scholarly journals Evolution of the Global Coupled Climate Response to Arctic Sea Ice Loss during 1990–2090 and Its Contribution to Climate Change

2018 ◽  
Vol 31 (19) ◽  
pp. 7823-7843 ◽  
Author(s):  
Lantao Sun ◽  
Michael Alexander ◽  
Clara Deser
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Sea Ice ◽  
Surface Temperature ◽  
Atmospheric Circulation ◽  
Arctic Sea Ice ◽  
Meridional Overturning Circulation ◽  
The Arctic ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

The role of transient Arctic sea ice loss in the projected greenhouse gas–induced late-twentieth- to late-twenty-first-century climate change is investigated using the Geophysical Fluid Dynamics Laboratory’s Coupled Model version 3. Two sets of simulations have been conducted, one with representative concentration pathway (RCP) 8.5 radiative forcing and the second with RCP forcing but with Arctic sea ice nudged to its 1990 state. The difference between the two five-member sets indicates the influence of decreasing Arctic sea ice on the climate system. Within the Arctic, sea ice loss is found to be a primary driver of the surface temperature and precipitation changes. Arctic sea ice depletion also plays a dominant role in projected Atlantic meridional overturning circulation weakening and changes in North Atlantic extratropical sea surface temperature and salinity, especially in the first half century. The effect of present-day Arctic sea ice loss on Northern Hemisphere (NH) extratropical atmospheric circulation is small relative to internal variability and the future sea ice loss effect on atmospheric circulation is distinct from the projected anthropogenic change. Arctic sea ice loss warms NH extratropical continents and is an important contributor to global warming not only over high latitudes but also in the eastern United States. Last, the Arctic sea ice loss displaces the Pacific intertropical convergence zone (ITCZ) equatorward and induces a “mini-global warming” in the tropical upper troposphere.

scholarly journals Atmospheric feedback explains disparate climate response to regional Arctic sea-ice loss

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Xavier J. Levine ◽  
Ivana Cvijanovic ◽  
Pablo Ortega ◽  
Markus G. Donat ◽  
Etienne Tourigny
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Sea Ice ◽  
Feedback Mechanism ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Climate Response ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss ◽  
Atmospheric Feedback

AbstractArctic sea-ice loss is a consequence of anthropogenic global warming and can itself be a driver of climate change in the Arctic and at lower latitudes, with sea-ice minima likely favoring extreme events over Europe and North America. Yet the role that the sea-ice plays in ongoing climate change remains uncertain, partly due to a limited understanding of whether and how the exact geographical distribution of sea-ice loss impacts climate. Here we demonstrate that the climate response to sea-ice loss can vary widely depending on the pattern of sea-ice change, and show that this is due to the presence of an atmospheric feedback mechanism that amplifies the local and remote signals when broader scale sea-ice loss occurs. Our study thus highlights the need to better constrain the spatial pattern of future sea-ice when assessing its impacts on the climate in the Arctic and beyond.

scholarly journals Arctic Sea Ice’s Amplification as a Complement to Anthropogenic Global Warming

2021 ◽  
Author(s):  
Marco Morando
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Future Climate Change ◽  
Number Count ◽  
Anthropogenic Global Warming ◽  
Arctic Sea ◽  
Scientific Subject

Abstract Climate Change is a widely debated scientific subject and Anthropogenic Global Warming is its main cause. Nevertheless, several authors have indicated solar activity and Atlantic Multi-decadal Oscillation variations may also influence Climate Change. This article considers the amplification of solar radiation’s and Atlantic Multi-decadal Oscillation’s variations, via sea ice cover albedo feedbacks in the Arctic regions, providing a conceptual advance in the application of Arctic Amplification for modelling historical climate change. A 1-dimensional physical model, using sunspot number count and Atlantic Multi-decadal Oscillation index as inputs, can simulate the average global temperature’s anomaly and the Arctic Sea Ice Extension for the past eight centuries. This model represents an innovative progress in understanding how existing studies on Arctic sea ice’s albedo feedbacks can help complementing the Anthropogenic Global Warming models, thus helping to define more precise models for future climate change.

