Arctic sea ice in the PlioMIP ensemble: is model performance for modern climates a reliable guide to performance for the past or the future?

Abstract. Eight general circulation models have simulated the mid-Pliocene Warm Period (mPWP, 3.264 to 3.025 Ma) as part of the Pliocene Modelling Intercomparison Project (PlioMIP). Here, we analyse and compare their simulation of Arctic sea ice for both the pre-industrial and the mid-Pliocene. Mid-Pliocene sea ice thickness and extent is reduced and displays greater variability within the ensemble compared to the pre-industrial. This variability is highest in the summer months, when the model spread in the mid-Pliocene is more than three times larger than the rest of the year. Correlations between mid-Pliocene Arctic temperatures and sea ice extents are almost twice as strong as the equivalent correlations for the pre-industrial simulations. It is suggested that the weaker relationship between pre-industrial Arctic sea ice and temperatures is likely due to the tuning of climate models to achieve an optimal pre-industrial sea ice cover, which may also affect future predictions of Arctic sea ice. Model tuning for the pre-industrial does not appear to be best suited for simulating the different climate state of the mid-Pliocene. This highlights the importance of evaluating climate models through simulation of past climates, and the urgent need for more proxy evidence of sea ice during the Pliocene.

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Arctic sea ice simulation in the PlioMIP ensemble

Climate of the Past ◽

10.5194/cp-12-749-2016 ◽

2016 ◽

Vol 12 (3) ◽

pp. 749-767 ◽

Cited By ~ 14

Author(s):

Fergus W. Howell ◽

Alan M. Haywood ◽

Bette L. Otto-Bliesner ◽

Fran Bragg ◽

Wing-Le Chan ◽

...

Keyword(s):

Sea Ice ◽

General Circulation ◽

General Circulation Models ◽

Arctic Sea Ice ◽

Proxy Data ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Intercomparison Project ◽

Proxy Reconstructions

Abstract. Eight general circulation models have simulated the mid-Pliocene warm period (mid-Pliocene, 3.264 to 3.025 Ma) as part of the Pliocene Modelling Intercomparison Project (PlioMIP). Here, we analyse and compare their simulation of Arctic sea ice for both the pre-industrial period and the mid-Pliocene. Mid-Pliocene sea ice thickness and extent is reduced, and the model spread of extent is more than twice the pre-industrial spread in some summer months. Half of the PlioMIP models simulate ice-free conditions in the mid-Pliocene. This spread amongst the ensemble is in line with the uncertainties amongst proxy reconstructions for mid-Pliocene sea ice extent. Correlations between mid-Pliocene Arctic temperatures and sea ice extents are almost twice as strong as the equivalent correlations for the pre-industrial simulations. The need for more comprehensive sea ice proxy data is highlighted, in order to better compare model performances.

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Sea-ice freeboard or thickness? Design choices in the context of data assimilation in the coupled numerical prediction system EC-Earth3 for seasonal Arctic sea ice prediction

10.5194/egusphere-egu21-8317 ◽

2021 ◽

Author(s):

