scholarly journals Assessment of sea ice simulations in the CMIP5 models

2015 ◽  
Vol 9 (1) ◽  
pp. 399-409 ◽  
Author(s):  
Q. Shu ◽  
Z. Song ◽  
F. Qiao
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Linear Trend ◽  
Ocean Modeling ◽  
The Arctic ◽  
Cmip5 Models ◽  
Output Data ◽  
Sea Ice Extent ◽  
The Antarctic ◽  
Assimilation System

Abstract. The historical simulations of sea ice during 1979 to 2005 by the Coupled Model Intercomparison Project Phase 5 (CMIP5) are compared with satellite observations, Global Ice-Ocean Modeling and Assimilation System (GIOMAS) output data and Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) output data in this study. Forty-nine models, almost all of the CMIP5 climate models and earth system models with historical simulation, are used. For the Antarctic, multi-model ensemble mean (MME) results can give good climatology of sea ice extent (SIE), but the linear trend is incorrect. The linear trend of satellite-observed Antarctic SIE is 1.29 (±0.57) × 105 km2 decade−1; only about 1/7 CMIP5 models show increasing trends, and the linear trend of CMIP5 MME is negative with the value of −3.36 (±0.15) × 105 km2 decade−1. For the Arctic, both climatology and linear trend are better reproduced. Sea ice volume (SIV) is also evaluated in this study, and this is a first attempt to evaluate the SIV in all CMIP5 models. Compared with the GIOMAS and PIOMAS data, the SIV values in both the Antarctic and the Arctic are too small, especially for the Antarctic in spring and winter. The GIOMAS Antarctic SIV in September is 19.1 × 103 km3, while the corresponding Antarctic SIV of CMIP5 MME is 13.0 × 103 km3 (almost 32% less). The Arctic SIV of CMIP5 in April is 27.1 × 103 km3, which is also less than that from PIOMAS SIV (29.5 × 103 km3). This means that the sea ice thickness simulated in CMIP5 is too thin, although the SIE is fairly well simulated.

scholarly journals Assessment of sea ice simulations in the CMIP5 Models

2014 ◽  
Vol 8 (3) ◽  
pp. 3413-3435
Author(s):  
Q. Shu ◽  
Z. Song ◽  
F. Qiao
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Linear Trend ◽  
Coupled Model ◽  
Ocean Modeling ◽  
The Arctic ◽  
Cmip5 Models ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Almost All

Abstract. The historical simulations of sea ice during 1979 to 2005 by the Coupled Model Intercomparison Project Phase 5 (CMIP5) are compared with satellite observations and Global Ice–Ocean Modeling and Assimilation System (GIOMAS) data in this study. Forty-nine models, almost all of the CMIP5 climate models and Earth System Models, are used. For the Antarctic, multi-model ensemble mean (MME) results can give good climatology of sea ice extent (SIE), but the linear trend is incorrect. The linear trend of satellite-observed Antarctic SIE is 1.56 × 105 km2 decade−1; only 1/7 CMIP5 models show increasing trends, and the linear trend of CMIP5 MME is negative (−3.36 × 105 km2 decade−1). For the Arctic, both climatology and linear trend are better reproduced. Sea ice volume (SIV) is also evaluated in this study, and this is a first attempt to evaluate the SIV in all CMIP5 models. Compared with the GIOMAS data, the SIV values in both Antarctic and Arctic are too small, especially in spring and winter. The GIOMAS SIV in September is 16.7 × 103 km3, while the corresponding Antarctic SIV of CMIP5 MME is 13.0 × 103 km3, almost 22% less. The Arctic SIV of CMIP5 in April is 26.8 × 103 km3, which is also less than the GIOMAS SIV (29.3 × 103 km3). This means that the sea ice thickness simulated in CMIP5 is too thin although the SIE is fairly well simulated.

scholarly journals A Regime Shift in Seasonal Total Antarctic Sea Ice Extent in the 20th Century

2021 ◽  
Author(s):  
Ryan Fogt ◽  
Amanda Sleinkofer ◽  
Marilyn Raphael ◽  
Mark Handcock
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
Climate Models ◽  
Historical Context ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Coupled Climate Models ◽  
High Degree ◽  
The Antarctic

