scholarly journals Sea-ice extent and its trend provide limited metrics of model performance

2014 ◽  
Vol 8 (1) ◽  
pp. 229-243 ◽  
Author(s):  
D. Notz
Keyword(s):  
Sea Ice ◽  
Internal Variability ◽  
Ice Cover ◽  
The Arctic ◽  
Cmip5 Models ◽  
High Concentration ◽  
Sea Ice Extent ◽  
Ice Coverage ◽  
Observational Uncertainty ◽  
Satellite Retrievals

Abstract. We examine how the evaluation of modelled sea-ice coverage against reality is affected by uncertainties in the retrieval of sea-ice coverage from satellite, by the usage of sea-ice extent to overcome these uncertainties, and by internal variability. We find that for Arctic summer sea ice, model biases in sea-ice extent can be qualitatively different from biases in sea-ice area. This is because about half of the CMIP5 models and satellite retrievals based on the Bootstrap and the ASI algorithm show a compact ice cover in summer with large areas of high-concentration sea ice, while the other half of the CMIP5 models and satellite retrievals based on the NASA Team algorithm show a loose ice cover. For the Arctic winter sea-ice cover, differences in grid geometry can cause synthetic biases in sea-ice extent that are larger than the observational uncertainty. Comparing the uncertainty arising directly from the satellite retrievals with those that arise from internal variability, we find that the latter by far dominates the uncertainty estimate for trends in sea-ice extent and area: most of the differences between modelled and observed trends can simply be explained by internal variability. For absolute sea-ice area and sea-ice extent, however, internal variability cannot explain the difference between model and observations for about half the CMIP5 models that we analyse here. All models that we examined have regional biases, as expressed by the root-mean-square error in concentration, that are larger than the differences between individual satellite algorithms.

scholarly journals Sea-ice extent provides a limited metric of model performance

2013 ◽  
Vol 7 (3) ◽  
pp. 3095-3131 ◽  
Author(s):  
D. Notz
Keyword(s):  
Sea Ice ◽  
Model Performance ◽  
Internal Variability ◽  
Ice Cover ◽  
Cmip5 Models ◽  
High Concentration ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Observational Uncertainty ◽  
Satellite Retrievals

Abstract. We examine the common practice of using sea-ice extent as the primary metric to evaluate modeled sea-ice coverage. Based on this analysis, we recommend a possible best practice for model evaluation. We find that for Arctic summer sea ice, model biases in sea-ice extent can be qualitatively different compared to biases in the geophysically more meaningful sea-ice area. These differences come about by a different frequency distribution of high-concentration sea-ice: while in summer about half of the CMIP5 models and satellite retrievals based on the Bootstrap and the ASI algorithm show a compact ice cover with large areas of high concentration sea ice, the other half of the CMIP5 models and satellite retrievals based on the NASA Team algorithm show a loose ice cover. The different behaviour of the CMIP5 models can be explained by their different distribution of excess heat between lateral melt and sea-ice thinning. Differences in grid geometry and round-off errors during interpolation only have a minor impact on the different biases in sea-ice extent and sea-ice area. Because of regional cancellation of biases in the integrative measures sea-ice extent and sea-ice area, these measures show little correlation with the more meaningful mean absolute bias in sea-ice concentration. Comparing the uncertainty arising directly from the satellite retrievals with those that arise from internal variability, we find that the latter by far dominates the uncertainty estimate for trends in sea-ice extent and area: much of the differences between modeled and observed trends can simply be explained by internal variability. Only for the absolute value of sea-ice area, differences between observations and models are so large that they cannot be explained by either observational uncertainty nor internal variability.

scholarly journals Late-Twentieth-Century Simulation of Arctic Sea Ice and Ocean Properties in the CCSM4

2012 ◽  
Vol 25 (5) ◽  
pp. 1431-1452 ◽  
Author(s):  
Alexandra Jahn ◽  
Kara Sterling ◽  
Marika M. Holland ◽  
Jennifer E. Kay ◽  
James A. Maslanik ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Sea Ice ◽  
Internal Variability ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Motion Field ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Ensemble Mean

