Summer Arctic Sea Ice Extent Research Articles

Impact of atmospheric rivers on Arctic sea ice variations

Abstract. Arctic sea ice has been declining rapidly in recent decades. We investigate how the poleward transport of moisture and heat from lower latitudes through atmospheric rivers (ARs) influences Arctic sea ice variations. We use hourly ERA5 (fifth-generation European Reanalysis) data for 1981–2020 at 0.25∘ × 0.25∘ resolution to examine the meteorological conditions and sea ice changes associated with ARs in the Arctic. In the years 2012 and 2020, which had an extremely low summer Arctic sea ice extent, we show that the individual AR events associated with large cyclones initiate a rapid sea ice decrease through turbulent heat fluxes and winds. We carry out further statistical analysis of the meteorological conditions and sea ice variations for 1981–2020 over the entire Arctic Ocean. We find that on weather timescales the atmospheric moisture content anticorrelates significantly with the sea ice concentration tendency almost everywhere in the Arctic Ocean, while the dynamic sea ice motion driven by northward winds further reduces the sea ice concentration.

Description of FIO-ESM version 2.1 and evaluation of its sea ice simulations

To improve Arctic sea ice simulations by the First Institute of Oceanography–Earth System Model (FIO–ESM), the model version has been updated from FIO–ESM v2.0 to FIO–ESM v2.1 by upgrading its sea ice component from Los Alamos Sea–Ice Model (CICE) version 4.0 (CICE4.0) to CICE6.0, and improving the ice–ocean heat exchange process from a two–equation boundary condition parameterization to a more realistic three–equation boundary condition parameterization. Numerical experiments show that the underestimation of Arctic summer sea ice extent (SIE) in FIO–ESM v2.0 is significantly improved by the model enhancements. The root mean square error of the simulated Arctic September SIE during 1979–2014 is reduced from 2.9 million to 0.7 million km2. Nevertheless, the biases of Antarctic SIE increase following the model version update. FIO–ESM v2.1 performs well for the simulations of surface air temperature, sea surface temperature, Atlantic Meridional Overturning Circulation, and Arctic SIE; however, it overestimates summer SIE in the Antarctic. Furthermore, future projections based on FIO–ESM v2.1 indicate that the first ice–free Arctic summer will occur in the 2050s and the 2040s under SSP2–4.5 and SSP5–8.5, respectively.

Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction

The Arctic sea ice plays a significant role in climate-related processes and has a considerable effect on humans, however accurately predicting the Arctic sea ice concentration is still challenging. Recently, with the rise and development of artificial intelligence, big data technology, machine learning has been widely used in the field of sea ice prediction. In this study, we utilized a sea ice concentration dataset obtained from satellite remote sensing and applied the k-nearest-neighbors (Ice-kNN) machine learning model to forecast the summer Arctic sea ice concentration and extent on 122 days prediction. Based on the physical characteristics of summer sea ice, different algorithms are employed to optimize the prediction model. A drift-ice correction algorithm is designed to address the unrealistic drift ice around the sea ice edge, and a distance function combined with the spatial pattern is proposed to enhance similarity detection. Deseasonalized and detrended sea ice datasets and an expanded training library are also utilized to improve model performance. Furthermore, sensitivity analysis reveals a positive impact of net surface heat flux on sea ice prediction. The modified Ice-kNN model outperforms climatological and anomaly persistence predictions, demonstrating its applicability to predicting summer Arctic sea ice. The September sea ice extent hindcasts of the modified Ice-kNN model are compared to a variety of models submitted to the Sea Ice Prediction Network, underscoring its potential to improve predictive skill for Arctic sea ice.

Are thunderstorms linked to the rapid Sea ice loss in the Arctic?

