Technical Note: Adjoint formulation of the  TOMCAT atmospheric transport scheme in the  Eulerian backtracking framework (RETRO-TOM)

Haines, Philip; Esler, J. G.; Carver, G. D.

doi:10.5194/acp-14-5477-2014

Cited by 7 publications

(10 citation statements)

References 34 publications

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“…A detailed description of the reciprocity property can be found in the literature Haines et al, 2014;Wilson et al, 2014). In Eq.…”

Section: Validation Of the Adjoint Modelmentioning

confidence: 99%

“…Furthermore, it has been found that such an adjoint model is ill-behaved due to discontinuities (Thuburn and Haine, 2001;Vukićevićet al, 2001). Therefore, alternative approaches have been proposed at the expense of linearity or the accuracy of numerical scheme (Vukićević and Hess, 2000;Vukićevićet al, 2001;Sandu et al, 2005;Hakami et al, 2007;Gou and Sandu, 2011;Haines et al, 2014). Most studies use either the "discrete adjoint" or "continuous adjoint".…”

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confidence: 99%

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A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 1: Offline forward and adjoint transport models

et al. 2017

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Abstract. A four-dimensional variational (4D-Var) method is a popular algorithm for inverting atmospheric greenhouse gas (GHG) measurements. In order to meet the computationally intense 4D-Var iterative calculation, offline forward and adjoint transport models are developed based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). By introducing flexibility into the temporal resolution of the input meteorological data, the forward model developed in this study is not only computationally efficient, it is also found to nearly match the transport performance of the online model. In a transport simulation of atmospheric carbon dioxide (CO 2 ), the data-thinning error (error resulting from reduction in the time resolution of the meteorological data used to drive the offline transport model) is minimized by employing high temporal resolution data of the vertical diffusion coefficient; with a low 6-hourly temporal resolution, significant concentration biases near the surface are introduced. The new adjoint model can be run in discrete or continuous adjoint mode for the advection process. The discrete adjoint is characterized by perfect adjoint relationship with the forward model that switches off the flux limiter, while the continuous adjoint is characterized by an imperfect but reasonable adjoint relationship with its corresponding forward model. In the latter case, both the forward and adjoint models use the flux limiter to ensure the monotonicity of tracer concentrations and sensitivities. Trajectory analysis for high CO 2 concentration events are performed to test adjoint sensitivities. We also demonstrate the potential usefulness of our adjoint model for diagnosing tracer transport. Both the offline forward and adjoint models have computational efficiency about 10 times higher than the online model. A description of our new 4D-Var system that includes an optimization method, along with its application in an atmospheric CO 2 inversion and the effects of using either the discrete or continuous adjoint method, is presented in an accompanying paper (Niwa et al., 2016).

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“…A detailed description of the reciprocity property can be found in the literature Haines et al, 2014;Wilson et al, 2014). In Eq.…”

Section: Validation Of the Adjoint Modelmentioning

confidence: 99%

mentioning

confidence: 99%

A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 1: Offline forward and adjoint transport models

et al. 2017

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“…Hitherto, the principal disadvantage has been that it is far from straightforward to obtain a high level of numerical accuracy in Eulerian backtracking calculations, e.g., Hourdin et al [], comparing direct and adjoint sensitivities for a short‐time test problem, report relative errors of order 10 −2 . To address this, a new and accurate Eulerian‐backtracking model (RETRO‐TOM) [ Haines et al , ], based on the transport component of the CTM TOMCAT [ Chipperfield , ] has been developed and tested by the authors. Relative accuracies of order 10 −8 are reported for RETRO‐TOM in comparable test problems.…”

Section: Introductionmentioning

confidence: 99%

Determination of the source regions for surface to stratosphere transport: An Eulerian backtracking approach

Haines

Esler

2014

Geophysical Research Letters

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A wide range of inverse problems in atmospheric transport and chemistry can be solved within the Eulerian backtracking framework. Here it is shown how a new and accurate numerical implementation can be used as an alternative to Lagrangian back trajectory methods in a wide class of process studies. As a key example, the question of how the (time‐averaged) stratospheric flux of a finite lifetime chemical species depends upon the location(s) of its surface source(s) is addressed. The resulting sensitivity maps are demonstrated to be robust features of the global atmospheric circulation, with relatively low interannual variability. The maps serve as an at‐a‐glance resource for policymakers wishing to compare the likely impact of proposed emission locations for very short lived halogenated species on the total loading of stratospheric chlorine and bromine.

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“…Adjoint models have numerous applications, including the assimilation of concentrations, inverse modeling of chemical source strengths, sensitivity analysis, and parameter sensitivity estimation (Enting, 2002;Haines et al, 2014). Recent studies have used this method to constrain estimates of the emissions of CO 2 using retrieved column integrals from the GOSAT satellite (Basu et al, 2013;Deng et al, 2014;Liu et al, 2015).…”

Section: A Belikov Et Al: A-gelca V10: Development and Validationmentioning

confidence: 99%

Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport model (A-GELCA v1.0): development and validation

et al. 2016

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Abstract. We present the development of the Adjoint of the Global Eulerian-Lagrangian Coupled Atmospheric (A-GELCA) model that consists of the National Institute for Environmental Studies (NIES) model as an Eulerian threedimensional transport model (TM), and FLEXPART (FLEXible PARTicle dispersion model) as the Lagrangian Particle Dispersion Model (LPDM). The forward tangent linear and adjoint components of the Eulerian model were constructed directly from the original NIES TM code using an automatic differentiation tool known as TAF (Transformation of Algorithms in Fortran; http://www.FastOpt.com), with additional manual pre-and post-processing aimed at improving transparency and clarity of the code and optimizing the performance of the computing, including MPI (Message Passing Interface). The Lagrangian component did not require any code modification, as LPDMs are self-adjoint and track a significant number of particles backward in time in order to calculate the sensitivity of the observations to the neighboring emission areas. The constructed Eulerian adjoint was coupled with the Lagrangian component at a time boundary in the global domain. The simulations presented in this work were performed using the A-GELCA model in forward and adjoint modes. The forward simulation shows that the coupled model improves reproduction of the seasonal cycle and short-term variability of CO 2 . Mean bias and standard deviation for five of the six Siberian sites considered decrease roughly by 1 ppm when using the coupled model. The adjoint of the Eulerian model was shown, through several numerical tests, to be very accurate (within machine epsilon with mismatch around to ±6 e −14 ) compared to direct forward sensitivity calculations. The developed adjoint of the coupled model combines the flux conservation and stability of an Eulerian discrete adjoint formulation with the flexibility, accuracy, and high resolution of a Lagrangian backward trajectory formulation. A-GELCA will be incorporated into a variational inversion system designed to optimize surface fluxes of greenhouse gases.

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Technical Note: Adjoint formulation of the TOMCAT atmospheric transport scheme in the Eulerian backtracking framework (RETRO-TOM)

Cited by 7 publications

References 34 publications

A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 1: Offline forward and adjoint transport models

A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 1: Offline forward and adjoint transport models

Determination of the source regions for surface to stratosphere transport: An Eulerian backtracking approach

Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport model (A-GELCA v1.0): development and validation

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