To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981–2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends.

scholarly journals An enhancement to sea ice motion and age products

2019 ◽  
Author(s):  
Mark A. Tschudi ◽  
Walter N. Meier ◽  
J. Scott Stewart
Keyword(s):  
Sea Ice ◽  
Ice Cover ◽  
The Arctic ◽  
Significant Shift ◽  
High Concentration ◽  
Sea Ice Extent ◽  
Previous Version ◽  
Arctic Ice ◽  
The Impact ◽  
Snow And Ice

Abstract. A new version of the sea ice motion and age products distributed at the National Snow and Ice Data Center's NASA Snow and Ice Distributed Active Archive Center has been developed. The new version, 4.0, includes several significant upgrades in processing, corrects known issues with the previous version, and updates the time series through 2018, with regular updates planned for the future. Here, we provide a history of the product development, discuss the improvements to the algorithms that create these products, and compare the Version 4 products to the previous version. While Version 4 algorithm changes were significant, the impact on the products is relatively minor, particularly for more recent years. Trends in motion and age are not substantially different between the versions. Changes in sea ice motion and age derived from the product show a significant shift in the Arctic ice cover, from a pack with a high concentration of older ice, to a sea ice cover dominated by first-year ice, which is more susceptible to summer melt. We also observe an increase in the speed of the ice in recent years, which is anticipated with the annual decrease in sea ice extent.

scholarly journals New Perspectives on Observed and Simulated Antarctic Sea Ice Extent Trends Using Optimal Fingerprinting Techniques*

2015 ◽  
Vol 28 (4) ◽  
pp. 1543-1560 ◽  
Author(s):  
William Richard Hobbs ◽  
Nathaniel L. Bindoff ◽  
Marilyn N. Raphael
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Ross Sea ◽  
Internal Variability ◽  
Ice Cover ◽  
Natural Variability ◽  
Forced Response ◽  
Cmip5 Models ◽  
Sea Ice Extent ◽  
Optimal Fingerprinting

Abstract Using optimal fingerprinting techniques, a detection analysis is performed to determine whether observed trends in Southern Ocean sea ice extent since 1979 are outside the expected range of natural variability. Consistent with previous studies, it is found that for the seasons of maximum sea ice cover (i.e., winter and early spring), the observed trends are not outside the range of natural variability and in some West Antarctic sectors they may be partially due to tropical variability. However, when information about the spatial pattern of trends is included in the analysis, the summer and autumn trends fall outside the range of internal variability. The detectable signal is dominated by strong and opposing trends in the Ross Sea and the Amundsen–Bellingshausen Sea regions. In contrast to the observed pattern, an ensemble of 20 CMIP5 coupled climate models shows that a decrease in Ross Sea ice cover would be expected in response to external forcings. The simulated decreases in the Ross, Bellingshausen, and Amundsen Seas for the autumn season are significantly different from unforced internal variability at the 95% confidence level. Unlike earlier work, the authors formally show that the simulated sea ice response to external forcing is different from both the observed trends and simulated internal variability and conclude that in general the CMIP5 models do not adequately represent the forced response of the Antarctic climate system.

scholarly journals Nonuniform Contribution of Internal Variability to Recent Arctic Sea Ice Loss

2019 ◽  
Vol 32 (13) ◽  
pp. 4039-4053 ◽  
Author(s):  
Mark England ◽  
Alexandra Jahn ◽  
Lorenzo Polvani
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Late Summer ◽  
Internal Variability ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Cmip5 Models ◽  
East Siberian Sea ◽  
Arctic Sea

Abstract Over the last half century, the Arctic sea ice cover has declined dramatically. Current estimates suggest that, for the Arctic as a whole, nearly one-half of the observed loss of summer sea ice cover is not due to anthropogenic forcing but rather is due to internal variability. Using the 40 members of the Community Earth System Model Large Ensemble (CESM-LE), our analysis provides the first regional assessment of the role of internal variability on the observed sea ice loss. The CESM-LE is one of the best available models for such an analysis, because it performs better than other CMIP5 models for many metrics of importance. Our study reveals that the local contribution of internal variability has a large range and strongly depends on the month and region in question. We find that the pattern of internal variability is highly nonuniform over the Arctic, with internal variability accounting for less than 10% of late summer (August–September) East Siberian Sea sea ice loss but more than 60% of the Kara Sea sea ice loss. In contrast, spring (April–May) sea ice loss, notably in the Barents Sea, has so far been dominated by internal variability.

