Trends In Sea Ice Extent Research Articles

Intersensor Calibration of Spaceborne Passive Microwave Radiometers and Algorithm Tuning for Long-Term Sea Ice Trend Analysis Based on AMSR-E Observations

Sea ice monitoring is key to analyzing the Earth’s climate system. Long-term sea ice extent (SIE) has been continuously monitored using various spaceborne passive microwave radiometers (PMRs) since November 1978. As the lifetime of a satellite is usually approximately 5 years, bias caused by differences in PMRs should be eliminated to obtain objective SIE trends. Most sea ice products have been analyzed for long-term trends with a bias adjustment based on the coarse resolution special sensor microwave imager (SSM/I) in operation for the longest period. However, since 2002, Japanese microwave radiometers of the Advanced Microwave Scanning Radiometer (AMSR) series, which have the highest spatial resolution in PMR, have been available. In this study, we developed standardization techniques for processing SIE including calibration of the brightness temperature (TB), tuning the sea ice concentration (SIC) algorithm, and adjusting the SIC threshold to retrieve a consistent SIE trend based on the AMSR for the Earth Observing System (AMSR-E, one of the AMSR that operated from May 2002 to October 2011). Analysis results showed that the root-mean-square error between AMSR-E SICs and those of moderate resolution imaging spectroradiometer (MODIS) was 15%. In this study, SIE was defined as the sum of the areas where the AMSR-E SIC was >15%. When retrieving SIE, we adjusted the SIC threshold for each PMR to be consistent with the SIE calculated based on the 15% SIC threshold for AMSR-E. We then calculated a time-series of the SIE trends over approximately 45 years using the adjusted SIE data. Therefore, we revealed the dramatic decrease in global sea ice extent since 1978. This technique enables retrieval of more accurate long-term sea ice trends for more than half a century in the future.

Implications of Snowpack Reactive Bromine Production for Arctic Ice Core Bromine Preservation

AbstractSnowpack emissions are recognized as an important source of gas‐phase reactive bromine in the Arctic and are necessary to explain ozone depletion events in spring caused by the catalytic destruction of ozone by halogen radicals. Quantifying bromine emissions from snowpack is essential for interpretation of ice‐core bromine. We present ice‐core bromine records since the pre‐industrial (1750 CE) from six Arctic locations and examine potential post‐depositional loss of snowpack bromine using a global chemical transport model. Trend analysis of the ice‐core records shows that only the high‐latitude coastal Akademii Nauk (AN) ice core from the Russian Arctic preserves significant trends since pre‐industrial times that are consistent with trends in sea ice extent and anthropogenic emissions from source regions. Model simulations suggest that recycling of reactive bromine on the snow skin layer (top 1 mm) results in 9–17% loss of deposited bromine across all six ice‐core locations. Reactive bromine production from below the snow skin layer and within the snow photic zone is potentially more important, but the magnitude of this source is uncertain. Model simulations suggest that the AN core is most likely to preserve an atmospheric signal compared to five Greenland ice cores due to its high latitude location combined with a relatively high snow accumulation rate. Understanding the sources and amount of photochemically reactive snow bromide in the snow photic zone throughout the sunlit period in the high Arctic is essential for interpreting ice‐core bromine, and warrants further lab studies and field observations at inland locations.

Estimating summer sea ice extent in the Weddell Sea during the early 19th century

Abstract. Over the past 3 decades, discordant trends in sea ice extent have been observed between the Arctic and Antarctic regions. Arctic sea ice extent has been characterised by a rapid decline, whereas Antarctic sea ice extent, while highly variable interannually, has tended to increase. Climate models have so far failed to capture these trends. Coupled with the limited pre-1970 sea ice dataset, this poses a significant challenge to quantifying the mechanisms responsible for driving such trends. However, historical records from early Antarctic expeditions contain a wealth of information regarding the nature and concentration of sea ice. Such records have been underutilised, and their analysis may enhance our understanding of recent Antarctic sea ice variability. For the purpose of this study, nine records from eight Antarctic expeditions have been examined. Summer sea ice positions recorded during 1820–1843 have been compared to satellite observations from 1987–2017, as well as historical data for the period 1897–1917. Through analysis of these three time series, estimates for the northern limits of summer sea ice in the Weddell Sea during the early 19th century have been produced. The key findings of this study indicate a 19th century average core summer northernmost sea ice latitude in much of the Weddell Sea that was further north than during the modern era, with 19th century February having significantly more sea ice by all measures. However, late summer sea ice was most extensive in the early years of the 20th century.

