#A04 Dust Aerosols in ACME and Sensitivity to Model Resolution

Abstract

Dust aerosols affect Earth’s climate through direct and indirect perturbations of global energy balance, and interactions with atmospheric chemistry and biosphere. Therefore, it is important to quantify the model representation of dust aerosols for implications on both coupled water cycle and biogeochemical simulations with ACME. Here we present dust simulations from the low-res (ne30) and high-res (ne120) F2000 runs, evaluation with observations, and discuss sensitivity of dust predictions to model resolution.

Compared to the low-res model, increase of model resolution (by 4 times) leads to higher dust emissions (+29%) and increases dust aerosol optical depth (AOD) from 0.03 to 0.037, shifting to the higher end of the current estimates of dust AOD [0.02~0.04]. In the meantime, total aerosol wet removal is enhanced in the high-res model, which compensates for the increase of dust AOD, resulting in a global mean AOD (0.145) similar to that by the low-res model (0.143). After tuning dust emissions to match the low-res model, the total AOD decreases by 16% (sea salt emissions are also reduced). This could potentially change the estimate of total aerosol forcing between the two model resolutions. We will present the resulting changes in spatial distribution of aerosol optical properties and compare with observations.

In addition to dust optical properties, model resolution also affects dust deposition. In the high-resolution model with similar dust emissions (also dust AOD), wet removal of dust is more efficient and its contribution to global dust deposition increases from 22% to 28%. Regional and seasonal changes of dust deposition are more significant especially over the open ocean and high latitudes, with implications on predictions of soluble iron flux and snow/ice albedo. We will perform one-year runs with meteorology nudged to the reanalysis data, and evaluate the model predictions of dust emissions, AOD, surface concentrations and deposition with observations or observationally constrained model estimates.