scholarly journals The Sensitivity of the Arctic Ocean Sea Ice Thickness and Its Dependence on the Surface Albedo Parameterization

2013 ◽  
Vol 26 (4) ◽  
pp. 1355-1370 ◽  
Author(s):  
Göran Björk ◽  
Christian Stranne ◽  
Karin Borenäs
Keyword(s):  
Sea Ice ◽  
Surface Albedo ◽  
Climate Models ◽  
Ice Cover ◽  
Global Climate Models ◽  
The Arctic ◽  
Ice Thickness ◽  
External Forcing ◽  
Sea Ice Thickness ◽  
Climate System Model

Abstract In this study, the response of sea ice thickness to changes in the external forcing is investigated and particularly how this response depends on the surface albedo formulation by means of a one-dimensional coupled ocean–ice–atmosphere model. The main focus is on the thickness response to the atmospheric heat advection Fwall, solar radiation FSW, and amount of snow precipitation Sprec. Different albedo parameterization schemes [ECHAM5, CSIRO, and Community Climate System Model, version 3 (CCSM3)] representing albedos commonly used in global climate models are compared together with more simplified schemes. Using different albedo schemes with the same external forcing produces large differences in ice thickness. The ice thickness response is similar for all realistic albedo schemes with a nearly linear decrease with increasing Fwall in the perennial ice regime and with a steplike transition into seasonal ice when Fwall exceeds a certain threshold. This transition occurs at an annual-mean ice thickness of 1.7–2.0 m. Latitudinal differences in solar insolation generally leads to increasing ice thickness toward the North Pole. The snow response varies significantly depending on which albedo scheme is used. The ECHAM5 scheme yields thinner ice with Sprec, the CSIRO scheme gives ice thickness nearly independent of Sprec, and with the CCSM3 scheme the ice thickness decreases with Sprec. A general result is that the modeled ice cover is rather sensitive to positive perturbations of the external heat supply when it is close to the transition such that just a small increase of, for example, Fwall can force the ice cover into the seasonal regime.

scholarly journals How Climate Model Complexity Influences Sea Ice Stability

2015 ◽  
Vol 28 (10) ◽  
pp. 3998-4014 ◽  
Author(s):  
Till J. W. Wagner ◽  
Ian Eisenman
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Model Complexity ◽  
The Arctic ◽  
Arctic Sea ◽  
The Stability ◽  
Low Order

Abstract Record lows in Arctic sea ice extent have been making frequent headlines in recent years. The change in albedo when sea ice is replaced by open water introduces a nonlinearity that has sparked an ongoing debate about the stability of the Arctic sea ice cover and the possibility of Arctic “tipping points.” Previous studies identified instabilities for a shrinking ice cover in two types of idealized climate models: (i) annual-mean latitudinally varying diffusive energy balance models (EBMs) and (ii) seasonally varying single-column models (SCMs). The instabilities in these low-order models stand in contrast with results from comprehensive global climate models (GCMs), which typically do not simulate any such instability. To help bridge the gap between low-order models and GCMs, an idealized model is developed that includes both latitudinal and seasonal variations. The model reduces to a standard EBM or SCM as limiting cases in the parameter space, thus reconciling the two previous lines of research. It is found that the stability of the ice cover vastly increases with the inclusion of spatial communication via meridional heat transport or a seasonal cycle in solar forcing, being most stable when both are included. If the associated parameters are set to values that correspond to the current climate, the ice retreat is reversible and there is no instability when the climate is warmed. The two parameters have to be reduced by at least a factor of 3 for instability to occur. This implies that the sea ice cover may be substantially more stable than has been suggested in previous idealized modeling studies.

