Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

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.

Retrieval of Antarctic sea ice freeboard and thickness from HY-2B satellite altimeter data

Antarctic sea ice is an important part of the Earth’s atmospheric system,and satellite remote sensing is an important technology for observing Antarctic sea ice.Whether Chinese Haiyang-2B(HY-2B)satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series,as other radar altimetry satellites can,needs further investigation.This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data.We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature(IST)product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images.Second,a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights.We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation.Finally,the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate(ASPeCt)ship-based observed sea ice thickness.The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable,and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m.The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products;this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.

The Arctic Sea Ice Thickness Change in CMIP6’s Historical Simulations

This study assesses sea ice thickness(SIT)from the historical run of the Coupled Model Inter-comparison Project Phase 6(CMIP6).The SIT reanalysis from the Pan-Arctic Ice Ocean Modeling and Assimilation System(PIOMAS)product is chosen as the validation reference data.Results show that most models can adequately reproduce the climatological mean,seasonal cycle,and long-term trend of Arctic Ocean SIT during 1979-2014,but significant inter-model spread exists.Differences in simulated SIT patterns among the CMIP6 models may be related to model resolution and sea ice model components.By comparing the climatological mean and trend for SIT among all models,the Arctic SIT change in different seas during 1979-2014 is evaluated.Under the scenario of historical radiative forcing,the Arctic SIT will probably exponentially decay at-18%(10 yr)-1 and plausibly reach its minimum(equilibrium)of 0.47 m since the 2070s.

Conditions of spatiotemporal variability of the thickness of the ice cover on lakes in the Tatra Mountains

This research aimed to identify the impact of local climatic and topographic conditions on the formation and development of the ice cover in highmountain lakes and the representativeness assessment of periodic point measurements of the ice cover thickness by taking into consideration the role of the avalanches on the icing of the lakes.Field works included measurement of the ice and snow cover thickness of seven lakes situated in the Tatra Mountains(UNESCO biosphere reserve)at the beginning and the end of the 2017/2018 winter season.In addition,morphometric,topographic and daily meteorological data of lakes from local IMGW(Polish Institute of Meteorology and Water Management)stations and satellite images were used.The obtained results enabled us to quantify the impact of the winter eolian snow accumulation on the variation in ice thickness.This variation was ranging from several centimetres up to about 2 meters and had a tendency to increase during the winter season.The thickest ice covers occurred in the most shaded places in the direct vicinity of rock walls.The obtained results confirm a dominating role of the snow cover in the variation of the ice thickness within individual lakes.

Influences of gas bubble and ice density on ice thickness measurement by GPR

Lake ice thickness changes with regional hydrometeorology and is closely associated with global climate change.We tested the detection of ice thickness using ground penetrating radar（GPR）in the Hongqipao reservoir.Ice crystals,gas bubbles,ice density and ice thickness were also determined by concurrently drilling for validation.During the tests the gas bubble content was high in the upper and low in the bottom,ice density varied with the bubble content,and the ice thickness was not homogeneous.By comparisons between radar detected and in-situ measured ice thicknesses with theoretical analyses of the influence of gas bubble content on the dielectric constant,a three-layer model with snow, granular ice,and columnar ice was established and the transmission speed of radar wave within the ice was determined.Experience reveals that the equivalent dielectric constant can be used to evaluate the wave speed and the values at 1/3 ice cover depth can be used as equivalent values.Besides,the difference between the theoretical transmission time and the real detection time induced by large gas bubbles increases nonlinearly with the theoretical transmission time or ice thickness.

Determination of the Thickness and Medium of Covering Soil for Land Reclamation

Land reclamation is a process of ecosystem reconstruction, for which it is very important to keep co-adaptation between plants and the below ground habitat. In order to keep the co-adaptation among plant species, thickness of covering soil and medium of covering soil to establish a self-regulating ecosystem, the thickness of covering soil of land reclamation for plants in different living forms by synusia structure of plant below-ground habitat and medium of covering soil by ecological factors of plant below-ground habitat were studied. Synusia structure of plant below-ground habitat was recognized through investigation on structure and root of plant community, and ecological factors were determined through soil profile investigation. The thickness and medium of covering soil of land reclamation for the tree, the shrub and the herb were proposed.

