Abstract
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 (CO2), 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 CO2 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 CO2 inversion and the effects of using either the discrete or continuous adjoint method, is presented in an accompanying paper Niwa et al.(2016).
Highlights
We have developed a new four-dimensional variational (4DVar) inversion system for estimating surface fluxes of greenhouse gases (GHGs; presently, primarily targets are carbon dioxide (CO2) and methane (CH4))
For the 1-year-long sensitivity test simulation discussed below, the offline forward model requires only 7 min, while the online model requires about 70 min
We have developed forward and adjoint models based on Nonhydrostatic ICosahedral Atmospheric Model (NICAM)-TM, as part of the 4D-Var system for atmospheric GHGs inversions
Summary
We have developed a new four-dimensional variational (4DVar) inversion system for estimating surface fluxes of greenhouse gases (GHGs; presently, primarily targets are carbon dioxide (CO2) and methane (CH4)). The new system is referred to as NICAM-TM 4D-Var, the 4D-Var inversion system based on the Transport Model version of the Nonhydrostatic ICosahedral Atmospheric Model. It consists mainly of forward and adjoint transport models and an optimization scheme. This paper presents derivation of the transport models and evaluate their performances. Y. Niwa et al.: NICAM-TM 4D-Var – Part 1 per (Niwa et al, 2016) describes the optimization scheme and demonstrates the application of the new system to an atmospheric CO2 inversion problem
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