Subseasonal-to-seasonal prediction of arctic sea ice Using a Fully Coupled dynamical ensemble forecast system

With the opening of Arctic passage for maritime transportation under global warming, accurate prediction of Arctic sea ice (ASI) on subseasonal-to-seasonal (S2S) timescales becomes increasingly crucial for the economy and society but challenging. This study examined the S2S hindcast skills of monthly ASI during 1992–2019 using Flexible Global Ocean-Atmosphere-Land System, Finite-Volume version 2 (FGOALS-f2), a global coupled model that includes an interactive dynamical sea ice component. Firstly, the prediction system can accurately capture the seasonal cycle of Arctic sea ice extent (SIE). Still, it slightly overestimates SIE in March–June and significantly underestimates SIE in August–December. Meanwhile, the declining trend of SIE is underestimated in predictions for all target seasons, particularly for spring. Secondly, relatively high skills with an average ACC of 0.44 are found in predicting monthly SIE anomalies (SIEA) after removing the long-term linear trend at a one-to-six-month lead. However, the one-to-six-month lead prediction skills in April drop significantly by 36% when the interdecadal variation is removed. We find that the variation of April SIEA after removing the linear trend is dominated by the Pacific Decadal Oscillation (PDO), which can be better predicted than interannual variability ahead of subseasonal timescales. Unlike the SIE error-based metrics considered in previous studies, the one-to-three-month lead prediction in FGOALS-f2 shows significantly better performance in predicting the spatial distribution of September ASI for extreme years compared to normal years. Finally, the skill for predicting minimum SIEA in FGOALS-f2 is considerably higher at a three-to-six-month lead than anomaly persistence, indicating the advantage of this dynamical prediction system at a longer lead time.

A frequent ice-free Arctic is likely to occur before the mid-21st century

Although the trend of sea-ice extent under global warming has been studied extensively in recent years, most climate models have failed to capture the recent rapid change in the Arctic environment, which has brought into question the reliability of climate model projections of sea ice and suggested a potential shift in Arctic climate dynamics. Here, based on the results of a time-variant emergent constraint method with a weighting scheme, we show that an ice-free Arctic might occur earlier (by at least 5 ~ 10 years) than previously estimated. In other words, Arctic ice will likely disappear before the 2050 s. The observationally constrained date for an ice-free Arctic in September under fossil-fuel-based development (i.e., Shared Socioeconomic Pathway (SSP) 5–8.5) scenarios yields a central estimate of 2050–2054 with a 66% confidence range (equivalent to the IPCC’s ‘likely’ range) of 2037–2066, while an ice-free Arctic will likely occur for another 20 years and 11 years under ambitious mitigation scenarios (i.e., SSP2-4.5) and SSP3-7.0. An ice-free Arctic is unlikely to occur under the sustainable development scenario (i.e., SSP1-2.6). Looking forward, this time-variant emergent constraint may also help detect tipping points in the climate system. Our findings provide useful information to help policy makers cope with climate change.

A new sea ice concentration product in the polar regions derived from the FengYun-3 MWRI sensors

Abstract. Sea ice concentration (SIC) is the main geophysical variable for quantifying change in sea ice in the polar regions. A continuous SIC product is key to informing climate and ecosystem studies in the polar regions. Our study generates a new SIC product covering the Arctic and Antarctic from November 2010 to December 2019. It is the first long-term SIC product derived from the Microwave Radiation Imager (MWRI) sensors on board the Chinese FengYun-3B, FengYun-3C, and FengYun-3D satellites, after a recent re-calibration of brightness temperature. We modified the previous Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie point algorithm mainly by changing input brightness temperature and initial tie points. The MWRI-ASI SIC was compared to the existing ASI SIC products and validated using ship-based SIC observations. Results show that the MWRI-ASI SIC mostly coincides with the ASI SIC obtained from the Special Sensor Microwave Imager series sensors, with overall biases of −1 ± 2 % in the Arctic and 0.5 ± 2 % in the Antarctic, respectively. The overall mean absolute deviation between the MWRI-ASI SIC and ship-based SIC is 16 % and 17 % in the Arctic and Antarctic, respectively, which is close to the existing ASI SIC products. The trend of sea ice extent (SIE) derived from the MWRI-ASI SIC closely agrees with the trends of the Sea Ice Index SIEs provided by the Ocean and Sea Ice Satellite Application Facility (OSI SAF) and the National Snow and Ice Data Center (NSIDC). Therefore, the MWRI-ASI SIC is comparable with other SIC products and may be applied alternatively. The MWRI-ASI SIC dataset is available at https://doi.org/10.1594/PANGAEA.945188 (Chen et al., 2022b).

