An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.

Application of the two different salinity parameterization schemes in the sea ice model

In this study, we mainly introduce two salinity parameterization schemes used in Sea Ice Simulator （SIS）, that is, isosaline scheme and salinity profile scheme. Comparing the equation of isosaline scheme with that of salinity profile scheme, we found that there was one different term between the two schemes named the salinity different term. The thermodynamic effect of the salinity difference term on sea ice thickness and sea ice concentration showed that： in the freezing processes from November to next May, the sea ice temperature could rise on the influence of the salinity difference term and restrain sea ice freezing; at the first melting phase from June to August, the upper ice melting rate was faster than the lower ice melting rate. Then sea ice temperature could rise and accelerate the sea ice melting; at the second melting phase from September to October, the upper ice melting rate was slower than the lower ice melting rate, then sea ice temperature could decrease and restrain sea ice melting. However, the effect of the salinity difference term on the sea ice thickness and sea ice concentration was weak. To analyze the impacts of the salinity different term on Arctic sea ice thickness and sea ice concentration, we also designed several experiments by introducing the two salinity parameterizations to the ice-ocean coupled model, Modular Ocean Model （MOM4）, respectively. The simulated results confirmed the previous results of formula derivation.

The physical structures of snow and sea ice in the Arctic section of 150°-180°W during the summer of 2010

The physical structures of snow and sea ice in the Arctic section of 150°-180°W were observed on the basis of snow-pit, ice-core, and drill-hole measurements from late July to late August 2010. Almost all the in- vestigated floes were first-year ice, except for one located north of Alaska, which was probably multi-year ice transported from north of the Canadian Arctic Archipelago during early summer. The snow covers over all the investigated floes were in the melting phase, with temperatures approaching 0℃and densities of 295-398 kg/m3. The snow covers can be divided into two to five layers of different textures, with most cases having a top layer of fresh snow, a round-grain layer in the middle, and slush and/or thin icing layers at the bottom. The first-year sea ice contained about 7%-17% granular ice at the top. There was no granular ice in the lower layers. The interior melting and desalination of sea ice introduced strong stratifications of temper- ature, salinity, density, and gas and brine volume fractions. The sea ice temperature exhibited linear cooling with depth, while the salinity and the density increased linearly with normalized depth from 0.2 to 0.9 and from 0 to 0.65, respectively. The top layer, especially the freeboard layer, had the lowest salinity and density, and consequently the largest gas content and the smallest brine content. Both the salinity and density in the ice basal layer were highly scattered due to large differences in ice porosity among the samples. The bulk average sea ice temperature, salinity, density, and gas and brine volume fractions were -0.8℃, 1.8, 837 kg/m3, 9.3% and 10.4%, respectively. The snow cover, sea ice bottom, and sea ice interior show evidences of melting during mid-August in the investigated floe located at about 87°N, 175°W.

Response of Arctic marine microalgae to changes of salinity

An algal assemblage collected from the bottom of floe in the Greenland Sea was batchcultured at 1±1℃ and 10 salinity gradients varied from 4 0 to 90 8 for 19 d.The growth for both the algal community and individual populations was characterized by an initial lag phase of six days followed by positive growth.Maximum growth rates were obtained as 0 19/d for the algal community and 0 32 to 0 39 d -1 for individual populations for the whole experiment period,which mostly occurred at the lower salinities.The competition between the algal species and the evolution of the algal assemblages under the salinity changes was checked.After 14 d culture,the dominating algae in the lower salinities were centric diatoms,pennate diatoms and phytoflagellates,while ones in the higher salinities almost belonged to pennate diatoms.It is suggested that the sea ice algal community from the Greenland Sea prefer lower salinities to higher ones,and the decrease in salinity in small ranges could stimulate the growth of sea ice algae.

Sensitivity of sea ice and ocean simulations to sea ice salinity in a coupled global climate model

The impacts of the spatiotemporal variations of sea ice salinity on sea ice and ocean characteristics have not been studied in detail, as the existing climate models neglect or misrepresent this process. To address this issue, this paper formulated a parameterization with more realistic sea ice salinity budget, and examined the sensitivity of sea ice and ocean simulations to the ice salinity variations and associated salt flux into the ocean using a coupled global climate model. Results show that the inclusion of such a parameterization leads to an increase and thickening of sea ice in the Eurasian Arctic and within the ice pack in the Antarctic circumpolar region, and a weakening of the North Atlantic Deep Water and a strengthening of the Antarctic Bottom Water. The atmospheric responses associated with the ice changes were also discussed.

Arctic Climate Changes Based on Historical Simulations(1900-2013) with the CAMS-CSM

The Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM) is a newly developed global climate model that will participate in the Coupled Model Intercomparison Project phase 6. Based on historical simulations(1900-2013), we evaluate the model performance in simulating the observed characteristics of the Arctic climate system, which includes air temperature, precipitation, the Arctic Oscillation(AO), ocean temperature/salinity,the Atlantic meridional overturning circulation(AMOC), snow cover, and sea ice. The model-data comparisons indicate that the CAMS-CSM reproduces spatial patterns of climatological mean air temperature over the Arctic(60°-90°N) and a rapid warming trend from 1979 to 2013. However, the warming trend is overestimated south of the Arctic Circle, implying a subdued Arctic amplification. The distribution of climatological precipitation in the Arctic is broadly captured in the model, whereas it shows limited skills in depicting the overall increasing trend. The AO can be reproduced by the CAMS-CSM in terms of reasonable patterns and variability. Regarding the ocean simulation, the model underestimates the AMOC and zonally averaged ocean temperatures and salinity above a depth of 500 m, and it fails to reproduce the observed increasing trend in the upper ocean heat content in the Arctic. The largescale distribution of the snow cover extent(SCE) in the Northern Hemisphere and the overall decreasing trend in the spring SCE are captured by the CAMS-CSM, while the biased magnitudes exist. Due to the underestimation of the AMOC and the poor quantification of air–sea interaction, the CAMS-CSM overestimates regional sea ice and underestimates the observed decreasing trend in Arctic sea–ice area in September. Overall, the CAMS-CSM reproduces a climatological distribution of the Arctic climate system and general trends from 1979 to 2013 compared with the observations, but it shows limited skills in modeling local trends and interannual variability.

T-MOSAiC——A new circumpolar collaboration

Atmospheric temperatures over northern regions of the world are rising at twice the global average,and one of the most conspicuous effects is the rapid and ongoing decline in summer sea ice in the Arctic Ocean.To improve understanding about the causes and consequences of this decline,the project‘Multidisciplinary drifting Observatory for the Study of Arctic Climate’(https://www.mosaicexpedition.org/)commenced in September 2019,under the auspices of the International Arctic Science Committee(IASC).This project is an ambitious year-round set of observations of multiple aspects of the Arctic Ocean,sea ice and atmosphere,involving five icebreakers(notably the German vessel Polarstern that was frozen into the Arctic pack ice),and 600 science personnel from more than 20 nations.