The global aerosol-climate model ECHAM-HAM, version 2: sensitivity to improvements in process representations
Abstract. This paper introduces and evaluates the second version of the global aerosol-climate model ECHAM-HAM. Major changes have been brought into the model, including new parameterizations for aerosol nucleation and water uptake, an explicit treatment of secondary organic aerosols, modified emission calculations for sea salt and mineral dust, the coupling of aerosol microphysics to a two-moment stratiform cloud microphysics scheme, and alternative wet scavenging parameterizations. These revisions extend the model's capability to represent details of the aerosol lifecycle and its interaction with climate. Sensitivity experiments are carried out to analyse the effects of these improvements in the process representation on the simulated aerosol properties and global distribution. The new parameterizations that have largest impact on the global mean aerosol optical depth and radiative effects turn out to be the water uptake scheme and cloud microphysics. The former leads to a significant decrease of aerosol water contents in the lower troposphere, and consequently smaller optical depth; the latter results in higher aerosol loading and longer lifetime due to weaker in-cloud scavenging. The combined effects of the new/updated parameterizations are demonstrated by comparing the new model results with those from the earlier version, and against observations. Model simulations are evaluated in terms of aerosol number concentrations against measurements collected from twenty field campaigns as well as from fixed measurement sites, and in terms of optical properties against the AERONET measurements. Results indicate a general improvement with respect to the earlier version. The aerosol size distribution and spatial-temporal variance simulated by HAM2 are in better agreement with the observations. Biases in the earlier model version in aerosol optical depth and in the Ångström parameter have been reduced. The paper also points out the remaining model deficiencies that need to be addressed in the future.