scholarly journals The potential of using Landsat 7 ETM+ for the classification of sea-ice surface conditions during summer

2002 ◽  
Vol 34 ◽  
pp. 415-419 ◽  
Author(s):  
Thorsten Markus ◽  
Donald J. Cavalieri ◽  
Alvaro Ivanoff
Keyword(s):  
Sea Ice ◽  
Near Infrared ◽  
Color Image ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Surface Conditions ◽  
Melt Ponds ◽  
Wet Snow ◽  
Landsat 7 ◽  
The Individual

AbstractDuring spring and summer, the surface of the Arctic sea-ice cover undergoes rapid changes that greatly affect the surface albedo and significantly impact the further decay of the sea ice. These changes are primarily the development of a wet snow cover and the development of melt ponds. As melt ponds generally do not exceed a couple of meters in diameter, the spatial resolutions of sensors like the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer are too coarse for their identification. Landsat 7, on the other hand, has a spatial resolution of 30 m (15 m for the panchromatic band) and thus offers the best chance to map the distribution of melt ponds from space. The different wavelengths (bands) from blue to near-infrared offer the potential to distinguish among different surface conditions. Landsat 7 data for the Baffin Bay region for June 2000 have been analyzed. The analysis shows that different surface conditions, such as wet snow and melt-ponded areas, have different signatures in the individual Landsat bands. Consistent with in situ albedo measurements, melt ponds show up as blueish, whereas dry and wet ice have a white to gray appearance in the Landsat true-color image. These spectral differences enable areas with high fractions of melt ponds to be distinguished.

scholarly journals Nonlinear Climate Responses to Increasing CO2 and Anthropogenic Aerosols Simulated by CESM1

2019 ◽  
Vol 33 (1) ◽  
pp. 281-301 ◽  
Author(s):  
Jiechun Deng ◽  
Aiguo Dai ◽  
Haiming Xu
Keyword(s):  
Sea Ice ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Nonlinear Effects ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Anthropogenic Aerosols ◽  
Aerosol Loading ◽  
Temperature And Precipitation ◽  
The Individual

Abstract Atmospheric CO2 and anthropogenic aerosols (AA) have increased simultaneously. Because of their opposite radiative effects, these increases may offset each other, which may lead to some nonlinear effects. Here the seasonal and regional characteristics of this nonlinear effect from the CO2 and AA forcings are investigated using the fully coupled Community Earth System Model. Results show that nonlinear effects are small in the global mean of the top-of-the-atmosphere radiative fluxes, surface air temperature, and precipitation. However, significant nonlinear effects exist over the Arctic and other extratropical regions during certain seasons. When both forcings are included, Arctic sea ice in September–November decreases less than the linear combination of the responses to the individual forcings due to a higher sea ice sensitivity to the CO2-induced warming than the sensitivity to the AA-induced cooling. This leads to less Arctic warming in the combined-forcing experiment due to reduced energy release from the Arctic Ocean to the atmosphere. Some nonlinear effects on precipitation in June–August are found over East Asia, with the northward-shifted East Asian summer rain belt to oppose the CO2 effect. In December–February, the aerosol loading over Europe in the combined-forcing experiment is higher than that due to the AA forcing, resulting from CO2-induced circulation changes. The changed aerosol loading results in regional thermal responses due to aerosol direct and indirect effects, weakening the combined changes of temperature and circulation. This study highlights the need to consider nonlinear effects from historical CO2 and AA forcings in seasonal and regional climate attribution analyses.

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 Arctic sea-ice conditions and the distribution of solar radiation during summer

1997 ◽  
Vol 25 ◽  
pp. 445-450 ◽  
Author(s):  
Donald K. Perovich ◽  
Walter B. Tucker
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Open Water ◽  
Ice Cover ◽  
Feedback Mechanism ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Melt Ponds ◽  
Arctic Ice ◽  
Median Area

