GLOBAL WARMING AND ARCTIC SEA ICE EXTENT: A BIBLIOGRAPHY

1. 1999: Rothrock, Drew A., Yanling Yu, and Gary A. Maykut. “Thinning of the Arctic sea‐ice cover.” Geophysical Research Letters26.23 (1999): 3469-3472. Comparison of sea‐ice draft data acquired on submarine cruises between 1993 and 1997 with similar data acquired between 1958 and 1976 indicates that the mean ice draft at the end of the melt season has decreased by about 1.3 m in most of the deep water portion of the Arctic Ocean, from 3.1 m in 1958–1976 to 1.8 m in the 1990s. The decrease is greater in the central and eastern Arctic than in the Beaufort and Chukchi seas. Preliminary evidence is that the ice cover has continued to become thinner in some regions during the 1990s.
2. 2000: Polyakov, Igor V., and Mark A. Johnson. “Arctic decadal and interdecadal variability.” Geophysical Research Letters 27.24 (2000): 4097-4100. Atmospheric and oceanic variability in the Arctic shows the existence of several oscillatory modes. The decadal‐scale mode associated with the Arctic Oscillation (AO) and a low‐frequency oscillation (LFO) with an approximate time scale of 60–80 years, dominate. Both modes were positive in the 1990s, signifying a prolonged phase of anomalously low atmospheric sea level pressure and above normal surface air temperature in the central Arctic. Consistent with an enhanced cyclonic component, the arctic anticyclone was weakened and vorticity of winds became positive. The rapid reduction of arctic ice thickness in the 1990s may be one manifestation of the intense atmosphere and ice cyclonic circulation regime due to the synchronous actions of the AO and LFO. Our results suggest that the decadal AO and multidecadal LFO drive large amplitude natural variability in the Arctic making detection of possible long‐term trends induced by greenhouse gas warming most difficult.
3. 2003: Cavalieri, D. J., C. L. Parkinson, and K. Ya Vinnikov. “30‐Year satellite record reveals contrasting Arctic and Antarctic decadal sea ice variability.” Geophysical Research Letters30.18 (2003). A 30‐year satellite record of sea ice extents derived mostly from satellite microwave radiometer observations reveals that the Arctic sea ice extent decreased by 0.30 ± 0.03 × 10⁶ km²/10 yr from 1972 through 2002, but by 0.36 ± 0.05 × 10⁶km²/10yr from 1979 through 2002, indicating an acceleration of 20% in the rate of decrease. In contrast, the Antarctic sea ice extent decreased dramatically over the period 1973–1977, then gradually increased. Over the full 30‐year period, the Antarctic ice extent decreased by 0.15 ± 0.08 × 10⁶ km²/10 yr. The trend reversal is attributed to a large positive anomaly in Antarctic sea ice extent in the early 1970’s, an anomaly that apparently began in the late 1960’s, as observed in early visible and infrared satellite images.
4. 2004: Johannessen, Ola M., et al. “Arctic climate change: observed and modelled temperature and sea-ice variability.” Tellus A: Dynamic Meteorology and Oceanography 56.4 (2004): 328-341. Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic’s response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of centuryand multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere’ice’ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased~8 · 10⁵ km² (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.
5. 2006: Divine, Dmitry V., and Chad Dick. “Historical variability of sea ice edge position in the Nordic Seas.” Journal of Geophysical Research: Oceans 111.C1 (2006). Historical ice observations in the Nordic Seas from April through August are used to construct time series of ice edge position anomalies spanning the period 1750–2002. While analysis showed that interannual variability remained almost constant throughout this period, evidence was found of oscillations in ice cover with periods of about 60 to 80 years and 20 to 30 years, superimposed on a continuous negative trend. The lower frequency oscillations are more prominent in the Greenland Sea, while higher frequency oscillations are dominant in the Barents. The analysis suggests that the recent well‐documented retreat of ice cover can partly be attributed to a manifestation of the positive phase of the 60–80 year variability, associated with the warming of the subpolar North Atlantic and the Arctic. The continuous retreat of ice edge position observed since the second half of the 19th century may be a recovery after significant cooling in the study area that occurred as early as the second half of the 18th century.
6. 2008: Stroeve, Julienne, et al. “Arctic sea ice extent plummets in 2007.” Eos, Transactions American Geophysical Union 89.2 (2008): 13-14.Arctic sea ice declined rapidly to unprecedented low extents in the summer of 2007, raising concern that the Arctic may be on the verge of a fundamental transition toward a seasonal ice cover. Arctic sea ice extent typically attains a seasonal maximum in March and minimum in September. Over the course of the modern satellite record (1979 to present), sea ice extent has declined significantly in all months, with the decline being most pronounced in September. By mid‐July 2007, it was clear that a new record low would be set during the summer of 2007
7. 2007: Stroeve, Julienne, et al. “Arctic sea ice decline: Faster than forecast.” Geophysical research letters 34.9 (2007). From 1953 to 2006, Arctic sea ice extent at the end of the melt season in September has declined sharply. All models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) show declining Arctic ice cover over this period. However, depending on the time window for analysis, none or very few individual model simulations show trends comparable to observations. If the multi‐model ensemble mean time series provides a true representation of forced change by greenhouse gas (GHG) loading, 33–38% of the observed September trend from 1953–2006 is externally forced, growing to 47–57% from 1979–2006. Given evidence that as a group, the models underestimate the GHG response, the externally forced component may be larger. While both observed and modeled Antarctic winter trends are small, comparisons for summer are confounded by generally poor model performance.
8. 2008: Comiso, Josefino C., et al. “Accelerated decline in the Arctic sea ice cover.” Geophysical research letters 35.1 (2008). Satellite data reveal unusually low Arctic sea ice coverage during the summer of 2007, caused in part by anomalously high temperatures and southerly winds. The extent and area of the ice cover reached minima on 14 September 2007 at 4.1 × 10⁶ km² and 3.6 × 10⁶ km², respectively. These are 24% and 27% lower than the previous record lows, both reached on 21 September 2005, and 37% and 38% less than the climatological averages. Acceleration in the decline is evident as the extent and area trends of the entire ice cover (seasonal and perennial ice) have shifted from about −2.2 and −3.0% per decade in 1979–1996 to about −10.1 and −10.7% per decade in the last 10 years. The latter trends are now comparable to the high negative trends of −10.2 and −11.4% per decade for the perennial ice extent and area, 1979–2007.
