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Independent estimation and verification of fossil CO2 emissions on a regional and national scale is crucial to evaluate the fossil CO2 emissions and reductions reported by countries as part of their nationally determined contributions (NDCs). Top-down methods, such as the assimilation of in situ and satellite observations of different tracers (e.g. CO2, CO, ∆14CO2, XCO2, have been increasingly used lately for this purpose. In this paper, we use the Lund University Modular Inversion Algorithm (LUMIA) to estimate fossil CO2 emissions and natural fluxes by inverting simultaneously in situ observations of CO2 and ∆14CO2 over Europe. We evaluate the inversion system by performing a series of Observing System Simulation Experiments (OSSEs). We find that in regions with a dense sampling network, such as Western/Central Europe, when we add ∆14CO2 observations in an experiment where the prior fossil CO2 and biosphere fluxes are set to zero, LUMIA is capable of recovering the time series of both categories, reducing the prior to truth RMSE from 1.26 TgC day-1 to 0.12 TgC day-1 in fossil CO2 and from 0.97 TgC day-1 to 0.17 in biosphere, and the true total CO2 budget in 91 %. In a second set of experiments, using realistic prior fluxes, we find that, in addition to retrieving the time series of the optimized fluxes, we are able to recover the true regional fossil CO2 budget in Western/Central Europe by 95 % and in Germany by 97 %. In regions with low sampling coverage, such as Southern Europe and the British Isles, the posterior fossil CO2 emissions are not well resolved in any scenario, and the biosphere fluxes can follow the seasonality with a significant bias that makes it impossible to close the total CO2 budget. We find that the prior uncertainty of fossil CO2 emissions does not significantly impact the posterior estimates, showing similar results in regions with good sampling coverage like Western/Central and Northern Europe. Finally, it is important to have a good prior estimate of the terrestrial isotopic disequilibrium to avoid including additional noise to the posterior fossil CO2 fluxes. [ABSTRACT FROM AUTHOR]

Drought and heat events are becoming more frequent in Europe due to human-induced climate change, affecting many aspects of human well-being and ecosystem functioning. However, the intensity of these drought and heat events is not spatially and temporally uniform. Understanding the spatial variability of drought impacts is important information for decision makers, supporting both planning and preparations to cope with the changing climatic conditions. Currently, data relating to the damage caused by extended drought episodes is scattered across languages and sources such as scientific publications, governmental reports and the media. In this review paper, we compiled data of damages caused by the drought and heat of 2018 until 2022 in forest ecosystems and relate it to large European data sets, providing support for decision making both on the regional and European levels. We partitioned data from 16 European countries to the following regions: Northern, Central, Alpine, and South. We focused on drought and heat damage to forests, and categorized them as (1) physiological (2) pest, and (3) fire damage. We were able to identify the following key trends: (1) Relative defoliation rates of broadleaves is higher than of conifers in every country with the exception of Czech Republic (2) the incidence of wood destroyed by insects is extremely high in Central Europe and Sweden (3) Although forest fires can be related to heat and drought, they are superimposed by other anthropogenic influences (4) In this period (2018–2022), forests in central Europe are particularly affected, while forests in the Northern and Alpine zones are less affected, and adaptations to heat and drought can still be observed in the Southern zone. (5) Although in several regions 2021 was an average year still high levels of damages were observed indicating strong legacy effects of 2018–2020. We note that the inventory should be continuously updated as new data appear. [ABSTRACT FROM AUTHOR]

In this paper, the implementation and validation of the torus mapping, a method to dealias radial wind observations by meteorological radars is presented. We apply the procedure to data from the Operational program for exchange of weather radar information (OPERA) over central part of Europe for the whole year 2021. The quality of the resulting radar winds is assessed by a comparison to colocated radiosonde and aircraft observations and also through analysis of first guess departures using a regional numerical weather prediction model. Performance of quality control on dealiased radial wind data is also evaluated. We show that the torus mapping method is a robust procedure on data with a sufficiently low amount of measurement noise. It produces datasets of comparable quality for a wide range of Nyquist velocity values and has potential for operational applications. [ABSTRACT FROM AUTHOR]