Understanding the interaction of solar radiation with the ice cover is critical in determining the heat and mass balance of the Arctic ice pack, and in assessing potential impacts due to climate change. Because of the importance of the ice-albedo feedback mechanism, information on the surface state of the ice cover is needed. Observations of the surface slate of sea ice were obtained from helicopter photography missions made during the 1994 Arctic Ocean Section cruise. Photographs from one flight, taken during the height of the melt season (31 July 1994) at 76° N, 172° W, were analyzed in detail. Bare ice covered 82% of the total area, melt ponds 12%, and open water 6%, There was considerable variability in these area fractions on scales < 1 km2. Sample areas >2 3 km2gave representative values of ice concentration and pond fraction. Melt ponds were numerous, with a number density of 1800 ponds km-2. The melt ponds had a mean area of 62 m2a median area of 14 m2, and a size distribution that was well lit by a cumulative lognormal distribution. While leads make up only a small portion of the total area, they are the source of virtually all of the solar energy input to the ocean.

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.

Drone-based sea ice albedo measurements and photogrammetry during the Arctic freeze-up in the MOSAiC expedition

2021 ◽  
Author(s):  
Roberta Pirazzini ◽  
Henna-Reetta Hannula ◽  
David Brus ◽  
Ruzica Dadic ◽  
Martin Scnheebeli
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Surface Topography ◽  
Near Infrared ◽  
Snow Accumulation ◽  
The Arctic ◽  
Ocean Optics ◽  
Surface Features ◽  
Ice Surface ◽  
Melt Ponds

<p>Aerial albedo measurements and detailed surface topography of sea ice are needed to characterize the distribution of the various surface types (melt ponds of different depth and size, ice of different thicknesses, leads, ridges) and to determine how they contribute to the areal-averaged albedo on different horizontal scales. These measurements represent the bridge between the albedo measured from surface-based platforms, which typically have metre-to-tens-of-meters footprint, and satellite observations or large-grid model outputs.</p><p>Two drones were operated in synergy to measure the albedo and map the surface topography of the sea ice during the leg 5 of the MOSAiC expedition (August-September 2020), when concurrent albedo and surface roughness measurements were collected using surface-based instruments. The drone SPECTRA was equipped with paired Kipp and Zonen CM4 pyranometers measuring broadband albedo and paired Ocean Optics STS VIS (350 – 800 nm) and NIR (650-1100 nm) micro-radiometers measuring visible and near-infrared spectral albedo, and the drone Mavic 2 Pro was equipped with camera to perform photography mapping of the area measured by the SPECTRA drone.</p><p>Here we illustrate the collected data, which show a drastic change in sea ice albedo during the observing period, from the initial melting state to the freezing and snow accumulation state, and demonstrate how this change is related to the evolution of the different surface features, melt ponds and leads above all. From the data analysis we can conclude that the 30m albedo is not significantly affected by the individual surface features and, therefore, it is potentially representative of the sea ice albedo in satellite footprint and model grid areas.</p><p>The Digital Elevation Models (DEMs) of the sea ice surface obtained from UAV photogrammetry are combined with the DEMs based on Structure From Motion technique that apply photos manually taken close to the surface. This will enable us to derive the surface roughness from sub-millimeter to meter scales, which is critical to interpret the observed albedo and to develop correction methods to eliminate the artefacts caused by shadows.</p><p>The UAV-based albedo and surface roughness are highly complementary also to analogous helicopter-based observations, and will be relevant for the interpretation of all the physical and biochemical processes observed at and near the sea ice surface during the transition from melting to freezing and growing.</p>

Modelling melt ponds in Global Circulation Models

2020 ◽  
Author(s):  
Jean Sterlin ◽  
Thierry Fichefet ◽  
François Massonnet ◽  
Olivier Lecomte ◽  
Martin Vancoppenolle
Keyword(s):  
Aspect Ratio ◽  
Sea Ice ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Global Circulation ◽  
Melt Pond ◽  
Global Circulation Models ◽  
Melt Ponds ◽  
The Impact