9. 2009: Kwok, Rothrock, and D. A. Rothrock. “Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008.” Geophysical Research Letters 36.15 (2009). The decline of sea ice thickness in the Arctic Ocean from ICESat (2003–2008) is placed in the context of estimates from 42 years of submarine records (1958–2000) described by Rothrock et al. (1999, 2008). While the earlier 1999 work provides a longer historical record of the regional changes, the latter offers a more refined analysis, over a sizable portion of the Arctic Ocean supported by a much stronger and richer data set. Within the data release area (DRA) of declassified submarine sonar measurements (covering ∼38% of the Arctic Ocean), the overall mean winter thickness of 3.64 m in 1980 can be compared to a 1.89 m mean during the last winter of the ICESat record—an astonishing decrease of 1.75 m in thickness. Between 1975 and 2000, the steepest rate of decrease is −0.08 m/yr in 1990 compared to a slightly higher winter/summer rate of −0.10/−0.20 m/yr in the five‐year ICESat record (2003–2008). Prior to 1997, ice extent in the DRA was >90% during the summer minimum. This can be contrasted to the gradual decrease in the early 2000s followed by an abrupt drop to <55% during the record setting minimum in 2007. This combined analysis shows a long‐term trend of sea ice thinning over submarine and ICESat records that span five decades.
10. 2009: Chylek, Petr, et al. “Arctic air temperature change amplification and the Atlantic Multidecadal Oscillation.” Geophysical Research Letters 36.14 (2009). Understanding Arctic temperature variability is essential for assessing possible future melting of the Greenland ice sheet, Arctic sea ice and Arctic permafrost. Temperature trend reversals in 1940 and 1970 separate two Arctic warming periods (1910–1940 and 1970–2008) by a significant 1940–1970 cooling period. Analyzing temperature records of the Arctic meteorological stations we find that (a) the Arctic amplification (ratio of the Arctic to global temperature trends) is not a constant but varies in time on a multi‐decadal time scale, (b) the Arctic warming from 1910–1940 proceeded at a significantly faster rate than the current 1970–2008 warming, and (c) the Arctic temperature changes are highly correlated with the Atlantic Multi‐decadal Oscillation (AMO) suggesting the Atlantic Ocean thermohaline circulation is linked to the Arctic temperature variability on a multi‐decadal time scale.
11. 2010: Ho, Joshua. “The implications of Arctic sea ice decline on shipping.” Marine Policy 34.3 (2010): 713-715. Although a ‘blue’ Arctic Ocean is predicted in the summertime to occur from the middle of this century, current rates of warming indicate an earlier realization. Also, routes along the coast of Siberia will be navigable much earlier. However, before the Arctic routes can reliably be used on a large scale for transit by shipping along its passages, more investments are required on infrastructure and the provision of marine services to ensure the safe and secure transit of shipping with minimal environmental impact.
12. 2010: Frankcombe, Leela M., Anna Von Der Heydt, and Henk A. Dijkstra. “North Atlantic multidecadal climate variability: an investigation of dominant time scales and processes.” Journal of climate 23.13 (2010): 3626-3638. The issue of multidecadal variability in the North Atlantic has been an important topic of late. It is clear that there are multidecadal variations in several climate variables in the North Atlantic, such as sea surface temperature and sea level height. The details of this variability, in particular the dominant patterns and time scales, are confusing from both an observational as well as a theoretical point of view. After analyzing results from observational datasets and a 500-yr simulation of an Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) climate model, two dominant time scales (20–30 and 50–70 yr) of multidecadal variability in the North Atlantic are proposed. The 20–30-yr variability is characterized by the westward propagation of subsurface temperature anomalies. The hypothesis is that the 20–30-yr variability is caused by internal variability of the Atlantic Meridional Overturning Circulation (MOC) while the 50–70-yr variability is related to atmospheric forcing over the Atlantic Ocean and exchange processes between the Atlantic and Arctic Oceans.
13. 2010: Screen, James A., and Ian Simmonds. “The central role of diminishing sea ice in recent Arctic temperature amplification.” Nature 464.7293 (2010): 1334. The rise in Arctic near-surface air temperatures has been almost twice as large as the global average in recent decades^1,2,3—a feature known as ‘Arctic amplification’. Increased concentrations of atmospheric greenhouse gases have driven Arctic and global average warming^1,4; however, the underlying causes of Arctic amplification remain uncertain. The roles of reductions in snow and sea ice cover^5,6,7 and changes in atmospheric and oceanic circulation^8,9,10, cloud cover and water vapour^11,12 are still matters of debate. A better understanding of the processes responsible for the recent amplified warming is essential for assessing the likelihood, and impacts, of future rapid Arctic warming and sea ice loss^13,14. Here we show that the Arctic warming is strongest at the surface during most of the year and is primarily consistent with reductions in sea ice cover. Changes in cloud cover, in contrast, have not contributed strongly to recent warming. Increases in atmospheric water vapour content, partly in response to reduced sea ice cover, may have enhanced warming in the lower part of the atmosphere during summer and early autumn. We conclude that diminishing sea ice has had a leading role in recent Arctic temperature amplification. The findings reinforce suggestions that strong positive ice–temperature feedbacks have emerged in the Arctic¹⁵, increasing the chances of further rapid warming and sea ice loss, and will probably affect polar ecosystems, ice-sheet mass balance and human activities in the Arctic.
