An Initial Assessment of Antarctic Sea Ice Extent in the CMIP5 Models

Turner, John; Bracegirdle, Thomas J.; Phillips, Tony; Marshall, Gareth J.; Hosking, J. Scott

doi:10.1175/jcli-d-12-00068.1

Cited by 303 publications

(322 citation statements)

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“…In the case of sea ice this is a necessary (i.e., a biased baseline climatology will produce a biased projection) but, not sufficient (i.e., an accurate baseline climatology will not necessarily produce an accurate projection of future change) condition for producing reliable projections of future change. Despite the large differences between observed and simulated climatological sea ice extent in many climate models (Turner et al, 2013), previous projections for Southern Hemisphere sea ice have either treated all CMIP models equally (Collins et al, 2013) or used weightings based on other variables (Bracegirdle et al, 2008).…”

Section: Discussion Sea Ice In Ipcc-class Modelsmentioning

confidence: 99%

“…Overall, sea ice extent around the Antarctic ranges from a late winter peak of ∼19 million km 2 to a minimum of ∼3-4 million km 2 in summer (Massom and Stammerjohn, 2010). Since the late 1970s the annual mean total Southern Hemisphere sea ice extent has increased by ∼3%, albeit with considerable regional contrast and variability (Turner et al, 2013;Gagné et al, 2015;Simmonds, 2015). However, as the impacts of shorter term variability (associated with natural variability and ozone depletion) recede over longer time frames, this overall increasing trend is projected to reverse in direction (Bracegirdle et al, 2008;Turner et al, 2013).…”

Section: Datamentioning

confidence: 99%

“…There is strong evidence that this has had important effects on the Southern Ocean, in particular sea ice (Ferreira et al, 2015). However, major issues remain with regard to reproducing the observed physical state of the Southern Ocean in global climate models, and these have implications for the reliability of predicted responses to future climate forcing (Turner et al, 2013;Meijers, 2014).…”

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confidence: 99%

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A Synergistic Approach for Evaluating Climate Model Output for Ecological Applications

et al. 2017

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Increasing concern about the impacts of climate change on ecosystems is prompting ecologists and ecosystem managers to seek reliable projections of physical drivers of change. The use of global climate models in ecology is growing, although drawing ecologically meaningful conclusions can be problematic. The expertise required to access and interpret output from climate and earth system models is hampering progress in utilizing them most effectively to determine the wider implications of climate change. To address this issue, we present a joint approach between climate scientists and ecologists that explores key challenges and opportunities for progress. As an exemplar, our focus is the Southern Ocean, notable for significant change with global implications, and on sea ice, given its crucial role in this dynamic ecosystem. We combined perspectives to evaluate the representation of sea ice in global climate models. With an emphasis on ecologically-relevant criteria (sea ice extent and seasonality) we selected a subset of eight models that reliably reproduce extant sea ice distributions. While the model subset shows a similar mean change to the full ensemble in sea ice extent (approximately 50% decline in winter and 30% decline in summer), there is a marked reduction in the range. This improved the precision of projected future sea ice distributions by approximately one third, and means they are more amenable to ecological interpretation. We conclude that careful multidisciplinary evaluation of climate models, in conjunction with ongoing modeling advances, should form an integral part of utilizing model output.

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Section: Discussion Sea Ice In Ipcc-class Modelsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

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A Synergistic Approach for Evaluating Climate Model Output for Ecological Applications

et al. 2017

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“…In general, coupled climate models fail to reproduce the observed expansion of Antarctic sea ice during the recent decades (Turner et al, 2013). This may be due to the large role of interannual variability in this increasing trend (Zunz et al, 2013;Mahlstein et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Impact of surface wind biases on the Antarctic sea ice concentration budget in climate models

et al. 2016

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Highlights• The ice concentration budget (ICB) is applied to several forced and coupled models.• Ice drift and 10 m wind vectors are evaluated against observations and reanalyses.• Biases in winds are largely responsible for biases in the ICB in free drift regions.• Errors in winds and sea ice model physics are equally important closer to the coast.• The method provides a new tool for climate model intercomparisons. AbstractWe derive the terms in the Antarctic sea ice concentration budget from the output of three models, and compare them to observations of the same terms. Those models include two climate models from the 5th Coupled Model Intercomparison Project (CMIP5) and one ocean-sea ice coupled model with prescribed atmospheric forcing. Sea ice drift and wind fields from those models, in average over April-October 1992-2005, all exhibit large differences with the available observational or reanalysis datasets. However, the discrepancies between the two distinct ice drift products or the two wind reanalyses used here are sometimes even greater than those differences. Two major findings stand out from the analysis. Firstly, large biases in sea ice drift speed and direction in exterior sectors of the sea ice covered region tend to be systematic and consistent with those in winds. This suggests that sea ice errors in these areas are most likely wind-driven, so as errors in the simulated ice motion vectors. The systematic nature of these biases is less prominent in interior sectors, nearer the coast, where sea ice is mechanically constrained and its motion in response to the wind forcing more depending on the model rheology. Second, the intimate relationship between winds, sea ice drift and the sea ice concentration budget gives insight on ways to categorize models with regard to errors in their ice dynamics. In exterior regions, models with seemingly too weak winds and slow ice drift consistently yield a lack of ice velocity divergence and hence a wrong wintertime sea ice growth rate. In interior sectors, too slow ice drift, presumably originating from issues in the physical representation of sea ice dynamics as much as from errors in surface winds, leads to wrong timing of the late winter ice retreat. Those results illustrate that the applied methodology provides a valuable tool for prioritizing model improvements based on the ice concentration budget-ice * Corresponding author

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“…processes responsible for the observed SIE increase over the last 30 years are not 1! being simulated correctly (Turner et al 2013c). …”

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confidence: 97%

From Ice to Penguins: The Role of Mathematics in Antarctic Research

Xavier

Hill

Belchier

et al. 2015

CIM Series in Mathematical Sciences

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An Initial Assessment of Antarctic Sea Ice Extent in the CMIP5 Models

Cited by 303 publications

References 13 publications

A Synergistic Approach for Evaluating Climate Model Output for Ecological Applications

A Synergistic Approach for Evaluating Climate Model Output for Ecological Applications

Impact of surface wind biases on the Antarctic sea ice concentration budget in climate models

From Ice to Penguins: The Role of Mathematics in Antarctic Research

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