The Impact of Milankovitch Solar Radiation Variations on Sea-Ice and Air Temperature in a Coupled Energy-Balance Climate-Sea-Ice Model

The sensitivity of thermodynamically-varying sea-ice and surface air temperature to variations in solar radiation on the 104 to 105 time scales is examined in this study. Model simulation results show the mean annual sea-ice thickness is very sensitive to the magnitude of midsummer solar radiation. During periods of high midsummer solar radiation between 115 ka B.P. and the present the sea ice is thinner, producing larger summer time leads and longer periods of open ocean. This has an effect on the mean annual sea-ice thickness, but not on the mean annual air temperature. However, the changes in sea ice are accompanied by similar variations in the summer surface air temperature, which are the result of the variations in the solar radiation and meridional energy transport.

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The Impact of Milankovitch Solar Radiation Variations on Sea-Ice and Air Temperature in a Coupled Energy-Balance Climate-Sea-Ice Model

Annals of Glaciology ◽

10.1017/s0260305500008454 ◽

1990 ◽

Vol 14 ◽

pp. 144-147 ◽

Cited By ~ 5

Author(s):

Tamara Shapiro Ledley

Keyword(s):

Solar Radiation ◽

Sea Ice ◽

Air Temperature ◽

Energy Transport ◽

Model Simulation ◽

Surface Air Temperature ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Mean ◽

The Impact

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Impact of Model Physics on Seasonal Forecasts of Surface Air Temperature in the Arctic

Monthly Weather Review ◽

10.1175/mwr-d-16-0272.1 ◽

2017 ◽

Vol 145 (3) ◽

pp. 773-782 ◽

Cited By ~ 3

Author(s):

Qiong Yang ◽

Muyin Wang ◽

James E. Overland ◽

Wanqiu Wang ◽

Thomas W. Collow

Keyword(s):

Sea Ice ◽

Air Temperature ◽

Surface Air Temperature ◽

The Arctic ◽

Ice Thickness ◽

Seasonal Forecasts ◽

Forecast System ◽

Climate Forecast System ◽

Sea Ice Thickness ◽

Forecast Bias

The impacts of model physics and initial sea ice thickness on seasonal forecasts of surface energy budget and air temperature in the Arctic during summer were investigated based on Climate Forecast System, version 2 (CFSv2), simulations. The model physics changes include the enabling of a marine stratus cloud scheme and the removal of the artificial upper limit on the bottom heat flux from ocean to sea ice. The impact of initial sea ice thickness was examined by initializing the model with relatively realistic sea ice thickness generated by the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Model outputs were compared to that from a control run that did not impose physics changes and used Climate Forecast System Reanalysis (CFSR) sea ice thickness. After applying the physics modification to either sea ice thickness initialization, the simulated total cloud cover more closely resembled the observed monthly variations of total cloud cover except for the midsummer reduction. Over the Chukchi–Bering Seas, the model physics modification reduced the seasonal forecast bias in surface air temperature by 24%. However, the use of initial PIOMAS sea ice thickness alone worsened the surface air temperature predictions. The experiment with physics modifications and initial PIOMAS sea ice thickness achieves the best surface air temperature improvement over the Chukchi–Bering Seas where the area-weighted forecast bias was reduced by 71% from 1.05 K down to −0.3 K compared with the control run. This study supports other results that surface temperatures and sea ice characteristics are highly sensitive to the Arctic cloud and radiation formulations in models and need priority in model formulation and validation.

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Seasonally changing contribution of sea ice and snow cover to uncertainty in multi-decadal Eurasian surface air temperature trends based on CESM simulations

Climate Dynamics ◽

10.1007/s00382-021-05746-4 ◽

2021 ◽

Author(s):

Zhaomin Ding ◽

Renguang Wu

Keyword(s):

