ALBERT

Description: Understanding how stalagmites grow under changing climate conditions is of great significance for their application as a paleoclimate archive. In this study, we present a shape modeling approach to stalagmite growth by combining three existing models accounting for climate variables, karst water chemistry, and speleothem deposition. The combined model requires only four input parameters: calcium concentration of the water drop, drip interval, cave temperature, and cave carbon dioxide (CO2) concentration. Using the output of the coupled atmosphere–ocean–land surface model MPI-ESM1.2 and the CaveCalc model for speleothem chemistry, we simulated stalagmite growth at Sofular Cave, Northern Turkey, (in the last 25 kyr) and compared the results to those of the existing So-1 stalagmite from the same cave. This approach allows simulating, completely independent of measured boundary conditions, a stalagmite geometry that follows the trend of the experimental data for the growth rate, with input parameters within the respective error ranges. When testing the sensitivity of the individual model parameters, the model suggests that the stalagmite radius mainly depends on the drip interval, whereas the growth rate is driven by the calcium concentration of the water drop. The model is also capable of showing some basic phenomena, like a decrease in growth rate (as observed in the real stalagmite), as CO2 concentration in the cave increases. The coupling of input parameters for the model to climate models represents the first attempt to understand an important climate archive in its shape and isotope content and opens the possibility for a new inverse approach to paleoclimate variables and model constraints.

Description: Comparable to carbon dioxide, dimethyl sulfide (DMS), and carbon monoxide (CO) are tiny gases that have a great impact on our climate. Though occurring only in very small amounts in the atmosphere they are climate influencers, especially in the Arctic. The Arctic is a unique place on Earth where all life is adapted to the extreme cold. Therefore, global warming is a great threat to the Arctic. DMS and CO are produced in the Arctic Ocean and can go into the atmosphere. There, CO may enhance the warming of the Arctic. On the other hand, DMS possibly cools the atmosphere because it helps forming clouds. The processes CO and DMS are involved in, are complex and will probably alter under a changing climate. It is important to understand these processes to get an idea of the future Arctic Ocean and climate to find ways to save the Arctic.

Description: Earth's climate can be understood as a dynamical system that changes due to external forcing and internal couplings. Essential climate variables, such as surface air temperature, describe this dynamics. Our current interglacial, the Holocene (11 700 yr ago to today), has been characterized by small variations in global mean temperature prior to anthropogenic warming. However, the mechanisms and spatiotemporal patterns of fluctuations around this mean, called temperature variability, are poorly understood despite their socioeconomic relevance for climate change mitigation and adaptation. Here we examine discrepancies between temperature variability from model simulations and paleoclimate reconstructions by categorizing the scaling behavior of local and global surface air temperature on the timescale of years to centuries. To this end, we contrast power spectral densities (PSD) and their power-law scaling using simulated and observation-based temperature series of the last 6000 yr. We further introduce the spectral gain to disentangle the externally forced and internally generated variability as a function of timescale. It is based on our estimate of the joint PSD of radiative forcing, which exhibits a scale break around the period of 7 yr. We find that local temperature series from paleoclimate reconstructions show a different scaling behavior than simulated ones, with a tendency towards stronger persistence (i.e., correlation between successive values within a time series) on periods of 10 to 200 yr. Conversely, the PSD and spectral gain of global mean temperature are consistent across data sets. Our results point to the limitation of climate models to fully represent local temperature statistics over decades to centuries. By highlighting the key characteristics of temperature variability, we pave a way to better constrain possible changes in temperature variability with global warming and assess future climate risks.