scholarly journals Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

2016 ◽  
Author(s):  
Anna M. Michalak ◽  
Nina A. Randazzo ◽  
Frédéric Chevallier
Keyword(s):  
Inverse Problems ◽  
Greenhouse Gases ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Ghg Emissions ◽  
Surface Fluxes ◽  
Diagnostic Methods ◽  
Clear Understanding ◽  
Atmospheric Observations ◽  
Atmospheric Inversions

Abstract. The ability to predict the trajectory of climate change requires a clear understanding of the emissions and uptake (a.k.a. surface fluxes) of long-lived greenhouse gases (GHGs). Furthermore, the development of climate policies is driving a need to constrain the budgets of anthropogenic GHG emissions. Inverse problems that couple atmospheric observations of GHG concentrations with an atmospheric chemistry and transport model have increasingly been used to gain insights into surface fluxes. Given the inherent technical challenges associated with their solution, it is imperative that objective approaches exist for the evaluation of such inverse problems. Because direct observation of fluxes at compatible spatiotemporal scales is rarely possible, diagnostics tools must rely on indirect measures. Here we review diagnostics that have been implemented in recent studies, and discuss their use in informing adjustments to model setup. We group the diagnostics along a continuum starting with those that are most closely related to the scientific question being targeted, and ending with those most closely tied to the statistical and computational setup of the inversion. We thus begin with diagnostics based on assessments against independent information (e.g., unused atmospheric observations, large-scale scientific constraints), followed by statistical diagnostics of inversion results, diagnostics based on sensitivity tests and analyses of robustness (e.g., tests focusing on the chemistry and transport model, the atmospheric observations, or the statistical and computational framework), and close with the use of synthetic data experiments (a.k.a. observing system simulation experiments (OSSEs)). We find that existing diagnostics provide a crucial toolbox for evaluating and improving flux estimates, but, not surprisingly, cannot overcome the fundamental challenges associated with limited atmospheric observations or the lack of direct flux measurements at compatible scales. As atmospheric inversions are increasingly expected to contribute to national reporting of GHG emissions, the need for developing and implementing robust and transparent evaluation approaches will only grow.

scholarly journals Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

2017 ◽  
Vol 17 (12) ◽  
pp. 7405-7421 ◽  
Author(s):  
Anna M. Michalak ◽  
Nina A. Randazzo ◽  
Frédéric Chevallier
Keyword(s):  
Inverse Problems ◽  
Greenhouse Gases ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Ghg Emissions ◽  
Surface Fluxes ◽  
Diagnostic Methods ◽  
Clear Understanding ◽  
Atmospheric Observations ◽  
Atmospheric Inversions

Abstract. The ability to predict the trajectory of climate change requires a clear understanding of the emissions and uptake (i.e., surface fluxes) of long-lived greenhouse gases (GHGs). Furthermore, the development of climate policies is driving a need to constrain the budgets of anthropogenic GHG emissions. Inverse problems that couple atmospheric observations of GHG concentrations with an atmospheric chemistry and transport model have increasingly been used to gain insights into surface fluxes. Given the inherent technical challenges associated with their solution, it is imperative that objective approaches exist for the evaluation of such inverse problems. Because direct observation of fluxes at compatible spatiotemporal scales is rarely possible, diagnostics tools must rely on indirect measures. Here we review diagnostics that have been implemented in recent studies and discuss their use in informing adjustments to model setup. We group the diagnostics along a continuum starting with those that are most closely related to the scientific question being targeted, and ending with those most closely tied to the statistical and computational setup of the inversion. We thus begin with diagnostics based on assessments against independent information (e.g., unused atmospheric observations, large-scale scientific constraints), followed by statistical diagnostics of inversion results, diagnostics based on sensitivity tests, and analyses of robustness (e.g., tests focusing on the chemistry and transport model, the atmospheric observations, or the statistical and computational framework), and close with the use of synthetic data experiments (i.e., observing system simulation experiments, OSSEs). We find that existing diagnostics provide a crucial toolbox for evaluating and improving flux estimates but, not surprisingly, cannot overcome the fundamental challenges associated with limited atmospheric observations or the lack of direct flux measurements at compatible scales. As atmospheric inversions are increasingly expected to contribute to national reporting of GHG emissions, the need for developing and implementing robust and transparent evaluation approaches will only grow.

scholarly journals On the parallelization of atmospheric inversions of CO<sub>2</sub> surface fluxes within a variational framework

