The color of melt ponds on Arctic sea ice

Pond color, which creates the visual appearance of melt ponds on Arctic sea ice in summer, is quantitatively investigated using a two-stream radiative transfer model for ponded sea ice. The upwelling irradiance from the pond surface is determined and then its spectrum is transformed into RGB (red, green, blue) color space using a colorimetric method. The dependence of pond color on various factors such as water and ice properties and incident solar radiation is investigated. The results reveal that increasing underlying ice thickness H-i enhances both the green and blue intensities of pond color, whereas the red intensity is mostly sensitive to H-i for thin ice (H-i 1.5 m), similar to the behavior of meltpond albedo. The distribution of the incident solar spectrum F-0 with wavelength affects the pond color rather than its intensity. The pond color changes from dark blue to brighter blue with increasing scattering in ice, and the influence of absorption in ice on pond color is limited. The pond color reproduced by the model agrees with field observations for Arctic sea ice in summer, which supports the validity of this study. More importantly, the pond color has been confirmed to contain information about meltwater and underlying ice, and therefore it can be used as an index to retrieve H-i and H-p. Retrievals of H-i for thin ice (H-i <1 m) agree better with field measurements than retrievals for thick ice, but those of H-p are not good. The analysis of pond color is a new potential method to obtain thin ice thickness in summer, although more validation data and improvements to the radiative transfer model will be needed in future.Peer reviewe

A new tracking algorithm for sea ice age distribution estimation

A new algorithm for estimating sea ice age (SIA) distribution based on the Eulerian advection scheme is presented. The advection scheme accounts for the observed divergence or convergence and freezing or melting of sea ice and predicts consequent generation or loss of new ice. The algorithm uses daily gridded sea ice drift and sea ice concentration products from the Ocean and Sea Ice Satellite Application Facility. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel of the output product or, in other words, to provide a frequency distribution of the ice age allowing to apply mean, median, weighted average or other statistical measures. Comparison with the National Snow and Ice Data Center SIA product revealed several improvements of the new SIA maps and time series. First, the application of the Eulerian scheme provides smooth distribution of the ice age parameters and prevents product undersampling which may occur when a Lagrangian tracking approach is used. Second, utilization of the new sea ice drift product void of artifacts from EUMETSAT OSI SAF resulted in more accurate and reliable spatial distribution of ice age fractions. Third, constraining SIA computations by the observed sea ice concentration expectedly led to considerable reduction of multi-year ice (MYI) fractions. MYI concentration is computed as a sum of all MYI fractions and compares well to the MYI products based on passive and active microwave and SAR products

Snow Depth on Arctic and Antarctic Sea Ice Derived from Snow Buoys

The snow cover on sea ice received more and more attention in recent sea ice studies and model simulations, because its physical properties dominate many sea ice and upper ocean processes. In particular; the temporal and spatial distribution of snow depth is of crucial importance for the energy and mass budgets of sea ice, as well as for the interaction with the atmosphere and the oceanic freshwater budget. Snow depth is also a crucial parameter for sea ice thickness retrieval algorithms from satellite altimetry data. Recent time series of Arctic sea ice volume only use monthly snow depth climatology, which cannot take into account annual changes of the snow depth and its properties. For Antarctic sea ice, no such climatology is available. With a few exceptions, snow depth on sea ice is determined from manual in-situ measurements with very limited coverage of space and time. Hence the need for more consistent observational data sets of snow depth on sea ice is frequently highlighted. Here, we present time series measurements of snow depths on Antarctic and Arctic sea ice, recorded by an innovative and affordable platform. This Snow Buoy is optimized to autonomously monitor the evolution of snow depth on sea ice and will allow new insights into its seasonality. In addition, the instruments report air temperature and atmospheric pressure directly into different international networks, e.g. the Global Telecommunication System (GTS) and the International Arctic Buoy Programme (IABP). We introduce the Snow Buoy concept together with technical specifications and results on data quality, reliability, and performance of the units. We highlight the findings from four buoys, which simultaneously drifted through the Weddell Sea for more than 1.5 years, revealing unique information on characteristic regional and seasonal differences. Finally, results from seven snow buoys co-deployed on Arctic sea ice throughout the winter season 2015/16 suggest the great importance of local effects, weather events, and potential influences of dynamic sea ice processes on snow accumulation

Snow depth on Antarctic sea ice from autonomous measurements

The snow cover on sea ice received more and more attention in recent sea ice studies and model simulations, because its physical properties dominate many sea ice and upper ocean processes. In particular; the temporal and spatial distribution of snow depth is of crucial importance for the energy and mass budgets of sea ice, as well as for the interaction with the atmosphere and the oceanic freshwater budget. Snow depth is also a crucial parameter for sea ice thickness retrieval algorithms from satellite altimetry data. Recent time series of Arctic sea ice volume only use monthly snow depth climatology, which cannot take into account annual changes of the snow depth and its properties. For Antarctic sea ice, no such climatology is available. With a few exceptions, snow depth on sea ice is determined from manual in-situ measurements with very limited coverage of space and time. Hence the need for more consistent observational data sets of snow depth on sea ice is frequently highlighted. Here, we present time series measurements of snow depths on Antarctic sea ice, recorded by an innovative and affordable platform. This Snow Buoy is optimized to autonomously monitor the evolution of snow depth on sea ice and will allow new insights into its seasonality. In addition, the instruments report air temperature and atmospheric pressure directly into different international networks, e.g. the Global Telecommunication System (GTS). We introduce the Snow Buoy concept together with technical specifications and results on data quality, reliability, and performance of the units. We highlight the findings from four buoys, which simultaneously drifted through the Weddell Sea for more than 1.5 years, revealing unique information on characteristic regional and seasonal differences

Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.</jats:p

Online sea ice data platform: www.seaiceportal.de

There is an increasing public interest in sea ice information from both Polar Regions, which requires up-to-date background information and data sets at different levels for various target groups. In order to serve this interest and need, seaiceportal.de (originally: meereisportal.de) was developed as a comprehensive German knowledge platform on sea ice and its snow cover in the Arctic and Antarctic. It was launched in April 2013. Since then, the content and selection of data sets increased and the data portal received increasing attention, also from the international science community. Meanwhile, we are providing near-real time and archive data of many key parameters of sea ice and its snow cover. The data sets result from measurements acquired by various platforms as well as numerical simulations. Satellite observations of sea ice concentration, freeboard, thickness and drift are available as gridded data sets. Sea ice and snow temperatures and thickness as well as atmospheric parameters are available from autonomous platforms (buoys). Additional ship observations, ice station measurements, and mooring time series are compiled as data collections over the last decade. In parallel, we are continuously extending our meta-data and uncertainty information for all data sets. In addition to the data portal, seaiceportal.de provides general comprehensive background information on sea ice and snow as well as expert statements on recent observations and developments. This content is mostly in German in order to complement the various existing international sites for the German speaking public. We will present the portal, its content and function, but we are also asking for direct user feedback

Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records

We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: first, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR &amp; SSM/I &amp; SSMIS or AMSR-E &amp; AMSR2), in the imaging frequency channels (37&thinsp;GHz and either 6 or 19&thinsp;GHz), in their horizontal resolution (25 or 50&thinsp;km), and in the time period they cover. We introduce the underlying algorithms and provide an evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover.</p