Ice thickness,internal layers,and surface and subglacial topography in the vicinity of Chinese Antarctic Taishan station in Princess Elizabeth Land,East Antarctica

We present the results of two ground-based radio-echo-sounding（RES） and GPS surveys performed in the vicinity of new Chinese Taishan station,Princess Elizabeth Land,East Antarctica,obtained in two austral summers during CHINARE 21（2004/2005） and CHINARE 29（2012/2013）.The radar surveys measured ice thickness and internal layers using 60- and 150-MHz radar systems,and GPS measurements showed smooth surface slopes around the station with altitudes of 2607-2636 m above sea level（a.s.l.）.Radar profiles indicate an average ice thickness of 1900 m,with a maximum of 1949 m and a minimum of 1856 m,within a square area measuring approximately 2 km × 2 km in the vicinity of the station.The ice thickness beneath the station site is 1870 m.The subglacial landscape beneath the station is quiet sharp and ranges from 662 to 770 m a.s.l.,revealing part of a mountainous topography.The ice volume in the grid is estimated to be 7.6 km^3.Along a 60-MHz radar profile with a length of 17.6 km at the region covering the station site,some disturbed internal layers are identified and traced;the geometry of internal layers within the englacial stratigraphy may imply a complex depositional process in the area.

Distribution of ice thickness and subglacial topography of the "Chinese Wall" around Kunlun Station,East Antarctica

As fundamental parameters of the Antarctic Ice Sheet,ice thickness and subglacial topography are critical factors for studying the basal conditions and mass balance in Antarctica.During CHINARE 24（the 24 th Chinese National Antarctic Research Expedition,2007/08）,the research team used a deep ice-penetrating radar system to measure the ice thickness and subglacial topography of the ＂Chinese Wall＂ around Kunlun Station,East Antarctica.Preliminary results show that the ice thickness varies mostly from 1600 m to 2800 m along the ＂Chinese Wall＂,with the thickest ice being 3444 m,and the thinnest ice 1255 m.The average bedrock elevation is 1722 m,while the minimum is just 604 m.Compared with the northern side of the ice divide,the ice thickness is a little greater and the subglacial topography lower on the southern side,which is also characterized by four deep valleys.We found no basal freeze-on ice in the Gamburtsev Subglacial Mountains area,subglacial lakes,or water bodies along the ＂Chinese Wall＂.Ice thickness and subglacial topography data extracted from the Bedmap 2 database along the ＂Chinese Wall＂ are consistent with our results,but their resolution and accuracy are very limited in areas where the bedrock fluctuates intensely.The distribution of ice thickness and subglacial topography detected by ice-penetrating radar clarifies the features of the ice sheet in this ＂inaccessible＂ region.These results will help to advance the study of ice sheet dynamics and the determination of future locations of the GSM＇s geological and deep ice core drilling sites in the Dome A region.

Effects of Different Thicknesses of Gravel Covering on Daily Soil Evaporation

[Objective] The research aimed to explore the most suitable gravel cover- ing thickness for selenium sand melon in arid region of central Ningxia. [Method] The natural gravel, which was from Nanshantai Region in Zhongwei City, Ningxia, was acted as test materials to study the effects of different thicknesses of gravel covering on daily evaporation using evaporator overall weighing method. [Result] The daily evaporation capacity order of the gravel covering thickness was as follows： CK〉HI（5 cm）〉 H2（8 cm）〉 H3（10 cm）〉 H4（15 cm）. Meanwhile, with the increase of test days, the difference of cumulative evaporation capacity between H3 （10 cm） and H4 （15 cm） decreased gradually. Soil evaporation capacity reduced at the pow- er function with the increase of gravel covering thickness, and the decision coeffi- cient of the fitted curve reached to 0.925 5. [Conclusion] With the increase of gravel covering thickness, evaporation capacity of soil reduced gradually, and the soil water content increased gradually. Gravel covering could effectively reduce the evapora- tion. The thicker of covering, the more obvious inhibition effect on evaporation. The thickness of covering should increase moderately to prevent moisture loss from e- vaporation. Gravel inhibition effect on the evaporation wasn＇t obvious when the gravel covering thickness reached more than 10 cm. 10 cm gravel covering was the most appropriate thickness for local natural condition. The soil evaporation capacity along with the change of gravel covering could be simulated with power function e- quation Y=at^b.

