scholarly journals Understanding model spread in sea ice volume by attribution of model differences in seasonal ice growth and melt

2021 ◽  
Author(s):  
Alex West ◽  
Ed Blockley ◽  
Mat Collins
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Surface Flux ◽  
Early Summer ◽  
Arctic Sea Ice ◽  
Ice Growth ◽  
Melt Pond ◽  
Ice Volume ◽  
Melt Ponds ◽  
Future Loss

Abstract. Arctic sea ice is declining rapidly, but predictions of its future loss are made difficult by the large spread both in present-day and in future sea ice area and volume; hence, there is a need to better understand the drivers of model spread in sea ice state. Here we present a framework for understanding differences between modelled sea ice simulations based on attributing seasonal ice growth and melt differences. In the method presented, the net downward surface flux is treated as the principal driver of seasonal sea ice growth and melt. A system of simple models is used to estimate the pointwise effect of model differences in key Arctic climate variables on this surface flux, and hence on seasonal sea ice growth and melt. We compare three models with very different historical sea ice simulations: HadGEM2-ES, HadGEM3-GC3.1 and UKESM1.0. The largest driver of differences in ice growth / melt between these models is shown to be the ice area in summer (representing the surface albedo feedback) and the ice thickness distribution in winter (the thickness-growth feedback). Differences in snow and melt-pond cover during the early summer exert a smaller effect on the seasonal growth and melt, hence representing the drivers of model differences in both this and in the sea ice volume. In particular, the direct impacts on sea ice growth / melt of differing model parameterisations of snow area and of melt-ponds are shown to be small but non-negligible.

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

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.

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

2019 ◽  
Vol 13 (1) ◽  
pp. 125-139 ◽  
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 ◽  
Component Community ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Melt Pond ◽  
The Impact

Abstract. Estimates of Arctic sea ice thickness have been 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 five ice thickness categories used in a sea ice component (Community Ice CodE, 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.

scholarly journals Impact of refreezing melt ponds on Arctic sea ice basal growth

2016 ◽  
Author(s):  
Daniela Flocco ◽  
Daniel L. Feltham ◽  
David Schroeder ◽  
Michel Tsamados
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Pond Water ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Growth ◽  
Melt Pond ◽  
Melt Ponds ◽  
Ocean Properties ◽  
The Impact

Abstract. Melt ponds forming over the sea ice cover in the Arctic profoundly impact the surface albedo inducing a positive feedback leading to further melting. Here we examine the processes involved in melt pond refreezing and their impact on basal sea ice growth. When ponds freeze, the ice that forms on them insulates the pond trapping it between the sea ice and the ice lid. Trapped melt ponds delay basal sea ice growth in Autumn: ice thickens only after (1) the pond water has been fully frozen and (2) a temperature gradient is established that will conduct heat away from the ocean. Sea ice thickening in the areas where ponds are present is mainly due to the pond's water refreezing. Pan-Arctic simulations with a stand-alone sea ice model and studies with a high-resolution one-dimensional, three-layer refreezing model are used to study the impact on sea ice growth of trapped melt ponds. Basal sea ice growth may be inhibited by up to two months. We estimate an inhibited basal growth of up to 228 km3, which represents 25 % of the basal sea ice growth estimated by PIOMAS during the months of September and October. The brine not released due to the inhibited basal growth during this period could have implications for the ocean properties and circulation. The impact of trapped melt ponds has not been accounted for so far in any climate model.

scholarly journals Investigation of spatiotemporal variability of melt pond fraction and its relationship with sea ice extent during 2000–2017 using a new data

2019 ◽  
Author(s):  
Yifan Ding ◽  
Xiao Cheng ◽  
Jiping Liu ◽  
Fengming Hui ◽  
Zhenzhan Wang
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Beaufort Sea ◽  
Arctic Sea Ice ◽  
Spatiotemporal Variability ◽  
First Year ◽  
Spatial And Temporal Variability ◽  
Sea Ice Extent ◽  
Melt Pond ◽  
Melt Ponds

