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Impact of the atmospheric sink and vertical mixing on nitrous oxide fluxes estimated using inversion methods

Identifieur interne : 006413 ( Main/Exploration ); précédent : 006412; suivant : 006414

Impact of the atmospheric sink and vertical mixing on nitrous oxide fluxes estimated using inversion methods

Auteurs : R. L. Thompson [France] ; P. Bousquet [France] ; F. Chevallier [France] ; P. J. Rayner [France, Australie] ; P. Ciais [France]

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RBID : ISTEX:A30AA400581D125D847754A71B8C7EC9B9ABFCDB

Descripteurs français

English descriptors

Abstract

This study investigates some of the principal errors arising in atmospheric inversion estimates of N2O surface fluxes. Using a synthetic data set of model‐generated atmospheric N2O mixing ratio data, representative of the current observation network, we investigate the influence of errors in the stratospheric N2O sink and in vertical transport. Our inversion framework uses a variational formulation of the Bayesian problem, and atmospheric transport is modeled using the global circulation model LMDz. When only optimizing the surface fluxes (with a prescribed sink), bias errors in the sink magnitude translate into substantial bias errors in the retrieved global total surface fluxes. Conversely, we find that errors only in the temporal and horizontal distribution of the N2O sink (nonbiased magnitude) have a very small impact on tropospheric mixing ratios and thus on the retrieved surface fluxes. Bias errors in the modeled vertical transport, however, lead to notable changes in tropospheric N2O and, in particular, in the phase of the seasonal cycle. This also leads to bias errors in the spatial distribution of the derived surface fluxes, although not in the global total. Last, the simultaneous optimization of the surface fluxes and the sink magnitude was tested as a means to avoid biasing the fluxes by incorrect prior assumptions about the N2O lifetime. With this approach, a significant reduction in the error of the sink magnitude was achieved and biases in the surface fluxes were largely avoided, and this result was further enhanced when aircraft data were included in the inversion.

Url:
DOI: 10.1029/2011JD015815


Affiliations:


