Abstract. Temperature is a key factor controlling plant growth and vitality in the temperate climates of the midlatitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies (LSTAD) and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.

Drought, caused by a prolonged deficit of precipitation, bears the risk of severe economic and ecological consequences for affected societies. The occurrence of this significant hydro-meteorological hazard is expected to strongly increase in many regions due to climate change, however, it is also subject to high internal climate variability. This calls for an assessment of climate trends and hot spots that considers the variations due to internal variability. In this study, the percent of normal index (PNI), an index that describes meteorological droughts by the deviation of a long-term reference mean, is analyzed in a single-model initial-condition large ensemble (SMILE) of the Canadian regional climate model version 5 (CRCM5) over Europe. A far future horizon under the Representative Concentration Pathway 8.5 is compared to the present-day climate and a pre-industrial reference, which is derived from pi-control runs of the CRCM5 representing a counterfactual world without anthropogenic climate change. Our analysis of the SMILE reveals a high internal variability of drought occurrence over Europe. Considering the high internal variability, our results show a clear overall increase in the duration, number and intensity of droughts toward the far future horizon. We furthermore find a strong seasonal divergence with a distinct increase in summer droughts and a decrease in winter droughts in most regions. Additionally, the percentage of summer droughts followed by wet winters is increasing in all regions except for the Iberian Peninsula. Because of particularly severe drying trends, the Alps, the Mediterranean, France and the Iberian Peninsula are suggested to be considered as drought hot spots. Due to the simplicity and intuitivity of the PNI, our results derived from this index are particularly appropriate for region-specific communication purposes and outreach.

High- and low pressure systems of the large-scale atmospheric circulation in the mid-latitudes drive European weather and climate. Potential future changes in the occurrence of circulation types are highly relevant for society. Classifying the highly dynamic atmospheric circulation into discrete classes of circulation types helps to categorize the linkages between atmospheric forcing and surface conditions (e.g. extreme events). Previous studies have revealed a high internal variability of projected changes of circulation types. Dealing with this high internal variability requires the employment of a single-model initial-condition large ensemble (SMILE) and an automated classification method, which can be applied to large climate data sets. One of the most established classifications in Europe are the 29 subjective circulation types called Grosswetterlagen by Hess & Brezowsky (HB circulation types). We developed, in the first analysis of its kind, an automated version of this subjective classification using deep learning. Our classifier reaches an overall accuracy of 41.1% on the test sets of nested cross-validation. It outperforms the state-of-the-art automatization of the HB circulation types in 20 of the 29 classes. We apply the deep learning classifier to the SMHI-LENS, a SMILE of the Coupled Model Intercomparison Project phase 6, composed of 50 members of the EC-Earth3 model under the SSP37.0 scenario. For the analysis of future frequency changes of the 29 circulation types, we use the signal-to-noise ratio to discriminate the climate change signal from the noise of internal variability. Using a 5%-significance level, we find significant frequency changes in 69% of the circulation types when comparing the future (2071–2100) to a reference period (1991–2020).

Vb cyclones are major drivers of extreme precipitation and floods in the study area of hydrological Bavaria (Germany). When assessing climate change impacts on Vb cyclones, internal variability of the climate system is an important underlying uncertainty. Here, we employ a 50-member single-model initial-condition large ensemble of a regional climate model to study climate variability and forced change on Vb cyclones. An artificial neural network detects cutoff lows over central Europe, which are associated with extreme precipitation Vb cyclones. Thus, machine learning filters the large ensemble prior to cyclone tracking. Our results show a striking change in Vb seasonality with a strong decrease of Vb cyclones in summer (−52%) and a large increase in spring (+73%) under the Representative Concentration Pathway 8.5. This change exceeds the noise of internal variability and leads to a peak shift from summer to spring. Additionally, we show significant increases in the daily precipitation intensity during Vb cyclones in all seasons.Plain Language Summary Bavaria, a state in the southeast of Germany, has been hit by several devastating floods in recent decades triggered by a storm type called Vb. For future flood risk in Bavaria it is crucial to understand how climate change affects Vb storms. This study uses high-resolution climate simulations over Europe to study changes in the frequency of Vb storms, their seasonal occurrence, and their rainfall intensity under a high greenhouse gas concentration scenario. However, Vb storms are rare events and a single simulation may not provide enough events to distinguish between climate change and random, natural variations. Therefore, we employ a large database of 50 climate simulations with the same settings and greenhouse gas concentration scenario, but slightly different starting conditions, in order to robustly estimate climate change effects on Vb storms. The drawback of using 50 simulations is the high amount of data. Therefore, we apply machine learning for pattern recognition to detect the low-pressure systems related to extreme precipitation Vb storms in the climate simulations. Our results show that climate change considerably affects the seasonal occurrence of Vb storms with a shift from summer to spring. Furthermore, the daily rainfall intensity in Bavaria increases during Vb storms significantly with climate change.

