Impact of the atmospheric sink and vertical mixing on nitrous oxide fluxes estimated using inversion methods
Identifieur interne : 001E87 ( Istex/Corpus ); précédent : 001E86; suivant : 001E88Impact of the atmospheric sink and vertical mixing on nitrous oxide fluxes estimated using inversion methods
Auteurs : R. L. Thompson ; P. Bousquet ; F. Chevallier ; P. J. Rayner ; P. CiaisSource :
- Journal of Geophysical Research: Atmospheres [ 0148-0227 ] ; 2011-09-16.
English descriptors
- KwdEn :
- Actinic flux, Adjoint model, Aggregation errors, Aircraft data, Archived fields, Atmos, Atmospheric, Atmospheric lifetime, Atmospheric observations, Atmospheric transport, Atmospheric transport model, Austral summer, Auxiliary material, Bayesian inversion framework, Bias errors, Cambridge univ, Caribic passenger aircraft, Central europe, Chem, Chevallier, Circulation model, Climate change, Convection mass fluxes, Corazza, Earth sciences, East coast, Emission estimates, Error covariance matrix, Error reduction, Error reductions, European emissions, Extra degrees, Flux, Fluxes table, Fourth assessment report, Free troposphere, Geophys, Global, Global biogeochem, Global inversions, Grid, Grid cell, Grid cells, Growth rate, Highest density, Horizontal distribution, Human activities, Increment, Intergovernmental panel, Inversion, Inversion estimates, Inversion framework, Inversion methods, Inversion results, Inversion tests, Latitudinal, Latitudinal band, Latitudinal bands, Little impact, Lmdz, Lmdz model, Lower stratosphere, Mace head, Mass fluxes, Maximum flux, Measurement errors, Mismatch, More freedom, Nitroeurope project, Nitrous, Nitrous oxide, Nitrous oxide fluxes, Northern hemisphere, Notable error reductions, Optimization, Optimized, Optimized surface fluxes, Oxide, Personal communication, Perturbed, Phys, Physical science basis, Positive increments, Pseudo, Pseudo aircraft data, Pseudo aircraft observations, Random errors, Reference inversion, Scalar, Scalar errors, Scalar value, Scalar values, Seasonal cycle, Second test, Simultaneous optimization, Southern hemisphere, Spatial distribution, Spatial resolution, Sref, State vector, Stratosphere, Stratospheric, Substantial errors, Sufficient information, Supplementary material, Surface flux, Surface flux errors, Surface flux estimates, Surface flux increments, Surface flux rmse, Surface flux uncertainties, Surface fluxes, Surface observations, Synthetic data, Temporal resolution, Test sref, Tests sref, Time step, Transport error, Transport errors, Transport model, Tropical africa, Troposphere, Tropospheric, True fluxes, True value, Variational formulation, Vertical column, Vertical gradient, Vertical mass flux, Vertical mass fluxes, Vertical transport, Vertical transport errors.
- Teeft :
- Actinic flux, Adjoint model, Aggregation errors, Aircraft data, Archived fields, Atmos, Atmospheric, Atmospheric lifetime, Atmospheric observations, Atmospheric transport, Atmospheric transport model, Austral summer, Auxiliary material, Bayesian inversion framework, Bias errors, Cambridge univ, Caribic passenger aircraft, Central europe, Chem, Chevallier, Circulation model, Climate change, Convection mass fluxes, Corazza, Earth sciences, East coast, Emission estimates, Error covariance matrix, Error reduction, Error reductions, European emissions, Extra degrees, Flux, Fluxes table, Fourth assessment report, Free troposphere, Geophys, Global, Global biogeochem, Global inversions, Grid, Grid cell, Grid cells, Growth rate, Highest density, Horizontal distribution, Human activities, Increment, Intergovernmental panel, Inversion, Inversion estimates, Inversion framework, Inversion methods, Inversion results, Inversion tests, Latitudinal, Latitudinal band, Latitudinal bands, Little impact, Lmdz, Lmdz model, Lower stratosphere, Mace head, Mass fluxes, Maximum flux, Measurement errors, Mismatch, More freedom, Nitroeurope project, Nitrous, Nitrous oxide, Nitrous oxide fluxes, Northern hemisphere, Notable error reductions, Optimization, Optimized, Optimized surface fluxes, Oxide, Personal communication, Perturbed, Phys, Physical science basis, Positive increments, Pseudo, Pseudo aircraft data, Pseudo aircraft observations, Random errors, Reference inversion, Scalar, Scalar errors, Scalar value, Scalar values, Seasonal cycle, Second test, Simultaneous optimization, Southern hemisphere, Spatial distribution, Spatial resolution, Sref, State vector, Stratosphere, Stratospheric, Substantial errors, Sufficient information, Supplementary material, Surface flux, Surface flux errors, Surface flux estimates, Surface flux increments, Surface flux rmse, Surface flux uncertainties, Surface fluxes, Surface observations, Synthetic data, Temporal resolution, Test sref, Tests sref, Time step, Transport error, Transport errors, Transport model, Tropical africa, Troposphere, Tropospheric, True fluxes, True value, Variational formulation, Vertical column, Vertical gradient, Vertical mass flux, Vertical mass fluxes, Vertical transport, Vertical transport errors.
