Aerial observations of sea ice and melt ponds near the North Pole during CHINARE2010

An aerial photography has been used to provide validation data on sea ice near the North Pole where most polar orbiting satellites cannot cover. This kind of data can also be used as a supplement for missing data and for reducing the uncertainty of data interpolation. The aerial photos are analyzed near the North Pole collected during the Chinese national arctic research expedition in the summer of 2010（CHINARE2010）. The result shows that the average fraction of open water increases from the ice camp at approximately 87°N to the North Pole, resulting in the decrease in the sea ice. The average sea ice concentration is only 62.0% for the two flights（16 and 19 August 2010）. The average albedo（0.42） estimated from the area ratios among snow-covered ice,melt pond and water is slightly lower than the 0.49 of HOTRAX 2005. The data on 19 August 2010 shows that the albedo decreases from the ice camp at approximately 87°N to the North Pole, primarily due to the decrease in the fraction of snow-covered ice and the increase in fractions of melt-pond and open-water. The ice concentration from the aerial photos and AMSR-E（The Advanced Microwave Scanning Radiometer-Earth Observing System） images at 87.0°–87.5°N exhibits similar spatial patterns, although the AMSR-E concentration is approximately 18.0%（on average） higher than aerial photos. This can be attributed to the 6.25 km resolution of AMSR-E, which cannot separate melt ponds/submerged ice from ice and cannot detect the small leads between floes. Thus, the aerial photos would play an important role in providing high-resolution independent estimates of the ice concentration and the fraction of melt pond cover to validate and/or supplement space-borne remote sensing products near the North Pole.

Melt Pond Scheme Parameter Estimation Using an Adjoint Model

Melt ponds significantly affect Arctic sea ice thermodynamic processes.The melt pond parameterization scheme in the Los Alamos sea ice model(CICE6.0) can predict the volume,area fraction(the ratio between melt pond area to sea ice area in a model grid),and depth of melt ponds.However,this scheme has some uncertain parameters that affect melt pond simulations.These parameters could be determined through a conventional parameter estimation method,which requires a large number of sensitivity simulations.The adjoint model can calculate the parameter sensitivity efficiently.In the present research,an adjoint model was developed for the CESM(Community Earth System Model) melt pond scheme.A melt pond parameter estimation algorithm was then developed based on the CICE6.0 sea ice model,melt pond adjoint model,and L-BFGS(Limited-memory Broyden-Fletcher-Goldfard-Shanno) minimization algorithm.The parameter estimation algorithm was verified under idealized conditions.By using MODIS(Moderate Resolution Imaging Spectroradiometer)melt pond fraction observation as a constraint and the developed parameter estimation algorithm,the melt pond aspect ratio parameter in CESM scheme,which is defined as the ratio between pond depth and pond area fraction,was estimated every eight days during summertime for two different regions in the Arctic.One region was covered by multi-year ice(MYI) and the other by first-year ice(FYI).The estimated parameter was then used in simulations and the results show that:(1) the estimated parameter varies over time and is quite different for MYI and FYI;(2) the estimated parameter improved the simulation of the melt pond fraction.

A concept for autonomous and continuous observation of melt pond morphology: Instrument design and test trail during the 4th CHINARE-Arctic in 2010

Accelerated decline of summer and winter Arctic sea ice has been demonstrated progressively. Melt ponds play a key role in enhancing the feedback of solar radiation in the ice/ocean-atmosphere system, and have thus been a focus of researchers and modelers. A new melt pond investigation system was designed to determine morphologic and hydrologic features, and their evolution. This system consists of three major parts： Temperature-salinity measuring, surface morphology monitoring, and water depth monitoring units. The setup was deployed during the ice camp period of the fourth Chinese National Arctic Research Expedition in summer 2010. The evolution of a typical Arctic melt pond was documented in terms of pond depth, shape and surface condition. These datasets are presented to scientifically reveal how involved parameters change, contributing to better understanding of the evolution mechanism of the melt pond. The main advantage of this system is its suitability for autonomous and long-term observation, over and within a melt pond. Further, the setup is portable and robust. It can be easily and quickly installed, which is most valuable for deployment under harsh conditions.

Determination of Arctic melt pond fraction and sea ice roughness from Unmanned Aerial Vehicle (UAV) imagery

Melt ponds on Arctic sea ice are of great significance in the study of the heat balance in the ocean mixed layer, mass and salt balances of Arctic sea ice, and other aspects of the earth-atmosphere system. During the 7th Chinese National Arctic Research Expedition, aerial photographs were taken from an Unmanned Aerial Vehicle over an ice floe in the Canada Basin. Using threshold discrimination and three-dimensional modeling, we estimated a melt pond fraction of 1.63% and a regionally averaged surface roughness of 0.12 for the study area. In view- of the particularly foggy environment of the Arctic, aerial images were defogged using an improved dark channel prior based image defog algorithm, especially adapted for the special conditions of sea ice images. An aerial photo mosaic was generated, melt ponds were identified from the mosaic image and melt pond fractions were calculated. Three-dimensional modeling techniques were used to generate a digital elevation model allowing relative elevation and roughness of the sea ice surface to be estimated. Analysis of the relationship between the distributions of melt ponds and sea ice surface roughness show-s that melt ponds are smaller on sea ice with higher surface roughness, while broader melt ponds usually occur in areas where sea ice surface roughness is lower.

