The SPRINTARS version 3.80/4D-Var data assimilation system: development and inversion experiments based on the observing system simulation experiment framework

We present an aerosol data assimilation system based on a global aerosol climate model (SPRINTARS – Spectral Radiation-Transport Model for Aerosol Species) and a four-dimensional variational data assimilation method (4D-Var). Its main purposes are to optimize emission estimates, improve composites, and obtain the best estimate of the radiative effects of aerosols in conjunction with observations. To reduce the huge computational cost caused by the iterative integrations in the models, we developed an offline model and a corresponding adjoint model, which are driven by pre-calculated meteorological, land, and soil data. The offline and adjoint model shortened the computational time of the inner loop by more than 30%.

By comparing the results with a 1 yr simulation from the original online model, the consistency of the offline model was verified, with correlation coefficient R > 0.97 and absolute value of normalized mean bias NMB < 7% for the natural aerosol emissions and aerosol optical thickness (AOT) of individual aerosol species. Deviations between the offline and original online models are mainly associated with the time interpolation of the input meteorological variables in the offline model; the smaller variability and difference in the wind velocity near the surface and relative humidity cause negative and positive biases in the wind-blown aerosol emissions and AOTs of hygroscopic aerosols, respectively.

The feasibility and capability of the developed system for aerosol inverse modelling was demonstrated in several inversion experiments based on the observing system simulation experiment framework. In the experiments, we used the simulated observation data sets of fine- and coarse-mode AOTs from sun-synchronous polar orbits to investigate the impact of the observational frequency (number of satellites) and coverage (land and ocean), and assigned aerosol emissions to control parameters. Observations over land have a notably positive impact on the performance of inverse modelling as compared with observations over ocean, implying that reliable observational information over land is important for inverse modelling of land-born aerosols. The experimental results also indicate that information that provides differentiations between aerosol species is crucial to inverse modelling over regions where various aerosol species coexist (e.g. industrialized regions and areas downwind of them).