Uncertainty in modeled Arctic sea ice volume

Schweiger, Axel; Lindsay, R. W.; Zhang, Jinlun; Steele, Michael; Stern, Harry L.; Kwok, R.

doi:10.1029/2011jc007084

Cited by 543 publications

(684 citation statements)

References 40 publications

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“…As a result the trend in September ice volume was also very different, 22.6 3 10 3 km 3 decade 21 for the NCEP-R1 forcing compared to a much weaker trend in the run with thinner ice, 21.6 3 10 3 km 3 decade 21 for the ERA-Interim forcing. In contrast, our range of differences in mean state and volume trends using different forcing datasets is substantially smaller and in line with uncertainties estimated through independent ice thickness validation (Schweiger et al 2011). The ability of this class of models to reproduce the mean state is important for assessing trend sensitivities of the models to different forcing datasets.…”

Section: Forcing An Ice-ocean Modelsupporting

confidence: 74%

“…Reanalysis datasets are used to provide surface atmospheric variables to link the historic state of the atmosphere to the dynamically evolving ice-ocean state in the model. A notable application of this class of models is the production of retrospective time series of sea ice thickness and volume (e.g., Zhang and Rothrock 2003;Hunke and Holland 2007;Lindsay et al 2009;Schweiger at al. 2011;Notz et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

et al. 2014

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Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice-ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981-2010: National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP-U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice-ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R 5 0.82). The trend in the total ice volume in September is greatest with MERRA (24.1 3 10 3 km 3 decade 21 ) and least with CFSR (22.7 3 10 3 km 3 decade 21 ).

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Section: Forcing An Ice-ocean Modelsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

et al. 2014

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“…The "best" estimate of sea ice volume (extent multiplied by thickness) from the recently updated Pan Arctic Ice Modeling and Assimilation System (11) shows that September sea ice volume has decreased ∼75% from 1979 to 2011, which is faster than the observed decrease of September sea ice extent over the same period (∼36%, ref. 12).…”

mentioning

confidence: 99%

Reducing spread in climate model projections of a September ice-free Arctic

Liu

Song

Horton

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

163

120

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This paper addresses the specter of a September ice-free Arctic in the 21st century using newly available simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We find that large spread in the projected timing of the September ice-free Arctic in 30 CMIP5 models is associated at least as much with different atmospheric model components as with initial conditions. Here we reduce the spread in the timing of an ice-free state using two different approaches for the 30 CMIP5 models: (i) model selection based on the ability to reproduce the observed sea ice climatology and variability since 1979 and (ii) constrained estimation based on the strong and persistent relationship between present and future sea ice conditions. Results from the two approaches show good agreement. Under a high-emission scenario both approaches project that September ice extent will drop to ∼1.7 million km 2 in the mid 2040s and reach the ice-free state (defined as 1 million km 2 ) in 2054-2058. Under a medium-mitigation scenario, both approaches project a decrease to ∼1.7 million km 2 in the early 2060s, followed by a leveling off in the ice extent.A rctic sea ice has undergone dramatic decline in recent years (1). The minimum sea ice extent set on September 16, 2012 (3.41 million km 2 , ref.2) was 48.5% below the long-term mean and broke the previous record minimum set on September 18, 2007. The last six years (2007)(2008)(2009)(2010)(2011)(2012) have featured the lowest September ice extents during the satellite era. This decline raises the specter of a September ice-free Arctic in the coming decades, which would have significant impacts on Arctic maritime activities and ecosystems, biogeochemical feedbacks, and extreme weather and climate in mid and high latitudes (3, 4).The Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) concluded that "Arctic sea ice responds sensitively to warming, . . . late-summer sea ice is projected to disappear almost completely towards the end of the 21st century under the A2 scenario in some models" (5). Subsequent research suggested the ice-free Arctic might occur from as early as the late 2030s to the end of the 21st century under the Special Report on Emissions Scenarios (SRES) A1B and A2 scenarios based on the IPCC AR4 model simulations (also referred to as the CMIP3). An abrupt reduction in sea ice cover (i.e., 2-6 million km 2 within a decade) during the 21st century seems to be a common feature in a number of climate projections by the IPCC AR4 models. This could result in ice-free September conditions (less than ∼1.5 million km 2 ) by 2040 according to one simulation from the Community Climate System Model (CCSM) (6). Using the observed September sea ice extent in 2007 as a starting point, together with the projections of a subset of the IPCC AR4 models that better reproduce the observed climatological September ice extent, one study suggested the Arctic could be ice-free in September (less than 1 million km 2 ) in the late 2030s (with a large uncertainty...

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“…Because of lacking sufficient long-term sea ice thickness observations, we compare our simulated solid FW content with the 25 estimate from the PIOMAS Arctic sea ice volume reanalysis (Schweiger et al, 2011). The simulated mean solid FW content in the period of 1980-2000 is about 20% higher than the PIOMAS estimate (Table 1).…”

Section: Sea Ice and Solid Freshwatermentioning

confidence: 94%

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM1.4

Wang¹,

Wekerle²,

Danilov³

et al. 2017

Preprint

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Abstract. In the framework of developing a global modeling system which can facilitate modeling studies on Arctic Ocean and high-mid latitude linkage, we evaluate the Arctic Ocean simulated by the multi-resolution ocean sea-ice model FESOM.To explore the value of using high horizontal resolution for Arctic Ocean modeling, we use two global meshes differing in Atlantic Water (AW) mean state and variability. The deepening and thickening bias of the AW layer, a common issue found in coarse resolution simulations, is significantly alleviated by using higher resolution. The topographic steering of the AW is stronger and the seasonal and interannual temperature variability along the ocean bottom topography is enhanced in the high resolution simulation. The high resolution also improves the ocean surface circulation, mainly through a better representation of the narrow straits in the Canadian Arctic Archipelago (CAA). The representation of CAA throughflow not only influences 10 the release of water masses through the other gateways, but also the circulation pathways inside the Arctic Ocean. However, the mean state and variability of Arctic freshwater content and the variability of freshwater transport through the Arctic gateways appear not to be very sensitive to the increase in resolution employed here. We also discuss model issues that are not directly linked to resolution, highlighting the need for further model development including improving parameterizations.

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Uncertainty in modeled Arctic sea ice volume

Cited by 543 publications

References 40 publications

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

Reducing spread in climate model projections of a September ice-free Arctic

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM1.4

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