scholarly journals Sensitivity of the recent methane budget to LMDz sub-grid-scale physical parameterizations

2015 ◽  
Vol 15 (17) ◽  
pp. 9765-9780 ◽  
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
M. Saunois ◽  
F. Chevallier ◽  
C. Cressot
Keyword(s):  
Inverse Modeling ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Deep Convection ◽  
Regional Scale ◽  
Global Scale ◽  
Methane Emissions ◽  
Data Sets ◽  
Methane Budget ◽  
Physical Parameterizations

Abstract. With the densification of surface observing networks and the development of remote sensing of greenhouse gases from space, estimations of methane (CH4) sources and sinks by inverse modeling are gaining additional constraining data but facing new challenges. The chemical transport model (CTM) linking the flux space to methane mixing ratio space must be able to represent these different types of atmospheric constraints for providing consistent flux estimations. Here we quantify the impact of sub-grid-scale physical parameterization errors on the global methane budget inferred by inverse modeling. We use the same inversion setup but different physical parameterizations within one CTM. Two different schemes for vertical diffusion, two others for deep convection, and one additional for thermals in the planetary boundary layer (PBL) are tested. Different atmospheric methane data sets are used as constraints (surface observations or satellite retrievals). At the global scale, methane emissions differ, on average, from 4.1 Tg CH4 per year due to the use of different sub-grid-scale parameterizations. Inversions using satellite total-column mixing ratios retrieved by GOSAT are less impacted, at the global scale, by errors in physical parameterizations. Focusing on large-scale atmospheric transport, we show that inversions using the deep convection scheme of Emanuel (1991) derive smaller interhemispheric gradients in methane emissions, indicating a slower interhemispheric exchange. At regional scale, the use of different sub-grid-scale parameterizations induces uncertainties ranging from 1.2 % (2.7 %) to 9.4 % (14.2 %) of methane emissions when using only surface measurements from a background (or an extended) surface network. Moreover, spatial distribution of methane emissions at regional scale can be very different, depending on both the physical parameterizations used for the modeling of the atmospheric transport and the observation data sets used to constrain the inverse system. When using only satellite data from GOSAT, we show that the small biases found in inversions using a coarser version of the transport model are actually masking a poor representation of the stratosphere–troposphere methane gradient in the model. Improving the stratosphere–troposphere gradient reveals a larger bias in GOSAT CH4 satellite data, which largely amplifies inconsistencies between the surface and satellite inversions. A simple bias correction is proposed. The results of this work provide the level of confidence one can have for recent methane inversions relative to physical parameterizations included in CTMs.

scholarly journals Sensitivity of the recent methane budget to LMDz sub-grid scale physical parameterizations

2015 ◽  
Vol 15 (8) ◽  
pp. 11853-11888
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
M. Saunois ◽  
F. Chevallier ◽  
C. Cressot
Keyword(s):  
Satellite Data ◽  
Transport Model ◽  
Deep Convection ◽  
Chemical Transport ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Chemical Transport Model ◽  
Methane Budget ◽  
Physical Parameterizations

Abstract. With the densification of surface observing networks and the development of remote sensing of greenhouse gases from space, estimations of methane (CH4) sources and sinks by inverse modelling face new challenges. Indeed, the chemical transport model used to link the flux space with the mixing ratio space must be able to represent these different types of constraints for providing consistent flux estimations. Here we quantify the impact of sub-grid scale physical parameterization errors on the global methane budget inferred by inverse modelling using the same inversion set-up but different physical parameterizations within one chemical-transport model. Two different schemes for vertical diffusion, two others for deep convection, and one additional for thermals in the planetary boundary layer are tested. Different atmospheric methane datasets are used as constraints (surface observations or satellite retrievals). At the global scale, methane emissions differ, on average, from 4.1 Tg CH4 per year due to the use of different sub-grid scale parameterizations. Inversions using satellite total-column retrieved by GOSAT satellite are less impacted, at the global scale, by errors in physical parameterizations. Focusing on large-scale atmospheric transport, we show that inversions using the deep convection scheme of Emanuel (1991) derive smaller interhemispheric gradient in methane emissions. At regional scale, the use of different sub-grid scale parameterizations induces uncertainties ranging from 1.2 (2.7%) to 9.4% (14.2%) of methane emissions in Africa and Eurasia Boreal respectively when using only surface measurements from the background (extended) surface network. When using only satellite data, we show that the small biases found in inversions using GOSAT-CH4 data and a coarser version of the transport model were actually masking a poor representation of the stratosphere–troposphere gradient in the model. Improving the stratosphere–troposphere gradient reveals a larger bias in GOSAT-CH4 satellite data, which largely amplifies inconsistencies between surface and satellite inversions. A simple bias correction is proposed. The results of this work provide the level of confidence one can have for recent methane inversions relatively to physical parameterizations included in chemical-transport models.

