A Robust Arctic Amplification Factor Throughout the Last 21,000 Years

Mapping Intimacies ◽

10.21203/rs.3.rs-1111875/v1 ◽

2022 ◽

Author(s):

Yuzhen Yan ◽

Xinyu Wen

Keyword(s):

Sea Ice ◽

Climate Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Climate Projections ◽

Arctic Amplification ◽

The Past ◽

Wide Range ◽

Arctic Sea ◽

New Evidence

Abstract Arctic amplification (AA), a phenomenon that a larger change in temperature near the Arctic areas than the Northern Hemisphere average in the past 100+ years, has significant impacts on mid-latitude weather and climate, and therefore is of great concern in current climate projections. Previous studies suggest a wide range of AA factors from 1.0 to 12.5 using either the 20th century observations or climate model hindcasts. In the present paper, we explore the diversity of AA factor in a long-term transient simulation covering the past glacial-to-interglacial years. It is shown that the natural AA phenomenon is essentially linked with North Atlantic sea ice changes through ice-albedo feedback with a narrowed and robust AA factor of 2.5±0.8 throughout the last 21,000 years. Current observed AA phenomenon is a mixed result combining sea ice melting induced AA mode with GHGs induced global uniform warming, and thus has an AA factor slightly less than 2.5. In the future, as Arctic sea ice gradually melts off, we speculate that AA phenomenon might fade off accordingly and the AA factor will decline close to 1.0 in 1-2 centuries. Our findings provide new evidence for better understanding the range of AA factor and associated key physical processes, and provide new insights for AA’s projection in current anthropogenic warming climate.

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Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

Journal of Climate ◽

10.1175/jcli-d-18-0195.1 ◽

2019 ◽

Vol 32 (5) ◽

pp. 1361-1380 ◽

Cited By ~ 3

Author(s):

J. Ono ◽

H. Tatebe ◽

Y. Komuro

Keyword(s):

Sea Ice ◽

Climate Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Ensemble Prediction ◽

Forecast Errors ◽

Drastic Reduction ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

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Atmospheric teleconnection processes linking winter air stagnation and haze extremes in China with regional Arctic sea ice decline

10.5194/acp-2019-1023 ◽

2019 ◽

Author(s):

Yufei Zou ◽

Yuhang Wang ◽

Zuowei Xie ◽

Hailong Wang ◽

Philip J. Rasch

Keyword(s):

Sea Ice ◽

Climate Model ◽

Teleconnection Pattern ◽

Arctic Sea Ice ◽

The Arctic ◽

Transient Eddy ◽

Regional Ventilation ◽

Atmospheric Teleconnection ◽

Upper Troposphere ◽

Arctic Sea

Abstract. Recent studies suggested significant impacts of boreal cryosphere changes on wintertime air stagnation and haze pollution extremes in China. However, the underlying mechanism of such a teleconnection relationship remains unclear. Here we used the Whole Atmosphere Community Climate Model (WACCM) to investigate dynamic processes leading to atmospheric circulation and air stagnation responses to Arctic sea ice changes. We conducted four climate sensitivity experiments by perturbing sea ice concentrations (SIC) and corresponding sea surface temperature (SST) in autumn and early winter over the whole Arctic and three sub-regions in the climate model. The results indicate different responses in the general circulation and regional ventilation to the region-specific Arctic changes, with the largest increase of both the probability (by 120 %) and the intensity (by 32 %) of air stagnation extreme events being found in the experiment driven by SIC and SST changes over the Pacific sector of the Arctic (the East Siberian and Chukchi Seas). The increased air stagnation extreme events are mainly driven by an amplified hemispheric-scale atmospheric teleconnection pattern that resembles the negative phase of the Eurasian (EU) pattern. Dynamical diagnostics suggest that convergence of transient eddy forcing in the vicinity of Scandinavia in winter is largely responsible for the amplification of the teleconnection pattern. Transient eddy vorticity fluxes dominate the transient eddy forcing and produce a barotropic anticyclonic anomaly near Scandinavia and wave-train propagation across Eurasia to the downstream regions in East Asia. The piecewise potential vorticity inversion analysis reveals that this long-range atmospheric teleconnection of the Arctic origin takes place primarily in the middle and upper troposphere. The anomalous ridge over East Asia in the middle and upper troposphere worsens regional ventilation conditions by weakening monsoon northwesterlies and enhancing temperature inversion near the surface, leading to more and stronger air stagnation and pollution extremes over eastern China in winter. Ensemble projections based on the state-of-the-art climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) corroborate this teleconnection relationship between high-latitude environmental changes and middle-latitude weather extremes, though the tendency and magnitude vary considerably among each participating model.

