scholarly journals Impact of Daily Arctic Sea Ice Variability in CAM3.0 during Fall and Winter

2013 ◽  
Vol 26 (6) ◽  
pp. 1939-1955 ◽  
Author(s):  
Dyre O. Dammann ◽  
Uma S. Bhatt ◽  
Peter L. Langen ◽  
Jeremy R. Krieger ◽  
Xiangdong Zhang
Keyword(s):  
Sea Ice ◽  
High Frequency ◽  
Temporal Frequency ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Climate Projections ◽  
Surface Cooling ◽  
Sea Ice Concentration ◽  
Storm Activity ◽  
Near Surface

Abstract Climate projections suggest that an ice-free summer Arctic Ocean is possible within several decades and with this comes the prospect of increased ship traffic and safety concerns. The daily sea ice concentration tendency in five Coupled Model Intercomparison Project phase 5 (CMIP5) simulations is compared with observations to reveal that many models underestimate this quantity that describes high-frequency ice movements, particularly in the marginal ice zone. To investigate whether high-frequency ice variability impacts the atmosphere, the Community Atmosphere Model, version 3.0 (CAM3.0), is forced by sea ice with and without daily fluctuations. Two 100-member ensemble experiments with daily varying (DAILY) and smoothly varying (SMTH) sea ice are conducted, along with a climatological control, for an anomalously low ice period (August 2006–November 2007). Results are presented for three periods: September 2006, October 2006, and December–February (DJF) 2006/07. The atmospheric response differs between DAILY and SMTH. In September, sea ice differences lead to an anomalous high and weaker storm activity over northern Europe. During October, the ice expands equatorward faster in DAILY than SMTH in the Siberian seas and leads to a local response of near-surface cooling. In DJF, there is a 1.5-hPa positive sea level pressure anomaly over North America, leading to anomalous northerly flow and anomalously cool continental U.S. temperatures. While the atmospheric responses are modest, the differences arising from high temporal frequency ice variability cannot be ignored. Increasing the accuracy of coupled model sea ice variations on short time scales is needed to improve short-term coupled model forecasts.

scholarly journals Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness

2018 ◽  
Vol 12 (11) ◽  
pp. 3419-3438 ◽  
Author(s):  
Edward W. Blockley ◽  
K. Andrew Peterson
Keyword(s):  
Sea Ice ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Forecast Errors ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Near Surface ◽  
Arctic Sea ◽  
The Impact

Abstract. Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue. The prospect of increased human industrial activity in the region, as well as scientific interest in the predictability of sea ice, provides important motivation for understanding, and improving, the skill of Arctic predictions. Several operational forecasting centres now routinely produce seasonal predictions of sea-ice cover using coupled atmosphere–ocean–sea-ice models. Although assimilation of sea-ice concentration into these systems is commonplace, sea-ice thickness observations, being much less mature, are typically not assimilated. However, many studies suggest that initialization of winter sea-ice thickness could lead to improved prediction of Arctic summer sea ice. Here, for the first time, we directly assess the impact of winter sea-ice thickness initialization on the skill of summer seasonal predictions by assimilating CryoSat-2 thickness data into the Met Office's coupled seasonal prediction system (GloSea). We show a significant improvement in predictive skill of Arctic sea-ice extent and ice-edge location for forecasts of September Arctic sea ice made from the beginning of the melt season. The improvements in sea-ice cover lead to further improvement of near-surface air temperature and pressure fields across the region. A clear relationship between modelled winter thickness biases and summer extent errors is identified which supports the theory that Arctic winter thickness provides some predictive capability for summer ice extent, and further highlights the importance that modelled winter thickness biases can have on the evolution of forecast errors through the melt season.

scholarly journals Arctic sea-ice variability revisited

2008 ◽  
Vol 48 ◽  
pp. 71-81 ◽  
Author(s):  
Julienne Stroeve ◽  
Allan Frei ◽  
James McCreight ◽  
Debjani Ghatak
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Warming Trend ◽  
Atmospheric Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Near Surface ◽  
Arctic Sea ◽  
Ice Concentration

AbstractThis paper explores spatial and temporal relationships between variations in Arctic sea-ice concentration (summer and winter) and near-surface atmospheric temperature and atmospheric pressure using multivariate statistical techniques. Trend, empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses are used to identify spatial patterns associated with covariances and correlations between these fields. Results show that (1) in winter, the Arctic Oscillation still explains most of the variability in sea-ice concentration from 1979 to 2006; and (2) in summer, a decreasing sea-ice trend centered in the Pacific sector of the Arctic basin is clearly correlated to an Arctic-wide air temperature warming trend. These results demonstrate the applicability of multivariate methods, and in particular SVD analysis, which has not been used in earlier studies for assessment of changes in the Arctic sea-ice cover. Results are consistent with the interpretation that a warming signal has now emerged from the noise in the Arctic sea-ice record during summer. Our analysis indicates that such a signal may also be forthcoming during winter.

