Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE

Fortems-Cheiney, Audrey; Pison, Isabelle; Dufour, G.; Broquet, Grégoire; Potier, Élise; Coman, Adriana; Siour, Guillaume; Costantino, Lorenzo

doi:10.5194/gmd-2019-186

Cited by 8 publications

(12 citation statements)

References 7 publications

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“…The posterior value of x , i.e. the solution minimizing this equation, can be found by solving the first-order derivative using a descent algorithm, also known as the variational approach, and is the approach used by four of the inversion frameworks in this study [ 8 , 9 , 19 – 21 ]. The fifth framework (NAME-HB) uses a Monte Carlo Markov Chain approach to find the solution for x [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Changes in net ecosystem exchange over Europe during the 2018 drought based on atmospheric observations

Thompson

Broquet

Gerbig

et al. 2020

Phil. Trans. R. Soc. B

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The 2018 drought was one of the worst European droughts of the twenty-first century in terms of its severity, extent and duration. The effects of the drought could be seen in a reduction in harvest yields in parts of Europe, as well as an unprecedented browning of vegetation in summer. Here, we quantify the effect of the drought on net ecosystem exchange (NEE) using five independent regional atmospheric inversion frameworks. Using a network of atmospheric CO 2 mole fraction observations, we estimate NEE with at least monthly and 0.5° × 0.5° resolution for 2009–2018. We find that the annual NEE in 2018 was likely more positive (less CO 2 uptake) in the temperate region of Europe by 0.09 ± 0.06 Pg C yr −1 (mean ± s.d.) compared to the mean of the last 10 years of −0.08 ± 0.17 Pg C yr −1 , making the region close to carbon neutral in 2018. Similarly, we find a positive annual NEE anomaly for the northern region of Europe of 0.02 ± 0.02 Pg C yr −1 compared the 10-year mean of −0.04 ± 0.05 Pg C yr −1 . In both regions, this was largely owing to a reduction in the summer CO 2 uptake. The positive NEE anomalies coincided spatially and temporally with negative anomalies in soil water. These anomalies were exceptional for the 10-year period of our study. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’.

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Section: Methodsmentioning

confidence: 99%

Changes in net ecosystem exchange over Europe during the 2018 drought based on atmospheric observations

Thompson

Broquet

Gerbig

et al. 2020

Phil. Trans. R. Soc. B

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“…This is because the methane lifetime depends on the concentration of the OH radical which, in turn, depends on the concentration of CO and methane as well as sources of OH. There are no perfect methods to constrain global OH concentrations, and more work should be done to constrain trends in the concentration and production of hydroxyl radicals (e.g., Fortems‐Cheiney et al, ; Li et al, ; Miyazaki et al, ; Wolfe et al, ). In decadal methane emissions estimates with fixed OH concentrations, we find a systematic and nonnegligible negative bias in inversions that do not consider this chemical feedback.…”

Section: Summary and Recommendationsmentioning

confidence: 99%

Effects of Chemical Feedbacks on Decadal Methane Emissions Estimates

Nguyen

Turner

Yin

et al. 2020

Geophysical Research Letters

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The coupled chemistry of methane, carbon monoxide (CO), and hydroxyl radical (OH) can modulate methane's 9‐year lifetime. This is often ignored in methane flux inversions, and the impacts of neglecting interactive chemistry have not been quantified. Using a coupled‐chemistry box model, we show that neglecting the effect of methane source perturbation on [OH] can lead to a 25% bias in estimating abrupt changes in methane sources after only 10 years. Further, large CO emissions, such as from biomass burning, can increase methane concentrations by extending the methane lifetime through impacts on [OH]. Finally, we quantify the biases of including (or excluding) coupled chemistry in the context of recent methane and CO trends. Decreasing CO concentrations, beginning in the 2000's, have notable impacts on methane flux inversions. Given these nonnegligible errors, decadal methane emissions inversions should incorporate chemical feedbacks for more robust methane trend analyses and source attributions.

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“…The target vector x includes the variables to be optimized by the inversion; it includes the main variables of interest, such as the surface fluxes, but also variables relating to atmospheric chemical sources and sinks, background concentrations in the case of limited-area transport models, model parameters, etc., which are required to make the inversion physically consistent. The observation operator H mainly includes the computation of atmospheric transport and chemistry (if relevant) by numerical Eulerian global circulation models (e.g., LMDZ, Chevallier et al 2010;TM5, Houweling et al 2014;GEOS-Chem, van der Laan-Luijkx et al 2017;Liu et al 2015;Palmer et al 2019;Feng et al 2017;NICAM, Niwa et al 2017), regional Eulerian chemistry-transport models (e.g., CHIMERE, Broquet et al 2011;Fortems-Cheiney et al 2019;WRF-CHEM, Zheng et al 2018;COSMO-GHG, Mizzi et al 2016;LOTOS-EUROS, Curier et al 2012) or Lagrangian Particle Dispersion models (e.g., FLEXPART, Thompson and Stohl 2014;STILT, Bagley et al 2017;Brioude et al 2013;Trusilova et al 2010). It also includes pre-and post-processing operations required to project the target vector to a format compatible with the model input and the model outputs to the observation vector; these operations can be the applications of e.g., averaging kernels in the case of satellite operations, or interpolation of the target vector to higher resolution model inputs.…”

Section: General Bayesian Data Assimilation Frameworkmentioning

confidence: 99%

“…Variational inversions are a numerical approximation to the solution of the inversion problem: they involve the gradient of the cost function in Eq. 5 and require to run forward and adjoint simulations iteratively (e.g., Meirink et al, 2008;Bergamaschi et al, 2010;Houweling et al, 2016Houweling et al, , 2014Fortems-Cheiney et al, 2019;Chevallier et al, 2010Chevallier et al, , 2005Thompson and Stohl, 2014;Wang et al, 2019). In variational inversions, the solution x is defined as being that with maximum posterior probability.…”

Section: Variational Inversionsmentioning

confidence: 99%

“…We will dedicate a future paper to the evaluation of the system on a real-life problem with a number of interfaced atmospheric (chemistry-)transport models. At the time of writing the present article, the following models are interfaced with the CIF: the Global Circulation Models LMDZ (Chevallier et al, 2005) and TM5 (Krol et al, 2005;van der Laan-Luijkx et al, 2017), the regional chemistrytransport Eulerian model CHIMERE (Fortems-Cheiney et al, 2019) and the Lagrangian Particle Dispersion models FLEXPART (Pisso et al, 2019) and STILT (Trusilova et al, 2010). For the sake of simplicity, we refer to all types of (chemistry-)transport models generically as CTMs in the following.…”

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confidence: 99%

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The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

Sollum¹,

Thompson²,

Pison³

et al. 2020

Preprint

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Abstract. Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface greenhouse gas (GHG) fluxes. In the past decade, the research community has developed various inversion softwares, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry-transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in-situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here a Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source python-based tool to estimate the fluxes of various GHGs both at global and regional scales. It will allow running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow a comprehensively assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and demonstrate how it operates in a simple academic case.

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Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE

Cited by 8 publications

References 7 publications

Changes in net ecosystem exchange over Europe during the 2018 drought based on atmospheric observations

Changes in net ecosystem exchange over Europe during the 2018 drought based on atmospheric observations

Effects of Chemical Feedbacks on Decadal Methane Emissions Estimates

The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

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