A1: Climate Variability and Predictability

Within this research topic, we investigate the variability and the predictability of climate taking both internal variations and external forcing into account. Internal variations determine climate predictability on a wide range of scales: While slow variations constitute predictability, short-term unpredictable fluctuations can notably limit predictability. External forcing factors affect climate predictability through the different climate responses to these perturbations. Our research is primarily based on numerical climate simulations.


Work within A1 investigates the variability and predictability of climate from three perspectives:

  1. Climate predictability affected by and resulting from internal variability: we aim to identify the masking effects of unpredictable fluctuations and their role for large-scale dynamics and investigate the mechanisms and deterministic time scales of predictable slow climate component.
  2. Quantifying and reducing uncertainties relevant for predictability: we aim to develop a parametrization of ocean mixing that depends on the climate state.
  3. Predictability originating from responses to perturbations in external forcing: we analyse past millennium simulations and apply methods of non-equilibrium statistical mechanics to the climate system.

Latest A1 Publications

  • Wiegand, K. N., Brune, S., & Baehr, J. (2019). Predictability of Multiyear Trends of the Pacific Decadal Oscillation in an MPI-ESM Hindcast Ensemble. Geophysical Research Letters, 46(1), 318-325. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059469952&doi=10.1029%2f2018GL080661&partnerID=40&md5=5e0b0a0b20669539d4136181c5cefbd1.
  • Blender, R., Gohlke, D., & Lunkeit, F. (2018). Fluctuation analysis of the atmospheric energy cycle. Physical Review E, 98: 023101, pp. 1-7. doi:10.1103/PhysRevE.98.023101.
  • Tantet, A., Lucarini, V., Lunkeit, F., & Dijkstra, H. A. (2018). Crisis of the chaotic attractor of a climate model: a transfer operator approach. Nonlinearity, 31(5), 2221-2251. doi:10.1088/1361-6544/aaaf42.
  • Bunzel, F., Müller, W. A., Dobrynin, M., Fröhlich, K., Hagemann, S., Pohlmann, H., Stacke, T., & Baehr, J. (2018). Improved seasonal prediction of European summer temperatures with new five-layer soil-hydrology scheme. Geophysical Research Letters, 45, 346-353. doi:10.1002/2017GL076204.
  • Risbey, J. S., O'Kane, T. J., Monselesan, D. P., Franzke, C., & Horenko, I. (2018). On the Dynamics of Austral Heat Waves. Journal of Geophysical Research-Atmospheres, 123, 38-57. doi:10.1002/2017JD027222.