Comment on tc-2022-246

<strong class="journal-contentHeaderColor">Abstract.</strong> Arctic sea ice outflow to the Atlantic Ocean is essential to Arctic sea ice mass loss and the hydrographical and ecological environments in the Barents and Greenland Seas (BGS). In the context of the extremely positive Arctic Oscillation (AO) in January&ndash;March 2020, the impacts and feedback mechanisms on a seasonal scale of anomalies in Arctic sea ice outflow on winter&ndash;spring sea ice and other marine environmental conditions in the subsequent months until early summer in the BGS were investigated. The results reveal that the total sea ice area flux (SIAF) through the Fram Strait, the Svalbard-Franz Josef Land, and the Franz Josef Land-Novaya Zemlya passageways in January&ndash;March and June 2020 were higher than the 1988&ndash;2020 climatology, mainly through the Fram Strait (77.6 %). The interannual variability of this total SIAF was dominated by changes in ice motion speed (<em>R</em> = +0.86, <em>P</em> &lt; 0.001). The relatively high ice speed along the Transpolar Drift in January&ndash;June 2020 was related to the positive phases of winter (JFM) AO and the winter-spring air pressure gradient across the western and eastern Arctic Ocean. The abnormally high Arctic sea ice outflow led to an increased sea ice area and thickness in the BGS, which has been observed since March 2020, especially in May&ndash;June. In this region, the April sea ice area was significantly negatively correlated with synchronous sea surface temperature (SST) as well as the lagging SST of 1&ndash;3 months. High sea ice area in spring (AMJ) 2020 also inhibited phytoplankton bloom, with an extremely low <em>Chlorophyll-a</em> concentration observed over the BGS in April. Therefore, this study suggests that winter&ndash;spring Arctic sea ice outflow can be considered as a predictor of changes in sea ice and other marine environmental conditions in the BGS in the subsequent months, at least until early summer. The results increase our understanding of the physical connection between the central Arctic Ocean and the peripheral seas.

Sea ice in the Arctic has been reducing rapidly in the past half century due to global warming. This study analyzes the variations of sea ice extent in the entire Arctic Ocean and its sub regions. The results indicate that sea ice extent reduction during 1979–2013 is most significant in summer, following by that in autumn, winter and spring. In years with rich sea ice, sea ice extent anomaly with seasonal cycle removed changes with a period of 4–6 years. The year of 2003–2006 is the ice-rich period with diverse regional difference in this century. In years with poor sea ice, sea ice margin retreats further north in the Arctic. Sea ice in the Fram Strait changes in an opposite way to that in the entire Arctic. Sea ice coverage index in melting-freezing period is an critical indicator for sea ice changes, which shows an coincident change in the Arctic and sub regions. Since 2002, Region C2 in north of the Pacific sector contributes most to sea ice changes in the central Aarctic, followed by C1 and C3. Sea ice changes in different regions show three relationships. The correlation coefficient between sea ice coverage index of the Chukchi Sea and that of the East Siberian Sea is high, suggesting good consistency of ice variation. In the Atlantic sector, sea ice changes are coincided with each other between the Kara Sea and the Barents Sea as a result of warm inflow into the Kara Sea from the Barents Sea. Sea ice changes in the central Arctic are affected by surrounding seas.

This chapter reviews the efforts under SIKU-ISIUOP to expand upon previous research that characterized the importance of sea ice processes, use, and change around the Baffin Island communities of Cape Dorset, Igloolik, and Pangnirtung, Nunavut. In these three communities, local ice conditions are intertwined with daily activities and provide a means of traveling and hunting, as well as sustaining marine wildlife and aspects of Inuit culture. In order for people to effectively travel and hunt on the sea ice, they have to become knowledgeable about the complexity and dynamism of the oceanic environment. Through these understandings and long-term experience and observation, local experts (such as Inuit elders and active hunters) are acutely aware of the local and regional manifestations of climate change, as indicated by long-term changes and increased unpredictability of sea ice. Specifically, Inuit have observed changes in floe edge position, weather, the timing of freeze-up and breakup, ice thickness, and the presence of multi-year ice. This chapter reviews specific indicators used to evaluate sea ice changes, offers a regional comparison of sea ice changes in the three communities, and provides an overview of some of the local implications of sea ice changes.