14. 2010: Bhatt, Uma S., et al. “Circumpolar Arctic tundra vegetation change is linked to sea ice decline.” Earth Interactions 14.8 (2010): 1-20. Linkages between diminishing Arctic sea ice and changes in Arctic terrestrial ecosystems have not been previously demonstrated. Here, the authors use a newly available Arctic Normalized Difference Vegetation Index (NDVI) dataset (a measure of vegetation photosynthetic capacity) to document coherent temporal relationships between near-coastal sea ice, summer tundra land surface temperatures, and vegetation productivity. The authors find that, during the period of satellite observations (1982–2008), sea ice within 50 km of the coast during the period of early summer ice breakup declined an average of 25% for the Arctic as a whole, with much larger changes in the East Siberian Sea to Chukchi Sea sectors (>44% decline). The changes in sea ice conditions are most directly relevant and have the strongest effect on the villages and ecosystems immediately adjacent to the coast, but the terrestrial effects of sea ice changes also extend far inland. Low-elevation (<300 m) tundra summer land temperatures, as indicated by the summer warmth index (SWI; sum of the monthly-mean temperatures above freezing, expressed as °C month⁻¹), have increased an average of 5°C month⁻¹ (24% increase) for the Arctic as a whole; the largest changes (+10° to 12°C month⁻¹) have been over land along the Chukchi and Bering Seas. The land warming has been more pronounced in North America (+30%) than in Eurasia (16%). When expressed as percentage change, land areas in the High Arctic in the vicinity of the Greenland Sea, Baffin Bay, and Davis Strait have experienced the largest changes (>70%). The NDVI has increased across most of the Arctic, with some exceptions over land regions along the Bering and west Chukchi Seas. The greatest change in absolute maximum NDVI occurred over tundra in northern Alaska on the Beaufort Sea coast [+0.08 Advanced Very High Resolution Radiometer (AVHRR) NDVI units]. When expressed as percentage change, large NDVI changes (10%–15%) occurred over land in the North America High Arctic and along the Beaufort Sea. Ground observations along an 1800-km climate transect in North America support the strong correlations between satellite NDVI observations and summer land temperatures. Other new observations from near the Lewis Glacier, Baffin Island, Canada, document rapid vegetation changes along the margins of large retreating glaciers and may be partly responsible for the large NDVI changes observed in northern Canada and Greenland. The ongoing changes to plant productivity will affect many aspects of Arctic systems, including changes to active-layer depths, permafrost, biodiversity, wildlife, and human use of these regions. Ecosystems that are presently adjacent to year-round (perennial) sea ice are likely to experience the greatest changes.
15. 2010: Fauria, M. Macias, et al. “Unprecedented low twentieth century winter sea ice extent in the Western Nordic Seas since AD 1200.” Climate Dynamics 34.6 (2010): 781-795. We reconstructed decadal to centennial variability of maximum sea ice extent in the Western Nordic Seas for A.D. 1200–1997 using a combination of a regional tree-ring chronology from the timberline area in Fennoscandia and δ¹⁸O from the Lomonosovfonna ice core in Svalbard. The reconstruction successfully explained 59% of the variance in sea ice extent based on the calibration period 1864–1997. The significance of the reconstruction statistics (reduction of error, coefficient of efficiency) is computed for the first time against a realistic noise background. The twentieth century sustained the lowest sea ice extent values since A.D. 1200: low sea ice extent also occurred before (mid-seventeenth and mid-eighteenth centuries, early fifteenth and late thirteenth centuries), but these periods were in no case as persistent as in the twentieth century. Largest sea ice extent values occurred from the seventeenth to the nineteenth centuries, during the Little Ice Age (LIA), with relatively smaller sea ice-covered area during the sixteenth century. Moderate sea ice extent occurred during thirteenth–fifteenth centuries. Reconstructed sea ice extent variability is dominated by decadal oscillations, frequently associated with decadal components of the North Atlantic Oscillation/Arctic Oscillation (NAO/AO), and multi-decadal lower frequency oscillations operating at ~50–120 year. Sea ice extent and NAO showed a non-stationary relationship during the observational period. The present low sea ice extent is unique over the last 800 years, and results from a decline started in late-nineteenth century after the LIA.
16. 2011: Kay, Jennifer E., Marika M. Holland, and Alexandra Jahn. “Inter‐annual to multi‐decadal Arctic sea ice extent trends in a warming world.” Geophysical Research Letters 38.15 (2011). A climate model (CCSM4) is used to investigate the influence of anthropogenic forcing on late 20th century and early 21st century Arctic sea ice extent trends. On all timescales examined (2–50+ years), the most extreme negative observed late 20th century trends cannot be explained by modeled natural variability alone. Modeled late 20th century ice extent loss also cannot be explained by natural causes alone, but the six available CCSM4 ensemble members exhibit a large spread in their late 20th century ice extent loss. Comparing trends from the CCSM4 ensemble to observed trends suggests that internal variability explains approximately half of the observed 1979–2005 September Arctic sea ice extent loss. In a warming world, CCSM4 shows that multi‐decadal negative trends increase in frequency and magnitude, and that trend variability on 2–10 year timescales increases. Furthermore, when internal variability counteracts anthropogenic forcing, positive trends on 2–20 year timescales occur until the middle of the 21st century.
17. 2011: Stroeve, Julienne C., et al. “Sea ice response to an extreme negative phase of the Arctic Oscillation during winter 2009/2010.” Geophysical Research Letters 38.2 (2011). Based on relationships established in previous studies, the extreme negative phase of the Arctic Oscillation (AO) that characterized winter of 2009/2010 should have favored retention of Arctic sea ice through the 2010 summer melt season. The September 2010 sea ice extent nevertheless ended up as third lowest in the satellite record, behind 2007 and barely above 2008, reinforcing the long‐term downward trend. This reflects pronounced differences in atmospheric circulation during winter of 2009/2010 compared to the mean anomaly pattern based on past negative AO winters, low ice volume at the start of the melt season, and summer melt of much of the multiyear ice that had been transported into the warm southerly reaches of the Beaufort and Chukchi seas.
18. 2011: Kay, Jennifer E., Marika M. Holland, and Alexandra Jahn. “Inter‐annual to multi‐decadal Arctic sea ice extent trends in a warming world.” Geophysical Research Letters 38.15 (2011). A climate model (CCSM4) is used to investigate the influence of anthropogenic forcing on late 20th century and early 21st century Arctic sea ice extent trends. On all timescales examined (2–50+ years), the most extreme negative observed late 20th century trends cannot be explained by modeled natural variability alone. Modeled late 20th century ice extent loss also cannot be explained by natural causes alone, but the six available CCSM4 ensemble members exhibit a large spread in their late 20th century ice extent loss. Comparing trends from the CCSM4 ensemble to observed trends suggests that internal variability explains approximately half of the observed 1979–2005 September Arctic sea ice extent loss. In a warming world, CCSM4 shows that multi‐decadal negative trends increase in frequency and magnitude, and that trend variability on 2–10 year timescales increases. Furthermore, when internal variability counteracts anthropogenic forcing, positive trends on 2–20 year timescales occur until the middle of the 21st century.
