Schirmacher, Imke ORCID: 0000-0003-4438-3077
(2024).
Characterization of Arctic low-level clouds and precipitation over the Fram Strait by airborne radar observations.
PhD thesis, Universität zu Köln.
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- Characterization of Arctic low-level clouds and precipitation over the Fram Strait by airborne radar observations. (deposited 04 Mar 2025 14:00) [Currently Displayed]
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Abstract
Clouds are dominant features in the Earth’s atmosphere, particularly in the Arctic. There, specifically, low-level clouds are common that frequently consist of both supercooled liquid and ice simultaneously and are thus called mixed-phase clouds. These clouds and their precipitation substantially impact the Earth’s radiative budget by warming the surface and also affect the Earth’s water cycle. However, limited observations of Arctic low-level clouds and precipitation lead to persistent uncertainties in their occurrence and the distribution of liquid and ice. The latter uncertainty has been indicated to cause a disagreement in the sign of the cloud feedback in the Arctic across climate models. To clarify the role of low-level clouds in the Arctic,accurate observations of their distribution, microphysics, and precipitation are required over ocean, sea ice, and land. An improved understanding of cloud microphysical processes enhances the understanding of cloud formation, evolution, and lifetime and is therefore necessary to improve weather and climate models. This thesis uses the unique capabilities of airborne radar observations to characterize Arctic low-level clouds and their precipitation with a particular focus on mixed-phase clouds and their development over the Fram Strait, a critical region for Arctic climate change. Airborne down-looking radar observations are especially suitable because they can cover large areas over open ocean and sea ice with a high spatial resolution and can reach close to the surface. This way, they can detect clouds that are not detected by other observational techniques. The investigated airborne observations, which cover more than 25,000 km over the ocean, were conducted during four campaigns that took place within the ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms ((AC)3) project in spring and summer between 2017 and 2022. The first study presented in this thesis highlights the need for airborne radar observations to observe low-level clouds due to limitations in spaceborne radar observations. By simulating spaceborne radar observations using the airborne radar measurements, limitations of spaceborne radars are assessed concerning their coarse horizontal and vertical resolution and their blind zone that covers the lowest kilometer of the atmosphere. Overall, the spaceborne simulations overestimate the observed cloud fraction, particularly when cold and dry air is advected from the central Arctic over the ocean, i. e., marine cold air outbreaks that foster low-level cloud formation. The blind zone of the spaceborne radar simultaneously misses half of the precipitation amount coming mostly from light precipitation events. The second study focuses on marine cold air outbreaks that offer unique conditions to study low-level mixed-phase clouds and their evolution. To characterize the clouds, metrics that describe the roll circulation, as well as cloud macro- and microphysics, are derived from radar observations and investigated along the fetch, i. e., the distance an air mass traveled over an open ocean. For two marine cold air outbreak cases of different strengths and with different microphysical preconditions, discrepancies in cloud and precipitation characteristics, including their evolution within cloud objects, are identified. In contrast, similarities are found regarding their evolution with fetch. Moreover, the study highlights that the evolution of snowfall is influenced by microphysical processes, i. e., riming, that are modified by the roll circulations. The third study aims to constrain snowfall estimates that highly differ among existing data sets. The focus lies on a statistical assessment of snowfall from low-level clouds over the Fram Strait during marine cold air outbreaks. The retrieved snowfall estimates enable an evaluation of the representation of snowfall in reanalyses. Snowfall rates are retrieved from the airborne radar reflectivity observations by applying a power law, a so-called Ze–S relation. An optimal relation has been derived from radar and precipitation gauge observations at Ny-Ålesund, Svalbard. The observed snowfall rates over the Fram Strait during marine cold air outbreaks are, on average, 330 mm year−1 and generally increase with the fetch. Reanalyses have different shortcomings in representing snowfall: the studied global reanalysis overestimates snowfall occurrence, particularly of light snowfall, resulting in a total overestimation of snow accumulation along the flight tracks. In contrast, the studied regional reanalysis underestimates snowfall occurrences and rates. Overall, this thesis provides an improved assessment and process understanding of low-level clouds over the Fram Strait and related snowfall. The findings are a benchmark to evaluate models and further data sets, such as precipitation estimates from the recently launched satellite EarthCARE.
Item Type: | Thesis (PhD thesis) | ||||||||||||
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URN: | urn:nbn:de:hbz:38-753489 | ||||||||||||
Date: | 2024 | ||||||||||||
Language: | English | ||||||||||||
Faculty: | Faculty of Mathematics and Natural Sciences | ||||||||||||
Divisions: | Faculty of Mathematics and Natural Sciences > Department of Geosciences > Institute for Geophysics and Meteorology | ||||||||||||
Subjects: | Natural sciences and mathematics Earth sciences |
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Date of oral exam: | 13 February 2025 | ||||||||||||
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Open access funding: | Deutsche Forschungsgemeinschaft - Project No. 268020496 | ||||||||||||
Projects: | TRR 172 - (AC)3: “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms ” | ||||||||||||
Refereed: | Yes | ||||||||||||
URI: | http://kups.ub.uni-koeln.de/id/eprint/75348 |
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