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
Links to Exploration step
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<term>Aircraft data</term>
<term>Archived fields</term>
<term>Atmos</term>
<term>Atmospheric</term>
<term>Atmospheric lifetime</term>
<term>Atmospheric observations</term>
<term>Atmospheric transport</term>
<term>Atmospheric transport model</term>
<term>Austral summer</term>
<term>Auxiliary material</term>
<term>Bayesian inversion framework</term>
<term>Bias errors</term>
<term>Cambridge univ</term>
<term>Caribic passenger aircraft</term>
<term>Central europe</term>
<term>Chem</term>
<term>Chevallier</term>
<term>Circulation model</term>
<term>Climate change</term>
<term>Convection mass fluxes</term>
<term>Corazza</term>
<term>Earth sciences</term>
<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>
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<term>Fluxes table</term>
<term>Fourth assessment report</term>
<term>Free troposphere</term>
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<term>Global biogeochem</term>
<term>Global inversions</term>
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<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>
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<term>Inversion methods</term>
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<term>Maximum flux</term>
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<term>More freedom</term>
<term>Nitroeurope project</term>
<term>Nitrous</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>Pseudo aircraft data</term>
<term>Pseudo aircraft observations</term>
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<term>Scalar errors</term>
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<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>
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<term>Sufficient information</term>
<term>Supplementary material</term>
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<term>Surface flux increments</term>
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<term>Synthetic data</term>
<term>Temporal resolution</term>
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<term>Transport errors</term>
<term>Transport model</term>
<term>Tropical africa</term>
<term>Troposphere</term>
<term>Tropospheric</term>
<term>True fluxes</term>
<term>True value</term>
<term>Variational formulation</term>
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<term>Vertical mass fluxes</term>
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<term>Adjoint model</term>
<term>Aggregation errors</term>
<term>Aircraft data</term>
<term>Archived fields</term>
<term>Atmos</term>
<term>Atmospheric</term>
<term>Atmospheric lifetime</term>
<term>Atmospheric observations</term>
<term>Atmospheric transport</term>
<term>Atmospheric transport model</term>
<term>Austral summer</term>
<term>Auxiliary material</term>
<term>Bayesian inversion framework</term>
<term>Bias errors</term>
<term>Cambridge univ</term>
<term>Caribic passenger aircraft</term>
<term>Central europe</term>
<term>Chem</term>
<term>Chevallier</term>
<term>Circulation model</term>
<term>Climate change</term>
<term>Convection mass fluxes</term>
<term>Corazza</term>
<term>Earth sciences</term>
<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>
<term>Flux</term>
<term>Fluxes table</term>
<term>Fourth assessment report</term>
<term>Free troposphere</term>
<term>Geophys</term>
<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>
<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>
<term>Little impact</term>
<term>Lmdz</term>
<term>Lmdz model</term>
<term>Lower stratosphere</term>
<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>
<term>Nitrous</term>
<term>Nitrous oxide</term>
<term>Nitrous oxide fluxes</term>
<term>Northern hemisphere</term>
<term>Notable error reductions</term>
<term>Optimization</term>
<term>Optimized</term>
<term>Optimized surface fluxes</term>
<term>Oxide</term>
<term>Personal communication</term>
<term>Perturbed</term>
<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>
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<term>Reference inversion</term>
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<term>Scalar values</term>
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<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>
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<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>
<term>Synthetic data</term>
<term>Temporal resolution</term>
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<term>Tests sref</term>
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<term>Transport errors</term>
<term>Transport model</term>
<term>Tropical africa</term>
<term>Troposphere</term>
<term>Tropospheric</term>
<term>True fluxes</term>
<term>True value</term>
<term>Variational formulation</term>
<term>Vertical column</term>
<term>Vertical gradient</term>