    
    Citing Literature
19. 2011: Medhaug, Iselin, and Tore Furevik. “North Atlantic 20th century multidecadal variability in coupled climate models: Sea surface temperature and ocean overturning circulation.” (2011) Atlantic Multidecadal Oscillation (AMO) and sea surface temperature in the Bay of Biscay and adjacent regions. Output from a total of 24 state-of-the-art Atmosphere-Ocean General Circulation Models is analyzed. The models were integrated with observed forcing for the period 1850–2000 as part of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. All models show enhanced variability at multi-decadal time scales in the North Atlantic sector similar to the observations, but with a large intermodel spread in amplitudes and frequencies for both the Atlantic Multidecadal Oscillation (AMO) and the Atlantic Meridional Overturning Circulation (AMOC). The models, in general, are able to reproduce the observed geographical patterns of warm and cold episodes, but not the phasing such as the early warming (1930s–1950s) and the following colder period (1960s–1980s). This indicates that the observed 20th century extreme in temperatures are due to primarily a fortuitous phasing of intrinsic climate variability and not dominated by external forcing. Most models show a realistic structure in the overturning circulation, where more than half of the available models have a mean overturning transport within the observed estimated range of 13–24 Sverdrup. Associated with a stronger than normal AMOC, the surface temperature is increased and the sea ice extent slightly reduced in the North Atlantic. Individual models show potential for decadal prediction based on the relationship between the AMO and AMOC, but the models strongly disagree both in phasing and strength of the covariability. This makes it difficult to identify common mechanisms and to assess the applicability for predictions.
20. 2011: Mahajan, Salil, Rong Zhang, and Thomas L. Delworth. “Impact of the Atlantic meridional overturning circulation (AMOC) on Arctic surface air temperature and sea ice variability.” Journal of Climate 24.24 (2011): 6573-6581. The simulated impact of the Atlantic meridional overturning circulation (AMOC) on the low-frequency variability of the Arctic surface air temperature (SAT) and sea ice extent is studied with a 1000-year-long segment of a control simulation of the Geophysical Fluid Dynamics Laboratory Climate Model version 2.1. The simulated AMOC variations in the control simulation are found to be significantly anticorrelated with the Arctic sea ice extent anomalies and significantly correlated with the Arctic SAT anomalies on decadal time scales in the Atlantic sector of the Arctic. The maximum anticorrelation with the Arctic sea ice extent and the maximum correlation with the Arctic SAT occur when the AMOC index leads by one year. An intensification of the AMOC is associated with a sea ice decline in the Labrador, Greenland, and Barents Seas in the control simulation, with the largest change occurring in winter. The recent declining trend in the satellite-observed sea ice extent also shows a similar pattern in the Atlantic sector of the Arctic in the winter, suggesting the possibility of a role of the AMOC in the recent Arctic sea ice decline in addition to anthropogenic greenhouse-gas-induced warming. However, in the summer, the simulated sea ice response to the AMOC in the Pacific sector of the Arctic is much weaker than the observed declining trend, indicating a stronger role for other climate forcings or variability in the recently observed summer sea ice decline in the Chukchi, Beaufort, East Siberian, and Laptev Seas.
21. 2012: Liu, Jiping, et al. “Impact of declining Arctic sea ice on winter snowfall.” Proceedings of the National Academy of Sciences(2012). While the Arctic region has been warming strongly in recent decades, anomalously large snowfall in recent winters has affected large parts of North America, Europe, and east Asia. Here we demonstrate that the decrease in autumn Arctic sea ice area is linked to changes in the winter Northern Hemisphere atmospheric circulation that have some resemblance to the negative phase of the winter Arctic oscillation. However, the atmospheric circulation change linked to the reduction of sea ice shows much broader meridional meanders in midlatitudes and clearly different interannual variability than the classical Arctic oscillation. This circulation change results in more frequent episodes of blocking patterns that lead to increased cold surges over large parts of northern continents. Moreover, the increase in atmospheric water vapor content in the Arctic region during late autumn and winter driven locally by the reduction of sea ice provides enhanced moisture sources, supporting increased heavy snowfall in Europe during early winter and the northeastern and midwestern United States during winter. We conclude that the recent decline of Arctic sea ice has played a critical role in recent cold and snowy winters. (??)
22. 2012: Garcia-Soto, Carlos, and Robin D. Pingree. “Atlantic Multidecadal Oscillation (AMO) and sea surface temperature in the Bay of Biscay and adjacent regions.” Journal of the Marine Biological Association of the United Kingdom 92.2 (2012): 213-234. The sea surface temperature (SST) variability of the Bay of Biscay and adjacent regions (1854–2010) has been examined in relation to the evolution of the Atlantic Multidecadal Oscillation (AMO), a major climate mode. The AMO index explains ~25% of the interannual variability of the annual SST during the last 150 years, while different indices of the North Atlantic Oscillation (NAO) explain ≤1% of the long-term record. NAO is a high frequency climate mode while AMO can modulate low frequency changes. Sixty per cent of the AMO variability is contained in periods longer than a decade. The basin-scale influence of NAO on SST over specific years (1995 to 1998) is presented and the SST anomalies explained. The period analysed represents an abrupt change in NAO and the North Atlantic circulation state as shown with altimetry and SST data. Additional atmospheric climate data over a shorter ~60 year period (1950–2008) show the influence on the Bay of Biscay SST of the East Atlantic (EA) pattern and the Scandinavia (SCA) pattern. These atmospheric teleconnections explain respectively ~25% and ~20% of the SST variability. The winter SST in the shelf-break/slope or poleward current region is analysed in relation to AMO. The poleward current shows a trend towards increasing SSTs during the last three decades as a result of the combined positive phase of AMO and global warming. The seasonality of this winter warm flow in the Iberian region is related to the autumn/winter seasonality of south-westerly (SW) winds. The SW winds are strengthened along the European shelf-break by the development of low pressure conditions in the region to the north of the Azores and therefore a negative NAO. AMO overall modulates multidecadal changes (~60% of the AMO variance). The long-term time-series of SST and SST anomalies in the Bay of Biscay show AMO-like cycles with maxima near 1870 and 1950 and minima near 1900 and 1980 indicating a period of 60–80 years during the last century and a half. Similar AMO-like variability is found in the Russell cycle of the Western English Channel (1924–1972). AMO relates at least to four mesozooplankton components of the Russell cycle: the abundance of the chaetognaths Parasagitta elegans and Parasagitta setosa (AMO −), the amount of the species Calanus helgolandicus (AMO −), the amount of the larvae of decapod crustaceans (AMO −) and the number of pilchard eggs (Sardine pilchardus; AMO +). In addition to AMO, the decadal to multidecadal (D2M) variability in the number of sunspots is analysed for the last 300 years. Several periodicities and a multi-secular linear increase are presented. There are secular minima near 1710, 1810, 1910 and 2010. The long term variability (>11 years) of the solar sunspot activity explains ~50% of the variance of the SST of the Bay of Biscay with periods longer than 11 years. AMO is finally compared with the Pacific Decadal Oscillation, the leading principal component of North Pacific SST anomalies.
23. 2012: Day, J. J., et al. “Sources of multi-decadal variability in Arctic sea ice extent.” Environmental Research Letters 7.3 (2012): 034011. The observed dramatic decrease in September sea ice extent (SIE) has been widely discussed in the scientific literature. Though there is qualitative agreement between observations and ensemble members of the Third Coupled Model Intercomparison Project (CMIP3), it is concerning that the observed trend (1979–2010) is not captured by any ensemble member. The potential sources of this discrepancy include: observational uncertainty, physical model limitations and vigorous natural climate variability. The latter has received less attention and is difficult to assess using the relatively short observational sea ice records. In this study multi-centennial pre-industrial control simulations with five CMIP3 climate models are used to investigate the role that the Arctic oscillation (AO), the Atlantic multi-decadal oscillation (AMO) and the Atlantic meridional overturning circulation (AMOC) play in decadal sea ice variability. Further, we use the models to determine the impact that these sources of variability have had on SIE over both the era of satellite observation (1979–2010) and an extended observational record (1953–2010). There is little evidence of a relationship between the AO and SIE in the models. However, we find that both the AMO and AMOC indices are significantly correlated with SIE in all the models considered. Using sensitivity statistics derived from the models, assuming a linear relationship, we attribute 0.5–3.1%/decade of the 10.1%/decade decline in September SIE (1979–2010) to AMO driven variability.
24. 2014: Beaugrand, Gregory, Xavier Harlay, and Martin Edwards. “Detecting plankton shifts in the North Sea: a new abrupt ecosystem shift between 1996 and 2003.” Marine Ecology Progress Series 502 (2014): 85-104. Global warming is now unequivocal, and studies suggest it has started to influence natural systems, including the oceans. Here, we quantify plankton changes in the North Sea for the period 1958 to 2007 using an approach we call Multi-Scale Multivariate Split Moving Window (MMS-SMW) analysis that we apply to 5 groups: (1) diatoms, (2) dinoflagellates, (3) copepods, (4) other holozooplankton and (5) meroplankton. Three temporally persistent shifts were identified in the 1960s, the 1980s and during the period 1996 to 2003. The present study therefore reveals for the first time an abrupt ecosystem shift between 1996 and 2003 in the North Sea, which had the same magnitude in terms of species response as the well-documented shift detected in the 1980s. All ecosystem shifts coincided with a significant change in hydro-climatic conditions and had consequences for the structure and the functioning of the ecosystems. We showed that the 3 shifts only impacted 40% of the plankton species or taxa considered in the analysis and that the timing of the shift varied according to the planktonic group and even among species within a group.
25. 2014: Vihma, Timo. “Effects of Arctic sea ice decline on weather and climate: A review.” Surveys in Geophysics 35.5 (2014): 1175-1214. The areal extent, concentration and thickness of sea ice in the Arctic Ocean and adjacent seas have strongly decreased during the recent decades, but cold, snow-rich winters have been common over mid-latitude land areas since 2005. A review is presented on studies addressing the local and remote effects of the sea ice decline on weather and climate. It is evident that the reduction in sea ice cover has increased the heat flux from the ocean to atmosphere in autumn and early winter. This has locally increased air temperature, moisture, and cloud cover and reduced the static stability in the lower troposphere. Several studies based on observations, atmospheric reanalyses, and model experiments suggest that the sea ice decline, together with increased snow cover in Eurasia, favours circulation patterns resembling the negative phase of the North Atlantic Oscillation and Arctic Oscillation. The suggested large-scale pressure patterns include a high over Eurasia, which favours cold winters in Europe and northeastern Eurasia. A high over the western and a low over the eastern North America have also been suggested, favouring advection of Arctic air masses to North America. Mid-latitude winter weather is, however, affected by several other factors, which generate a large inter-annual variability and often mask the effects of sea ice decline. In addition, the small sample of years with a large sea ice loss makes it difficult to distinguish the effects directly attributable to sea ice conditions. Several studies suggest that, with advancing global warming, cold winters in mid-latitude continents will no longer be common during the second half of the twenty-first century. Recent studies have also suggested causal links between the sea ice decline and summer precipitation in Europe, the Mediterranean, and East Asia.
26. 2014: Msadek, Rym, et al. “Importance of initial conditions in seasonal predictions of Arctic sea ice extent.” Geophysical Research Letters 41.14 (2014): 5208-5215. We present seasonal predictions of Arctic sea ice extent (SIE) over the 1982–2013 period using two suites of retrospective forecasts initialized from a fully coupled ocean‐atmosphere‐sea ice assimilation system. High skill scores are found in predicting year‐to‐year fluctuations of Arctic SIE, with significant correlations up to 7 month ahead for September detrended anomalies. Predictions over the recent era, which coincides with an improved observational coverage, outperform the earlier period for most target months. We find, however, a degradation of skill in September during the last decade, a period of sea ice thinning in observations. The two prediction models, Climate Model version 2.1 (CM2.1) and Forecast‐oriented Low Ocean Resolution (FLOR), share very similar ocean and ice component and initialization but differ by their atmospheric component. FLOR has improved climatological atmospheric circulation and sea ice mean state, but its skill is overall similar to CM2.1 for most seasons, which suggests a key role for initial conditions in predicting seasonal SIE fluctuations.
