Climate processes

Abstract:

Aerosols strongly influence Earth's climate as they scatter and absorb radiation and serve as condensation nuclei for cloud droplets and ice particles. New Earth system models that run at kilometer resolutions allow us to examine long-standing questions related to these interactions. To perform kilometer-scale simulations with the Earth system model ICON-MPIM, we developed the one-moment aerosol module HAM-lite. HAM-lite was derived from the two-moment module HAM. Like in HAM, aerosols are represented as an ensemble of log-normal modes. Unlike in HAM, aerosol sizes and compositions are prescribed, which reduces the computational costs significantly. Here, we present a first global simulation with four aerosol modes at a resolution of five kilometers and over a period of one year. The simulation captured key aerosol processes including, for example, the emission of dust aerosols by convective storms in the Sahara and the interactions between sea salt aerosols and tropical cyclones in the Pacific.

Abstract:

Fog and low stratus clouds (FLS) form as a result of complex interactions of multiple factors in the atmosphere and at the land surface and impact both the anthropogenic and natural environments. Here, we analyze the role of synoptic conditions and aerosol loading on FLS occurrence and persistence in the Po valley in northern Italy. By applying k‐means clustering to reanalysis data, we find that FLS formation in the Po valley is either based on radiative processes or moisture advection from the Mediterranean sea. Satellite‐based data on FLS persistence shows longer persistence of radiatively formed FLS events, likely due to air mass stagnation and a temperature inversion. Ground‐based aerosol optical depth observations further reveal that FLS event duration is significantly higher under high aerosol loading. The results underline the combined effect of topography, moisture advection and aerosol loading on the FLS life cycle in the Po valley.

Abstract:

Abstract:

Clouds are one of the largest sources of uncertainty in climate predictions. Global km‐scale models need to simulate clouds and precipitation accurately to predict future climates. To isolate issues in their representation of clouds, models need to be thoroughly evaluated with observations. Here, we introduce multifractal analysis as a method for evaluating km‐scale simulations. We apply it to outgoing longwave radiation fields to investigate structural differences between observed and simulated anvil clouds. We compute fractal parameters which compactly characterize the scaling behavior of clouds and can be compared across simulations and observations. We use this method to evaluate the nextGEMS ICON simulations via comparison with observations from the geostationary satellite GOES‐16. We find that multifractal scaling exponents in the ICON model are significantly lower than in observations. We conclude that too much variability is contained in the small scales ( < 100 k m ) $(< 100\ \mathrm{k}\mathrm{m})$ leading to less organized convection and smaller, isolated anvils.