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Precipitation is a major component of the hydrologic cycle and plays a significant role in the sea ice mass balance in the polar regions. Over the Southern Ocean, precipitation is particularly uncertain due to the lack of direct observations in this remote and harsh environment. Here we demonstrate that precipitation estimates from eight global reanalyses produce similar spatial patterns between 2000 and 2010, although their annual means vary by about 250 mm yr−1 (or 26% of the median values) and there is little similarity in their representation of interannual variability. ERA-Interim produces the smallest and CFSR produces the largest amount of precipitation overall. Rainfall and snowfall are partitioned in five reanalyses; snowfall suffers from the same issues as the total precipitation comparison, with ERA-Interim producing about 128 mm less snowfall and JRA-55 about 103 mm more rainfall compared to the other reanalyses. When compared to CloudSat-derived snowfall, these five reanalyses indicate similar spatial patterns, but differ in their magnitude. All reanalyses indicate precipitation on nearly every day of the year, with spurious values occurring on an average of about 60 days yr−1, resulting in an accumulation of about 4.5 mm yr−1. While similarities in spatial patterns among the reanalyses suggest a convergence, the large spread in magnitudes points to issues with the background models in adequately reproducing precipitation rates, and the differences in the model physics employed. Further improvements to model physics are required to achieve confidence in precipitation rate, as well as the phase and frequency of precipitation in these products.

Taking stock of our losses Earth's ice sheets are melting and sea levels are rising, so it behooves us to understand better which climate processes are responsible for how much of the mass loss. Smith et al. estimated grounded and floating ice mass change for the Greenland and Antarctic ice sheets from 2003 to 2019 using satellite laser altimetry data from NASA's ICESat and ICESat-2 satellites. They show how changing ice flow, melting, and precipitation affect different regions of ice and estimate that grounded-ice loss averaged close to 320 gigatons per year over that period and contributed 14 millimeters to sea level rise. Science , this issue p. 1239

The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currently configured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean through the accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two (vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection–divergence, blowing snow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis-derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim, and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus median daily snowfall dataset from these reanalyses. The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate–strong correlations and root mean squared errors of ∼ 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

Precipitation over the Arctic Ocean has a significant impact on the basin-scale freshwater and energy budgets but is one of the most poorly constrained variables in atmospheric reanalyses. Precipitation controls the snow cover on sea ice, which impedes the exchange of energy between the ocean and atmosphere, inhibiting sea ice growth. Thus, accurate precipitation amounts are needed to inform sea ice modeling, especially for the production of thickness estimates from satellite altimetry freeboard data. However, obtaining a quantitative estimate of the precipitation distribution in the Arctic is notoriously difficult because of a number of factors, including a lack of reliable, long-term in situ observations; difficulties in remote sensing over sea ice; and model biases in temperature and moisture fields and associated uncertainty of modeled cloud microphysical processes in the polar regions. Here, we compare precipitation estimates over the Arctic Ocean from eight widely used atmospheric reanalyses over the period 2000–16 (nominally the “new Arctic”). We find that the magnitude, frequency, and phase of precipitation vary drastically, although interannual variability is similar. Reanalysis-derived precipitation does not increase with time as expected; however, an increasing trend of higher fractions of liquid precipitation (rainfall) is found. When compared with drifting ice mass balance buoys, three reanalyses (ERA-Interim, MERRA, and NCEP R2) produce realistic magnitudes and temporal agreement with observed precipitation events, while two products [MERRA, version 2 (MERRA-2), and CFSR] show large, implausible magnitudes in precipitation events. All the reanalyses tend to produce overly frequent Arctic precipitation. Future work needs to be undertaken to determine the specific factors in reanalyses that contribute to these discrepancies in the new Arctic.

The potential of deriving snow depth estimates using differences in freeboard heights from CryoSat-2 (CS-2) and ICESat-2 (IS-2) is examined. In our analysis, we use lidar freeboard from the Airborne Topographic Mapper (ATM) on Operation IceBridge (OIB) as proxy of IS-2 total (snow + ice) freeboard. Snow depths are estimates from the OIB snow radar. Differences in height between the total (ATM) and ice (CS-2) freeboards are related to snow depth by the refractive index of the snow layer ( η s ) , which is dependent on snow density. For two years (2014 and 2015), regression of the ATM and CS-2 freeboard differences against OIB snow depth gives correlations of ∼0.80, estimated η s of ∼1.21, and standard errors of ∼8 cm. The resulting refractive index, η s , can be compared to that expected of the Arctic snow cover in early spring (1.25 ± 0.05). The expected biases and variability in the regression analysis are discussed. Results suggest that snow depth can be estimated from the freeboard differences. The benefits of adjusting the orbit of CS-2 for providing more optimized overlaps between IS-2 and CS-2 are considered.

In this study, we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March–May, while the SIC forecasts produce the highest skill in June–August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance. Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.

