In Situ Observations of the Interplay Between Sea Ice and the Atmosphere and Ocean

The International Arctic Buoy Programme (IABP) maintains fundamental in situ components of the Arctic Observing Network. Automated Drifting Stations (ADS) consisting of sea ice, meteorological, and oceanographic buoys are collectively deployed at many sites with webcams to help understand the intric...

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Main Authors: Wu¹, Lily, Rigor², Ignatius, Wang², Muyin
Format: Other/Unknown Material
Language:unknown
Published: Authorea, Inc. 2023
Subjects:
Online Access:http://dx.doi.org/10.22541/essoar.170317286.65084423/v1
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spelling crwinnower:10.22541/essoar.170317286.65084423/v1 2024-06-02T08:00:41+00:00 In Situ Observations of the Interplay Between Sea Ice and the Atmosphere and Ocean Wu¹, Lily Rigor², Ignatius Wang², Muyin 2023 http://dx.doi.org/10.22541/essoar.170317286.65084423/v1 unknown Authorea, Inc. posted-content 2023 crwinnower https://doi.org/10.22541/essoar.170317286.65084423/v1 2024-05-07T14:19:21Z The International Arctic Buoy Programme (IABP) maintains fundamental in situ components of the Arctic Observing Network. Automated Drifting Stations (ADS) consisting of sea ice, meteorological, and oceanographic buoys are collectively deployed at many sites with webcams to help understand the intricate and complex interactions between sea ice, the atmosphere, and the ocean. While passive microwave satellites provide substantial information about the Arctic, remote sensing still has resolution limitations despite broad spatial coverage. Climate modeling and atmospheric reanalysis help surmount these limitations, but traditional observational methods of in situ data collection still have many advantages. Buoys and webcams can monitor Arctic sea ice changes above and below, allowing for more direct observations of localized ice floes when deployed in close proximity. Using data from webcams in the Arctic, we have stitched together images into time-lapse animations that provide insight into physical sea ice processes. Coupled with buoy data, we compare physical measurements (like temperature) with webcam observations (like cloud cover) to explain trends and anomalies. For example, isothermal periods in the buoy temperature data match time-lapse images with cloudy skies, while the opposite is also true: high variability correlates with sunny skies. Hence, these instruments allow for the verification of Arctic observations both visually and statistically. Although significant challenges like camera lifetimes and temporal resolution still persist, we argue that buoys and time-lapse videos can help validate satellite data and offer cheaper solutions to collecting vital information that increases our understanding of geophysical processes. We’ve compiled these datasets and present case studies showing the use of time-lapse videos to help monitor and understand the interplay and processes of the Arctic environment. Other/Unknown Material Arctic Sea ice The Winnower Arctic
institution Open Polar
collection The Winnower
op_collection_id crwinnower
language unknown
description The International Arctic Buoy Programme (IABP) maintains fundamental in situ components of the Arctic Observing Network. Automated Drifting Stations (ADS) consisting of sea ice, meteorological, and oceanographic buoys are collectively deployed at many sites with webcams to help understand the intricate and complex interactions between sea ice, the atmosphere, and the ocean. While passive microwave satellites provide substantial information about the Arctic, remote sensing still has resolution limitations despite broad spatial coverage. Climate modeling and atmospheric reanalysis help surmount these limitations, but traditional observational methods of in situ data collection still have many advantages. Buoys and webcams can monitor Arctic sea ice changes above and below, allowing for more direct observations of localized ice floes when deployed in close proximity. Using data from webcams in the Arctic, we have stitched together images into time-lapse animations that provide insight into physical sea ice processes. Coupled with buoy data, we compare physical measurements (like temperature) with webcam observations (like cloud cover) to explain trends and anomalies. For example, isothermal periods in the buoy temperature data match time-lapse images with cloudy skies, while the opposite is also true: high variability correlates with sunny skies. Hence, these instruments allow for the verification of Arctic observations both visually and statistically. Although significant challenges like camera lifetimes and temporal resolution still persist, we argue that buoys and time-lapse videos can help validate satellite data and offer cheaper solutions to collecting vital information that increases our understanding of geophysical processes. We’ve compiled these datasets and present case studies showing the use of time-lapse videos to help monitor and understand the interplay and processes of the Arctic environment.
format Other/Unknown Material
author Wu¹, Lily
Rigor², Ignatius
Wang², Muyin
spellingShingle Wu¹, Lily
Rigor², Ignatius
Wang², Muyin
In Situ Observations of the Interplay Between Sea Ice and the Atmosphere and Ocean
author_facet Wu¹, Lily
Rigor², Ignatius
Wang², Muyin
author_sort Wu¹, Lily
title In Situ Observations of the Interplay Between Sea Ice and the Atmosphere and Ocean
title_short In Situ Observations of the Interplay Between Sea Ice and the Atmosphere and Ocean
title_full In Situ Observations of the Interplay Between Sea Ice and the Atmosphere and Ocean
title_fullStr In Situ Observations of the Interplay Between Sea Ice and the Atmosphere and Ocean
title_full_unstemmed In Situ Observations of the Interplay Between Sea Ice and the Atmosphere and Ocean
title_sort in situ observations of the interplay between sea ice and the atmosphere and ocean
publisher Authorea, Inc.
publishDate 2023
url http://dx.doi.org/10.22541/essoar.170317286.65084423/v1
geographic Arctic
geographic_facet Arctic
genre Arctic
Sea ice
genre_facet Arctic
Sea ice
op_doi https://doi.org/10.22541/essoar.170317286.65084423/v1
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