A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO2 fluxes and 3-D atmospheric CO2 concentrations from observations

摘要

We have developed a novel framework ("Tan-Tracker") for assimilatingobservations of atmospheric CO concentrations, based on the POD-based(proper orthogonal decomposition) ensemble four-dimensional variational dataassimilation method (PODEn4DVar). The high flexibility and the highcomputational efficiency of the PODEn4DVar approach allow us to include boththe atmospheric CO concentrations and the surface CO fluxes aspart of the large state vector to be simultaneously estimated fromassimilation of atmospheric CO observations. Compared to mostmodern top-down flux inversion approaches, where only surface fluxes areconsidered as control variables, one major advantage of our joint dataassimilation system is that, in principle, no assumption on perfecttransport models is needed. In addition, the possibility for Tan-Trackerto use a complete dynamic model to consistently describe the time evolutionof CO surface fluxes (CFs) and the atmospheric CO concentrationsrepresents a better use of observation information for recycling theanalyses at each assimilation step in order to improve the forecasts for thefollowing assimilations. An experimental Tan-Tracker system has been builtbased on a complete augmented dynamical model, where (1) the surfaceatmosphere CO exchanges are prescribed by using a persistentforecasting model for the scaling factors of the first-guess net CO surface fluxes and (2) the atmospheric CO transport is simulatedby using the GEOS-Chem three-dimensional global chemistry transport model.Observing system simulation experiments (OSSEs) for assimilating syntheticin situ observations of surface CO concentrations are carefullydesigned to evaluate the effectiveness of the Tan-Tracker system. Inparticular, detailed comparisons are made with its simplified version(referred to as TT-S) with only CFs taken as the prognostic variables. It isfound that our Tan-Tracker system is capable of outperforming TT-S withhigher assimilation precision for both CO concentrations andCO fluxes, mainly due to the simultaneous estimation of CO concentrations and CFs in our Tan-Tracker data assimilation system.A experiment for assimilating the real dry-air column CO retrievals (CO) from the Japanese Greenhouse Gases ObservationSatellite (GOSAT) further demonstrates its potential wide applications.