Abstract In stark contrast to the Arctic, there have been statistically significant positive trends in total Antarctic sea ice extent since 1979, despite a sudden decline in sea ice in 2016(1–5) and increasing greenhouse gas concentrations. Attributing Antarctic sea ice trends is complicated by the fact that most coupled climate models show negative trends in sea ice extent since 1979, opposite of that observed(6–8). Additionally, the short record of sea ice extent (beginning in 1979), coupled with the high degree of interannual variability, make the record too short to fully understand the historical context of these recent changes(9). Here we show, using new robust observation-based reconstructions, that 1) these observed recent increases in Antarctic sea ice extent are unique in the context of the 20th century and 2) the observed trends are juxtaposed against statistically significant decreases in sea ice extent throughout much of the early and middle 20th century. These reconstructions are the first to provide reliable estimates of total sea ice extent surrounding the continent; previous proxy-based reconstructions are limited(10). Importantly, the reconstructions continue to show the high degree of interannual Antarctic sea ice extent variability that is marked with frequent sudden changes, such as observed in 2016, which stress the importance of a longer historical context when assessing and attributing observed trends in Antarctic climate(9). Our reconstructions are skillful enough to be used in climate models to allow better understanding of the interconnected nature of the Antarctic climate system and to improve predictions of the future state of Antarctic climate.

scholarly journals Recent changes in Antarctic Sea Ice

2015 ◽  
Vol 373 (2045) ◽  
pp. 20140163 ◽  
Author(s):  
John Turner ◽  
J. Scott Hosking ◽  
Thomas J. Bracegirdle ◽  
Gareth J. Marshall ◽  
Tony Phillips
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Climate Models ◽  
Surface Wind ◽  
The Arctic ◽  
Cmip5 Models ◽  
Intrinsic Variability ◽  
Sea Ice Extent ◽  
Amundsen Sea ◽  
Near Surface

In contrast to the Arctic, total sea ice extent (SIE) across the Southern Ocean has increased since the late 1970s, with the annual mean increasing at a rate of 186×10 3  km 2 per decade (1.5% per decade; p <0.01) for 1979–2013. However, this overall increase masks larger regional variations, most notably an increase (decrease) over the Ross (Amundsen–Bellingshausen) Sea. Sea ice variability results from changes in atmospheric and oceanic conditions, although the former is thought to be more significant, since there is a high correlation between anomalies in the ice concentration and the near-surface wind field. The Southern Ocean SIE trend is dominated by the increase in the Ross Sea sector, where the SIE is significantly correlated with the depth of the Amundsen Sea Low (ASL), which has deepened since 1979. The depth of the ASL is influenced by a number of external factors, including tropical sea surface temperatures, but the low also has a large locally driven intrinsic variability, suggesting that SIE in these areas is especially variable. Many of the current generation of coupled climate models have difficulty in simulating sea ice. However, output from the better-performing IPCC CMIP5 models suggests that the recent increase in Antarctic SIE may be within the bounds of intrinsic/internal variability.

scholarly journals Modeled Arctic sea ice evolution through 2300 in CMIP5 extended RCPs

2014 ◽  
Vol 8 (1) ◽  
pp. 1383-1406 ◽  
Author(s):  
P. J. Hezel ◽  
T. Fichefet ◽  
F. Massonnet
Keyword(s):  
Sea Ice ◽  
Radiative Forcing ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

Abstract. Almost all global climate models and Earth system models that participated in the Coupled Model Intercomparison Project 5 (CMIP5) show strong declines in Arctic sea ice extent and volume under the highest forcing scenario of the Radiative Concentration Pathways (RCPs) through 2100, including a transition from perennial to seasonal ice cover. Extended RCP simulations through 2300 were completed for a~subset of models, and here we examine the time evolution of Arctic sea ice in these simulations. In RCP2.6, the summer Arctic sea ice extent increases compared to its minimum following the peak radiative forcing in 2044 in all 9 models. RCP4.5 demonstrates continued summer Arctic sea ice decline due to continued warming on longer time scales. These two scenarios imply that summer sea ice extent could begin to recover if and when radiative forcing from greenhouse gas concentrations were to decrease. In RCP8.5 the Arctic Ocean reaches annually ice-free conditions in 7 of 9 models. The ensemble of simulations completed under the extended RCPs provide insight into the global temperature increase at which sea ice disappears in the Arctic and reversibility of declines in seasonal sea ice extent.

scholarly journals Positive Trend in the Antarctic Sea Ice Cover and Associated Changes in Surface Temperature

2017 ◽  
Vol 30 (6) ◽  
pp. 2251-2267 ◽  
Author(s):  
Josefino C. Comiso ◽  
Robert A. Gersten ◽  
Larry V. Stock ◽  
John Turner ◽  
Gay J. Perez ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Climate Models ◽  
Ice Cover ◽  
Southern Annular Mode ◽  
Positive Trend ◽  
Growth Season ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
The Antarctic