<p>Melt ponds appear during the Arctic summer on the sea ice cover when meltwater and liquid precipitation collect in the depressions of the ice surface. The albedo of the melt ponds is lower than that of surrounding ice and snow areas. Consequently, the melt ponds are an important factor for the ice-albedo feedback, a mechanism whereby a decrease in albedo results in greater absorption of solar radiation, further ice melt, and lower albedos </p><p>To account for the effect of melt ponds on the climate, several numerical schemes have been introduced for Global Circulation Models. They can be classified into two groups. The first group makes use of an explicit relation to define the aspect ratio of the melt ponds. The scheme of Holland et al. (2012) uses a constant ratio of the melt pond depth to the fraction of sea ice covered by melt ponds. The second group relies on theoretical considerations to deduce the area and volume of the melt ponds. The scheme of Flocco et al. (2012) uses the ice thickness distribution to share the meltwater between the ice categories and determine the melt ponds characteristics.</p><p>Despite their complexity, current melt pond schemes fail to agree on the trends in melt pond fraction of sea ice area during the last decades. The disagreement casts doubts on the projected melt pond changes. It also raises questions on the definition of the physical processes governing the melt ponds in the schemes and their sensitivity to atmospheric surface conditions.</p><p>In this study, we aim at identifying 1) the conceptual difference of the aspect ratio definition in melt pond schemes; 2) the role of refreezing for melt ponds; 3) the impact of the uncertainties in the atmospheric reanalyses. To address these points, we have run the Louvain-la-Neuve Ice Model (LIM), part of the Nucleus for European Modelling of the Ocean (NEMO) version 3.6 along with two different atmospheric reanalyses as surface forcing sets. We used the reanalyses in association with Holland et al. (2012) and Flocco et al. (2012) melt pond schemes. We selected Holland et al. (2012) pond refreezing formulation for both schemes and tested two different threshold temperatures for refreezing. </p><p>From the experiments, we describe the impact on Arctic sea ice and state the importance of including melt ponds in climate models. We attempt at disentangling the separate effects of the type of melt pond scheme, the refreezing mechanism, and the atmospheric surface forcing method, on the climate. We finally formulate a recommendation on the use of melt ponds in climate models. </p>

scholarly journals Arctic sea ice variability and trends, 1979–2010

2012 ◽  
Vol 6 (2) ◽  
pp. 957-979 ◽  
Author(s):  
D. J. Cavalieri ◽  
C. L. Parkinson
Keyword(s):  
Sea Ice ◽  
Bering Sea ◽  
Arctic Sea Ice ◽  
Negative Trend ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Regions ◽  
Arctic Sea ◽  
The Individual ◽  
The Bering Sea

Abstract. Analyses of 32 yr (1979–2010) of Arctic sea ice extents and areas derived from satellite passive microwave radiometers are presented for the Northern Hemisphere as a whole and for nine Arctic regions. There is an overall negative yearly trend of −51.5 ± 4.1 × 103 km2 yr−1 (−4.1 ± 0.3% decade−1) in sea ice extent for the hemisphere. The sea ice extent trends for the individual Arctic regions are all negative except for the Bering Sea: −3.9 ± 1.1 × 103 km2 yr−1 (−8.7 ± 2.5% decade−1) for the Seas of Okhotsk and Japan, +0.3 ± 0.8 × 103 km2 yr−1 (+1.2 ± 2.7% decade−1) for the Bering Sea, −4.4 ± 0.7 × 103 km2 yr−1 (−5.1 ± 0.9% decade−1) for Hudson Bay, −7.6 ± 1.6 × 103 km2 yr−1 (−8.5 ± 1.8% decade−1) for Baffin Bay/Labrador Sea, −0.5 ± 0.3 × 103 km2 yr−1 (−5.9 ± 3.5% decade−1) for the Gulf of St. Lawrence, −6.5 ± 1.1 × 103 km2 yr−1 (−8.6 ± 1.5% decade−1) for the Greenland Sea, −13.5 ± 2.3 × 103 km2 yr−1 (−9.2 ± 1.6% decade−1) for the Kara and Barents Seas, −14.6 ± 2.3 × 103 km2 yr−1 (−2.1 ± 0.3% decade−1) for the Arctic Ocean, and −0.9 ± 0.4 × 103 km2 yr−1 (−1.3 ± 0.5% decade−1) for the Canadian Archipelago. Similarly, the yearly trends for sea ice areas are all negative except for the Bering Sea. On a seasonal basis for both sea ice extents and areas, the largest negative trend is observed for summer with the next largest negative trend being for autumn.