ClimeApp is a newly developed web-based processing tool for the state-of-the-art ModE-RA paleo-climate reanalysis. It presents temperature, precipitation and pressure reconstructions with global coverage and monthly resolution for the period 1422 to 2008 C.E. These can be visualized as maps or timeseries and compared with historical or other climate-related information through composite, correlation and regression functions. Alongside ModE-RA, ClimeApp allows access to the ModE-Sim climate simulation, which is the basis of ModE-RA before assimilating early instrumental, documentary and proxy data. Together with the sensitivity experiment ModE-RAclim, these three data sets allow researchers to separate the effects of external forcing from internal climate variability. The app is designed to allow quick data processing for climatologists and easy use for non-climatologists. Specifically, it aims to help bring climate into the humanities, where climatological data still has huge potential to advance research. This paper outlines the development, processing and applications of ClimeApp, and presents an updated analysis of the calamitous Tambora volcanic eruption and the 1816 'year without a summer' in Europe, using the new ModE datasets. ClimeApp is available at https://mode-ra.unibe.ch/climeapp/. [ABSTRACT FROM AUTHOR]

In this study we present a series of sensitivity experiments conducted for the Last Glacial Maximum (LGM, ~21000 years ago) over Europe using the regional climate Weather Research and Forecasting model (WRF). Using a 4 step 2-way nesting approach, we are able to reach a convection-permitting horizontal resolution over the inner part of the study area, covering Central Europe and the Alpine region. The main objective of the paper is to evaluate a model version including a series of new developments better suitable for the simulation of paleo-glacial time slices with respect to the ones employed in former studies. The evaluation of the model is conducted against newly available pollen-based reconstructions of the LGM European climate and takes into account the effect of two main sources of model uncertainty: a different height of continental glaciers at higher latitudes of the northern hemisphere and different land cover. Model results are in good agreement with evidence from the proxies, in particular for temperatures. Importantly, the consideration of different ensemble members for characterizing model uncertainty allows to increase the agreement of the model against the proxy reconstructions that would be obtained when considering a single model realization. The spread of the produced ensemble is relatively small for temperature, beside areas surrounding glaciers in summer. On the other hand, differences between the different ensemble members are very pronounced for precipitation, in particular in winter over areas highly affected by moisture advection from the Atlantic. This highlights the importance of the considered sources of uncertainty for the study of European LGM climate and allows to determine where the results of an RCM are more likely to be uncertain for the considered case study. Finally, the results are also used to demonstrate the added value of convection-permitting resolutions, at both local and regional scales, under glacial conditions. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of multi-decadal climatic variability on the temporal evolution of root zone storage capacities (Sr,max) and its implications for streamflow predictions at the catchment scale. Through a comprehensive analysis of 286 catchments across Europe and the US, we analyse the deviations in evaporative ratios (IE) from expected values based on catchment aridity (IA) and their subsequent impact on Sr,max predictions. Our findings reveal that while catchments do not strictly adhere to their specific parametric Budyko curves over time, the deviations in IE are generally very minor, with an average ΔIE = 0.01 and an interquartile range IQR= -0.01 to 0.03. Consequently, these minor deviations lead to limited changes in predictions of Sr,max, mostly ranging between -10.5 and +21.5 mm (-5.1 % to +9.9 %). When these uncertainties in Sr,max are incorporated into hydrological models, the impact on streamflow predictions is found to be marginal, with the most significant shifts in monthly evaporation and streamflow not exceeding 4 % and 12 %, respectively. Our study underscores the utility of parametric Budyko-style equations for first order estimates of future Sr,max in hydrological models, even in the face of climate change and variability. This research contributes to a more nuanced understanding of hydrological responses to changing climatic conditions and offers valuable insights for future climate impact studies in hydrology. [ABSTRACT FROM AUTHOR]

This paper describes the GENerator of Reduced Organic Aerosol Mechanisms (GENOA) that produces semi-explicit mechanisms for simulating the formation and evolution of secondary organic aerosol (SOA) in air-quality models. Using a series of predefined reduction strategies and evaluation criteria, GENOA trains and reduces SOA mechanisms from explicit chemical mechanisms (e.g., the master chemical mechanism (MCM)) under representative atmospheric conditions. As a consequence, these trained SOA mechanisms can preserve the accuracy of explicit VOC mechanisms on SOA formation (e.g., molecular structures of crucial compounds, effect of non-ideality and hydrophilic/hydrophobic partitioning of aerosols), with a size (in terms of reaction and species numbers) that is manageable for three-dimensional aerosol modeling (e.g., regional chemical transport models). Applied to the degradation of a sesquiterpene (β-caryophyllene) from MCM, GENOA builds a concise SOA mechanism (2% of the MCM size), consisting of 23 reactions and 15 species, six of them being condensable. The generated SOA mechanism has been evaluated for its ability to reproduce SOA concentrations under varying atmospheric conditions encountered over Europe, with an average error lower than 3%. [ABSTRACT FROM AUTHOR]