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<p>Communicating the uncertainty of natural climate variability to the public and researchers from other fields remains challenging. In this context, the concept of time of emergence (ToE) i.e., the year or decade when the climate signal emerges from the natural climate variability, has been well established over the past years. In addition, global warming levels (GWLs) are used more and more frequently to define the future projection horizon. However, only a few studies combined these two approaches. In this study, we utilize multiple initial condition large ensembles from CMIP6, to more robustly sample extreme events and account for natural climate variability, to estimate the global warming level of emergence (GWLoE) of various ETCCDI indicators. These indicators were selected to represent both precipitation and temperature extremes. Further, we analyze the impact of incremental temperature changes on the emergence of these indicators. Additionally, the GWLs are analyzed in relation to changes in the probability risk ratio to highlight that every degree of additional warming counts. Different scenarios for population changes are applied to estimate the population affected by the emergence of indicators as well as for a doubling in probability risk ratio. The combined GWLoE of all large ensembles highlights considerable regional differences among the individual ensembles. Similarly, regional differences arise for the GWL related to a doubling in probability risk ratio. The changes in population affected by these changes in risk ratio highlight the need to limit global warming as much as possible.</p>

<p>Internal climate variability describes the natural random fluctuation of the climate system, which arise from non-linear dynamic processes in the atmosphere and ocean and are intrinsic to the climate system. It is one of the major sources of uncertainty in climate projections, besides model error and scenario uncertainty. The research branch of <em>initial-condition large ensembles (SMILEs) </em>investigates internal climate variability by employing large ensembles of dozens of climate simulations that are generated using the same model and scenario but slightly different initial conditions. With this, SMILEs enable to statistically capture the spread of internal variability of the climate system.</p><p>Regarding science communication with the general public, internal climate variability is an especially challenging topic. This is for one thing due to the large ensembles involved, which call for an adequate presentation format; for another, the possibilities to capture the concept of internal climate variability with real-life experiences are limited since observations represent only one realization of the climate state and, in the case of extreme events, deliver only a limited number of events that are insufficient to cover the spread of internal climate variability.</p><p>Here, we present three approaches to encounter the challenges in climate communication with respect to internal climate variability. The first approach involves a study on the extreme event of meteorological droughts in Europe and uses the intuitive drought index “<em>percent-of-normal</em>” to prepare the results of a regional SMILE over Europe for the purpose of science communication. “<em>Drying stripes</em>”, an illustration form inspired by the <em>“warming stripes”</em> by Ed Hawkins, are used to tailor the obtained results in an appealing way for the media (press releases, television and newspapers). The second study approach uses several global SMILEs from the <em>Coupled Intercomparison Project 6 (CMIP6)</em> to investigate climate change effects and the range of internal climate variability for important temperature- and precipitation-based climate indices. The concepts of <em>time of emergence</em> and <em>global warming levels</em> are used to derive concise statements about the degree of global warming at which the analyzed climate indices exceed the noise of internal climate variability. The third approach targets at the preparation of the results from the ClimEx project (“Climate Change and Hydrological Extremes”), which deals with internal climate variability over the study area of Bavaria. The results are presented in form of a flyer and a hydrological atlas, which has been used for communication purposes in cooperation with the Bavarian State Office for the Environment.</p><p>These three approaches are initiatives to facilitate a condensed and comprehensible communication of the uncertainty of internal climate variability in climate projections to the general public. The presented formats include analysis, figures, statements and flyers that are targeted for media presentation and agency-based science communication.</p>

<p>Climate change is altering the Earth’s atmospheric circulation and the dynamic drivers of extreme events. Extreme weather events pose a great potential risk to infrastructure and human security. In Montréal (Québec, Canada) long-duration mixed precipitation events (freezing rain and/or ice pellets) are high-impact cold-season hazards and an understanding of how climate change alters their occurrence is of high societal interest.</p><p>Here, we introduce a two-staged deep learning approach that uses the synoptic-scale drivers of mixed precipitation to identify these extreme events in archived climate model data. The approach is destined for the application on regional climate model (RCM) data over the Montréal area. The dominant dynamic mechanism leading to mixed precipitation in Montréal is pressure-driven channeling of winds along the St. Lawrence river valley. The identification of the synoptic-scale pressure pattern related to pressure-driven channeling is a visual image classification task that is addressed with supervised machine learning. A convolutional neural network (CNN) is trained on the classification of the synoptic-scale pressure patterns by using a large training database derived from an ensemble of the Canadian Regional Climate Model version 5 (CRCM5). The CRCM5 is to our knowledge the only RCM available so far that employs the diagnostic method by Bourgouin to simulate mixed precipitation inline and thus delivers training examples and labels for this supervised classification task.</p><p>The CNN correctly identifies 90 % of the Bourgouin mixed precipitation cases in the test set. The weak point of the approach is a high type I error, which is enhanced in a second stage by applying a temperature condition. The evaluation on an CRCM5 run driven by ERA-Interim reanalysis reveals a still low precision of 21 % and thus a Matthews correlation coefficient of 0.39. The deep learning approach can be applied to ensembles of regional climate models on the North America grid of the Coordinated Regional Downscaling Experiment (CORDEX-NA).</p>