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Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations
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| Zeitschriftentitel: | The Cryosphere |
|---|---|
| Personen und Körperschaften: | , , |
| In: | The Cryosphere, 16, 2022, 5, S. 1653-1673 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Copernicus GmbH
|
| Schlagwörter: |
| author_facet |
Gregory, William Stroeve, Julienne Tsamados, Michel Gregory, William Stroeve, Julienne Tsamados, Michel |
|---|---|
| author |
Gregory, William Stroeve, Julienne Tsamados, Michel |
| spellingShingle |
Gregory, William Stroeve, Julienne Tsamados, Michel The Cryosphere Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations Earth-Surface Processes Water Science and Technology |
| author_sort |
gregory, william |
| spelling |
Gregory, William Stroeve, Julienne Tsamados, Michel 1994-0424 Copernicus GmbH Earth-Surface Processes Water Science and Technology http://dx.doi.org/10.5194/tc-16-1653-2022 <jats:p>Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer timescales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in Northern Hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations, respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the adjusted Rand index – a method for comparing spatial patterns of variability – and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability in the AO relatively well, although they overestimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean and underestimate the variability over northern Africa and southern Europe. They also underestimate the importance of regions such as the Beaufort, East Siberian, and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice. </jats:p> Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations The Cryosphere |
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| title |
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_unstemmed |
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_full |
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_fullStr |
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_full_unstemmed |
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_short |
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_sort |
network connectivity between the winter arctic oscillation and summer sea ice in cmip6 models and observations |
| topic |
Earth-Surface Processes Water Science and Technology |
| url |
http://dx.doi.org/10.5194/tc-16-1653-2022 |
| publishDate |
2022 |
| physical |
1653-1673 |
| description |
<jats:p>Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer timescales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in Northern Hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations, respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the adjusted Rand index – a method for comparing spatial patterns of variability – and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability in the AO relatively well, although they overestimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean and underestimate the variability over northern Africa and southern Europe. They also underestimate the importance of regions such as the Beaufort, East Siberian, and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.
</jats:p> |
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| description | <jats:p>Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer timescales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in Northern Hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations, respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the adjusted Rand index – a method for comparing spatial patterns of variability – and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability in the AO relatively well, although they overestimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean and underestimate the variability over northern Africa and southern Europe. They also underestimate the importance of regions such as the Beaufort, East Siberian, and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice. </jats:p> |
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| spelling | Gregory, William Stroeve, Julienne Tsamados, Michel 1994-0424 Copernicus GmbH Earth-Surface Processes Water Science and Technology http://dx.doi.org/10.5194/tc-16-1653-2022 <jats:p>Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer timescales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in Northern Hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations, respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the adjusted Rand index – a method for comparing spatial patterns of variability – and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability in the AO relatively well, although they overestimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean and underestimate the variability over northern Africa and southern Europe. They also underestimate the importance of regions such as the Beaufort, East Siberian, and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice. </jats:p> Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations The Cryosphere |
| spellingShingle | Gregory, William, Stroeve, Julienne, Tsamados, Michel, The Cryosphere, Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations, Earth-Surface Processes, Water Science and Technology |
| title | Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_full | Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_fullStr | Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_full_unstemmed | Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_short | Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| title_sort | network connectivity between the winter arctic oscillation and summer sea ice in cmip6 models and observations |
| title_unstemmed | Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations |
| topic | Earth-Surface Processes, Water Science and Technology |
| url | http://dx.doi.org/10.5194/tc-16-1653-2022 |