scholarly journals Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Qi Liu 1 ◽  
Yawen Zhang 1
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Satellite Data ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Neural Network Approach ◽  
Melt Pond ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Visible Imagery

During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multi-period visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results.

scholarly journals Inorganic carbon dynamics of melt pond-covered first year sea ice in the Canadian Arctic

2014 ◽  
Vol 11 (5) ◽  
pp. 7485-7519 ◽  
Author(s):  
N.-X. Geilfus ◽  
R. J. Galley ◽  
O. Crabeck ◽  
T. Papakyriakou ◽  
J. Landy ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Ice Melt ◽  
Melt Pond ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Melt Water

Abstract. Melt pond formation is a common feature of the spring and summer Arctic sea ice. However, the role of the melt ponds formation and the impact of the sea ice melt on both the direction and size of CO2 flux between air and sea is still unknown. Here we describe the CO2-carbonate chemistry of melting sea ice, melt ponds and the underlying seawater associated with measurement of CO2 fluxes across first year landfast sea ice in the Resolute Passage, Nunavut, in June 2012. Early in the melt season, the increase of the ice temperature and the subsequent decrease of the bulk ice salinity promote a strong decrease of the total alkalinity (TA), total dissolved inorganic carbon (TCO2) and partial pressure of CO2 (pCO2) within the bulk sea ice and the brine. Later on, melt pond formation affects both the bulk sea ice and the brine system. As melt ponds are formed from melted snow the in situ melt pond pCO2 is low (36 μatm). The percolation of this low pCO2 melt water into the sea ice matrix dilutes the brine resulting in a strong decrease of the in situ brine pCO2 (to 20 μatm). As melt ponds reach equilibrium with the atmosphere, their in situ pCO2 increase (up to 380 μatm) and the percolation of this high concentration pCO2 melt water increase the in situ brine pCO2 within the sea ice matrix. The low in situ pCO2 observed in brine and melt ponds results in CO2 fluxes of −0.04 to −5.4 mmol m–2 d–1. As melt ponds reach equilibrium with the atmosphere, the uptake becomes less significant. However, since melt ponds are continuously supplied by melt water their in situ pCO2 still remains low, promoting a continuous but moderate uptake of CO2 (~ −1mmol m–2 d–1). The potential uptake of atmospheric CO2 by melting sea ice during the Arctic summer has been estimated from 7 to 16 Tg of C ignoring the role of melt ponds. This additional uptake of CO2 associated to Arctic sea ice needs to be further explored and considered in the estimation of the Arctic Ocean's overall CO2 budget.

scholarly journals Processes controlling surface, bottom and lateral melt of Arctic sea ice in a state of the art sea ice model

2015 ◽  
Vol 373 (2052) ◽  
pp. 20140167 ◽  
Author(s):  
Michel Tsamados ◽  
Daniel Feltham ◽  
Alek Petty ◽  
David Schroeder ◽  
Daniela Flocco
Keyword(s):  
Sea Ice ◽  
Research Priorities ◽  
Sensitivity Study ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Future Research ◽  
Melt Ponds ◽  
Equation Boundary ◽  
Arctic Sea ◽  
Ice Model

We present a modelling study of processes controlling the summer melt of the Arctic sea ice cover. We perform a sensitivity study and focus our interest on the thermodynamics at the ice–atmosphere and ice–ocean interfaces. We use the Los Alamos community sea ice model CICE, and additionally implement and test three new parametrization schemes: (i) a prognostic mixed layer; (ii) a three equation boundary condition for the salt and heat flux at the ice–ocean interface; and (iii) a new lateral melt parametrization. Recent additions to the CICE model are also tested, including explicit melt ponds, a form drag parametrization and a halodynamic brine drainage scheme. The various sea ice parametrizations tested in this sensitivity study introduce a wide spread in the simulated sea ice characteristics. For each simulation, the total melt is decomposed into its surface, bottom and lateral melt components to assess the processes driving melt and how this varies regionally and temporally. Because this study quantifies the relative importance of several processes in driving the summer melt of sea ice, this work can serve as a guide for future research priorities.

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