The ICESat-2 mission will provide routine estimates of sea ice freeboard from profiles of surface heights acquired by its photon-counting lidar: the Advanced Topographic Laser Altimeter System (ATLAS). In this paper, we describe and test procedures devised to separate returns of ice from open water — a crucial step in the estimation of local sea levels for freeboard calculations. The two data sets used in these tests, one each from winter and summer, were acquired by a Multiple Altimeter Beam Experimental Lidar (MABEL) implemented to support pre-launch development of retrieval approaches. Our approach first identifies likely open water returns using surface photon and background count rates as proxy indicators of apparent surface reflectance. Since these measured rates are noisy estimates of expected reflectance, relative surface heights are used to refine the selection of the candidate sea surface samples. Results show that winter freeboard distributions are consistent with expected regional variability, and the nearly identical repeat-track freeboard distributions during summer show retrieval consistency. From coincident lidar coverage, variability of sea level samples identified in the MABEL and Airborne Topographic Mapper (ATM) lidar profiles are 1.6 cm and 2.6 cm, with the overall difference between the distributions at 0.00 ± 0.15 m. This demonstrates the viability of the algorithms. The parameters used in these procedures will serve as a baseline and as a guide for understanding algorithm reliability. It is expected that they will be adjusted post-launch to reflect the on-orbit performance of ATLAS.

Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is $ 5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than $\pm$ 3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models.

NASA's Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission is a decadal survey mission (2016 launch). The mission objectives are to measure land ice elevation, sea ice freeboard, and changes in these variables, as well as to collect measurements over vegetation to facilitate canopy height determination. Two innovative components will characterize the ICESat-2 lidar: 1) collection of elevation data by a multibeam system and 2) application of micropulse lidar (photon-counting) technology. A photon-counting altimeter yields clouds of discrete points, resulting from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of the returned points to reflectors of interest. The objective of this paper is to derive an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2 data, based on airborne observations with a Sigma Space micropulse lidar. The mathematical algorithm uses spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors, and geostatistical classification parameters and hyperparameters. Validation shows that ground and canopy elevation, and hence canopy height, can be expected to be observable with high accuracy by ICESat-2 for all expected beam energies considered for instrument design (93.01%-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp), and 72.85%-98.68% for 0.48 msp). The algorithm derived here is generally applicable for elevation determination from photon-counting lidar altimeter data collected over forested areas, land ice, sea ice, and land surfaces, as well as for cloud detection.

[1] The Arctic sea ice acts as a barrier between the ocean and lower atmosphere, reducing the exchange of heat and moisture. In recent years the ice pack has undergone many changes, in particular a rapid reduction in sea ice extent and compactness in summer and autumn. This, along with modeling studies, would cause one to believe that the moisture flux would be increasing. We estimate the daily moisture flux from 2003 to 2011 using geophysical data from multiple sensors onboard NASA's Aqua satellite, taking advantage of observations being collected at the same time and along the same track. Our findings show the moisture flux, averaged over the entire Arctic, has had large interannual variations, with smallest fluxes in 2010, 2003, and 2004, and largest ones in 2007, 2008, and 2005. Increases in air specific humidity tend to reduce the moisture flux, whereas the decrease in sea ice cover tends to increase the flux. Statistically significant seasonal decreasing trends are seen in December, January, and February because of the dominating effect of increase in 2 m air specific humidity increasing, reducing the surface-air specific humidity difference by −0.0547 kg/kg in the Kara/Barents Seas, E. Greenland Sea, and Baffin Bay regions where there is some open water year round. Our results also show that the contribution of the sea ice zone to the total moisture flux (from the open ocean and sea ice zone) has increased by 3.6% because the amount of open water within the sea ice zone has increased by 4.3%.

[1] Snow depth on sea ice (SD) is a key geophysical variable, knowledge of which is critical for calculating the energy and mass balance budgets. Moreover, accurate knowledge of the SD distribution is important to retrieve sea-ice thicknesses from altimetry data. So far, only space-based microwave radiometers (e.g., Advanced Microwave Scanning Radiometer for Earth Observing System; AMSR-E) provide operational SD on seasonal sea-ice retrievals. A thorough assessment of these retrievals is needed on a large scale and on a variety of sea-ice types. Our study presents such an assessment on Arctic sea ice using NASA's airborne Operation IceBridge (OIB) SDs, retrieved from radar measurements. Between 2009 and 2011, ∼610 12.5 km satellite grid cells were covered by seasonal sea ice where both satellite SD retrievals and OIB data were available. Using all the available data, the difference between the AMSR-E product and the averaged OIB snow-radar-derived SD is 0.00±0.07 m. Satellite-derived SD was accurate in the Beaufort Sea and the Canadian Archipelago but underestimated (∼0.07 m) in the Nares Strait. The RMSE between the two products ranges between 0.03 and 0.15 m. The RMSE is less than 0.06 m over a shallow snow cover ( 0.10 m) have been identified and related to the presence of either low ice concentration or significant fraction of multiyear ice within the grid cell that has not been flagged.