Francois Massonnet ◽

Sara Fleury ◽

Florent Garnier ◽

Ed Blockley ◽

Pablo Ortega Montilla ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

General Circulation ◽

Seasonal Prediction ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration

<p>It is well established that winter and spring Arctic sea-ice thickness anomalies are a key source of predictability for late summer sea-ice concentration. While numerical general circulation models (GCMs) are increasingly used to perform seasonal predictions, they are not systematically taking advantage of the wealth of polar observations available. Data assimilation, the study of how to constrain GCMs to produce a physically consistent state given observations and their uncertainties, remains, therefore, an active area of research in the field of seasonal prediction. With the recent advent of satellite laser and radar altimetry, large-scale estimates of sea-ice thickness have become available for data assimilation in GCMs. However, the sea-ice thickness is never directly observed by altimeters, but rather deduced from the measured sea-ice freeboard (the height of the emerged part of the sea ice floe) based on several assumptions like the depth of snow on sea ice and its density, which are both often poorly estimated. Thus, observed sea-ice thickness estimates are potentially less reliable than sea-ice freeboard estimates. Here, using the EC-Earth3 coupled forecasting system and an ensemble Kalman filter, we perform a set of sensitivity tests to answer the following questions: (1) Does the assimilation of late spring observed sea-ice freeboard or thickness information yield more skilful predictions than no assimilation at all? (2) Should the sea-ice freeboard assimilation be preferred over sea-ice thickness assimilation? (3) Does the assimilation of observed sea-ice concentration provide further constraints on the prediction? We address these questions in the context of a realistic test case, the prediction of 2012 summer conditions, which led to the all-time record low in Arctic sea-ice extent. We finally formulate a set of recommendations for practitioners and future users of sea ice observations in the context of seasonal prediction.</p>

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Constraining projections of summer Arctic sea ice

The Cryosphere ◽

10.5194/tc-6-1383-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 1383-1394 ◽

Cited By ~ 158

Author(s):

F. Massonnet ◽

T. Fichefet ◽

H. Goosse ◽

C. M. Bitz ◽

G. Philippon-Berthier ◽

...

Keyword(s):

Sea Ice ◽

General Circulation ◽

Thickness Distribution ◽

Coupled Model ◽

General Circulation Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

Arctic Sea ◽

Volume Characteristics

Abstract. We examine the recent (1979–2010) and future (2011–2100) characteristics of the summer Arctic sea ice cover as simulated by 29 Earth system and general circulation models from the Coupled Model Intercomparison Project, phase 5 (CMIP5). As was the case with CMIP3, a large intermodel spread persists in the simulated summer sea ice losses over the 21st century for a given forcing scenario. The 1979–2010 sea ice extent, thickness distribution and volume characteristics of each CMIP5 model are discussed as potential constraints on the September sea ice extent (SSIE) projections. Our results suggest first that the future changes in SSIE with respect to the 1979–2010 model SSIE are related in a complicated manner to the initial 1979–2010 sea ice model characteristics, due to the large diversity of the CMIP5 population: at a given time, some models are in an ice-free state while others are still on the track of ice loss. However, in phase plane plots (that do not consider the time as an independent variable), we show that the transition towards ice-free conditions is actually occurring in a very similar manner for all models. We also find that the year at which SSIE drops below a certain threshold is likely to be constrained by the present-day sea ice properties. In a second step, using several adequate 1979–2010 sea ice metrics, we effectively reduce the uncertainty as to when the Arctic could become nearly ice-free in summertime, the interval [2041, 2060] being our best estimate for a high climate forcing scenario.

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The Arctic and Antarctic Sea-Ice Area Index Records versus Measured and Modeled Temperature Data

Advances in Meteorology ◽

10.1155/2015/481834 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Nicola Scafetta ◽

Adriano Mazzarella

Keyword(s):

Sea Ice ◽

General Circulation ◽

General Circulation Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Climate Indices ◽

Area Index ◽

Antarctic Sea Ice ◽

Arctic Sea ◽

The Antarctic

Here we study the Arctic and Antarctic sea-ice area records provided by the National Snow and Ice Data Center (NSIDC). These records reveal an opposite climatic behavior: since 1978 the Arctic sea-ice area index decreased, that is, the region has warmed, while the Antarctic sea-ice area index increased, that is, the region has cooled. During the last 7 years the Arctic sea-ice area has stabilized while the Antarctic sea-ice area has increased at a rate significantly higher than during the previous decades; that is, the sea-ice area of both regions has experienced a positive acceleration. This result is quite robust because it is confirmed by alternative temperature climate indices of the same regions. We also found that a significant 4-5-year natural oscillation characterizes the climate of these sea-ice polar areas. On the contrary, we found that the CMIP5 general circulation models have predicted significant warming in both polar sea regions and failed to reproduce the strong 4-5-year oscillation. Because the CMIP5 GCM simulations are inconsistent with the observations, we suggest that important natural mechanisms of climate change are missing in the models.