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Mechanisms causing reduced Arctic sea ice loss in a coupled climate model

The Cryosphere Discussions ◽

10.5194/tcd-6-2653-2012 ◽

2012 ◽

Vol 6 (4) ◽

pp. 2653-2687 ◽

Cited By ~ 1

Author(s):

A. E. West ◽

A. B. Keen ◽

H. T. Hewitt

Keyword(s):

Sea Ice ◽

Climate Model ◽

Historical Record ◽

Arctic Sea Ice ◽

The Arctic ◽

Slight Reduction ◽

Coupled Climate Model ◽

Arctic Sea ◽

Ensembles Project ◽

Arctic Sea Ice Loss

Abstract. The fully-coupled climate model HadGEM1 produces one of the most accurate simulations of the historical record of Arctic sea ice seen in the IPCC AR4 multi-model ensemble. In this study, we examine projections of sea ice decline out to 2030, produced by two ensembles of HadGEM1 with natural and anthropogenic forcings included. These ensembles project a significant slowing of the rate of ice loss to occur after 2010, with some integrations even simulating a small increase in ice area. We use an energy budget of the Arctic to examine the causes of this slowdown. A negative feedback effect by which rapid reductions in ice thickness north of Greenland reduce ice export is found to play a major role. A slight reduction in ocean-to-ice heat flux in the relevant period, caused by changes in the MOC and subpolar gyre in some integrations, is also found to play a part. Finally, we assess the likelihood of a slowdown occurring in the real world due to these causes.

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Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

10.5194/egusphere-egu21-3106 ◽

2021 ◽

Author(s):

Vladimir Semenov ◽

Tatiana Matveeva

Keyword(s):

Sea Ice ◽

20Th Century ◽

Climate Model ◽

Surface Air Temperature ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

The North ◽

Arctic Sea ◽

Ice Concentration

Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20th century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model&#8217;s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20th century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km2 in September and 0.7 million km2 in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21st century.

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Mid-Holocene Northern Hemisphere warming driven by Arctic amplification

Science Advances ◽

10.1126/sciadv.aax8203 ◽

2019 ◽

Vol 5 (12) ◽

pp. eaax8203 ◽

Cited By ~ 3

Author(s):

Hyo-Seok Park ◽

Seong-Joong Kim ◽

Andrew L. Stewart ◽

Seok-Woo Son ◽

Kyong-Hwan Seo

Keyword(s):

Sea Ice ◽

Northern Hemisphere ◽

High Latitude ◽

Climate Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Arctic Amplification ◽

The Holocene ◽

Holocene Thermal Maximum ◽

Thermal Maximum

The Holocene thermal maximum was characterized by strong summer solar heating that substantially increased the summertime temperature relative to preindustrial climate. However, the summer warming was compensated by weaker winter insolation, and the annual mean temperature of the Holocene thermal maximum remains ambiguous. Using multimodel mid-Holocene simulations, we show that the annual mean Northern Hemisphere temperature is strongly correlated with the degree of Arctic amplification and sea ice loss. Additional model experiments show that the summer Arctic sea ice loss persists into winter and increases the mid- and high-latitude temperatures. These results are evaluated against four proxy datasets to verify that the annual mean northern high-latitude temperature during the mid-Holocene was warmer than the preindustrial climate, because of the seasonally rectified temperature increase driven by the Arctic amplification. This study offers a resolution to the “Holocene temperature conundrum”, a well-known discrepancy between paleo-proxies and climate model simulations of Holocene thermal maximum.

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neXtSIM: a new Lagrangian sea ice model

The Cryosphere ◽

10.5194/tc-10-1055-2016 ◽

2016 ◽

Vol 10 (3) ◽

pp. 1055-1073 ◽

Cited By ~ 39

Author(s):

Pierre Rampal ◽

Sylvain Bouillon ◽

Einar Ólason ◽

Mathieu Morlighem

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

Dynamical Response ◽

Ice Dynamics ◽

Ice Drift ◽

Wide Range ◽

Arctic Sea ◽

Sea Ice Drift ◽

Ice Model

Abstract. The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multi-scale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales.