[1] A new satellite passive microwave (PMW) melt onset retrieval algorithm based on temporal variations in the differences of the brightness temperature between 19 and 37 GHz is shown to be as effective as radar (e.g., QuikScat) measurements. The PMW technique shows improved melt estimates that are more closely linked to observed snow-off dates than previous studies. An integrated pan-Arctic (north of 50°N) melt onset date (MOD) dataset is produced by combining estimates on land and sea ice for the entire satellite PMW record. During the 1979–2011 period, significant trends of 2~3 days (decade)−1 to earlier MOD are mainly concentrated over the Eurasian land sector of the Arctic, consistent with changes in spring snow cover extent observed with visible satellite data. The variability and change in melt onset are largely driven by spring surface air temperature, with insignificant influence from low-frequency modes of atmospheric circulation.

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission is currently under development by NASA. The primary mission of ICESat-2 will be to measure elevation changes of the Greenland and Antarctic ice sheets, document changes in sea ice thickness distribution, and derive important information about the current state of the global ice coverage. To make this important measurement, NASA is implementing a new type of satellite-based surface altimetry based on sensing of laser pulses transmitted to, and reflected from, the surface. Because the ICESat-2 measurement approach is different from that used for previous altimeter missions, a high-fidelity aircraft instrument, the Multiple Altimeter Beam Experimental Lidar (MABEL), was developed to demonstrate the measurement concept and provide verification of the ICESat-2 methodology. The MABEL instrument will serve as a prototype for the ICESat-2 mission and also provides a science tool for studies of land surface topography. This paper outlines the science objectives for the ICESat-2 mission, the current measurement concept for ICESat-2, and the instrument concept and preliminary data from MABEL.

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km × 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.

An assessment of the standard Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sea-ice concentrations for the Antarctic winter is made from a comparison of nearly 40 000 AMSR-E sea-ice concentration values with geolocated sea-ice concentrations derived from ten Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) scenes acquired on October 1st and 2nd of 2005 and 2006. The standard AMSR-E sea-ice concentration products are produced using the National Aeronautics and Space Administration Team 2 sea-ice algorithm. The ten MODIS scenes cover portions of almost all the sea-ice regions surrounding the Antarctic continent. The AMSR-E averaged ice concentration biases relative to MODIS (AMSR-E minus MODIS) ranged from less than -0.5% to - 18%, and the corresponding averaged root-mean-square (rms) errors ranged from 2% to 24%. One scene [October 1, 2006 (0550 UT)] had both the largest bias (-18%) and rms error (24%), whereas the other nine scenes had an average bias of - 1.5% and an average rms error of 4.9%. The biases and rms errors are correlated with the fractions of new ice and open water. This is consistent with the findings that the largest errors in ice concentration derived from the AMSR-E occur in the marginal ice zone (MIZ) and along the ice edge and are likely caused by sea-ice flooding in the MIZ and new-ice production at the ice edge.

Satellite and aircraft observations have revealed that remarkable changes in the Earth's 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. NASA's 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-2's 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.

Snow depth (SD) can be retrieved from spaceborne data through linear regression against the microwave brightness temperature difference between 19 and 37 GHz (or similar frequencies). Other methods use snow physical and/or snow electromagnetic (EM) models to estimate SD. Here, we introduce novel retrieval approaches that dynamically combine ancillary SD information (e.g., from snow physical models driven with surface meteorological data) with established algorithms based on regression or EM modeling. The basic idea is to recalibrate regression coefficients (or the effective grain size in the case of EM models) once per week in a simple data assimilation scheme. SD is retrieved from Special Sensor Microwave Imager brightness temperature data and evaluated against in situ observations from 37 stations throughout the Northern Hemisphere. As expected, the SD retrievals perform better with (weekly) ancillary SD inputs from in situ measurements (not used in validation) than with (weekly) ancillary SD inputs from snow physical modeling. The best results are obtained with the regression-based approach using dynamically recalibrated coefficients and ancillary SD inputs from in situ observations (rmse = 6 cm). The regression approach still performs better with the time average of the dynamic coefficients (rmse = 8 cm) than with standard literature values based on climatology ("REGR-CLIM"; rmse = 50 cm). For SD retrieval with an EM model, we obtain results comparable to REGR-CLIM (rmse = 44 cm). Driving the novel regression approaches with SD estimates from snow physical modeling still results in improvements over REGR-CLIM for all approaches (rmse = 15 cm). Comparable SD estimates are obtained from the snow physical model alone.

Continuous monitoring of sea ice and its changes is mainly done by passive microwave sensors on satellites. One frequently used technique of retrieving sea-ice concentrations is the Arctic Radiation and Turbulence Interaction STudy Sea Ice (ASI) algorithm, which uses the near-90-GHz channels, here those of the Advanced Microwave Scanning Radiometer-Earth Observing System to calculate sea-ice concentrations. The ASI ice concentrations are compared with ice concentrations derived from the following: 1) the multispectral imager Enhanced Thematic Mapper Plus operating on Landsat and 2) from Envisat and Radarsat SAR images. In this paper, we focus on marginal ice zones, as the ice concentrations in those regions are in general observed with higher errors. First-year ice (bias: -1%-0% and rms error: 1%-4%) and young ice (bias: -4%-0% and rms error: 3%-9%) are fairly well recognized with little underestimation of ASI ice concentrations with respect to Landsat ice concentrations. New ice is identified with less accuracy by the ASI algorithm (bias: -16%-9% and rms error: 18.3%-26.2%). Averaged over all ice types, the bias ranges between -8.4% and 4.5%, and the rms error ranges between 2.0% and 17.4%. Discrepancies mainly occur in polynya areas (underestimation by ASI) and along the ice edge (overestimation by ASI). The results of the ASI-SAR comparison yield contrasting results. ASI underestimates the ice concentrations near the ice edge but overestimates them in some interior areas (bias: -2.9%-2.5% and rms error: 16.9%-20.1%). The discrepancies between both comparisons may be due to the different interaction mechanisms of the different sensor types, particularly with the newly formed ice.