scholarly journals Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

2013 ◽  
Vol 13 (4) ◽  
pp. 10961-11021
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
F. Chevallier ◽  
A. Fortems-Cheney ◽  
S. Szopa ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Spatial Scales ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Model Errors ◽  
Inverse Systems ◽  
Set Up ◽  
The Impact

Abstract. A modelling experiment has been conceived to assess the impact of transport model errors on the methane emissions estimated by an atmospheric inversion system. Synthetic methane observations, given by 10 different model outputs from the international TransCom-CH4 model exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the PYVAR-LMDZ-SACS inverse system to produce 10 different methane emission estimates at the global scale for the year 2005. The same set-up has been used to produce the synthetic observations and to compute flux estimates by inverse modelling, which means that only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg CH4 per year at the global scale, representing 5% of the total methane emissions. At continental and yearly scales, transport model errors have bigger impacts depending on the region, ranging from 36 Tg CH4 in north America to 7 Tg CH4 in Boreal Eurasian (from 23% to 48%). At the model gridbox scale, the spread of inverse estimates can even reach 150% of the prior flux. Thus, transport model errors contribute to significant uncertainties on the methane estimates by inverse modelling, especially when small spatial scales are invoked. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher resolution models. The analysis of methane estimated fluxes in these different configurations questions the consistency of transport model errors in current inverse systems. For future methane inversions, an improvement in the modelling of the atmospheric transport would make the estimations more accurate. Likewise, errors of the observation covariance matrix should be more consistently prescribed in future inversions in order to limit the impact of transport model errors on estimated methane fluxes.

scholarly journals A European-wide <sup>222</sup>Radon and <sup>222</sup>Radon progeny comparison study

2016 ◽  
Author(s):  
Dominik Schmithüsen ◽  
Scott Chambers ◽  
Bernd Fischer ◽  
Stefan Gilge ◽  
Juha Hatakka ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Ground Level ◽  
Data Sets ◽  
Correction Factors ◽  
Comparison Study ◽  
Data Set ◽  
Parallel Data ◽  
Linear Regressions ◽  
Atmospheric Transport Model

Abstract. A European-wide 222Radon/222Radon progeny comparison study has been conducted in order to determine correction factors that could be applied to existing atmospheric 222Radon data sets for quantitative use of this tracer in atmospheric transport model validation. Two compact and easy-to-transport Heidelberg Radon Monitors (HRM) were moved around to run for at least one month at each of the nine European measurement stations that were included in the comparison. Linear regressions between parallel data sets were calculated, yielding correction factors relative to the HRM ranging from 0.68 to 1.45. A calibration bias between ANSTO (Australian Nuclear Science and Technology Organisation) two-filter radon monitors and the HRM of ANSTO/HRM = 1.11 ± 0.05 was found. For continental stations, which use one-filter systems, preliminary 214Po/222Rn disequilibrium values were estimated to lie between 0.8 at mountain stations (e.g. Schauinsland) and 0.9 at non-mountain sites for sampling heights around 20 to 30 m above ground level. Respective corrections need to be applied to obtain a consistent European 222Radon data set for further applications.

scholarly journals Multi-species inversion of CH<sub>4</sub>, CO and H<sub>2</sub> emissions from surface measurements

2009 ◽  
Vol 9 (14) ◽  
pp. 5281-5297 ◽  
Author(s):  
I. Pison ◽  
P. Bousquet ◽  
F. Chevallier ◽  
S. Szopa ◽  
D. Hauglustaine
Keyword(s):  
Transport Model ◽  
Regional Scale ◽  
Surface Concentration ◽  
Temporal Variations ◽  
Global Scale ◽  
Chemical Transport Model ◽  
Methyl Chloroform ◽  
Emission Fluxes ◽  
Surface Measurements ◽  
One Year