Do we miss something about «traditional" media such as handwritten letters and photography before the digital age? Some of the authors remember this age fondly, and we wanted to see if this fondness could be translated into a science dialogue project with school classes. We designed and carried out a communication process with 4 classes at different schools across Europe. During this process, each class would interact with a single scientist primarily via hand-written questions & letters, and a Polaroid photo album. The scientists would make this unique, one-of-a-kind album whilst on board a research expedition in the Barents Sea. We asked the question whether this process might show any benefits to the school students involved. To answer this, we asked the students to write up their thoughts on communicating with a scientist in this way. We analysed the texts and found that most students thought the letters and polaroid albums were a "beautiful experience". Others commented on how important it is to actually put pen to paper and write, since they use (almost) only digital media these days. Most importantly, the students learnt different elements of the science connected to the research expedition, but also about the scientific process in general. And, equally important, some of the students were surprised and thankful that the scientists took the time to communicate with them in such a personal way. These results could possibly have been achieved using other media, however the hand-written letters and Polaroids worked very well. They also seemed to conjure up some of the personal memories that we have about communication not so long ago. Maybe there is something to be said for slowing things down with our science communication projects and making them more personal and unique. This is something that snail-mail and making photo albums forces us to do. [ABSTRACT FROM AUTHOR]

Adopting soil and crop management practices that conserve or enhance 1 soil structure is critical for supporting the sustainable adaptation of agriculture to climate change, as it should help maintain agricultural production in the face of increasing drought or water excess without impairing environmental quality. In this paper, we evaluate the evidence for this assertion by synthesizing the results of 34 published meta-analyses of the effects of such practices on soil physical and hydraulic properties relevant for climate change adaptation in European agriculture. We also review an additional 127 meta-analyses that investigated synergies and trade-offs or help to explain the effects of soil and crop management in terms of the underlying processes and mechanisms. Finally, we identify how responses to alternative soil-crop management systems vary under contrasting agro9 environmental conditions across Europe. This information may help practitioners and policymakers to draw context-specific conclusions concerning the efficacy of management practices as climate adaptation tools. Our synthesis demonstrates that organic soil amendments and the adoption of practices that maintain "continuous living cover" result in significant benefits for the water regulation function of soils, mostly arising from the additional carbon inputs to soil and the stimulation of biological processes. These effects are clearly related to improved soil aggregation and enhanced bio-porosity, both of which reduce surface runoff and increase infiltration. One potentially negative consequence of these systems is a reduction in soil water storage and groundwater recharge, which may be problematic in dry climates. Some important synergies are reductions in nitrate leaching to groundwater and greenhouse gas emissions for non-leguminous cover crop systems. The benefits of reducing tillage intensity appear much less clear-cut. Increases in soil bulk density due to traffic compaction are commonly reported. However, biological activity is enhanced under reduced tillage intensity, which should improve soil structure, infiltration capacity, and reduce surface runoff and the losses of agro chemicals to surface water. However, the evidence for these beneficial effects is inconclusive, while significant trade-offs include yield penalties and increases in greenhouse gas emissions and the risks of leaching of pesticides and nitrate. [ABSTRACT FROM AUTHOR]