[1] The perennial (September) Arctic sea ice cover exhibits large interannual variability, with changes of over a million square kilometers from one year to the next. Here we explore the role of changes in Arctic cyclone activity, and related factors, in driving these pronounced year-to-year changes in perennial sea ice cover. Strong relationships are revealed between the September sea ice changes and the number of cyclones in the preceding late spring and early summer. In particular, fewer cyclones over the central Arctic Ocean during the months of May, June, and July appear to favor a low sea ice area at the end of the melt season. Years with large losses of sea ice are characterized by abnormal cyclone distributions and tracks: they lack the normal maximum in cyclone activity over the central Arctic Ocean, and cyclones that track from Eurasia into the central Arctic are largely absent. Fewer storms are associated with above-average mean sea level pressure, strengthened anticyclonic winds, an intensification of the transpolar drift stream, and reduced cloud cover, all of which favor ice melt. It is also shown that a strengthening of the central Arctic cyclone maximum helps preserve the ice cover, although the association is weaker than that between low cyclone activity and reduced sea ice. The results suggest that changes in cyclone occurrence during late spring and early summer have preconditioning effects on the sea ice cover and exert a strong influence on the amount of sea ice that survives the melt season.

The sea ice concentration dataset (covering the period 1992-2008) used in this study is a new dataset based on the Sea Ice Climate Change Initiative (SICCI) algorithm. We investigate whether the SICCI dataset is on a par with other datasets for studying sea ice cover changes in the Southern Ocean. We then examine spatiotemporal variations in sea ice derived from the SICCI dataset over the Southern Ocean, and analyse relationships of sea ice with sea surface temperature (SST). The results indicate that there is no significant difference between the SICCI dataset and the NASA Team dataset, and therefore the former can also be used for studying sea ice changes. Both sea ice extent (SIE) and sea ice area (SIA) derived from the SICCI dataset over the Southern Ocean increased slightly from 1992 to 2008, at rates of (17.75 ± 11.50) × 10^3 and (17.37 ± 9.51) × 10^3 km^2 yr^(–1), respectively. Antarctic sea ice has significant seasonal variations; all seasonally averaged SIE and SIA show an increase, with spring showing the largest positive changing rate. The Weddell Sea, Ross Sea, and Indian Ocean have positive yearly changing rates in SIE and SIA, while the Bellingshausen/Amundsen seas and western Pacific Ocean have negative yearly changing rates. However, overall sea ice over the Southern Ocean has a slight positive trend, which is the same as the sea ice change pattern derived from the NASA Team dataset. This indicates that the contributions to the change in sea ice over the whole Southern Ocean due to the Weddell Sea, Ross Sea, and Indian Ocean dominate over those by the Bellingshausen/Amundsen seas and western Pacific Ocean. Further analysis shows that both SIE and SIA are negatively correlated with SST in the Southern Ocean or each of the 5 longitudinal sectors, and sea ice is more sensitive to SST in spring and autumn.

Abstract. The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×106 km2, compared to 6.46×106 km2 for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×106 km2 for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 % yr−1) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

Variability and trends of Fram Strait sea ice area and volume exports are examined for the period of 1990–2010. Simulations from a high-resolution version of the MPIOM model (STORM project) reproduce area and volume export well when compared with NSIDC and ICESat satellite data and in-situ ice thickness observations. The fluxes derived from ice thickness and drift satellite products vary considerably, indicating a high uncertainty in these estimates which we mostly assign to the drift observations. The model captures the observed average seasonal cycles and interannual variability of ice export. The simulated mean annual sea ice area export is 860 × 103 km2 a− 1 (1990–2010), and the correlation with the NSIDC-based area fluxes is r = 0.67. The simulated mean annual volume export is 3.3 × 103 km3 a− 1 (1990–2010), close to the ICESat/ULS values, with a correlation of r = 0.58. The simulated monthly area export has a significant positive trend of + 10% per decade, explained by wind forcing. The major contribution to the robust trend in area export between June and September. Fram Strait ice volume export variability is mainly controlled by ice drift with a dominant role of the Transpolar Drift and, to a lesser extent thickness variability. The area export increase reflects increasing ice-drift speed, but is balanced with a reduced thickness over time when it comes to volume export, giving no significant trend in volume export. The spatial variability of ice drift indicates that the export influences a large area upstream in the Trans-Polar Drift stream, and that high volume export events lead to a thinner thickness there. The central Arctic is well connected drift-wise to the Fram Strait via the Transpolar Drift while for thickness, the region north of Greenland is dominated and controlled by the Fram Strait ice export.