scholarly journals Assessment and Ranking of Climate Models in Arctic Sea Ice Cover Simulation: From CMIP5 to CMIP6

2021 ◽  
Vol 34 (9) ◽  
pp. 3609-3627
Author(s):  
Zili Shen ◽  
Anmin Duan ◽  
Dongliang Li ◽  
Jinxiao Li
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Substantial Reduction ◽  
Coupled Model ◽  
Internal Variability ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea

AbstractThe capability of 36 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and their 24 CMIP5 counterparts in simulating the mean state and variability of Arctic sea ice cover for the period 1979–2014 is evaluated. In addition, a sea ice cover performance score for each CMIP5 and CMIP6 model is provided that can be used to reduce the spread in sea ice projections through applying weighted averages based on the ability of models to reproduce the historical sea ice state. Results show that the seasonal cycle of the Arctic sea ice extent (SIE) in the multimodel ensemble (MME) mean of the CMIP6 simulations agrees well with observations, with a MME mean error of less than 15% in any given month relative to the observations. CMIP6 has a smaller intermodel spread in climatological SIE values during summer months than its CMIP5 counterpart. In terms of the monthly SIE trends, the CMIP6 MME mean shows a substantial reduction in the positive bias relative to the observations compared with that of CMIP5. The spread of September SIE trends is very large, not only across different models but also across different ensemble members of the same model, indicating a strong influence of internal variability on SIE evolution. Based on the assumptions that the simulations of CMIP6 models are from the same distribution and that models have no bias in response to external forcing, we can infer that internal variability contributes to approximately 22% ± 5% of the September SIE trend over the period 1979–2014.

scholarly journals Antarctic sea ice variability and trends, 1979–2010

2012 ◽  
Vol 6 (2) ◽  
pp. 931-956 ◽  
Author(s):  
C. L. Parkinson ◽  
D. J. Cavalieri
Keyword(s):  
Indian Ocean ◽  
Sea Ice ◽  
Ross Sea ◽  
Ice Cover ◽  
Weddell Sea ◽  
The Arctic ◽  
Future Research ◽  
Positive Trend ◽  
Antarctic Sea Ice ◽  
Ice Coverage

Abstract. In sharp contrast to the decreasing sea ice coverage of the Arctic, in the Antarctic the sea ice cover has, on average, expanded since the late 1970s. More specifically, satellite passive-microwave data for the period November 1978–December 2010 reveal an overall positive trend in ice extents of 17 100 ± 2300 km2 yr−1. Much of the increase, at 13 700 ± 1500 km2 yr−1, has occurred in the region of the Ross Sea, with lesser contributions from the Weddell Sea and Indian Ocean. One region, that of the Bellingshausen/Amundsen Seas, has, like the Arctic, instead experienced significant sea ice decreases, with an overall ice extent trend of −8200 ± 1200 km2 yr−1. When examined through the annual cycle over the 32-yr period 1979–2010, the Southern Hemisphere sea ice cover as a whole experienced positive ice extent trends in every month, ranging in magnitude from a low of 9100 ± 6300 km2 yr−1 in February to a high of 24 700 ± 10 000 km2 yr−1 in May. The Ross Sea and Indian Ocean also had positive trends in each month, while the Bellingshausen/Amundsen Seas had negative trends in each month, and the Weddell Sea and Western Pacific Ocean had a mixture of positive and negative trends. Comparing ice-area results to ice-extent results, in each case the ice-area trend has the same sign as the ice-extent trend, but differences in the magnitudes of the two trends identify regions with overall increasing ice concentrations and others with overall decreasing ice concentrations. The strong pattern of decreasing ice coverage in the Bellingshausen/Amundsen Seas region and increasing ice coverage in the Ross Sea region is suggestive of changes in atmospheric circulation. This is a key topic for future research.