In order to improve observations of the atmospheric boundary layer (ABL), Europe's network of meteorological and hydrological services EUMETNET as well as the European Research Infrastructure ACTRIS are currently working on building networks of microwave radiometers (MWRs). Elevation-scanning MWRs are well suited to obtain temperature profiles of the atmosphere, especially within the ABL. Understanding and assessing measurement uncertainties of scanning MWRs is therefore crucial for accurate temperature profiling. In this paper we discuss measurement uncertainties due to the instrument set-up and originating from external sources, namely (1) horizontal inhomogeneities of the atmosphere, (2) pointing errors or a tilt of the instrument, (3) physical obstacles in line of sight of the instrument, and (4) radio frequency interference (RFI). Horizontal inhomogeneities from observations at the Jülich Observatory of Cloud Evolution (JOYCE) are shown to have a small impact on retrieved temperature profiles (< |0.22 K| in the 25th/75th percentiles below 3000 m). Typical instrument tilts, that could be caused by uncertainties during the instrument set-up, also have a very small impact on temperature profiles and are smaller than 0.1 K below 3000 m for up to 1° of tilt. Physical obstacles at ambient temperatures and in line of sight and filling the complete beam of the MWR at the lowest elevation angle of 5.4° have to be at least 600 m away from the instrument in order to have an impact of less than 0.1 K on obtained temperature profiles. If the obstacle is 5 K warmer than its surroundings then the obstacle should be at least 2700 m away. Finally, we present an approach on how to detect RFI with an MWR with azimuth and elevation scanning capabilities. In this study we detect RFIs in a water vapor channel that does not influence the temperature retrieval, but would be relevant if the MWR were used to detect horizontal humidity inhomogeneities. [ABSTRACT FROM AUTHOR]

Atmospheric waveguides have been linked to amplified atmospheric Rossby waves and therefore to extreme weather events in the mid-latitudes. Waveguides have often been calculated on zonal-mean data, and/or on timescales of a month or longer. Here, I develop an objective algorithm to detect barotropic waveguides, and create a dataset of time- and spatially-varying waveguides in both summer and winter for both the Northern and Southern Hemisphere (NH/SH), including a metric of waveguide depth. In this dataset, waveguides for waves of zonal wavenumber 5 exist in the extra-tropics on more than 40 % of days across many longitudes, with the frequency of occurrence reducing for higher zonal wavenumbers. Waveguides tend to be more frequent, and deeper, in summer than in winter, and more frequent in the NH than the SH. Composites of days with high spatial mean waveguide depth over particular regions show a double jet structure associated with strong waveguide occurrence, consistent with previous research. Significant positive correlations exist between waveguide depth and the presence/strength of quasi-stationary waves. In the SH these correlations are strong across much of the mid-latitudes in both seasons, whilst in the NH significant correlations are found only over the Atlantic, Europe and Asia during NH summer, with the strongest correlations over the Atlantic and western Europe, a region notable for its strong positive trend in extreme heat temperature events in recent decades. [ABSTRACT FROM AUTHOR]

There is considerable academic interest in the potential for air quality improvement as a co-benefit of climate change mitigation. Few studies use regional air quality models for simulating future co-benefits, but many use global chemistry-climate model output. Using regional atmospheric chemistry could provide a better representation of air quality changes than global chemistry-climate models, especially by improving the representation of elevated urban concentrations. We use a detailed regional atmospheric chemistry model (WRF-Chem v 4.2) to model European air quality in 2050 compared to 2014 following three climate change mitigation scenarios. We represent different climate futures by using air pollutant emissions and chemical boundary conditions (from CESM2-WACCM output) for three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0; a high, medium and low mitigation pathway). We find that in 2050, following SSP1-2.6, mean population-weighted PM2.5 concentrations across European countries reduces by 52 % compared to 2014. Whilst under SSP2-4.5, this average reduction is 34 %. The smallest average reduction was 18 % by following SSP3-7.0. Maximum 6-monthly-mean daily-maximum 8 h (6mDM8h) ozone (O3) is reduced across Europe by 15 % following SSP1-2.6, and 3 % following SSP2-4.5, but increases by 13 % following SSP3-7.0. This demonstrates clear co-benefits of climate mitigation. The additional resolution allows us to analyse regional differences and identify key sectors. We find that mitigation of agricultural emissions will be key for attaining meaningful co-benefits of mitigation policies, evidenced by the importance of changes in NO3 aerosol mass to determining future PM2.5 air quality and changes in CH4 emissions to future O3 air quality. [ABSTRACT FROM AUTHOR]

Precipitation is arguably one of the most relevant surface variables impacting human lives on the planet, but global-coverage, high-resolution and good-quality observations are not readily available. In particular, gridded observational datasets are much needed for model development and forecast quality assessment. Here we compare the quality of four types of gridded precipitation products over Europe, namely: a rain-gauge interpolation; a satellite-derived product; a radar composite; and a reanalysis. Each product has its own strengths and weaknesses, and since each precipitation estimate uses different measuring techniques, we can employ a triangulation method to estimate the error variance of each product with respect to the unknown true values. Results show that: a) the satellite product has limited quality over Europe and may be problematic to use in quantitative forecast evaluation and diagnostics; b) the radar composite has spurious features that need to be considered when used in verification; c) all products struggle in topographically complex areas; d) the rain-gauge interpolation is not free of errors, despite rain gauges being often treated as ground truth in the literature; and e) the reanalysis dataset produces in some cases the best available estimates, in particular over the European near-coastal waters. [ABSTRACT FROM AUTHOR]

