scholarly journals A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

2021 ◽  
Author(s):  
William Gregory ◽  
Isobel Lawrence ◽  
Michel Tsamados
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Data Sets ◽  
Sea Ice Thickness ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Arctic Sea ◽  
Thickness Variability ◽  
Satellite Radar ◽  
Validation Experiments

<p>Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thicknessestimates, which in turn inform on sea ice forecasts, volume budgets, and productivity rates. Current spatio-temporalresolution of radar freeboard is limited as 30 days are required in order to generate pan-Arctic coverage fromCryoSat-2, or 27 days from Sentinel-3 satellites. This therefore hinders our ability to understand physical processesthat drive sea ice thickness variability on sub-monthly time scales. In this study we exploit the consistency betweenCryoSat-2, Sentinel-3A and Sentinel-3B radar freeboards in order to produce daily gridded pan-Arctic freeboardestimates between December 2018 and April 2019. We use the Bayesian inference approach of Gaussian Process Regressionto learn functional mappings between radar freeboard observations in space and time, and to subsequently retrievepan-Arctic freeboard, as well as uncertainty estimates. The estimated daily fields are, on average across the 2018-2019season, equivalent to CryoSat-2 and Sentinel-3 freeboards to within 2 mm, and cross-validation experiments show thaterrors in predictions are, on average, within 3 mm across the same period. This method presents as a robust frameworkwhich can be used to model a wide range of statistical problems, from interpolation of altimetry data sets, to timeseries forecasting.</p>

Sea ice representation in CMIP6 simulations with EC-Earth3-Veg

2020 ◽  
Author(s):  
Torben Koenigk ◽  
Evelien Dekker
Keyword(s):  
Sea Ice ◽  
Emission Scenario ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
First Year ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Arctic Sea

<p>In this study, we compare the sea ice in ensembles of historical and future simulations with EC-Earth3-Veg to the sea ice of the NSIDC and OSA-SAF satellite data sets. The EC-Earth3-Veg Arctic sea ice extent generally matches well to the observational data sets, and the trend over 1980-2014 is captured correctly. Interestingly, the summer Arctic sea ice area minimum occurs already in August in the model. Mainly east of Greenland, sea ice area is overestimated. In summer, Arctic sea ice is too thick compared to PIOMAS. In March, sea ice thickness is slightly overestimated in the Central Arctic but in the Bering and Kara Seas, the ice thickness is lower than in PIOMAS.</p><p>While the general picture of Arctic sea ice looks good, EC-Earth suffers from a warm bias in the Southern Ocean. This is also reflected by a substantial underestimation of sea ice area in the Antarctic.</p><p>Different ensemble members of the future scenario projections of sea ice show a large range of the date of first year with a minimum ice area below 1 million square kilometers in the Arctic. The year varies between 2024 and 2056. Interestingly, this range does not differ very much with the emission scenario and even under the low emission scenario SSP1-1.9 summer Arctic sea ice almost totally disappears.</p>

scholarly journals Brief Communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates

2019 ◽  
Author(s):  
Robbie D. C. Mallett ◽  
Isobel R. Lawrence ◽  
Julienne C. Stroeve ◽  
Jack C. Landy ◽  
Michel Tsamados
Keyword(s):  
Growth Rate ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Snow Layer ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Satellite Radar ◽  
Arctic Sea Ice Thickness ◽  
Radar Wave

Abstract. Pan-Arctic sea ice thickness has been monitored over recent decades by satellite radar altimeters such as CryoSat-2, which emit Ku-band radar waves that are conventionally assumed to penetrate overlying snow and scatter from the ice-snow interface. Here we examine two expressions for the time delay caused by slower radar wave propagation through the snow layer and related assumptions concerning the time-evolution of overlying snow density. Two conventional treatments lead to systematic underestimates of winter ice thickness and thermodynamic growth rate of up to 15 cm over multiyear ice. Correcting these biases would improve the accuracy of sea ice thickness products, which feed a wide variety of model projections, calibrations, validations and reanalyses.

