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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 2
The Cryosphere, 10, 879–894, 2016
https://doi.org/10.5194/tc-10-879-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 10, 879–894, 2016
https://doi.org/10.5194/tc-10-879-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 25 Apr 2016

Research article | 25 Apr 2016

Analyzing airflow in static ice caves by using the calcFLOW method

Christiane Meyer1, Ulrich Meyer2, Andreas Pflitsch3, and Valter Maggi1 Christiane Meyer et al.
  • 1Universita di Milano-Bicocca, Dipartimento di Scienze Ambiente e Territorio e Scienze della Terra, Piazza della Scienza 1, 20126 Milan, Italy
  • 2University of Bern, Astronomical Institute, Sidlerstrasse 5, 3012 Bern, Switzerland
  • 3Ruhr-University Bochum, Geography Department, Working Group Cave- and Subway-Climatology, Universitätsstrasse 150/Building NA, 44780 Bochum, Germany

Abstract. In this paper we present a method to detect airflow through ice caves and to quantify the corresponding airflow speeds by the use of temperature loggers. The time series of temperature observations at different loggers are cross-correlated. The time shift of best correlation corresponds to the travel time of the air and is used to derive the airflow speed between the loggers. We apply the method to test data observed inside Schellenberger Eishöhle (ice cave). The successful determination of airflow speeds depends on the existence of distinct temperature variations during the time span of interest. Moreover the airflow speed is assumed to be constant during the period used for the correlation analysis. Both requirements limit the applicability of the correlation analysis to determine instantaneous airflow speeds. Nevertheless the method is very helpful to characterize the general patterns of air movement and their slow temporal variations. The correlation analysis assumes a linear dependency between the correlated data. The good correlation we found for our test data confirms this assumption. We therefore in a second step estimate temperature biases and scale factors for the observed temperature variations by a least-squares adjustment. The observed phenomena, a warming and an attenuation of temperature variations, depending on the distance the air traveled inside the cave, are explained by a mixing of the inflowing air with the air inside the cave. Furthermore we test the significance of the determined parameters by a standard F test and study the sensitivity of the procedure to common manipulations of the original observations like smoothing. In the end we will give an outlook on possible applications and further development of this method.

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In the paper a new method to calculate airflow speeds in static ice caves by using air temperature data is presented. As most study sites are in very remote places, where it is often not possible to use sonic anemometers and other devices for the analysis of the cave climate, we show how one can use the given database for calculating airflow speeds. Understanding/quantifying all elements of the specific cave climate is indispensable for understanding the evolution of the ice body in ice caves.
In the paper a new method to calculate airflow speeds in static ice caves by using air...
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