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Internal variability in a changing climate: A large ensemble perspective on tropical Atlantic rainfall

MPS-Authors
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Milinski,  Sebastian
IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society;
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Fulltext (public)

WEB_BzE_219_Milinski_Sebastian.pdf
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Citation

Milinski, S. (2019). Internal variability in a changing climate: A large ensemble perspective on tropical Atlantic rainfall. PhD Thesis, Universität Hamburg, Hamburg. doi:10.17617/2.3165869.


Cite as: http://hdl.handle.net/21.11116/0000-0004-BC5E-0
Abstract
In this dissertation I investigate the temporal development of internal variability under global warming. Understanding internal variability is essential to understand the past and possible future trajectories of our climate, yet it is often assumed to be a property of the climate system that does not change under global warming. I use a novel large ensemble, the Max Planck Institute Grand Ensemble, to introduce a new perspective on internal variability. Internal variability can be described as the seemingly random fluc- tuations of the climate system over time. Due to nonlinearity in the climate system, small perturbations may grow to large anomalies over time that are associated with anomalous or even extreme events. In my first chapter, I quantify internal variability and investigate whether it changes under global warming. The change in the external forcing is the same for all of these realisations, the initial conditions are different for each realisation. Thus, each realisation follows its own, unique tra- jectory. For each time step, the distribution of all realisations provides an estimate of the possible states of the climate system. I develop an analysis framework based on a large ensemble to detect, quantify and attribute changes in internal variability in a transient climate. Rather than analysing variability over time, I use the ensemble dimension of a large ensemble to quantify internal variability. This approach allows a clean separation of the forced signal from internal variability and ensures stationarity of the statistics even when the forcing is changing with time. My non-parametric approach provides an objective quantification of changes in internal variability and their robustness. In my second chapter I apply this analysis framework to investigate rainfall in the tropical Atlantic region in the past and its possible future trajectories. I can show that simulated internal variability in the Sahel encompasses all observed values for the 20th century. The model suggests an externally forced increase in rainfall towards the end of the 20th century. However, due to large internal variability, it is not possible to detect this forced change in a single realisation. In future projections, I find an increase in the mean rainfall over the Sahel, accompanied by an increase in the variability. This implies that the average rainfall will increase, but individual years may show deviations from this mean value that are larger than under present-day conditions. In the tropical Atlantic region, most state-of-the-art coupled climate models show large biases in the simulated sea surface temperature and rainfall when compared to observations. These model errors