The Antarctic ice sheet (AIS) is sensitive to short‐term extreme meteorological events that can leave long‐term impacts on the continent's surface mass balance (SMB). We investigate the impacts of atmospheric rivers (ARs) on the AIS precipitation budget using an AR detection algorithm and a regional climate model (Modèle Atmosphérique Régional) from 1980 to 2018. While ARs and their associated extreme vapor transport are relatively rare events over Antarctic coastal regions (∼3 days per year), they have a significant impact on the precipitation climatology. ARs are responsible for at least 10% of total accumulated snowfall across East Antarctica (localized areas reaching 20%) and a majority of extreme precipitation events. Trends in AR annual frequency since 1980 are observed across parts of AIS, most notably an increasing trend in Dronning Maud Land; however, interannual variability in AR frequency is much larger. This AR behavior appears to drive a significant portion of annual snowfall trends across East Antarctica, while controlling the interannual variability of precipitation across most of the AIS. AR landfalls are most likely when the circumpolar jet is highly amplified during blocking conditions in the Southern Ocean. There is a fingerprint of the Southern Annular Mode (SAM) on AR variability inmore »
We characterize the sensitivity of atmospheric river (AR)‐derived seasonal snowfall estimates to their atmospheric reanalysis‐based detection over Sierra Nevada, USA. We use an independent snow data set and the ARs identified with a single detection method applied to multiple atmospheric reanalyses of varying horizontal resolutions, to evaluate orographic relationships and contributions of individual ARs to the seasonal cumulative snowfall (CS). Spatial resolution differences have relatively minor effects on the number of ARs diagnosed, with higher‐resolution data sets identifying four more AR days per year, on average, during the 1985–2015 winters. However, this can lead to ~10% difference in AR attribution to the mean domain‐wide seasonal CS and differences up to 47% snowfall attribution at the seasonal scale. We show that identifying snow‐bearing ARs provides more information about the seasonal CS than simply knowing how many ARs occurred. Overall, we find that higher‐resolution atmospheric reanalyses imply greater attribution of seasonal CS to ARs.
- Award ID(s):
- 1725789
- Publication Date:
- NSF-PAR ID:
- 10457252
- Journal Name:
- Geophysical Research Letters
- Volume:
- 46
- Issue:
- 2
- Page Range or eLocation-ID:
- p. 794-803
- ISSN:
- 0094-8276
- Publisher:
- DOI PREFIX: 10.1029
- Sponsoring Org:
- National Science Foundation
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