scholarly journals Summertime low clouds mediate the impact of the large-scale circulation on Arctic sea ice

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yiyi Huang ◽  
Qinghua Ding ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Ian Baxter
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Climate Models ◽  
Lower Troposphere ◽  
Arctic Sea Ice ◽  
Albedo Feedback ◽  
Ice Melt ◽  
Low Clouds ◽  
Arctic Sea ◽  
The Impact

AbstractThe rapid Arctic sea ice retreat in the early 21st century is believed to be driven by several dynamic and thermodynamic feedbacks, such as ice-albedo feedback and water vapor feedback. However, the role of clouds in these feedbacks remains unclear since the causality between clouds and these processes is complex. Here, we use NASA CERES satellite products and NCAR CESM model simulations to suggest that summertime low clouds have played an important role in driving sea ice melt by amplifying the adiabatic warming induced by a stronger anticyclonic circulation aloft. The upper-level high pressure regulates low clouds through stronger downward motion and increasing lower troposphere relative humidity. The increased low clouds favor more sea ice melt via emitting stronger longwave radiation. Then decreased surface albedo triggers a positive ice-albedo feedback, which further enhances sea ice melt. Considering the importance of summertime low clouds, accurate simulation of this process is a prerequisite for climate models to produce reliable future projections of Arctic sea ice.

scholarly journals Arctic Energy Budget in Relation to Sea Ice Variability on Monthly-to-Annual Time Scales

2015 ◽  
Vol 28 (16) ◽  
pp. 6335-6350 ◽  
Author(s):  
F. Krikken ◽  
W. Hazeleger
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Energy Budget ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Albedo Feedback ◽  
Arctic Sea ◽  
Cloud Ice

Abstract The large decrease in Arctic sea ice in recent years has triggered a strong interest in Arctic sea ice predictions on seasonal-to-decadal time scales. Hence, it is important to understand physical processes that provide enhanced predictability beyond persistence of sea ice anomalies. This study analyzes the natural variability of Arctic sea ice from an energy budget perspective, using 15 climate models from phase 5 of CMIP (CMIP5), and compares these results to reanalysis data. The authors quantify the persistence of sea ice anomalies and the cross correlation with the surface and top-of-atmosphere energy budget components. The Arctic energy balance components primarily indicate the important role of the seasonal ice–albedo feedback, through which sea ice anomalies in the melt season reemerge in the growth season. This is a robust anomaly reemergence mechanism among all 15 climate models. The role of the ocean lies mainly in storing heat content anomalies in spring and releasing them in autumn. Ocean heat flux variations play only a minor role. Confirming a previous (observational) study, the authors demonstrate that there is no direct atmospheric response of clouds to spring sea ice anomalies, but a delayed response is evident in autumn. Hence, there is no cloud–ice feedback in late spring and summer, but there is a cloud–ice feedback in autumn, which strengthens the ice–albedo feedback. Anomalies in insolation are positively correlated with sea ice variability. This is primarily a result of reduced multiple reflection of insolation due to an albedo decrease. This effect counteracts the ice-albedo effect up to 50%. ERA-Interim and Ocean Reanalysis System 4 (ORAS4) confirm the main findings from the climate models.

Evaluation of a multilayer snow scheme in NEMO-LIM3

2020 ◽  
Author(s):  
Gaëlle Gilson ◽  
Thierry Fichefet ◽  
Olivier Lecomte ◽  
Pierre-Yves Barriat ◽  
Jean Sterlin ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Climate Models ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Optimum Number ◽  
Ice Growth ◽  
Arctic Sea ◽  
The Impact