Atmospheric blocking is a synoptic-scale phenomenon that consists in an obstruction of the normal easterly progression of weather patterns in the midlatitudes, leading to persistent atmospheric conditions sometimes associated with extreme weather. State-of-the-art climate models systematically underestimate winter atmospheric blocking frequency, especially over Europe. This is often attributed to a poor representation of small-scale processes that are fundamental for the onset and maintenance of blocking events. Here, we explore how the implementation of two stochastic parameterizations, namely the Stochastically Perturbed Parameterization Tendencies (SPPT) scheme and the Stochastic Kinetic Energy Backscatter (SKEB) scheme, influences the representation of Northern Hemisphere winter blocking in EC-Earth3. Surprisingly, the activation of the two stochastic schemes has detrimental effects on blocking representation. Such deterioration is attributed to changes in the mean winter atmospheric circulation, primarily manifested in a strengthening of the mid-latitude jet stream and an intensification of the Hadley Cell. Ultimately, these circulation differences arise from a modified condensation process in tropical clouds that impacts the tropical stationary eddy activity, which in turn modifies the zonal momentum balance. Our findings reconnect with earlier literature on similar experiments and suggest that the activation of stochastic parameterizations may require a retuning of the model to correct for significant biases in the mean atmospheric circulation. [ABSTRACT FROM AUTHOR]

The concentration of carbonaceous aerosols, black carbon (BC) and organic aerosol (OA), in the atmosphere is related to co-emitted or co-produced trace gases. In this study, we investigate the most relevant proportional relationships between both BC and OA with the following trace gases: carbon monoxide (CO), formaldehyde (HCHO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2). One motivation for selecting these trace gases is that they can be observed using remote sensing measurements from satellite instrumentation, and could therefore be used to predict spatial changes in the amounts of BC and OA. Airborne measurements are optimal for the analysis of both the composition of aerosols and trace gases in different environments ranging from unpolluted oceanic air masses to those in heavily polluted city plumes. The two aircraft campaigns of the EMeRGe (Effect of Megacities on the Transport and Transformation of Pollutants on the Regional to Global Scales) project have created a unique database, with flight plans dedicated to studying city plumes in two regions, Europe (2017) and East Asia (2018), along with identical instrumental payload. Using linear regression analysis, three relevant relationships between carbonaceous aerosol and trace gases are identified: - The BC/OA ratio observed in the Asian campaign is three times higher (≈ 0.3) than in the European campaign (≈ 0.1), whereas the Pearson correlation coefficient (R) between BC and OA is much higher in Europe (R ≈ 0.8) than in Asia (R ≈ 0.6). - The CO/BC ratio is also observed higher in the Asian campaign (≈ 240) than in the European campaign (≈ 170), whereas the R-value between CO and BC is similar for both campaigns (R ≈ 0.7). - The HCHO/OA ratio is similar in both campaigns (≈0.32), but the observed R-values between HCHO and OA is higher in Europe than in the Asia (R ≈ 0.7 compared to ≈ 0.3). By focusing on heavily polluted air masses sampled downwind in the city plumes, the ratios between the observed carbonaceous aerosols and the five trace gases change, and the R-values increase with O3 for both BC and OA (R ≈ 0.5). To assess the performance of atmospheric models with respect to the most relevant observed relationships, an air quality model ensemble is used to represent the current state of atmospheric modeling, consisting of two global and two regional simulations. The evaluation shows that these proportional relationships are not satisfactorily reproduced by the model ensemble. The relationships between BC and OA or between CO and BC are modeled with stronger correlations than the observed ones, and their higher ratios observed in Asia compared to Europe are not reproduced. Furthermore, the modeled HCHO/OA ratio is underestimated in the Asian campaign and overestimated in the European campaign. This analysis of the proportional relationships between carbonaceous aerosols and trace gases implies that the observed relationships can be used to constrain models and improve anthropogenic emission inventories. In addition, it implies that information about the lower tropospheric concentration of carbonaceous aerosols can potentially be inferred from satellite retrievals of trace gases, particularly in the plumes from megacities. [ABSTRACT FROM AUTHOR]