Sea ice in the Arctic grows during the winter and retreats in the summer. The highly reflective white surface of sea ice reflects solar energy, cooling the planet. However, when it melts, the darker ocean absorbs more solar radiation, reinforcing the cycle of melting sea ice. Since sea ice plays a critical role in regulating Earth's climate, changes in sea ice may influence global weather patterns and ocean circulations. In addition to the rise in greenhouse gases in the Arctic, upper tropospheric water vapor (UTWV) or the specific humidity (SH) is also increasing, while acting as an additional greenhouse gas trapping in heat released from the Earth's surface. While annual greenhouse gas concentrations in the Arctic are increasing rather smoothly over time, the interannual variability in sea ice is shown here to be linked to the variability of UTWV at 400 hPa in the Arctic. We propose that increasing thunderstorm activity in the Arctic in the last decade is increasing the water vapor in the upper troposphere, partially explaining the interannual variability, and long term decrease, in the Arctic summer sea ice extent. Both the temporal and spatial analysis between UTWV and Sea Ice changes show that the decline of the summer sea ice cover in the Arctic may be linked to the increasing thunderstorm activity in the Arctic in the last decades.

Simulating the Impact of the Sustained Melting Arctic on the Global Container Sea–Rail Intermodal Shipping

Global warming trends and the rapid reduction of summer Arctic sea ice extent have increased the feasibility of transarctic transport. How the process of glacier melting affects the existing containerized sea–rail shipping network and container flow assignment has become a challenging economic and policy issue. This paper first examines the meteorological influences on glacier melting and the assignment of container flow over the existing sea–rail network. Then, a three-layer simulation framework is constructed, with the upper layer simulating glacier melting based on the raster grid, the middle layer combining a grid and topology analysis to simulate the evolution of the global sea–rail network and the lower layer establishing a concave cost network flow model to simulate the container flow assignment. Finally, we use MicroCity to achieve the dynamic optimization and simulation of global container flow assignment, solving the large-scale sea–rail shipping network traffic assignment problem. The simulation results show that the proposed model and solution algorithm are feasible and effective, revealing the variation of container flow assignment in the global sea–rail shipping network under different Arctic ice melting scenarios. For instance, in the summer of 2050, the Arctic routes will share the global container flows, resulting in a significant reduction of container flows in the Malacca Strait, Suez Canal and Panama Canal.

New insight into the influence of the Greenland high on summer Arctic sea ice

The Greenland high (GL-high) coincides with a local center of action of the summer North Atlantic Oscillation and is known to have significant influence on Greenland ice sheet melting and summer Arctic sea ice. However, the mechanism behind the influence on regional Arctic sea ice is not yet clear. In this study, using reanalysis datasets and satellite observations, the influence of the GL-high in early summer on Arctic sea ice variability, and the mechanism behind it, are investigated. In response to an intensified GL-high, sea ice over the Beaufort Sea shows significant decline in both concentration and thickness from June through September. This decline in sea ice is primarily due to thermodynamic and mechanical redistribution processes. Firstly, the intensified GL-high increases subsidence over the Canadian Basin, leading to an increase in surface air temperature by adiabatic heating, and a substantial decrease in cloud cover and thus increased downward shortwave radiation. Secondly, the intensified GL-high increases easterly wind frequency and wind speed over the Beaufort Sea, pushing sea ice over the Canadian Basin away from the coastlines. Both processes contribute to an increase in open water areas, amplifying ice–albedo feedback and leading to sea ice decline. The mechanism identified here differs from previous studies that focused on northward moisture and heat transport and the associated increase in downward longwave radiation over the Arctic. The impact of the GL-high on the regional sea ice (also Arctic sea ice extent) can persist from June into fall, providing an important source for seasonal prediction of Arctic sea ice. The GL-high has an upward trend and reached a record high in 2012 that coincided with a record minimum summer Arctic sea ice extent, and has strong implications for summer Arctic sea ice changes.

