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

ABSTRACT

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.

KEYWORDS:

• Snow
• sea ice
• ice thickness
• ice mass balance buoy
• polar oceans

Acknowledgments

The authors are grateful for the SIMBA deployment made by our international collaborators. The FMI02 was deployed by Dr. Marcel Nicolaus from AWI in autumn 2012; FMI20 was deployed during the NICE2015 field campaign. NMEFC-Ant2014 was deployed by Mr. Xiaopeng Han from NMEFC in autumn 2014.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Academy of Finland [grant number 317999], Natural Science Foundation of China [grant numbers 41376005, 41406218, 41428603, 41506221, 11571383], European Union’s Horizon 2020 research and innovation programme [No 727890 – INTAROS], the Key Research Program of Frontier Sciences of CAS [QYZDY-SSW-DQC021] and the Science and Technology Program Guangzhou, China [201804020053].