2023 Vol. 40, No. 12
Display Method:
2023, 40(12): 1-1.
Abstract:
2023, 40(12): 2138-2153.
doi: 10.1007/s00376-022-2201-4
Abstract:
An enhanced Warm Arctic–Cold Eurasia (WACE) pattern has been a notable feature in recent winters of the Northern Hemisphere. However, divergent results between model and observational studies of the WACE still remain. This study evaluates the performance of 39 climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the WACE pattern in winter of 1980–2014 and explores the key factors causing the differences in the simulation capability among the models. The results show that the multimodel ensemble (MME) can better simulate the spatial distribution of the WACE pattern than most single models. Models that can/cannot simulate both the climatology and the standard deviation of the Eurasian winter surface air temperature well, especially the latter, usually can/cannot simulate the WACE pattern well. This mainly results from the different abilities of the models to simulate the range and intensity of the warm anomaly in the Barents Sea–Kara seas (BKS) region. Further analysis shows that a good performance of the models in the BKS area is usually related to their ability to simulate location and persistence of Ural blocking (UB), which can transport heat to the BKS region, causing the warm Arctic, and strengthen the westerly trough downstream, cooling central Eurasia. Therefore, simulation of UB is key and significantly affects the model’s performance in simulating the WACE.
An enhanced Warm Arctic–Cold Eurasia (WACE) pattern has been a notable feature in recent winters of the Northern Hemisphere. However, divergent results between model and observational studies of the WACE still remain. This study evaluates the performance of 39 climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the WACE pattern in winter of 1980–2014 and explores the key factors causing the differences in the simulation capability among the models. The results show that the multimodel ensemble (MME) can better simulate the spatial distribution of the WACE pattern than most single models. Models that can/cannot simulate both the climatology and the standard deviation of the Eurasian winter surface air temperature well, especially the latter, usually can/cannot simulate the WACE pattern well. This mainly results from the different abilities of the models to simulate the range and intensity of the warm anomaly in the Barents Sea–Kara seas (BKS) region. Further analysis shows that a good performance of the models in the BKS area is usually related to their ability to simulate location and persistence of Ural blocking (UB), which can transport heat to the BKS region, causing the warm Arctic, and strengthen the westerly trough downstream, cooling central Eurasia. Therefore, simulation of UB is key and significantly affects the model’s performance in simulating the WACE.
2023, 40(12): 2154-2168.
doi: 10.1007/s00376-023-2227-2
Abstract:
Arctic sea ice loss and the associated enhanced warming has been related to midlatitude weather and climate changes through modulate meridional temperature gradients linked to circulation. However, contrasting lines of evidence result in low confidence in the influence of Arctic warming on midlatitude climate. This study examines the additional perspectives that palaeoclimate evidence provides on the decadal relationship between autumn sea ice extent (SIE) in the Barents–Kara (B–K) Seas and extreme cold wave events (ECWEs) in southern China. Reconstruction of the winter Cold Index and SIE in the B–K Seas from 1289 to 2017 shows that a significant anti-phase relationship occurred during most periods of decreasing SIE, indicating that cold winters are more likely in low SIE years due to the “bridge” role of the North Atlantic Oscillation and Siberian High. It is confirmed that the recent increase in ECWEs in southern China is closely related to the sea ice decline in the B–K Seas. However, our results show that the linkage is unstable, especially in high SIE periods, and it is probably modulated by atmospheric internal variability.
Arctic sea ice loss and the associated enhanced warming has been related to midlatitude weather and climate changes through modulate meridional temperature gradients linked to circulation. However, contrasting lines of evidence result in low confidence in the influence of Arctic warming on midlatitude climate. This study examines the additional perspectives that palaeoclimate evidence provides on the decadal relationship between autumn sea ice extent (SIE) in the Barents–Kara (B–K) Seas and extreme cold wave events (ECWEs) in southern China. Reconstruction of the winter Cold Index and SIE in the B–K Seas from 1289 to 2017 shows that a significant anti-phase relationship occurred during most periods of decreasing SIE, indicating that cold winters are more likely in low SIE years due to the “bridge” role of the North Atlantic Oscillation and Siberian High. It is confirmed that the recent increase in ECWEs in southern China is closely related to the sea ice decline in the B–K Seas. However, our results show that the linkage is unstable, especially in high SIE periods, and it is probably modulated by atmospheric internal variability.
2023, 40(12): 2169-2182.
doi: 10.1007/s00376-023-2255-y
Abstract:
Arctic changes influence not only temperature and precipitation in the midlatitudes but also contribute to severe convection. This study investigates an extreme gale event that occurred on 30 April 2021 in East China and was forced by an Arctic potential vorticity (PV) anomaly intrusion. Temperature advection steered by storms contributed to the equatorward propagation of Arctic high PV, forming the Northeast China cold vortex (NCCV). At the upper levels, a PV southward intrusion guided the combination of the polar jet and the subtropical jet, providing strong vertical wind shear and downward momentum transportation to the event. The PV anomaly cooled the upper troposphere and the northern part of East China, whereas the lower levels over southern East China were dominated by local warm air, thus establishing strong instability and baroclinicity. In addition, the entrainment of Arctic dry air strengthened the surface pressure gradient by evaporation cooling. Capturing the above mechanism has the potential to improve convective weather forecasts under climate change. This study suggests that the more frequent NCCV-induced gale events in recent years are partly due to high-latitude waviness and storm activities, and this hypothesis needs to be investigated using more cases.