Impacts of glacier and sea ice melt on methane pathways on the Northeast Greenland shelf

Global warming has led to a sharp decrease in Arctic summer sea ice extent and a dramatic ice mass loss of the Greenland Ice Sheet over the past three decades. The Northeast Greenland continental shelf is a site of intense water mass transformation involving both sea ice processes and glacier dynamics. The Arctic shelf waters are considered to be a net source of atmospheric methane (CH4); however, the effect of glacier and sea ice melt on oceanic CH4 concentrations still needs to be investigated. To better understand the effect of meltwater on the CH4 budget of the ocean, our study constrains the CH4 pathways by following changes in water mass properties and infers potential CH4 sources and sinks. Based on measurements of concentration and carbon isotope delta (δ13C) of CH4, the water mass tracer δ18O(H2O) and physical properties of the water masses, we detected CH4 excess in surface waters, which we attribute to brine release during sea ice formation. We show that this CH4 excess is sustained throughout the melt season, due to a freshwater lid formed at the ocean surface. The meltwater hardly alters the CH4 excess, but enhances water stratification, which, in turn, restricts the sea-to-air flux. The CH4 excess is subject to mixing with surrounding shelf waters influenced by basal glacial meltwater discharge. We suggest that the CH4 excess of Northeast Greenland continental shelf waters is redistributed in the marine environment, while CH4 emission to the atmosphere is limited to regions not covered by sea ice.

Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations

Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer timescales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in Northern Hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations, respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the adjusted Rand index – a method for comparing spatial patterns of variability – and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability in the AO relatively well, although they overestimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean and underestimate the variability over northern Africa and southern Europe. They also underestimate the importance of regions such as the Beaufort, East Siberian, and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.

Highly Productive Ice Algal Mats in Arctic Melt Ponds: Primary Production and Carbon Turnover

Arctic summer sea ice extent is decreasing and thinning, forming melt ponds that cover more than 50% of the sea ice area during the peak of the melting season. Despite of this, ice algal communities in melt ponds are understudied and so are their contribution to the Arctic Ocean primary production and carbon turnover. While melt ponds have been considered as low productive, recent studies suggest that accumulated ice algal potentially facilitate high and yet overlooked rates of carbon turnover. Here we report on ice algal communities forming dense mats not previously described, collected from melt ponds in the northern Barents Sea in July. We document on distinct layered and brown colored mats with high carbon assimilation and net primary production rates compared to ice algal communities and aggregates, in fact comparable to benthic microalgae at temperate tidal flats. Rates of gross and net primary production, as well as community respiration rates were obtained from oxygen micro profiling, and carbon assimilation calculations were supported by 14C incubations, pigment analysis and light microscopy examinations. The melt pond algal mats consisted of distinct colored layers and differed from aggregates with a consisted layered structure. We accordingly propose the term melt pond algal mats, and further speculate that these dense ice algal mats may provide an important yet overlooked source of organic carbon in the Arctic food-web. A foodweb component likely very sensitive to climate driven changes in the Arctic Ocean and pan-Arctic seas.

Impact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea ice

Abstract. Recent years have seen a rapid reduction in the summer Arctic sea ice extent. To both understand this trend and project the future evolution of the summer Arctic sea ice, a better understanding of the physical processes that drive the seasonal loss of sea ice is required. The marginal ice zone, here defined as regions with between 15 % and 80 % sea ice cover, is the region separating pack ice from the open ocean. Accurate modelling of this region is important to understand the dominant mechanisms involved in seasonal sea ice loss. Evolution of the marginal ice zone is determined by complex interactions between the atmosphere, sea ice, ocean, and ocean surface waves. Therefore, this region presents a significant modelling challenge. Sea ice floes span a range of sizes but sea ice models within climate models assume they adopt a constant size. Floe size influences the lateral melt rate of sea ice and momentum transfer between atmosphere, sea ice, and ocean, all important processes within the marginal ice zone. In this study, the floe size distribution is represented as a power law defined by an upper floe size cut-off, lower floe size cut-off, and power-law exponent. This distribution is also defined by a new tracer that varies in response to lateral melting, wave-induced break-up, freezing conditions, and advection. This distribution is implemented within a sea ice model coupled to a prognostic ocean mixed-layer model. We present results to show that the use of a power-law floe size distribution has a spatially and temporally dependent impact on the sea ice, in particular increasing the role of the marginal ice zone in seasonal sea ice loss. This feature is important in correcting existing biases within sea ice models. In addition, we show a much stronger model sensitivity to floe size distribution parameters than other parameters used to calculate lateral melt, justifying the focus on floe size distribution in model development. We also find that the attenuation rate of waves propagating under the sea ice cover modulates the impact of wave break-up on the floe size distribution. It is finally concluded that the model approach presented here is a flexible tool for assessing the importance of a floe size distribution in the evolution of sea ice and is a useful stepping stone for future development of floe size modelling.