Surface elevation and roughness measurements from NASA's Ice, Cloud, and land Elevation Satellite (ICESat) are compared with high-resolution airborne laser altimeter measurements over the Arctic sea ice north of Alaska, which were taken during the March 2006 EOS Aqua Advanced Microwave Scanning Radiometer sea ice validation campaign. The comparison of the elevation measurements shows that they agree quite well with correlations of around 0.9 for individual shots and a bias of less than 2 cm. The differences are found to decrease quite rapidly when applying running means. The comparison of the roughness measurements show that there are significant differences between the two data sets, with ICESat generally having higher values. The roughness values are only moderately correlated on an individual-shot basis, but applying running means to the data significantly improves the correlations to as high as 0.9. For the conversion of the elevation measurements into snow-ice freeboard, ocean surface elevation estimates are made with the high-resolution laser altimeter data, as well as several methods using lower resolution ICESat data. Under optimum conditions, i.e., when leads that are larger than the ICESat footprint are present, the ICESat- and Airborne Topographic Mapper-derived freeboards are found to agree to within 2 cm. For other areas, ICESat tends to underestimate the freeboard by up to 9 cm.

An assessment of Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) sea ice concentrations under winter conditions using ice concentrations derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) imagery obtained during the March 2003 Arctic sea ice validation field campaign is presented. The National Oceanic and Atmospheric Administration Environmental Technology Laboratory's Airborne Polarimetric Scanning Radiometer Measurements, which were made from the National Aeronautics and Space Administration P 3B aircraft during the campaign, were used primarily as a diagnostic tool to understand the comparative results and to suggest improvements to the AMSR-E ice concentration algorithm. Based on the AMSR-E/ETM+ comparisons, a good overall agreement with little bias (~1%) for areas of first year and young sea ice was found. Areas of new ice production result in a negative bias of about 5% in the AMSR-E ice concentration retrievals, with a root mean square error of 8%. Some areas of deep snow also resulted in an underestimate of the ice concentration (~10%). For all ice types combined and for the full range of ice concentrations, the bias ranged from 0% to 3%, and the rms errors ranged from 1% to 7%, depending on the region. The new-ice and deep-snow biases are expected to be reduced through an adjustment of the new-ice and ice-type C algorithm tie points

This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate AMSR-E Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea ice algorithms, namely: (1) the National Aeronautics and Space Administration Team (NT) algorithm and (2) the enhanced NT algorithm, as well as the impact of roughness on the AMSR-E snow depth algorithm. Surface snow and ice data collected during the AMSR-Ice03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over- or underestimation of the true amount of sea ice in the pixel area viewed. For example, this paper showed that if the sea ice areas modeled in this paper were assumed to be completely smooth, sea ice concentrations were underestimated by nearly 14% using the NT sea ice algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7, 18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled ice. Using the PR at 10.7 GHz to derive an "effective" viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea ice concentration for both algorithms. The AMSR-E snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and 18.7 GHz to these factors to improve snow depth retrievals from spaceborne passive microwave sensors

An overview of the March 2003 coordinated sea ice field campaign in the Alaskan Arctic is presented with reference to the papers in this special section. This campaign is part of the program to validate the Aqua Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice products. Standard AMSR-E sea ice products include sea ice concentration, sea ice temperature, and snow depth on sea ice. The validation program consists of three elements, namely: 1) satellite data comparisons; 2) coordinated satellite/aircraft/surface measurements; and 3) modeling and sensitivity analyses. Landsat-7 and RADARSAT observations were used in comparative studies with the retrieved AMSR-E sea ice concentrations. The aircraft sensors provided high-resolution microwave imagery of the surface, atmospheric profiles of temperature and humidity, and digital records of sea ice conditions. When combined with in situ measurements, aircraft data were used to validate the AMSR-E sea ice temperature and snow-depth products. The modeling studies helped interpret the field-data comparisons, provided insight on the limitations of the AMSR-E sea ice algorithms, and suggested potential improvements to the AMSR-E retrieval algorithms

