In order to understand and predict the human influence on the earth system, it is particularly important to quantify the structure of internal climate and environmental variability and the sensitivity of the earth system to external forcing. We develop and use a systematic approach to constrain the possible range of future climates and environments through the use of (proxy) observations, statistics and models.

Space-time structure of climate change (SPACE)

European Research Council (ERC) Starting Grant

SPACE determines and uses the space-time structure of climate change from years to millennia to test climate models, fundamentally improve the understanding of climate variability and provide a stronger basis for the quantitative use of paleoclimate records.

DEEPICE

Horizon 2020 research and innovation programme

Fyntan Shaw's PhD project "Estimating and accounting for diffusion in deep ice using advanced statistical methods" is funded by the DEEPICE training network.

PalMod

Federal Ministry of Education and Research (BMBF)

As part of the national project PalMod, ESD members work on quantifying of transient proxy uncertainty.

CorCliV

Seasonal to decadal tropical Sea Surface Temperature variability from corals: timescale dependent fidelity of δ18O and Sr/Ca records

CorCliV is a DFG funded project and part of SPP 2299: Tropical Climate Variability and Coral Reefs. A Past to Future Perspective on Current Rates of Change at Ultra-High Resolution.

HEIBRiDS - Helmholtz Einstein International Berlin Research School in Data Science

HEIBRiDS Logo (Photo: HEIBRiDS)

Spatial climate variability patterns reconstructed with Bayesian Hierarchical Learning

The project aims to reconstruct spatial patterns of timescale-dependent climate variability. For that a Bayesian Hierarchical Model will be developed that incorporates a variety of proxy data while considering proxy processes and noise. It aims to quantify limitations and uncertainties of derived climate variability reconstructions related to the covariance structure used and the sparseness, spatial heterogeneity and noisiness of the observational data through Bayesian posterior distributions. We will use the climate variability map to investigate regional patterns of low-frequency variability and the corresponding implications e.g. for the range of possible future climate trends in natural variability and of the frequency of extreme events.