Abstract. In order to study the spatial and temporal variations of the emissions of greenhouse gases and of their precursors, we developed a data assimilation system and applied it to infer emissions of CH4, CO and H2 for one year. It is based on an atmospheric chemical transport model and on a simplified scheme for the oxidation chain of hydrocarbons, including methane, formaldehyde, carbon monoxide and molecular hydrogen together with methyl chloroform. The methodology is exposed and a first attempt at evaluating the inverted fluxes is made. Inversions of the emission fluxes of CO, CH4 and H2 and concentrations of HCHO and OH were performed for the year 2004, using surface concentration measurements of CO, CH4, H2 and CH3CCl3 as constraints. Independent data from ship and aircraft measurements and satellite retrievals are used to evaluate the results. The total emitted mass of CO is 30% higher after the inversion, due to increased fluxes by up to 35% in the Northern Hemisphere. The spatial distribution of emissions of CH4 is modified by a decrease of fluxes in boreal areas up to 60%. The comparison between mono- and multi-species inversions shows that the results are close at a global scale but may significantly differ at a regional scale because of the interactions between the various tracers during the inversion.

Quantifying the contribution of regional methane emissions to the global methane budget between 2008 and 2018 using the TOMCAT chemical transport model

2021 ◽  
Author(s):  
Emily Dowd ◽  
Christopher Wilson ◽  
Martyn Chipperfield ◽  
Manuel Gloor
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
Transport Model ◽  
Chemical Transport ◽  
Methane Emissions ◽  
Anthropogenic Sources ◽  
Chemical Transport Model ◽  
Methane Budget

&lt;p&gt;Methane (CH&lt;sub&gt;4&lt;/sub&gt;) is the second most important atmospheric greenhouse gas after carbon dioxide. Global concentrations of CH&lt;sub&gt;4&lt;/sub&gt; have been rising in the last decade and our understanding of what is driving the increase remains incomplete. Natural sources, such as wetlands, contribute to the uncertainty of the methane budget. However, anthropogenic sources, such as fossil fuels, present an opportunity to mitigate the human contribution to climate change on a relatively short timescale, since CH&lt;sub&gt;4&lt;/sub&gt; has a much shorter lifetime than carbon dioxide. Therefore, it is important to know the relative contributions of these sources in different regions.&lt;/p&gt;&lt;p&gt;We have investigated the inter-annual variation (IAV) and rising trend of CH&lt;sub&gt;4&lt;/sub&gt; concentrations using a global 3-D chemical transport model, TOMCAT. We independently tagged several regional natural and anthropogenic CH&lt;sub&gt;4&lt;/sub&gt; tracers in TOMCAT to identify their contribution to the atmospheric CH&lt;sub&gt;4&lt;/sub&gt; concentrations over the period 2009 &amp;#8211; 2018. The tagged regions were selected based on the land surface types and the predominant flux sector within each region and include subcontinental regions, such as tropical South America, boreal regions and anthropogenic regions such as Europe. We used surface CH&lt;sub&gt;4&lt;/sub&gt; fluxes derived from a previous TOMCAT-based atmospheric inversion study (Wilson et al., 2020). These atmospheric inversions were constrained by satellite and surface flask observations of CH&lt;sub&gt;4&lt;/sub&gt;, giving optimised monthly estimates for fossil fuel and non-fossil fuel emissions on a 5.6&amp;#176; horizontal grid. During the study period, the total optimised CH&lt;sub&gt;4&lt;/sub&gt; flux grew from 552 Tg/yr to 593 Tg/yr. This increase in emissions, particularly in the tropics, contributed to the increase in atmospheric CH&lt;sub&gt;4 &lt;/sub&gt;concentrations and added to the imbalance in the CH&lt;sub&gt;4&lt;/sub&gt; budget. We will use the results of the regional tagged tracers to quantify the contribution of regional methane emissions at surface observation sites, and to quantify the contributions of the natural and anthropogenic emissions from the tagged regions to the IAV and the rising methane concentrations.&lt;/p&gt;&lt;p&gt;Wilson, C., Chipperfield, M. P., Gloor, M., Parker, R. J., Boesch, H., McNorton, J., Gatti, L. V., Miller, J. B., Basso, L. S., and Monks, S. A.: Large and increasing methane emissions from Eastern Amazonia derived from satellite data, 2010&amp;#8211;2018, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1136, in review, 2020.&lt;/p&gt;

scholarly journals The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM-MACH-GHG v.137-reg

