A 4D-Var CO2 inversion system with NICAM-TM: development and sensitivity analyses

Yosuke Niwa1, Yosuke Fujii1, Yousuke Sawa2, Dr. Akihiko Ito3, Yosuke Iida4, Hirofumi Tomita5, Satoh Masaki6,7, Ryoichi Imasu7, Hidekazu Matsueda8, Toshinobu Machida9 and Nobuko Saigusa9, (1)Meteorological Research Institute, Ibaraki, Japan, (2)Meteorological Research Institute, Tsukuba, Japan, (3)The University of Tokyo, Graduate School of Agricultural and Life Sciences, Tokyo, Japan, (4)Atmosphere and Ocean Department, Japan Meteorological Agency, Tokyo, Japan, (5)RIKEN, Kobe, Japan, (6)Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan, (7)AORI The University of Tokyo, Chiba, Japan, (8)Dokkyo University, Tsukuba, Japan, (9)CGER-NIES, Tsukuba, Japan
Abstract:
Our understanding of the global carbon cycle and its feedback mechanism to climate changes is limited due to high uncertainties in estimates of regional CO2 fluxes at the earth surface. Recently, a large amount of CO2 concentration data are becoming available from high-frequency aircraft measurements (e.g., CONTRAIL) and satellite measurements (e.g., GOSAT and OCO-2), in addition to expansion of surface measurement networks. To exploit those observational data, a new inversion system has been developed with the four-dimensional variational (4D-Var) method. The system is based on Nonhydrostatic ICosahedral Atmospheric Model-based Transport Model (NICAM-TM), which consists of forward and adjoint transport modes. For the a priori fluxes at terrestrial biospheres and oceans, CO2 flux data from Vegetation Integrative SImulator for Trace Gases (VISIT) and the diagnostic model of Japan Meteorological Agency (JMA) are respectively used. In the iterative calculation, the quasi-Newton method of Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar) is used. In this study, we present the structure of the newly developed system and performances for CO2 flux estimates in ideal twin experiments. By the twin experiments, sensitivities to prior error covariance, numerical algorithms, and observational networks are investigated.

Acknowledgment: This study is supported by the Environment Research and Technology Development Fund (2-1401) of the Ministry of the Environment, Japan.

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