Does a real-world speleothem look like the prediction: A comprehensive study of the Sofular Cave

For many years, there have been ongoing works on modelling the growth processes of stalagmites to obtain climatic information from their shape and stratigraphy. However, knowledge is still limited and it is therefore essential to improve our understanding of the underlying processes. Several studies focus on developing new algorithms to describe the equilibrium radius and the growth rate (Romanov et al., 2008) but there are only a few attempts to drive the Shape Model with time series. Kaufmann for example, focuses on the temperature as the driving force for growth variations (Kaufmann, 2003). Here, we introduce a coupling of three existing models in order to simulate the shape and growth rate of the So-1 stalagmite from the Sofular Cave in Northern Turkey. 

The presented Shape Model only needs 4 input parameters to simulate the stalagmite: cave temperature, calcium concentration of the water drop, drip rate and the CO₂ concentration in the cave. To determine these values we use modelled data from the Max Planck Institute Earth System Model version 1.2 (MPI-ESMI1.2) and ice core data. Additionally, we use CaveCalc, a numerical model for speleothem chemistry and isotopes, to calculate the chemical reactions in the soil and karst above the cave. Through this approach we were able to simulate a stalagmite, which follows the trend of the experimental data for the growth rate, using the input parameters inside the respective error ranges. Real-world growth variations under 5 kyr are not visible. Furthermore, the effect of the individual parameters can be tested. Here, the model suggests that the radius mainly depends on the drip rate, whereas the growth rate is driven by the calcium concentration of the water drop. The model is also capable of showing some basic principles like a decrease in height as the distance to the entrance and hence CO₂ concentration increases.

This new coupling opens the possibility of adjusting the data till the model corresponds better to the experimental data in order to get insights into difficult values like the drip rate. Further, it can be the start for a new inverse approach by calculating which input values correspond to the measured data while keeping some parameters fixed.

References:

Romanov, D., Kaufmann, G., and Dreybrod, W. (2008). Modeling stalagmite growth by first principles of chemistry and physics of calcite precipitation.
Geochimica et Cosmochimica Acta, 72(2):423–437.

Kaufmann, G. (2003). Stalagmite growth and palaeo-climate: the numerical perspective. Earth and Planetary Science Letters, 214(1-2):251–266