We present non-coupled (atmosphere only) simulations using EC-Earth version 3 with the existing and the newly introduced aerosol distributions taken from the CMIP5 dataset. The CMIP5 aerosols tend to underestimate aerosol optical depth (AOD), as illustrated against MODIS-MISR observations (Figure 1). An exception are dust affected regions, where AOD tends to be overestimated.
The distribution of cloud condensation nuclei (CCN) density currently used in the radiative scheme for the calculation of droplet liquid effective radius (Martin et al., 1994) is uniform with values 120 cm-3 over land and 60 cm-3 ocean, as global and annual average. The new CCN distribution obtained using the empirical relationship form Menon et al. (2002) and CMIP5 aerosol mass distributions is more realistic with values 200 cm-3 in the aerosol source regions and below 90 cm-3 in pristine oceanic regions, as global and annual average (Figure 2). The higher CCN number density tends to decrease the droplet effective radius and increase the cloud radiative forcing (Figure 3).
The changes in cloud microphysics due to aerosols is parameterized via the explicit dependence of the critical cloud liquid water mixing ratio for autoconversion on the CCN density. Currently the CCN density in the radiation and microphysics are inconsistent with the latter based on Menon et al. (2002) and aerosol mass distributions from Tegen et al. (1997). We consistently apply the same CCN distributions, based on Menon et al. (2002) and CMIP5 aerosols, in the microphysics and in the radiation. The critical cloud mixing ratio for autoconversion tends to increase when replacing Tegen et al. (1997) with the CMIP5 aerosols (Figure 4). This will modify the cloud and precipitation patterns.
The impact of the new aerosol representation on the model biases is being currently evaluated against the CFMIP observations for model evaluation (CFMIP-OBS). Of interest is the impact of the aerosol loadings globally and in the Southern ocean, a region greatly influenced by clouds and where the model tends to display a warm surface bias.
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