2013 ◽  
Vol 6 (3) ◽  
pp. 783-790 ◽  
Author(s):  
F. Chevallier
Keyword(s):  
Transport Model ◽  
Spatial Scales ◽  
Computational Cost ◽  
Surface Fluxes ◽  
Atmospheric Measurements ◽  
Transport Modelling ◽  
Time Space ◽  
Variational Minimization ◽  
Analytical Approaches ◽  
Atmospheric Inversions

Abstract. The variational formulation of Bayes' theorem allows inferring CO2 sources and sinks from atmospheric concentrations at much higher time–space resolution than the ensemble or analytical approaches. However, it usually exhibits limited scalable parallelism. This limitation hinders global atmospheric inversions operated on decadal time scales and regional ones with kilometric spatial scales because of the computational cost of the underlying transport model that has to be run at each iteration of the variational minimization. Here, we introduce a physical parallelization (PP) of variational atmospheric inversions. In the PP, the inversion still manages a single physically and statistically consistent window, but the transport model is run in parallel overlapping sub-segments in order to massively reduce the computation wall-clock time of the inversion. For global inversions, a simplification of transport modelling is described to connect the output of all segments. We demonstrate the performance of the approach on a global inversion for CO2 with a 32 yr inversion window (1979–2010) with atmospheric measurements from 81 sites of the NOAA global cooperative air sampling network. In this case, we show that the duration of the inversion is reduced by a seven-fold factor (from months to days), while still processing the three decades consistently and with improved numerical stability.

scholarly journals Limitations of the Radon Tracer Method (RTM) to estimate regional Greenhouse Gases (GHG) emissions – a case study for methane in Heidelberg

2021 ◽  
Author(s):  
Ingeborg Levin ◽  
Ute Karstens ◽  
Samuel Hammer ◽  
Julian DellaColetta ◽  
Fabian Maier ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Catchment Area ◽  
Ghg Emissions ◽  
Point Sources ◽  
Atmospheric Methane ◽  
Tracer Method ◽  
Night Time ◽  
Ch4 Emissions ◽  
Annual Variations ◽  
Atmospheric Observations

Abstract. Correlations of night-time atmospheric methane (CH4) and 222Radon (222Rn) observations in Heidelberg, Germany, were evaluated with the Radon Tracer Method (RTM) to estimate the trend of annual CH4 emissions from 1996–2020 in the catchment area of the station. After an initial 30 % decrease of emissions from 1996 to 2004, no further systematic trend but small inter-annual variations were observed thereafter. This is in accordance with the trend of emissions until 2010 reported by the EDGARv6.0 inventory for the surroundings of Heidelberg. We show that the reliability of total CH4 emission estimates with the RTM critically depends on the accuracy and representativeness of the 222Rn exhalation rate from soils in the catchment area of the site. Simply using 222Rn fluxes as estimated by Karstens et al. (2015) could lead to biases in the estimated greenhouse gases (GHG) fluxes as large as a factor of two. RTM-based GHG flux estimates also depend on the parameters chosen for the night-time correlations of CH4 and 222Rn, such as the night-time period for regressions as well as the R2 cut-off value for the goodness of the fit. Quantitative comparison of total RTM-based top-down with bottom-up emission inventories requires representative high-resolution footprint modelling, particularly in polluted areas where CH4 emissions show large heterogeneity. Even then, RTM-based estimates are likely biased low if point sources play a significant role in the station/observation footprint as their emissions are not captured by the RTM method. Long-term representative 222Rn flux observations in the catchment area of a station are indispensable in order to apply the RTM method for reliable quantitative flux estimations of GHG emissions from atmospheric observations.

scholarly journals On the parallelization of atmospheric inversions of CO<sub>2</sub> surface fluxes within a variational framework

2013 ◽  
Vol 6 (1) ◽  
pp. 37-57 ◽  
Author(s):  
F. Chevallier
Keyword(s):  
Transport Model ◽  
Spatial Scales ◽  
Computational Cost ◽  
Surface Fluxes ◽  
Atmospheric Measurements ◽  
Transport Modelling ◽  
Sources And Sinks ◽  
Variational Framework ◽  
Variational Minimization ◽  
Atmospheric Inversions