Arctic changes influence not only temperature and precipitation in the midlatitudes but also contribute to severe convection. This study investigates an extreme gale event that occurred on 30 April 2021 in East China and was forced by an Arctic potential vorticity (PV) anomaly intrusion. Temperature advection steered by storms contributed to the equatorward propagation of Arctic high PV, forming the Northeast China cold vortex (NCCV). At the upper levels, a PV southward intrusion guided the combination of the polar jet and the subtropical jet, providing strong vertical wind shear and downward momentum transportation to the event. The PV anomaly cooled the upper troposphere and the northern part of East China, whereas the lower levels over southern East China were dominated by local warm air, thus establishing strong instability and baroclinicity. In addition, the entrainment of Arctic dry air strengthened the surface pressure gradient by evaporation cooling. Capturing the above mechanism has the potential to improve convective weather forecasts under climate change. This study suggests that the more frequent NCCV-induced gale events in recent years are partly due to high-latitude waviness and storm activities, and this hypothesis needs to be investigated using more cases.
2023, 40(12): 2183-2194.
doi: 10.1007/s00376-023-2272-x
Abstract:
Recent research has shown that snow cover induces extreme wintertime cooling and has detrimental impacts. Although the dramatic loss of Arctic sea ice certainly has contributed to a more extreme climate, the mechanism connecting sea-ice loss to extensive snow cover is still up for debate. In this study, a significant relationship between sea ice concentration (SIC) in the Barents-Kara (B-K) seas in November and snow cover extent over Eurasia in winter (November–January) has been found based in observational datasets and through numerical experiments. The reduction in B-K sea ice gives rise to a negative phase of Arctic Oscillation (AO), a deepened East Asia trough, and a shallow trough over Europe. These circulation anomalies lead to colder-than-normal Eurasian mid-latitude temperatures, providing favorable conditions for snowfall. In addition, two prominent cyclonic anomalies near Europe and Lake Baikal affect moisture transport and its divergence, which results in increased precipitation due to moisture advection and wind convergence. Furthermore, anomalous E-P flux shows that amplified upward propagating waves associated with the low SIC could contribute to the weakening of the polar vortex and southward breakouts of cold air. This work may be helpful for further understanding and predicting the snowfall conditions in the middle latitudes.
Recent research has shown that snow cover induces extreme wintertime cooling and has detrimental impacts. Although the dramatic loss of Arctic sea ice certainly has contributed to a more extreme climate, the mechanism connecting sea-ice loss to extensive snow cover is still up for debate. In this study, a significant relationship between sea ice concentration (SIC) in the Barents-Kara (B-K) seas in November and snow cover extent over Eurasia in winter (November–January) has been found based in observational datasets and through numerical experiments. The reduction in B-K sea ice gives rise to a negative phase of Arctic Oscillation (AO), a deepened East Asia trough, and a shallow trough over Europe. These circulation anomalies lead to colder-than-normal Eurasian mid-latitude temperatures, providing favorable conditions for snowfall. In addition, two prominent cyclonic anomalies near Europe and Lake Baikal affect moisture transport and its divergence, which results in increased precipitation due to moisture advection and wind convergence. Furthermore, anomalous E-P flux shows that amplified upward propagating waves associated with the low SIC could contribute to the weakening of the polar vortex and southward breakouts of cold air. This work may be helpful for further understanding and predicting the snowfall conditions in the middle latitudes.
2023, 40(12): 2195-2210.
doi: 10.1007/s00376-023-2313-5
Abstract:
This study explores the linkage between summertime temperature fluctuations over midlatitude Eurasia and the preceding Arctic sea ice concentration (SIC) by utilizing the squared norm of the temperature anomaly, the essential part of local eddy available potential energy, as a metric to quantify the temperature fluctuations with weather patterns on various timescales. By comparing groups of singular value decomposition (SVD) analysis, we suggest a significant linkage between strong (weak) August 10-to-30-day temperature fluctuations over mid-west Asia and enhanced (decreased) Barents-Kara Sea ice in the previous February. We find that when the February SIC increases in the Barents-Kara Sea, a zonal dipolar pattern of SST anomalies appears in the Atlantic subpolar region and lasts from February into the summer months. Evidence suggests that in such a background state, the atmospheric circulation changes evidently from July to August, so that the August is characterized by an amplified meridional circulation over Eurasia, weakened westerlies, and high-pressure anomalies along the Arctic coast. Moreover, the 10-to-30-day wave becomes more active in the North Atlantic–Barents-Kara Sea–Central Asia regions and manifests a more evident southward propagation from the Barents-Kara Sea into the Ural region, which is responsible for the enhanced 10-to-30-day wave activity and temperature fluctuations in the region.
