Dirk Olonscheck

Single-model initial-condition large ensembles are powerful tools to quantify the forced response, internal climate variability, and their evolution under global warming. I present the CMIP6 version of the Max Planck Institute Grand Ensemble (MPI-GE CMIP6). The ensemble has currently 30 realisations for the historical period and for each five emission scenarios. The power of MPI-GE CMIP6 goes beyond its predecessor ensemble MPI-GE CMIP5 (Maher et al., 2019) by providing high-frequency output, the full range of emission scenarios including the highly policy-relevant low emission scenarios SSP1-1.9 and SSP1-2.6, and the opportunity to compare the ensemble to complementary high-resolution simulations. I evaluate the new ensemble with observations and reanalyses, and compare it to MPI-GE CMIP5. I further demonstrate with six application examples how to use the power of the ensemble to better quantify and understand present and future climate extremes, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI-GE CMIP6 allows one to show that the recently observed Siberian and Pacific North American heatwaves would only avoid reaching 1-2 year return periods in 2071-2100 with low emission scenarios, that recently observed European precipitation extremes are captured only by complementary high-resolution simulations, and that 3-hourly output projects a decreasing activity of storms in mid-latitude oceans. Further, the ensemble is ideal for estimates of probabilities of crossing global warming limits and the irreducible uncertainty introduced by internal variability, and is sufficiently large to be used for infilling surface temperature observations with artificial intelligence.

Reference: Olonscheck, D., Suarez‐Gutierrez, L., Milinski, S., Beobide‐Arsuaga, G., Baehr, J., Fröb, F., et al. (2023), The New Max Planck Institute Grand Ensemble with CMIP6 forcing and high‐frequency model output, Journal of Advances in Modeling Earth Systems

I here show that observed and simulated trends in sea surface temperature (SST) patterns are globally consistent when accounting for internal variability. Some individual ensemble members of single-model initial-condition large ensembles simulate trends in large-scale SST patterns that closely resemble the observed ones. Observed regional trends that lie at the edge of the simulated distribution of internal variability - such as for the tropical Pacific east-to-west SST gradient - are hard to interpret. The observed trends could be an unusual realization of the Earth's possible behavior and/or the models are systematically biased but large internal variability leads to good matches with the observations.

Reference: Olonscheck et al. (2020), Broad consistency between observed and simulated trends in sea surface temperature patterns, Geophysical Research Letters.

I show that human-caused changes in internal year-to-year temperature variability are expected to emerge from the unforced range by the end of the 21^st century. This emergence is shown based on climate model initial-condition large ensembles forced with strong global warming scenarios. I reveal that different simulated changes in globally averaged temperature variability between models can be explained by a trade-off between strong increases in variability on tropical land and substantial decreases in high latitudes. This latitudinal pattern of temperature variability change is consistent with the observed and projected loss of sea ice in high latitudes and changes in vegetation cover in the tropics. Instrumental records support this emerging pattern, but have data gaps in key regions. Comparison of the model output with paleoclimate proxy reconstructions supports the simulated magnitude and distribution of temperature variability. The findings strengthen the need for urgent mitigation to avoid unprecedented changes in temperature variability.

Reference: Olonscheck et al. (2021), Large-scale emergence of regional changes in year-to-year temperature variability by the end of the 21st century, Nature Communications, featured as Editors’ Highlights (Climate Change Impacts).

I systematically quantify the possible drivers of the sea-ice variability in the Arctic. By isolating the impact of the individual drivers in an Earth system model, I show that the internal variability of the Arctic sea-ice area is primarily caused directly by atmospheric temperature fluctuations. In contrast to the wide-spread belief that feedbacks related to surface albedo, clouds and water vapour, or the forcing by poleward ocean heat transport are crucial, I find that these processes are only of minor relevance for driving sea-ice variability.

Reference: Olonscheck et al. (2019), Arctic sea-ice variability is primarily driven by atmospheric temperature fluctuations, Nature Geoscience.

I investigate how internal climate variability changes under global warming. To do so, I develop a new method that relates the preindustrial internal variability to the internal variability for a changing forcing. The estimate for the forced time period is derived from the ensemble spread of single-model ensembles of a multi-model ensemble. This practically applies the quasi-ergodic assumption, and allows one to assess whether the range of internal variability of any climate variable is projected to change.

Reference: Olonscheck & Notz (2017), Consistently Estimating Internal Climate Variability from Climate Model Simulations, Journal of Climate.

Climate extremes often have substantial impacts on society. However, it is unclear, how and why they will change in the future. Using an empirical threshold approach, I quantitatively attribute the future probability of changes in daily temperature, wind and precipitation extremes to anthropogenic global warming and associated changes in internal variability. I show that this different origin of future changes in climate extremes is important, because their origin determines the frequency and intensity of extremes. This is because climate extremes are, on average, much more severe when caused by an increased variability than when caused by a mere shift in the mean climate.

Reference: Olonscheck (2018), Understanding internal variability of sea ice and surface air temperature, PhD Thesis.

I communicate my research, and our knowledge on the polar climate and climate variability by giving talks in schools and by demonstrating small physical experiments at public events. Together with colleagues, I also developed the strategy board game "Cold Cooperation" that highlights the challenge of international cooperation to both satisfy partial interests of individual countries and to avoid the most dramatic consequences of global warming. The game can be downloaded here: owncloud.gwdg.de/index.php/s/n8tghGRSGg74Hn0