A multimodel comparison of stratospheric ozone data assimilation based on an ensemble Kalman filter approach

For future development of a high-performance ozone analysis system, we investigated the impact of model performance on stratospheric ozone analysis by using four different models with a common data assimilation framework. For assimilation of ozone and meteorological field variables, we used a local ensemble transform Kalman filter with the CCSR/NIES chemistry-climate model (CCM), the MIROC3.2 CCM, the MRI CCM, and the CHASER chemical transport model. We examined the effects of model biases on forecast/analysis of ozone based on multimodel comparisons of assimilation results. We assimilated ozone profiles provided by Aura/Microwave Limb Sounder (MLS) and total ozone provided by the Ozone Monitoring Instrument (OMI)-Total Ozone Mapping Spectrometer (TOMS). For all models, meteorological fields obtained from a global reanalysis dataset (JMA Climate Data Assimilation System) were also assimilated to provide a common framework without any spatiotemporal dependence of data observation quality. Ozone profiles obtained from assimilation of MLS observations showed good agreement with independent ozonesonde observations, with a mean bias of less than 5% in the stratosphere. We found that model bias originating from ozone chemistry degraded the assimilation performance of not only ozone but also temperature in the stratosphere. Assimilation of OMI-TOMS total ozone data agreed with the independent SCIAMACHY total ozone with a bias of less than 3%. However, a model bias in the tropospheric ozone concentration deteriorated the stratospheric ozone analysis. Finally, the use of both stratospheric ozone profile data and total ozone data greatly improved the overall performance of the ozone analysis, regardless of the model biases.