Characteristics of Arctic Sea-Ice Thickness Variability in GCMs

2014 ◽  
Vol 27 (21) ◽  
pp. 8244-8258 ◽  
Author(s):  
Edward Blanchard-Wrigglesworth ◽  
Cecilia M. Bitz
Keyword(s):  
Sea Ice ◽  
Time Scale ◽  
Spatial Scales ◽  
Arctic Basin ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Thickness Variability ◽  
Arctic Sea Ice Thickness

Abstract Skillful Arctic sea ice forecasts may be possible for lead times of months or even years owing to the persistence of thickness anomalies. In this study sea ice thickness variability is characterized in fully coupled GCMs and sea ice–ocean-only models (IOMs) that are forced with an estimate of observations derived from atmospheric reanalysis and satellite measurements. Overall, variance in sea ice thickness is greatest along Arctic Ocean coastlines. Sea ice thickness anomalies have a typical time scale of about 6–20 months, a time scale that lengthens about a season when accounting for ice transport, and a typical length scale of about 500–1000 km. The range of these scales across GCMs implies that an estimate of the number of thickness monitoring locations needed to characterize the full Arctic basin sea ice thickness variability field is model dependent and would vary between 3 and 14. Models with a thinner mean ice state tend to have ice-thickness anomalies that are generally shorter lived and smaller in amplitude but have larger spatial scales. Additionally, sea ice thickness variability in IOMs is damped relative to GCMs in part due to strong negative coupling between the dynamic and thermodynamic processes that affect sea ice thickness. The significance for designing prediction systems is discussed.

scholarly journals Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates

2020 ◽  
Vol 14 (1) ◽  
pp. 251-260 ◽  
Author(s):  
Robbie D. C. Mallett ◽  
Isobel R. Lawrence ◽  
Julienne C. Stroeve ◽  
Jack C. Landy ◽  
Michel Tsamados
Keyword(s):  
Growth Rate ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Snow Layer ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Satellite Radar ◽  
Arctic Sea Ice Thickness ◽  
Radar Wave

Abstract. Pan-Arctic sea ice thickness has been monitored over recent decades by satellite radar altimeters such as CryoSat-2, which emits Ku-band radar waves that are assumed in publicly available sea ice thickness products to penetrate overlying snow and scatter from the ice–snow interface. Here we examine two expressions for the time delay caused by slower radar wave propagation through the snow layer and related assumptions concerning the time evolution of overlying snow density. Two conventional treatments introduce systematic underestimates of up to 15 cm into ice thickness estimates and up to 10 cm into thermodynamic growth rate estimates over multi-year ice in winter. Correcting these biases would impact a wide variety of model projections, calibrations, validations and reanalyses.

scholarly journals A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

2021 ◽  
Author(s):  
William Gregory ◽  
Isobel R. Lawrence ◽  
Michel Tsamados
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Gaussian Process Regression ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Uncertainty Estimates ◽  
Spatio Temporal ◽  
Satellite Radar

Abstract. Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thickness estimates, which in turn inform on sea ice forecasts, volume budgets, and productivity rates. Current spatio-temporal resolution of radar freeboard is limited as 30 days are required in order to generate pan-Arctic coverage from CryoSat-2, or 27 days from Sentinel-3 satellites. This therefore hinders our ability to understand physical processes that drive sea ice thickness variability on sub-monthly time scales. In this study we exploit the consistency between CryoSat-2, Sentinel-3A and Sentinel-3B radar freeboards in order to produce daily gridded pan-Arctic freeboard estimates between December 2018 and April 2019. We use the Bayesian inference approach of Gaussian Process Regression to learn functional mappings between radar freeboard observations in space and time, and to subsequently retrieve pan-Arctic freeboard, as well as uncertainty estimates. The estimated daily fields are, on average across the 2018–2019 season, equivalent to CryoSat-2 and Sentinel-3 freeboards to within 2 mm (standard deviations < 5 cm), and cross-validation experiments show that errors in predictions are, on average, within 3 mm across the same period. We also demonstrate the improved temporal variability of a pan-Arctic daily product by comparing time series of the predicted freeboards, with time series from CryoSat-2 and Sentinel-3 freeboards, across 9 sectors of the Arctic. The mean of predicted and CryoSat-2 or Sentinel-3 time series are generally consistent to within 3 mm, except for the Canadian Archipelago and Greenland, Iceland and Norwegian Seas, which show discrepancies greater than 1 cm due, in part, to biases between CryoSat-2 and Sentinel-3 observations in these locations.

