Simone Tilmes, Jean-Francois Lamarque, Louisa Emmons, CAMchem and WACCM team
The IGAC/SPARC Chemistry-Climate Model Initiative (CCMI) was established to coordinate SPARC chemistry-climate model evaluation and associated modeling activities. A set of reference and sensitivity simulations have been defined to address emerging science questions, improve process understanding and support of upcoming ozone and climate assessments (Eyring et al., 2014). The Community Earth System Model, CESM1 CAM4-chem has been used to perform the Chemistry Climate Model Initiative (CCMI) reference and sensitivity simulations, as described in Tilmes et al. (2016). Important improvements of CESM1 CAM4-chem in recent years resulted in a very good representation of tropospheric ozone mixing ratios and trends, and a good representation of aerosols in comparison to observations (Figure 1).
For the troposphere, near-surface ozone mixing ratios and trends are very well reproduced and within 25% of the values from ozonesonde and satellite observations throughout the troposphere. Some biases in the model have not been resolved compared to earlier versions of the model. CO is still biased low in all model experiments in the NH, especially in spring. Some differences between the experiments may be attributed to differences in biogenic emissions. Correspondingly, methane lifetime is low compared to observational estimates, which is likely related not only to shortcomings in emissions, but also to too large an oxidation capacity of the atmosphere. Significant shortcomings of hydrocarbons (shown for ethane) are identified in particular in the NH. The hemispheric gradient of BC in the model is reproduced well in most seasons, while the fall and winter values in mid-latitudes are often overestimated in mid-latitudes. BC in the tropics is largely overestimated for most seasons. This points to potential shortcomings in emissions, but also loss processes in the model. Ongoing work is focusing on reducing existing biases in the model in the troposphere, for example through improvements in the chemical mechanisms, aerosol descriptions, understanding and improvement of biases in emission data sets.