Sea Ice ◽

Air Temperature ◽

Western Siberia ◽

Russian Far East ◽

Far East ◽

Surface Air Temperature ◽

The Caspian Sea ◽

Sea Ice Concentration ◽

Northern Siberia ◽

The Impact

AbstractThis study investigates the impact of sea ice and snow changes on surface air temperature (SAT) trends on the multidecadal time scale over the mid- and high-latitudes of Eurasia during boreal autumn, winter and spring based on a 30-member ensemble simulations of the Community Earth System Model (CESM). A dynamical adjustment method is used to remove the internal component of circulation-induced SAT trends. The leading mode of dynamically adjusted SAT trends is featured by same-sign anomalies extending from northern Europe to central Siberia and to the Russian Far East, respectively, during boreal spring and autumn, and confined to western Siberia during winter. The internally generated component of sea ice concentration trends over the Barents-Kara Seas contributes to the differences in the thermodynamic component of internal SAT trends across the ensemble over adjacent northern Siberia during all the three seasons. The sea ice effect is largest in autumn and smallest in winter. Eurasian snow changes contribute to the spread in dynamically adjusted SAT trends as well around the periphery of snow covered region by modulating surface heat flux changes. The snow effect is identified over northeast Europe-western Siberia in autumn, north of the Caspian Sea in winter, and over eastern Europe-northern Siberia in spring. The effects of sea ice and snow on the SAT trends are realized mainly by modulating upward shortwave and longwave radiation fluxes.

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Antarctic sea-ice thickness and volume estimates from ice charts between 1995 and 1998

Annals of Glaciology ◽

10.3189/2015aog69a763 ◽

2015 ◽

Vol 56 (69) ◽

pp. 383-393 ◽

Cited By ~ 3

Author(s):

E. Rachel Bernstein ◽

Cathleen A. Geiger ◽

Tracy L. Deliberty ◽

Mary D. Lemcke-Stampone

Keyword(s):

Sea Ice ◽

Regional Scale ◽

Weighted Average ◽

Ice Thickness ◽

Average Thickness ◽

Sea Ice Thickness ◽

Subjective Analysis ◽

Stage Of Development ◽

Volume Estimates ◽

The Impact

AbstractThis work evaluates two distinct calculations of central tendency for sea-ice thickness and quantifies the impact such calculations have on ice volume for the Southern Ocean. The first calculation, area-weighted average thickness, is computed from polygonal ice features and then upscaled to regions. The second calculation, integrated thickness, is a measure of the central value of thickness categories tracked across different scales and subsequently summed to chosen regions. Both methods yield the same result from one scale to the next, but subsequent scales develop diverging solutions when distributions are strongly non-Gaussian. Data for this evaluation are sea-ice stage-of-development records from US National Ice Center ice charts from 1995 to 1998, as proxy records of ice thickness. Results show regionally integrated thickness exceeds area-weighted average thickness by as much as 60% in summer with as few as five bins in thickness distribution. Year-round, the difference between the two calculations yields volume differences consistently >10%. The largest discrepancies arise due to bimodal distributions which are common in ice charts based on current subjective-analysis protocols. We recommend that integrated distribution be used for regional-scale sea-ice thickness and volume estimates from ice charts and encourage similar testing of other large-scale thickness data archives.

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The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

10.5194/egusphere-egu21-12240 ◽

2021 ◽

Author(s):

Isolde Glissenaar ◽

Jack Landy ◽

Alek Petty ◽

Nathan Kurtz ◽

Julienne Stroeve

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Satellite Altimetry ◽

Region Of Interest ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Baffin Bay ◽

The Impact

The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.

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New insight from CryoSat-2 sea ice thickness for sea ice modelling

10.5194/tc-2018-159 ◽

2018 ◽

Author(s):

David Schröder ◽

Danny L. Feltham ◽

Michel Tsamados ◽

Andy Ridout ◽

Rachel Tilling

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Atmospheric Conditions ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Ice Growth ◽

Melt Pond ◽

The Impact

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

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ICESat observations of seasonal and interannual variations of sea-ice freeboard and estimated thickness in the Weddell Sea, Antarctica (2003–2009)

Annals of Glaciology ◽

10.3189/172756411795931480 ◽

2011 ◽

Vol 52 (57) ◽

pp. 43-51 ◽

Cited By ~ 52

Author(s):

Donghui Yi ◽

H. Jay Zwally ◽

John W. Robbins

Keyword(s):

Sea Ice ◽

Weddell Sea ◽

Ice Thickness ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Earth Observing System ◽

