projection uncertainty; climate scenarios; multi model ensembles; joint projections; internal climate variability
Saffioti Claudio, Fischer Erich M., Scherrer Simon C., Knutti Reto (2016), Reconciling observed and modeled temperature and precipitation trends over Europe by adjusting for circulation variability, in Geophysical Research Letters
, 43(15), 8189-8198.
Fischer E. M., Knutti R. (2015), Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes, in Nature Climate Change
, 5(6), 560-564.
Saffioti Claudio, Fischer Erich M., Knutti Reto (2015), Contributions of atmospheric circulation variability and data coverage bias to the warming hiatus, in Geophysical Research Letters
, 42(7), 2385-2391.
Addor Nans, Fischer Erich (2015), The influence of natural variability and interpolation errors on bias characterization in RCM simulations, in Journal of Geophysical Research-Atmospheres
, 120(19), 180-10.
Fischer Erich M. (2014), Detection of spatially aggregated changes in temperature and precipitation extremes, in Knutti, Reto
, 41(2), 547-554.
Fischer E. M., Sedláček J., Hawkins E., Knutti R. (2014), Models agree on forced response pattern of precipitation and temperature extremes, in Geophysical Research Letters
, 41(23), 8554-8562.
Huber Markus, Knutti Reto (2014), Natural variability, radiative forcing and climate response in the recent hiatus reconciled, in Nature Geoscience
, 7(9), 651-656.
Fischer Erich M., Beyerle Urs, Knutti Reto (2013), Robust spatially aggregated projections of climate extremes, in Nature Climate change
, 3(12), 1033-1038.
Adaptation to ongoing climate change is vital to reduce negative impacts and to take advantage of opportunities, and adaptation benefits from projections of future climate change with robust uncertainty estimates. This project will systematically assess the potential and limitations of a new set of global climate model simulations and a large initial condition ensemble of regional model simulations to provide impact-relevant climate information at regional to local scale for the coming decades. While climate model uncertainty is widely recognized as being important and is partly quantified using multiple models, the role of internal unforced climate variability (which can be dominant on regional to local scales for some variables) and the relationships across variables have received little attention. The first subproject will quantify and evaluate the simulated internal climate variability for different locations, timescales, and spatial scales, compare internal variability in unforced and forced simulations and will quantify the forced signals in climate extremes and uncertainty ranges induced by internal variability. The second subproject will focus on relationships across multiple variables (e.g. temperature and precipitation) and variables that combine multiple quantities (e.g. heat stress indicators combining temperature and humidity). It will evaluate the model performance in joint variability in present-day conditions, relate this to the projected changes in multiple variables and explore possible observational constraints on projections. Finally, it will focus on the uncertainty in combined variables and joint projections. The project will yield systematic insight into the temporal and spatial scales for which internal variability is most relevant, and provide methods to combine projections from multiple models that (unlike averaging) maintain the information of variability. It will identify variables, scales and regions where relationships between variables exist in principal, test whether they can constrain future projections, help in deciding for which cases the desired climate model projections of joint variables are feasible, and quantify uncertainties in impact relevant combined quantities when they are much smaller or much larger than expected from considering the uncertainties in the individual variables alone. Together, the project will lead to a better understanding of the processes that determine the magnitudes and timescales of internal variability, how different variables are related. It will contribute to the evaluation of a new set of climate models which, ultimately, will feed back into the model development process. In a broader sense, the results will provide important guidance for public authorities or private companies for adaptation by quantifying uncertainties in climate projections. The proposed project will strongly emphasize the communication of the results and involves a strong potential to trigger new high-impact research that is visible even outside the science community.