Accurate knowledge of snow-depth distribution over sea ice is critical for polar climate studies. Current snow-depth-over-sea-ice retrieval algorithms do not sufficiently account for variations in snow and ice physical properties that can affect the accuracy of retrievals. For this reason, airborne microwave observations were coordinated with ground-based measurements of snow depth and snow properties in the vicinity of Barrow, AK, in March 2003. In this paper, the effects of snowpack properties and ice conditions on microwave signatures are examined using detailed surface-based measurements and airborne observations in conjunction with a thermal microwave-emission model. A comparison of the Microwave Emission Model of Layered Snowpacks (MEMLS) simulations with detailed snowpack and ice data from stakes along the Elson Lagoon and the Beaufort Sea and radiometer data taken from low-level flights using a Polarimetric Scanning Radiometer (PSR-A) shows that MEMLS can be used to simulate snow on sea ice and is a useful tool for understanding the limitations of the snow-depth algorithm. Analysis of radiance data taken over the Elson Lagoon and the Beaufort Sea using MEMLS suggests that the radiometric differences between the two locations are due to the differences in sea-ice emissivity. Furthermore, measured brightness temperatures suggest that the current snow-depth retrieval algorithm is sufficient for areas of smooth first-year sea ice, whereas new algorithm coefficients are needed for rough first-year sea ice. Snowpack grain size and density remain an unresolved issue for snow-depth retrievals using passive-microwave radiances

Ice concentration retrieved from spaceborne passive-microwave observations is a prime input to operational sea-ice-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)-York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate ice concentration from SpecialSensor Microwave/Imager observations. This paper furnishes a comparison between ice concentrations (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian Ice Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total ice concentration by 18.35% and 9.66% concentration counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved concentrations of thin and thick ice are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total ice concentration approaches 100%. If thin and thick ice types coexist in comparable concentrations, the algorithms' estimates agree with CIS's estimates. In terms of ice concentration retrieval, thin ice is more problematic than thick ice. The concept of using a single tie point to represent a thin ice surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total ice concentration in slightly more agreement with CIS's estimates, NT2 provides better agreement in retrieving thin and thick ice concentrations

Snow fall and snow accumulation are key climate parameters due to the snow's 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 Administration's 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 Administration's 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 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 melt period of the Arctic sea ice cover is of particular interest in studies of climate change due to the albedo feedback mechanisms associated with meltponds and openings in the ice pack. The traditionally used satellite passive microwave sea ice concentration algorithms have deficiencies during the summer months due to the period's highly variable surface properties. A newly developed ice concentration algorithm overcomes some of these deficiencies. It corrects for low ice concentration biases caused by surface effects through the use of 85 GHz data in addition to the commonly used 19 and 37 GHz data and, thus, the definition of an additional ice type representing layering and inhomogeneities in the snow layer. This new algorithm will be the standard algorithm for Arctic sea ice concentration retrievals with the EOS Aqua advanced microwave scanning radiometer (AMSR-E) instrument. In this paper, we evaluate the performance of this algorithm for the summer period of 1996 using data from the special sensor microwave imager (SSM/I) which has frequencies similar to the AMSR instrument. The temporal evolution of summertime passive microwave sea ice signatures are investigated and sea ice concentration retrievals from the standard NASA team and the new algorithm are compared. The results show that the introduction of the additional sea ice type in the new algorithm leads to improved summertime sea ice concentrations. The SSM/I sea ice retrievals are validated using SAR-derived ice concentrations that have been convolved with the SSM/I antenna pattern to ensure an appropriate comparison. For the marginal ice zone, with ice concentrations ranging from 40% to 100%, the correlation coefficient of SAR and SSM/I retrievals is 0.66 with a bias of 5% toward higher SAR ice concentrations. For the central Arctic, where ice concentrations varied between 60% and 100%, the correlation coefficient is 0.87 with a negligible bias.

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.

A bulk mixed layer model of the entire Southern Ocean forced by European Centre for Medium-Range Weather Forecasts (ECMWF) near-surface air temperatures and winds is presented. In addition, the Southern Ocean sea ice concentration determined from daily Defense Meteorological Satellite Program special sensor microwave imager (SSM/I) observations obviates the need for a dynamic-thermodynamic sea ice model. The oceanic mixed layer serves as the interface between the deep ocean and the atmosphere or sea ice. Measurements have shown that in the Southern Ocean entrainment of relatively warm deep water into the mixed layer has a significant impact on the oceanic stability and is the primary reason for the thinness of its sea ice cover. The use of satellite-derived ice concentrations reduces greatly the required accuracy in air temperature and wind needed by the model compared to the accuracy needed by coupled sea ice ocean models because these data are only needed here in the calculation of salt and heat fluxes and not to derive ice cover or ice dynamics. The results show that the seasonal cycle and the spatial distribution of mixed layer temperature, salinity, and depth can be reproduced and are in good agreement with in situ observations. Limitations in accuracy result predominantly still from uncertainties in air temperature, cloud cover, and wind speed during the summer months for areas with no or little ice coverage, ice concentration, and snow depth during the winter months as well as ocean currents.