Pack Ice ◽

Snow Water ◽

The Mean ◽

Seasonal And Interannual Variations

AbstractSea-ice freeboard heights for 17 ICESat campaign periods from 2003 to 2009 are derived from ICESat data. Freeboard is combined with snow depth from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) data and nominal densities of snow, water and sea ice, to estimate sea-ice thickness. Sea-ice freeboard and thickness distributions show clear seasonal variations that reflect the yearly cycle of growth and decay of the Weddell Sea (Antarctica) pack ice. During October–November, sea ice grows to its seasonal maximum both in area and thickness; the mean freeboards are 0.33–0.41m and the mean thicknesses are 2.10–2.59 m. During February–March, thinner sea ice melts away and the sea-ice pack is mainly distributed in the west Weddell Sea; the mean freeboards are 0.35–0.46m and the mean thicknesses are 1.48–1.94 m. During May–June, the mean freeboards and thicknesses are 0.26–0.29m and 1.32–1.37 m, respectively. the 6 year trends in sea-ice extent and volume are (0.023±0.051)×106 km2 a–1 (0.45% a–1) and (0.007±0.092)×103 km3 a–1 (0.08% a–1); however, the large standard deviations indicate that these positive trends are not statistically significant.

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Better constraints on the sea-ice state using global sea-ice data assimilation

Geoscientific Model Development Discussions ◽

10.5194/gmdd-5-1627-2012 ◽

2012 ◽

Vol 5 (2) ◽

pp. 1627-1667 ◽

Cited By ~ 1

Author(s):

P. Mathiot ◽

C. König Beatty ◽

T. Fichefet ◽

H. Goosse ◽

F. Massonnet ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Ice Thickness ◽

Initial State ◽

Concentration Data ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Ice Concentration ◽

The Impact

Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice-ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an Ensemble Kalman Filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged sea ice) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the assimilation of synthetic data (i.e., model-generated data) and, secondly, through the assimilation of satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

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Seasonal development of the sea-ice thickness distribution in East Antarctica: measurements from upward-looking sonar

Annals of Glaciology ◽

10.3189/172756401781818167 ◽

2001 ◽

Vol 33 ◽

pp. 177-180 ◽

Cited By ~ 14

Author(s):

A. P. Worby ◽

G. M. Bush ◽

I. Allison

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

East Antarctica ◽

Seasonal Development ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Seasonal Evolution ◽

Pack Ice ◽

The Mean ◽

Prototype Instrument

AbstractUpward-looking sonar (ULS) data are presented from a prototype instrument deployed at 63° 18’ S, 107°49’ E in 1994. These data show the seasonal evolution of the ice-draft distribution from May when predominantly thin ice is present, through October when substantially thicker ice has been formed by deformation. The mean ice draft reaches a maximum in August at 1.21 m, the same month in which ship-based observations from the same region show a peak in ice thickness. The observed distribution from ULS data is only for drafts > 0.3 m due to data losses caused by the low acoustic reflectivity of actively forming ice. The spring distributions show very little development of drafts > 3.0 m, and it is hypothesized that this is due to the cyclical nature of deformation in the East Antarctic pack-ice zone, and that periods of sustained pressure required to form very thick ice are uncommon in this region

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The impact of snow products on detecting trends in sea ice thickness during the CryoSat-2 era

10.5194/egusphere-egu2020-18304 ◽

2020 ◽

Author(s):

Heidi Sallila ◽

Samantha Buzzard ◽

Eero Rinne ◽

Michel Tsamados

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Ice Age ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Seasonal Snow ◽

Uncertainty Estimates ◽

The One ◽

The Impact ◽

First Time

Retrieval of sea ice depth from satellite altimetry relies on knowledge of snow depth in the conversion of freeboard measurements to sea ice thickness. This remains the largest source of uncertainty in calculating sea ice thickness. In order to go beyond the use of a seasonal snow climatology, namely the one by Warren created from measurements collected during the drifting stations in 1937 and 1954&#8211;1991, we have developed as part of an ESA Arctic+ project several novel snow on sea ice pan-Arctic products, with the ultimate goal to resolve for the first time inter-annual and seasonal snow variability.Our products are inter-compared and calibrated with each other to guarantee multi-decadal continuity, and also compared with other recently developed snow on sea ice modelling and satellite based products. Quality assessment and uncertainty estimates are provided at a gridded level and as a function of sea ice cover characteristics such as sea ice age, and sea ice type.We investigate the impact of the spatially and temporally varying snow products on current satellite estimates of sea ice thickness and provide an update on the sea ice thickness uncertainties. We pay particular attention to potential biases of the seasonal ice growth and inter-annual trends.

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