Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness and mitigation of extreme events. Previously thought to peak mainly in winter, recent research reveals that ARs exhibit region-specific seasonality. However, AR analysis is heavily influenced by the chosen detection algorithm. Our study examines how AR seasonality varies based on both location, year and algorithm selection. We investigate the link between year-to-year consistency of peak AR activity and the presence of a dominant seasonal pattern. We categorize regions based on their year-to-year seasonality characteristics, including consistent patterns (e.g., India, Central Asia), patterns with occasional outliers (e.g., British Columbia coast, Gulf of Alaska), and regions lacking a clear dominant season of peak AR frequency (e.g., South Atlantic, parts of Australia). Hence, not all regions exhibit a consistent seasonal cycle of AR activity. Additionally, different algorithms may detect a consistent seasonal pattern for the same region but disagree on the exact dominant season. This is exemplified by the conflicting results obtained for China. Integrated Vapor Transport (IVT) often corroborates consistent or inconsistent patterns across regions. In conclusion, this study suggests that variations in the consistency of seasonal patterns are related not only to the detection technique but also to atmospheric circulation, synoptic and low-frequency anomalies. Understanding the variations in the consistency of seasonal pattern in areas like Britain remains challenging due to algorithmic and physical differences. These findings emphasize the need for a multi-faceted approach to AR research, considering not just detection methodologies but also regional characteristics and atmospheric processes. Understanding the specific reasons for inconsistent seasonal patterns is an important next step for future research to improve forecasts and preparedness.

The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi-model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA-2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design.

Atmospheric rivers (ARs) are efficient mechanisms for transporting atmospheric moisture from low latitudes to the Antarctic Ice Sheet (AIS). While AR events occur infrequently, they can lead to extreme precipitation and surface melt events on the AIS. Here we estimate the contribution of ARs to total Antarctic precipitation, by combining precipitation from atmospheric reanalyses and an polar-specific AR detection algorithm. We show that ARs contribute substantially to Antarctic precipitation, especially on East Antarctica at elevations below 3000 meters. ARs play a vital role in explaining the substantial year-to-year variability in Antarctic precipitation. Our results highlight that ARs are an important component for understanding present and future Antarctic mass balance trends and variability.

Antarctic atmospheric rivers (ARs) are driven by their synoptic environments and lead to profound and varying impacts along the coastlines and over the continent. The definition and detection of ARs specifically over Antarctica accounts for large uncertainty in AR metrics, and consequently, impacts quantification. We find that Antarctic-specific detection tools consistently capture the AR footprint inland over the ice sheets, whereas most global detection tools do not. Large-scale synoptic environments and associated ARs, however, are broadly consistent across detection tools. Using data from the Atmospheric River Tracking Method Intercomparison Project and global reanalyses, we quantify the uncertainty in Antarctic AR metrics as well as evaluate large-scale environments in the context of decadal and interannual modes of variability. The Antarctic western hemisphere has stronger connections to both decadal and interannual modes of variability compared to East Antarctica, and the IOD’s influence on Antarctic ARs is stronger while in phase with ENSO.