The potential of sea ice leads as a predictor for summer Arctic sea ice extent

Abstract. The Arctic sea ice extent throughout the melt season is closely associated with initial sea ice state in winter and spring. Sea ice leads are important sites of energy fluxes in the Arctic Ocean, which may play an important role in the evolution of Arctic sea ice. In this study, we examine the potential of sea ice leads as a predictor for summer Arctic sea ice extent forecast using a recently developed daily sea ice lead product retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS). Our results show that July pan-Arctic sea ice extent can be predicted from the area of sea ice leads integrated from midwinter to late spring, with a prediction error of 0.28 million km2 that is smaller than the standard deviation of the observed interannual variability. However, the predictive skills for August and September pan-Arctic sea ice extent are very low. When the area of sea ice leads integrated in the Atlantic and central and west Siberian sector of the Arctic is used, it has a significantly strong relationship (high predictability) with both July and August sea ice extent in the Atlantic and central and west Siberian sector of the Arctic. Thus, the realistic representation of sea ice leads (e.g., the areal coverage) in numerical prediction systems might improve the skill of forecast in the Arctic region.

Comparison of Mechanisms for Low-Frequency Variability of Summer Arctic Sea Ice in Three Coupled Models

Abstract In this study the mechanisms for low-frequency variability of summer Arctic sea ice are analyzed using long control simulations from three coupled models (GFDL CM2.1, GFDL CM3, and NCAR CESM). Despite different Arctic sea ice mean states, there are many robust features in the response of low-frequency summer Arctic sea ice variability to the three key predictors (Atlantic and Pacific oceanic heat transport into the Arctic and the Arctic dipole) across all three models. In all three models, an enhanced Atlantic (Pacific) heat transport into the Arctic induces summer Arctic sea ice decline and surface warming, especially over the Atlantic (Pacific) sector of the Arctic. A positive phase of the Arctic dipole induces summer Arctic sea ice decline and surface warming on the Pacific side, and opposite changes on the Atlantic side. There is robust Bjerknes compensation at low frequency, so the northward atmospheric heat transport provides a negative feedback to summer Arctic sea ice variations. The influence of the Arctic dipole on summer Arctic sea ice extent is more (less) effective in simulations with less (excessive) climatological summer sea ice in the Atlantic sector. The response of Arctic sea ice thickness to the three key predictors is stronger in models that have thicker climatological Arctic sea ice.

Linkages between Arctic summer circulation regimes and regional sea ice anomalies

AbstractThe downward trend in overall Arctic summer sea ice extent has been substantial, particularly in the last few decades. Departures in ice extent from year to year can be very large, however, in part due to the high variability in summer atmospheric circulation patterns. Anomalies in the Pacific sector ice cover can be partially compensated by anomalies of opposite sign in the Atlantic sector. An assessment of linkages between summer atmospheric patterns and sectoral anomalies in the area of maximum open water north of 70°N demonstrates that there is asymmetry in the mechanisms. Years with low ice extent and high open water fraction are uniformly associated with positive temperature anomalies and southerly flow in both the Atlantic and Pacific sectors. However, years with high extent and low open water fraction in both sectors reveal two dominant mechanisms. Some years with anomalously low maximum open water fraction are associated with negative temperature anomalies and southerly transport—a cool summer pattern that allows ice to persist over larger areas. However, other low open water years are characterized by an “ice factory” mechanism, whereby—even when melting—ice cover is continually replenished by advection from the north.