Abstract. The accurate knowledge of variations of melt ponds is important for understanding Arctic energy budget due to its albedo-transmittance-melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) from the MODIS surface reflectance. We construct an ensemble-based deep neural network and use in-situ observations of MPF from multi-sources to train the network. The results show that our derived MPF is in good agreement with the observations, and relatively outperforms the MPF retrieved by University of Hamburg. Built on this, we create a new MPF data from 2000 to 2017 (the longest data in our knowledge), and analyze the spatial and temporal variability of MPF. It is found that the MPF has significant increasing trends from late July to early September, which is largely contributed by the MPF over the first-year sea ice. The analysis based on our MPF during 2000–2017 confirms that the integrated MPF to late June does promise to improve the prediction skill of seasonal Arctic sea ice minimum. However, our MPF data shows concentrated significant correlations first appear in a band, extending from the eastern Beaufort Sea, through the central Arctic, to the northern East Siberian and Laptev Seas in early-mid June, and then shifts towards large areas of the Beaufort Sea, Canadian Arctic, the northern Greenland Sea and the central Arctic basin.

scholarly journals Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

2020 ◽  
Vol 61 (82) ◽  
pp. 154-163
Author(s):  
Qing Li ◽  
Chunxia Zhou ◽  
Lei Zheng ◽  
Tingting Liu ◽  
Xiaotong Yang
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Arctic Sea Ice ◽  
First Year ◽  
Landsat 8 ◽  
Canadian Arctic Archipelago ◽  
Melt Pond ◽  
Melt Ponds ◽  
Validation Experiment ◽  
Multiyear Ice

AbstractThe evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation (R = −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.

scholarly journals Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery

2020 ◽  
Vol 12 (16) ◽  
pp. 2623 ◽  
Author(s):  
Marcel König ◽  
Gerit Birnbaum ◽  
Natascha Oppelt
Keyword(s):  
Sea Ice ◽  
Hyperspectral Imagery ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Arctic Sea Ice ◽  
Atmospheric Conditions ◽  
Melt Pond ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Highly Correlated

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (RMSE) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (RMSE < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate (RMSE = 4.04 cm), and the model explains a large portion of the variability (R2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground.

scholarly journals Melt pond distribution and geometry in high Arctic sea ice derived from aerial investigations

2016 ◽  
Vol 57 (73) ◽  
pp. 105-118 ◽  
Author(s):  
W. Huang ◽  
P. Lu ◽  
R. Lei ◽  
H. Xie ◽  
Z. Li
Keyword(s):  
Sea Ice ◽  
Ground Truth ◽  
High Arctic ◽  
Arctic Sea Ice ◽  
Melt Pond ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Areal Fraction ◽  
Pond Size ◽  
Arctic Ice

ABSTRACTAerial photography was conducted in the high Arctic Ocean during a Chinese research expedition in summer 2010. By partitioning the images into three distinct surface categories (sea ice/snow, water and melt ponds), the areal fraction of each category, ice concentration and the size and geometry of individual melt ponds, are determined with high-spatial resolution. The ice concentration and melt pond coverage have large spatial deviations between flights and even between images from the marginal ice zone to the pack ice zone in the central Arctic. Ice concentration and pond coverage over high Arctic (from 84°N to north) was ~75% and ~6.8%, respectively, providing ‘ground truth’ for the unusual transpolar reduction strip of ice indicated concurrently by AMSR-E data and for the regions (north of 88°N) where no passive microwave sensors can cover. Melt pond size and shape distributions are examined in terms of pond area (S), perimeter (P), mean caliper dimension (MCD) (L), roundness (R), convex degree (C), the ratio of P/S and fractal dimension (D). Power-law relationships are developed between pond size and number. Some general trends in geometric metrics are identified as a function of pond area including R, C, P/S and D. The scale separation of pond complexity is demonstrated by analyzing area-perimeter data. The results will potentially help the modelling of melt pond evolution and the determination of heterogeneity of under-ice transmitted light fields.

scholarly journals Comparison of different retrieval techniques for melt ponds on Arctic sea ice from Landsat and MODIS satellite data

2011 ◽  
Vol 52 (57) ◽  
pp. 185-191 ◽  
Author(s):  
Anja Rösel ◽  
Lars Kaleschke
Keyword(s):  
Sea Ice ◽  
Principal Component ◽  
Arctic Sea Ice ◽  
Additional Advantage ◽  
Melt Pond ◽  
Spectral Bands ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Very High Spatial Resolution ◽  
Landsat 7

