Variations in the conditions of sea ice in the northern part of Baffin Bay and North Open Water polynya influence human activity in northwestern Greenland through oceanic circulation and heat balance between air and sea. To evaluate the impact of variations in sea ice conditions on the surrounding environment, it is important to understand the mechanism of sea ice variations over long periods. In this study, we estimated the age of the SIGMA‐A ice core collected northwestern Greenland Ice Sheet and researched the relationship between annual or seasonal deuterium excess (d‐excess) and seasonal sea ice concentration. We found that a temporal variation in the spring d‐excess in the ice core negatively correlated significantly with that of sea ice concentration in February–April in northern Baffin Bay from 1979–2005 (r = −0.61,p < 0.001). Using this relationship, we reconstructed the temporal variations in sea ice concentrations for 100 years from the ice core drilled in the northwestern Greenland Ice Sheet. The sea ice concentration in the early twentieth century was lower than that in the present. The decrease in sea ice concentration was consistent with analytical results for marine sediments obtained from Baffin Bay. We also suggested that the sea ice concentration was controlled by atmospheric conditions from the 1920s to 1940s based on examinations of correlations with the North Atlantic Oscillation index and air temperature in Ilulissat and by oceanographic conditions from 1945–1955, 1959–1969, and 1982–1992 based on the Atlantic Multidecadal Oscillation index and meridional heat transport to western Greenland.

We proposed new snow grain model and snow impurity mixture models for the purpose of accurate retrievals of snow grain size and concentration of light‐absorbing particles (LAP) in snow from the optical remote sensing data. Two kinds of ice crystal models, irregularly shaped Voronoi columns, and Voronoi aggregates were employed. LAP can be captured by the snow through two processes: dry and wet deposition. Two different snow impurity mixture models were proposed. One is an external mixture model. We employed a coated sphere model in which soot were hydrophilic particles to represent a hygroscopic property of aerosol and assumed the hydrophilic soot particles to be externally mixed with snow particles. The other is an internal mixture model. We employed a dynamic effective medium approximation method in which soot particles were randomly located within snow particle with any size distribution and number concentration. Validation of these models is conducted using a ground‐based spectral radiometer system with in situ measurement data. For snow grain size retrievals, a Voronoi mixture model seamlessly representing a geometrical shape and an optical properties of various snow types can provide accurate retrievals. For the retrieval of LAP concentration in snow, employing different mixing state models depending on season and measurement site gives accurate results. We also discussed the uncertainty of retrieved snow parameters on the surface slope involved in the retrieval accuracy. These models are expected to be useful for advanced airborne/satellite remote sensing and climate studies via radiative transfer modeling in the atmosphere‐snow system.

Depositions of light‐absorbing particles (LAPs), such as black carbon (BC) and dust, on the snow surface modulate the snow albedo; therefore, they are considered key factors of snow‐atmosphere interaction in the present‐day climate system. However, their detailed roles have not yet been fully elucidated, mainly due to the lack of in‐situ measurements. Here, we develop a new model chain NHM‐Chem‐SMAP, which is composed of a detailed regional meteorology‐chemistry model and a multilayered physical snowpack model, and evaluate it using LAPs concentrations data measured at Sapporo, Japan during the 2011–2012 winter. NHM‐Chem‐SMAP successfully reproduces the in‐situ measured seasonal variations in the mass concentrations of BC and dust in the surface snowpack. Furthermore, we find that LAPs from domestic and foreign sources played a role in shortening the snow cover duration by 5 and 10 days, respectively, compared to the completely pure snow condition.

Sea ice, which forms in polar and nonpolar areas, transmits light to ice-associated (sympagic) algal communities. To noninvasively study the distribution of sea-ice algae, empirical relations to estimate its biomass from under-ice hyperspectral irradiance have been developed in the Arctic and Antarctica but lack for nonpolar regions. This study examines relationships between normalised difference indices (NDI) calculated from hyperspectral transmittance and sympagic algal biomass in the nonpolar Saroma-ko Lagoon. We analysed physico-biogeochemical properties of snow and land-fast sea ice supporting 27 paired bio-optical measurements along three transects covering an area of over 250 m × 250 m in February 2019. Snow depth (0.08 ± 0.01 m) and ice-bottom brine volume fraction (0.21 ± 0.02) showed low (0.06) and high (0.58) correlations with sea-ice core bottom section chlorophylla(Chl.a), respectively. Spatial analyses unveiled the patch size of sea-ice Chl.ato be ~65 m, which is in the same range reported from previous studies. A selected NDI (669, 596 nm) explained 63% of algal biomass variability. This reflects the bio-optical properties and environmental conditions of the lagoon that favour the wavelength pair in the orange/red part of the spectrum and suggests the necessity of a specific bio-optical relationship for Saroma-ko Lagoon.

There is a strong need for measuring sea ice thickness with passive microwave radiometers onboard satellites. However, since the footprint size of passive microwave radiometers is 5 to 50 km, it is not easy to verify the possibility using satellite data. To study how the microwave brightness temperature and emissivity change with sea ice thickness and salinity, the authors have been performing a sea ice tank experiment for the past few years. The tank, which has a diameter of 2.6 m, was filled with seawater having a salinity of 32.5 ppt to a depth of 0.85 m. The tank was installed outdoors and covered with a hood to keep the surface from being covered by snow. The continuous measurement of microwave brightness temperature, salinity and thickness of artificially grown sea ice in a tank was performed under subfreezing temperatures. The brightness temperature measurements were made at 7 GHz, 18 GHz, and 36 GHz for both vertical (V) and horizontal (H) polarizations using a portable microwave radiometer MMRS2 developed by MELOS. During the experiment, the sea ice thickness went up to 54.5 cm. The brightness temperature and emissivity measured by the potable microwave radiometer saturated at sea ice thickness of 3.0 cm for 36 GHz, 5.0 cm for 18 GHz, and 13.5 cm for 7 GHz. The relationship between PR and ice thickness as well as the relationship between GR and ice thickness were also examined. The result suggested the limitation and possibility of estimating sea ice thickness with passive microwave radiometers.

We developed a Handheld Integrating Sphere Snow Grain Sizer (HISSGraS) for field use to measure the specific surface area (SSA) of snow. In addition to snow samples, HISSGraS can directly measure snow surfaces and snow pit walls. The basic measurement principle is the same as that of the IceCube SSA instrument. The retrieval algorithm for SSA from reflectance employs two conversion equations formulated using spherical and nonspherical grain shape models. We observed SSAs using HISSGraS, IceCube and the gas adsorption method in a snowfield in Hokkaido, Japan. Intercomparison of the results confirmed that with HISSGraS direct measurement, SSA profile observations can be completed in just ~1/10 the time required for measurement of snow samples. Our results also suggest that HISSGraS and IceCube have similar accuracy when the same snow samples are measured using the same grain shape model. However, SSAs of near-surface snow layers measured using the three techniques exhibited some biases, possibly due to rapid snow metamorphism or melting during measurement and some technical issues with optical techniques. When excluding SSA data for the surface layer, which metamorphosed remarkably during measurement, IceCube- and HISSGraS-derived SSAs correlated strongly with those obtained by gas adsorption and HISSGraS accuracy is 21–34%.