Recent Decline in Antarctic Sea Ice Cover From 2016 to 2022: Insights From Satellite Observations, Argo Floats, and Model Reanalysis

Ever since the abrupt drop in Antarctic sea ice extent (SIE) began in spring of 2016, as opposed to its consistent growth (1.95% decade–1 from 1979 to 2015), the SIE in the satellite era has reached record lows in 2017 and 2022. From spring 2016, the satellite-based SIE remained consistently lower than the long-term mean, with the trend dropping to 0.11% decade–1 from 1979 to 2022. The top record lowest SIE years were observed from 2016 to 2022, corresponding to the warmest years dating back to 1979. With this background, the rare features of Antarctic polynyas reoccurred frequently and the west Antarctic Peninsula remained ice-free throughout 2022. Recently, the SIE dropped to a record low in June 2022, July 2022, August 2022, January 2023, and February 2023, which were 13.67%, 9.91%, 6.79%, 39.29%, 39.56% below the long-term mean value, respectively for months described above. We find that the observed decline in SIE during 2016–2022 occurred due to the combined influences from the intensification of atmospheric zonal waves with enhanced poleward transport of warm-moist air and anomalous warming in the Southern Ocean mixed layer (>1°C). Although the sudden sea ice decline in spring of 2016 occurred corresponding to the transitional climate shift from IPO– (Interdecadal Pacific Oscillation, 2000–2014) to IPO+ (2014–2016), the recent decline after 2016 occurred in a dominant IPO– and Southern Annular Mode (SAM+). CMIP6 models showed a consistent decrease in ensemble-mean SIE from 1979 to 2022. The model trend exhibits similarities to the recent declining trend in SIE from satellite observations since 2016, suggesting a possible shift towards a warmer climatic regime.

Revisiting Antarctic sea-ice decadal variability since 1980

Sea-ice extent (SIE) satellite data has been stored and studied over the past four decades. Antarctica’s SIE has recently attained both its maximum (2014) and its minimum (2017) values, which raised questions regarding how SIE variability is evolving. Here we discuss a new approach to improve our understanding of SIE variability for the 1982–1993 and 2006–2017 periods. Using a probability density function and examining the mean and standard deviation of SIE distributions as a function of seasonality. Results show that the whole Southern Ocean presented both mean and standard deviation growth in all seasons. The largest SIE distribution difference for Southern Ocean between 1982–1993 and 2006–2017 periods was observed in July, August and September (JAS), during which all individual Sea sectors presented growth of the SIE mean. Not only different Sea sectors showed different variations, but the SIE data distributions from the same Sea sector yielded different changes with differing seasons. January, February and March (JFM) together with April, May and June were the seasons with the largest SIE decadal variability for Weddell, Indian, Amundsen and West Pacific Sea Sectors, while the Ross Sea showed greater SIE variability during JAS and October November, December. This methodology showed consistent results with the traditional SIE trend calculation and introduced a new quantification index for evaluating the differences between two distributions not necessarily connected in time.

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

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.

Evaluation of Arctic Sea-ice Cover and Thickness Simulated by MITgcm

A regional Arctic Ocean configuration of the Massachusetts Institute of Technology General Circulation Model (MITgcm) is applied to simulate the Arctic sea ice from 1991 to 2012. The simulations are evaluated by comparing them with observations from different sources. The results show that MITgcm can reproduce the interannual and seasonal variability of the sea-ice extent, but underestimates the trend in sea-ice extent, especially in September. The ice concentration and thickness distributions are comparable to those from the observations, with most deviations within the observational uncertainties and less than 0.5 m, respectively. The simulated sea-ice extents are better correlated with observations in September, with a correlation coefficient of 0.95, than in March, with a correlation coefficient of 0.83. However, the distributions of sea-ice concentration are better simulated in March, with higher pattern correlation coefficients (0.98) than in September. When the model underestimates the atmospheric influence on the sea-ice evolution in March, deviations in the sea-ice concentration arise at the ice edges and are higher than those in September. In contrast, when the model underestimates the oceanic boundaries’ influence on the September sea-ice evolution, disagreements in the distribution of the sea-ice concentration and its trend are found over most marginal seas in the Arctic Ocean. The uncertainties of the model, whereby it fails to incorporate the atmospheric information in March and oceanic information in September, contribute to varying model errors with the seasons.