2019 ◽  
Author(s):  
Jinwoong Kim ◽  
Saroja Polavarapu ◽  
Douglas Chan ◽  
Michael Neish
Keyword(s):  
Greenhouse Gas ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Forecast Error ◽  
Regional Scale ◽  
The United States ◽  
Regional Model ◽  
Boreal Summer ◽  
Chemical Transport Model ◽  
Atmospheric Transport Model

Abstract. In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high spatio-temporal resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km × 10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations, based on comparisons to surface and aircraft observations. In addition, reduced bias and standard deviation of forecast error in boreal summer are obtained by the regional model. Better representation of model topography in the regional model reduces transport and representation errors significantly compared to the global model, especially in regions of complex topography, as revealed by the more precise and detailed structure of the CO2 diurnal cycle produced at observation sites and in model space. The new regional model will form the basis of a flux inversion system that estimates regional scale fluxes of GHGs over Canada.

scholarly journals The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM–MACH–GHG v.137-reg

2020 ◽  
Vol 13 (1) ◽  
pp. 269-295
Author(s):  
Jinwoong Kim ◽  
Saroja M. Polavarapu ◽  
Douglas Chan ◽  
Michael Neish
Keyword(s):  
Greenhouse Gas ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Forecast Error ◽  
Regional Scale ◽  
The United States ◽  
Regional Model ◽  
Boreal Summer ◽  
Spatiotemporal Resolution ◽  
Atmospheric Transport Model

Abstract. In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high-spatiotemporal-resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km×10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower-resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate the high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations based on comparisons to surface and aircraft observations. In addition, the bias and standard deviation of forecast error in boreal summer are reduced by the regional model. Better representation of model topography in the regional model results in improved simulation of the CO2 diurnal cycle compared to the global model at Walnut Grove, California. The new regional model will form the basis of a flux inversion system that estimates regional-scale fluxes of GHGs over Canada.

scholarly journals Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

2013 ◽  
Vol 13 (19) ◽  
pp. 9917-9937 ◽  
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
F. Chevallier ◽  
A. Fortems-Cheney ◽  
S. Szopa ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Model Errors ◽  
Inverse Systems ◽  
The Impact

Abstract. A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.

scholarly journals Clouds, photolysis and regional tropospheric ozone budgets

2009 ◽  
Vol 9 (3) ◽  
pp. 13889-13916 ◽  
Author(s):  
A. Voulgarakis ◽  
O. Wild ◽  
N. H. Savage ◽  
G. D. Carver ◽  
J. A. Pyle
Keyword(s):  
Tropospheric Ozone ◽  
Large Scale ◽  
Transport Model ◽  
Regional Scale ◽  
Three Dimensional ◽  
Global Scale ◽  
Chemical Production ◽  
Chemical Transport Model ◽  
Photolysis Rates ◽  
Boundary Layer Ozone

Abstract. We use a three-dimensional chemical transport model to examine the shortwave radiative effects of clouds on the tropospheric ozone budget. In addition to looking at changes in global concentrations as previous studies have done, we examine changes in ozone chemical production and loss caused by clouds and how these vary in different parts of the troposphere. On a global scale, we find that clouds have a modest effect on ozone chemistry, but on a regional scale their role is much more significant, with the size of the response dependent on the region. The largest averaged changes in chemical budgets (±10–14%) are found in the marine troposphere, where cloud optical depths are high. We demonstrate that cloud effects are small on average in the middle troposphere because this is a transition region between reduction and enhancement in photolysis rates. We show that increases in boundary layer ozone due to clouds are driven by large-scale changes in downward ozone transport from higher in the troposphere rather than by decreases in in-situ ozone chemical loss rates. Increases in upper tropospheric ozone are caused by higher production rates due to backscattering of radiation and consequent increases in photolysis rates, mainly J(NO2). The global radiative effect of clouds on isoprene is stronger than on ozone. Tropospheric isoprene lifetime increases by 7% when taking clouds into account. We compare the importance of clouds in contributing to uncertainties in the global ozone budget with the role of other radiatively-important factors. The budget is most sensitive to the overhead ozone column, while surface albedo and clouds have smaller effects. However, uncertainty in representing the spatial distribution of clouds may lead to a large sensitivity on regional scales.

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