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

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 Estimating the Continental Response to Global Warming Using Pattern-Scaled Sea Surface Temperatures and Sea Ice

2016 ◽  
Vol 29 (24) ◽  
pp. 9125-9139 ◽  
Author(s):  
Adeline Bichet ◽  
Paul J. Kushner ◽  
Lawrence Mudryk
Keyword(s):  
Global Warming ◽  
Sea Ice ◽  
General Circulation ◽  
Circulation Model ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Climate Projections ◽  
Western Boundary ◽  
Sea Ice Concentration ◽  
Continental Climate

Abstract Better constraining the continental climate response to anthropogenic forcing is essential to improve climate projections. In this study, pattern scaling is used to extract, from observations, the patterned response of sea surface temperature (SST) and sea ice concentration (SICE) to anthropogenically dominated long-term global warming. The SST response pattern includes a warming of the tropical Indian Ocean, the high northern latitudes, and the western boundary currents. The SICE pattern shows seasonal variations of the main locations of sea ice loss. These SST–SICE response patterns are used to drive an ensemble of an atmospheric general circulation model, the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 5 (CAM5), over the period 1980–2010 along with a standard AMIP ensemble using observed SST—SICE. The simulations enable attribution of a variety of observed trends of continental climate to global warming. On the one hand, the warming trends observed in all seasons across the entire Northern Hemisphere extratropics result from global warming, as does the snow loss observed over the northern midlatitudes and northwestern Eurasia. On the other hand, 1980–2010 precipitation trends observed in winter over North America and in summer over Africa result from the recent decreasing phase of the Pacific decadal oscillation and the recent increasing phase of the Atlantic multidecadal oscillation, respectively, which are not part of the global warming signal. The method holds promise for near-term decadal climate prediction but as currently framed cannot distinguish regional signals associated with oceanic internal variability from aerosol forcing and other sources of short-term forcing.

Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period 

2021 ◽  
Author(s):  
Harry Heorton ◽  
Michel Tsamados ◽  
Paul Holland ◽  
Jack Landy
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Volume Growth ◽  
Arctic Sea Ice ◽  
First Year ◽  
Sea Ice Concentration ◽  
Marginal Seas ◽  
Ice Volume ◽  
Arctic Sea ◽  
Ice Concentration

<p><span>We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations</span><span>. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice</span><span>. Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.</span></p>

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.

scholarly journals Stationary Wave Interference and Its Relation to Tropical Convection and Arctic Warming

2016 ◽  
Vol 29 (4) ◽  
pp. 1369-1389 ◽  
Author(s):  
Michael Goss ◽  
Steven B. Feldstein ◽  
Sukyoung Lee
Keyword(s):  
Sea Ice ◽  
Stationary Wave ◽  
Warm Pool ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Boreal Winter ◽  
Destructive Interference ◽  
Constructive Interference ◽  
Sea Ice Concentration ◽  
Stratospheric Polar Vortex

Abstract The interference between transient eddies and climatological stationary eddies in the Northern Hemisphere is investigated. The amplitude and sign of the interference is represented by the stationary wave index (SWI), which is calculated by projecting the daily 300-hPa streamfunction anomaly field onto the 300-hPa climatological stationary wave. ERA-Interim data for the years 1979 to 2013 are used. The amplitude of the interference peaks during boreal winter. The evolution of outgoing longwave radiation, Arctic temperature, 300-hPa streamfunction, 10-hPa zonal wind, Arctic sea ice concentration, and the Arctic Oscillation (AO) index are examined for days of large SWI values during the winter. Constructive interference during winter tends to occur about one week after enhanced warm pool convection and is followed by an increase in Arctic surface air temperature along with a reduction of sea ice in the Barents and Kara Seas. The warming of the Arctic does occur without prior warm pool convection, but it is enhanced and prolonged when constructive interference occurs in concert with enhanced warm pool convection. This is followed two weeks later by a weakening of the stratospheric polar vortex and a decline of the AO. All of these associations are reversed in the case of destructive interference. Potential climate change implications are briefly discussed.

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