Ice bottom evolution derived from thermistor string-based ice mass balance buoy observations

Digital information on sea ice extent,thickness,volume,and distribution is crucial for understanding Earth's climate system.The Snow and Ice Mass Balance Apparatus(SIMBA)is used to determine snow and ice temperatures in Arctic,Antarctic,ice-covered seas,and boreal lakes.Snow depth and ice thickness are derived from SIMBA temperature regimes(SIMBA_ET and SIMBA_HT).In warm conditions,SiMBA_ET temperature-based ice thickness may have errors due to the isothermal vertical profile.SIMBA_HT provides a visible ice-bottom interface for manual quantification.We propose an unmanned approach,combining neural networks,wavelet analysis,and Kalman filtering(NWK),to mathematically establish NwK and retrieve ice bottoms from various SIMBA_HT datasets.In the Arctic,NWK-derived total thickness showed a bias range of-5.64 cm to 4.01 cm and a correlation coefficient of 95%-99%.For Baltic Sea ice,values ranged from 1.31 cm to 2.41 cm(88%-98%correlation),and for boreal lake ice,-0.7 cm to 2.6 cm(75%-83%correlation).During ice growth,thermal equilibrium,and melting,the bias varied from-3.93 cm to 2.37 cm,-1.92 cm to 0.04 cm,and-4.90 cm to 3.96 cm,with correlation coefficients of 76%-99%.These results demonstrate NWK's robustness in retrieving ice bottom evolution in different water environments.

Snow depth and ice thickness derived from SIMBA ice mass balance buoy data using an automated algorithm

An ice mass balance buoy(IMB)monitors the evolution of snow and ice cover on seas,ice caps and lakes through the measurement of various variables.The crucial measurement of snow and ice thickness has been achieved using acoustic sounders in early devices but a more recently developed IMB called the Snow and Ice Mass Balance Array(SIMBA)measures vertical temperature profiles through the air-snow-ice-water column using a thermistor string.The determination of snow depth and ice thickness from SIMBA temperature profiles is presently a manual process.We present an automated algorithm to perform this task.The algorithm is based on heat flux continuation,limit ratio between thermal heat conductivity of snow and ice,and minimum resolution(±0.0625°C)of the temperature sensors.The algorithm results are compared with manual analyses,in situ borehole measurements and numerical model simulation.The bias and root mean square error between algorithm and other methods ranged from 1 to 9 cm for ice thickness counting 2%–7%of the mean observed values.The algorithm works well in cold condition but becomes less reliable in warmer conditions where the vertical temperature gradient is reduced.