Impact of Winds and Southern Ocean SSTs on Antarctic Sea Ice Trends and Variability

AbstractAntarctic sea ice extent (SIE) has slightly increased over the satellite observational period (1979 to the present) despite global warming. Several mechanisms have been invoked to explain this trend, such as changes in winds, precipitation, or ocean stratification, yet there is no widespread consensus. Additionally, fully coupled Earth system models run under historic and anthropogenic forcing generally fail to simulate positive SIE trends over this time period. In this work, we quantify the role of winds and Southern Ocean SSTs on sea ice trends and variability with an Earth system model run under historic and anthropogenic forcing that nudges winds over the polar regions and Southern Ocean SSTs north of the sea ice to observations from 1979 to 2018. Simulations with nudged winds alone capture the observed interannual variability in SIE and the observed long-term trends from the early 1990s onward, yet for the longer 1979–2018 period they simulate a negative SIE trend, in part due to faster-than-observed warming at the global and hemispheric scale in the model. Simulations with both nudged winds and SSTs show no significant SIE trends over 1979–2018, in agreement with observations. At the regional scale, simulated sea ice shows higher skill compared to the pan-Antarctic scale both in capturing trends and interannual variability in all nudged simulations. We additionally find negligible impact of the initial conditions in 1979 on long-term trends.

Arctic and Antarctic Sea Ice Mean State in the Community Earth System Model Version 2 and the Influence of Atmospheric Chemistry

AbstractArctic and Antarctic sea ice has undergone significant and rapid change with the changing climate. Here, we present preindustrial and historical results from the newly released Community Earth System Model Version 2 (CESM2) to assess the Arctic and Antarctic sea ice. Two configurations of the CESM2 are available that differ only in their atmospheric model top and the inclusion of comprehensive atmospheric chemistry, including prognostic aerosols. The CESM2 configuration with comprehensive atmospheric chemistry has significantly thicker Arctic sea ice year‐round and better captures decreasing trends in sea ice extent and volume over the satellite period. In the Antarctic, both CESM configurations have similar mean state ice extent and volume, but the ice extent trends are opposite to satellite observations. We find that differences in the Arctic sea ice between CESM2 configurations are the result of differences in liquid clouds. Over the Arctic, the CESM2 configuration without prognostic aerosol formation has fewer aerosols to form cloud condensation nuclei, leading to thinner liquid clouds. As a result, the sea ice receives much more shortwave radiation early in the melt season, driving a stronger ice albedo feedback and leading to additional sea ice loss and significantly thinner ice year‐round. The aerosols necessary for the Arctic liquid cloud formation are produced from different precursor emissions and transported to the Arctic. Thus, the main reason sea ice differs in the Arctic is the transport of cloud‐impacting aerosols into the region, while the Antarctic remains relatively pristine from extrapolar aerosol transport.

Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections

Arctic sea ice extent has been utilized to monitor sea ice changes since the late 1970s using remotely sensed sea ice data derived from passive microwave (PM) sensors. A 15% sea ice concentration threshold value has been used traditionally when computing sea ice extent (SIE), although other threshold values have been employed. Does the rapid depletion of Arctic sea ice potentially alter the basic characteristics of Arctic ice extent? In this paper, we explore whether and how the statistical characteristics of Arctic sea ice have changed during the satellite data record period of 1979–2017 and examine the sensitivity of sea ice extents and their decadal trends to sea ice concentration threshold values. Threshold choice can affect the timing of annual SIE minimums: a threshold choice as low as 30% can change the timing to August instead of September. Threshold choice impacts the value of annual SIE minimums: in particular, changing the threshold from 15% to 35% can change the annual SIE by more than 10% in magnitude. Monthly SIE data distributions are seasonally dependent. Although little impact was seen for threshold choice on data distributions during annual minimum times (August and September), there is a strong impact in May. Threshold choices were not found to impact the choice of optimal statistical models characterizing annual minimum SIE time series. However, the first ice-free Arctic summer year (FIASY) estimates are impacted; higher threshold values produce earlier FIASY estimates and, more notably, FIASY estimates amongst all considered models are more consistent. This analysis suggests that some of the threshold choice impacts to SIE trends may actually be the result of biased data due to surface melt. Given that the rapid Arctic sea ice depletion appears to have statistically changed SIE characteristics, particularly in the summer months, a more extensive investigation to verify surface melt impacts on this data set is warranted.

