Thorsten Markus

Changes in Arctic melt season and implications for sea ice loss

The Arctic-wide melt season has lengthened at a rate of 5 days decade−1 from 1979 to 2013, dominated by later autumn freezeup within the Kara, Laptev, East Siberian, Chukchi, and Beaufort seas between 6 and 11 days decade−1. While melt onset trends are generally smaller, the timing of melt onset has a large influence on the total amount of solar energy absorbed during summer. The additional heat stored in the upper ocean of approximately 752 MJ m−2 during the last decade increases sea surface temperatures by 0.5 to 1.5 °C and largely explains the observed delays in autumn freezeup within the Arctic Oceans adjacent seas. Cumulative anomalies in total absorbed solar radiation from May through September for the most recent pentad locally exceed 300–400 MJ m−2 in the Beaufort, Chukchi, and East Siberian seas. This extra solar energy is equivalent to melting 0.97 to 1.3 m of ice during the summer.

An enhancement of the NASA Team sea ice algorithm

An enhancement of the NASA Team sea ice concentration algorithm overcomes the problem of a low ice concentration bias associated with surface snow effects that are particularly apparent in Southern Ocean sea ice retrievals. The algorithm has the same functional form as the NASA Team algorithm, but uses a wider range of frequencies (19-85 GHz). It accommodates ice temperature variability through the use of radiance ratios as in the original NASA Team algorithm, and has the added advantage of providing weather-corrected sea ice concentrations through the utilization of a forward atmospheric radiative transfer model. Retrievals of sea ice concentration with this new algorithm for both the Arctic and Antarctic do not reveal the deficiencies present in either the NASA Team or Bootstrap algorithms. Furthermore, quantitative comparisons with infrared AVHRR data show that the enhanced algorithm provides more accurate ice concentrations with much less bias than the other two algorithms.

Sea ice concentration, ice temperature, and snow depth using AMSR-E data

A summary of the theoretical basis and initial performance of the algorithms that are used to derive sea ice concentration, ice temperature, and snow depth on sea ice from newly acquired Earth Observing System-Aqua/Advanced Microwave Scanning Radiometer-EOS (AMSR-E) radiances is presented. The algorithms have been developed and tested using historical satellite passive microwave data and are expected to provide more accurate products, since they are designed to take advantage of the wider range of frequencies and higher spatial resolution of the AMSR-E microwave instrument. Validation programs involving coordinated satellite, aircraft, and surface measurements to determine the accuracies of these sea ice products and to improve further our capability to monitor global sea ice are currently underway.

The ICESat-2 Laser Altimetry Mission

Satellite and aircraft observations have revealed that remarkable changes in the Earths polar ice cover have occurred in the last decade. The impacts of these changes, which include dramatic ice loss from ice sheets and rapid declines in Arctic sea ice, could be quite large in terms of sea level rise and global climate. NASAs Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), currently planned for launch in 2015, is specifically intended to quantify the amount of change in ice sheets and sea ice and provide key insights into their behavior. It will achieve these objectives through the use of precise laser measurements of surface elevation, building on the groundbreaking capabilities of its predecessor, the Ice Cloud and Land Elevation Satellite (ICESat). In particular, ICESat-2 will measure the temporal and spatial character of ice sheet elevation change to enable assessment of ice sheet mass balance and examination of the underlying mechanisms that control it. The precision of ICESat-2s elevation measurement will also allow for accurate measurements of sea ice freeboard height, from which sea ice thickness and its temporal changes can be estimated. ICESat-2 will provide important information on other components of the Earth System as well, most notably large-scale vegetation biomass estimates through the measurement of vegetation canopy height. When combined with the original ICESat observations, ICESat-2 will provide ice change measurements across more than a 15-year time span. Its significantly improved laser system will also provide observations with much greater spatial resolution, temporal resolution, and accuracy than has ever been possible before.