Satellite data were applied to calculate the moisture flux from the North Water polynya during a series of events spanning 2003-2009. The fluxes were calculated using bulk aerodynamic formulas with the stability effects according to the Monin-Obukhov similarity theory. Input parameters were taken from three sources: air relative humidity, air temperature, and surface temperature from the Atmospheric Infrared Sounder (AIRS) onboard NASA's Earth Observing System (EOS) Aqua satellite, sea ice concentration from the Advanced Microwave Scanning Radiometer (AMSR-E, also onboard Aqua), and wind speed from the ECMWF ERA-Interim reanalysis. Our results show the progression of the moisture fluxes from the polynya during each event, as well as their atmospheric effects after the polynya has closed up. These results were compared to results from studies on other polynyas, and fall within one standard deviation of the moisture flux estimates from these studies. Although the estimated moisture fluxes over the entire study region from AIRS are smaller in magnitude than ERA-Interim, they are more accurate due to improved temperature and relative humidity profiles and ice concentration estimates over the polynya. Error estimates were calculated to be 5.56 x10(exp -3) g/sq. m/ s, only 25% of the total moisture flux, thus suggesting that AIRS and AMSR-E can be used with confidence to study smaller scale features in the Arctic sea ice pack and can capture their atmospheric effects. These findings bode well for larger-scale studies of moisture fluxes over the entire Arctic Ocean and the thinning ice pack.

Using recently developed techniques we estimate snow and sea ice thickness distributions for the Arctic basin through the combination of freeboard data from the Ice, Cloud, and land Elevation Satellite (ICESat) and a snow depth model. These data are used with meteorological data and a thermodynamic sea ice model to calculate ocean-atmosphere heat exchange and ice volume production during the 2003-2008 fall and winter seasons. The calculated heat fluxes and ice growth rates are in agreement with previous observations over multiyear ice. In this study, we calculate heat fluxes and ice growth rates for the full distribution of ice thicknesses covering the Arctic basin and determine the impact of ice thickness change on the calculated values. Thinning of the sea ice is observed which greatly increases the 2005-2007 fall period ocean-atmosphere heat fluxes compared to those observed in 2003. Although there was also a decline in sea ice thickness for the winter periods, the winter time heat flux was found to be less impacted by the observed changes in ice thickness. A large increase in the net Arctic ocean-atmosphere heat output is also observed in the fall periods due to changes in the areal coverage of sea ice. The anomalously low sea ice coverage in 2007 led to a net ocean-atmosphere heat output approximately 3 times greater than was observed in previous years and suggests that sea ice losses are now playing a role in increasing surface air temperatures in the Arctic.

Recent satellite lidar measurements of cloud properties spanning a period of five years are used to examine a possible connection between Arctic sea ice amount and polar cloud fraction and vertical distribution. We find an anti-correlation between sea ice extent and cloud fraction with maximum cloudiness occurring over areas with little or no sea ice. We also find that over ice free regions, there is greater low cloud frequency and average optical depth. Most of the optical depth increase is due to the presence of geometrically thicker clouds over water. In addition, our analysis indicates that over the last 5 years, October and March average polar cloud fraction has increased by about 7 and 10 percent, respectively, as year average sea ice extent has decreased by 5 to 7 percent. The observed cloud changes are likely due to a number of effects including, but not limited to, the observed decrease in sea ice extent and thickness. Increasing cloud amount and changes in vertical distribution and optical properties have the potential to affect the radiative balance of the Arctic region by decreasing both the upwelling terrestrial longwave radiation and the downward shortwave solar radiation. Since longwave radiation dominates in the long polar winter, the overall effect of increasing low cloud cover is likely a warming of the Arctic and thus a positive climate feedback, possibly accelerating the melting of Arctic sea ice.

[1] Sea ice conditions and melt season duration within the Canadian Arctic Archipelago (CAA) were investigated from 1979–2008. The CAA is exhibiting statistically significant decreases in average September total sea ice area at −8.7% decade−1. The melt season duration within the CAA is increasing significantly at 7 days decade−1. 2008 represented the longest melt season duration within the CAA over the satellite record at 129 days. Average September multi-year ice (MYI) area is decreasing at −6.4% decade−1 but has yet to reach statistical significance as a result of increasing MYI dynamic import from the Arctic Ocean. Results also find that the Western Parry Channel (WPC) region of the Northwest Passage (NWP) will continue to be susceptible to MYI as the transition to a summer-time sea ice free Arctic continues. The processes responsible for the temporary clearing of the WPC region of the NWP in 2007 were also identified.

[1] Passive microwave snow depth, ice concentration, and ice motion estimates are combined with snowfall from the European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis (ERA-40) from 1979–2001 to estimate the prevalence of snow-to-ice conversion (snow-ice formation) on level sea ice in the Antarctic for April–October. Snow ice is ubiquitous in all regions throughout the growth season. Calculated snow-ice thicknesses fall within the range of estimates from ice core analysis for most regions. However, uncertainties in both this analysis and in situ data limit the usefulness of snow depth and snow-ice production to evaluate the accuracy of ERA-40 snowfall. The East Antarctic is an exception, where calculated snow-ice production exceeds observed ice thickness over wide areas, suggesting that ERA-40 precipitation is too high there. Snow-ice thickness variability is strongly controlled not just by snow accumulation rates, but also by ice divergence. Surprisingly, snow-ice production is largely independent of snow depth, indicating that the latter may be a poor indicator of total snow accumulation. Using the presence of snow-ice formation as a proxy indicator for near-zero freeboard, we examine the possibility of estimating level ice thickness from satellite snow depths. A best estimate for the mean level ice thickness in September is 53 cm, comparing well with 51 cm from ship-based observations. The error is estimated to be 10–20 cm, which is similar to the observed interannual and regional variability. Nevertheless, this is comparable to expected errors for ice thickness determined by satellite altimeters. Improvement in satellite snow depth retrievals would benefit both of these methods.

