Articles | Volume 10, issue 3
Geosci. Model Dev., 10, 1157–1174, 2017
Geosci. Model Dev., 10, 1157–1174, 2017

Development and technical paper 17 Mar 2017

Development and technical paper | 17 Mar 2017

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

Yosuke Niwa1, Hirofumi Tomita2, Masaki Satoh3,4, Ryoichi Imasu3, Yousuke Sawa1, Kazuhiro Tsuboi1, Hidekazu Matsueda1, Toshinobu Machida5, Motoki Sasakawa5, Boris Belan6, and Nobuko Saigusa5 Yosuke Niwa et al.
  • 1Oceanography and Geochemistry Research Department, Meteorological Research Institute, Tsukuba, Japan
  • 2RIKEN Advanced Institute for Computational Science, Kobe, Japan
  • 3Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan
  • 4Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
  • 5Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan
  • 6V. E. Zuev Institute of Atmospheric Optics, Russian Academy of Sciences, Siberian Branch, Tomsk, Russia

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).

Short summary
We have developed forward and adjoint models based on NICAM-TM, as part of the 4D-Var system for atmospheric GHGs inversions. The models are computationally efficient enough to make the 4D-Var iterative calculation feasible. 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.