Data collected by the International Arctic Buoy Programme from 1979 to 1998 are analyzed to obtain statistics of sea level pressure (SLP) and sea ice motion (SIM). The annual and seasonal mean fields agree with those obtained in previous studies of Arctic climatology. The data show a 3-hPa decrease in decadal mean SLP over the central Arctic Ocean between 1979–88 and 1989–98. This decrease in SLP drives a cyclonic trend in SIM, which resembles the structure of the Arctic Oscillation (AO). Regression maps of SIM during the wintertime (January–March) AO index show 1) an increase in ice advection away from the coast of the East Siberian and Laptev Seas, which should have the effect of producing more new thin ice in the coastal flaw leads; 2) a decrease in ice advection from the western Arctic into the eastern Arctic; and 3) a slight increase in ice advection out of the Arctic through Fram Strait. Taken together, these changes suggest that at least part of the thinning of sea ice recently observed over the Arctic Ocean can be attributed to the trend in the AO toward the high-index polarity. Rigor et al. showed that year-to-year variations in the wintertime AO imprint a distinctive signature on surface air temperature (SAT) anomalies over the Arctic, which is reflected in the spatial pattern of temperature change from the 1980s to the 1990s. Here it is shown that the memory of the wintertime AO persists through most of the subsequent year: spring and autumn SAT and summertime sea ice concentration are all strongly correlated with the AO index for the previous winter. It is hypothesized that these delayed responses reflect the dynamical influence of the AO on the thickness of the wintertime sea ice, whose persistent “footprint” is reflected in the heat fluxes during the subsequent spring, in the extent of open water during the subsequent summer, and the heat liberated in the freezing of the open water during the subsequent autumn.

The Hudson Bay system is undergoing climate-driven changes in sea ice and freshwater inflow and has seen an increase in winter river inflow since the 1960s due in part to flow regulation for hydropower production. Southeast Hudson Bay and adjacent James Bay are at the forefront of these changes, with more than 1-month shortening of the season of sea ice cover as defined using satellite data, increases in winter inflow from the regulated La Grande River complex, and changes in coastal ice and polynya behavior described by Belcher Islands’ Inuit. In summer, there is a fresh coastal domain in southeast Hudson Bay fueled by river runoff and sea ice melt. To investigate winter oceanographic conditions and potential interactions between runoff and ice melt or brine in southeast Hudson Bay, we initiated the first winter study of the shallow waters surrounding the Belchers, collecting conductivity-temperature-depth (CTD) profiles and conductivity-temperature (CT) time series using under-ice moorings, and collecting water samples and ice cores during four campaigns between January 2014 and March 2015. Tandem measurements of salinity and δ18O were made for the water and ice samples to discriminate between freshwater sources (river runoff and sea ice melt). We find that southeast Hudson Bay, and particularly the nearshore domain southeast of the Belchers, is distinguished in winter by the presence of river water and strong surface stratification, which runs counter to expectations for a system in which local freshwater remains frozen on land until spring freshet (May–June) and sea ice growth is adding brine to surface waters. The amount of river water around the Belcher Islands increased significantly from fall through to late winter according to δ18O records of ice. The accumulation of river water in surface waters during the winter is directly associated with an accumulation of brine, which considerably exceeds the capacity of local ice formation to produce brine. We therefore infer that brine is advected into the study area together with river water, and that interplay between these properties establishes and maintains the level of surface stratification throughout winter. With reference to a NEMO ocean model simulation of winter circulation in the study area, we propose a conceptual model in which winter river inflow into James Bay drives the northward transport of both river water and brine captured near the surface, with reductions in brine-driven deep convection in the area’s flaw leads. While past changes in winter oceanographic conditions and sea ice cannot be reconstructed from the few available scientific data, the presence of significant runoff in winter in southeast Hudson Bay implies heightened sensitivity to delayed freeze-up under a warmer climate, which will have the effect of reducing brine early in the winter, also promoting increased stratification and river plume transport.

Although subject to annual fluctuations, sea ice in the Sea of Okhotsk has decreased to a maximum extent at a rate of approximately 3.4% per decade since the 1970s. Thus far, few studies have focused on projections of sea ice in the Sea of Okhotsk. This study focused on sea ice in the Sea of Okhotsk and examined its past and future characteristics using a climate projection dataset termed the Future Ocean Regional Projection dataset. Historical sea ice areas have been reported to be larger than satellite observations, and some data contain biases of approximately double the actual value. Therefore, a simple bias correction was performed based on the ratio of historical to satellite observation sea-ice areas, and the bias was corrected. Furthermore, we performed future projections using two bias-corrected scenarios (RCP2.6 and RCP8.5). Results revealed that for the future analysis period of 2006–2100, sea ice loss would be approximately 12.3 (102 km2/year) under RCP2.6 and approximately 37.3 (102 km2/year) under RCP8.5, indicating that under both scenarios, there would be almost no sea ice in the southern Sea of Okhotsk between 2071 and 2100. The results of this study provide useful information for researchers to predict sea ice in related physical fields.