27. 2014: Miles, Martin W., et al. “A signal of persistent Atlantic multidecadal variability in Arctic sea ice.” Geophysical Research Letters 41.2 (2014): 463-469. Satellite data suggest an Arctic sea ice‐climate system in rapid transformation, yet its long‐term natural modes of variability are poorly known. Here we integrate and synthesize a set of multicentury historical records of Atlantic Arctic sea ice, supplemented with high‐resolution paleoproxy records, each reflecting primarily winter/spring sea ice conditions. We establish a signal of pervasive and persistent multidecadal (~60–90 year) fluctuations that is most pronounced in the Greenland Sea and weakens further away. Covariability between sea ice and Atlantic multidecadal variability as represented by the Atlantic Multidecadal Oscillation (AMO) index is evident during the instrumental record, including an abrupt change at the onset of the early twentieth century warming. Similar covariability through previous centuries is evident from comparison of the longest historical sea ice records and paleoproxy reconstructions of sea ice and the AMO. This observational evidence supports recent modeling studies that have suggested that Arctic sea ice is intrinsically linked to Atlantic multidecadal variability. This may have implications for understanding the recent negative trend in Arctic winter sea ice extent, although because the losses have been greater in summer, other processes and feedbacks are also important.
28. 2015: Peterson, K. Andrew, et al. “Assessing the forecast skill of Arctic sea ice extent in the GloSea4 seasonal prediction system.” Climate dynamics 44.1-2 (2015): 147-162. An assessment of the ability of the Met Office seasonal prediction system, GloSea4, to accurately forecast Arctic sea ice concentration and extent over seasonal time scales is presented. GloSea4 was upgraded in November 2010 to include the initialization of the observed sea ice concentration from satellite measurements. GloSea4 is one of only a few operational seasonal prediction systems to include both the initialization of observed sea ice followed by its prognostic determination in a coupled dynamical model of sea ice. For the forecast of the September monthly mean ice extent the best skill in GloSea4, as judged from the historical forecast period of 1996–2009, is when the system is initialized in late March and early April near to the sea ice maxima with correlation skills in the range of 0.6. In contrast, correlation skills using May initialization dates are much lower due to thinning of the sea ice at the start of the melt season which allows ice to melt too rapidly. This is likely to be due both to a systematic bias in the ice-ocean forced model as well as biases in the ice analysis system. Detailing the forecast correlation skill throughout the whole year shows that for our system, the correlation skill for ice extent at five to six months lead time is highest leading up to the September minimum (from March/April start dates) and leading up to the March maximum (from October/November start dates). Conversely, little skill is found for the shoulder seasons of November and May at any lead time.
29. 2015: Zhang, Rong. “Mechanisms for low-frequency variability of summer Arctic sea ice extent.” Proceedings of the National Academy of Sciences (2015): 201422296. Satellite observations reveal a substantial decline in September Arctic sea ice extent since 1979, which has played a leading role in the observed recent Arctic surface warming and has often been attributed, in large part, to the increase in greenhouse gases. However, the most rapid decline occurred during the recent global warming hiatus period. Previous studies are often focused on a single mechanism for changes and variations of summer Arctic sea ice extent, and many are based on short observational records. The key players for summer Arctic sea ice extent variability at multidecadal/centennial time scales and their contributions to the observed summer Arctic sea ice decline are not well understood. Here a multiple regression model is developed for the first time, to the author’s knowledge, to provide a framework to quantify the contributions of three key predictors (Atlantic/Pacific heat transport into the Arctic, and Arctic Dipole) to the internal low-frequency variability of Summer Arctic sea ice extent, using a 3,600-y-long control climate model simulation. The results suggest that changes in these key predictors could have contributed substantially to the observed summer Arctic sea ice decline. If the ocean heat transport into the Arctic were to weaken in the near future due to internal variability, there might be a hiatus in the decline of September Arctic sea ice. The modeling results also suggest that at multidecadal/centennial time scales, variations in the atmosphere heat transport across the Arctic Circle are forced by anticorrelated variations in the Atlantic heat transport into the Arctic.
30. 2015: Frey, Karen E., et al. “Divergent patterns of recent sea ice cover across the Bering, Chukchi, and Beaufort seas of the Pacific Arctic Region.” Progress in Oceanography 136 (2015): 32-49. Over the past three decades of the observed satellite record, there have been significant changes in sea ice cover across the Bering, Chukchi, and Beaufort seas of the Pacific Arctic Region (PAR). Satellite data reveal that patterns in sea ice cover have been spatially heterogeneous, with significant declines in the Chukchi and Beaufort seas, yet more complex multi-year variability in the Bering Sea south of St. Lawrence Island. These patterns in the Chukchi and Beaufort seas have intensified since 2000, indicating a regime shift in sea ice cover across the northern portion of the PAR. In particular, satellite data over 1979–2012 reveal localized decreases in sea ice presence of up to −1.64 days/year (Canada Basin) and −1.24 days/year (Beaufort Sea), which accelerated to up to −6.57 days/year (Canada Basin) and −12.84 days/year (Beaufort Sea) over the 2000–2012 time period. In contrast, sea ice in the Bering Sea shows more complex multi-year variability with localized increases in sea ice presence of up to +8.41 days/year since 2000. The observed increases in sea ice cover since 2000 in the southern Bering Sea shelf region are observed in wintertime, whereas sea ice losses in the Canada Basin and Beaufort Sea have occurred during summer. We further compare sea ice variability across the region with the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) wind and air temperature fields to determine the extent to which this recent variability is driven by thermal vs. wind-driven processes. Results suggest that for these localized areas that are experiencing the most rapid shifts in sea ice cover, those in the Beaufort Sea are primarily wind driven, those offshore in the Canada Basin are primarily thermally driven, and those in the Bering Sea are influenced by elements of both. Sea ice variability (and its drivers) across the PAR provides critical insight into the forcing effects of recent shifts in climate and its likely ultimate profound impacts on ecosystem productivity across all trophic levels.