scholarly journals Antarctic sea ice variability and trends, 1979–2010

2012 ◽  
Vol 6 (4) ◽  
pp. 871-880 ◽  
Author(s):  
C. L. Parkinson ◽  
D. J. Cavalieri
Keyword(s):  
Indian Ocean ◽  
Sea Ice ◽  
Ross Sea ◽  
Ice Cover ◽  
Weddell Sea ◽  
The Arctic ◽  
Future Research ◽  
Positive Trend ◽  
Antarctic Sea Ice ◽  
Ice Coverage

Abstract. In sharp contrast to the decreasing sea ice coverage of the Arctic, in the Antarctic the sea ice cover has, on average, expanded since the late 1970s. More specifically, satellite passive-microwave data for the period November 1978–December 2010 reveal an overall positive trend in ice extents of 17 100 ± 2300 km2 yr−1. Much of the increase, at 13 700 ± 1500 km2 yr−1, has occurred in the region of the Ross Sea, with lesser contributions from the Weddell Sea and Indian Ocean. One region, that of the Bellingshausen/Amundsen Seas, has (like the Arctic) instead experienced significant sea ice decreases, with an overall ice extent trend of −8200 ± 1200 km2 yr−1. When examined through the annual cycle over the 32-yr period 1979–2010, the Southern Hemisphere sea ice cover as a whole experienced positive ice extent trends in every month, ranging in magnitude from a low of 9100 ± 6300 km2 yr−1 in February to a high of 24 700 ± 10 000 km2 yr−1 in May. The Ross Sea and Indian Ocean also had positive trends in each month, while the Bellingshausen/Amundsen Seas had negative trends in each month, and the Weddell Sea and western Pacific Ocean had a mixture of positive and negative trends. Comparing ice-area results to ice-extent results, in each case the ice-area trend has the same sign as the ice-extent trend, but the magnitudes of the two trends differ, and in some cases these differences allow inferences about the corresponding changes in sea ice concentrations. The strong pattern of decreasing ice coverage in the Bellingshausen/Amundsen Seas region and increasing ice coverage in the Ross Sea region is suggestive of changes in atmospheric circulation. This is a key topic for future research.

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 Sea-ice and North Atlantic climate response to CO2-induced warming and cooling conditions

2006 ◽  
Vol 52 (178) ◽  
pp. 433-439 ◽  
Author(s):  
Larissa Nazarenko ◽  
Nickolai Tausnev ◽  
James Hansen
Keyword(s):  
Sea Ice ◽  
Northern Hemisphere ◽  
Surface Pressure ◽  
Climate Model ◽  
Global Climate Model ◽  
Ice Cover ◽  
The Arctic ◽  
Normal Surface ◽  
Sea Ice Extent ◽  
Polar Cell

AbstractUsing a global climate model coupled with an ocean and a sea-ice model, we compare the effects of doubling CO2 and halving CO2 on sea-ice cover and connections with the atmosphere and ocean. An overall warming in the 2 × CO2 experiment causes reduction of sea-ice extent by 15%, with maximum decrease in summer and autumn, consistent with observed seasonal sea-ice changes. The intensification of the Northern Hemisphere circulation is reflected in the positive phase of the Arctic Oscillation (AO), associated with higher-than-normal surface pressure south of about 50° N and lower-than-normal surface pressure over the high northern latitudes. Strengthening the polar cell causes enhancement of westerlies around the Arctic perimeter during winter. Cooling, in the 0.5 × CO2 experiment, leads to thicker and more extensive sea ice. In the Southern Hemisphere, the increase in ice-covered area (28%) dominates the ice-thickness increase (5%) due to open ocean to the north. In the Northern Hemisphere, sea-ice cover increases by only 8% due to the enclosed land/sea configuration, but sea ice becomes much thicker (108%). Substantial weakening of the polar cell due to increase in sea-level pressure over polar latitudes leads to a negative trend of the winter AO index. The model reproduces large year-to-year variability under both cooling and warming conditions.

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