scholarly journals Internal Variability in Projections of Twenty-First-Century Arctic Sea Ice Loss: Role of the Large-Scale Atmospheric Circulation

2014 ◽  
Vol 27 (2) ◽  
pp. 527-550 ◽  
Author(s):  
Justin J. Wettstein ◽  
Clara Deser
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Wave Train ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
Rossby Wave Train ◽  
Arctic Sea Ice Loss

Abstract Internal variability in twenty-first-century summer Arctic sea ice loss and its relationship to the large-scale atmospheric circulation is investigated in a 39-member Community Climate System Model, version 3 (CCSM3) ensemble for the period 2000–61. Each member is subject to an identical greenhouse gas emissions scenario and differs only in the atmospheric model component's initial condition. September Arctic sea ice extent trends during 2020–59 range from −2.0 × 106 to −5.7 × 106 km2 across the 39 ensemble members, indicating a substantial role for internal variability in future Arctic sea ice loss projections. A similar nearly threefold range (from −7.0 × 103 to −19 × 103 km3) is found for summer sea ice volume trends. Higher rates of summer Arctic sea ice loss in CCSM3 are associated with enhanced transpolar drift and Fram Strait ice export driven by surface wind and sea level pressure patterns. Over the Arctic, the covarying atmospheric circulation patterns resemble the so-called Arctic dipole, with maximum amplitude between April and July. Outside the Arctic, an atmospheric Rossby wave train over the Pacific sector is associated with internal ice loss variability. Interannual covariability patterns between sea ice and atmospheric circulation are similar to those based on trends, suggesting that similar processes govern internal variability over a broad range of time scales. Interannual patterns of CCSM3 ice–atmosphere covariability compare well with those in nature and in the newer CCSM4 version of the model, lending confidence to the results. Atmospheric teleconnection patterns in CCSM3 suggest that the tropical Pacific modulates Arctic sea ice variability via the aforementioned Rossby wave train. Large ensembles with other coupled models are needed to corroborate these CCSM3-based findings.

Deep learning for monthly Arctic sea ice concentration prediction

2020 ◽  
Author(s):  
Tom Andersson ◽  
Fruzsina Agocs ◽  
Scott Hosking ◽  
María Pérez-Ortiz ◽  
Brooks Paige ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Deep Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Prediction System ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

<p>Over recent decades, the Arctic has warmed faster than any region on Earth. The rapid decline in Arctic sea ice extent (SIE) is often highlighted as a key indicator of anthropogenic climate change. Changes in sea ice disrupt Arctic wildlife and indigenous communities, and influence weather patterns as far as the mid-latitudes. Furthermore, melting sea ice attenuates the albedo effect by replacing the white, reflective ice with dark, heat-absorbing melt ponds and open sea, increasing the Sun’s radiative heat input to the Arctic and amplifying global warming through a positive feedback loop. Thus, the reliable prediction of sea ice under a changing climate is of both regional and global importance. However, Arctic sea ice presents severe modelling challenges due to its complex coupled interactions with the ocean and atmosphere, leading to high levels of uncertainty in numerical sea ice forecasts.</p><p>Deep learning (a subset of machine learning) is a family of algorithms that use multiple nonlinear processing layers to extract increasingly high-level features from raw input data. Recent advances in deep learning techniques have enabled widespread success in diverse areas where significant volumes of data are available, such as image recognition, genetics, and online recommendation systems. Despite this success, and the presence of large climate datasets, applications of deep learning in climate science have been scarce until recent years. For example, few studies have posed the prediction of Arctic sea ice in a deep learning framework. We investigate the potential of a fully data-driven, neural network sea ice prediction system based on satellite observations of the Arctic. In particular, we use inputs of monthly-averaged sea ice concentration (SIC) maps since 1979 from the National Snow and Ice Data Centre, as well as climatological variables (such as surface pressure and temperature) from the European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) dataset. Past deep learning-based Arctic sea ice prediction systems tend to overestimate sea ice in recent years - we investigate the potential to learn the non-stationarity induced by climate change with the inclusion of multi-decade global warming indicators (such as average Arctic air temperature). We train the networks to predict SIC maps one month into the future, evaluating network prediction uncertainty by ensembling independent networks with different random weight initialisations. Our model accounts for seasonal variations in the drivers of sea ice by controlling for the month of the year being predicted. We benchmark our prediction system against persistence, linear extrapolation and autoregressive models, as well as September minimum SIE predictions from submissions to the Sea Ice Prediction Network's Sea Ice Outlook. Performance is evaluated quantitatively using the root mean square error and qualitatively by analysing maps of prediction error and uncertainty.</p>