Abstract. The variational formulation of Bayes' theorem allows inferring CO2 sources and sinks from atmospheric concentrations at much higher space-time resolution than the ensemble approach or the analytical one. However, it usually exhibits limited scalable parallelism. This limitation hinders global atmospheric inversions operated on decadal time scales and regional ones with kilometric spatial scales, because of the computational cost of the underlying transport model that has to be run at each iteration of the variational minimization. Here, we introduce a Physical Parallelisation (PP) of variational atmospheric inversions. In the PP, the inversion still manages a single physically and statistically consistent window, but the transport model is run in parallel overlapping sub-segments in order to massively reduce the computation wall clock time of the inversion. For global inversions, a simplification of transport modelling is described to connect the output of all segments. We demonstrate the performance of the approach on a global inversion for CO2 with a 32-yr inversion window (1979–2010) with atmospheric measurements from 81 sites of the NOAA global cooperative air sampling network. In this case, we show that the duration of the inversion is reduced by a seven-fold factor (from months to days) while still processing the three decades consistently and with improved numerical stability.

Time-series of GhG emissions estimation for the learning of inverse modeling

2021 ◽  
Author(s):  
Carlos Gómez-Ortiz ◽  
Guillaume Monteil ◽  
Marko Scholze
Keyword(s):  
Time Series ◽  
Inverse Modeling ◽  
Fossil Fuel ◽  
Transport Model ◽  
A Priori ◽  
Ghg Emissions ◽  
Atmospheric Concentration ◽  
Training Tool ◽  
Sources And Sinks ◽  
Atmospheric Inversions

&lt;p&gt;Inverse modeling is a commonly used method to infer greenhouse gases (GhGs) sources and sinks based on their observed concentrations in the atmosphere. It is a Bayesian framework and requires a priori fluxes of all the evaluated sources and sinks, atmospheric observations, an atmospheric transport model that relates the observations to the a priori fluxes and the uncertainties of both fluxes and observations. Various techniques exist to solve the inversion.&lt;/p&gt;&lt;p&gt;Atmospheric inverse modeling is and will become even more important in the future quantification of GhGs to monitor the compliance of the Nationally Determined Contributions (NDCs) under the Paris Agreement. Therefore, the scientific and educational communities are becoming more interested in using atmospheric inversions and this has risen a necessity of creating tools that facilitate understanding as well as training in these techniques.&lt;/p&gt;&lt;p&gt;Quantifying anthropogenic GhG emissions, such as CO&lt;sub&gt;2&lt;/sub&gt; from fossil fuel burning or CH&lt;sub&gt;4 &lt;/sub&gt;from human activities, from atmospheric concentration observations is difficult since the carbon from all sources, both natural and anthropogenic, is mixed in the atmosphere, making it necessary to use other signals or tracers to separate anthropogenic emissions from natural sources. For fossil fuel CO&lt;sub&gt;2&lt;/sub&gt; emissions radiocarbon (&lt;sup&gt;14&lt;/sup&gt;CO&lt;sub&gt;2&lt;/sub&gt;) is an excellent emission tracer because, due to its radioactive decay (~ 5000 years), it cannot be found in fossil fuels, which have been deposited millions of years ago as organic material. We have developed a Jupyter Notebook based on Python for the quantification of multi-tracer GhGs fossil fuel emissions and its isotopes. The notebook solves for the emissions by applying atmospheric inversions within a practical two-box model. The inverse modeling notebook is based on the analytical maximum a posteriori (MAP) solution of the Bayes&amp;#8217; theorem and allows to assess the error in the state vector and its uncertainty.&lt;/p&gt;&lt;p&gt;This basic but powerful notebook is meant to be an educational and training tool for university students and new researchers in the field as well as for researchers interested in the estimation of long-term (&gt;centennial) time-series of GhG emissions since it is built as a modular algorithm to be easily modified, coupled or expanded to other approaches or models depending on the application. The notebook was initially developed for the inverse modeling of CO&lt;sub&gt;2&lt;/sub&gt; and &lt;sup&gt;14&lt;/sup&gt;CO&lt;sub&gt;2&lt;/sub&gt; simultaneously and it is being expanded for additional GHG such as CH&lt;sub&gt;4&lt;/sub&gt; and &lt;sup&gt;13&lt;/sup&gt;CH&lt;sub&gt;4&lt;/sub&gt;.&lt;/p&gt;

scholarly journals Effect of Asymmetric Membrane Structure on Hydrogen Transport Resistance and Performance of a Catalytic Membrane Reactor for Ethanol Steam Reforming