This study explores the linkage between summertime temperature fluctuations over midlatitude Eurasia and the preceding Arctic sea ice concentration (SIC) by utilizing the squared norm of the temperature anomaly, the essential part of local eddy available potential energy, as a metric to quantify the temperature fluctuations with weather patterns on various timescales. By comparing groups of singular value decomposition (SVD) analysis, we suggest a significant linkage between strong (weak) August 10-to-30-day temperature fluctuations over mid-west Asia and enhanced (decreased) Barents-Kara Sea ice in the previous February. We find that when the February SIC increases in the Barents-Kara Sea, a zonal dipolar pattern of SST anomalies appears in the Atlantic subpolar region and lasts from February into the summer months. Evidence suggests that in such a background state, the atmospheric circulation changes evidently from July to August, so that the August is characterized by an amplified meridional circulation over Eurasia, weakened westerlies, and high-pressure anomalies along the Arctic coast. Moreover, the 10-to-30-day wave becomes more active in the North Atlantic–Barents-Kara Sea–Central Asia regions and manifests a more evident southward propagation from the Barents-Kara Sea into the Ural region, which is responsible for the enhanced 10-to-30-day wave activity and temperature fluctuations in the region.
2023, 40(12): 2211-2223.
doi: 10.1007/s00376-022-2057-7
Abstract:
How atmospheric and oceanic circulations respond to Arctic warming at different timescales are revealed with idealized numerical simulations. Induced by local forcing and feedback, Arctic warming appears and leads to sea-ice melting. Deep-water formation is inhibited, which weakens the Atlantic Meridional Overturning Circulation (AMOC). The flow and temperature in the upper layer does not respond to the AMOC decrease immediately, especially at mid-low latitudes. Thus, nearly uniform surface warming in mid-low latitudes enhances (decreases) the strength (width) of the Hadley cell (HC). With the smaller northward heat carried by the weaker AMOC, the Norwegian Sea cools significantly. With strong warming in Northern Hemisphere high latitudes, the long-term response triggers the “temperature-wind-gyre-temperature” cycle, leading to colder midlatitudes, resulting in strong subsidence and Ferrel cell enhancement, which drives the HC southward. With weaker warming in the tropics and stronger warming at high latitudes, there is a stronger HC with decreased width. A much warmer Southern Hemisphere appears due to a weaker AMOC that also pushes the HC southward. Our idealized model results suggest that the HC strengthens under both warming conditions, as tropical warming determines the strength of the HC convection. Second, extreme Arctic warming led by artificially reduced surface albedo decreases the meridional temperature gradient between high and low latitudes, which contracts the HC. Third, a warmer mid-high latitude in the Northern (Southern) Hemisphere due to surface albedo feedback (weakened AMOC) in our experiments pushes the HC northward (southward). In most seasons, the HC exhibits the same trend as that described above.
How atmospheric and oceanic circulations respond to Arctic warming at different timescales are revealed with idealized numerical simulations. Induced by local forcing and feedback, Arctic warming appears and leads to sea-ice melting. Deep-water formation is inhibited, which weakens the Atlantic Meridional Overturning Circulation (AMOC). The flow and temperature in the upper layer does not respond to the AMOC decrease immediately, especially at mid-low latitudes. Thus, nearly uniform surface warming in mid-low latitudes enhances (decreases) the strength (width) of the Hadley cell (HC). With the smaller northward heat carried by the weaker AMOC, the Norwegian Sea cools significantly. With strong warming in Northern Hemisphere high latitudes, the long-term response triggers the “temperature-wind-gyre-temperature” cycle, leading to colder midlatitudes, resulting in strong subsidence and Ferrel cell enhancement, which drives the HC southward. With weaker warming in the tropics and stronger warming at high latitudes, there is a stronger HC with decreased width. A much warmer Southern Hemisphere appears due to a weaker AMOC that also pushes the HC southward. Our idealized model results suggest that the HC strengthens under both warming conditions, as tropical warming determines the strength of the HC convection. Second, extreme Arctic warming led by artificially reduced surface albedo decreases the meridional temperature gradient between high and low latitudes, which contracts the HC. Third, a warmer mid-high latitude in the Northern (Southern) Hemisphere due to surface albedo feedback (weakened AMOC) in our experiments pushes the HC northward (southward). In most seasons, the HC exhibits the same trend as that described above.
2023, 40(12): 2224-2241.
doi: 10.1007/s00376-023-3010-0
Abstract:
Utilizing the Community Atmosphere Model, version 4, the influence of Arctic sea-ice concentration (SIC) on the extended-range prediction of three simulated cold events (CEs) in East Asia is investigated. Numerical results show that the Arctic SIC is crucial for the extended-range prediction of CEs in East Asia. The conditional nonlinear optimal perturbation approach is adopted to identify the optimal Arctic SIC perturbations with the largest influence on CE prediction on the extended-range time scale. It shows that the optimal SIC perturbations are more inclined to weaken the CEs and cause large prediction errors in the fourth pentad, as compared with random SIC perturbations under the same constraint. Further diagnosis reveals that the optimal SIC perturbations first modulate the local temperature through the diabatic process, and then influence the remote temperature by horizontal advection and vertical convection terms. Consequently, the optimal SIC perturbations trigger a warming center in East Asia through the propagation of Rossby wave trains, leading to the largest prediction uncertainty of the CEs in the fourth pentad. These results may provide scientific support for targeted observation of Arctic SIC to improve the extended-range CE prediction skill.