scholarly journals The Role of Extratropical Ocean Warming in the Coupled Climate Response to Arctic Sea Ice Loss

2018 ◽  
Vol 31 (22) ◽  
pp. 9193-9206 ◽  
Author(s):  
Russell Blackport ◽  
Paul J. Kushner
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
General Circulation ◽  
Arctic Sea Ice ◽  
Ocean Warming ◽  
The Arctic ◽  
Climate Response ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

The role of extratropical ocean warming in the coupled climate response to Arctic sea ice loss is investigated using coupled atmosphere–ocean general circulation model (AOGCM) and uncoupled atmospheric-only (AGCM) experiments. Coupled AOGCM experiments driven by sea ice albedo reduction and greenhouse gas–dominated radiative forcing are used to diagnose the extratropical sea surface temperature (SST) response to sea ice loss. Sea ice loss is then imposed in AGCM experiments both with and without these extratropical SST changes, which are found to extend beyond the regions where sea ice is lost. Sea ice loss in isolation drives warming that is confined to the Arctic lower troposphere and only a weak atmospheric circulation response. When the extratropical SST response caused by sea ice loss is also included in the forcing, the warming extends into the Arctic midtroposphere during winter. This coincides with a stronger atmospheric circulation response, including an equatorward shift in the eddy-driven jet, a deepening of the Aleutian low, and an expansion of the Siberian high. Similar results are found whether the extratropical SST forcing is taken directly from the AOGCM driven by sea ice loss, or whether they are diagnosed using a two-parameter pattern scaling technique where tropical adjustment to sea ice loss is removed. These results suggest that AGCM experiments that are driven by sea ice loss and only local SST increases will underestimate the Arctic midtroposphere warming and atmospheric circulation response to sea ice loss, compared to AOGCM simulations and the real world.

scholarly journals Simulation of observed climate changes in 1850–2014 with climate model INM-CM5

2018 ◽  
Vol 9 (4) ◽  
pp. 1235-1242 ◽  
Author(s):  
Evgeny Volodin ◽  
Andrey Gritsun
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Climate Changes ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ocean Heat Uptake ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss ◽  
Global Mean

Abstract. Climate changes observed in 1850–2014 are modeled and studied on the basis of seven historical runs with the climate model INM-CM5 under the scenario proposed for the Coupled Model Intercomparison Project Phase 6 (CMIP6). In all runs global mean surface temperature rises by 0.8 K at the end of the experiment (2014) in agreement with the observations. Periods of fast warming in 1920–1940 and 1980–2000 as well as its slowdown in 1950–1975 and 2000–2014 are correctly reproduced by the ensemble mean. The notable change here with respect to the CMIP5 results is the correct reproduction of the slowdown in global warming in 2000–2014 that we attribute to a change in ocean heat uptake and a more accurate description of the total solar irradiance in the CMIP6 protocol. The model is able to reproduce the correct behavior of global mean temperature in 1980–2014 despite incorrect phases of the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation indices in the majority of experiments. The Arctic sea ice loss in recent decades is reasonably close to the observations in just one model run; the model underestimates Arctic sea ice loss by a factor of 2.5. The spatial pattern of the model mean surface temperature trend during the last 30 years looks close to the one for the ERA-Interim reanalysis. The model correctly estimates the magnitude of stratospheric cooling.

scholarly journals Recent summer Arctic atmospheric circulation anomalies in a historical perspective

