The Atmospheric motion vectors (AMVs) represent horizontal wind derived by tracking the cloud or water vapor features on successive satellite images. The launch of the Geostationary Operational Environmental Satellite-R Series (GOES-R), including GOES-16 (GOES-East) and GOES-17 (GOES-West), significantly enhanced data volume and geographic coverage over the contiguous U.S. and adjacent oceans. AMVs from GOES-16/17 products can augment wind data in data-sparse areas like oceanic atmospheric rivers (ARs). However, AMVs exhibit biases and uncertainties, especially due to height assignment issues, and there are fewer conventional data (e.g., radiosondes) to calibrate GOES-17 over oceans. The AR Reconnaissance (AR Recon) samples ARs to improve forecast skill over the U.S. West and provides a great opportunity to validate GOES-16/17 AMVs. This study quantifies biases and uncertainties in GOES-16/17 AMVs in the Northeast Pacific using dropsondes from AR Recon and assesses model analyses and background from the GFS at National Centers for Environmental Prediction (NCEP). Results for four representative AR cases show that GOES-16/17 AMVs improved wind data distribution, particularly in the upper and lower troposphere. A comparison with dropsondes reveals negative biases of AMVs in both wind components, with a slow wind speed bias of -0.7 m/s, particularly in upper levels. The uncertainty for AMVs is estimated at 5-6 m/s. Validation of GFS model background shows small biases, with RMSD of 3.2 m/s for dropsondes and 2.1 m/s for AMVs. Data assimilation reduces RMSD, but biases in operational AMVs need further attention, as they are a dominant wind data source in NWP models.

This study investigates the impacts of aerosol atmospheric rivers (AARs) on extreme Particulate Matter 2.5 (PM2.5) levels (PM2.5 > 15mgm-3, as per the WHO) and on aerosol optical depth (AOD) extremes (AOD > 98th percentile) over the US and the globe, respectively, between 1997-2020. Results show that over various regions over the US, extreme PM2.5values are associated with AARs up to 70% of the time. Dust (sulfate) AARs are responsible for extreme PM2.5 levels over the southwestern (northeastern and the east coastal) US. Organic and black carbon AARs are associated with extreme PM2.5 levels over the Midwest region of the US. Globally, AARs are associated with 40-80% of the extreme AOD levels over the US, Sahel, Europe, Middle East, US, South America, East Asia, India, and South Africa. Such associations often lead to the highest or the second highest PM2.5 and AOD levels recorded over those stations between 1997-2020.

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth’s hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a pre-industrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

Atmospheric rivers (ARs) transport vast amounts of moisture from low to high latitude regions. One region particularly impacted by ARs is Interior Alaska (AK). We analyze the impact of ARs on the annual river ice breakup date for 25 locations in AK. We investigate the AR-driven rise in local air temperatures and explore the relationship between ARs and precipitation, including extremes and interannual variability. We found that AR events lead to an increase in local air temperatures for up to one week (by ≈ 1°C). Interannually, ARs account for 36% of total precipitation, explain 48% of precipitation variability, and make up 57% of extreme precipitation events. By estimating the heat transfer between winter precipitation and the river ice surface, we conclude that increased precipitation during the coldest period of the year delays river ice breakup dates, while precipitation occurring close to the breakup date has little impact on breakup timing.

In recent decades, Arctic sea ice coverage experienced a drastic decline in winter, when sea ice is expected to recover following the melting season. Using observations and climate model simulations, we found a robust frequency increase in atmospheric rivers (ARs, intense corridors of moisture transport) over Barents-Kara Seas and the neighboring central Arctic (ABK) in early winter. The extensive moisture carried by more frequent ARs has intensified surface downward longwave radiation and liquid rainfall, caused stronger melting of thin, fragile ice cover, and slowed the seasonal recovery of sea ice, contributing to the sea ice cover decline in ABK. A series of model ensemble experiments suggests that, in addition to a uniform AR increase in response to anthropogenic forcing, the contribution of tropical Pacific variability is indispensable in the observed Arctic AR changes. These findings have significant implications for understanding the rapidly changing Arctic hydroclimate and the cryosphere.

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.

Despite strong impacts that aerosols have on climate and air quality, significant gaps remain in our knowledge concerning their long-range transport, especially extreme transport events. With this consideration in mind and by leveraging the “atmospheric river” concept, this work develops an objective global algorithm for detecting aerosol atmospheric rivers (AARs), shows a climatology of AARs, elucidates their contributions to major global aerosol transport pathways, and illustrates how AARs can drive extreme cases of poor air quality conditions. Our methodology separately accounts for dust, carbonaceous (accounting for organic and black carbon separately where appropriate), sea salt and sulfate aerosols. Findings show there are a number of long-range regional transport pathways where AARs account for a sizable fraction (40-80%) of the total transport in relatively few events (20-40 AAR days/year). This study highlights the role of AARs in establishing source-receptor relationships that can drive regional air-quality and extremes.