Membranes ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 332
Author(s):  
Ludmilla Bobrova ◽  
Nikita Eremeev ◽  
Nadezhda Vernikovskaya ◽  
Vladislav Sadykov ◽  
Oleg Smorygo
Keyword(s):  
Steam Reforming ◽  
Transport Model ◽  
Hydrogen Permeation ◽  
Ethanol Steam Reforming ◽  
Clear Understanding ◽  
Catalytic Membrane ◽  
Structure Property ◽  
Asymmetric Membrane ◽  
Supported Membrane ◽  
Ethanol Steam

The performance of catalytic membrane reactors (CMRs) depends on the specific details of interactions at different levels between catalytic and separation parts. A clear understanding of decisive factors affecting their operational parameters can be provided via mathematical simulations. In the present paper, main results of numerical studies of ethanol steam reforming, followed by downstream hydrogen permeation through an asymmetric supported membrane, are reported. The membrane module consists of a thin selective layer supported on a substrate with graded porous structure. One-dimensional isothermal reaction–transport model for the CMR has been developed, and its validation has been carried out by using performance data from a lab-scale reactor with a disk-shaped membrane. Simulations demonstrate the model’s capabilities to analyze local concentrations gradients, as required to provide accurate estimates of the relationship between structure–property–performance. It was shown that transport properties of multilayer asymmetric membranes are highly related to the structural properties of each single layer.

scholarly journals Sunset–sunrise difference in solar occultation ozone measurements (SAGE II, HALOE, and ACE–FTS) and its relationship to tidal vertical winds

2015 ◽  
Vol 15 (2) ◽  
pp. 829-843 ◽  
Author(s):  
T. Sakazaki ◽  
M. Shiotani ◽  
M. Suzuki ◽  
D. Kinnison ◽  
J. M. Zawodny ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Seasonal Variations ◽  
Climate Model ◽  
Transport Model ◽  
Stratospheric Aerosol ◽  
Data Sets ◽  
Chemical Transport Model ◽  
Latitude Range ◽  
Solar Occultation ◽  
Vertical Winds

Abstract. This paper contains a comprehensive investigation of the sunset–sunrise difference (SSD, i.e., the sunset-minus-sunrise value) of the ozone mixing ratio in the latitude range of 10° S–10° N. SSD values were determined from solar occultation measurements based on data obtained from the Stratospheric Aerosol and Gas Experiment (SAGE) II, the Halogen Occultation Experiment (HALOE), and the Atmospheric Chemistry Experiment–Fourier transform spectrometer (ACE–FTS). The SSD was negative at altitudes of 20–30 km (−0.1 ppmv at 25 km) and positive at 30–50 km (+0.2 ppmv at 40–45 km) for HALOE and ACE–FTS data. SAGE II data also showed a qualitatively similar result, although the SSD in the upper stratosphere was 2 times larger than those derived from the other data sets. On the basis of an analysis of data from the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) and a nudged chemical transport model (the specified dynamics version of the Whole Atmosphere Community Climate Model: SD–WACCM), we conclude that the SSD can be explained by diurnal variations in the ozone concentration, particularly those caused by vertical transport by the atmospheric tidal winds. All data sets showed significant seasonal variations in the SSD; the SSD in the upper stratosphere is greatest from December through February, while that in the lower stratosphere reaches a maximum twice: during the periods March–April and September–October. Based on an analysis of SD–WACCM results, we found that these seasonal variations follow those associated with the tidal vertical winds.

scholarly journals Accelerating simulations using REDCHEM_v0.0 for atmospheric chemistry mechanism reduction

2018 ◽  
Vol 11 (8) ◽  
pp. 3391-3407 ◽  
Author(s):  
Zacharias Marinou Nikolaou ◽  
Jyh-Yuan Chen ◽  
Yiannis Proestos ◽  
Jos Lelieveld ◽  
Rolf Sander
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
A Priori ◽  
Chemical Mechanism ◽  
Climate Modelling ◽  
Mechanism Reduction ◽  
Computational Speed ◽  
Speed Up ◽  
Spatio Temporal ◽  
Skeletal Mechanism

Abstract. Chemical mechanism reduction is common practice in combustion research for accelerating numerical simulations; however, there have been limited applications of this practice in atmospheric chemistry. In this study, we employ a powerful reduction method in order to produce a skeletal mechanism of an atmospheric chemistry code that is commonly used in air quality and climate modelling. The skeletal mechanism is developed using input data from a model scenario. Its performance is then evaluated both a priori against the model scenario results and a posteriori by implementing the skeletal mechanism in a chemistry transport model, namely the Weather Research and Forecasting code with Chemistry. Preliminary results, indicate a substantial increase in computational speed-up for both cases, with a minimal loss of accuracy with regards to the simulated spatio-temporal mixing ratio of the target species, which was selected to be ozone.

