With a nearly twice as strongly pronounced temperature increase compared to that of the Northern Hemisphere, the Arctic is especially susceptible to global climate change. The effect of clouds on the Arctic warming is especially uncertain, which is caused by misrepresented cloud microphysical processes in atmospheric models. This thesis aims at employing a scale- and definition-aware comparison of models and observations and will propose changes how to better parameterize Arctic clouds in atmospheric models.
In the first part of this thesis, ECHAM6, which is the atmospheric component of the MPI-ESM global climate model, is compared to spaceborne lidar observations of clouds from the CALIPSO satellite. This comparison shows that ECHAM6 overestimates Arctic low-level, liquid containing clouds over snow- and ice-covered surfaces, which consequently leads to an overestimated amount of radiative energy received by the surface. Using sensitivity studies, it is shown that the probable cause of the model biases in cloud amount and phase is related to misrepresented cloud microphysical parameterization (i.e., parameterization of the Wegener-Bergeron-Findeisen process and of the cloud cover scheme) in ECHAM6. By revising those processes, a better representation of cloud amount and cloud phase is achieved, which helps to more accurately simulated the amount of radiative energy received by the Arctic in ECHAM6.
The second part of this thesis will focus on a comparison of kilometer-scale simulation with the ICON model to aircraft observations from the ACLOUD campaign that took place in May/June 2017 over the sea ice-covered Arctic Ocean north of Svalbard, Norway. By comparing measurements of solar and terrestrial surface irradiances during ACLOUD flights to the respective quantities in ICON, it is shown that the model systematically overestimates the transmissivity of the mostly liquid clouds during the campaign. This model bias is traced back to the way cloud condensation nuclei get activated into cloud droplets in the two-moment, bulk microphysical scheme used. By parameterizing subgrid-scale vertical motion as a function of turbulent kinetic energy, a more realistic CCN activation into cloud droplets is achieved. This consequently results in an improved representation of cloud optical properties in the ICON simulations.
Furthermore, the results of two studies to which contributions have been made during the Ph.D. will be summarized. In Petersik et al. 2018, the impact of subgrid-scale variability in clear-sky relative humidity on hygroscopic growth of aerosols in the aerosol-climate model ECHAM6-HAM2 has been explored. It was shown that the revised parameterization of hygroscopic growth of aerosols resulted in a stronger swelling of aerosol particles, which consequently causes an increased backscattering of solar radiation. In the study of Costa-Suros et al. 2019, it is explored whether it is possible to detect and attribute aerosol-cloud interactions in large-eddy simulation over Germany. It was shown that an increase in cloud droplet number concentration could be attributed to an increased aerosol load, while such an attribution was not possible for other cloud micro- and macrophysical variables.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:75240 |
| Date | 29 June 2021 |
| Creators | Kretzschmar, Jan |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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