Effects of recent decreases in arctic sea ice on an ice-associated marine bird

Recent major reductions in summer arctic sea ice extent could be expected to be affecting the distributions and life histories of arctic marine biota adapted to living adjacent to sea ice. Of major concern are the effects of ice reductions, and associated increasing SST, on the most abundant forage fish in the Arctic, Arctic cod (Boreogadus saida), the primary prey for the region’s upper trophic level marine predators. The black guillemot (Cepphus grylle mandtii) is an ice-obligate diving seabird specializing in feeding on Arctic cod and has been studied annually since 1975 at a breeding colony in the western Beaufort Sea. The data set is one of the few allowing assessment of the response of an upper trophic marine predator to recent decadal changes in the region’s cryosphere. Analysis of oceanographic conditions north of the colony from 1975 to 2012 for the annual period when parents provision young (mid-July to early September), found no major regime shifts in ice extent or SST until the late 1990s with major decreases in ice and increases in SST in the first decade of the 21st Century. We examined decadal variation in late summer oceanographic conditions, nestling diet and success, and overwinter adult survival, comparing a historical period (1975–1984) with a recent (2003–2012) one. In the historical period sea ice retreated an average of 1.8km per day from 15 July to 1 September to an average distance of 95.8km from the colony, while in the recent period ice retreat averaged 9.8km per day to an average distance of 506.9km for the same time period. SST adjacent to the island increased an average of 2.9°C between the two periods.While Arctic cod comprised over 95% of the prey provided to nestlings in the historical period, in the recent period 80% of the years had seasonal decreases, with Arctic cod decreasing to <5% of the nestling diet, and nearshore demersals, primarily sculpin (Cottidae), comprising the majority of the diet. A five-fold increase in the rate of nestling starvation and reductions in nestling growth and fledging mass were associated with the shift from Arctic cod. Annual adult survival during the nonbreeding season (September–May), showed no significant difference between the two periods, indicating no major change in availability of Arctic cod or other prey in the wintering area in the Bering Sea. Our findings of a substantial decrease in Arctic cod availability in late summer in response to decreased ice extent and increasing SST have implications for the entire Arctic given the ongoing and predicted basin-wide reductions in sea ice.

Mechanisms for low-frequency variability of summer Arctic sea ice extent

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.

Late winter biogeochemical conditions under sea ice in the Canadian High Arctic

With the Arctic summer sea-ice extent in decline, questions are arising as to how changes in sea-ice dynamics might affect biogeochemical cycling and phenomena such as carbon dioxide (CO2) uptake and ocean acidification. Recent field research in these areas has concentrated on biogeochemical and CO2 measurements during spring, summer or autumn, but there are few data for the winter or winter–spring transition, particularly in the High Arctic. Here, we present carbon and nutrient data within and under sea ice measured during the Catlin Arctic Survey, over 40 days in March and April 2010, off Ellef Ringnes Island (78° 43.11′ N, 104° 47.44′ W) in the Canadian High Arctic. Results show relatively low surface water (1–10 m) nitrate (&lt;1.3 µM) and total inorganic carbon concentrations (mean±SD=2015±5.83 µmol kg−1), total alkalinity (mean±SD=2134±11.09 µmol kg−1) and under-ice pCO2sw (mean±SD=286±17 µatm). These surprisingly low wintertime carbon and nutrient conditions suggest that the outer Canadian Arctic Archipelago region is nitrate-limited on account of sluggish mixing among the multi-year ice regions of the High Arctic, which could temper the potential of widespread under-ice and open-water phytoplankton blooms later in the season.Keywords: Sea ice; carbon cycling; biogeochemical cycles; nutrients; Arctic Ocean; ocean acidification.(Published: 9 December 2015)Citation: Polar Research 2015, 34, 24170, http://dx.doi.org/10.3402/polar.v34.24170

The importance of spring atmospheric conditions for predictions of the Arctic summer sea ice extent

AbstractRecent studies have shown that atmospheric processes in spring play an important role for the initiation of the summer ice melt and therefore may strongly influence the September sea ice concentration (SSIC). Here a simple statistical regression model based on only atmospheric spring parameters is applied in order to predict the SSIC over the major part of the Arctic Ocean. By using spring anomalies of downwelling longwave radiation or atmospheric water vapor as predictor variables, correlation coefficients between observed and predicted SSIC of up to 0.5 are found. These skills of seasonal SSIC predictions are similar to those obtained using more complex dynamical forecast systems, despite the fact that the simple model applied here takes neither information of the sea ice state, oceanic conditions nor feedback mechanisms during summer into account. The results indicate that a realistic representation of spring atmospheric conditions in the prediction system plays an important role for the predictive skills of a model system.

