Kai Uwe Jeggle


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Jeggle

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Kai Uwe

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0000-0002-3098-9484

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2024 - 20253
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Publications 1 - 3 of 3
  • Dust-driven droplet freezing explains cloud-top phase in the northern extratropics
    Item type: Journal Article
    Villanueva, Diego; Stengel, Martin S.; Hoose, Corinna; et al. (2025)
    Science
    Clouds between −39° and 0°C can be topped by a liquid or ice layer, which affects their radiative forcing and precipitation. The cloud-top ice-to-total frequency (ITF) quantifies the occurrence of clouds with an ice top relative to total cloud occurrence, but the factors controlling ITF are poorly understood. using 35 years of satellite data, we show that in the Northern Hemisphere, between −15° and −30°C, dust aerosol is strongly correlated with ITF in both time and space. Furthermore, we found that the sensitivities of ITF to temperature and dust are in a ratio that agrees with laboratory measurements of droplet freezing, showing that ITF can be attributed to dust aerosol.
  • Towards an improved understanding of cirrus clouds - Machine learning enabled insights from satellite data
    Item type: Doctoral Thesis
    Jeggle, Kai Uwe (2024)
    Clouds are key components of the climate system by modulating solar and terrestrial radiation, controlling the hydrological cycle, and affecting climate dynamics on multiple spatial and temporal scales. Despite their climatological relevance, clouds are a source of large uncertainties in climate models. This thesis focuses on cirrus clouds, which consist solely of ice crystals and occur at high altitudes in the upper troposphere. Gaps in understanding of cirrus cloud formation processes and their interactions with aerosols contribute significantly to the uncertainties in climate sensitivity. In times of global warming it is more important than ever to reduce these uncertainties in order to improve future climate projections and assess the risk and feasibility of climate intervention methods targeting cirrus clouds. Satellite observations of cirrus clouds present tremendous potential for improving the understanding of cirrus clouds. Large amounts of observations from satellites motivate the use of machine learning methods as a tool to discover novel scientific insights from the data. However, retrievals from commonly used active polar-orbiting satellites, such as CALIPSO and CloudSat, are limited by sparse temporal sampling, impeding studies on cloud formation and development. Given that clouds are constantly evolving, it is crucial to adopt an approach that takes cloud formation and evolution into consideration. To this end we extend the satellite retrieval product DARDAR, a synergistic dataset combining CALIPSO and CloudSat data, with Lagrangian backward trajectories of reanalysis data. Each trajectory contains information about meteorological and aerosol conditions of an air parcel in which a cirrus cloud has formed. This dataset is the basis for quantifying the dependencies of cirrus cloud properties on meteorological and aerosol drivers using explainable machine learning. Additionally, we identify different cirrus cloud formation regimes and characterize their microphysical properties using a clustering approach. We also disentangle the effect of dust aerosol on cirrus cloud properties from other dependencies, such as regional influences, formation regime, and meteorological conditions. \\ While Lagrangian trajectories provide information about meteorological and aerosol conditions of an air parcel that led to a cirrus cloud, information about cirrus cloud properties throughout their lifetime is still lacking. An observational data source that does provide a high temporal sampling of cloud observations, are passive geostationary satellite instruments, such as SEVIRI. Since they only provide a 2D view on clouds from space, their usefulness for studying cloud processes is limited, however. To combine the strengths of different satellite products, we introduce IceCloudNet, a deep learning model that learns to predict 3D cloud structures as retrieved by active polar-orbiting satellites from 2D passive geostationary satellite observations. IceCloudNet is able to adequately reconstruct the vertical cloud structure and predict cloud ice microphysical properties with high skill. The dataset generated by IceCloudNet merges the spatial and temporal resolution of geostationary satellite observations with the vertical resolution of DARDAR. The produced dataset increases the availability of vertical cloud ice profiles by a factor of a million and is thus a starting point for new research on cloud formation and evolution. Additionally, it can be used to validate modern high-resolution climate models.
  • Cirrus formation regimes - data-driven identification and quantification of mineral dust effect
    Item type: Journal Article
    Jeggle, Kai Uwe; Neubauer, David; Binder, Hanin; et al. (2025)
    Atmospheric Chemistry and Physics
    The microphysical and radiative properties of cirrus clouds are strongly dependent on the ice nucleation mechanism and origin of the ice crystals. Due to sparse temporal coverage of satellite data and limited observations of ice-nucleating particles (INPs) at cirrus levels, it is notoriously hard to determine the origin of the ice and the nucleation mechanism of cirrus clouds in satellite observations. In this work we combine 3 years of satellite observations of cirrus clouds from the DARDAR-Nice retrieval product with Lagrangian trajectories of reanalysis data of meteorological and aerosol variables calculated 12 h backward in time for each observed cirrus cloud. In a first step, we identify typical cirrus cloud formation regimes by clustering the Lagrangian trajectories and characterize observed microphysical properties for in situ and liquid origin cirrus clouds in mid-latitudes and the tropics. On average, in situ cirrus clouds have smaller ice water content (IWC) and lower ice crystal number concentration (Nice) and a strong negative temperature dependence of Nice, while liquid origin cirrus have a larger IWC, higher Nice and a strong positive temperature dependence of IWC. In a second step, we use MERRA2 reanalysis data to quantify the sensitivity of cirrus cloud microphysical properties to a change in the concentration of dust particles that may act as INPs. By identifying similar cirrus cloud formation pathways, we can condition on ice origin, region, and meteorological dependencies, and quantify the impact of dust particles for different formation regimes. We find that at cloud-top median Nice decreases with increasing dust concentrations for liquid origin cirrus. Specifically, the sensitivities are between 6 % and 7 % per order of magnitude increase in dust in the tropics and between 15 % in the mid-latitudes. The decrease in Nice can be explained by increased heterogeneous ice nucleation in the mixed-phase regime, leading to fewer cloud droplets freezing homogeneously once the cloud enters the cirrus temperatures and glaciates. The resulting fewer, but larger, ice crystals are more likely to sediment, leading to reduced IWC, as for example observed for liquid origin cirrus in the mid-latitudes. In contrast, for high-altitude in situ cirrus in the tropics, we find an increase of Nice median values of 11 % and IWC median values of 17 % per unit increase of dust aerosol in logarithmic space. We assume that this is caused by heterogeneous nucleation of ice initiated by dust INPs in INP-limited conditions with supersaturations between the heterogeneous and homogeneous freezing thresholds. Such conditions frequently occur at high altitudes, especially in tropical regions at temperatures below 200 K.Our results provide an observational line of evidence that the climate intervention method of seeding cirrus clouds with potent INPs may potentially result in an undesired positive cloud radiative effect (CRE), i.e., a warming effect. Instead of producing fewer but larger ice crystals, we show that additional INPs can lead to an increase in Nice and IWC, an effect called overseeding.
Publications 1 - 3 of 3