Abstract. This work analyzes overshoot simulations from 13 models, including the SSP5-3.4OS and SSP1-1.9 experiments from the Coupled Model Intercomparison Project Phase 6 (CMIP6), to assess how regional temperatures after overshoot differ from those of before, and how these differences may be impacted by changes in sea ice, the Atlantic Ocean Heat Transport (OHT) and the Atlantic Meridional Overturning Circulation (AMOC). The overshoot scenarios, characterized by a peak in radiative forcing levels followed by a decline, show that changes during the CO2 increasing phase are not necessarily compensated during the CO2 decreasing phase, particularly at the regional level. Even if the global mean temperature may recover after the overshoot, regional conditions post-overshoot may still differ from those pre-overshoot, with spatial patterns characterized by large-scale temperature asymmetries. These asymmetries are found between Northern (NH) and Southern Hemisphere (SH), between high and mid-latitudes of the NH, and between western and eastern areas of the Southern Ocean. Changes in sea ice and ocean circulation are identified as potential sources of hysteresis, highlighting the impact of oceanic changes in the behavior of atmospheric variables in case of overshoot. The analyses from this work show that the relative contribution of each mechanism strongly depends on the model. Inter-model differences in the contributions of the meridional overturning can be associated with different climatologies of Mixed Layer Depth (MLD) in the northern North Atlantic (NNA) and in certain areas of the Southern Ocean. Despite these differences across models, both ocean circulation and sea ice changes contribute to shaping the regional temperatures after overshoot, with the temperature asymmetries between NH and SH mainly explained by changes in the AMOC, those between high and mid-latitudes of the NH by sea ice changes, and those between western and eastern areas of the Southern Ocean by the Southern Meridional Overturning Circulation (SMOC). These results highlight the importance of model intercomparison and analysis of ocean dynamics to understand the regional impacts of an overshoot, and more generally the responses to forcing changes.

In this study, hindcast skill for near-surface air temperature (TAS), sea surface temperature (SST), sea ice concentration, and sea ice area is assessed for the Arctic region using decadal simulations with the MiKlip decadal prototype prediction system. The prototype MiKlip system is based on the low-resolution version of the MPI-ESM model. In the simulations, a full field initialization of atmospheric and oceanic variables was used, but sea ice was not initialized. The hypothesis is that the increase in hindcast skill due to initialization found for TAS and SST in the North Atlantic in the prototype system compared to the historical simulations leads to enhanced skill also in the Arctic. However, the skill enhancement compared to the uninitialized experiments in the Arctic is generally weak. The hindcast skill only increases for SST and sea ice concentration along the east coast of Greenland and in the Fram Strait in lead years 2–5. Initialization additionally improves the skill in regionally integrated sea ice area (detrended) in the Greenland Sea, but only in lead year 1 and only in winter, and not in other Arctic regions. In order to assess whether additional initialization of sea ice concentration improves skill, we also analyse hindcasts and historical simulations performed with the MiKlip preoperational system that is based on the high-resolution version of the MPI-ESM. These simulations have nonetheless a negative bias in sea ice area in late summer of 1 to 3 million km2 . Noting that this is a much smaller ensemble than for the prototype system, the hindcast skill in North Atlantic TAS and SSTs is significantly reduced and not present when evaluated against ERA-Interim instead of HadCRUT4 data. Accordingly, in the Arctic, no additional skill compared to the prototype hindcasts is found. Our results underline the importance to assess the robustness of skill with different observational datasets and metrics. For future MiKlip simulations, we recommend to additionally initialize sea ice thickness or age, and to initialize the simulations in a different month to potentially enhance sea ice skill in the Arctic.

Pan-Arctic melt pond fraction trend, variability, and contribution to sea ice changes

Sea ice is influential in regulating energy exchanges between the ocean and the atmosphere, and has figured prominently in scientific studies of climate change and climate feedbacks. However, sea ice is also a vital component of everyday life in Inuit communities of the circumpolar Arctic. Therefore, it is important to understand the links between the potential impacts of climate change on Arctic sea ice extent, distribution, and thickness as well as the related consequences for northern coastal populations. This paper explores the relationship between sea ice and climate change from both scientific and Inuit perspectives. Based on an overview of diverse literature the experiences, methods, and goals which differentiate local and scientific sea ice knowledge are examined. These efforts are considered essential background upon which to develop more accurate assessments of community vulnerability to climate, and resulting sea ice, change. Inuit and scientific perspectives may indeed be the ideal complement when investigating the links between sea ice and climate change, but effective and appropriate conceptual bridges need to be built between the two types of expertise. The complementary nature of these knowledge systems may only be realized, in a practical sense, if significant effort is expended to: (i) understand sea ice from both Inuit and scientific perspectives, along with their underlying differences; (ii) investigate common interests or concerns; (iii) establish meaningful and reciprocal research partnerships with Inuit communities; (iv) engage in, and improve, collaborative research methods; and, (v) maintain ongoing dialogue.

<strong class="journal-contentHeaderColor">Abstract.</strong> The Arctic sea ice cover is strongly influenced by internal variability on decadal time scales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea ice variability and trends, and thus help to disentangle the role of forced and internal sea ice changes over the satellite record. In particular, we identify a mode of decadal ocean-atmosphere-sea ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea, but has contributed less to trends in the Barents and Kara Seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments.