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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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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1766319377388404736 |