A method to estimate subpixel-scale coastal polynyas with satellite passive microwave data

The importance of Antarctic coastal polynyas for heat exchange between ocean and atmosphere, for high ice production, and thus, with the resultant brine rejection, for a large amount of the Antarctic bottom water is widely recognized. To obtain full understanding of their influence, continuous measurement of even small polynyas is necessary. Only passive microwave sensors, with their global coverage and their ability to penetrate cloud cover, can provide this information. However, because of their coarse resolution, a special method to estimate the area of subpixel-scale coastal polynyas has been developed. It uses 85- and 37-GHz data successively in order to take full advantage of the higher resolution at 85 GHz while compensating for its sensitivity to atmospheric effects with the 37-GHz data. This method is based on simulating microwave images of polynya events by convolving an assumed brightness temperature distribution with the satellite antenna pattern. These images are compared with measured microwave data and the polynya area iteratively modified until best agreement is found. Application of the method to synthetic images produces a maximum error of 200 km2 but a mean error of 80 km2. The method shows distinct improvement over the more traditional, i.e., ice concentration, methods. Analysis of coincident infrared data indicate that ice with a thickness up to 0.06 m is included in the estimated open water area. Area time series derived with the method for a coastal polynya near Halley Bay show day-to-day changes from 50 km2 to 450 km2 during austral spring. Interpretation of these results in terms of wind forcing and ice growth in polynyas is aided by comparison with a one-dimensional model of polynya development.

Seasonal evolution and interannual variability of the local solar energy absorbed by the Arctic sea ice–ocean system

The melt season of the Arctic sea ice cover is greatly affected by the partitioning of the incident solar radiation between reflection to the atmosphere and absorption in the ice and ocean. This partitioning exhibits a strong seasonal cycle and significant interannual variability. Data in the period 1998, 2000-2004 were analyzed in this study. Observations made during the 1997-1998 SHEBA (Surface HEat Budget of the Arctic Ocean) field experiment showed a strong seasonal dependence of the partitioning, dominated by a five-phase albedo evolution. QuikSCAT scatterometer data from the SHEBA region in 1999-2004 were used to further investigate solar partitioning in summer. The time series of scatterometer data were used to determine the onset of melt and the beginning of freezeup. This information was combined with SSM/I-derived ice concentration, TOVS-based estimates of incident solar irradiance, and SHEBA results to estimate the amount of solar energy absorbed in the ice-ocean system for these years. The average total solar energy absorbed in the ice-ocean system from April through September was 900 MJ m(sup -2). There was considerable interannual variability, with a range of 826 to 1044 MJ m(sup -2). The total amount of solar energy absorbed by the ice and ocean was strongly related to the date of melt onset, but only weakly related to the total duration of the melt season or the onset of freezeup. The timing of melt onset is significant because the incident solar energy is large and a change at this time propagates through the entire melt season, affecting the albedo every day throughout melt and freezeup.

Solar partitioning in a changing Arctic sea-ice cover

Abstract The summer extent of the Arctic sea-ice cover has decreased in recent decades and there have been alterations in the timing and duration of the summer melt season. These changes in ice conditions have affected the partitioning of solar radiation in the Arctic atmosphere–ice–ocean system. the impact of sea-ice changes on solar partitioning is examined on a pan-Arctic scale using a 25 km × 25 km Equal-Area Scalable Earth Grid for the years 1979–2007. Daily values of incident solar irradiance are obtained from NCEP reanalysis products adjusted by ERA-40, and ice concentrations are determined from passive microwave satellite data. the albedo of the ice is parameterized by a five-stage process that includes dry snow, melting snow, melt pond formation, melt pond evolution, and freeze-up. the timing of these stages is governed by the onset dates of summer melt and fall freeze-up, which are determined from satellite observations. Trends of solar heat input to the ice were mixed, with increases due to longer melt seasons and decreases due to reduced ice concentration. Results indicate a general trend of increasing solar heat input to the Arctic ice–ocean system due to declines in albedo induced by decreases in ice concentration and longer melt seasons. the evolution of sea-ice albedo, and hence the total solar heating of the ice–ocean system, is more sensitive to the date of melt onset than the date of fall freeze-up. the largest increases in total annual solar heat input from 1979 to 2007, averaging as much as 4%a–1, occurred in the Chukchi Sea region. the contribution of solar heat to the ocean is increasing faster than the contribution to the ice due to the loss of sea ice.

Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the Antarctic Ocean

Abstract Antarctic coastal polynyas are important areas of high sea ice production and dense water formation, and thus their detection including an estimate of thin ice thickness is essential. In this paper, the authors propose an algorithm that estimates thin ice thickness and detects fast ice using Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) data in the Antarctic Ocean. Detection and estimation of sea ice thicknesses of <0.2 m are based on the SSM/I 85- and 37-GHz polarization ratios (PR85 and PR37) through a comparison with sea ice thicknesses estimated from the Advanced Very High Resolution Radiometer (AVHRR) data. The exclusion of data affected by atmospheric water vapor is discussed. Because thin ice and fast ice (specifically ice shelves, glacier tongues, icebergs, and landfast ice) have similar PR signatures, a scheme was developed to separate these two surface types before the application of the thin ice algorithm to coastal polynyas. The probability th...

Sensitivity of passive microwave snow depth retrievals to weather effects and snow evolution

Snow fall and snow accumulation are key climate parameters due to the snows high albedo, its thermal insulation, and its importance to the global water cycle. Satellite passive microwave radiometers currently provide the only means for the retrieval of snow depth and/or snow water equivalent (SWE) over land as well as over sea ice from space. All algorithms make use of the frequency-dependent amount of scattering of snow over a high-emissivity surface. Specifically, the difference between 37- and 19-GHz brightness temperatures is used to determine the depth of the snow or the SWE. With the availability of the Advanced Microwave Scanning Radiometer (AMSR-E) on the National Aeronautics and Space Administrations Earth Observing System Aqua satellite (launched in May 2002), a wider range of frequencies can be utilized. In this study we investigate, using model simulations, how snow depth retrievals are affected by the evolution of the physical properties of the snow (mainly grain size growth and densification), how they are affected by variations in atmospheric conditions and, finally, how the additional channels may help to reduce errors in passive microwave snow retrievals. The sensitivity of snow depth retrievals to atmospheric water vapor is confirmed through the comparison with precipitable water retrievals from the National Oceanic and Atmospheric Administrations Advanced Microwave Sounding Unit (AMSU-B). The results suggest that a combination of the 10-, 19-, 37-, and 89-GHz channels may significantly improve retrieval accuracy. Additionally, the development of a multisensor algorithm utilizing AMSR-E and AMSU-B data may help to obtain weather-corrected snow retrievals.

A First Assessment of IceBridge Snow and Ice Thickness Data Over Arctic Sea Ice

We present a first assessment of airborne laser and radar altimeter data over snow-covered sea ice, gathered during the National Aeronautics and Space Administration Operation IceBridge Mission. We describe a new technique designed to process radar echograms from the University of Kansas snow radar to estimate snow depth. We combine IceBridge laser altimetry with radar-derived snow depths to determine sea ice thickness. Results are validated through comparison with direct measurements of snow and ice thickness collected in situ at the Danish GreenArc 2009 sea ice camp located on fast ice north of Greenland. The IceBridge instrument suite provides accurate measurements of snow and ice thickness, particularly over level ice. Mean IceBridge snow and ice thickness agree with in situ measurements to within ~ 0.01 and ~ 0.05 m, respectively, while modal snow and ice thickness estimates agree to within 0.02 and 0.10 m, respectively. IceBridge snow depths were correlated with in situ measurements (R = 0.7, for an averaging length of 55 m). The uncertainty associated with the derived IceBridge sea ice thickness estimates is 0.40 m. The results demonstrate the retrieval of both first-year and multiyear ice thickness from IceBridge data. The airborne data were however compromised in heavily ridged ice where snow depth, and hence ice thickness, could not be measured. Techniques developed as part of this study will be used for routine processing of IceBridge retrievals over Arctic sea ice. The limitations of the GreenArc study are discussed, and recommendations for future validation of airborne measurements via field activities are provided.