Abstract The Antarctic sea ice extent has been slowly increasing contrary to expected trends due to global warming and results from coupled climate models. After a record high extent in 2012 the extent was even higher in 2014 when the magnitude exceeded 20 × 106 km2 for the first time during the satellite era. The positive trend is confirmed with newly reprocessed sea ice data that addressed inconsistency issues in the time series. The variability in sea ice extent and ice area was studied alongside surface ice temperature for the 34-yr period starting in 1981, and the results of the analysis show a strong correlation of −0.94 during the growth season and −0.86 during the melt season. The correlation coefficients are even stronger with a one-month lag in surface temperature at −0.96 during the growth season and −0.98 during the melt season, suggesting that the trend in sea ice cover is strongly influenced by the trend in surface temperature. The correlation with atmospheric circulation as represented by the southern annular mode (SAM) index appears to be relatively weak. A case study comparing the record high in 2014 with a relatively low ice extent in 2015 also shows strong sensitivity to changes in surface temperature. The results suggest that the positive trend is a consequence of the spatial variability of global trends in surface temperature and that the ability of current climate models to forecast sea ice trend can be improved through better performance in reproducing observed surface temperatures in the Antarctic region.

scholarly journals How Well Do the CMIP5 Models Simulate the Antarctic Atmospheric Energy Budget?

2015 ◽  
Vol 28 (20) ◽  
pp. 7933-7942 ◽  
Author(s):  
Michael Previdi ◽  
Karen L. Smith ◽  
Lorenzo M. Polvani
Keyword(s):  
Sea Ice ◽  
Energy Budget ◽  
General Circulation ◽  
Climate Model ◽  
General Circulation Models ◽  
Cmip5 Models ◽  
Secondary Importance ◽  
Sea Ice Extent ◽  
Atmospheric Energy ◽  
The Antarctic

Abstract The authors evaluate 23 coupled atmosphere–ocean general circulation models from phase 5 of CMIP (CMIP5) in terms of their ability to simulate the observed climatological mean energy budget of the Antarctic atmosphere. While the models are shown to capture the gross features of the energy budget well [e.g., the observed two-way balance between the top-of-atmosphere (TOA) net radiation and horizontal convergence of atmospheric energy transport], the simulated TOA absorbed shortwave (SW) radiation is too large during austral summer. In the multimodel mean, this excessive absorption reaches approximately 10 W m−2, with even larger biases (up to 25–30 W m−2) in individual models. Previous studies have identified similar climate model biases in the TOA net SW radiation at Southern Hemisphere midlatitudes and have attributed these biases to errors in the simulated cloud cover. Over the Antarctic, though, model cloud errors are of secondary importance, and biases in the simulated TOA net SW flux are instead driven mainly by biases in the clear-sky SW reflection. The latter are likely related in part to the models’ underestimation of the observed annual minimum in Antarctic sea ice extent, thus underscoring the importance of sea ice in the Antarctic energy budget. Finally, substantial differences in the climatological surface energy fluxes between existing observational datasets preclude any meaningful assessment of model skill in simulating these fluxes.

scholarly journals A simple approach to providing a more consistent Arctic sea ice extent time series from the 1950s to present

2012 ◽  
Vol 6 (6) ◽  
pp. 1359-1368 ◽  
Author(s):  
W. N. Meier ◽  
J. Stroeve ◽  
A. Barrett ◽  
F. Fetterer
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Linear Trend ◽  
Statistical Significance ◽  
Recent Decade ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Combined Time Series

Abstract. Observations from passive microwave satellite sensors have provided a continuous and consistent record of sea ice extent since late 1978. Earlier records, compiled from ice charts and other sources exist, but are not consistent with the satellite record. Here, a method is presented to adjust a compilation of pre-satellite sources to remove discontinuities between the two periods and create a more consistent combined 59-yr time series spanning 1953–2011. This adjusted combined time series shows more realistic behavior across the transition between the two individual time series and thus provides higher confidence in trend estimates from 1953 through 2011. The long-term time series is used to calculate linear trend estimates and compare them with trend estimates from the satellite period. The results indicate that trends through the 1960s were largely positive (though not statistically significant) and then turned negative by the mid-1970s and have been consistently negative since, reaching statistical significance (at the 95% confidence level) by the late 1980s. The trend for September (when Arctic extent reaches its seasonal minimum) for the satellite period, 1979–2011 is −12.9% decade−1, nearly double the 1953–2011 trend of −6.8% decade−1 (percent relative to the 1981–2010 mean). The recent decade (2002–2011) stands out as a period of persistent decline in ice extent. The combined 59-yr time series puts the strong observed decline in the Arctic sea ice cover during 1979–2011 in a longer-term context and provides a useful resource for comparisons with historical model estimates.

scholarly journals Evaluation of Arctic warming in mid-Pliocene climate simulations

2020 ◽  
Author(s):  
Wesley de Nooijer ◽  
Qiong Zhang ◽  
Qiang Li ◽  
Qiang Zhang ◽  
Xiangyu Li ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Future Climate ◽  
The Arctic ◽  
Future Climate Change ◽  
Sea Ice Extent ◽  
Current Form ◽  
Arctic Warming ◽  
Amplification Ratio ◽  
Climate Simulations

