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The objective of the present work is to characterize the temporal evolution of the electromagnetic signatures of sea ice from initial formation through the development of first-year ice on the basis of the temporal variations in the physical properties of the ice.
A network of autonomous, ice-tethered buoys was deployed around the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) experiment in late September 2019 for a year-long drift in the Arctic Transpolar Drift Stream. The buoys were deployed as part of the MOSAiC distributed network (DN) which included 12 multi-instrumented ice stations and an additional 116 GPS buoys distributed primarily within a 40 km radius of the MOSAiC Central Observatory. Buoy coverage within the DN was maintained with additional deployments throughout the year-long drift allowing for collection of data over a full sea ice growth and melt cycle. All GPS position data from buoys deployed within the DN have been assembled and processed into the collection of 216 quality-controlled buoy drift tracks presented in this dataset covering the period 26 September 2019 – 23 May 2021. The drift tracks in this collection are ideal for studies of dynamic sea ice motion around the MOSAiC experiment at cascading spatial scales ranging from 100s of meters to 100s of km.
In order to demonstrate the feasibility of the microwave radiometers to be carried aboard the Nimbus 5 and 6 satellites and proposed for one of the earth observatory satellites, remote measurements of microwave radiation at wavelengths ranging from 0.8 to 21 cm have been made of a variety of the earth's surfaces from the NASA CV-990 A/C. Brightness temperatures of sea water surfaces of varying roughness, of terrain with varying soil moisture, and of sea ice of varying structure were observed. In each case, around truth information was available for correlation with the microwave brightness temperature. The utility of passive microwave radiometry in determining ocean surface wind speeds, at least for values higher than 7 meters/second has been demonstrated. In addition, it was shown that radiometric signatures can be used to determine soil moisture in unvegetated terrain to within five percentage points by weight. Finally, it was demonstrated that first year thick, multi-year, and first year thin sea ice can be distinguished by observing their differing microwave emissivities at various wavelengths.
Intensification of global warming in recent decades has caused a rise of interest in year-to-year and decadal-scale climate variability in the Arctic. This is because the Arctic is believed to be one of the most sensitive and vulnerable regions to climatic changes. For over two decades satellite passive microwave observations have been utilized to continuously monitor the Arctic environment. Derived parameters include sea ice cover, snow cover and snow water equivalent over land, and Greenland melt extent and length of melt season. Most studies have primarily concentrated on trends and variations of individual variables. In this study we investigated how variations in sea ice cover, Greenland surface melt, and boreal snow cover are correlated. This was done on hemispheric as well as on regional scales. Latest results will be presented including data from the summer of 2004.
Results are presented on surface-based multifrequency passive microwave observations of multiyear (MY) sea ice in the eastern Arctic Basin, the Beaufort Sea, the Canadian archipelago, and the northern Greenland Sea. The analyses of these data show that the magnitude of the spectral gradient of emissivity is directly related to the existence and the thickness of a decomposed surface ice layer with very high porosity. Spectra for melt ponds with a frozen surface layer closely resembled those of lake ice and showed a positive spectral gradient. The variance among emissivity spectra for MY ice was caused primarily by the distributions of melt ponds and by the presence of significant amounts of scattering inhomogeneities in the snow and the upper 20-30-cm layer of the ice.
Variations in the extent and concentration of sea ice cover on the Southern Ocean are described for the three-year period 1973-75 using information derived from the Nimbus-5 passive microwave imager.
The characteristics of sea ice models, developed to study the physics of the growth and melt of ice at the ocean surface and the variations in ice extent, depend on the size of the time step. Thus, to study longer-term variations within a reasonable computer budget, a model with a scheme allowing longer time steps has been constructed. However, the results produced by the model can definitely depend on the length of the time step. The sensitivity of a model to time-step size can be reduced by appropriate approaches. The present investigation is concerned with experiments which use a formulation of a lead parameterization that can be considered as a first step toward the development of a lead parameterization suitable for a use in long-term climate studies.
Sea ice remote sensing and estimation of concentrations of each of several ice types from passive microwave satellite data is described. The approach is based on the Kalman filter; it incorporates surface temperature, ice advection, and ice deformation data derived from drifting buoys and uses the whole temporal microwave record to make a smoothed estimate of ice concentration. The method allows resolution of previously ambiguous surface types. An example using time histories of two SMMR measurements to resolve the fractional areas of four surface types: open water, first-year, second-year and older multiyear ice is shown.