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.

A new technique is described that detects when and where new snow falls on ice sheets and then determines the thickness of new accumulation. Measurements of vertically polarized passive emission at 85 GHz are filtered with the Hilbert–Huang Transform to identify periods where the surface snow has changed significantly. These are shown to be commonly the result of new snow by comparison with both field observations and in situ instrumentation. Temperature, atmospheric emission and clouds all affect the passive microwave signal but each is examined and shown not to prevent the identification of new snow events. The magnitude of the brightness temperature change is not strongly correlated with snowfall amount. To quantify the amount of new snow, the spatial extent and timing of new snowfalls are examined with ICESat/GLAS laser altimetry data. Crossover differences between altimetric profiles taken before, during, and after the snowfall event provide a measure of the thickness of new snow. Specific cases are presented where 11 and 13 cm of new snow were detected over large regions.

A multidisciplinary, multi-institution team of scientists has been working for over three years to evaluate the performance of sea ice parameter algorithms applied to data from the AMSR-E (Advanced Microwave Scanning Radiometer - EOS) carried aboard NASA's Aqua platform. The AMSR-E data and derived sea ice geophysical products have been compared against a variety of measurements, including ground truth data from an ice field camp, imagery from aerosondes and an aircraft-borne microwave radiometer, and imagery from RADARSAT, MODIS, and AVHRR. Arctic ice environments examined include first-year and multiyear pack ice in the Beaufort and Chukchi Seas, polynyas and flaw leads in the Bering Sea, and the ice edge. This paper will outline the AMSRIce03 project, cover the validation methodology in detail, and discuss the results and their implications for use of sea ice products derived from the AMSR-E.

For enhanced accuracy in the determination of heat fluxes in the polar regions, estimates of ice thickness are necessary. Only passive microwave sensors provide daily global surface information, but ice thickness is not directly measurable from this data source. Nevertheless, different ice types, associated with a certain thickness, are distinguishable resulting from different emissivities. Two methods using SSM/I data for this discrimination are compared with satellite infrared data and aircraft measurements. The SSM/I results are in good agreement with those data. Therefore, either method should greatly improve heat flux calculations.

The focus of this paper is on the representation of Antarctic coastal polynyas in global ice-ocean general circulation models (OGCMs), in particular their local, regional, and high-frequency behavior. This is verified with the aid of daily ice concentration derived from satellite passive microwave data using the NASATeam 2 (NT2) and the bootstrap (BS) algorithms. Large systematic regional and temporal discrepancies arise, some of which are related to the type of convection parameterization used in the model. An attempt is made to improve the fresh-water flux associated with melting and freezing in Antarctic coastal polynyas by ingesting (assimilating) satellite ice concentration where it comes to determining the thermodynamics of the open-water fraction of a model grid cell. Since the NT2 coastal open-water fraction (polynyas) tends to be less extensive than the simulated one in the decisive season and region, assimilating NT2 coastal ice concentration yields overall reduced net freezing rates, smaller formation rates of Antarctic Bottom Water, and a stronger southward flow of North Atlantic Deep Water across 30 S. Enhanced net freezing rates occur regionally when NT2 coastal ice concentration is assimilated, concomitant with a more realistic ice thickness distribution and accumulation of High-Salinity Shelf Water. Assimilating BS rather than NT2 coastal ice concentration, the differences to the non-assimilated simulation are generally smaller and of opposite sign. This suggests that the model reproduces coastal ice concentration in closer agreement with the BS data than with the NT2 data, while more realistic features emerge when NT2 data are assimilated.

Open water areas within the sea ice (polynyas) are sources of intense heat exchange between the ocean and the atmosphere. In this paper, we used microwave and visible/infrared satellite data together with a sea ice model to investigate the polynya opening mechanisms. The satellite data and the model show significant agreement and prove that tides play an active role in the polynya dynamics.