Utilizing the Community Atmosphere Model, version 4, the influence of Arctic sea-ice concentration (SIC) on the extended-range prediction of three simulated cold events (CEs) in East Asia is investigated. Numerical results show that the Arctic SIC is crucial for the extended-range prediction of CEs in East Asia. The conditional nonlinear optimal perturbation approach is adopted to identify the optimal Arctic SIC perturbations with the largest influence on CE prediction on the extended-range time scale. It shows that the optimal SIC perturbations are more inclined to weaken the CEs and cause large prediction errors in the fourth pentad, as compared with random SIC perturbations under the same constraint. Further diagnosis reveals that the optimal SIC perturbations first modulate the local temperature through the diabatic process, and then influence the remote temperature by horizontal advection and vertical convection terms. Consequently, the optimal SIC perturbations trigger a warming center in East Asia through the propagation of Rossby wave trains, leading to the largest prediction uncertainty of the CEs in the fourth pentad. These results may provide scientific support for targeted observation of Arctic SIC to improve the extended-range CE prediction skill.
2023, 40(12): 2242-2261.
doi: 10.1007/s00376-023-2371-8
Abstract:
The influence of Arctic sea ice concentration (SIC) on the subseasonal prediction of the North Atlantic Oscillation (NAO) event is investigated by utilizing the Community Atmospheric Model version 4. The optimal Arctic SIC perturbations which exert the greatest influence on the onset of an NAO event from a lead of three pentads (15 days) are obtained with a conditional nonlinear optimal perturbation approach. Numerical results show that there are two types of optimal Arctic SIC perturbations for each NAO event, with one weakening event (marked as type-1) and another strengthening event (marked as type-2). For positive NAO events, type-1 optimal SIC perturbations mainly show positive SIC anomalies in the Greenland, Barents, and Okhotsk Seas, while type-2 perturbations mainly feature negative SIC anomalies in these regions. For negative NAO events, the optimal SIC perturbations have almost opposite patterns to those in positive events, although there are some differences among these SIC perturbations due to different atmospheric initial conditions. Further diagnosis reveals that the optimal Arctic SIC perturbations first modify the surface turbulent heat flux and the temperature in the lower troposphere via diabatic processes. Afterward, the temperature in the low troposphere is mainly affected by dynamic advection. Finally, potential vorticity advection plays a crucial role in the 500-hPa geopotential height prediction in the northern North Atlantic sector during pentad 4, which influences NAO event prediction. These results highlight the importance of Arctic SIC on NAO event prediction and the spatial characteristics of the SIC perturbations may provide scientific support for target observations of SIC in improving NAO subseasonal predictions.
The influence of Arctic sea ice concentration (SIC) on the subseasonal prediction of the North Atlantic Oscillation (NAO) event is investigated by utilizing the Community Atmospheric Model version 4. The optimal Arctic SIC perturbations which exert the greatest influence on the onset of an NAO event from a lead of three pentads (15 days) are obtained with a conditional nonlinear optimal perturbation approach. Numerical results show that there are two types of optimal Arctic SIC perturbations for each NAO event, with one weakening event (marked as type-1) and another strengthening event (marked as type-2). For positive NAO events, type-1 optimal SIC perturbations mainly show positive SIC anomalies in the Greenland, Barents, and Okhotsk Seas, while type-2 perturbations mainly feature negative SIC anomalies in these regions. For negative NAO events, the optimal SIC perturbations have almost opposite patterns to those in positive events, although there are some differences among these SIC perturbations due to different atmospheric initial conditions. Further diagnosis reveals that the optimal Arctic SIC perturbations first modify the surface turbulent heat flux and the temperature in the lower troposphere via diabatic processes. Afterward, the temperature in the low troposphere is mainly affected by dynamic advection. Finally, potential vorticity advection plays a crucial role in the 500-hPa geopotential height prediction in the northern North Atlantic sector during pentad 4, which influences NAO event prediction. These results highlight the importance of Arctic SIC on NAO event prediction and the spatial characteristics of the SIC perturbations may provide scientific support for target observations of SIC in improving NAO subseasonal predictions.
2023, 40(12): 2262-2278.
doi: 10.1007/s00376-022-2033-2
Abstract:
This study examines the dependence of Arctic stratospheric polar vortex (SPV) variations on the meridional positions of the sea surface temperature (SST) anomalies associated with the first leading mode of North Pacific SST. The principal component 1 (PC1) of the first leading mode is obtained by empirical orthogonal function decomposition. Reanalysis data, numerical experiments, and CMIP5 model outputs all suggest that the PC1 events (positive-minus-negative PC1 events), located relatively northward (i.e., North PC1 events), more easily weaken the Arctic SPV compared to the PC1 events located relatively southward (i.e., South PC1 events). The analysis indicates that the North PC1-related Aleutian low anomaly is located over the northern North Pacific and thus enhances the climatological trough, which strengthens the planetary-scale wave 1 at mid-to-high latitudes and thereby weakens the SPV. The weakened stratospheric circulation further extends into the troposphere and favors negative surface temperature anomalies over Eurasia. By contrast, the South PC1-related Aleutian low anomaly is located relatively southward, and its constructive interference with the climatological trough is less efficient at high latitudes. Thus, the South PC1 events could not induce an evident enhancement of the planetary-scale waves at high latitudes and thereby a weakening of the SPV on average. The Eurasian cooling associated with South PC1 events (positive-minus-negative PC1 events) is also not prominent. The results of this study suggest that the meridional positions of the PC1 events may be useful for predicting the Arctic SPV and Eurasian surface temperature variations.