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Arctic Sea Ice Reemergence: The Role of Large-Scale Oceanic and Atmospheric Variability*

Journal of Climate ◽

10.1175/jcli-d-14-00354.1 ◽

2015 ◽

Vol 28 (14) ◽

pp. 5477-5509 ◽

Cited By ~ 25

Author(s):

Mitchell Bushuk ◽

Dimitrios Giannakis ◽

Andrew J. Majda

Keyword(s):

Sea Ice ◽

Large Scale ◽

Climate Model ◽

Surface Air Temperature ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

Arctic Sea ◽

Spring Sea Ice ◽

Phase Relationships

Abstract Arctic sea ice reemergence is a phenomenon in which spring sea ice anomalies are positively correlated with fall anomalies, despite a loss of correlation over the intervening summer months. This work employs a novel data analysis algorithm for high-dimensional multivariate datasets, coupled nonlinear Laplacian spectral analysis (NLSA), to investigate the regional and temporal aspects of this reemergence phenomenon. Coupled NLSA modes of variability of sea ice concentration (SIC), sea surface temperature (SST), and sea level pressure (SLP) are studied in the Arctic sector of a comprehensive climate model and in observations. It is found that low-dimensional families of NLSA modes are able to efficiently reproduce the prominent lagged correlation features of the raw sea ice data. In both the model and observations, these families provide an SST–sea ice reemergence mechanism, in which melt season (spring) sea ice anomalies are imprinted as SST anomalies and stored over the summer months, allowing for sea ice anomalies of the same sign to reappear in the growth season (fall). The ice anomalies of each family exhibit clear phase relationships between the Barents–Kara Seas, the Labrador Sea, and the Bering Sea, three regions that compose the majority of Arctic sea ice variability. These regional phase relationships in sea ice have a natural explanation via the SLP patterns of each family, which closely resemble the Arctic Oscillation and the Arctic dipole anomaly. These SLP patterns, along with their associated geostrophic winds and surface air temperature advection, provide a large-scale teleconnection between different regions of sea ice variability. Moreover, the SLP patterns suggest another plausible ice reemergence mechanism, via their winter-to-winter regime persistence.

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Impact of Reduced Arctic Sea Ice on Northern Hemisphere Climate and Weather in Autumn and Winter

Journal of Climate ◽

10.1175/jcli-d-20-0515.1 ◽

2021 ◽

pp. 1-61

Author(s):

Svenya Chripko ◽

Rym Msadek ◽

Emilia Sanchez-Gomez ◽

Laurent Terray ◽

Laurent Bessières ◽

...

Keyword(s):

North America ◽

Sea Ice ◽

Central Asia ◽

Northern Hemisphere ◽

Climate Model ◽

Polar Vortex ◽

Arctic Sea Ice ◽

The Arctic ◽

Arctic Sea ◽

Autumn And Winter

AbstractThe Northern Hemisphere transient atmospheric response to Arctic sea decline is investigated in autumn and winter, using sensitivity experiments performed with the CNRMCM6-1 high-top climate model. Arctic sea ice albedo is reduced to the ocean value, yielding ice-free conditions during summer and a more moderate sea ice reduction during the following months. A strong ampli_cation of temperatures over the Arctic is induced by sea ice loss, with values reaching up to 25°C near the surface in autumn. Signi_cant surface temperature anomalies are also found over the mid-latitudes, with a warming reaching 1°C over North America and Europe, and a cooling reaching 1°C over central Asia. Using a dynamical adjustment method based on a regional reconstruction of circulation analogs, we show that the warming over North America and Europe can be explained both by changes in the atmospheric circulation and by the advection of warmer oceanic air by the climatological ow. In contrast, we demonstrate that the sea-ice induced cooling over central Asia is solely due to dynamical changes, involving an intensi_cation of the Siberian High and a cyclonic anomaly over the Sea of Okhotsk. In the troposphere, the abrupt Arctic sea ice decline favours a narrowing of the subtropical jet stream and a slight weakening of the lower part of the polar vortex that is explained by a weak enhancement of upward wave activity toward the stratosphere. We further show that reduced Arctic sea ice in our experiments is mainly associated with less severe cold extremes in the mid-latitudes.

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An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models

10.5194/tc-2019-314 ◽

2020 ◽

Author(s):

Ann Keen ◽

Ed Blockley ◽

David Bailey ◽

Jens Boldingh Debernard ◽

Mitchell Bushuk ◽

...