Regional Trends in Weather Systems Help Explain Antarctic Sea Ice Trends

AbstractIn contrast to Arctic sea ice extent, Antarctic sea ice extent has increased since 1979. On a regional scale, however, the trends exhibit high spatial variability and are even of the opposite sign. This study examines connections between sea ice extent and the frequencies of extratropical cyclones and blocks. Consideration is given to regions that exhibit long‐term sea ice trends during spring and autumn. Significant connections exist in almost all examined regions. Typically, the region of maximum correlation is shifted upstream or downstream of the sea ice target region, which indicates that the 10‐m wind associated with the examined weather systems is the chief thermodynamic and dynamic agent underlying sea ice variability. Along the ice edge of the Weddell and Ross Seas, the correlation between springtime sea ice extent and cyclone frequencies displays a wave number 3 pattern. Eastward of the Ross Sea, along the transition into the Amundsen Sea, spring sea ice extent is connected to cyclone and blocking frequencies during spring and the preceding autumn. Westward of the Ross Sea, autumn sea ice extent is strongly connected to blocking frequencies during the preceding spring. For the Bellingshausen Sea, an inverse relationship exists between autumn cyclone frequencies and autumn sea ice extent. Significant cyclone and blocking trends that are consistent with long‐term sea ice trends exist in most examined regions. These findings point toward identifying regional trends in extratropical cyclone and blocking frequencies as a useful step toward a better understanding of couplings between Southern Hemisphere climate and regional trends in sea ice extent.

Variability of Arctic Sea Ice Thickness Using PIOMAS and the CESM Large Ensemble

Abstract Because of limited high-quality satellite and in situ observations, less attention has been given to the trends in Arctic sea ice thickness and therefore sea ice volume than to the trends in sea ice extent. This study evaluates the spatial and temporal variability in Arctic sea ice thickness using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Additionally, the Community Earth System Model Large Ensemble Project (LENS) is used to quantify the forced response and internal variability in the model. A dipole spatial pattern of sea ice thickness variability is shown in both PIOMAS and LENS with opposite signs of polarity between the East Siberian Sea and near the Fram Strait. As future sea ice thins, this dipole structure of variability is reduced, and the largest interannual variability is found only along the northern Greenland coastline. Under a high-emissions scenario (RCP8.5) projection, average September sea ice thickness falls below 0.5 m by the end of the twenty-first century. However, a regional analysis shows internal variability contributes to an uncertainty of 10 to 20 years for the timing of the first September sea ice thickness less than 0.5 m in the marginal seas.

Challenges of Sea-Ice Prediction for Arctic Marine Policy and Planning

ABSTRACTSea ice presents an important challenge for trans-border coordination of Arctic maritime infrastructure. Widespread summer sea-ice melt and anticipated expansion of shipping and offshore oil and gas drilling have highlighted a need for seasonal forecasts and decadal projections of sea ice for strategic planning efforts. While the long-term trend in sea-ice extent is expected to remain negative, ice conditions exhibit large spatial and temporal variability, raising uncertainty and operational risks of navigation in seasonally ice-covered areas. Given the potential trans-border impacts of a maritime accident on the marine and coastal environment, predicting ice conditions is of critical interest to government, industry, and community stakeholders. Seasonal ice forecasts have shown promise for short-term operational decision-making, while decadal projections from general circulation models are increasingly being used for long-term planning of energy, security, and environmental policy. However, numerous issues complicate the application of sea-ice prediction methods for policy and planning. This paper examines the potential of sea-ice prediction as a tool to support strategic planning, with a focus on the trans-border marine space of the US and Canadian Arctic. The utility and limitations of seasonal and decadal sea-ice prediction are reviewed, followed by a discussion of the infrastructure and policy context within which sea-ice forecasts may be used to enhance safety and mitigate risk in the Arctic.