Thermodynamic model of melt pond and its application during summer of 2010 in the central Arctic Ocean

A one-dimensional thermodynamic model of melt pond is established in this paper. The observation data measured in the summer of 2010 by the Chinese National Arctic Research Expedition （CHINARE-2010） are used to partially parameterize equations and to validate results of the model. About 85% of the incident solar radiation passed through the melt pond surface, and some of it was released in the form of sensible and latent heat. However, the released energy was very little （about 15%）, compared to the incident solar radiation. More than 58.6% of the incident energy was absorbed by melt pond water, which caused pond-covered ice melting and variation of pond water temperature. The simulated temperature of melt pond had a diurnal variation and its value ranged between 0.0~C and 0.3~C. The melting rate of upper pond-covered ice is estimated to be around two times faster than snow-covered ice. At same time, the change of melting rate was relatively quick for pond depth less than 0.4 m, while the melting rate kept relatively constant （about 1.0 cm/d） for pond depth greater than 0.4 m.

Retrievals of Arctic sea ice melt pond depth and underlying ice thickness using optical data

Melt pond is a distinctive characteristic of the summer Arctic,which affects energy balance in the Arctic system.The Delta-Eddington model(BL)and Two-strEam rAdiative transfer model(TEA)are employed to retrieving pond depth Hpand underlying ice thickness Hi according to the ratio X of the melt-pond albedo in two bands.Results showed that whenλ1=359 nm andλ2=605 nm,the Pearson’s correlation coefficient r between X and Hp is 0.99 for the BL model.The result of TEA model was similar to the BL model.The retrievals of Hp for the two models agreed well with field observations.For Hi,the highest r(0.99)was obtained whenλ1=447 nm andλ2=470 nm for the BL model,λ1=447 nm andλ2=451 nm for the TEA model.Furthermore,the BL model was more suitable for the retrieval of thick ice(0<Hi<3.5 m,R2=0.632),while the TEA model is on the contrary(Hi<1 m,R2=0.842).The present results provide a potential method for the remote sensing on melt pond and ice in the Arctic summer.

Arctic summer sea ice phenology including ponding from 1982 to 2017

Information on the Arctic sea ice climate indicators is crucial to business strategic planning and climate monitoring.Data on the evolvement of the Arctic sea ice and decadal trends of phenology factors during melt season are necessary for climate prediction under global warming.Previous studies on Arctic sea ice phenology did not involve melt ponds that dramatically lower the ice surface albedo and tremendously affect the process of sea ice surface melt.Temporal means and trends of the Arctic sea ice phenology from 1982 to 2017 were examined based on satellite-derived sea ice concentration and albedo measurements.Moreover,the timing of ice ponding and two periods corresponding to it were newly proposed as key stages in the melt season.Therefore,four timings,i.e.,date of snow and ice surface melt onset(MO),date of pond onset(PO),date of sea ice opening(DOO),and date of sea ice retreat(DOR);and three durations,i.e.,melt pond formation period(MPFP,i.e.,MO–PO),melt pond extension period(MPEP,i.e.,PO–DOR),and seasonal loss of ice period(SLIP,i.e.,DOO–DOR),were used.PO ranged from late April in the peripheral seas to late June in the central Arctic Ocean in Bootstrap results,whereas the pan-Arctic was observed nearly 4 days later in NASA Team results.Significant negative trends were presented in the MPEP in the Hudson Bay,the Baffin Bay,the Greenland Sea,the Kara and Barents seas in both results,indicating that the Arctic sea ice undergoes a quick transition from ice to open water,thereby extending the melt season year to year.The high correlation coefficient between MO and PO,MPFP illustrated that MO predominates the process of pond formation.

Physical and optical characteristics of sea ice in the Pacific Arctic Sector during the summer of 2018

The reduction in Arctic sea ice in summer has been reported to have a significant impact on the global climate.In this study,Arctic sea ice/snow at the end of the melting season in 2018 was investigated during CHINARE-2018,in terms of its temperature,salinity,density and textural structure,the snow density,water content and albedo,as well as morphology and albedo of the refreezing melt pond.The interior melting of sea ice caused a strong stratification of temperature,salinity and density.The temperature of sea ice ranged from–0.8℃ to 0℃,and exhibited linear cooling with depth.The average salinity and density of sea ice were approximately 1.3 psu and 825 kg/m3,respectively,and increased slightly with depth.The first-year sea ice was dominated by columnar grained ice.Snow cover over all the investigated floes was in the melt phase,and the average water content and density were 0.74%and 241 kg/m3,respectively.The thickness of the thin ice lid ranged from 2.2 cm to 7.0 cm,and the depth of the pond ranged from 1.8 cm to 26.8 cm.The integrated albedo of the refreezing melt pond was in the range of 0.28–0.57.Because of the thin ice lid,the albedo of the melt pond improved to twice as high as that of the mature melt pond.These results provide a reference for the current state of Arctic sea ice and the mechanism of its reduction.

Characterization of sea ice and its snow cover in the Arctic Pacific sector during the summer of 2016

A comprehensive analysis of sea ice and its snow cover during the summer in the Arctic Pacific sector was conducted using the observations recorded during the 7th Chinese National Arctic Research Expedition(CHIANRE-2016)and the satellite-derived parameters of the melt pond fraction(MPF)and snow grain size(SGS)from MODIS data.The results show that there were many low-concentration ice areas in the south of 78°N,while the ice concentration and thickness increased significantly with the latitude above the north of 78°N during CHIANRE-2016.The average MPF presented a trend of increasing in June and then decreasing in early September for 2016.The average snow depth on sea ice increased with latitude in the Arctic Pacific sector.We found a widely developed depth hoar layer in the snow stratigraphic profiles.The average SGS generally increased from June to early August and then decreased from August to September in 2016,and two valley values appeared during this period due to snowfall incidents.