This study examines the dependence of Arctic stratospheric polar vortex (SPV) variations on the meridional positions of the sea surface temperature (SST) anomalies associated with the first leading mode of North Pacific SST. The principal component 1 (PC1) of the first leading mode is obtained by empirical orthogonal function decomposition. Reanalysis data, numerical experiments, and CMIP5 model outputs all suggest that the PC1 events (positive-minus-negative PC1 events), located relatively northward (i.e., North PC1 events), more easily weaken the Arctic SPV compared to the PC1 events located relatively southward (i.e., South PC1 events). The analysis indicates that the North PC1-related Aleutian low anomaly is located over the northern North Pacific and thus enhances the climatological trough, which strengthens the planetary-scale wave 1 at mid-to-high latitudes and thereby weakens the SPV. The weakened stratospheric circulation further extends into the troposphere and favors negative surface temperature anomalies over Eurasia. By contrast, the South PC1-related Aleutian low anomaly is located relatively southward, and its constructive interference with the climatological trough is less efficient at high latitudes. Thus, the South PC1 events could not induce an evident enhancement of the planetary-scale waves at high latitudes and thereby a weakening of the SPV on average. The Eurasian cooling associated with South PC1 events (positive-minus-negative PC1 events) is also not prominent. The results of this study suggest that the meridional positions of the PC1 events may be useful for predicting the Arctic SPV and Eurasian surface temperature variations.
2023, 40(12): 2279-2287.
doi: 10.1007/s00376-023-2188-5
Abstract:
The global wave model WAVEWATCH III® works well in open water. To simulate the propagation and attenuation of waves through ice-covered water, existing simulations have considered the influence of sea ice by adding the sea ice concentration in the wind wave module; however, they simply suppose that the wind cannot penetrate the ice layer and ignore the possibility of wind forcing waves below the ice cover. To improve the simulation performance of wind wave modules in the marginal ice zone (MIZ), this study proposes a parameterization scheme by directly including the sea ice thickness. Instead of scaling the wind input with the fraction of open water, this new scheme allows partial wind input in ice-covered areas based on the ice thickness. Compared with observations in the Barents Sea in 2016, the new scheme appears to improve the modeled waves in the high-frequency band. Sensitivity experiments with and without wind wave modules show that wind waves can play an important role in areas with low sea ice concentration in the MIZ.
The global wave model WAVEWATCH III® works well in open water. To simulate the propagation and attenuation of waves through ice-covered water, existing simulations have considered the influence of sea ice by adding the sea ice concentration in the wind wave module; however, they simply suppose that the wind cannot penetrate the ice layer and ignore the possibility of wind forcing waves below the ice cover. To improve the simulation performance of wind wave modules in the marginal ice zone (MIZ), this study proposes a parameterization scheme by directly including the sea ice thickness. Instead of scaling the wind input with the fraction of open water, this new scheme allows partial wind input in ice-covered areas based on the ice thickness. Compared with observations in the Barents Sea in 2016, the new scheme appears to improve the modeled waves in the high-frequency band. Sensitivity experiments with and without wind wave modules show that wind waves can play an important role in areas with low sea ice concentration in the MIZ.
2023, 40(12): 2288-2301.
doi: 10.1007/s00376-023-2338-9
Abstract:
A robust phenomenon termed the Arctic Amplification (AA) refers to the stronger warming taking place over the Arctic compared to the global mean. The AA can be confirmed through observations and reproduced in climate model simulations and shows significant seasonality and inter-model spread. This study focuses on the influence of surface type on the seasonality of AA and its inter-model spread by dividing the Arctic region into four surface types: ice-covered, ice-retreat, ice-free, and land. The magnitude and inter-model spread of Arctic surface warming are calculated from the difference between the abrupt-4 × CO2 and pre-industrial experiments of 17 CMIP6 models. The change of effective thermal inertia (ETI) in response to the quadrupling of CO2 forcing is the leading mechanism for the seasonal energy transfer mechanism, which acts to store heat temporarily in summer and then release it in winter. The ETI change is strongest over the ice-retreat region, which is also responsible for the strongest AA among the four surface types. The lack of ETI change explains the nearly uniform warming pattern across seasons over the ice-free (ocean) region. Compared to other regions, the ice-covered region shows the maximum inter-model spread in JFM, resulting from a stronger inter-model spread in the oceanic heat storage term. However, the weaker upward surface turbulent sensible and latent heat fluxes tend to suppress the inter-model spread. The relatively small inter-model spread during summer is caused by the cancellation of the inter-model spread in ice-albedo feedback with that in the oceanic heat storage term.