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Constraining projections of summer Arctic sea ice

The Cryosphere Discussions ◽

10.5194/tcd-6-2931-2012 ◽

2012 ◽

Vol 6 (4) ◽

pp. 2931-2959 ◽

Cited By ~ 15

Author(s):

F. Massonnet ◽

T. Fichefet ◽

H. Goosse ◽

C. M. Bitz ◽

G. Philippon-Berthier ◽

...

Keyword(s):

Sea Ice ◽

General Circulation ◽

Thickness Distribution ◽

Coupled Model ◽

General Circulation Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

Arctic Sea ◽

Volume Characteristics

Abstract. We examine the recent (1979–2010) and future (2011–2100) characteristics of the summer Arctic sea ice cover as simulated by 29 Earth system and general circulation models from the Coupled Model Intercomparison Project, phase 5 (CMIP5). As was the case with CMIP3, a large inter-model spread persists in the simulated summer sea ice losses over the 21st century for a given forcing scenario. The initial 1979–2010 sea ice properties (including the sea ice extent, thickness distribution and volume characteristics) of each CMIP5 model are discussed as potential constraints on the September sea ice extent (SSIE) projections. Our results suggest first that the SSIE anomalies (compared to the 1979–2010 model SSIE) are related in a complicated manner to the initial 1979–2010 sea ice model characteristics, due to the large diversity of the CMIP5 population (at a given time, some models are in an ice-free state while others are still on the track of ice loss). In a new diagram (that does not consider the time as an independent variable) we show that the transition towards ice-free conditions is actually occuring in a very similar manner for all models. For these reasons, some quantities that do not explicitly depend on time, such as the year at which SSIE drops below a certain threshold, are likely to be constrained. In a second step, using several adequate 1979–2010 sea ice metrics, we effectively reduce the uncertainty as to when the Arctic could become nearly ice-free in summertime (between 2041 and 2060 for a high climate forcing scenario).

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Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss ?

Journal of Climate ◽

10.1175/jcli-d-20-0613.1 ◽

2021 ◽

pp. 1-54

Author(s):

Y. Peings ◽

Z. Labe ◽

G. Magnusdottir

Keyword(s):

Sea Ice ◽

General Circulation ◽

Internal Variability ◽

General Circulation Models ◽

Arctic Sea Ice ◽

Model Assessment ◽

Atmospheric Response ◽

Arctic Sea ◽

Ocean Atmosphere ◽

Arctic Sea Ice Loss

AbstractThis study presents results from the Polar Amplification Multimodel Intercomparison Project (PAMIP) single-year time-slice experiments that aim to isolate the atmospheric response to Arctic sea ice loss at global warming levels of +2°C. Using two General Circulation Models (GCMs), the ensemble size is increased up to 300 ensemble members, beyond the recommended 100 members. After partitioning the response in groups of 100-ensemble members, the reproducibility of the results is evaluated, with a focus on the response of the mid-latitude jet streams in the North Atlantic and North Pacific. Both atmosphere-only and coupled ocean-atmosphere PAMIP experiments are analyzed. Substantial differences in the mid-latitude response are found among the different experiment subsets, suggesting that 100-member ensembles are still significantly influenced by internal variability, which can mislead conclusions. Despite an overall stronger response, the coupled ocean-atmosphere runs exhibit greater spread due to additional ENSO-related internal variability when the ocean is interactive. The lack of consistency in the response is true for anomalies that are statistically significant according to Student’s-t and False Discovery Rate tests. This is problematic for the multi-model assessment of the response, as some of the spread may be attributed to different model sensitivities while it is due to internal variability. We propose a method to overcome this consistency issue, that allows for more robust conclusions when only 100 ensemble members are used.