Assessment of decadal prediction skills and sensitivity to sea-ice thickness initialization in the Arctic when seasonal ice becomes dominant in the Arctic

2020 ◽  
Author(s):  
Tian Tian ◽  
Shuting Yang ◽  
Pasha Karami ◽  
François Massonnet ◽  
Tim Kruschke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Ice Cover ◽  
Climate Prediction ◽  
Arctic Sea Ice ◽  
Added Value ◽  
The Arctic ◽  
Ice Thickness ◽  
Decadal Prediction ◽  
Sea Ice Thickness

<p>The Arctic has lost more than 50% multiyear sea ice (MYI) area during 1999-2017. Observation analysis suggests that if the decline of the MYI coverage continues, changes in the Arctic ice cover (i.e. area and volume) will be more controlled by seasonal ice than the effect of global warming. To investigate how large and where the source of Arctic prediction skill is given a large losses of thick MYI during the last two decades, we explore the decadal prediction skills and sensitivity to sea ice thickness (SIT) initialization from the EC-Earth3 Climate Prediction System with Anomaly Initialization (EC-Earth3-CPSAI). Three sets of ensemble hind-cast experiments following the protocol for the CMIP6 Decadal Climate Prediction Project (DCPP) are carried out in which the predictions start from: 1) a baseline system with ocean only initialization; 2) with ocean and sea ice concentration (SIC) initialization; 3) with ocean, SIC and SIT initialization. The hind-cast experiments are initialized and validated based on the ERA-Interim-reanalysis for the atmosphere and ORAS5 for ocean and sea-ice, with a focus period 1997-2016. All initialized experiments show better agreement with ORAS5 than the CMIP6 historical run (i.e. the Free run) for the first winter sea ice forecast. The SIT initialized experiments show the best skill in predicting SIT (or volume) and the added value by greatly reducing errors of near surface air temperature over the Greenland and its surrounding waters. In the Central Arctic, the Beaufort and East Siberian Seas, there are only minor differences in prediction skills on seasonal to decadal time scales between the ocean-only initialized and the SIT initialized experiments, indicating that the source of predictability in these regions are mainly from the ocean; while the ocean-only initialization degrades skill with larger RMSE than the Free run, e.g. during the ice-freezing season in the GIN and Barents Seas, or at  the summer minimum in the Kara Sea, the added value from the SIT initialized experiment is present, and it may have long-term effect (>4 years) probably associated with sea-ice recirculation. In all cases, the improvement from the ocean-only initialization to also including SIC initialization is found negligible, even somehow degrading the skills. This highlights the important use of SIT in predicting changes in the Arctic sea ice cover at various time scales during the study period. Therefore, the sea-ice initialization with constraint on SIT is recommended as the most effective initialization strategy in our EC-Earth3-CPSAI for present climate prediction from seasonal to decadal time scales.</p>

scholarly journals On the retrieval of sea-ice thickness using SMOS polarization differences

2019 ◽  
Vol 65 (251) ◽  
pp. 481-493
Author(s):  
MUKESH GUPTA ◽  
CAROLINA GABARRO ◽  
ANTONIO TURIEL ◽  
MARCOS PORTABELLA ◽  
JUSTINO MARTINEZ
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Operational Application ◽  
Ocean Salinity ◽  
Ice Concentration

ABSTRACTArctic sea ice is going through a dramatic change in its extent and volume at an unprecedented rate. Sea-ice thickness (SIT) is a controlling geophysical variable that needs to be understood with greater accuracy. For the first time, a SIT-retrieval method that exclusively uses only airborne SIT data for training the empirical algorithm to retrieve SIT from Soil Moisture Ocean Salinity (SMOS) brightness temperature (TB) at different polarization is presented. A large amount of airborne SIT data has been used from various field campaigns in the Arctic conducted by different countries during 2011–15. The algorithm attempts to circumvent the issue related to discrimination between TB signatures of thin SIT versus low sea-ice concentration. The computed SIT has a rms error of 0.10 m, which seems reasonably good (as compared to the existing algorithms) for analysis at the used 25 km grid. This new SIT retrieval product is designed for direct operational application in ice prediction/climate models.

scholarly journals Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models

2020 ◽  
Author(s):  
Wang Yangjun ◽  
Liu Kefeng ◽  
Zhang Ren ◽  
Qian Longxia ◽  
Zhang Yu
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Ensemble Methods ◽  
Global Climate Models ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Advanced Method ◽  
Multi Criteria Evaluation ◽  
Better Than