Impact of the Shrinkage of Arctic Sea Ice on Eurasian Snow Cover Changes in 1979-2021

Recent research has shown that snow cover induces extreme wintertime cooling and has detrimental impacts.Although the dramatic loss of Arctic sea ice certainly has contributed to a more extreme climate,the mechanism connecting sea-ice loss to extensive snow cover is still up for debate.In this study,a significant relationship between sea ice concentration(SIC)in the Barents-Kara(B-K)seas in November and snow cover extent over Eurasia in winter(November-January)has been found based in observational datasets and through numerical experiments.The reduction in B-K sea ice gives rise to a negative phase of Arctic Oscillation(AO),a deepened East Asia trough,and a shallow trough over Europe.These circulation anomalies lead to colder-than-normal Eurasian mid-latitude temperatures,providing favorable conditions for snowfall.In addition,two prominent cyclonic anomalies near Europe and Lake Baikal affect moisture transport and its divergence,which results in increased precipitation due to moisture advection and wind convergence.Furthermore,anomalous E-P flux shows that amplified upward propagating waves associated with the low SIC could contribute to the weakening of the polar vortex and southward breakouts of cold air.This work may be helpful for further understanding and predicting the snowfall conditions in the middle latitudes.

Soil seed bank is affected by transferred soil thickness and properties in the reclaimed coal mine in the Qilian Mountains, China

Reclamation of lands abandoned after mining in mountain areas is critical to erosion control,safety from landslides,and ecological protection of mountain ecosystems.However,little is known about alpine coal mine reclamation using the soil seed bank as a potential source for revegetation.We collected samples of persistent soil seed bank for germination experiments from nine reclaimed sites with different soil cover thicknesses and from six control sites in the Qilian Mountains of China.Soil properties of each site were determined(including soil water content,soil available potassium,soil available phosphorus,soil total nitrogen,pH,soil organic matter,soil total phosphorus,and soil total potassium,and soil alkali-hydrolyzable nitrogen),and the relationships of the characteristics of the soil seed bank with soil cover thickness and soil properties were examined.The results showed that the density,number of species,and diversity of the topsoil seed bank were significantly correlated with soil cover thickness,and all increased with the increment of soil cover thickness.Soil cover thickness controlled the soil seed bank by influencing soil properties.With the increase in soil cover thickness,soil properties(e.g.,soil organic matter,soil total nitrogen,etc.)content increased while soil pH decreased.The soil seed bank had the potential to restored the pre-mining habitat at reclaimed sites with approximately 20-cm soil cover thickness.Soil properties of reclaimed sites were lower than that of natural sites.The relationship between the soil seed bank and soil cover thickness determined in this study provides a foundation for improving reclamation measures used in coal mines,as well as for the management and monitoring of reclaimed areas.

An Arctic sea ice thickness variability revealed from satellite altimetric measurements

A modified algorithm taking into account the first year（FY） and multiyear（MY） ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat（2003–2008）. Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4–5 month separation, were found. Interannually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns（2005–2008） leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors.

Sea ice thickness analyses for the Bohai Sea using MODIS thermal infrared imagery

Level ice thickness distribution pattern in the Bohai Sea in the winter of 2009-2010 was investigated in this paper using MODIS night-time thermal infrared imagery. The cloud cover in the imagery was masked out manually. Level ice thickness was calculated using MODIS ice surface temperature and an ice surface heat balance equation. Weather forcing data was from the European Centre for Medium-Range Weather Forecasts （ECMWF） analyses. The retrieved ice thickness agreed reasonable well with in situ observations from two off-shore oil platforms. The overall bias and the root mean square error of the MODIS ice thickness are -1.4 cm and 3.9 cm, respectively. The MODIS results under cold conditions （air temperature 〈 -10~C） also agree with the estimated ice growth from Lebedev and Zubov models. The MODIS ice thickness is sensitive to the changes of the sea ice and air temperature, in particular when the sea ice is relatively thin. It is less sensitive to the wind speed. Our method is feasible for the Bohai Sea operational ice thickness analyses during cold freezing seasons.

Sea ice thickness estimation in the Bohai Sea using geostationary ocean color imager data

A method to estimate the thickness of the sea ice of the Bohai Sea is proposed using geostationary ocean color imager （GOCI） data and then applied to the dynamic monitoring of the sea ice thickness in the Bohal Sea during the winter of 2014 to 2015. First of all, a model is given between the GOCI shortwave broadband albedo and the reflectance of each band with high temporal resolution GOCI data. Then, the relationship model between the sea ice thickness and the GOCI shortwave broadband albedo is established and applied to the thickness extraction of the sea ice in the Bohai Sea. Finally, the sea ice thickness extraction method is tested by the results based on the MODIS data, thermodynamic empirical models （Lebedev and Zubov）, and the in situ ice thickness data. The test results not only indicated that the sea ice thickness retrieval method based on the GOCI data was a good correlation （r2〉0.66） with the sea ice thickness retrieved by the MODIS and thermodynamic empirical models, but also that the RMS is only 6.82 cm different from the thickness of the sea ice based on the GOCI and in situ data.