Understanding black carbon (BC) levels and their sources in urban environments is of paramount importance due to their far-reaching health, climate and air quality implications. While several recent studies have assessed BC concentrations at specific fixed urban locations, there is a notable lack of knowledge in the existing literature on spatially resolved data alongside source estimation methods. This study aims to fill this gap by conducting a comprehensive investigation of BC levels and sources in Modena (Po Valley, Italy), which serves as a representative example of a medium-sized urban area in Europe. Using a combination of multi-wavelength micro-aethalometer measurements and a hybrid Eulerian-Lagrangian modelling system, we studied two consecutive winter seasons (February–March 2020 and December 2020–January 2021). Leveraging the multi-wavelength absorption analyser (MWAA) model, we differentiate sources (fossil fuel combustion, FF, and biomass burning, BB) and components (BC vs. brown carbon, BrC) from micro-aethalometer measurements. The analysis reveals consistent, minimal diurnal variability in BrC absorption, in contrast to FF-related sources, which exhibit distinctive diurnal peaks during rush hours, while BB sources show less diurnal variation. The city itself contributes significantly to BC concentrations (52 % ± 10 %), with BB and FF playing a prominent role (35 % ± 15 % and 9 % ± 4 %, respectively). Long-distance transport also influences BC concentrations, especially in the case of BB and FF emissions, with 28 % ± 1 % and 15 % ± 2 %, respectively. When analysing the traffic related concentrations, Euro 4 diesel passenger cars considerably contribute to the exhaust emissions. These results provide valuable insights for policy makers and urban planners to manage BC levels in medium-sized urban areas, taking into account local and long-distance sources. [ABSTRACT FROM AUTHOR]

Atmospheric volatile organic compounds (VOC) constitute a wide range of species, acting as precursors to ozone and aerosol formation. Atmospheric chemistry and transport models (CTMs) are crucial to understanding the emissions, distribution, and impacts of VOCs. Given the uncertainties in VOC emissions, lack of evaluation studies, and recent changes in emissions, this work adapts the European Monitoring and Evaluation Programme Meteorological Synthesizing Centre – West (EMEP MSC-W) CTM to evaluate emission inventories in Europe. Here we undertake the first intensive model-measurement comparison of VOCs in two decades. The modelled surface concentrations are evaluated both spatially and temporally, using measurements from the regular EMEP monitoring network in 2018 and 2019, and a 2022 campaign. To achieve this, we utilised the UK National Atmospheric Emission Inventory to derive explicit emission profiles for individual species and employed a 'tracer' method to produce pure concentrations that are directly comparable to observations. Model simulations for 2018 compare the use of two European inventories, CAMS and CEIP, and of two chemical mechanisms, CRIv2R5Em and EmChem19rc; those for 2019 and 2022 use CAMS and CRIv2R5Em only. The degree to which the modelled and measured VOCs agree varies depending on the specific species. The model successfully captures the overall spatial and temporal variations of major alkanes (e.g., ethane, n-butane) and unsaturated species (e.g., ethene, benzene), but less though for propane, i-butane, and ethyne. This discrepancy underscores potential issues in the boundary conditions for these latter species and in their primary emissions from in particular the solvent and road transport sectors. Specifically, potential missing propane emissions and issues with its boundary conditions are highlighted by large model underestimations and smaller propane to ethane ratios compared to the measurement. Meanwhile, both the model and measurement show strong linear correlations among butane isomers and among pentane isomers, indicating common sources for these pairs of isomers. However, modelled ratios of i- to n-butane and i- to n-pentane are approximately one-third of the measured ratios, which is largely driven by significant emissions of n-butane and n-pentane from the solvent sector. This suggests issues with the speciation profile of the solvent sector, or underrepresented contributions from transport and fuel evaporation sectors in current inventories, or both. Furthermore, the modelled ethene-to-ethyne and benzene-to-ethyne ratios differ significantly from measured ratios. The different model performance strongly points to shortcomings in the spatial and temporal patterns and magnitudes of ethyne emissions, especially during winter. For OVOCs, modelled and measured methanal and methylglyoxal display a good agreement, which demonstrates that the model captures the overall photo-oxidation processes reasonably well. However, the insufficiency of suitable measurements limits the evaluation of other OVOCs. Finally, the model exhibits very similar performance across simulations using different inventories, which suggests that the emission profiles are likely to exert a more significant impact on the agreement between modelled and measured data than the total emissions reported for each sector. Therefore, the future focus may need to shift towards refining these speciation profiles through for example new emission measurement campaigns to improve the model accuracy. [ABSTRACT FROM AUTHOR]

