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 How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent?

2012 ◽  
Vol 6 (5) ◽  
pp. 3539-3573 ◽  
Author(s):  
V. Zunz ◽  
H. Goosse ◽  
F. Massonnet
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Climate Models ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Positive Trend ◽  
External Forcing ◽  
Sea Ice Extent ◽  
The One ◽  
The Impact

Abstract. Observations over the last 30 yr have shown that the sea ice extent in the Southern Ocean has slightly increased since 1979. Mechanisms responsible for this positive trend have not been well established yet and climate models are generally unable to simulate correctly this expansion. In this study, we focus on two related hypotheses that could explain the misrepresentation of the positive trend in sea ice extent by climate models: an unrealistic internal variability and an inadequate initialization of the system. For that purpose, we analyze the evolution of sea ice around the Antarctic simulated by 24 different general circulation models involved in the 5th Coupled Model Intercomparison Project (CMIP5). On the one hand, historical simulations, driven by external forcing and initialized without observations, are examined. They provide information about the mean state, the variability and the trend in sea ice extent simulated by each model. On the other hand, decadal prediction experiments, driven by external forcing and initialized with some observed fields, allow us to assess the impact of the representation of the observed initial state on the quality of model predictions. Our analyses show that CMIP5 models respond to the forcing, including the one induced by stratospheric ozone depletion, by reducing the sea ice cover in the Southern Ocean. Some simulations display an increase in sea ice extent. However, models strongly overestimate the variability of sea ice extent and the initialization methods currently used in models do not improve systematically the simulated trends in sea ice extent. On the basis of those results, a critical role of the internal variability in the observed increase in the sea ice extent in the Southern Ocean could not be ruled out but current models results appear inadequate to test more precisely this hypothesis.

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 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 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 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.

Plumbing the depths - the waters of the Ross Sea

2003 ◽  
Vol 15 (1) ◽  
pp. 1-1
Author(s):  
STANLEY S. JACOBS
Keyword(s):  
Sea Ice ◽  
Continental Shelf ◽  
Ocean Circulation ◽  
Ross Sea ◽  
Ice Cover ◽  
Surface Layers ◽  
Sea Ice Extent ◽  
The Antarctic

The first oceanographic measurements in the Ross Sea were made by its discoverer James Clark Ross, from the Erebus, on 18 January 1841. Since that time its continental shelf, seasonally ice free in most years, has proved a magnet to explorers and scientists, if not to fishermen and tourists. Nevertheless, our knowledge of this environment is rapidly being outpaced by our ignorance of its variability. For example, the Ross Sea contains two of the largest, most persistent polynyas on the Antarctic coastline, but its sea ice extent has increased over recent decades while its salinity has steadily declined. Are regional winds now stronger, the ocean circulation faster, and the ice thinner now than at the time of the IGY? Are its winter polynyas characterized more by upwelling driven by offshore winds, or downwelling due to brine release when sea ice is formed? How are polynya surface layers stabilized and iron-enriched, reportedly enhancing summer productivity, if the ice cover is blown away before it can melt in situ?

Trends in the CERES Dataset, 2000–13: The Effects of Sea Ice and Jet Shifts and Comparison to Climate Models

2014 ◽  
Vol 27 (6) ◽  
pp. 2444-2456 ◽  
Author(s):  
Dennis L. Hartmann ◽  
Paulo Ceppi
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Climate Models ◽  
Radiant Energy ◽  
Shortwave Radiation ◽  
Radiative Energy ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Sea Ice Extent ◽  
Intercomparison Project

Abstract The Clouds and the Earth’s Radiant Energy System (CERES) observations of global top-of-atmosphere radiative energy fluxes for the period March 2000–February 2013 are examined for robust trends and variability. The trend in Arctic ice is clearly evident in the time series of reflected shortwave radiation, which closely follows the record of ice extent. The data indicate that, for every 106 km2 decrease in September sea ice extent, annual-mean absorbed solar radiation averaged over 75°–90°N increases by 2.5 W m−2, or about 6 W m−2 between 2000 and 2012. CMIP5 models generally show a much smaller change in sea ice extent over the 1970–2012 period, but the relationship of sea ice extent to reflected shortwave is in good agreement with recent observations. Another robust trend during this period is an increase in reflected shortwave radiation in the zonal belt from 45° to 65°S. This trend is mostly related to increases in sea ice concentrations in the Southern Ocean and less directly related to cloudiness trends associated with the annular variability of the Southern Hemisphere. Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) produce a scaling of cloud reflection to zonal wind increase that is similar to trend observations in regions separated from the direct effects of sea ice. Atmospheric Model Intercomparison Project (AMIP) model responses over the Southern Ocean are not consistent with each other or with the observed shortwave trends in regions removed from the direct effect of sea ice.

Arctic and Antarctic Sea Ice Change: Contrasts, Commonalities, and Causes

2019 ◽  
Vol 11 (1) ◽  
pp. 187-213 ◽  
Author(s):  
Ted Maksym
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Ice Cover ◽  
Natural Variability ◽  
Arctic Sea Ice ◽  
Regional Variability ◽  
Antarctic Sea Ice ◽  
Induced Changes ◽  
Natural Climate ◽  
The Impact

Arctic sea ice has declined precipitously in both extent and thickness over the past four decades; by contrast, Antarctic sea ice has shown little overall change, but this masks large regional variability. Climate models have not captured these changes. But these differences do not represent a paradox. The processes governing, and impacts of, natural variability and human-induced changes differ markedly at the poles largely because of the ways in which differences in geography control the properties of and interactions among the atmosphere, ice, and ocean. The impact of natural variability on the ice cover is large at both poles, so modeled ice trends are not entirely inconsistent with contributions from both natural variability and anthropogenic forcing. Despite this concurrence, the coupling of natural climate variability, climate feedbacks, and sea ice is not well understood, and significant biases remain in model representations of the ice cover and the processes that drive it.

Sign in / Sign up

Export Citation Format

Share Document