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<front><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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<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.</abstract>
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<title level="a" type="short">INVERSION ESTIMATES OF N2O FLUXES</title>
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<titleGroup><title type="main">Impact of the atmospheric sink and vertical mixing on nitrous oxide fluxes estimated using inversion methods</title>
<title type="short">INVERSION ESTIMATES OF N<sub>2</sub>
O FLUXES</title>
<title type="shortAuthors">Thompson <i>et al</i>
.</title>
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<caption>Figure S5. Temporal evolution of the difference in zonal mean vertical profile of N2O relative to scenario P1 (in ppb) for the year 2008.</caption>
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<abstractGroup><abstract type="main"><p xml:id="jgrd17190-para-0004" label="1">This study investigates some of the principal errors arising in atmospheric inversion estimates of N<sub>2</sub>
O surface fluxes. Using a synthetic data set of model‐generated atmospheric N<sub>2</sub>
O mixing ratio data, representative of the current observation network, we investigate the influence of errors in the stratospheric N<sub>2</sub>
O 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 N<sub>2</sub>
O 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 N<sub>2</sub>
O 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 N<sub>2</sub>
O 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.</p>
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<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.</abstract>
<abstract type="short">Inversion estimates of N2O fluxes are sensitive to bias errors in the sink Simultaneous optimization of N2O surface fluxes and sink is possible Model vertical transport errors lead to errors in flux spatial distribution</abstract>
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<note type="content"> Auxiliary material for this article contains additional figures. Auxiliary material files may require downloading to a local drive depending on platform, browser, configuration, and size. To open auxiliary materials in a browser, click on the label. To download, Right‐click and select “Save Target As…” (PC) or CTRL‐click and select “Download Link to Disk” (Mac). Additional file information is provided in the readme.txt. Auxiliary material for this article contains additional figures. Auxiliary material files may require downloading to a local drive depending on platform, browser, configuration, and size. To open auxiliary materials in a browser, click on the label. To download, Right‐click and select “Save Target As…” (PC) or CTRL‐click and select “Download Link to Disk” (Mac). Additional file information is provided in the readme.txt. Auxiliary material for this article contains additional figures. Auxiliary material files may require downloading to a local drive depending on platform, browser, configuration, and size. To open auxiliary materials in a browser, click on the label. To download, Right‐click and select “Save Target As…” (PC) or CTRL‐click and select “Download Link to Disk” (Mac). Additional file information is provided in the readme.txt.Supporting Info Item: readme.txt - Figure S1. N2O mixing ratios simulated by coupling the true fluxes to LMDz shown for 6 example sites. - Figure S2. Simulated N2O seasonal cycles for 2007 shown for the same 6 example sites and 4 scenarios as described for Figure S1. - Figure S3a. Difference (scenario P4 ‐ P1) in N2O mixing ratio in the stratosphere owing to the change in N2O loss distribution, shown for Jun‐2008 at 5 hPa. - Figure S3b. Difference (scenario P4 ‐ P1) in N2O mixing ratio in the stratosphere owing to the change in N2O loss distribution, shown for Dec‐2008 at 5 hPa. - Figure S3c. Difference (scenario P4 ‐ P1) in N2O mixing ratio in the stratosphere owing to the change in N2O loss distribution, shown for Jun‐2008 at 183 hPa. - Figure S3d. Difference (scenario P4 ‐ P1) in N2O mixing ratio in the stratosphere owing to the change in N2O loss distribution, shown for Dec‐2008 at 183 hPa. - Figure S4. Temporal evolution of the zonal mean vertical profile of N2O (in ppb) for the year 2008. - Figure S5. Temporal evolution of the difference in zonal mean vertical profile of N2O relative to scenario P1 (in ppb) for the year 2008. - Figure S6. Difference in the interhemispheric gradients of scenarios P2 to P4 relative to P1 shown for the lowermost model layer (surface up to 995 hPa) and for the year 2008. - Figure S7a. The temporal evolution of the zonal mean N2O mixing ratio (ppb) for the scenario P4 shown at 45°N for the year 2008 (the x‐axis is the day‐of‐year). - Figure S7b. Same as Figure 7a but showing the difference between scenario P4 and P1. - Tab‐delimited Table 1. - Tab‐delimited Table 2. - Tab‐delimited Table 3. - Tab‐delimited Table 4. - Tab‐delimited Table 5. - </note>
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