Heterogeneous radiative forcing in mid-latitudes, such as that exerted by aerosols, has been found to affect the Arctic climate, though the mechanisms remain debated. In this study, we leverage Deep Learning (DL) techniques to explore the complex response of the Arctic climate system to local radiative forcing over Europe. We conducted sensitivity experiments using the Max Planck Institute Earth System Model (MPI-ESM1.2) coupled with atmosphere-ocean–land surface components. Utilizing a DL-based clustering method, we classify atmospheric circulation patterns in a lower-dimensional space, focusing on Poleward Moist Static Energy Transport (PMSET) as our primary parameter. We developed a novel method to analyze the circulation patterns' contributions to various climatic parameter anomalies. Our findings indicate that the negative forcing over Europe alters existing circulation patterns and their occurrence frequency without introducing new ones. Specifically, we identify changes in a circulation pattern with a high-pressure system over Scandinavia as a key driver for reduced Sea Ice Concentration (SIC) in the Barents-Kara Sea during autumn. This circulation pattern also influences middle atmospheric dynamics, although its contribution is relatively minor compared to other circulation patterns that resemble the phases of the North Atlantic Oscillation (NAO). Our multidimensional approach combines DL algorithms and human expertise to offer a novel analytical tool that could have broader applications in climate science. [ABSTRACT FROM AUTHOR]

We develop a harmonized earthquake forecasting model for Europe based on the Epidemic-type Aftershock Sequence (ETAS) model to describe the spatio-temporal evolution of aftershock sequences. We propose a method modification that integrates information from the European Seismic Hazard Model (ESHM20) about the spatial variation of background seismicity during ETAS parameter inversion based on the expectation-maximization (EM) algorithm. Other modifications to the basic ETAS model are explored, namely fixing the productivity term to a higher value to balance the more productive triggering by high-magnitude events with their much rarer occurrence, and replacing the b -value estimate with one relying on the b -positive method to observe the possible effect of short-term incompleteness on model parameters. Retrospective and pseudo-prospective tests demonstrate that ETAS-based models outperform the time-independent benchmark model as well as an ETAS model calibrated on global data. The background-informed ETAS variant achieves the highest score in the pseudo-prospective experiment, but the performance difference to the second-best model is not significant. Our findings highlight promising areas for future exploration, such as avoiding the simplification of using a single b -value for the entire region or reevaluating the completeness of the used seismic catalogs. [ABSTRACT FROM AUTHOR]

Floods regularly cause substantial damage worldwide. Changing flood characteristics, for instance due to climate change, pose challenges to flood risk management. The spatial extent of floods is an important indicator for potential impacts, as consequences of widespread floods are particularly difficult to mitigate. The highly uneven station distribution in space and time, however, limits the ability to quantify changes in flood characteristics, and in particular flood extent, over large regions. Here we use observation-driven routed runoff simulations over the last 70 years in Europe from a state-of-the-art hydrological model (mHM) to identify large spatio-temporally connected flood events. Our identified spatio-temporal flood events compare well against an independent flood impact database. We find that flood extents increase by 11.3 % on average across Europe. This increase occurs over most of Europe, except for parts of eastern Europe (e.g., Ukraine, Belarus) and southern Europe (e.g., Spain). Over northern Europe, the increase in flood extent is mainly driven by the overall increase in flood magnitude caused by increasing precipitation and snowmelt. In contrast, the increasing trend in flood extent over central Europe can be attributed to an increase in the spatial extent of heavy precipitation. Overall, our study illustrates the opportunities of combining long-term consistent regional runoff simulations with a spatio-temporal flood detection algorithm to identify large-scale trends in key flood characteristics and their drivers. The identified change in flood extent poses challenges to flood control and water resource management. [ABSTRACT FROM AUTHOR]