An Arctic sea ice thickness variability revealed from satellite altimetric measurements

2014 ◽  
Vol 33 (11) ◽  
pp. 134-140 ◽  
Author(s):  
Haibo Bi ◽  
Haijun Huang ◽  
Qiao Su ◽  
Liwen Yan ◽  
Yanxia Liu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Thickness Variability ◽  
Arctic Sea Ice Thickness ◽  
Altimetric Measurements

scholarly journals Improved Arctic sea ice thickness projections using bias corrected CMIP5 simulations

2015 ◽  
Vol 9 (4) ◽  
pp. 3821-3857 ◽  
Author(s):  
N. Melia ◽  
K. Haines ◽  
E. Hawkins
Keyword(s):  
Sea Ice ◽  
Bias Correction ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Climatic Fluctuations ◽  
Wide Range ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Projections of Arctic sea ice thickness (SIT) have the potential to inform stakeholders about accessibility to the region, but are currently rather uncertain. The latest suite of CMIP5 Global Climate Models (GCMs) produce a wide range of simulated SIT in the historical period (1979–2014) and exhibit various spatial and temporal biases when compared with the Pan-Arctic Ice Ocean Modelling and Assimilation System (PIOMAS) sea ice reanalysis. We present a new method to constrain such GCM simulations of SIT to narrow projection uncertainty via a statistical bias correction technique. The bias correction successfully constrains the spatial SIT distribution and temporal variability in the CMIP5 projections whilst retaining the climatic fluctuations from individual ensemble members. The bias correction acts to reduce the uncertainty in projections of SIT and reveals the significant contributions of sea ice internal variability in the first half of the century and of scenario uncertainty from mid-century onwards. The projected date of ice-free conditions in the Arctic under the RCP8.5 high emission scenario occurs in the 2050s, which is a decade earlier than without the bias correction, with potentially significant implications for stakeholders in the Arctic such as the shipping industry. The bias correction methodology developed could be similarly applied to other variables to narrow uncertainty in climate projections more generally.

scholarly journals Improved Arctic sea ice thickness projections using bias-corrected CMIP5 simulations

2015 ◽  
Vol 9 (6) ◽  
pp. 2237-2251 ◽  
Author(s):  
N. Melia ◽  
K. Haines ◽  
E. Hawkins
Keyword(s):  
Sea Ice ◽  
Bias Correction ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Climatic Fluctuations ◽  
Wide Range ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Projections of Arctic sea ice thickness (SIT) have the potential to inform stakeholders about accessibility to the region, but are currently rather uncertain. The latest suite of CMIP5 global climate models (GCMs) produce a wide range of simulated SIT in the historical period (1979–2014) and exhibit various biases when compared with the Pan-Arctic Ice–Ocean Modelling and Assimilation System (PIOMAS) sea ice reanalysis. We present a new method to constrain such GCM simulations of SIT via a statistical bias correction technique. The bias correction successfully constrains the spatial SIT distribution and temporal variability in the CMIP5 projections whilst retaining the climatic fluctuations from individual ensemble members. The bias correction acts to reduce the spread in projections of SIT and reveals the significant contributions of climate internal variability in the first half of the century and of scenario uncertainty from the mid-century onwards. The projected date of ice-free conditions in the Arctic under the RCP8.5 high emission scenario occurs in the 2050s, which is a decade earlier than without the bias correction, with potentially significant implications for stakeholders in the Arctic such as the shipping industry. The bias correction methodology developed could be similarly applied to other variables to reduce spread in climate projections more generally.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

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