate the spatial resolution of emission constraints provided by the observations, and find that significant, spatially explicit constraints can be achieved in locations near and immediately upwind of surface measurements and the HIPPO flight tracks; however, these are mostly confined to North America, Europe, and Australia. None of the current observing networks are able to provide significant spatial information on tropical N[subscript 2]O emissions. There, averaging kernels (describing the sensitivity of the inversion to emissions in each grid square) are highly smeared spatially and extend even to the midlatitudes, so that tropical emissions risk being conflated with those elsewhere. For global inversions, therefore, the current lack of constraints on the tropics also places an important limit on our ability to understand extratropical emissions. Based on the error reduction statistics from the inverse Hessian, we characterize the atmospheric distribution of unconstrained N[subscript 2]O, and identify regions in and downwind of South America, central Africa, and Southeast Asia where new surface or profile measurements would have the most value for reducing present uncertainty in the global N[subscript 2]O budget. United States. National Oceanic and Atmospheric Administration (Grant NA13OAR4310086) Minnesota Supercomputing Institute 2015-11-02T19:37:48Z 2015-11-02T19:37:48Z 2015-10 2015-09 Article http://purl.org/eprint/type/JournalArticle 1991-9603 1991-959X http://hdl.handle.net/1721.1/99661 Wells, K. C., D. B. Millet, N. Bousserez, D. K. Henze, S. Chaliyakunnel, T. J. Griffis, Y. Luan, et al. “Simulation of Atmospheric N[subscript 2]O with GEOS-Chem and Its Adjoint: Evaluation of Observational Constraints.” Geosci. Model Dev. 8, no. 10 (2015): 3179–3198. https://orcid.org/0000-0001-5925-3801 en_US http://dx.doi.org/10.5194/gmd-8-3179-2015 Geoscientific Model Development Creative Commons Attribution http://creativecommons.org/licenses/by/3.0/ application/pdf Copernicus GmbH Copernicus Publications
spellingShingle Wells, K. C.
Millet, D. B.
Bousserez, N.
Henze, D. K.
Chaliyakunnel, S.
Griffis, T. J.
Luan, Y.
Dlugokencky, E. J.
O'Doherty, Simon
Weiss, R. F.
Dutton, G. S.
Elkins, J. W.
Krummel, P. B.
Langenfelds, R. L.
Steele, L. P.
Kort, E. A.
Wofsy, S. C.
Umezawa, T.
Prinn, Ronald G.
Simulation of atmospheric N[subscript 2]O with GEOS-Chem and its adjoint: evaluation of observational constraints
title Simulation of atmospheric N[subscript 2]O with GEOS-Chem and its adjoint: evaluation of observational constraints
title_full Simulation of atmospheric N[subscript 2]O with GEOS-Chem and its adjoint: evaluation of observational constraints
title_fullStr Simulation of atmospheric N[subscript 2]O with GEOS-Chem and its adjoint: evaluation of observational constraints
title_full_unstemmed Simulation of atmospheric N[subscript 2]O with GEOS-Chem and its adjoint: evaluation of observational constraints
title_short Simulation of atmospheric N[subscript 2]O with GEOS-Chem and its adjoint: evaluation of observational constraints
title_sort simulation of atmospheric n subscript 2 o with geos chem and its adjoint evaluation of observational constraints
url http://hdl.handle.net/1721.1/99661
https://orcid.org/0000-0001-5925-3801
work_keys_str_mv AT wellskc simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT milletdb simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT bousserezn simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT henzedk simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT chaliyakunnels simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT griffistj simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT luany simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT dlugokenckyej simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT odohertysimon simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT weissrf simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT duttongs simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT elkinsjw simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT krummelpb simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT langenfeldsrl simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT steelelp simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT kortea simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT wofsysc simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT umezawat simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints
AT prinnronaldg simulationofatmosphericnsubscript2owithgeoschemanditsadjointevaluationofobservationalconstraints