31. 2015: Swart, Neil C., et al. “Influence of internal variability on Arctic sea-ice trends.” Nature Climate Change 5.2 (2015): 86. Internal climate variability can mask or enhance human-induced sea-ice loss on timescales ranging from years to decades. It must be properly accounted for when considering observations, understanding projections and evaluating models.
32. 2015: Liu, Jiping, et al. “Revisiting the potential of melt pond fraction as a predictor for the seasonal Arctic sea ice extent minimum.” Environmental Research Letters 10.5 (2015): 054017. The rapid change in Arctic sea ice in recent decades has led to a rising demand for seasonal sea ice prediction. A recent modeling study that employed a prognostic melt pond model in a stand-alone sea ice model found that September Arctic sea ice extent can be accurately predicted from the melt pond fraction in May. Here we show that satellite observations show no evidence of predictive skill in May. However, we find that a significantly strong relationship (high predictability) first emerges as the melt pond fraction is integrated from early May to late June, with a persistent strong relationship only occurring after late July. Our results highlight that late spring to mid summer melt pond information is required to improve the prediction skill of the seasonal sea ice minimum. Furthermore, satellite observations indicate a much higher percentage of melt pond formation in May than does the aforementioned model simulation, which points to the need to reconcile model simulations and observations, in order to better understand key mechanisms of melt pond formation and evolution and their influence on sea ice state..” Environmental Research Letters 10.5 (2015): 054017.
33. 2015: Serreze, Mark C., and Julienne Stroeve. “Arctic sea ice trends, variability and implications for seasonal ice forecasting.” Phil. Trans. R. Soc. A 373.2045 (2015): 20140159. September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability.
34. 2015: Hobbs, William Richard, Nathaniel L. Bindoff, and Marilyn N. Raphael. “New perspectives on observed and simulated Antarctic sea ice extent trends using optimal fingerprinting techniques.” Journal of Climate 28.4 (2015): 1543-1560. 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.
35. 2016: Walsh, John E., and William L. Chapman. “Variability of sea ice extent over decadal and longer timescales.” Climate change: multidecadal and beyond. 2016. 203-217. Recent syntheses of sea ice and related proxy information have provided an improved picture of Arctic sea ice variability over decadal to century timescales. A spectrum of variability is superimposed on a recent decrease of Arctic sea ice. An outstanding feature is the correspondence with the Atlantic Multidecadal Oscillation, which has timescales of 50–120 years. The linkage appears to arise through the inflow of Atlantic Water to the Arctic Ocean. Less robust, and by all indications non-stationary, associations with atmospheric modes such as the North Atlantic Oscillation have also been documented, primarily in recent decades. One possible reason for the nonstationarity of such associations is that the centers of action of major atmospheric modes may change over the timescale of centuries or even less. While the recent decrease of summer ice in the Arctic appears to be unique in the past 1,450 years, paleo reconstructions also suggest a minimum in Arctic ice coverage during the early Holocene. Unlike the Arctic, Antarctic sea ice shows essentially no trend over the past 30 years. The absence of a trend has been attributed to wind forcing and possibly ocean interactions. Observational information on Antarctic sea ice variability is virtually nonexistent beyond the past 100–150 years, so proxy information provides the only clues to longer-term Antarctic sea ice variability. Such information obtained from ice cores suggests that wintertime ice extent in the East Antarctic sector has decreased by about 20% since 1950, and that multicentury variations also characterize Antarctic ice extent.
36. 2016: Otto-Bliesner, Bette L., et al. “Climate variability and change since 850 CE: An ensemble approach with the Community Earth System Model.” Bulletin of the American Meteorological Society 97.5 (2016): 735-754. The climate of the past millennium provides a baseline for understanding the background of natural climate variability upon which current anthropogenic changes are superimposed. As this period also contains high data density from proxy sources (e.g., ice cores, stalagmites, corals, tree rings, and sediments), it provides a unique opportunity for understanding both global and regional-scale climate responses to natural forcing. Toward that end, an ensemble of simulations with the Community Earth System Model (CESM) for the period 850–2005 (the CESM Last Millennium Ensemble, or CESM-LME) is now available to the community. This ensemble includes simulations forced with the transient evolution of solar intensity, volcanic emissions, greenhouse gases, aerosols, land-use conditions, and orbital parameters, both together and individually. The CESM-LME thus allows for evaluation of the relative contributions of external forcing and internal variability to changes evident in the paleoclimate data record, as well as providing a longer-term perspective for understanding events in the modern instrumental period. It also constitutes a dynamically consistent framework within which to diagnose mechanisms of regional variability. Results demonstrate an important influence of internal variability on regional responses of the climate system during the past millennium. All the forcings, particularly large volcanic eruptions, are found to be regionally influential during the preindustrial period, while anthropogenic greenhouse gas and aerosol changes dominate the forced variability of the mid- to late twentieth century.
37. 2017: Ding, Qinghua, et al. “Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice.” Nature Climate Change 7.4 (2017): 289. The Arctic has seen rapid sea-ice decline in the past three decades, whilst warming at about twice the global average rate. Yet the relationship between Arctic warming and sea-ice loss is not well understood. Here, we present evidence that trends in summertime atmospheric circulation may have contributed as much as 60% to the September sea-ice extent decline since 1979. A tendency towards a stronger anticyclonic circulation over Greenland and the Arctic Ocean with a barotropic structure in the troposphere increased the downwelling longwave radiation above the ice by warming and moistening the lower troposphere. Model experiments, with reanalysis data constraining atmospheric circulation, replicate the observed thermodynamic response and indicate that the near-surface changes are dominated by circulation changes rather than feedbacks from the changing sea-ice cover. Internal variability dominates the Arctic summer circulation trend and may be responsible for about 30–50% of the overall decline in September sea ice since 1979.