A robust phenomenon termed the Arctic Amplification (AA) refers to the stronger warming taking place over the Arctic compared to the global mean. The AA can be confirmed through observations and reproduced in climate model simulations and shows significant seasonality and inter-model spread. This study focuses on the influence of surface type on the seasonality of AA and its inter-model spread by dividing the Arctic region into four surface types: ice-covered, ice-retreat, ice-free, and land. The magnitude and inter-model spread of Arctic surface warming are calculated from the difference between the abrupt-4 × CO2 and pre-industrial experiments of 17 CMIP6 models. The change of effective thermal inertia (ETI) in response to the quadrupling of CO2 forcing is the leading mechanism for the seasonal energy transfer mechanism, which acts to store heat temporarily in summer and then release it in winter. The ETI change is strongest over the ice-retreat region, which is also responsible for the strongest AA among the four surface types. The lack of ETI change explains the nearly uniform warming pattern across seasons over the ice-free (ocean) region. Compared to other regions, the ice-covered region shows the maximum inter-model spread in JFM, resulting from a stronger inter-model spread in the oceanic heat storage term. However, the weaker upward surface turbulent sensible and latent heat fluxes tend to suppress the inter-model spread. The relatively small inter-model spread during summer is caused by the cancellation of the inter-model spread in ice-albedo feedback with that in the oceanic heat storage term.
2023, 40(12): 2302-2317.
doi: 10.1007/s00376-023-2320-6
Abstract:
This study evaluates the Arctic sea-ice simulation of the SODA3 dataset driven by different atmospheric forcing fields and explores the errors of the Arctic sea-ice simulation caused by the forcing field. We find that the SODA3 data driven by different forcing fields represent a significant systematical error in the simulation of Arctic sea-ice concentration, showing a low concentration of thick ice and a high concentration of thin ice. In terms of sea-ice extent, the SODA3 data from different versions well characterize the interannual variability and declining trend in the observed data, but they overestimate the overall Arctic sea-ice extent, which is related to excessive simulation of ice in the sea-ice margin. Compared to observations, all the chosen SODA3 reanalysis versions driven by different atmospheric forcing generally tend to underestimate the Arctic sea-ice thickness, especially for thick ice in the multi-year sea-ice regions. Inaccurate simulations of Arctic sea-ice transport may partly explain the error in SODA3 sea-ice thickness in multi-year sea-ice areas. The results of different SDOA3 versions differ greatly in the Beaufort Sea, the Fram Strait, and the Central Arctic Sea. The difference in sea-ice thickness among different SODA3 versions is primarily due to the thermodynamic contribution, which may come from the diversity of atmospheric forcing fields. Our work provides a reference for using SODA3 data to study Arctic sea ice.
This study evaluates the Arctic sea-ice simulation of the SODA3 dataset driven by different atmospheric forcing fields and explores the errors of the Arctic sea-ice simulation caused by the forcing field. We find that the SODA3 data driven by different forcing fields represent a significant systematical error in the simulation of Arctic sea-ice concentration, showing a low concentration of thick ice and a high concentration of thin ice. In terms of sea-ice extent, the SODA3 data from different versions well characterize the interannual variability and declining trend in the observed data, but they overestimate the overall Arctic sea-ice extent, which is related to excessive simulation of ice in the sea-ice margin. Compared to observations, all the chosen SODA3 reanalysis versions driven by different atmospheric forcing generally tend to underestimate the Arctic sea-ice thickness, especially for thick ice in the multi-year sea-ice regions. Inaccurate simulations of Arctic sea-ice transport may partly explain the error in SODA3 sea-ice thickness in multi-year sea-ice areas. The results of different SDOA3 versions differ greatly in the Beaufort Sea, the Fram Strait, and the Central Arctic Sea. The difference in sea-ice thickness among different SODA3 versions is primarily due to the thermodynamic contribution, which may come from the diversity of atmospheric forcing fields. Our work provides a reference for using SODA3 data to study Arctic sea ice.
2023, 40(12): 2318-2330.
doi: 10.1007/s00376-023-2235-2
Abstract:
Dramatic changes in the sea ice characteristics in the Barents Sea have potential consequences for the weather and climate systems of mid-latitude continents, Arctic ecosystems, and fisheries, as well as Arctic maritime navigation. Simulations and projections of winter sea ice in the Barents Sea based on the latest 41 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) are investigated in this study. Results show that most CMIP6 models overestimate winter sea ice in the Barents Sea and underestimate its decreasing trend. The discrepancy is mainly attributed to the simulation bias towards an overly weak ocean heat transport through the Barents Sea Opening and the underestimation of its increasing trend. The methods of observation-based model selection and emergent constraint were used to project future winter sea ice changes in the Barents Sea. Projections indicate that sea ice in the Barents Sea will continue to decline in a warming climate and that a winter ice-free Barents Sea will occur for the first time during 2042–2089 under the Shared Socioeconomic Pathway 585 (SSP5-8.5). Even in the observation-based selected models, the sensitivity of winter sea ice in the Barents Sea to global warming is weaker than observed, indicating that a winter ice-free Barents Sea might occur earlier than projected by the CMIP6 simulations.
