El Niño–Southern Oscillation (ENSO) is one of the dominant climatic phenomena in the equatorial Pacific. Understanding and predicting how ENSO might change in a warmer climate is both societally and scientifically important. We use 1000-year-long simulations from seven climate models to analyze ENSO in an idealized stable climate. We show that ENSO will be weaker and last shorter under the warming, while the skill of ENSO prediction will unlikely change.

This paper presents an analysis of wind sensitivity experiments in order to provide insight into the wind forcing dependence of the AMOC by understanding the behavior of its depth scale(s).

Novel techniques from computer science are used to study extreme weather events. Inspired by the interactive 3-D visual analysis of the recently released ERA5 reanalysis data, we improve commonly used metrics for measuring polar winter storms and outbreaks of cold air. The software (Met.3D) that we have extended and applied as part of this study is freely available and can be used generically for 3-D visualization of a broad variety of atmospheric processes in weather and climate data.

This work questions the influence of the Atlantic Meridional Overturning Circulation, an important component of the climate system, on the variability in North Atlantic sea surface temperature (SST) a season ahead, particularly how this influence affects SST prediction credibility 2–4 months into the future. While we find this relationship is relevant for assessing SST predictions, it strongly depends on the time period and season we analyse and is more subtle than what is found in observations.

We investigate the stratospheric influence on marine cold air outbreaks (MCAOs) in the North Atlantic using ERA-Interim reanalysis data. MCAOs are associated with severe Arctic weather, such as polar lows and strong surface winds. Sudden stratospheric events are found to be associated with more frequent MCAOs in the Barents and the Norwegian seas, affected by the anomalous circulation over Greenland and Scandinavia. Identification of MCAO precursors is crucial for improved long-range prediction.

The Standardized Precipitation Index (SPI) is a widely accepted drought index. SPI normalizes the precipitation distribution via a probability density function (PDF). However, which PDF properly normalizes SPI is still disputed. We suggest using a previously mostly overlooked PDF, namely the exponentiated Weibull distribution. The proposed PDF ensures the normality of the index. We demonstrate this – for the first time – for all common accumulation periods in both observations and simulations.

A new method for probabilistic spatial reconstructions of past climate states is presented, which combines pollen data with a multi-model ensemble of climate simulations in a Bayesian framework. The approach is applied to reconstruct summer and winter temperature in Europe during the mid-Holocene. Our reconstructions account for multiple sources of uncertainty and are well suited for quantitative statistical analyses of the climate under different forcing conditions.

Ensemble forecast verification treats the issues of forecast errors and uncertainty estimated from ensemble spread. We suggest measures based on relative entropy. For continuous variables correlation and the mean ratio of the ensemble spread to climate variance (analysis of variance (anova)) are related to these entropies. For categorical data corresponding scores are deduced that allow the comparison with continuous data.

In a climate projection experiment with the Max Planck Institute Earth System Model (MPI-ESM), we find that a decline in the Atlantic Ocean meridional heat transport (OHT) is accompanied by a change in the seasonal cycle of the total OHT and its components. We found a northward shift of 5° and latitude-dependent shifts between 1 and 6 months in the seasonal cycle that are mainly associated with changes in the meridional velocity field rather than the temperature field.

This study applies numerical clustering for the classification of about 1500 ozone data sets in Europe. We show the usefulness of cluster analysis (CA) for the quantitative evaluation of a global model: pre-selection of stations and validation of a global model in a phase-space produce clearer and more interpretable results. CA can be easily updated for new stations, different length of data, and other type of input properties, as well as other type of data (for example, meteorological).