ALBERT

Description: The dataset contains hourly detected Atmospheric Rivers (ARs) for ERA5 reanalysis (1979 – 2021). For the detection of ARs, the global detection algorithm introduced by Guan and Waliser (2015) (first version v1), and refined by Guan et al. (2018) (second version v2) is applied. The algorithm considers different requirements: the intensity of integrated water vapor transport IVT, direction, and geometry (described in detail by Guan and Waliser (2015), Guan et al. (2018), and Lauer et al. (2023)). (1) IVT intensity: The IVT must exceed the 85th percentile of IVT for each grid cell and the lower limit of 100 kg m⁻¹ s⁻¹. (2) IVT direction: The IVT direction at individual grid cells have to be coherent, and the direction of object-mean IVT has to be within 45° of the AR shape orientation, with an appreciable poleward component. (3) Geometry: The length has to be larger than 2000 km, and the length-to-width ratio should be higher than two. In case an object exceeds the IVT percentile, but (2) and (3) are not fulfilled, the process is repeated for higher IVT thresholds (up to the 95th percentile with 2.5 steps). Thus, an object surrounded by an increased moisture content can be detected as an AR. To apply the detection algorithm, the input variables - zonal and meridional components of the IVT (IVTx and IVTy) and the IVT percentiles - are first calculated using ERA5 reanalysis. When these variables have been inserted and the algorithm has been applied, an nc-file is output. This nc-file includes the AR shape, axis, geometric characteristics such as length and width, the coordinates of the AR's head, tail, and centroid, mean of zonal and meridional IVT, the direction of mean IVT, and the landfall location (if the AR made landfall).

Description: Distinct events of warm and moist air intrusions (WAIs) from mid-latitudes have pronounced impacts on the Arctic climate system. We present a detailed analysis of a record-breaking WAI observed during the MOSAiC expedition in mid-April 2020. By combining Eulerian with Lagrangian frameworks and using simulations across different scales, we investigate aspects of air mass transformations via cloud processes and quantify related surface impacts. The WAI is characterized by two distinct pathways, Siberian and Atlantic. A moist static energy transport across the Arctic Circle above the climatological 90th percentile is found. Observations at research vessel Polarstern show a transition from radiatively clear to cloudy state with significant precipitation and a positive surface energy balance (SEB), i.e., surface warming. WAI air parcels reach Polarstern first near the tropopause, and only 1–2 days later at lower altitudes. In the 5 days prior to the event, latent heat release during cloud formation triggers maximum diabatic heating rates in excess of 20 K d-1. For some poleward drifting air parcels, this facilitates strong ascent by up to 9 km. Based on model experiments, we explore the role of two key cloud-determining factors. First, we test the role moisture availability by reducing lateral moisture inflow during the WAI by 30%. This does not significantly affect the liquid water path, and therefore the SEB, in the central Arctic. The cause are counteracting mechanisms of cloud formation and precipitation along the trajectory. Second, we test the impact of increasing Cloud Condensation Nuclei concentrations from 10 to 1,000 cm-3 (pristine Arctic to highly polluted), which enhances cloud water content. Resulting stronger longwave cooling at cloud top makes entrainment more efficient and deepens the atmospheric boundary layer. Finally, we show the strongly positive effect of the WAI on the SEB. This is mainly driven by turbulent heat fluxes over the ocean, but radiation over sea ice. The WAI also contributes a large fraction to precipitation in the Arctic, reaching 30% of total precipitation in a 9-day period at the MOSAiC site. However, measured precipitation varies substantially between different platforms. Therefore, estimates of total precipitation are subject to considerable observational uncertainty.