Faster Arctic Sea Ice Retreat in CMIP5 than in CMIP3 due to Volcanoes

Rosenblum, Erica; Eisenman, Ian

doi:10.1175/jcli-d-16-0391.1

Cited by 44 publications

(36 citation statements)

References 34 publications

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“…These estimates cover the range of findings by Rosenblum and Eisenman (2016) and Screen and Williamson (2017). There is substantial observational uncertainty of Arctic sea ice sensitivity for a given degree of global warming.…”

Section: Discussionsupporting

confidence: 54%

“…Rosenblum and Eisenman (2016) estimate that the Arctic Ocean loses its summer sea ice once global warming rises above 1 ∘ C relative to the period 1980-1999. For the high-sensitivity observational estimate, we find that sea ice area in September drops below this threshold for about 1.7 ∘ C global warming.…”

Section: Sea Ice Area As a Function Of Global-mean Surface Temperaturementioning

confidence: 99%

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Arctic Sea Ice in a 1.5°C Warmer World

Niederdrenk

Notz

2018

Geophysical Research Letters

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We examine the seasonal cycle of Arctic sea ice in scenarios with limited future global warming. To do so, we analyze two sets of observational records that cover the observational uncertainty of Arctic sea ice loss per degree of global warming. The observations are combined with 100 simulations of historical and future climate evolution from the Max Planck Institute Earth System Model Grand Ensemble. Based on the high‐sensitivity observations, we find that Arctic September sea ice is lost with low probability (P≈ 10%) for global warming of +1.5°C above preindustrial levels and with very high probability (P> 99%) for global warming of +2°C above preindustrial levels. For the low‐sensitivity observations, September sea ice is extremely unlikely to disappear for +1.5°C warming (P≪ 1%) and has low likelihood (P≈ 10%) to disappear even for +2°C global warming. For March, both observational records suggest a loss of 15% to 20% of Arctic sea ice area for 1.5°C to 2°C global warming.

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Section: Discussionsupporting

confidence: 54%

Section: Sea Ice Area As a Function Of Global-mean Surface Temperaturementioning

confidence: 99%

Arctic Sea Ice in a 1.5°C Warmer World

Niederdrenk

Notz

2018

Geophysical Research Letters

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“…The cause of this reduction in model bias is analyzed in a companion paper (Rosenblum and Eisenman 2016). The ensemblemean Arctic sea ice trend in CMIP5 is still slower than observed (Stroeve et al 2012;IPCC 2013), but the observations fall within the range of simulations (Figures 1b,e).…”

Section: Introductionmentioning

confidence: 89%

Sea Ice Trends in Climate Models Only Accurate in Runs with Biased Global Warming

2017

Self Cite

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Observations indicate that the Arctic sea ice cover is rapidly retreating while the Antarctic sea ice cover is steadily expanding. State-of-the-art climate models, by contrast, typically simulate a moderate decrease in both the Arctic and Antarctic sea ice covers. However, in each hemisphere there is a small subset of model simulations that have sea ice trends similar to the observations. Based on this, a number of recent studies have suggested that the models are consistent with the observations in each hemisphere when simulated internal climate variability is taken into account. Here we examine sea ice changes during 1979-2013 in simulations from the most recent Coupled Model Intercomparison Project (CMIP5) as well as the Community Earth System Model Large Ensemble (CESM-LE), drawing on previous work that found a close relationship in climate models between global-mean surface temperature and sea ice extent. We find that all of the simulations with 1979-2013 Arctic sea ice retreat as fast as observed have considerably more global warming than observations during this time period. Using two separate methods to estimate the sea ice retreat that would occur under the observed level of global warming in each simulation in both ensembles, we find that simulated Arctic sea ice retreat as fast as observed would occur less than 1% of the time. This implies that the models are not consistent with the observations. In the Antarctic, we find that simulated sea ice expansion as fast as observed typically corresponds with too little global warming, although these results are more equivocal. We show that because of this, the simulations do not capture the observed asymmetry between Arctic and Antarctic sea ice trends. This suggests that the models may be getting the right sea ice trends for the wrong reasons in both polar regions.

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“…The IPCC fifth assessment report shows that the models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5, http://pcmdi3.llnl.gov/esgcet) have a higher performance than those in the Coupled Model Intercomparison Project phase 3 (CMIP3) in their capability of reproducing the long-term trends of sea ice. Rosenblum and Eisenman (2016) found that the inclusion of 5 volcanic activity, rather than improvement of sea ice physics or model resolution, accounts for the priority of the CMIP5 over the CMIP3 in simulating the Arctic sea ice trends. Nevertheless, the CMIP5 model simulation results are far from satisfying, especially in the Antarctic region.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Arctic sea ice simulations in CMIP5 models

Yang

2018

Preprint

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Abstract. The Arctic sea ice cover has experienced an unprecedented decline since the late 20 th century. As a result, the feedback of sea ice 5 anomalies to atmospheric circulation has been increasingly evidenced. While the climate models almost consistently reproduce the downward trend of sea ice cover, great dispersion between them still exists. To evaluate the model performance in simulating Arctic sea ice and its potential role in climate change, we constructed a reasonable metric by synthesizing the linear trends and anomalies of the sea ice. We particularly focus on the Barents and Kara seas, where the sea ice anomalies have the greatest potential to feedback the atmosphere. Models can be grouped into three categories according to this criterion. The strong contrast among the multi-model ensemble means in different groups demonstrates the robustness 10 and rationality of this method. The potential factors accounting for the different performance of climate models are further explored. The result shows that the model performance depends more on the ozone datasets prescribed by model rather than on the chemistry representation of ozone.

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Faster Arctic Sea Ice Retreat in CMIP5 than in CMIP3 due to Volcanoes

Cited by 44 publications

References 34 publications

Arctic Sea Ice in a 1.5°C Warmer World

Arctic Sea Ice in a 1.5°C Warmer World

Sea Ice Trends in Climate Models Only Accurate in Runs with Biased Global Warming

Assessment of Arctic sea ice simulations in CMIP5 models

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