Arctic sea ice in CMIP5 climate model projections and their seasonal variability

This paper is focused on the seasonality change of Arctic sea ice extent (SIE) from 1979 to 2100 using newly available simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A new approach to compare the simulation metric of Arctic SIE between observation and 31 CMIP5 models was established. The approach is based on four factors including the climatological average, linear trend of SIE, span of melting season and annual range of SIE. It is more objective and can be popularized to other comparison of models. Six good models (GFDL-CM3, CESM1-BGC, MPI-ESM-LR, ACCESS-1.0, HadGEM2-CC, and HadGEM2-AO in turn) are found which meet the criterion closely based on above approach. Based on ensemble mean of the six models, we found that the Arctic sea ice will continue declining in each season and firstly drop below 1 million km2 (defined as the ice-free state) in September 2065 under RCP4.5 scenario and in September 2053 under RCP8.5 scenario. We also study the seasonal cycle of the Arctic SIE and find out the duration of Arctic summer (melting season) will increase by about 100 days under RCP4.5 scenario and about 200 days under RCP8.5 scenario relative to current circumstance by the end of the 21st century. Asymmetry of the Arctic SIE seasonal cycle with later freezing in fall and early melting in spring, would be more apparent in the future when the Arctic climate approaches to “tipping point”, or when the ice-free Arctic Ocean appears. Annual range of SIE (seasonal melting ice extent) will increase almost linearly in the near future 30–40 years before the Arctic appears ice-free ocean, indicating the more ice melting in summer, the more ice freezing in winter, which may cause more extreme weather events in both winter and summer in the future years.

Attributing Causes of 2015 Record Minimum Sea-Ice Extent in the Sea of Okhotsk

In 2015, the sea ice extent (SIE) over the Sea of Okhotsk (Okhotsk SIE) hit a record low since 1979 during February–March, the period when the sea ice extent generally reaches its annual maximum. To quantify the role of anthropogenic influences on the changes observed in Okhotsk SIE, this study employed a fraction of attributable risk (FAR) analysis to compare the probability of occurrence of extreme Okhotsk SIE events and long-term SIE trends using phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel simulations performed with and without anthropogenic forcing. It was found that because of anthropogenic influence, both the probability of extreme low Okhotsk SIEs that exceed the 2015 event and the observed long-term trends during 1979–2015 have increased by more than 4 times (FAR = 0.76 to 1). In addition, it is suggested that a strong negative phase of the North Pacific Oscillation (NPO) during midwinter (January–February) 2015 also contributed to the 2015 extreme SIE event. An analysis based on multiple linear regression was conducted to quantify relative contributions of the external forcing (anthropogenic plus natural) and the NPO (internal variability) to the observed SIE changes. About 56.0% and 24.7% of the 2015 SIE anomaly was estimated to be attributable to the external forcing and the strong negative NPO influence, respectively. The external forcing was also found to explain about 86.1% of the observed long-term SIE trend. Further, projections from the CMIP5 models indicate that a sea ice–free condition may occur in the Sea of Okhotsk by the late twenty-first century in some models.

On the discrepancy between observed and CMIP5 multi-model simulated Barents Sea winter sea ice decline

This study aims to understand the relative roles of external forcing versus internal climate variability in causing the observed Barents Sea winter sea ice extent (SIE) decline since 1979. We identify major discrepancies in the spatial patterns of winter Northern Hemisphere sea ice concentration trends over the satellite period between observations and CMIP5 multi-model mean externally forced response. The CMIP5 externally forced decline in Barents Sea winter SIE is much weaker than that observed. Across CMIP5 ensemble members, March Barents Sea SIE trends have little correlation with global mean surface air temperature trends, but are strongly anti-correlated with trends in Atlantic heat transport across the Barents Sea Opening (BSO). Further comparison with control simulations from coupled climate models suggests that enhanced Atlantic heat transport across the BSO associated with regional internal variability may have played a leading role in the observed decline in winter Barents Sea SIE since 1979.

Effect of climate variability on weaning mass in a declining population of southern elephant seals Mirounga leonina

With the global climate predicted to continue to change over the coming century, quantifying how animal populations respond to environmental variation is central for the prediction of future responses and consequences. To quantify how environmental change affects the foraging success of Macquarie Island female elephant seals Mirounga leonina, we related weaning mass—a function of maternal foraging success—to a series of intrinsic and extrinsic (environmental) covariates. We found that the weaning mass of elephant seals was positively related to the number of females ashore during the breeding season and negatively related to maximum sea ice extent in the Ross Sea region. Weaners weighed on average 3 kg more in years with more females ashore and 17 kg less in years of high ice extent. These relationships suggest that in years of poor female foraging conditions (i.e. high ice extent), Macquarie Island population growth is affected not only by a reduction in the number of females ashore for reproduction but also, given that weaning mass dictates first-year survival, by reduced pup survival rates. The decline in the Macquarie Island population over the past 60 yr has occurred concurrently with a positive trend in sea ice extent in the Ross Sea region. The negative relationship between weaning mass and sea ice extent suggests that the decline in the Macquarie Island population trajectory may, in part, be caused by increasing sea ice in the Ross Sea and mediated by a reduction in weaning mass, first-year survival and recruitment rates.