Abstract. Palaeoclimate simulations improve our understanding of the climate, inform us about the performance of climate models in a different climate scenario, and help to identify robust features of the climate system. Here, we analyse Arctic warming in an ensemble of 16 simulations of the mid-Pliocene Warm Period (mPWP), derived from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). The PlioMIP2 ensemble simulates Arctic (60–90° N) annual mean surface air temperature (SAT) increases of 3.7 to 11.6 °C compared to the pre-industrial, with a multi-model mean (MMM) increase of 7.2 °C. The Arctic warming amplification ratio relative to global SAT anomalies in the ensemble ranges from 1.8 to 3.1 (MMM is 2.3). Sea ice extent anomalies range from −3.0 to −10.4 × 06 km2 with a MMM anomaly of −5.6 × 106 km2, which constitutes a decrease of 53 % compared to the pre-industrial. The majority (11 out of 16) models simulate summer sea ice-free conditions (≤ 1 × 06 km2) in their mPWP simulation. The ensemble tends to underestimate SAT in the Arctic when compared to available reconstructions. The simulations with the highest Arctic SAT anomalies tend to match the proxy dataset in its current form better. The ensemble shows some agreement with reconstructions of sea ice, particularly with regards to seasonal sea ice. Large uncertainties limit the confidence that can be placed in the findings and the compatibility of the different proxy datasets. We show that, while reducing uncertainties in the reconstructions could decrease the SAT data-model discord substantially, further improvements are likely to be found in enhanced boundary conditions or model physics. Lastly, we compare the Arctic warming in the mPWP to projections of future Arctic warming and find that the PlioMIP2 ensemble simulates greater Arctic amplification, an increase instead of a decrease in AMOC strength compared to pre-industrial, and a lesser strengthening of northern modes of variability than CMIP5 future climate simulations. The results highlight the importance of slow feedbacks in equilibrium climate simulations, and that caution must be taken when using simulations of the mPWP as an analogue for future climate change.

scholarly journals September Arctic Sea Ice minimum prediction – a new skillful statistical approach

2018 ◽  
Author(s):  
Monica Ionita ◽  
Klaus Grosfeld ◽  
Patrick Scholz ◽  
Renate Treffeisen ◽  
Gerrit Lohmann
Keyword(s):  
Sea Ice ◽  
Statistical Model ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Polar Regions ◽  
Sea Ice Extent ◽  
Important Indicator ◽  
Predictive Skill

Abstract. Sea ice in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad interest exists on sea ice coverage, variability and long term change. However, its predictability is complex and it depends on various atmospheric and oceanic parameters. In order to provide insights into the potential development of a monthly/seasonal signal of sea ice evolution, we developed a robust statistical model based on oceanic and different atmospheric variables to calculate an estimate of the September sea ice extent (SSIE) on monthly time scale. Although previous statistical attempts of monthly/seasonal SSIE forecasts show a relatively reduced skill, when the trend is removed, we show here that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' atmospheric and oceanic conditions. Our statistical model skillfully captures the interannual variability of the SSIE and could provide a valuable tool for identifying relevant regions and atmospheric parameters that are important for the sea ice development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on sea ice formation.

scholarly journals Influence of sea surface temperature in the tropics on the Antarctic sea ice under global warming

2019 ◽  
Vol 59 (2) ◽  
pp. 213-221 ◽  
Author(s):  
G. V. Alekseev ◽  
N. I. Glok ◽  
A. E. Vyasilova ◽  
N. E. Ivanov ◽  
N. E. Kharlanenkova ◽  
...  
Keyword(s):  
Global Warming ◽  
Sea Ice ◽  
Polar Front ◽  
Global Climate Models ◽  
The Arctic ◽  
Antarctic Polar Front ◽  
Sea Ice Extent ◽  
Low Latitudes ◽  
The Antarctic ◽  
Sst Anomalies

Sea ice fields in the Antarctic, in contrast to the Arctic ones, did not show a reduction in observed global warming, whereas the global climate models indicate its certain decrease. The purpose of the study is to explain this climatic phenomenon on the basis of the idea of joint dynamics of oceanic structures in the Southern Ocean – the Antarctic polar front and the margin of the maximum distribution of sea ice. We used data from the ERA/Interim and HadISST as well as the database on the sea ice for 1979–2017. Relationship between the SST-anomalies in low latitudes of the Northern hemisphere and positions of the Antarctic polar front and maximum sea-ice extent was investigated. It was found that locations of these structures changed under the influence of the SST anomalies in low latitudes. The results obtained confirm existence of the opposite trends in changes in the sea ice extent in the Arctic and Antarctic under the influence of the SST anomalies in the central North Atlantic Ocean. When positive, the anomalies cause a shift of the Intertropical Convergence Zone (ITCZ) and the Hadley circulation to the North, while, on the contrary, the negative anomaly promotes the corresponding shift of the Antarctic polar front, followed by the boundary of sea ice.

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