Knowledge of the atmospheric temperature profile is of critical importance for climatological and meteorological studies. For the polar regions, especially in the southern hemisphere, in-situ measurements from radiosonde are extremely sparse. Only satellite measurements can provide this information with good spatial coverage. The Special Sensor Microwave Temperature sounder (SSM/T1) on the polar orbiting satellite in the Defense Meteorological Satellite Program (DMSP) is well suited to derive temperature profiles continuously. Operationally-used retrieval methods account for the influence of the surface by utilizing only the surface channel, and significant retrieval errors can appear in the lower troposphere. In polar regions the surface conditions are complex with mixtures of open water and different types of ice, which possess different emissivities and coverage in the footprints of SSM/T1. The authors have combined SSM/T1 data with coincident data from the Special Sensor Microwave Imager (SSM/I) and the Operational Linescan System (OLS) to get surface information, specifically ice concentration and surface temperature, from which the emissivities of open water and two ice types at a frequency of 50.5 GHz are estimated. The temperature retrieval algorithm is based on the constrained linear inversion method. Temperature profiles are retrieved for the Weddell Sea region during July 1992, and compared to radiosonde data from the German research vessel Polarstern. The results show that multisensor retrieval on the average gives a 1 K (rms) improvement in the lower troposphere (h

As a part of the NASA Aqua satellite validation program the NOAA Environmental Research Laboratory's Polarimetric Scanning Radiometer (PSR) was flown on a Navy P-3 aircraft in June-July of 2000. Several flights over melting ice floes in Baffin Bay and Melville Sound were performed with the aim of imaging microwave brightness temperatures at high spatial resolution and under a variety of ice melt states. Conditions observed range from open water to completely frozen ice pack, including melting ponds. Two PSR scanheads were used during the experiment: (1) the PSR/C scanhead provided a C-band microwave radiometer with vertically and horizontally polarized channels at 6.00, 6.50, and 7.325 GHz and a fully polarimetric channel at the AMSR band of 6.92 GHz, (2) the PSR/A scanhead provided fully polarimetric channels at 10.7 and 18.7 GHz, tri-polarimetric channels at 37 and 89 GHz, and a dual-polarimetric channel at 21.5 GHz. Both scanheads provided an infrared radiometer operating from 9.5-11 /spl mu/m. Using the two scanheads all AMSR-E frequencies were covered. In addition, L-band data was obtained using the Scanning Low-Frequency Microwave Radiometer (SLFMR). Results from the calibration of the PSR brightness imagery are presented for both ice and water. These results from PSR and SLFMR show an excellent ability of high-resolution microwave imagery to distinguish melting ice from water, and provide a means of determining the fractal structure of sea ice coverage.

Given the importance of the relatively long-term passive microwave record for climate studies, we evaluated the sensitivity of passive microwave brightness temperatures and corresponding sea ice concentrations to variations in surface and atmospheric conditions. Based on model results, this effort allows sources and magnitudes of error in the available algorithms and the existing passive, microwave derived time series or sea ice concentrations to be better quantified.

[1] The surface energy budget of coastal polynyas in the southern Weddell Sea has been evaluated for the period 1992–1998 using a combination of satellite observations, meteorological data, and simple physical models. The study focuses on polynyas that habitually form off the Ronne Ice Shelf. The coastal polynya areal data are derived from an advanced multichannel polynya detection algorithm applied to passive microwave brightness temperatures. The surface sensible and latent heat fluxes are calculated via a fetch-dependent model of the convective-thermal internal boundary layer. The radiative fluxes are calculated using well-established empirical formulae and an innovative cloud model. Standard meteorological variables that are required for the flux calculations are taken from automatic weather stations and from the National Centers for Environmental Production/National Center for Atmospheric Research reanalyses. The 7 year surface energy budget shows an overall oceanic warming due to the presence of coastal polynyas. For most of the period the summertime oceanic warming, due to the absorption of shortwave radiation, is approximately in balance with the wintertime oceanic cooling. However, the anomalously large summertime polynya of 1997–1998 allowed a large oceanic warming of the region. Wintertime freezing seasons are characterized by episodes of high heat fluxes interspersed with more quiescent periods and controlled by coastal polynya dynamics. The high heat fluxes are primarily due to the sensible heat flux component, with smaller complementary latent and radiative flux components. The average freezing season area-integrated energy exchange is 3.48 × 1019 J, with contributions of 63, 22, and 15% from the sensible, latent, and radiative components, respectively. The average melting season area-integrated energy exchange is −5.31 × 1019 J, almost entirely due to the radiative component. There is considerable interannual variability in the surface energy budget. The standard deviation of the energy exchange during the freezing (melting) season is 28% (95%) of the mean. During the freezing season, positive surface heat fluxes are equated with ice production rates. The average annual coastal polynya ice production is 1.11 × 1011 m3 (or 24 m per unit area), with a range from 0.71 × 1011 (in 1994) to 1.55 × 1011 m3 (in 1995). This can be compared to the estimated total ice production for the entire Weddell Sea: on average the coastal polynya ice production makes up 6.08% of the total, with a range from 3.65 (in 1994) to 9.11% (in 1995).

[1] Satellite imagery has been used to document for the first time the potential for large icebergs to substantially alter the dynamics of a marine ecosystem. The B-15 iceberg (∼10,000 km 2 ), which calved off the Ross Ice Shelf in the biologically productive southwestern Ross Sea, Antarctica, restricted the normal drift of pack ice, resulting in heavier spring/summer pack ice cover than previously recorded. Extensive ice cover reduced both the area suitable for phytoplankton growth and the length of the algal growing season. Consequently, primary productivity throughout the region was >40% below normal, which changed both the abundance and behavior of upper trophic level organisms.