Abstract/Summary

Interpretations of high-resolution proxy datasets from stalagmites require support from long-term cave monitoring data and quantified changes in sample growth rate. One cave site for which the modern climate signal transfer systematics are relatively well characterised by cave monitoring is New St Michael's Cave, Gibraltar. This site provides a rare opportunity to reconstruct modern calcite growth, to link growth with the cave environment and local climate, and to test the sufficiency of existing growth rate theory on monthly to inter-annual timescales. Here, we use a numerical time-series growth rate model, driven by cave monitoring and local meteorological data, and the results of an experimental investigation into variation in dripwater film thickness as a function of stalagmite apex morphology to reconstruct the modern growth (AD 1951–2004) of ‘Gib04a’, a stalagmite retrieved from New St Michael's Cave. Our experimental measurements demonstrate that dripwater film thickness decreases linearly with increasing stalagmite curvature and that the presence of millimetre-scale surface microtopography reduces film thickness by an order of magnitude. We identified changes in growth laminae curvature from a Gib04a cut section to determine film thickness variability through time and combined this with estimated dripwater [Ca2+] and cave air pCO2 seasonality to drive the model. Our reconstruction exhibits strong seasonality and tracks variability in calcite [Sr2+], a trace metal whose incorporation into calcite is partially growth rate-controlled. Reconstructed growth also shows co-variation with seasonal changes in calcite fabric, with high growth corresponding to a greater density of calcite grain boundaries. We also link secular trends in karst recharge, film thickness and Gib04a growth, and assess the overall sensitivity of vertical growth rate to film thickness variability. This approach could be used to characterise the growth of other samples retrieved from well-monitored cave systems and may prove particularly useful in quantifying seasonal bias in geochemical proxy datasets, facilitating greater robustness of palaeoclimate reconstructions.