2014 ◽  
Vol 8 (5) ◽  
pp. 4823-4847 ◽  
Author(s):  
A. Belleflamme ◽  
X. Fettweis ◽  
M. Erpicum
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Greenland Ice Sheet ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
Circulation Anomalies ◽  
Arctic Sea Ice Loss ◽  
The Arctic Region

Abstract. A significant increase in the summertime occurrence of a high pressure area over the Beaufort Sea and Greenland has been observed from the beginning of the 2000's, and particularly between 2007 and 2012. These circulation anomalies are likely partly responsible for the enhanced Greenland ice sheet melt as well as the Arctic sea ice loss observed since 2007. Therefore, it is interesting to analyse whether similar conditions might have happened since the late 19th century over the Arctic region. We have used an atmospheric circulation type classification based on daily mean sea level pressure and 500 hPa geopotential height data from four reanalysis datasets (ERA-Interim, ERA-40, NCEP/NCAR, and 20CRv2) to put the recent circulation anomalies in perspective with the atmospheric circulation variability since 1871. We found that circulation conditions similar to 2007–2012 have occurred in the past, despite a higher uncertainty of the reconstructed circulation before 1940. But the recent anomalies largely exceed the interannual variability of the atmospheric circulation of the Arctic region. These circulation anomalies are linked with the North Atlantic Oscillation suggesting that they are not limited to the Arctic. Finally, they favour summertime Arctic sea ice loss.

scholarly journals Changes in sea-surface temperature and atmospheric circulation patterns associated with reductions in Arctic sea ice cover in recent decades

2018 ◽  
Vol 18 (19) ◽  
pp. 14149-14159 ◽  
Author(s):  
Lejiang Yu ◽  
Shiyuan Zhong
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
The Arctic ◽  
Atmospheric Circulation Patterns ◽  
Arctic Sea

Abstract. In recent decades, the Arctic sea ice has been declining at a rapid pace as the Arctic warms at a rate of twice the global average. The underlying physical mechanisms for the Arctic warming and accelerated sea ice retreat are not fully understood. In this study, we apply a relatively novel statistical method called self-organizing maps (SOM) along with composite analysis to examine the trend and variability of autumn Arctic sea ice in the past three decades and their relationships to large-scale atmospheric circulation changes. Our statistical results show that the anomalous autumn Arctic dipole (AD) (Node 1) and the Arctic Oscillation (AO) (Node 9) could explain in a statistical sense as much as 50 % of autumn sea ice decline between 1979 and 2016. The Arctic atmospheric circulation anomalies associated with anomalous sea-surface temperature (SST) patterns over the North Pacific and North Atlantic influence Arctic sea ice primarily through anomalous temperature and water vapor advection and associated radiative feedback.

scholarly journals The contributions of the leading modes of the North Pacific sea surface temperature variability to the Arctic sea ice depletion in recent decades

2019 ◽  
Author(s):  
Lejiang Yu ◽  
Shiyuan Zhong ◽  
Timo Vihma
Keyword(s):  
Global Warming ◽  
Sea Ice ◽  
North Pacific ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Boreal Summer ◽  
The North ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss ◽  
The North Pacific

Abstract. Arctic sea ice decrease in extent in recent decades has been linked to sea surface temperature (SST) anomalies in the North Pacific Ocean. In this study, we assess the relative contributions of the two leading modes in North Pacific SST anomalies representing external forcing related to global warming and internal forcing related to Pacific Decadal Oscillation (PDO) to the Arctic sea ice loss in boreal summer and autumn. For the 1979–2017 period, the time series of the global warming and PDO modes show significant positive and negative trends, respectively. The global warming mode accounts for 44.9 % and 50.1 % of the Arctic sea ice loss in boreal summer and autumn during this period, compared to the 20.0 % and 22.2 % from the PDO mode. There is also a seasonal difference in the response of atmospheric circulations to the two modes. The PDO mode excites a wavetrain from North Pacific to the Arctic; the wavetrain is not seen in the response of atmospheric circulation to the global warming mode. Both dynamic and thermodynamic forcings work in the relationship of atmospheric circulation and sea ice anomalies.

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