Keyword(s):

Sea Ice ◽

Climate Model ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Formation ◽

Mass Budget ◽

Ice Growth ◽

Arctic Sea ◽

Basal Melting

Abstract. We compare the mass budget of the Arctic sea ice for 14 models submitted to the latest Climate Model Inter-comparison Project (CMIP6), using new diagnostics that have not been available for previous model inter-comparisons. Using these diagnostics allows us to look beyond the standard metrics of ice cover and thickness, to compare the processes of sea ice growth and loss in climate models in a more detailed way than has previously been possible. For the 1960–89 multi-model mean, the dominant processes causing annual ice growth are basal growth and frazil ice formation, which both occur during the winter. The main processes by which ice is lost are basal melting, top melting and advection of ice out of the Arctic. The first two processes occur in summer, while the latter process is present all year. The sea-ice budgets for individual models are strikingly similar overall in terms of the major processes causing ice growth and loss, and in terms of the time of year during which each process is important. However, there are also some key differences between the models. The relative amounts of frazil and basal ice formation varies between the models. This is, to some extent at least, attributable to exactly how the frazil growth is formulated within each model. There are also differences in the relative amounts of top and basal melting. As the ice cover and mass decline during the 21st century, we see a shift in the timing of the top and basal melting in the multi-model mean, with more melt occurring earlier in the year, and less melt later in the summer. The amount of basal growth in the autumn reduces, but the amount of basal growth later in the winter increases due to the ice being thinner. Overall, extra ice loss in May–June and reduced ice growth in October-November is partially offset by reduced ice melt in August and increased ice growth in January–February. For the individual models, changes in the budget components vary considerably in terms of magnitude and timing of change. However, when the evolving budget terms are considered as a function of the changing ice state itself, behaviours common to all the models emerge, suggesting that the sea ice components of the models are fundamentally responding in a broadly consistent way to the warming climate. Additional results from a forced ocean-ice model show that although atmospheric forcing is crucial for the sea ice mass budget, the sea ice physics also plays an important role.

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Assessment of the Regional Arctic System Model Intra-Annual Ensemble Predictions of Arctic Sea Ice

10.5194/egusphere-egu2020-12849 ◽

2020 ◽

Author(s):

Wieslaw Maslowski ◽

Younjoo Lee ◽

Anthony Craig ◽

Mark Seefeldt ◽

Robert Osinski ◽

...

Keyword(s):

Sea Ice ◽

Time Scales ◽

Weather Prediction ◽

Arctic Sea Ice ◽

System Model ◽

The Arctic ◽

Ensemble Prediction ◽

Ensemble Forecasts ◽

The Past ◽

Arctic Sea

The Regional Arctic System Model (RASM) has been developed and used to investigate the past to present evolution of the Arctic climate system and to address increasing demands for Arctic forecasts beyond synoptic time scales. RASM is a fully coupled ice-ocean-atmosphere-land hydrology model configured over the pan-Arctic domain with horizontal resolution of 50 km or 25 km for the atmosphere and land and 9.3 km or 2.4 km for the ocean and sea ice components. As a regional model, RASM requires boundary conditions along its lateral boundaries and in the upper atmosphere, which for simulations of the past to present are derived from global atmospheric reanalyses, such as the National Center for Environmental Predictions (NCEP) Coupled Forecast System version 2 and Reanalysis (CFSv2/CFSR). This dynamical downscaling approach allows comparison of RASM results with observations, in place and time, to diagnose and reduce model biases. This in turn allows a unique capability not available in global weather prediction and Earth system models to produce realistic and physically consistent initial conditions for prediction without data assimilation.More recently, we have developed a new capability for an intra-annual (up to 6 months) ensemble prediction of the Arctic sea ice and climate using RASM forced with the routinely produced (every 6 hours) NCEP CFSv2 global 9-month forecasts. RASM intra-annual ensemble forecasts have been initialized on the 1st of each month starting in 2019 with forcing for each ensemble member derived from CSFv2 forecasts, 24-hr apart from the month preceding the initial forecast date. &#160;Several key processes and feedbacks will be discussed with regard to their impact on model physics, the representation of initial state and ensemble prediction skill of Arctic sea ice variability at time scales from synoptic to decadal. The skill of RASM ensemble forecasts will be assessed against available satellite observations with reference to reanalysis as well as hindcast data using several metrics, including the standard deviation, root mean square difference, Taylor diagrams and integrated ice-edge error.

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