38. 2017: Smedsrud, Lars H., et al. “Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years.” The Cryosphere 11.1 (2017): 65-79. A new long-term data record of Fram Strait sea ice area export from 1935 to 2014 is developed using a combination of satellite radar images and station observations of surface pressure across Fram Strait. This data record shows that the long-term annual mean export is about 880 000 km², representing 10 % of the sea-ice-covered area inside the basin. The time series has large interannual and multi-decadal variability but no long-term trend. However, during the last decades, the amount of ice exported has increased, with several years having annual ice exports that exceeded 1 million km². This increase is a result of faster southward ice drift speeds due to stronger southward geostrophic winds, largely explained by increasing surface pressure over Greenland. Evaluating the trend onwards from 1979 reveals an increase in annual ice export of about +6 % per decade, with spring and summer showing larger changes in ice export (+11 % per decade) compared to autumn and winter (+2.6 % per decade). Increased ice export during winter will generally result in new ice growth and contributes to thinning inside the Arctic Basin. Increased ice export during summer or spring will, in contrast, contribute directly to open water further north and a reduced summer sea ice extent through the ice–albedo feedback. Relatively low spring and summer export from 1950 to 1970 is thus consistent with a higher mid-September sea ice extent for these years. Our results are not sensitive to long-term change in Fram Strait sea ice concentration. We find a general moderate influence between export anomalies and the following September sea ice extent, explaining 18 % of the variance between 1935 and 2014, but with higher values since 2004.
39. 2017: Kirchmeier-Young, Megan C., Francis W. Zwiers, and Nathan P. Gillett. “Attribution of extreme events in Arctic sea ice extent.” Journal of Climate 30.2 (2017): 553-571. Arctic sea ice extent (SIE) has decreased over recent decades, with record-setting minimum events in 2007 and again in 2012. A question of interest across many disciplines concerns the extent to which such extreme events can be attributed to anthropogenic influences. First, a detection and attribution analysis is performed for trends in SIE anomalies over the observed period. The main objective of this study is an event attribution analysis for extreme minimum events in Arctic SIE. Although focus is placed on the 2012 event, the results are generalized to extreme events of other magnitudes, including both past and potential future extremes. Several ensembles of model responses are used, including two single-model large ensembles. Using several different metrics to define the events in question, it is shown that an extreme SIE minimum of the magnitude seen in 2012 is consistent with a scenario including anthropogenic influence and is extremely unlikely in a scenario excluding anthropogenic influence. Hence, the 2012 Arctic sea ice minimum provides a counterexample to the often-quoted idea that individual extreme events cannot be attributed to human influence.
40. 2017: Walsh, John E., et al. “A database for depicting Arctic sea ice variations back to 1850.” Geographical Review 107.1 (2017): 89-107. Arctic sea ice data from a variety of historical sources have been synthesized into a database extending back to 1850 with monthly time‐resolution. The synthesis procedure includes interpolation to a uniform grid and an analog‐based estimation of ice concentrations in areas of no data. The consolidated database shows that there is no precedent as far back as 1850 for the 21^st century’s minimum ice extent of sea ice on the pan‐Arctic scale. A regional‐scale exception to this statement is the Bering Sea. The rate of retreat since the 1990s is also unprecedented and especially large in the Beaufort and Chukchi Seas. Decadal and multidecadal variations have occurred in some regions, but their magnitudes are smaller than that of the recent ice loss. Interannual variability is prominent in all regions and will pose a challenge to sea ice prediction efforts.
41. 2018: Slawinska, Joanna, and Alan Robock. “Impact of volcanic eruptions on decadal to centennial fluctuations of Arctic sea ice extent during the last millennium and on initiation of the Little Ice Age.” Journal of Climate 31.6 (2018): 2145-2167. This study evaluates different hypotheses of the origin of the Little Ice Age, focusing on the long-term response of Arctic sea ice and oceanic circulation to solar and volcanic perturbations. The authors analyze the Last Millennium Ensemble of climate model simulations carried out with the Community Earth System Model at the National Center for Atmospheric Research. The authors examine the duration and strength of volcanic perturbations, and the effects of initial and boundary conditions, such as the phase of the Atlantic multidecadal oscillation. They evaluate the impacts of these factors on decadal-to-multicentennial perturbations of the cryospheric, oceanic, and atmospheric components of the climate system. The authors show that, at least in the Last Millennium Ensemble, volcanic eruptions are followed by a decadal-scale positive response of the Atlantic multidecadal overturning circulation, followed by a centennial-scale enhancement of the Northern Hemispheric sea ice extent. It is hypothesized that a few mechanisms, not just one, may have to play a role in consistently explaining such a simulated climate response at both decadal and centennial time scales. The authors argue that large volcanic forcing is necessary to explain the origin and duration of Little Ice Age–like perturbations in the Last Millennium Ensemble. Other forcings might play a role as well. In particular, prolonged fluctuations in solar irradiance associated with solar minima potentially amplify the enhancement of the magnitude of volcanically triggered anomalies of Arctic sea ice extent.
42. Responsiveness of Polar Sea Ice Extent to Air Temperature 1979-2016  Detrended correlation analysis of mean monthly sea ice extent with air temperature at an annual time scale in both Polar Oceans shows the expected negative correlation in 14 out of 36 cases studied. The other 22 cases, including the high profile case of September sea ice extent in the Arctic, show no evidence that temperature alone explains sea ice extent. We conclude that other factors such as wind, clouds, solar irradiance, and ocean circulation may be relevant in the study of differences in mean monthly sea ice extent for the same calendar month from year to year
43. Trends in Polar Sea Ice Extent 1979-2015  A survey of trends in dispersed and concentrated sea ice extent in the Arctic in the northern summer and northern winter and in the Antarctic in the southern summer and southern winter for the period 1979-2015 shows a negative trend in dispersed and concentrated sea ice extent in the Arctic in the northern summer amid rising surface temperature in the northern hemisphere. The trend in concentrated sea ice extent in the Arctic summer is not uniform across the study period but mostly a phenomenon of the latter half from 1998-2014. A positive trend for dispersed sea ice extent in the Antarctic in winter amid rising winter temperature in the southern hemisphere is not matched by trends in concentrated sea ice extent and the degree of dispersion and is discounted as spurious. In the southern summer, we found no trends in sea ice extent in the Antarctic and no trend in mean surface temperature in the southern hemisphere. This work concerns only sea ice extent without considerations of the age, thickness, and total volume of sea ice.
44. A General Linear Model for Sea Ice Extent  A general linear model is used for simultaneous identification of short term seasonal variations and long term trends in deseasonalized sea ice extent. It shows a sustained decline of Arctic sea ice extent over the entire study period from 1978 to 2014. No decline of sea ice extent is evident in the Antarctic.
45. Does Global Warming Drive Changes in Arctic Sea Ice?