Atmospheric carbon dioxide concentrations are being closely monitored by remote sensing experiments which rely on knowing line intensities with an uncertainty of 0.5% or better. We report a theoretical study providing rotationvibration line intensities substantially within the required accuracy based on the use of a highly accurate ab initio dipole moment surface (DMS). The theoretical model developed is used to compute CO 2 intensities with uncertainty estimates informed by cross comparing line lists calculated using pairs of potential energy surfaces (PES) and DMSs of similar high quality. This yields lines sensitivities which are utilized in reliability analysis of our results. The final outcome is compared to recent accurate measurements as well as the HITRAN2012 database. Transition frequencies are obtained from effective Hamiltonian calculations to produce a comprehensive line list covering all

A global, high accuracy ab initio dipole moment surface for the electronic ground state of the water molecule

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Remote sensing experiments require high-accuracy, preferably sub-percent, line intensities and in response to this need we present computed room temperature line lists for six symmetric isotopologues of carbon dioxide: 13 C 16 O 2 , 14 C 16 O 2 , 12 C 17 O 2 , 12 C 18 O 2 , 13 C 17 O 2 and 13 C 18 O 2 , covering the range 0-8000 cm −1 . Our calculation scheme is based on variational nuclear motion calculations and on a reliability analysis of the generated line intensities. Rotationvibration wavefunctions and energy levels are computed using the DVR3D software suite and a high quality semi-empirical potential energy surface (PES), followed by computation of intensities using an ab initio dipole moment surface (DMS). Four line lists are computed for each isotopologue to quantify sensitivity to minor distortions of the PES/DMS. Reliable lines are benchmarked against recent state-of-the-art measurements and against the HITRAN2012 database, supporting the claim that the majority of line intensities for strong bands are predicted with sub-percent accuracy. Accurate line positions are generated using an effective Hamiltonian. We recommend the use of these line lists for future remote sensing studies and their inclusion in databases.

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