Modeled Arctic sea ice evolution through 2300 in CMIP5 extended RCPs

Abstract. Almost all global climate models and Earth system models that participated in the Coupled Model Intercomparison Project 5 (CMIP5) show strong declines in Arctic sea ice extent and volume under the highest forcing scenario of the representative concentration pathways (RCPs) through 2100, including a transition from perennial to seasonal ice cover. Extended RCP simulations through 2300 were completed for a~subset of models, and here we examine the time evolution of Arctic sea ice in these simulations. In RCP2.6, the summer Arctic sea ice extent increases compared to its minimum following the peak radiative forcing in 2044 in all nine models. RCP4.5 demonstrates continued summer Arctic sea ice decline after the forcing stabilizes due to continued warming on longer timescales. Based on the analysis of these two scenarios, we suggest that Arctic summer sea ice extent could begin to recover if and when radiative forcing from greenhouse gas concentrations were to decrease. In RCP8.5 the Arctic Ocean reaches annually ice-free conditions in seven of nine models. The ensemble of simulations completed under the extended RCPs provide insight into the global temperature increase at which sea ice disappears in the Arctic and the reversibility of declines in seasonal sea ice extent.

Improved mapping of sea ice production in the Arctic Ocean using AMSR‐E thin ice thickness algorithm

AbstractNew and improved estimates of sea ice production in the Arctic Ocean are derived from AMSR‐E satellite and atmospheric reanalysis data for the period 2002–2011, at a spatial resolution of 6.25 km and using a newly developed fast‐ice mask. High ice production in the major coastal polynyas is well demonstrated. The total annual cumulative ice production in the major 10 polynya regions is about . The interannual variability of the ice production for each polynya is presented during 2002–2011. No obvious relationship is noted between the ice production and the recent drastic reduction in the preceding summer Arctic sea ice extent. Most polynya regions exhibit maximum ice production in autumn (October – November), before areas offshore have been covered with consolidated pack ice. Sea ice production from October to November in the marginal ice zone of the Pacific Ocean sector is negatively correlated with summer ice extent there. The ice production from October to November of 2007 (a record minimum summer ice extent) was about twice as large as that in other years. The high ice production area shifted to higher latitudes i.e., toward the deep Canada Basin, due to the retreat of the summer ice edge. We speculate that the resultant increase in brine input could change the oceanic structure in the basin, specifically deepening the winter mixed layer.

North American Climate in CMIP5 Experiments: Part III: Assessment of Twenty-First-Century Projections*

AbstractIn part III of a three-part study on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) models, the authors examine projections of twenty-first-century climate in the representative concentration pathway 8.5 (RCP8.5) emission experiments. This paper summarizes and synthesizes results from several coordinated studies by the authors. Aspects of North American climate change that are examined include changes in continental-scale temperature and the hydrologic cycle, extremes events, and storm tracks, as well as regional manifestations of these climate variables. The authors also examine changes in the eastern North Pacific and North Atlantic tropical cyclone activity and North American intraseasonal to decadal variability, including changes in teleconnections to other regions of the globe. Projected changes are generally consistent with those previously published for CMIP3, although CMIP5 model projections differ importantly from those of CMIP3 in some aspects, including CMIP5 model agreement on increased central California precipitation. The paper also highlights uncertainties and limitations based on current results as priorities for further research. Although many projected changes in North American climate are consistent across CMIP5 models, substantial intermodel disagreement exists in other aspects. Areas of disagreement include projections of changes in snow water equivalent on a regional basis, summer Arctic sea ice extent, the magnitude and sign of regional precipitation changes, extreme heat events across the northern United States, and Atlantic and east Pacific tropical cyclone activity.