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 SPACE project, I’m coordinating a small team of researchers and students working on a better use of the paleoclimate record to sharpen our knowledge about the climate system.
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).
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.