<p>Arctic sea ice is a major component of the Earth’s climate system and has been experiencing a drastic decline over the past decades, with important consequences regionally and globally. With the sustained warming of the Arctic, sea ice loss is expected to continue in the future. However, the estimation of its magnitude is model-dependent. As a result, the representation of sea ice in climate models requires further consideration. A major issue relates to the long-standing misrepresentation of snow properties on sea ice. However, the presence of snow strongly impacts sea ice growth and surface energy balance. Through its high albedo, snow reflects more solar radiation than bare sea ice does. When a snow cover is present, sea ice growth is reduced because snow is an effective insulator, with a thermal conductivity an order of magnitude lower than that of sea ice. Ocean circulation models usually use multiple layers to resolve sea ice thermodynamics but only one single layer for snow. Lecomte et al. (2013) developed a multilayer snow scheme for ocean circulation models and improved the snow depth distribution by considering the macroscopic effects of wind packing and redeposition. Since then, this snow scheme has been revisited and implemented in a more recent and much more robust NEMO-LIM version, using a simpler technical approach. In addition, new instrumental observations of snow thickness, distribution and density are available since these exploratory works. They are used in the current study to: 1) evaluate the performance of the multilayer snow scheme for sea ice in the NEMO-LIM3 model, and 2) investigate the climatic importance of this snow scheme. Here, we present results of simulations with a varying number of snow layers. By comparing these to the latest observational datasets, we recommend an optimum number of snow  layers to be used in ocean circulation models in both hemispheres. Finally, we explore the impact of a few specific parameterizations of snow thermophysical properties on the representation of sea ice in climate models.</p>

scholarly journals Arctic sea-ice melt in 2008 and the role of solar heating

2011 ◽  
Vol 52 (57) ◽  
pp. 355-359 ◽  
Author(s):  
Donald K. Perovich ◽  
Jacqueline A. Richter-Menge ◽  
Kathleen F. Jones ◽  
Bonnie Light ◽  
Bruce C. Elder ◽  
...  
Keyword(s):  
Sea Ice ◽  
Heat Input ◽  
Large Scale ◽  
Beaufort Sea ◽  
Arctic Sea Ice ◽  
Solar Heat ◽  
Ice Melt ◽  
Ice Surface ◽  
Arctic Sea ◽  
Ice Concentration

AbstractThere has been a marked decline in the summer extent of Arctic sea ice over the past few decades. Data from autonomous ice mass-balance buoys can enhance our understanding of this decline. These buoys monitor changes in snow deposition and ablation, ice growth, and ice surface and bottom melt. Results from the summer of 2008 showed considerable large-scale spatial variability in the amount of surface and bottom melt. Small amounts of melting were observed north of Greenland, while melting in the southern Beaufort Sea was quite large. Comparison of net solar heat input to the ice and heat required for surface ablation showed only modest correlation. However, there was a strong correlation between solar heat input to the ocean and bottom melting. As the ice concentration in the Beaufort Sea region decreased, there was an increase in solar heat to the ocean and an increase in bottom melting.

scholarly journals Improving model-satellite comparisons of sea ice melt onset with a satellite simulator

2021 ◽  
Author(s):  
Abigail Smith ◽  
Alexandra Jahn ◽  
Clara Burgard ◽  
Dirk Notz
Keyword(s):  
Sea Ice ◽  
Satellite Data ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
Satellite Observations ◽  
The Arctic ◽  
Physical Processes ◽  
Ice Melt ◽  
Brightness Temperatures ◽  
Arctic Sea

Abstract. Seasonal transitions in Arctic sea ice, such as the melt onset, have been found to be useful metrics for evaluating sea ice in climate models against observations. However, comparisons of melt onset dates between climate models and satellite observations are indirect. Satellite data products of melt onset rely on observed brightness temperatures, while climate models do not currently simulate brightness temperatures, and therefore must define melt onset with other modeled variables. Here we adapt a passive microwave sea ice satellite simulator (ARC3O) to produce simulated brightness temperatures that can be used to diagnose the timing of the earliest snowmelt in climate models, as we show here using CESM2 ocean-ice hindcasts. By producing simulated brightness temperatures and earliest snowmelt estimation dates using CESM2 and ARC3O, we facilitate new and previously impossible comparisons between the model and satellite observations by removing the uncertainty that arises due to definition differences. Direct comparisons between the model and satellite data allow us to identify an early bias across large areas of the Arctic at the beginning of the CESM2 ocean-ice hindcast melt season, as well as improve our understanding of the physical processes underlying seasonal changes in brightness temperatures. In particular, the ARC3O allows us to show that satellite algorithm-based melt onset dates likely occur after significant snowmelt has already taken place.