The seasonal cycle of surface albedo of sea ice in the Arctic is estimated from measurements made with the Advanced Very High Resolution Radiometer (AVHRR) on the polar-orbiting satellites NOAA-10 and NOAA-11. The albedos of 145 200-km-square cells are analyzed. The cells are from March through September 1989 and include only those for which the sun is more than 10 deg above the horizon. Cloud masking is performed manually. Corrections are applied for instrument calibration, nonisotropic reflection, atmospheric interference, narrowband to broadband conversion, and normalization to a common solar zenith angle. The estimated albedos are relative, with the instrument gain set to give an albedo of 0.80 for ice floes in March and April. The mean values for the cloud-free portions of individual cells range from 0.18 to 0.91. Monthly averages of cells in the central Arctic range from 0.76 in April to 0.47 in August. The monthly averages of the within-cell standard deviations in the central Arctic are 0.04 in April and 0.06 in September. The surface albedo and surface temperature are correlated most strongly in March (R = -0.77) with little correlation in the summer. The monthly average lead fraction is determined from the mean potential open water, a scaled representation of the temperature or albedo between 0.0 (for ice) and 1.0 (for water); in the central Arctic it rises from an average 0.025 in the spring to 0.06 in September. Sparse data on aerosols, ozone, and water vapor in the atmospheric column contribute uncertainties to instantaneous, area-average albedos of 0.13, 0.04, and 0.08. Uncertainties in monthly average albedos are not this large. Contemporaneous estimation of these variables could reduce the uncertainty in the estimated albedo considerably. The poor calibration of AVHRR channels 1 and 2 is another large impediment to making accurate albedo estimates.
Comparison of polar sea ice results from 11 major global climate models and satellite-derived observations for 1979-2004 reveals that each of the models is simulating seasonal cycles that are phased at least approximately correctly in both hemispheres. Each is also simulating various key aspects of the observed ice cover distributions, such as winter ice not only throughout the central Arctic basin but also throughout Hudson Bay, despite its relatively low latitudes. However, some of the models simulate too much ice, others too little ice (in some cases varying depending on hemisphere and/or season), and some match the observations better in one season versus another. Several models do noticeably better in the Northern Hemisphere than in the Southern Hemisphere, and one does noticeably better in the Southern Hemisphere. In the Northern Hemisphere all simulate monthly average ice extents to within +/-5.1 x 10(exp 6)sq km of the observed ice extent throughout the year; and in the Southern Hemisphere all except one simulate the monthly averages to within +/-6.3 x 10(exp 6) sq km of the observed values. All the models properly simulate a lack of winter ice to the west of Norway; however, most do not obtain as much absence of ice immediately north of Norway as the observations show, suggesting an under simulation of the North Atlantic Current. The spread in monthly averaged ice extents amongst the 11 model simulations is greater in the Southern Hemisphere than in the Northern Hemisphere and greatest in the Southern Hemisphere winter and spring.
The inter-relationship between subtropical western–central Pacific sea surface temperatures (STWCPSST), sea ice concentrations in the Beaufort Sea (SICBS), and the North Atlantic Oscillation (NAO) in summer are investigated over the period 1980–2016. It is shown that the Arctic response to the remote impact of the Pacific SST is more dominant in recent summers, leading to a frequent occurrence of the negative phase of the NAO following the STWCPSST increase. Lag–correlations of STWCPSST positive (negative) anomalies in spring with the negative (positive) NAO and SICBS loss (recovery) in summer have increased over the last two decades, reaching r = 0.4–0.5 with significance at the 5 percent level. Both observations and the atmospheric general circulation model experiments suggest that the positive STWCPSST anomaly and subsequent planetary-scale wave propagation act to increase the Arctic upper-level geopotential heights and temperatures in the following season. This response extends to Greenland, providing favorable conditions for developing the negative phase of the NAO. Connected with this atmospheric response, SIC and surface albedo decrease with an increase in the surface net shortwave flux over the Beaufort Sea. Examination of the surface energy balance (radiative and turbulent fluxes) reveals that surplus energy that can heat the surface increases over the Arctic, enhancing the SIC reduction.
Measurements from microwave sounders and imagers provide a valuable source of information including atmospheric temperature and water vapor in Numerical Weather Prediction (NWP) systems that assimilate these observations directly over water surfaces (oceans and other large water bodies). In a recent decadal survey, targeted observables in the Planetary Boundary Layer (PBL) were cited as a key need for future observations (NASEM, 2018). Microwave observations which sense in the PBL are currently available, however, utilizing surface-sensitive microwave observations for atmospheric data assimilation remains a challenge over land, snow and sea ice. This is in part due to the inability of surface emissivity models used by NWP data assimilation systems to simulate observations with sufficient accuracy. The GEOS-ADAS (Todling and el Akkraoui, 2018) which utilizes the Community Radiative Transfer Model (CRTM) (Han, 2006; Chen 2009) is no exception. The ECMWF system has retrieved instantaneous surface emissivity from surface-sensitive channels for SSMI/S and MHS radiance observations, and apply these estimates to the closest channels higher in frequency (Baordo and Geer 2016) in the calculation of simulated radiances. This approach currently is also being tested in the GEOS-ADAS for AMSU-A and ATMS radiances (Zhu et al. 2021). No or minimal emissivity spectral variability has been assumed in the above-mentioned studies. Recently, work by Munchak et al., 2020 (hereby referred to as M2020) provided a new database for emissivity over land, snow and sea ice retrieved from the NASA Global Precipitation Mission (GPM). Compared with Tool to Estimate Land Surface Emissivities at Microwave (TELSEM2; Wang et al., 2017), M2020 provides emissivities for more frequencies(i.e., 10.7 GHz V/H). Moreover, this database is unique in that it utilizes both active and passive data to retrieve surface emissivity and normalized radar cross section. While the emissivity values may be useful for other sensors, they are most applicable to the GPM Microwave Imager (GMI). In this work the GEOS-ADAS is modified to utilize emissivity values from Munchak et al, 2020 in place of values used by CRTM. Presently, only GMI radiances over ocean are used in the operational GEOS-ADAS. This study will focus on the GMI radiances over land, snow, and ice, as a first attempt to evaluate GMI radiances over these non-water surface types. Two cases are then presented, one with one week of observation minus background departures using the modified GEOS-ADAS, and one utilizing the original GEOS-ADAS. It should be noted that the surface emissivity models in CRTM are not state of the art and are scheduled to be replaced by the Community Surface Emissivity Module (CSEM; Chen and Weng, 2016). Simulations using default CRTM emissivity values are used merely as reference comparing against M2020, and is not a thorough comparison against other more state of the art modules such as CSEM.