Dramatic changes in the sea ice characteristics in the Barents Sea have potential consequences for the weather and climate systems of mid-latitude continents, Arctic ecosystems, and fisheries, as well as Arctic maritime navigation. Simulations and projections of winter sea ice in the Barents Sea based on the latest 41 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) are investigated in this study. Results show that most CMIP6 models overestimate winter sea ice in the Barents Sea and underestimate its decreasing trend. The discrepancy is mainly attributed to the simulation bias towards an overly weak ocean heat transport through the Barents Sea Opening and the underestimation of its increasing trend. The methods of observation-based model selection and emergent constraint were used to project future winter sea ice changes in the Barents Sea. Projections indicate that sea ice in the Barents Sea will continue to decline in a warming climate and that a winter ice-free Barents Sea will occur for the first time during 2042–2089 under the Shared Socioeconomic Pathway 585 (SSP5-8.5). Even in the observation-based selected models, the sensitivity of winter sea ice in the Barents Sea to global warming is weaker than observed, indicating that a winter ice-free Barents Sea might occur earlier than projected by the CMIP6 simulations.
2023, 40(12): 2331-2343.
doi: 10.1007/s00376-022-1460-4
Abstract:
This study assesses sea ice thickness (SIT) from the historical run of the Coupled Model Inter-comparison Project Phase 6 (CMIP6). The SIT reanalysis from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) product is chosen as the validation reference data. Results show that most models can adequately reproduce the climatological mean, seasonal cycle, and long-term trend of Arctic Ocean SIT during 1979–2014, but significant inter-model spread exists. Differences in simulated SIT patterns among the CMIP6 models may be related to model resolution and sea ice model components. By comparing the climatological mean and trend for SIT among all models, the Arctic SIT change in different seas during 1979–2014 is evaluated. Under the scenario of historical radiative forcing, the Arctic SIT will probably exponentially decay at –18% (10 yr)–1 and plausibly reach its minimum (equilibrium) of 0.47 m since the 2070s.
This study assesses sea ice thickness (SIT) from the historical run of the Coupled Model Inter-comparison Project Phase 6 (CMIP6). The SIT reanalysis from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) product is chosen as the validation reference data. Results show that most models can adequately reproduce the climatological mean, seasonal cycle, and long-term trend of Arctic Ocean SIT during 1979–2014, but significant inter-model spread exists. Differences in simulated SIT patterns among the CMIP6 models may be related to model resolution and sea ice model components. By comparing the climatological mean and trend for SIT among all models, the Arctic SIT change in different seas during 1979–2014 is evaluated. Under the scenario of historical radiative forcing, the Arctic SIT will probably exponentially decay at –18% (10 yr)–1 and plausibly reach its minimum (equilibrium) of 0.47 m since the 2070s.
2023, 40(12): 2344-2361.
doi: 10.1007/s00376-022-2176-1
Abstract:
In recent decades, Arctic summer sea ice extent (SIE) has shown a rapid decline overlaid with large interannual variations, both of which are influenced by geopotential height anomalies over Greenland (GL-high) and the central Arctic (CA-high). In this study, SIE along coastal Siberia (Sib-SIE) and Alaska (Ala-SIE) is found to account for about 65% and 21% of the Arctic SIE interannual variability, respectively. Variability in Ala-SIE is related to the GL-high, whereas variability in Sib-SIE is related to the CA-high. A decreased Ala-SIE is associated with decreased cloud cover and increased easterly winds along the Alaskan coast, promoting ice–albedo feedback. A decreased Sib-SIE is associated with a significant increase in water vapor and downward longwave radiation (DLR) along the Siberian coast. The years 2012 and 2020 with minimum recorded ASIE are used as examples. Compared to climatology, summer 2012 is characterized by a significantly enhanced GL-high with major sea ice loss along the Alaskan coast, while summer 2020 is characterized by an enhanced CA-high with sea ice loss focused along the Siberian coast. In 2012, the lack of cloud cover along the Alaskan coast contributed to an increase in incoming solar radiation, amplifying ice–albedo feedback there; while in 2020, the opposite occurs with an increase in cloud cover along the Alaskan coast, resulting in a slight increase in sea ice there. Along the Siberian coast, increased DLR in 2020 plays a dominant role in sea ice loss, and increased cloud cover and water vapor both contribute to the increased DLR.
In recent decades, Arctic summer sea ice extent (SIE) has shown a rapid decline overlaid with large interannual variations, both of which are influenced by geopotential height anomalies over Greenland (GL-high) and the central Arctic (CA-high). In this study, SIE along coastal Siberia (Sib-SIE) and Alaska (Ala-SIE) is found to account for about 65% and 21% of the Arctic SIE interannual variability, respectively. Variability in Ala-SIE is related to the GL-high, whereas variability in Sib-SIE is related to the CA-high. A decreased Ala-SIE is associated with decreased cloud cover and increased easterly winds along the Alaskan coast, promoting ice–albedo feedback. A decreased Sib-SIE is associated with a significant increase in water vapor and downward longwave radiation (DLR) along the Siberian coast. The years 2012 and 2020 with minimum recorded ASIE are used as examples. Compared to climatology, summer 2012 is characterized by a significantly enhanced GL-high with major sea ice loss along the Alaskan coast, while summer 2020 is characterized by an enhanced CA-high with sea ice loss focused along the Siberian coast. In 2012, the lack of cloud cover along the Alaskan coast contributed to an increase in incoming solar radiation, amplifying ice–albedo feedback there; while in 2020, the opposite occurs with an increase in cloud cover along the Alaskan coast, resulting in a slight increase in sea ice there. Along the Siberian coast, increased DLR in 2020 plays a dominant role in sea ice loss, and increased cloud cover and water vapor both contribute to the increased DLR.
