Julia Kukulies

and 5 more

Frozen cloud particles are an important link in the hydrological cycle and significantly influence the Earth’s energy budget. Despite their important role, observational records constraining concentrations of atmospheric ice remain severely limited. While combined radar and lidar estimates from the CloudSat and CALIPSO missions offer over a decade of high-quality data on ice hydrometeor concentrations, these estimates remain sparse. In contrast, products derived from passive satellite sensors typically provide better spatiotemporal coverage but disagree with CloudSat-baed measurements.   To address these limitations, we present a novel climate data record of total ice water path (TIWP), the Chalmers Cloud Ice Climatology (CCIC). It spans 40 years, from 1983 to the present, covering latitudes from 70 degree South to 70 degree North. CCIC offers TIWP estimates at three-hourly resolution from 1983 and half-hourly resolution from 2000 onwards. We demonstrate the long-term stability of CCIC by directly comparing it with CloudSat/CALIPSO-based estimates over the entire mission lifetime. Additionally, we assess CCIC against other long-term TIWP records, revealing that CCIC yields most accurate TIWP estimates compared to CloudSat/CALIPSO-based reference estimates. An investigation of the regional trends in TIWP shows good agreement between four observational datasets and ERA5 for the most recent 20 years. However, the consistency decreases for 40-year trends.   The CCIC climate record closes the gap between existing long-term TIWP records and CloudSat/CALIPSO-based reference measurements. The estimates’ continuous coverage and demonstrated accuracy make it a valuable resource for lifecycle studies of storms and the analysis of fine-scale cloud features in a changing climate.

Robin Ekelund

and 1 more

Improved observations of ice hydrometeors can lead to better weather predictions and understanding of the hydrological cycle. Global coverage is best achieved by satellites, using active and passive microwave remote sensing due to the inherent penetration capability and sensitivity to the snow and ice cloud particles which scatter and absorb the radiation. Snow and cirrus cloud particles are to a large degree composed of aggregates. While it is known that the shape of these aggregates influence microwave measurements to various degrees, it is currently not fully clear to what extent certain particle features are of importance. For example, of what importance is the shape of individual crystals in the aggregates? This work is an attempt at improving our knowledge on the impact of ice aggregate microphysics on microwave and sub-millimetre scattering properties from a modelling point of view. A large amount of aggregates (roughly 4000) where modelled through several semi-physical stochastic simulations. The aggregates are composed of hexagonal ice crystals of varying axis ratio, ranging from 1/15 (plates) to 15 (columns), and assumed to be oriented in the horizontal plane. Single scattering properties of over 1000 aggregates were then assessed for zenith/nadir observations, using the discrete dipole approximation (DDA) at three typical radar bands (13.4, 35.6, 94.1 GHz) and three passive microwave frequencies (183.3, 325.15 and 664 GHz). An analysis on the sensitivity of these scattering data to various aggregate parameters is presented. In general, extinction was found to be less sensitive to shape than back-scattering at investigated at frequencies. Extinction at 664 GHz in particular was found to be shape insensitive; promising for the sake of the Ice Cloud Imager (ICI) on the upcoming Metop-SG satellite. In contrast, evaluation of triple frequency signatures showed relatively high shape sensitivity; of relevance to future multi-frequency radars.