scholarly journals Surface fluxes of bromoform and dibromomethane over the tropical western Pacific inferred from airborne in situ measurements

2017 ◽  
Author(s):  
Liang Feng ◽  
Paul I. Palmer ◽  
Robyn Butler ◽  
Stephen J. Andrews ◽  
Elliot L. Atlas ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Chemistry ◽  
Marine Boundary Layer ◽  
Current Knowledge ◽  
A Priori ◽  
Surface Fluxes ◽  
Western Pacific ◽  
Study Region ◽  
The Western Pacific ◽  
Aircraft Data

Abstract. We infer surface fluxes of bromoform (CHBr3) and dibromoform (CH2Br2) from aircraft observations over the western Pacific using a tagged version of the GEOS-Chem global 3-D atmospheric chemistry model and a Maximum A Posteriori inverse model. The distribution of a priori ocean emissions of these gases are reasonably consistent with observed atmospheric mole fractions of CHBr3 (r = 0.62) and CH2Br2 (r = 0.38). These a priori emissions result in a positive model bias in CHBr3 peaking in the marine boundary layer, but capture observed values of CH2Br2 with no significant bias by virtue of its longer atmospheric lifetime. Using GEOS-Chem, we find that observed variations in atmospheric CHBr3 are determined equally by sources over the western Pacific and those outside the study region, but observed variations in CH2Br2 are determined mainly by sources outside the western Pacific. Numerical closed-loop experiments show that the spatial and temporal distribution of boundary layer aircraft data have the potential to substantially improve current knowledge of these fluxes, with improvements related to data density. Using the aircraft data, we estimate aggregated regional fluxes of 3.6 ± 0.3 × 108 g/month and 0.7 ± 0.1 × 108 g/month for CHBr3 and CH2Br2 over 130°–155° E and 0°–12° N, respectively, which represent reductions of 20–40 % and substantial spatial deviations from the a priori inventory. We find no evidence to support a robust linear relationship between CHBr3 and CH2Br2 oceanic emissions, as used by previous studies.

scholarly journals A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 2: Optimization scheme and identical twin experiment of atmospheric CO<sub>2</sub> inversion

2017 ◽  
Vol 10 (6) ◽  
pp. 2201-2219 ◽  
Author(s):  
Yosuke Niwa ◽  
Yosuke Fujii ◽  
Yousuke Sawa ◽  
Yosuke Iida ◽  
Akihiko Ito ◽  
...  
Keyword(s):  
Transport Model ◽  
Surface Fluxes ◽  
Identical Twin ◽  
Inversion Problem ◽  
Model Errors ◽  
Optimization Scheme ◽  
Error Covariance ◽  
Optimal Setting ◽  
Source Sink ◽  
Quasi Newton

Abstract. A four-dimensional variational method (4D-Var) is a popular technique for source/sink inversions of atmospheric constituents, but it is not without problems. Using an icosahedral grid transport model and the 4D-Var method, a new atmospheric greenhouse gas (GHG) inversion system has been developed. The system combines offline forward and adjoint models with a quasi-Newton optimization scheme. The new approach is then used to conduct identical twin experiments to investigate optimal system settings for an atmospheric CO2 inversion problem, and to demonstrate the validity of the new inversion system. In this paper, the inversion problem is simplified by assuming the prior flux errors to be reasonably well known and by designing the prior error correlations with a simple function as a first step. It is found that a system of forward and adjoint models with smaller model errors but with nonlinearity has comparable optimization performance to that of another system that conserves linearity with an exact adjoint relationship. Furthermore, the effectiveness of the prior error correlations is demonstrated, as the global error is reduced by about 15 % by adding prior error correlations that are simply designed when 65 weekly flask sampling observations at ground-based stations are used. With the optimal setting, the new inversion system successfully reproduces the spatiotemporal variations of the surface fluxes, from regional (such as biomass burning) to global scales. The optimization algorithm introduced in the new system does not require decomposition of a matrix that establishes the correlation among the prior flux errors. This enables us to design the prior error covariance matrix more freely.

Sign in / Sign up

Export Citation Format

Share Document