Abstract. This paper aims to find a possible ensemble method to combine the global climate models, providing an accuracy forecast of sea ice thickness. Conventional multimodel superensemble, the advanced method that is widely used in atmosphere, ocean and other fields, cannot be well performed in sea ice thickness simulation. Hence, an adaptive forecasting through exponential re-weighting (AFTER) algorithm is adopted to improve the conventional multimodel superensemble. Results show our proposed methods perform better than any other mainstream ensemble methods by using a multi-criteria evaluation. The proposed method is used to predict the future sea ice thickness in the period of 2020–2049, where the possible biases are discussed.

scholarly journals Fluctuating Arctic Sea Ice Thickness Changes Estimated by an In Situ Learned and Empirically Forced Neural Network Model

2008 ◽  
Vol 21 (4) ◽  
pp. 716-729 ◽  
Author(s):  
G. I. Belchansky ◽  
D. C. Douglas ◽  
N. G. Platonov
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Spatiotemporal Variability ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Volume

Abstract Sea ice thickness (SIT) is a key parameter of scientific interest because understanding the natural spatiotemporal variability of ice thickness is critical for improving global climate models. In this paper, changes in Arctic SIT during 1982–2003 are examined using a neural network (NN) algorithm trained with in situ submarine ice draft and surface drilling data. For each month of the study period, the NN individually estimated SIT of each ice-covered pixel (25-km resolution) based on seven geophysical parameters (four shortwave and longwave radiative fluxes, surface air temperature, ice drift velocity, and ice divergence/convergence) that were cumulatively summed at each monthly position along the pixel’s previous 3-yr drift track (or less if the ice was <3 yr old). Average January SIT increased during 1982–88 in most regions of the Arctic (+7.6 ± 0.9 cm yr−1), decreased through 1996 Arctic-wide (−6.1 ± 1.2 cm yr−1), then modestly increased through 2003 mostly in the central Arctic (+2.1 ± 0.6 cm yr−1). Net ice volume change in the Arctic Ocean from 1982 to 2003 was negligible, indicating that cumulative ice growth had largely replaced the estimated 45 000 km3 of ice lost by cumulative export. Above 65°N, total annual ice volume and interannual volume changes were correlated with the Arctic Oscillation (AO) at decadal and annual time scales, respectively. Late-summer ice thickness and total volume varied proportionally until the mid-1990s, but volume did not increase commensurate with the thickening during 1996–2002. The authors speculate that decoupling of the ice thickness–volume relationship resulted from two opposing mechanisms with different latitudinal expressions: a recent quasi-decadal shift in atmospheric circulation patterns associated with the AO’s neutral state facilitated ice thickening at high latitudes while anomalously warm thermal forcing thinned and melted the ice cap at its periphery.

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.

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

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

<p>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.</p><p>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.</p><p>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.</p>

scholarly journals Near Real Time Arctic sea ice thickness and volume from CryoSat-2

2016 ◽  
Author(s):  
R. L. Tilling ◽  
A. Ridout ◽  
A. Shepherd
Keyword(s):  
Sea Ice ◽  
Real Time ◽  
Arctic Sea Ice ◽  
Arctic Climate ◽  
The Arctic ◽  
Ice Thickness ◽  
Satellite Orbits ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Timely observations of sea ice thickness help us to understand Arctic climate, and can support maritime activities in the Polar Regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the final release dataset is typically one month, due to the time required to determine precise satellite orbits. We use a new fast delivery CryoSat-2 dataset based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT), and analyse this data for one sea ice growth season from October 2014 to April 2015. We show that this NRT sea ice thickness product is of comparable accuracy to that produced using the final release CryoSat-2 data, with an average thickness difference of 5 cm, demonstrating that the satellite orbit is not a critical factor in determining sea ice freeboard. In addition, the CryoSat-2 fast delivery product also provides measurements of Arctic sea ice thickness within three days of acquisition by the satellite, and a measurement is delivered, on average, within 10, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT sea ice thickness dataset provides an additional constraint for seasonal predictions of Arctic climate change, and will allow industries such as tourism and transport to navigate the polar oceans with safety and care.

