Arctic sea ice decline: Faster than forecast

Stroeve, Julienne; Holland, Marika M.; Meier, Walter N.; Scambos, Ted; Serreze, Mark C.

doi:10.1029/2007gl029703

Cited by 1,626 publications

(1,175 citation statements)

References 33 publications

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“…In recent years, a drastic decrease in sea ice has been reported for the western Arctic Ocean during the summer months, and even greater related changes in sea surface temperatures have been reported (Stroeve et al, 2007;Steele et al, 2008). The changes in sea surface temperatures, increases in the frequency and intensity of cyclones, and northward shifts from their tracks during the summer months as well as during other seasons have also been reported (Serreze et al, 2000;McCabe et al, 2001;Sepp and Jaagus, 2011).…”

Section: Introductionmentioning

confidence: 88%

Short-term changes in a microplankton community in the Chukchi Sea during autumn: consequences of a strong wind event

et al. 2016

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Abstract.Recent studies indicate an increase in atmospheric turbulence in the Chukchi Sea due to the recent drastic sea-ice reduction during summer months. The importance of the effects of this atmospheric turbulence on the marine ecosystem in this region, however, is not fully understood. To evaluate the effects of atmospheric turbulence on the marine ecosystem, high-frequency sampling (daily) from five layers of the microplankton community between 0 and 30 m at a fixed station in the Chukchi Sea from 10 through 25 September 2013 was conducted. During the study period, a strong wind event (SWE) was observed on 18 and 19 September. The abundance of microplankton was 2.6 to 17.6 cells mL −1 , with a maximum abundance being reported at 20 m on 22 September, while diatoms were the most dominant taxa throughout the study period. The abundance of diatoms, dinoflagellates and ciliates ranged between 1.6 and 14.1, 0.5 and 2.4 and 0.1 and 2.8 cells mL −1 , respectively. Diatoms belonging to 7 genera consisting of 35 species (Cylindrotheca closterium and Leptocylindrus danicus were dominant), dinoflagellates belonging to 7 genera consisting of 25 species (Prorocentrum balticum and Gymnodinium spp. were dominant) and ciliates belonging to 7 genera consisting of 8 species (Strobilidium spp. and Strombidium spp. were dominant) were identified. Within the microplankton species, there were 11 species with abundances that increased after the SWE, while there was no species with an abundance that decreased following the SWE. It is conjectured that atmospheric turbulences, such as that of an SWE, may supply sufficient nutrients to the surface layer that subsequently enhance the small bloom under the weak stratification of the Chukchi Sea Shelf during the autumn months. After the bloom, the dominant diatom community then shifts from centric-dominated to one where centric/pennate are more equal in abundance.

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Section: Introductionmentioning

confidence: 88%

Short-term changes in a microplankton community in the Chukchi Sea during autumn: consequences of a strong wind event

et al. 2016

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“…Arctic change has been reflected in decreasing sea ice thickness (Kwok and Rothrock 2009), reduced summer sea ice extent (Stroeve et al 2007(Stroeve et al , 2008Comiso 2012), changed freshwater content and distribution (McPhee et al 2009;Rabe et al 2011;Morison et al 2012;Giles et al 2012), and increased atmospheric temperature (Overland et al 2008) and oceanic heat content (Steele et al 2008;Polyakov et al 2010). Changes in all these properties are of concern given their possible linkages with global climate, for example by controlling stratification in the sub-Arctic seas and thereby modulating convection and the meridional overturning circulation (Aagaard and Carmack 1989;Hu et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Arctic Ocean Circulation Patterns Revealed by GRACE

et al. 2014

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Measurements of ocean bottom pressure (OBP) anomalies from the satellite mission Gravity Recovery and Climate Experiment (GRACE), complemented by information from two ocean models, are used to investigate the variations and distribution of the Arctic Ocean mass from 2002 through 2011. The forcing and dynamics associated with the observed OBP changes are explored. Major findings are the identification of three primary temporal-spatial modes of OBP variability at monthly-to-interannual time scales with the following characteristics. Mode 1 (50% of the variance) is a wintertime basin-coherent Arctic mass change forced by southerly winds through Fram Strait, and to a lesser extent through Bering Strait. These winds generate northward geostrophic current anomalies that increase the mass in the Arctic Ocean. Mode 2 (20%) reveals a mass change along the Siberian shelves, driven by surface Ekman transport and associated with the Arctic Oscillation. Mode 3 (10%) reveals a mass dipole, with mass decreasing in the Chukchi, East Siberian, and Laptev Seas, and mass increasing in the Barents and Kara Seas. During the summer, the mass decrease on the East Siberian shelves is due to the basin-scale anticyclonic atmospheric circulation that removes mass from the shelves via Ekman transport. During the winter, the forcing mechanisms include a large-scale cyclonic atmospheric circulation in the eastern-central Arctic that produces mass divergence into the Canada Basin and the Barents Sea. In addition, strengthening of the Beaufort high tends to remove mass from the East Siberian and Chukchi Seas. Supporting previous modeling results, the month-to-month variability in OBP associated with each mode is predominantly of barotropic character.

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“…For the integrations considered here, the atmosphere model (CAM3) (Collins et al 2006b) is run at T85 resolution (approximately 1.4 degrees) with 26 vertical levels. The ocean model (Smith and Gent 2004) includes an isopycnal transport parameterization (Gent and McWilliams 1990) and a surface boundary layer formulation following Large et al (1994). The dynamic-thermodynamic sea ice model (Briegleb et al 2004;Holland et al 2006b) uses the elasticviscous-plastic rheology (Hunke and Dukowicz 1997), a sub-gridscale ice thickness distribution (Thorndike et al 1975;Lipscomb 2001) and the thermodynamics of Bitz and Lipscomb (1999).…”

Section: Climate Model Integrationsmentioning

confidence: 99%

Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3

2010

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Seasonal predictions of Arctic sea ice have typically been based on statistical regression models or on results from ensemble ice model forecasts driven by historical atmospheric forcing. However, in the rapidly changing Arctic environment, the predictability characteristics of summer ice cover could undergo important transformations. Here global coupled climate model simulations are used to assess the inherent predictability of Arctic sea ice conditions on seasonal to interannual timescales within the Community Climate System Model, version 3. The role of preconditioning of the ice cover versus intrinsic variations in determining sea ice conditions is examined using ensemble experiments initialized in January with identical ice-ocean-terrestrial conditions. Assessing the divergence among the ensemble members reveals that sea ice area exhibits potential predictability during the first summer and for winter conditions after a year. The ice area exhibits little potential predictability during the spring transition season. Comparing experiments initialized with different mean ice conditions indicates that ice area in a thicker sea ice regime generally exhibits higher potential predictability for a longer period of time. In a thinner sea ice regime, winter ice conditions provide little ice area predictive capability after approximately 1 year. In all regimes, ice thickness has high potential predictability for at least 2 years.

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Arctic sea ice decline: Faster than forecast

Cited by 1,626 publications

References 33 publications

Short-term changes in a microplankton community in the Chukchi Sea during autumn: consequences of a strong wind event

Short-term changes in a microplankton community in the Chukchi Sea during autumn: consequences of a strong wind event

Arctic Ocean Circulation Patterns Revealed by GRACE

Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3

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