What online archive should we use this spring for
near-real time melt ponds? So many papers but no daily archive? Looking in the rear view mirror provides little of value during times of rapid change:
Revisiting the potential of melt pond fraction as predictor for seasonal Arctic sea ice extent minimum
Jiping Liu et al 19 May 2015
http://iopscience.iop.org/article/10.1088/1748-9326/10/5/054017/metaA recent modeling study [Schroder 2014] that employed a prognostic melt pond model in a stand-alone sea ice model found that September Arctic sea ice extent can be accurately predicted from the melt pond fraction in May. Here we show that satellite observations show
no evidence of predictive skill in May. However, we find that a significantly strong relationship (high predictability) first emerges as the melt pond fraction is integrated from early May to late June, with a persistent strong relationship only occurring after late July.
Melt pond fraction is derived from MODIS surface reflectance as processed by a neural network using spectral characteristics of melt ponds relative to open water, snow and ice. The melt pond fraction is available at 8 day interval from 9 May to 6 September with a spatial resolution of 12.5 km from 2000 to 2011 [Rösel et al 2012].
The observed climatological melt pond fraction is ~11% in early May and increases rapidly in late May and June (~23% in late June and reaching a peak ~25% in early July), followed by a gradual decrease (still retaining ~20% in late August and early September) as shown in Fig1a.
This archive is based on Modis but has no data beyond Sept 2011:
https://icdc.cen.uni-hamburg.de/1/daten/cryosphere/arctic-meltponds.html ftp://ftp-icdc.cen.uni-hamburg.de/arctic_meltponds/The impact of melt ponds on microwave brightness temperatures and sea-ice concentrations
S Kern et al Sept 2016
https://www.the-cryosphere.net/10/2217/2016/tc-10-2217-2016.pdfSea-ice concentrations derived from satellite microwave brightness temperatures are less accurate during summer. In the Arctic Ocean the lack of accuracy is primarily caused by melt ponds, but also by changes in the properties of snow and the sea-ice surface itself. We investigate the sensitivity of eight sea-ice concentration retrieval algorithms to melt ponds by comparing sea-ice concentration with the melt-pond fraction.
One potential reason for the reduced accuracy is the change in microphysical properties inside the sea ice, for instance, the desalination of the sea ice during the melt process or the flushing of air voids in multiyear ice with meltwater and other melt processes (Scharien 2010).
The three key surface features of summer melt on Arctic sea ice are a metamorphou wet snow cover, a porous, wet sea-ice surface, and melt ponds. During summer, the snow cover on sea ice is usually wet or even saturated with meltwater. Its density is usually considerably larger during summer than during winter.
Diurnal melt–refreeze cycles, episodes of intermittent melting and refreezing of the snow, which is a common phenomenon during late spring, result in an increase in the snow grain size. Wet snow is an efficient absorber of microwave radiation and has a microwave emissivity close to 1. It can effectively block microwave emission from underneath.
Satellite microwave sensors which have been used for sea-ice concentration retrieval allow for footprint sizes between 5 and 70 km. Melt ponds, cracks, and leads are therefore sub-footprint size surface features, and cannot be resolved individually. A satellite brightness temperature measurement of a mixed scene is therefore composed of contributions from the open water, i.e., cracks, leads, melt ponds, and from the (snow covered) sea ice.
This has two main consequences for a sea-ice concentration product computed from such coarse-resolution satellite measurements. The sea-ice concentration in the presence of melt ponds is likely to be underestimated because melt ponds are seen as open water. Whether the footprint contains, for example, (case A) 100 % sea ice with 40 % melt ponds or (case B) 60% sea ice with 40 % open water from leads and openings, is
ambiguous. In both cases, satellite microwave radiometry retrieves 60 % sea-ice concentration because the net sea-ice surface fraction of sea ice in the grid cells is 60 %
Melt pond fraction and spectral sea ice albedo retrieval from MERIS data
L Istomina et al
The Cryosphere, 9, 1551–1566, doi:10.5194/tc-9-1551-2015, 2015a.
The Cryosphere, 9, 1567– 1578, doi:10.5194/tc-9-1567-2015, 2015b.
The data used for the present study are the pond fraction and broadband sea ice albedo swath data products retrieved from MERIS (Medium Resolution Imaging Spectrometer) swath Level 1b data over the ice-covered Arctic Ocean using the MPD retrieval. The present chapter presents a short summary of the MPD retrieval. T
The full description of the algorithm can be found in EP Zege et al
https://epic.awi.de/38709/1/1-s20-S003442571500108X-main.pdf"The input to the current version of the MPD algorithm is the MERIS Level 1B data, including the radiance coefficients at ten wavelengths and the solar and observation angles (zenith and azimuth). Also, specific parameters describing surface and atmospheric state can be set in a configuration input file. The software output is the map of the melt ponds area fraction and the spectral albedo of sea-ice in HDF5 format. Currently, the MPD code is arranged as a Linux console application and works in the MERIS processing chain in theUniversity of Bremen, providing a comprehensive melt pond data product based on the complete MERIS data set
2002–2012Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery
AF Fors et al March 2017
The Cryosphere 11(2) DOI 10.5194/tc-11-755-2017
Melt pond fractions retrieved from a helicopter-borne camera system were compared to polarimetric features extracted from four dual-polarimetric X-band SAR scenes, revealing significant relationships. The correlations were strongly dependent on wind speed and SAR incidence angle. Co-polarization ratio was found to be the most promising SAR feature for melt pond fraction estimation at intermediate wind speeds.
A spectral mixture analysis approach to quantify Arctic first-year sea ice melt pond fraction using QuickBird and MODIS reflectance data
JJ Yackel et al Sept 2017 DOI10.1016/j.rse.2017.09.030
Despite its requirement for thermodynamic sea ice modeling,measurement of melt pond areal coverage using satellite remote sensing has proven difficult due to significantspatiotemporal variability in the timing and evolution of melt ponds. Less than optimal results from prior studiesemploying a spectral mixture analysis (SMA) towards the determination of melt pond areal coverage from sa-tellite remote sensing data provided the incentive for a multiple endmember spectral mixture analysis (MESMA)approach. The MESMA was performed on Moderate Resolution Imaging Spectroradiometer (MODIS) imageryusing endmember spectra obtained from atmospherically corrected coincident high resolution imagery, surfaceobservations and modeling. Results were validated against a high resolution Quickbird image acquired coin-cident to the MODIS image.
Melt ponds over Arctic sea ice
D Flocco March 2017
http://blogs.reading.ac.uk/weather-and-climate-at-reading/2017/melt-ponds-over-arctic-sea-ice/Where ponds form, the ice beneath becomes thinner due to increased melting. Towards the end of the summer, the air temperature drops and a thin layer of ice forms over melt ponds. The ponds’ melt water trapped in the ice acts as a heat store and does not allow the underlying ice to start thickening until all the pond’s water is frozen. Ponds are up to 1.5 m deep and it can take over two months to freeze their volume of water.