Development of CarbonTracker Europe-CH4 – Part 1: system set-up and sensitivity analyses
Aki Tsuruta1, Tuula Aalto1, Leif Backman1, Janne Hakkarainen2, Ingrid T. van der Laan-Luijkx3, Maarten C. Krol3,5,6, Renato Spahni4, Sander Houweling5,6, Marko Laine2, Marcel van der Schoot7, Ray Langenfelds7, Raymond Ellul8, and Wouter Peters3,91Climate Research, Finnish Meteorological Institute, Helsinki, Finland 2Earth Observation, Finnish Meteorological Institute, Helsinki, Finland 3Meteorology and Air Quality, Wageningen University, Wageningen, the Netherlands 4Climate and Environmental Physics, Physics Institute, and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland 5SRON Netherlands Institute for Space Research, Utrecht, the Netherlands 6Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands 7CSIRO Oceans and Atmosphere, Aspendale, Australia 8Atmospheric Research, Department of Geosciences, University of Malta, Msida, Malta 9Centre for Isotope Research, University of Groningen, Groningen, the Netherlands
Received: 21 Jul 2016 – Accepted for review: 15 Aug 2016 – Discussion started: 19 Aug 2016
Abstract. CarbonTracker Europe-CH4 (CTE-CH4) inverse model versions 1.0 and 1.1 are presented. The model optimizes global surface methane emissions from biosphere and anthropogenic sources using an ensemble Kalman filter (EnKF) based optimization method, using the TM5 chemistry transport model as an observation operator, and assimilating global in-situ atmospheric methane mole fraction observations. In this study, we examine sensitivity of our CH4 emission estimates on the ensemble size, covariance matrix, prior estimates, observations to be assimilated, assimilation window length, convection scheme in TM5, and model structure in the emission estimates by performing CTE-CH4 with several set-ups. The analyses show that the model is sensitive to most of the parameters and inputs that were examined. Firstly, using a large enough ensemble size stabilises the results. Secondly, using an informative covariance matrix reduces uncertainty estimates. Thirdly, agreement with discrete observations became better when assimilating continuous observations. Finally, the posterior emissions were found sensitive to the choice of prior estimates, convection scheme and model structure, particularly to their spatial distribution. The distribution of posterior mole fractions derived from posterior emissions is consistent with the observations to the extent prescribed in the various covariance estimates, indicating a satisfactory performance of our system.
Tsuruta, A., Aalto, T., Backman, L., Hakkarainen, J., van der Laan-Luijkx, I. T., Krol, M. C., Spahni, R., Houweling, S., Laine, M., van der Schoot, M., Langenfelds, R., Ellul, R., and Peters, W.: Development of CarbonTracker Europe-CH4 – Part 1: system set-up and sensitivity analyses, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-181, in review, 2016.