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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/23/5995 |
| _version_ | 1827642579123961856 |
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| author | Bohai Zhang Furong Li Huiyan Sang Noel Cressie |
| author_facet | Bohai Zhang Furong Li Huiyan Sang Noel Cressie |
| author_sort | Bohai Zhang |
| collection | DOAJ |
| 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 <inline-formula><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></inline-formula>–<inline-formula><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></inline-formula>, and of most concern is that this may reflect the future behavior of sea ice at ever-higher latitudes under climate change. |
| first_indexed | 2024-03-09T17:34:13Z |
| format | Article |
| id | doaj.art-d37a10cad0e04eb0a405e9759fa6cfa6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T17:34:13Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d37a10cad0e04eb0a405e9759fa6cfa62023-11-24T12:04:00ZengMDPI AGRemote Sensing2072-42922022-11-011423599510.3390/rs14235995Inferring Changes in Arctic Sea Ice through a Spatio-Temporal Logistic Autoregression Fitted to Remote-Sensing DataBohai Zhang0Furong Li1Huiyan Sang2Noel Cressie3School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, ChinaSchool of Mathematical Sciences, Ocean University of China, Qingdao 266100, ChinaDepartment of Statistics, Texas A&M University, College Station, TX 77843, USANational Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW 2522, AustraliaArctic 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 <inline-formula><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></inline-formula>–<inline-formula><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></inline-formula>, and of most concern is that this may reflect the future behavior of sea ice at ever-higher latitudes under climate change.https://www.mdpi.com/2072-4292/14/23/5995boxplot time seriesclimate changedynamic spatio-temporal modelreflected solar radiationsea ice extent |
| spellingShingle | 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 Remote Sensing boxplot time series climate change dynamic spatio-temporal model reflected solar radiation sea ice extent |
| title | 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_short | 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 |
| topic | boxplot time series climate change dynamic spatio-temporal model reflected solar radiation sea ice extent |
| url | https://www.mdpi.com/2072-4292/14/23/5995 |
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