2023, 40(12): 2362-2377.
doi: 10.1007/s00376-023-3004-y
Abstract:
Quantifying the contributions to Arctic sea level (ASL) variability is critical to understand how the Arctic is responsing to ongoing climate change. Here, we use Ocean Reanalysis System 5 (ORAS5) reanalysis data and tide gauge and satellite altimetry observations to quantify contributions from different physical processes on the ASL variability. The ORAS5 reanalysis shows that the ASL is rising with a trend of 2.5 ± 0.3 mm yr−1 (95% confidence level) over 1979–2018, which can be attributed to four components: (i) the dominant component from the global sea level increase of 1.9 ± 0.5 mm yr−1, explaining 69.7% of the total variance of the ASL time series; (ii) the Arctic Oscillation–induced mass redistribution between the deep central basin and shallow shelves, with no significant trend and explaining 6.3% of the total variance; (iii) the steric sea level increase centering on the Beaufort Gyre region with a trend of 0.5 ± 0.1 mm yr−1 and explaining 29.1% of the total variance of the ASL time series; and (iv) the intrusion of Pacific water into the Arctic Ocean, with no significant trend and contributing 14.2% of the total ASL variability. Furthermore, the dramatic sea ice melting and the larger area of open water changes the impact of the large-scale atmospheric forcing on the ASL variability after 1995, and the ocean dynamic circulation plays a more important role in the ASL variability.
Quantifying the contributions to Arctic sea level (ASL) variability is critical to understand how the Arctic is responsing to ongoing climate change. Here, we use Ocean Reanalysis System 5 (ORAS5) reanalysis data and tide gauge and satellite altimetry observations to quantify contributions from different physical processes on the ASL variability. The ORAS5 reanalysis shows that the ASL is rising with a trend of 2.5 ± 0.3 mm yr−1 (95% confidence level) over 1979–2018, which can be attributed to four components: (i) the dominant component from the global sea level increase of 1.9 ± 0.5 mm yr−1, explaining 69.7% of the total variance of the ASL time series; (ii) the Arctic Oscillation–induced mass redistribution between the deep central basin and shallow shelves, with no significant trend and explaining 6.3% of the total variance; (iii) the steric sea level increase centering on the Beaufort Gyre region with a trend of 0.5 ± 0.1 mm yr−1 and explaining 29.1% of the total variance of the ASL time series; and (iv) the intrusion of Pacific water into the Arctic Ocean, with no significant trend and contributing 14.2% of the total ASL variability. Furthermore, the dramatic sea ice melting and the larger area of open water changes the impact of the large-scale atmospheric forcing on the ASL variability after 1995, and the ocean dynamic circulation plays a more important role in the ASL variability.
2023, 40(12): 2378-2390.
doi: 10.1007/s00376-022-2040-3
Abstract:
Sea ice, one of the most dominant barriers to Arctic shipping, has decreased dramatically over the past four decades. Arctic maritime transport is hereupon growing in recent years. To produce a long-term assessment of trans-Arctic accessibility, we systematically revisit the daily Arctic navigability with a view to the combined effects of sea ice thickness and concentration throughout the period 1979−2020. The general trends of Navigable Windows (NW) in the Northeast Passage show that the number of navigable days is steadily growing and reached 89±16 days for Open Water (OW) ships and 163±19 days for Polar Class 6 (PC6) ships in the 2010s, despite high interannual and interdecadal variability in the NWs. More consecutive NWs have emerged annually for both OW ships and PC6 ships since 2005 because of the faster sea ice retreat. Since the 1980s, the number of simulated Arctic routes has continuously increased, and optimal navigability exists in these years of record-low sea ice extent (e.g., 2012 and 2020). Summertime navigability in the East Siberian and Laptev Seas, on the other hand, varies dramatically due to changing sea ice conditions. This systematic assessment of Arctic navigability provides a reference for better projecting the future trans-Arctic shipping routes.
Sea ice, one of the most dominant barriers to Arctic shipping, has decreased dramatically over the past four decades. Arctic maritime transport is hereupon growing in recent years. To produce a long-term assessment of trans-Arctic accessibility, we systematically revisit the daily Arctic navigability with a view to the combined effects of sea ice thickness and concentration throughout the period 1979−2020. The general trends of Navigable Windows (NW) in the Northeast Passage show that the number of navigable days is steadily growing and reached 89±16 days for Open Water (OW) ships and 163±19 days for Polar Class 6 (PC6) ships in the 2010s, despite high interannual and interdecadal variability in the NWs. More consecutive NWs have emerged annually for both OW ships and PC6 ships since 2005 because of the faster sea ice retreat. Since the 1980s, the number of simulated Arctic routes has continuously increased, and optimal navigability exists in these years of record-low sea ice extent (e.g., 2012 and 2020). Summertime navigability in the East Siberian and Laptev Seas, on the other hand, varies dramatically due to changing sea ice conditions. This systematic assessment of Arctic navigability provides a reference for better projecting the future trans-Arctic shipping routes.
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