Assessing water vapour from state-of-the-art observations and models in the central Arctic and the impact of inversions on downwelling longwave radiation
Observations have revealed that the rapidly warming Arctic is also moistening in certain regions and seasons. As water vapour is the strongest greenhouse gas, it contributes to the enhanced warming of the Arctic via the water vapour feedback. Water vapour estimates are uncertain in the Arctic due to...
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| Format: | Doctoral or Postdoctoral Thesis |
| Language: | German English |
| Published: |
2025
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| Online Access: | https://kups.ub.uni-koeln.de/75237/ |
| Summary: | Observations have revealed that the rapidly warming Arctic is also moistening in certain regions and seasons. As water vapour is the strongest greenhouse gas, it contributes to the enhanced warming of the Arctic via the water vapour feedback. Water vapour estimates are uncertain in the Arctic due to the low amount of ground stations and challenges in satellite remote sensing. Thus, it is not surprising to see uncertainties in water vapour trends across reanalyses, which use these observations. In contrast to lower latitudes, Arctic humidity profiles feature inversions where the specific humidity increases with height. The representation of humidity inversions in current models and satellite products and the radiative effect of humidity inversions is poorly studied. Furthermore, the ability of ground-based microwave radiometers (MWRs) to capture humidity inversions has yet to be analyzed. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Arctic Ocean provides excellent reference water vapour observations to evaluate the water vapour products of models and satellites. Radiosonde observations are complemented by two MWRs with complementary water vapour sensitivity. The first part of this thesis includes two studies to quantify the benefit of the synergy of the two MWRs for water vapour products compared to the use of single MWRs. In the first study, the measurements of each MWR were quality controlled and atmospheric parameters, including coarse humidity profiles and integrated water vapour (IWV), were retrieved using regression and Neural Networks. The single MWR retrievals were evaluated with the MOSAiC radiosondes. In the second study, measurements from both MWRs were combined in a Neural Network approach to exploit their complementary moisture sensitivity. The synergy benefit was determined by comparing the errors computed in the synergy evaluation to those of the single MWR retrievals. The synergy reduces lower tropospheric specific humidity errors ... |
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