Inferring Changes in Arctic Sea Ice through a Spatio-Temporal Logistic Autoregression Fitted to Remote-Sensing Data

Arctic sea ice extent (SIE) has drawn increasing attention from scientists in recent years because of its fast decline in the Boreal summer and early fall. The measurement of SIE is derived from remote sensing data and is both a lagged and leading indicator of climate change. To characterize at a lo...

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Published in:Remote Sensing
Main Authors: Bohai Zhang, Furong Li, Huiyan Sang, Noel Cressie
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2022
Subjects:
Q
Online Access:https://doi.org/10.3390/rs14235995
https://doaj.org/article/d37a10cad0e04eb0a405e9759fa6cfa6
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spelling ftdoajarticles:oai:doaj.org/article:d37a10cad0e04eb0a405e9759fa6cfa6 2023-05-15T14:48:17+02:00 Inferring Changes in Arctic Sea Ice through a Spatio-Temporal Logistic Autoregression Fitted to Remote-Sensing Data Bohai Zhang Furong Li Huiyan Sang Noel Cressie 2022-11-01T00:00:00Z https://doi.org/10.3390/rs14235995 https://doaj.org/article/d37a10cad0e04eb0a405e9759fa6cfa6 EN eng MDPI AG https://www.mdpi.com/2072-4292/14/23/5995 https://doaj.org/toc/2072-4292 doi:10.3390/rs14235995 2072-4292 https://doaj.org/article/d37a10cad0e04eb0a405e9759fa6cfa6 Remote Sensing, Vol 14, Iss 5995, p 5995 (2022) boxplot time series climate change dynamic spatio-temporal model reflected solar radiation sea ice extent Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14235995 2022-12-30T20:12:27Z Arctic sea ice extent (SIE) has drawn increasing attention from scientists in recent years because of its fast decline in the Boreal summer and early fall. The measurement of SIE is derived from remote sensing data and is both a lagged and leading indicator of climate change. To characterize at a local level the decline in SIE, we use remote-sensing data at 25 km resolution to fit a spatio-temporal logistic autoregressive model of the sea-ice evolution in the Arctic region. The model incorporates last year’s ice/water binary observations at nearby grid cells in an autoregressive manner with autoregressive coefficients that vary both in space and time. Using the model-based estimates of ice/water probabilities in the Arctic region, we propose several graphical summaries to visualize the spatio-temporal changes in Arctic sea ice beyond what can be visualized with the single time series of SIE. In ever-higher latitude bands, we observe a consistently declining temporal trend of sea ice in the early fall. We also observe a clear decline in and contraction of the sea ice’s distribution between <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>70</mn><mo>∘</mo></msup><mi mathvariant="normal">N</mi></mrow></semantics></math> – <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>75</mn><mo>∘</mo></msup><mi mathvariant="normal">N</mi></mrow></semantics></math> , and of most concern is that this may reflect the future behavior of sea ice at ever-higher latitudes under climate change. Article in Journal/Newspaper Arctic Climate change Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 14 23 5995
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic boxplot time series
climate change
dynamic spatio-temporal model
reflected solar radiation
sea ice extent
Science
Q
spellingShingle boxplot time series
climate change
dynamic spatio-temporal model
reflected solar radiation
sea ice extent
Science
Q
Bohai Zhang
Furong Li
Huiyan Sang
Noel Cressie
Inferring Changes in Arctic Sea Ice through a Spatio-Temporal Logistic Autoregression Fitted to Remote-Sensing Data
topic_facet boxplot time series
climate change
dynamic spatio-temporal model
reflected solar radiation
sea ice extent
Science
Q
description Arctic sea ice extent (SIE) has drawn increasing attention from scientists in recent years because of its fast decline in the Boreal summer and early fall. The measurement of SIE is derived from remote sensing data and is both a lagged and leading indicator of climate change. To characterize at a local level the decline in SIE, we use remote-sensing data at 25 km resolution to fit a spatio-temporal logistic autoregressive model of the sea-ice evolution in the Arctic region. The model incorporates last year’s ice/water binary observations at nearby grid cells in an autoregressive manner with autoregressive coefficients that vary both in space and time. Using the model-based estimates of ice/water probabilities in the Arctic region, we propose several graphical summaries to visualize the spatio-temporal changes in Arctic sea ice beyond what can be visualized with the single time series of SIE. In ever-higher latitude bands, we observe a consistently declining temporal trend of sea ice in the early fall. We also observe a clear decline in and contraction of the sea ice’s distribution between <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>70</mn><mo>∘</mo></msup><mi mathvariant="normal">N</mi></mrow></semantics></math> – <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>75</mn><mo>∘</mo></msup><mi mathvariant="normal">N</mi></mrow></semantics></math> , and of most concern is that this may reflect the future behavior of sea ice at ever-higher latitudes under climate change.
format Article in Journal/Newspaper
author Bohai Zhang
Furong Li
Huiyan Sang
Noel Cressie
author_facet Bohai Zhang
Furong Li
Huiyan Sang
Noel Cressie
author_sort Bohai Zhang
title Inferring Changes in Arctic Sea Ice through a Spatio-Temporal Logistic Autoregression Fitted to Remote-Sensing Data
title_short Inferring Changes in Arctic Sea Ice through a Spatio-Temporal Logistic Autoregression Fitted to Remote-Sensing Data
title_full Inferring Changes in Arctic Sea Ice through a Spatio-Temporal Logistic Autoregression Fitted to Remote-Sensing Data
title_fullStr Inferring Changes in Arctic Sea Ice through a Spatio-Temporal Logistic Autoregression Fitted to Remote-Sensing Data
title_full_unstemmed Inferring Changes in Arctic Sea Ice through a Spatio-Temporal Logistic Autoregression Fitted to Remote-Sensing Data
title_sort inferring changes in arctic sea ice through a spatio-temporal logistic autoregression fitted to remote-sensing data
publisher MDPI AG
publishDate 2022
url https://doi.org/10.3390/rs14235995
https://doaj.org/article/d37a10cad0e04eb0a405e9759fa6cfa6
geographic Arctic
geographic_facet Arctic
genre Arctic
Climate change
Sea ice
genre_facet Arctic
Climate change
Sea ice
op_source Remote Sensing, Vol 14, Iss 5995, p 5995 (2022)
op_relation https://www.mdpi.com/2072-4292/14/23/5995
https://doaj.org/toc/2072-4292
doi:10.3390/rs14235995
2072-4292
https://doaj.org/article/d37a10cad0e04eb0a405e9759fa6cfa6
op_doi https://doi.org/10.3390/rs14235995
container_title Remote Sensing
container_volume 14
container_issue 23
container_start_page 5995
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