Articles | Volume 18, issue 1
https://doi.org/10.5194/tc-18-121-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-121-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of atmospheric rivers on Arctic sea ice variations
Linghan Li
CORRESPONDING AUTHOR
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, California, USA
Forest Cannon
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, California, USA
Matthew R. Mazloff
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, California, USA
Aneesh C. Subramanian
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
Anna M. Wilson
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, California, USA
Fred Martin Ralph
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, California, USA
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Sarah E. Ogle, Garrett McGurk, Anahita Jensen, Fred Martin Ralph, and Morgan C. Levy
EGUsphere, https://doi.org/10.5194/egusphere-2025-2297, https://doi.org/10.5194/egusphere-2025-2297, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Intermittent streams are vital to ecosystems and water supply but are hard to monitor and increasingly affected by climate change. To address this, we used field camera images from 2017–2023 at a stream in northern California to train a machine learning model that classifies streamflow as dry, low, or high. This low-cost method enables monitoring of changing intermittent stream conditions and supports water management in data-scarce regions.
Aurora Roth, Fiamma Straneo, James Holte, Margaret Lindeman, and Matthew Mazloff
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The fjords of Kalaallit Nunaat/Greenland play a critical role in our climate system and support thriving ecosystems that Greenlanders call home. As our climate warms, fjords are hotspots of change and more scientists are collecting data in fjords. These data need to be available, useable, and intuitive across a wide range of users– from scientists to local people interested in their home. We provide an example of gridded data from Sermilik Fjord, Southeast Greenland as a way to achieve this.
Bing Cao, Jennifer S. Haase, Michael J. Murphy Jr., and Anna M. Wilson
Atmos. Meas. Tech., 18, 3361–3392, https://doi.org/10.5194/amt-18-3361-2025, https://doi.org/10.5194/amt-18-3361-2025, 2025
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This paper describes an airborne radio occultation (ARO) observation system installed on a reconnaissance aircraft that uses GPS signal refraction in the atmosphere to retrieve information about the temperature and moisture in the storm environment as far away as 400 km surrounding the flight track. The characteristics and quality of 1700 ARO refractivity profiles were assessed. These observations are collected to help understand atmospheric rivers and improve their forecasting.
Yuan Yang, Ming Pan, Dapeng Feng, Mu Xiao, Taylor Dixon, Robert Hartman, Chaopeng Shen, Yalan Song, Agniv Sengupta, Luca Delle Monache, and F. Martin Ralph
EGUsphere, https://doi.org/10.5194/egusphere-2025-1708, https://doi.org/10.5194/egusphere-2025-1708, 2025
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We explore a machine learning-based data integration method that integrates streamflow (Q) and snow water equivalent (SWE) to improve streamflow estimates at various lag times (1–10 days, 1–6 months) and timescales (daily and monthly) over Western U.S. basins. Benefits rank as: integrating Q at the daily scale > Q at the monthly scale > SWE at the monthly scale > SWE at the daily scale. Results highlight the method’s potential for short- and long-term streamflow forecasting in the Western U.S.
Tyler Pelle, Paul G. Myers, Andrew Hamilton, Matthew Mazloff, Krista Soderlund, Lucas Beem, Donald D. Blankenship, Cyril Grima, Feras Habbal, Mark Skidmore, and Jamin S. Greenbaum
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Here, we develop and run a high resolution ocean model of Jones Sound from 2003–2016 and characterize circulation into, out of, and within the sound as well as associated sea ice and productivity cycles. Atmospheric and ocean warming drive sea ice decline, which enhance biological productivity due to the increased light availability. These results highlight the utility of high resolution models in simulating complex waterways and the need for sustained oceanographic measurements in the sound.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
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Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Zhenhai Zhang, F. Martin Ralph, Xun Zou, Brian Kawzenuk, Minghua Zheng, Irina V. Gorodetskaya, Penny M. Rowe, and David H. Bromwich
The Cryosphere, 18, 5239–5258, https://doi.org/10.5194/tc-18-5239-2024, https://doi.org/10.5194/tc-18-5239-2024, 2024
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Atmospheric rivers (ARs) are long, narrow corridors of strong water vapor transport in the atmosphere. ARs play an important role in extreme weather in polar regions, including heavy rain and/or snow, heat waves, and surface melt. The standard AR scale is developed based on the midlatitude climate and is insufficient for polar regions. This paper introduces an extended version of the AR scale tuned to polar regions, aiming to quantify polar ARs objectively based on their strength and impact.
Rui Sun, Alison Cobb, Ana B. Villas Bôas, Sabique Langodan, Aneesh C. Subramanian, Matthew R. Mazloff, Bruce D. Cornuelle, Arthur J. Miller, Raju Pathak, and Ibrahim Hoteit
Geosci. Model Dev., 16, 3435–3458, https://doi.org/10.5194/gmd-16-3435-2023, https://doi.org/10.5194/gmd-16-3435-2023, 2023
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In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS. We then performed a case study using the newly implemented model to study Tropical Cyclone Mekunu, which occurred in the Arabian Sea. We found that the coupled model better simulates the cyclone than the uncoupled model, but the impact of waves on the cyclone is not significant. However, the waves change the sea surface temperature and mixed layer, especially in the cold waves produced due to the cyclone.
David S. Trossman, Caitlin B. Whalen, Thomas W. N. Haine, Amy F. Waterhouse, An T. Nguyen, Arash Bigdeli, Matthew Mazloff, and Patrick Heimbach
Ocean Sci., 18, 729–759, https://doi.org/10.5194/os-18-729-2022, https://doi.org/10.5194/os-18-729-2022, 2022
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How the ocean mixes is not yet adequately represented by models. There are many challenges with representing this mixing. A model that minimizes disagreements between observations and the model could be used to fill in the gaps from observations to better represent ocean mixing. But observations of ocean mixing have large uncertainties. Here, we show that ocean oxygen, which has relatively small uncertainties, and observations of ocean mixing provide information similar to the model.
Qian Shi, Qinghua Yang, Longjiang Mu, Jinfei Wang, François Massonnet, and Matthew R. Mazloff
The Cryosphere, 15, 31–47, https://doi.org/10.5194/tc-15-31-2021, https://doi.org/10.5194/tc-15-31-2021, 2021
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The ice thickness from four state-of-the-art reanalyses (GECCO2, SOSE, NEMO-EnKF and GIOMAS) are evaluated against that from remote sensing and in situ observations in the Weddell Sea, Antarctica. Most of the reanalyses can reproduce ice thickness in the central and eastern Weddell Sea but failed to capture the thick and deformed ice in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis in Antarctic climate research.
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Short summary
We investigate how the moisture transport through atmospheric rivers influences Arctic sea ice variations using hourly atmospheric ERA5 for 1981–2020 at 0.25° × 0.25° resolution. We show that individual atmospheric rivers initiate rapid sea ice decrease through surface heat flux and winds. We find that the rate of change in sea ice concentration has significant anticorrelation with moisture, northward wind and turbulent heat flux on weather timescales almost everywhere in the Arctic Ocean.
We investigate how the moisture transport through atmospheric rivers influences Arctic sea ice...