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Abstract. Atmospheric dynamics are described by a set of partial differential equations yielding an infinite-dimensional phase space. However, the actual trajectories followed by the system appear to be constrained to a finite-dimensional phase space, i.e. a strange attractor. The dynamical properties of this attractor are difficult to determine due to the complex nature of atmospheric motions. A first step to simplify the problem is to focus on observables which affect -or are linked to phenomena which affect -human welfare and activities, such as sea-level pressure, 2 m temperature, and precipitation frequency. We make use of recent advances in dynamical systems theory to estimate two instantaneous dynamical properties of the above fields for the Northern Hemisphere: local dimension and persistence. We then use these metrics to characterize the seasonality of the different fields and their interplay. We further analyse the large-scale anomaly patterns corresponding to phase-space extremes -namely time steps at which the fields display extremes in their instantaneous dynamical properties. The analysis is based on the NCEP/NCAR reanalysis data, over the period 1948-2013. The results show that (i) despite the high dimensionality of atmospheric dynamics, the Northern Hemisphere sea-level pressure and temperature fields can on average be described by roughly 20 degrees of freedom; (ii) the precipitation field has a higher dimensionality; and (iii) the seasonal forcing modulates the variability of the dynamical indicators and affects the occurrence of phase-space extremes. We further identify a number of robust correlations between the dynamical properties of the different variables.

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The atmosphere’s chaotic nature limits its short-term predictability. Furthermore, there is little knowledge on how the difficulty of forecasting weather may be affected by anthropogenic climate change. Here, we address this question by employing metrics issued from dynamical systems theory to describe the atmospheric circulation and infer the dynamical properties of the climate system. Specifically, we evaluate the changes in the sub-seasonal predictability of the large-scale atmospheric circulation over the North Atlantic for the historical period and under anthropogenic forcing, using centennial reanalyses and CMIP5 simulations. For the future period, most datasets point to an increase in the atmosphere’s predictability. AMIP simulations with 4K warmer oceans and 4 × atmospheric CO2 concentrations highlight the prominent role of a warmer ocean in driving this increase. We term this the hammam effect. Such effect is linked to enhanced zonal atmospheric patterns, which are more predictable than meridional configurations.

It is of fundamental importance to evaluate the ability of climate models to capture the large-scale atmospheric circulation patterns and, in the context of a rapidly increasing greenhouse forcing, the robustness of the changes simulated in these patterns over time. Here we approach this problem from an innovative point of view based on dynamical systems theory. We characterize the atmospheric circulation over the North Atlantic in the CMIP5 historical simulations (1851–2000) in terms of two instantaneous metrics: local dimension of the attractor and stability of phase-space trajectories. We then use these metrics to compare the models to the Twentieth Century Reanalysis version 2c (20CRv2c) over the same historical period. The comparison suggests that (i) most models capture to some degree the median attractor properties, and models with finer grids generally perform better; (ii) in most models the extremes in the dynamical systems metrics match large-scale patterns similar to those found in the reanalysis; (iii) changes in the attractor properties observed for the ensemble-mean 20CRv2c are artifacts resulting from inhomogeneities in the standard deviation of the ensemble over time; and (iv) the long-term trends in local dimension observed among the 56 members of the 20CR ensemble have the same sign as those observed in the CMIP5 multimodel mean, although the multimodel trend is much weaker.

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We analyse and quantify the recurrences of European temperature extremes using 32 historical simulations (1900–1999) of the fifth Coupled Model Intercomparison Project (CMIP5) and 8 historical simulations (1971–2005) from the EUROCORDEX experiment. We compare the former simulations to the 20th Century Reanalysis (20CRv2c) dataset to compute recurrence spectra of temperature in Europe. We find that, (1) the spectra obtained by the model ensemble mean are generally consistent with those of 20CR; (2) spectra biases have a strong regional dependence; (3) the resolution does not change the order of magnitude of spectral biases between models and reanalysis, (4) the spread in recurrence biases is larger for cold extremes. Our analysis of biases provides a new way of selecting a subset of the CMIP5 ensemble to obtain an optimal estimate of temperature recurrences for a range of time-scales.

Summer hot temperatures have many impacts on health, economy (agriculture, energy, and transports), and ecosystems. In Western Europe, the recent summers of 2003 and 2015 were exceptionally warm. Many studies have shown that the genesis of the major heat events of the last decades was linked to anticyclonic atmospheric circulation and to spring precipitation deficit in Southern Europe. Such results were obtained for the second part of the 20th century and projections into the 21st century. In this paper, we challenge this vision by investigating the earlier part of the 20th century from an ensemble of 20CR reanalyses. We propose an innovative description of Western-European heat events applying the dynamical system theory. We argue that the atmospheric circulation patterns leading to the most intense heat events have changed during the last century. We also show that the increasing temperature trend during major heatwaves is encountered during episodes of Scandinavian Blocking, while other circulation patterns do not yield temperature trends during extremes.