The role of air-sea ice-ocean interaction processes for Arctic-midlatitude linkages

2020 ◽  
Author(s):  
Sara Khosravi ◽  
Annette Rinke ◽  
Wolfgang Dorn ◽  
Christof Lüpkes ◽  
Vladimir Gryanik ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Sea Ice ◽  
Large Scale ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
Surface Fluxes ◽  
The Arctic ◽  
Russian Science ◽  
Large Scale Circulation ◽  
The Impact

<p>Climate models have deficits in reproducing Arctic circulation and sea ice development. The air-sea ice-ocean interaction parametrizations could be a potential reason of this shortcoming. In most climate models air-sea ice-ocean interaction are parametrized based on mid-latitude conditions which is not appropriate for polar region. The POLEX project, funded by Helmholtz Association and Russian Science Foundation, is studying the impact of improved representation of Arctic air-sea ice-ocean interaction on changes in Arctic atmospheric circulation and Arctic-midlatitude linkages. We have used a new suite of parametrizations, which are easily applicable for climate simulations and have been developed based on SHEBA expedition data by Gryanik and Lüpkes (2018). We implemented the new parametrizations in the global atmospheric model (ECHAM6) in the framework of POLEX to estimate its effect on regional Arctic and large-scale circulation changes. Several steps have been defined for implementing the new parameterization to be able to distinguish and understand better the impact of its parameters. Roughness length and stability functions for stable stratification have been modified. Here the initial results of ECHAM6 sensitivity runs for different steps of the parameterization will be presented. We will present first results from process-oriented evaluation over the Arctic sea ice, e.g. how is the impact on the simulation of the two states of the Arctic boundary layer in winter. Furthermore, we will show that the large-scale circulation reacts to the new parametrization in different months and years differently.<br>Reference:<br>Gryanik, V.M. and C. Lüpkes (2018) An efficient non-iterative bulk parametrization of surface fluxes for stable atmospheric conditions over polar sea-ice, Boundary-Layer Meteorol., 166, 301-325</p>

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

2020 ◽  
Vol 14 (2) ◽  
pp. 403-428 ◽  
Author(s):  
Adam W. Bateson ◽  
Daniel L. Feltham ◽  
David Schröder ◽  
Lucia Hosekova ◽  
Jeff K. Ridley ◽  
...  
Keyword(s):  
Sea Ice ◽  
Size Distribution ◽  
Power Law ◽  
Climate Models ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Marginal Ice Zone ◽  
Break Up ◽  
The Impact

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.

scholarly journals Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data

2019 ◽  
Vol 11 (21) ◽  
pp. 2481 ◽  
Author(s):  
Tatiana Alekseeva ◽  
Vasiliy Tikhonov ◽  
Sergei Frolov ◽  
Irina Repina ◽  
Mikhael Raev ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Interaction Study ◽  
Sea Ice Concentration ◽  
Ice Melt ◽  
Antarctic Sea Ice ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Satellite Microwave ◽  
The Impact

The paper presents a comparison of sea ice concentration (SIC) derived from satellite microwave radiometry data and dedicated ship observations. For the purpose, the NASA Team (NT), Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI), and Variation Arctic/Antarctic Sea Ice Algorithm 2 (VASIA2) algorithms were used as well as the database of visual ice observations accumulated in the course of 15 Arctic expeditions. The comparison was performed in line with the SIC gradation (in tenths) into very open (1–3), open (4–6), close (7–8), very close and compact (9–10,10) ice, separately for summer and winter seasons. On average, in summer NT underestimates SIC by 0.4 tenth as compared to ship observations, while ASI and VASIA2 by 0.3 tenth. All three algorithms overestimate total SIC in regions of very open ice and underestimate it in regions of close, very close, and compact ice. The maximum average errors are typical of open ice regions that are most common in marginal ice zones. In winter, NT and ASI also underestimate SIC on average by 0.4 and 0.8 tenths, respectively, while VASIA2, on the contrary, overestimates by 0.2 tenth against the ship data, however, for open and close ice the average errors are significantly higher than in summer. In the paper, we also estimate the impact of ice melt stage and presence of new ice and nilas on SIC derived from NT, ASI, and VASIA2.

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

scholarly journals What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?

Climate ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 15 ◽  
Author(s):  
Ge Peng ◽  
Jessica L. Matthews ◽  
Muyin Wang ◽  
Russell Vose ◽  
Liqiang Sun
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Adaptation ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Climate Projections ◽  
Emission Scenarios ◽  
Ice Coverage ◽  
Arctic Sea

The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.

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