Adjoint of the Global Eulerian–Lagrangian Coupled Atmospheric transport model (A-GELCA v1.0): development and validation

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. IntroductionForecasts of CO 2 levels in the atmosphere and predictions of future climate depend on our scientific understanding of the natural carbon cycle (IPCC, 2007;Peters et al., 2007). To estimate the spatial and temporal distribution of carbon sources and sinks, inverse methods are used to infer carbon fluxes from geographically sparse observations of the atmospheric CO 2 mixing ratio (Tans et al., 1989). The first com-Published by Copernicus Publications on behalf of the European Geosciences Union.Geosci. Model Dev., 9, 749-764, 2016 www.geosci-model-dev.net/9/749/2016/

Attribution of PM2.5 exposure in Beijing–Tianjin–Hebei region to emissions: implication to control strategies

“…Although GEOS-Chem was originally developed as a global CTM [49], several versions of its nested model have become available in recent years [31,50,51], allowing GEOS-Chem to be applied as a regional CTM. The adjoint method, which has excellent efficiency in calculating SS in a single simulation for many sources, has been developed for several models [31,32,[52][53][54][55]. As a representative application, the GEOS-Chem adjoint was used to efficiently estimate the sensitivity of a user-defined cost function to model parameters (e.g., emission rates and reaction rates) [23].…”

Study of the footprints of short-term variation in XCO<sub>2</sub> observed by TCCON sites using NIES and FLEXPART atmospheric transport models

“…The potential for further improvement includes moving from gross annual averaging to more accurate seasonal or monthly averaging. In addition, it is possible to study the sensitivity of XCO 2 observations using the adjoint of the global Eulerian-Lagrangian coupled atmospheric transport model (Belikov et al, 2016), which can resolve long-term, synoptic and hourly variation patterns.…”

“…(2013b) reported that the NIES model is able to successfully reproduce the vertical profile of CO 2 as well as the seasonal and interannual variability in XCO 2 . A comparison of modelled output with TCCON observations (Belikov et al, 2013b) revealed model biases of ±0.2 % for XCO 2 ; on this basis we assume that the NIES TM is able to successfully reproduce the vertical profile of CO 2 at the locations of TC-CON sites.…”

“…The key features of the NIES TM are as follows: a reduced horizontal latitude-longitude grid with a spatial resolution of 2.5 • × 2.5 • near the equator , a vertical flexible hybrid sigma-isentropic (σ -θ) grid with 32 levels up to the level of 5 hPa (Belikov et al, 2013b), separate parameterisation of the turbulent diffusivity in the PBL and free troposphere (provided by the European Centre for MediumRange Weather Forecasts (ECMWF) ERA-Interim reanalysis) and a modified Kuo-type parameterisation scheme for cumulus convection (Belikov et al, 2013a).…”

“…A comparison of collocation methods was performed for five GOSAT XCO 2 products: NIES v02.11 and the photon path length probability density function method retrievals (PPDF-S v02.11; Oshchepkov et al, 2013) Butz et al, 2011;Guerlet et al, 2013); and the University of Leicester Full Physics retrieval (UoL-FP v4;Boesch et al, 2011;Cogan et al, 2012). The mean percentage of discarded coincident TCCON-GOSAT observation is around 7-14 %.…”