scholarly journals Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations

2020 ◽  
Vol 14 (7) ◽  
pp. 2189-2203
Author(s):  
H. Jakob Belter ◽  
Thomas Krumpen ◽  
Stefan Hendricks ◽  
Jens Hoelemann ◽  
Markus A. Janout ◽  
...  
Keyword(s):  
Sea Ice ◽  
Source Region ◽  
European Space Agency ◽  
The Arctic ◽  
Ice Thickness ◽  
Laptev Sea ◽  
Validation Data ◽  
Data Set ◽  
Sea Ice Thickness ◽  
The Laptev Sea

Abstract. The gridded sea ice thickness (SIT) climate data record (CDR) produced by the European Space Agency (ESA) Sea Ice Climate Change Initiative Phase 2 (CCI-2) is the longest available, Arctic-wide SIT record covering the period from 2002 to 2017. SIT data are based on radar altimetry measurements of sea ice freeboard from the Environmental Satellite (ENVISAT) and CryoSat-2 (CS2). The CCI-2 SIT has previously been validated with in situ observations from drilling, airborne remote sensing, electromagnetic (EM) measurements and upward-looking sonars (ULSs) from multiple ice-covered regions of the Arctic. Here we present the Laptev Sea CCI-2 SIT record from 2002 to 2017 and use newly acquired ULS and upward-looking acoustic Doppler current profiler (ADCP) sea ice draft (VAL) data for validation of the gridded CCI-2 and additional satellite SIT products. The ULS and ADCP time series provide the first long-term satellite SIT validation data set from this important source region of sea ice in the Transpolar Drift. The comparison of VAL sea ice draft data with gridded monthly mean and orbit trajectory CCI-2 data, as well as merged CryoSat-2–SMOS (CS2SMOS) sea ice draft, shows that the agreement between the satellite and VAL draft data strongly depends on the thickness of the sampled ice. Rather than providing mean sea ice draft, the considered satellite products provide modal sea ice draft in the Laptev Sea. Ice drafts thinner than 0.7 m are overestimated, while drafts thicker than approximately 1.3 m are increasingly underestimated by all satellite products investigated for this study. The tendency of the satellite SIT products to better agree with modal sea ice draft and underestimate thicker ice needs to be considered for all past and future investigations into SIT changes in this important region. The performance of the CCI-2 SIT CDR is considered stable over time; however, observed trends in gridded CCI-2 SIT are strongly influenced by the uncertainties of ENVISAT and CS2 and the comparably short investigation period.

scholarly journals Modeled Arctic sea ice evolution through 2300 in CMIP5 extended RCPs

2014 ◽  
Vol 8 (1) ◽  
pp. 1383-1406 ◽  
Author(s):  
P. J. Hezel ◽  
T. Fichefet ◽  
F. Massonnet
Keyword(s):  
Sea Ice ◽  
Radiative Forcing ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

Abstract. Almost all global climate models and Earth system models that participated in the Coupled Model Intercomparison Project 5 (CMIP5) show strong declines in Arctic sea ice extent and volume under the highest forcing scenario of the Radiative Concentration Pathways (RCPs) through 2100, including a transition from perennial to seasonal ice cover. Extended RCP simulations through 2300 were completed for a~subset of models, and here we examine the time evolution of Arctic sea ice in these simulations. In RCP2.6, the summer Arctic sea ice extent increases compared to its minimum following the peak radiative forcing in 2044 in all 9 models. RCP4.5 demonstrates continued summer Arctic sea ice decline due to continued warming on longer time scales. These two scenarios imply that summer sea ice extent could begin to recover if and when radiative forcing from greenhouse gas concentrations were to decrease. In RCP8.5 the Arctic Ocean reaches annually ice-free conditions in 7 of 9 models. The ensemble of simulations completed under the extended RCPs provide insight into the global temperature increase at which sea ice disappears in the Arctic and reversibility of declines in seasonal sea ice extent.

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