Clouds interfere with the distribution of short-wave and long-wave radiations over sea ice, and thereby strongly affect the surface energy balance in polar regions.
The RADARSAT synthetic aperature radar is curently providing repeat surveys of the artic ocean sea ice on a near 3-day basis with its wide-swath ScanSAR mode.
Active microwave measurements were made of various sea ice forms in March and April 1987 during the Marginal Ice Zone Experiment, at 1, 5, 10, 18, and 35 GHz using a synthetic aperture radar (SAR) and helicopter and ship-based scatterometers. The X-band (9.8 GHz) SAR data were compared to the scatterometer data and it was determined that for 5 GHz and higher frequencies both the SAR and scatterometers can differentiate open water, new ice (5 to 30 cm), first-year ice with rubble (0.60 -1.5 m), and multiyear ice. The analysis further confirmed that the C-band (5 GHz) SAR's flying on ESA ERS-1 and Radarsat will differentiate the mentioned ice types.
From November 1978 through December 1996, the areal extent of sea ice decreased by 2.9 +/- 0.4 percent per decade in the Arctic and increased by 1.3 +/- 0.2 percent per decade in the Antarctic. The observed hemispheric asymmetry in these trends is consistent with a modeled response to a carbon dioxide-induced climate warming. The interannual variations, which are 2.3 percent of the annual mean in the Arctic, with a predominant period of about 5 years, and 3.4 percent of the annual mean in the Antarctic, with a predominant period of about 3 years, are uncorrelated.
The author has identified the following significant results. The results of the analysis of data collected during the spring and summer demonstrate that ERTS-1 imagery has a high potential for monitoring arctic sea ice conditions during the time for maximum ice extent through ice-breakup season. In the eastern Beaufort Sea area, the combination of ERTS-1 orbital overlap and a high incidence of cloud-free conditions during the spring assures a high frequency of repetitive satellite coverage. In the mid-Beaufort Sea, numerous fractures and leads can be identified, even in the early spring data. Ice features that can be identified include: development of fractures leading to the formation of distinct ice floes; growth and deterioration of leads; evidence of shearing movements of ice masses; formation of new grey ice within leads; distinction between grey, grey-white, and older forms of ice; and the deterioration of the ice surface evidenced by the formation of puddles, thaw holes, and drainage patterns. Ice conditions in the Bering Sea near St. Lawrence Island reported by aircraft observers participating in the Bering Sea Expedition are in close agreement with the ice conditions mapped from the corresponding ERTS-1 imagery. Ice features identified were: boundaries between grey ice and first year ice, shear leads, and occurrence of open water.
The inter-relationship between subtropical western-central Pacific sea surface temperatures (STWCPSST), sea ice concentration in the Beaufort Sea (SICBS), and the North Atlantic Oscillation (NAO) are investigated for the last 37 summers and winters (1980-2016). Lag-correlation of the STWCPSST×(-1) in spring with the NAO phase and SICBS in summer increases over the last two decades, reaching r = 0.4-0.5 with significance at 5 percent, while winter has strong correlations in approximately 1985-2005. Observational analysis and the atmospheric general circulation model experiments both suggest that STWCPSST warming acts to increase the Arctic geopotential height and temperature in the following season. This atmospheric response extends to Greenland, providing favorable conditions for developing the negative phase of the NAO. SIC and surface albedo tend to decrease over the Beaufort Sea in summer, linked to the positive surface net shortwave flux. Energy balance considering radiative and turbulent fluxes reveal that available energy that can heat surface is larger over the Arctic and Greenland and smaller over the south of Greenland, in response to the STWCPSST warming in spring. XXXX Arctic & Atlantic: Positive upper-level height/T anomaly over the Arctic and Greenland, and a negative anomaly over the central-eastern Atlantic, resembling the (-) phase of the NAO. Pacific: The negative height/T anomaly over the mid-latitudes, along with the positive anomaly over the STWCP, where 1degC warming above climatology is prescribed. Discussion: It is likely that the Arctic gets warm and the NAO is in the negative phase in response to the STWCP warming. But, there are other factors (e.g., internal variability) that contribute to determination of the NAO phase: not always the negative phase of the NAO in the event of STWCP warming (e.g.: recent winters and near neutral NAO in 2017 summer).