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Ocean Heat Transport as a Cause for Model Uncertainty in Projected Arctic Warming

Journal of Climate ◽

10.1175/2010jcli3713.1 ◽

2011 ◽

Vol 24 (5) ◽

pp. 1451-1460 ◽

Cited By ~ 56

Author(s):

Irina Mahlstein ◽

Reto Knutti

Keyword(s):

Sea Ice ◽

Heat Transport ◽

General Circulation ◽

General Circulation Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Ocean Heat Transport ◽

Sea Ice Extent ◽

Arctic Sea ◽

Future Warming

Abstract The Arctic climate is governed by complex interactions and feedback mechanisms between the atmosphere, ocean, and solar radiation. One of its characteristic features, the Arctic sea ice, is very vulnerable to anthropogenically caused warming. Production and melting of sea ice is influenced by several physical processes. The authors show that the northward ocean heat transport is an important factor in the simulation of the sea ice extent in the current general circulation models. Those models that transport more energy to the Arctic show a stronger future warming, in the Arctic as well as globally. Larger heat transport to the Arctic, in particular in the Barents Sea, reduces the sea ice cover in this area. More radiation is then absorbed during summer months and is radiated back to the atmosphere in winter months. This process leads to an increase in the surface temperature and therefore to a stronger polar amplification. The models that show a larger global warming agree better with the observed sea ice extent in the Arctic. In general, these models also have a higher spatial resolution. These results suggest that higher resolution and greater complexity are beneficial in simulating the processes relevant in the Arctic and that future warming in the high northern latitudes is likely to be near the upper range of model projections, consistent with recent evidence that many climate models underestimate Arctic sea ice decline.

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Past and future interannual variability in Arctic sea ice in coupled climate models

The Cryosphere ◽

10.5194/tc-13-113-2019 ◽

2019 ◽

Vol 13 (1) ◽

pp. 113-124 ◽

Cited By ~ 8

Author(s):

John R. Mioduszewski ◽

Stephen Vavrus ◽

Muyin Wang ◽

Marika Holland ◽

Laura Landrum

Keyword(s):

Sea Ice ◽

Climate Models ◽

Coupled Model ◽

Arctic Sea Ice ◽

Cmip5 Models ◽

Coupled Climate Models ◽

Arctic Sea ◽

Ice Retreat ◽

Intercomparison Project ◽

Ice Pack

Abstract. The diminishing Arctic sea ice pack has been widely studied, but previous research has mostly focused on time-mean changes in sea ice rather than on short-term variations that also have important physical and societal consequences. In this study we test the hypothesis that future interannual Arctic sea ice area variability will increase by utilizing 40 independent simulations from the Community Earth System Model's Large Ensemble (CESM-LE) for the 1920–2100 period and augment this with simulations from 12 models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Both CESM-LE and CMIP5 models project that ice area variability will indeed grow substantially but not monotonically in every month. There is also a strong seasonal dependence in the magnitude and timing of future variability increases that is robust among CESM ensemble members. The variability generally correlates with the average ice retreat rate, before there is an eventual disappearance in both terms as the ice pack becomes seasonal in summer and autumn by late century. The peak in variability correlates best with the total area of ice between 0.2 and 0.6 m monthly thickness, indicating that substantial future thinning of the ice pack is required before variability maximizes. Within this range, the most favorable thickness for high areal variability depends on the season, especially whether ice growth or ice retreat processes dominate. Our findings suggest that thermodynamic melting (top, bottom, lateral) and growth (frazil, congelation) processes are more important than dynamical mechanisms, namely ice export and ridging, in controlling ice area variability.

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Assessment of Arctic sea ice simulations in CMIP5 models

10.5194/tc-2018-26 ◽

2018 ◽

Author(s):

Liping Wu ◽

Xiao-Yi Yang ◽

Jianyu Hu

Keyword(s):

Sea Ice ◽

Climate Models ◽

Model Performance ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

Cmip5 Models ◽

Late 20Th Century ◽

Arctic Sea ◽

Potential Factors

Abstract. The Arctic sea ice cover has experienced an unprecedented decline since the late 20th century. As a result, the feedback of sea ice 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 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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