Observed and modelled snow and ice thickness in the Arctic Ocean with CHINARE buoy data

Sea ice and the snow pack on top of it were investigated using Chinese National Arctic Research Expedition （CHINARE） buoy data. Two polar hydrometeorological drifters, known as Zeno ice stations, were deployed during CHINARE 2003. A new type of high-resolution Snow and Ice Mass Balance Arrays, known as SIMBA buoys, were deployed during CHINARE 2014. Data from those buoys were applied to investigate the thickness of sea ice and snow in the CHINARE domain. A simple approach was applied to estimate the average snow thickness on the basis of Zeno temperature data. Snow and ice thicknesses were also derived from vertical temperature profile data based on the SIMBA buoys. A one-dimensional snow and ice thermodynamic model （HIGHTSI） was applied to calculate the snow and ice thickness along the buoy drift trajectories. The model forcing was based on forecasts and analyses of the European Centre for Medium-Range Weather Forecasts （ECMWF）. The Zeno buoys drifted in a confined area during 2003-2004. The snow thickness modelled applying HIGHTSI was consistent with results based on Zeno buoy data. The SIMBA buoys drifted from 81. 1°N, 157.4°W to 73.5°N, 134.9°W in 15 months during 2014-2015. The total ice thickness increased from an initial August 2014 value of 1.97 m to a maximum value of 2.45 in before the onset of snow melt in May 2015; the last observation was approximately 1 m in late November 2015. The ice thickness based on HIGHTSI agreed with SIMBA measurements, in particular when the seasonal variation of oceanic heat flux was taken into account, but the modelled snow thickness differed from the observed one. Sea ice thickness derived from SIMBA data was reasonably good in cold conditions, but challenges remain in both snow and ice thickness in summer.

Analysis of multi-dimensional SAR for determining the thickness of thin sea ice in the Bohai Sea

Flat thin ice (<30 cm thick) is a common ice type in the Bohai Sea, China. Ice thickness detection is important to offshore exploration and marine transport in winter. Synthetic aperture radar (SAR) can be used to acquire sea ice data in all weather conditions, and it is a useful tool for monitoring sea ice conditions. In this paper, we combine a multi-layered sea ice electromagnetic (EM) scattering model with a sea ice thermodynamic model to assess the determination of the thickness of flat thin ice in the Bohai Sea using SAR at different frequencies, polarization, and incidence angles. Our modeling studies suggest that co-polarization backscattering coefficients and the co-polarized ratio can be used to retrieve the thickness of flat thin ice from C- and X-band SAR, while the co-polarized correlation coefficient can be used to retrieve flat thin ice thickness from L-, C-, and X-band SAR. Importantly, small or moderate incidence angles should be chosen to avoid the effect of speckle noise.

Measuring Ice Thicknesses along the Red River in Canada Using RADARSAT-2 Satellite Imagery

The spring flood of 2009 in the Red River Valley was exacerbated with severe ice breakup and ice jamming. To assist ice jam mitigation by cutting and breaking up the river ice cover before the flood season and to support the operation of the Red River Floodway, Manitoba Water Stewardship is striving to model the occurrence of ice breakup and simulate the behaviour of ice jamming along the river. An important parameter in ice breakup forecasting is the ice thickness. RADARSAT-2 standard satellite images were collected along the course of the Red River in Manitoba during the 2009-2010 winter to help determine ice thicknesses along the river. Standard images can have transmit-receive polarizations in the horizontal-horizontal (HH) or horizontal-vertical (HV) configurations. Ice thickness measurements were taken in the field during the same time frame when the satellite passed over the Red River Valley. Good correlations were obtained between the HH-backscatter readings and the surveyed ice thicknesses. HV-backscatter readings correlate better with fresh snow depth measurements. Additionally, using same sensor incident angle and flight geometry allows ice thickening rate behavior over the course of the winter to be determined.

Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness(PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer(MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index(DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.