AbstractMelt ponds are regularly observed on the surface of Arctic sea ice in late spring and summer. They strongly reduce the surface albedo and accelerate the decay of Actic sea ice. Until now, only a few studies have looked at the spatial extent of melt ponds on Arctic sea ice. Knowledge of the melt-pond distribution on the entire Arctic sea ice would provide a solid basis for the parameterization of melt ponds in existing sea-ice models. Due to the different spectral properties of snow, ice and water, a multispectral sensor such as Landsat 7 ETM+ is generally applicable for the analysis of distribution. an additional advantage of the ETM+ sensor is the very high spatial resolution (30 m). an algorithm based on a principal component analysis (PCA) of two spectral channels has been developed in order to determine the melt-pond fraction. PCA allows differentiation of melt ponds and other surface types such as snow, ice or water. Spectral bands 1 and 4 with central wavelengths at 480 and 770 nm, respectively, are used as they represent the differences in the spectral albedo of melt ponds. A Landsat 7 ETM+ scene from 19 July 2001 was analysed using PCA. the melt-pond fraction determined by the PCA method yields a different spatial distribution of the ponded areas from that developed by others. A MODIS subset from the same date and area is also analysed. the classification of MODIS data results in a higher melt-pond fraction than both Landsat classifications.

scholarly journals Regional melt-pond fraction and albedo of thin Arctic first-year drift ice in late summer

2015 ◽  
Vol 9 (1) ◽  
pp. 255-268 ◽  
Author(s):  
D. V. Divine ◽  
M. A. Granskog ◽  
S. R. Hudson ◽  
C. A. Pedersen ◽  
T. I. Karlsen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Surface Type ◽  
Melt Pond ◽  
Melt Ponds

Abstract. The paper presents a case study of the regional (≈150 km) morphological and optical properties of a relatively thin, 70–90 cm modal thickness, first-year Arctic sea ice pack in an advanced stage of melt. The study combines in situ broadband albedo measurements representative of the four main surface types (bare ice, dark melt ponds, bright melt ponds and open water) and images acquired by a helicopter-borne camera system during ice-survey flights. The data were collected during the 8-day ICE12 drift experiment carried out by the Norwegian Polar Institute in the Arctic, north of Svalbard at 82.3° N, from 26 July to 3 August 2012. A set of > 10 000 classified images covering about 28 km2 revealed a homogeneous melt across the study area with melt-pond coverage of ≈ 0.29 and open-water fraction of ≈ 0.11. A decrease in pond fractions observed in the 30 km marginal ice zone (MIZ) occurred in parallel with an increase in open-water coverage. The moving block bootstrap technique applied to sequences of classified sea-ice images and albedo of the four surface types yielded a regional albedo estimate of 0.37 (0.35; 0.40) and regional sea-ice albedo of 0.44 (0.42; 0.46). Random sampling from the set of classified images allowed assessment of the aggregate scale of at least 0.7 km2 for the study area. For the current setup configuration it implies a minimum set of 300 images to process in order to gain adequate statistics on the state of the ice cover. Variance analysis also emphasized the importance of longer series of in situ albedo measurements conducted for each surface type when performing regional upscaling. The uncertainty in the mean estimates of surface type albedo from in situ measurements contributed up to 95% of the variance of the estimated regional albedo, with the remaining variance resulting from the spatial inhomogeneity of sea-ice cover.

Snow topography on undeformed Arctic sea ice captured by an idealized model

2020 ◽  
Author(s):  
Predrag Popovic ◽  
Justin Finkel ◽  
Mary Silber ◽  
Dorian Abbot
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Early Stage ◽  
Arctic Sea Ice ◽  
Conductive Heat ◽  
Melt Pond ◽  
Melt Ponds ◽  
Detailed Surface ◽  
Arctic Sea ◽  
Analytic Expressions

&lt;p&gt;Our ability to predict the future of Arctic sea ice is limited by ice's sensitivity to detailed surface conditions such as the distribution of snow and melt ponds. Snow on top of the ice decreases ice's thermal conductivity, increases its reflectivity, and provides a source of meltwater for melt ponds during summer that decrease the ice's albedo. Here, we develop a simple model of pre-melt ice surface topography that accurately describes snow cover on flat, undeformed ice. The model considers a surface that is a sum of randomly sized and placed ``snow dunes'' represented as Gaussian mounds. This model generalizes the &quot;void model&quot; of Popovic et al. (2018) and, as such, accurately describes the statistics of melt pond geometry. We test this model against detailed LiDAR measurements of the pre-melt snow topography. We show that the model snow-depth distribution is statistically indistinguishable from the measurements on flat ice, while small disagreement exists if the ice is deformed. We then use this model to determine analytic expressions for the conductive heat flux through the ice and for melt pond coverage evolution during an early stage of pond formation. We also formulate a criterion for ice to remain pond-free throughout the summer. Results from our model could be directly included in large-scale models, thereby improving our understanding of energy balance on sea ice and allowing for more reliable predictions of Arctic sea ice in a future climate.&amp;#160;&lt;/p&gt;

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