Earth System Diagnostics
Team members' research profiles:
Quantitiative interpretation of paleoclimate data
I enjoy working across disciplines and combining observational data, statistics and modeling. My current research focus is the estimation of climate variability and the mean temperature changes in the current warm period, the Holocene. This knowledge is relevant for predicting the spread of future climate changes and allows a direct test of climate models. In the ECUS project, I’m coordinating a small team of researchers and students and working on a better use of the paleoclimate record to sharpen our knowledge about the climate system.
Interpretation of temperature signals derived from ice cores
Ice cores are a key archive to reconstruct millennial-scale climate changes in temperature, but are, due to the inherent noise levels of the proxy data, less reliable in recording the smaller Holocene climate variations. However, quantitative knowledge of the natural Holocene polar climate variability is a key to determine the range of plausible future anthropogenic climate change.
My Ph.D. project aims at improving our understanding of the climate signal and the
non-climate variability recorded in water isotopes from polar ice cores. Currently, I use extensive isotope data obtained from the two-dimensional sampling of snow trenches at Kohnen station, Antarctica, to disentangle these two contributions. Of my work I particularly like to combine the observations with statistical modeling and numerical approaches to understand the physics of the ice-core proxy recorder system.
Climate proxy uncertainty
I am an ecologist with a focus on working with environmental data, statistics and models. In previous projects I have studied the response of phytoplankton to nutrient enrichment and their impact on water quality in lakes and rivers, and prior to this I worked on the dynamics of coral cover and its impact on reef associated fishes. My current work, as part of the PalMod project, is about quantifying and understanding uncertainty in climate proxies. To do this we are constructing proxy system models to simulate the creation of climate proxies. We can analyse the structure of the error introduced during the proxy creation process and this enables us to better understand the error in real climate proxies.
Investigation of the process of signal formation in paleo climate archives
My PhD project aims for a better understanding of the process of signal formation in paleo climate archives (e.g. ice cores, marine sediments). For this, I am investigating the imprint of the temperature signal in firn and ice cores by analysing the relationship between changes in the snow surface height and changes in the isotopic composition in the upper snow layer. This project contains practical work during field work and in laboratories as well as theoretical work and statistical analyses. A comparison of the measured isotopic composition in surface snow to a simple isotope model based on meteorological parameters from our study site, allows us to estimate the contribution of different processes (e. g. snowfall, wind-driven redistribution, diffusion, sublimation) to the final isotopic signal. This work aims to contribute to a (possible) improvement of climate reconstructions from ice cores.
As a second climate archive, I will explore the horizontal and vertical variability and heterogeneity of marine sediments by measuring and analysing the parameters d18O and Mg/Ca, which are usually used to reconstruct temperature, as well as the radiocarbon age in a two dimensional way from a box core.
A spectral view on the space-time structure of climate variability
My research focus is on the characterization of the space-time structure of climate variability across a large range of scales. In particular, a spectral view is adopted such that the scaling behaviour in both space and time (i.e., in the wavenumber and frequency domain) can be investigated. This type of analysis is applied to the output of long paleo-climate model simulations, to reanalysis data, and is compared to stochastic-diffusive energy balance models, for which the spectral scaling behaviour can be obtained analytically.
All in all, my aim is to achieve an as simple as possible conceptual picture of the spectral space-time structure, in order to (i) provide a systematic basis for time-scale-dependent interpretation of paleo-climate proxies, and (ii) to help distinguish between externally driven versus internal variability of the climate system, by searching for characteristic spectral space-time fingerprints of these components.
Specifically, I am currently developing spectral measures of the effective spatial degrees of freedom of large-scale climate fields, and I am also investigating the frequency-dependent spatial spectra of climate model simulations with different external forcings by decomposing fields into spherical harmonics.
Space-time dependencies of climate variability
Within my work I investigate space-time dependencies of climate variability across scales. Climate models are generally consistent with observational records at the scale of global averages but tend to underestimate regional variability. A deeper examination of this mismatch might help to improve the understanding of regional climate variability as well the interpretation of such model output. For this purpose I started to analyse high resolution model data of the AWI Climate Model (FESOM coupled with ECHAM6).
Reconstructing past ocean water conditions using geochemistry in foraminifera
I am a paleoceanographer using the geochemistry of the calcite shells of foraminifera, single-celled organisms, to reconstruct how water mass conditions have changed in the past and how they may change in the future. I also work to develop the tools we use for these reconstructions, which are called proxies by investigating how under modern conditions different geochemical signatures are incorporated into the shells. Within ECUS and SPACE I am contributing the geochemical part to the question what is determining proxy-variability, i.e. what variability is natural and what is caused by analytical and procedural uncertainties. This will show how we can use the natural variability in proxy data as additional parameters recording climate change, e.g. seasonal or ENSO-variability.