The application of an ensemble reduction technique to the European branch of the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) ensemble at resolution “EUR-11” (~12.5 km) under the RCP8.5 scenario is presented. The technique is based on monthly mean changes between a reference and two future time periods, calculated for eight regions in Germany, of the parameters near-surface air temperature (tas), precipitation totals (pr), contribution of precipitation from very wet days to precipitation totals (R95pTOT), near-surface specific humidity (huss), and surface downwelling shortwave radiation (rsds). The sensitivity of the reduction procedure with respect to a number of tuning parameters is investigated. When the optimal combination of tuning parameters is applied, the technique allows the reduction from 15 to 7 ensemble members, while the reduced ensemble reproduces about 94% of the spread of the full ensemble. Keeping in mind that climate projection ensembles are expected to grow substantially in the near future, this ensemble reduction technique can be useful to limit the computational efforts necessary for further processing and applications such as impact modeling.

Abstract. In climatological research, the evaluation of climate models is one of the central research subjects. As an expression of large-scale dynamical processes, global teleconnections play a major role in interannual to decadal climate variability. Their realistic representation is an indispensable requirement for the simulation of climate change, both natural and anthropogenic. Therefore, the evaluation of global teleconnections is of utmost importance when assessing the physical plausibility of climate projections. We present an application of the graph-theoretical analysis tool δ-MAPS, which constructs complex networks on the basis of spatio-temporal gridded data sets, here sea surface temperature and geopotential height at 500 hPa. Complex networks complement more traditional methods in the analysis of climate variability, like the classification of circulation regimes or empirical orthogonal functions, assuming a new non-linear perspective. While doing so, a number of technical tools and metrics, borrowed from different fields of data science, are implemented into the δ-MAPS framework in order to overcome specific challenges posed by our target problem. Those are trend empirical orthogonal functions (EOFs), distance correlation and distance multicorrelation, and the structural similarity index. δ-MAPS is a two-stage algorithm. In the first place, it assembles grid cells with highly coherent temporal evolution into so-called domains. In a second step, the teleconnections between the domains are inferred by means of the non-linear distance correlation. We construct 2 unipartite and 1 bipartite network for 22 historical CMIP6 climate projections and 2 century-long coupled reanalyses (CERA-20C and 20CRv3). Potential non-stationarity is taken into account by the use of moving time windows. The networks derived from projection data are compared to those from reanalyses. Our results indicate that no single climate projection outperforms all others in every aspect of the evaluation. But there are indeed models which tend to perform better/worse in many aspects. Differences in model performance are generally low within the geopotential height unipartite networks but higher in sea surface temperature and most pronounced in the bipartite network representing the interaction between ocean and atmosphere.

The variability of the sea level pressure in the North Atlantic sector is the most important driver of weather and climate in Europe. The main mode of this variability, the North Atlantic Oscillation (NAO), explains up to 50% of the total variance. Other modes, known as the Scandinavian index, East Atlantic, and East Atlantic/West Russian pattern, complement the variability of the sea level pressure, thereby influencing the European climate. It has been shown previously that a seasonal prediction system with enhanced winter NAO skill due to ensemble subsampling entails an improved prediction of the surface climate variables as well. Here, we show that a refined subselection procedure that accounts both for the NAO index and for the three additional modes of sea level pressure variability is able to further increase the prediction skill of wintertime mean sea level pressure, near‐surface temperature, and precipitation across Europe.

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The variability of the sea level pressure in the North Atlantic sector is the most important driver of weather and climate in Europe. The main mode of this variability, the North Atlantic Oscillation (NAO), explains up to 50% of the total variance. Other modes, known as the Scandinavian index, East Atlantic, and East Atlantic/West Russian pattern, complement the variability of the sea level pressure, thereby influencing the European climate. It has been shown previously that a seasonal prediction system with enhanced winter NAO skill due to ensemble subsampling entails an improved prediction of the surface climate variables as well. Here, we show that a refined subselection procedure that accounts both for the NAO index and for the three additional modes of sea level pressure variability is able to further increase the prediction skill of wintertime mean sea level pressure, near-surface temperature, and precipitation across Europe.Plain Language Summary Atmospheric winter conditions in Europe are primarily controlled by the varying pressure field over the North Atlantic, influencing temperature and precipitation in Europe. Current seasonal forecasts of European winter climate, though highly desirable for society and economy, are as yet not fully reliable. There exist a number of autumn predictors, such as sea surface and stratospheric temperature, Eurasian snow depth, and Arctic sea ice, that impact on the upcoming pressure regimes in a predictable way. The present dynamical seasonal forecast systems respond still too weakly to these known seasonal predictors. But the relationship is reproduced quite well by means of statistics. In combination, statistical and dynamical forecasts have the potential to improve forecasts of the North Atlantic pressure conditions and thereby affected variables like temperature and precipitation in Europe considerably. We extend an existing hybrid seasonal forecast procedure by considering more modes of variability of the Atlantic pressure regimes than just the North Atlantic Oscillation. In this way, we are able to improve the forecasts for temperature and precipitation over wider regions in Europe. Cohen et al. (2019) argue that new statistical techniques can increase the accuracy of seasonal forecasts and advocate the development of hybrid dynamical-statistical forecasts to produce more robust seasonal predictions. Hybrid forecasts based on circulation specification were presented, for example, by Baker, Shaffrey, and Scaife (2018) and Dobrynin et al. (2018).In boreal winter, European weather and climate is dominated by the zonal propagation of planetary and synoptic-scale waves. This large-scale circulation is an extremely high-dimensional phenomenon in real RESEARCH LETTER