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Le document en format XML

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<term>Aggregation errors</term>
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<term>Archived fields</term>
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<term>Atmospheric</term>
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<term>Auxiliary material</term>
<term>Bayesian inversion framework</term>
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<term>Caribic passenger aircraft</term>
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<term>East coast</term>
<term>Emission estimates</term>
<term>Error covariance matrix</term>
<term>Error reduction</term>
<term>Error reductions</term>
<term>European emissions</term>
<term>Extra degrees</term>
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<term>Fluxes table</term>
<term>Fourth assessment report</term>
<term>Free troposphere</term>
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<term>Global</term>
<term>Global biogeochem</term>
<term>Global inversions</term>
<term>Grid</term>
<term>Grid cell</term>
<term>Grid cells</term>
<term>Growth rate</term>
<term>Highest density</term>
<term>Horizontal distribution</term>
<term>Human activities</term>
<term>Increment</term>
<term>Intergovernmental panel</term>
<term>Inversion</term>
<term>Inversion estimates</term>
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<term>Inversion methods</term>
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<term>Latitudinal bands</term>
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<term>Mace head</term>
<term>Mass fluxes</term>
<term>Maximum flux</term>
<term>Measurement errors</term>
<term>Mismatch</term>
<term>More freedom</term>
<term>Nitroeurope project</term>
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<term>Optimized</term>
<term>Optimized surface fluxes</term>
<term>Oxide</term>
<term>Personal communication</term>
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<term>Phys</term>
<term>Physical science basis</term>
<term>Positive increments</term>
<term>Pseudo</term>
<term>Pseudo aircraft data</term>
<term>Pseudo aircraft observations</term>
<term>Random errors</term>
<term>Reference inversion</term>
<term>Scalar</term>
<term>Scalar errors</term>
<term>Scalar value</term>
<term>Scalar values</term>
<term>Seasonal cycle</term>
<term>Second test</term>
<term>Simultaneous optimization</term>
<term>Southern hemisphere</term>
<term>Spatial distribution</term>
<term>Spatial resolution</term>
<term>Sref</term>
<term>State vector</term>
<term>Stratosphere</term>
<term>Stratospheric</term>
<term>Substantial errors</term>
<term>Sufficient information</term>
<term>Supplementary material</term>
<term>Surface flux</term>
<term>Surface flux errors</term>
<term>Surface flux estimates</term>
<term>Surface flux increments</term>
<term>Surface flux rmse</term>
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<term>Temporal resolution</term>
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<term>Tests sref</term>
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<term>True value</term>
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<term>Vertical mass fluxes</term>
<term>Vertical transport</term>
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<term>Adjoint model</term>
<term>Aggregation errors</term>
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<term>Atmos</term>
<term>Atmospheric</term>
<term>Atmospheric lifetime</term>
<term>Atmospheric observations</term>
<term>Atmospheric transport</term>
<term>Atmospheric transport model</term>
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<term>Auxiliary material</term>
<term>Bayesian inversion framework</term>
<term>Bias errors</term>
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<term>Caribic passenger aircraft</term>
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<term>Inversion estimates</term>
<term>Inversion framework</term>
<term>Inversion methods</term>
<term>Inversion results</term>
<term>Inversion tests</term>
<term>Latitudinal</term>
<term>Latitudinal band</term>
<term>Latitudinal bands</term>
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<term>Mace head</term>
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<term>Maximum flux</term>
<term>Measurement errors</term>
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<term>More freedom</term>
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<term>Scalar errors</term>
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<term>Simultaneous optimization</term>
<term>Southern hemisphere</term>
<term>Spatial distribution</term>
<term>Spatial resolution</term>
<term>Sref</term>
<term>State vector</term>
<term>Stratosphere</term>
<term>Stratospheric</term>
<term>Substantial errors</term>
<term>Sufficient information</term>
<term>Supplementary material</term>
<term>Surface flux</term>
<term>Surface flux errors</term>
<term>Surface flux estimates</term>
<term>Surface flux increments</term>
<term>Surface flux rmse</term>
<term>Surface flux uncertainties</term>
<term>Surface fluxes</term>
<term>Surface observations</term>
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<term>Temporal resolution</term>
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<term>Tests sref</term>
<term>Time step</term>
<term>Transport error</term>
<term>Transport errors</term>
<term>Transport model</term>
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<term>Vertical mass fluxes</term>
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<term>Vertical transport errors</term>
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<div type="abstract">This study investigates some of the principal errors arising in atmospheric inversion estimates of N2O surface fluxes. Using a synthetic data set of model‐generated atmospheric N2O mixing ratio data, representative of the current observation network, we investigate the influence of errors in the stratospheric N2O sink and in vertical transport. Our inversion framework uses a variational formulation of the Bayesian problem, and atmospheric transport is modeled using the global circulation model LMDz. When only optimizing the surface fluxes (with a prescribed sink), bias errors in the sink magnitude translate into substantial bias errors in the retrieved global total surface fluxes. Conversely, we find that errors only in the temporal and horizontal distribution of the N2O sink (nonbiased magnitude) have a very small impact on tropospheric mixing ratios and thus on the retrieved surface fluxes. Bias errors in the modeled vertical transport, however, lead to notable changes in tropospheric N2O and, in particular, in the phase of the seasonal cycle. This also leads to bias errors in the spatial distribution of the derived surface fluxes, although not in the global total. Last, the simultaneous optimization of the surface fluxes and the sink magnitude was tested as a means to avoid biasing the fluxes by incorrect prior assumptions about the N2O lifetime. With this approach, a significant reduction in the error of the sink magnitude was achieved and biases in the surface fluxes were largely avoided, and this result was further enhanced when aircraft data were included in the inversion.</div>
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