By transporting warm and salty water poleward, the Gulf Stream system maintains a mild climate in northwestern Europe while also facilitating the dense water formation that feeds the deep ocean. The sensitivity of North Atlantic circulation to future greenhouse gas emissions seen in climate models has prompted an increasing effort to monitor the various ocean circulation components in recent decades. Here, we synthesise available ocean transport measurements from several observational programs in the North Atlantic and Nordic Seas, as well as an ocean state estimate (ECCOv4-r4), for an enhanced understanding of the Gulf Stream and its poleward extensions as an interconnected circulation system. We see limited coherence between the records on interannual time scales, highlighting the local oceanic response to atmospheric circulation patterns and variable recirculation time scales within the gyres. On decadal time scales, we find a weakening subtropical circulation between the mid-2000s and mid-2010s, while the inflow and circulation in the Nordic Seas remained stable. Differing decadal trends in the subtropics, subpolar North Atlantic, and Nordic Seas warrant caution in using observational records at a single latitude to infer large-scale circulation change. [ABSTRACT FROM AUTHOR]

Humans change the climate in many ways. In addition to greenhouse gases, climate model inputs thus include a number of other forcings like land-use change. While studies typically investigate the joint effects of all forcings, we here isolate the impact of afforestation and deforestation on winds in the lowermost 350 m of the atmosphere to quantify the relevance of the lower boundary condition for large-scale wind energy assessments. We use vertically resolved sub-daily output from two regional climate models instead of extrapolating near-surface winds with simplified profiles. Comparing two extreme scenarios, we report that afforestation reduces wind speeds by more than 1 m/s in many locations across Europe even 300 m above ground and thus remains relevant at hub heights of current and future wind turbines. We show that standard extrapolation with modified parameters approximates long-term means well but fails to capture essential spatio-temporal details, such as changes in the daily cycle, and is thus insufficient to estimate wind energy potentials. Using adjacent climate model levels to account for spatio-temporal wind profile complexity, we report that wind energy capacity factors are strongly impacted by afforestation and deforestation: they differ by more than 0.1 in absolute terms and up to 50 % in relative terms. Our results confirm earlier studies that land use change impacts on wind energy can be severe and that they are generally misrepresented with common extrapolation techniques. [ABSTRACT FROM AUTHOR]

Central European winters have warmed markedly since the mid-20th century. Yet cold winters are still associated with severe societal impacts on energy systems, infrastructure and public health. It is therefore crucial to anticipate storylines of worst-case cold winter conditions, and to understand whether an extremely cold winter, such as the coldest winter in the historical record of Germany in 1963 (−6.3 °C or −3.4 σ seasonal DJF temperature anomaly relative to 1981–2010), is still possible in a warming climate. Here, we first show based on multiple attribution methods that a winter of similar circulation conditions to 1963 would still lead to an extreme seasonal cold anomaly of about −4.9 to −4.7 °C (best estimates across methods) under present-day climate. This would rank as second-coldest winter in the last 75 years. Second, we conceive storylines of worst-case cold winter conditions based on two independent rare event sampling methods (climate model boosting and empirical importance sampling): winter as cold as 1963 is still physically possible in Central Europe today, albeit very unlikely. While cold winter hazards become less frequent and less intense in a warming climate overall, it remains crucial to anticipate the possibility of an extreme cold winter to avoid potential maladaptation and increased vulnerability. [ABSTRACT FROM AUTHOR]

In this study, we investigate applying deep learning (DL) models on a regional climate simulation produced by the Terrestrial Systems Modelling Platform (TSMP Ground to Atmosphere G2A) for vegetation health modeling and agricultural drought assessment. The TSMP simulation is performed in a free mode and the DL model is used in an intermediate step to synthesize Normalized Difference Vegetation Index (NDVI) and Brightness Temperature (BT) images from the TSMP simulation over Europe. These predicted images are then used to derive different vegetation and drought indices like NDVI anomaly, BT anomaly, Vegetation Condition Index (VCI), Thermal Condition Index (TCI), and Vegetation Health Index (VHI). To ensure reliability and to assess the model applicability with different seasonality and spatial variability, we provide an analysis of model biases and uncertainties across different regions over the Pan-Europe domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation on the long-term TSMP using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K° with coefficient of determination (R2) scores of 0.88 and 0.92 for NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. Our study could be used as a complimentary evaluation framework for climate change simulations with TSMP. Moreover, the developed DL model could be integrated with data assimilation and used for down-stream tasks, i.e., modelling the impact of extreme events on vegetation responses with different climate change scenarios. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP). [ABSTRACT FROM AUTHOR]

In May–June 2019, smoke plumes from wildfires in Alberta, Canada, were advected all the way to Europe. To analyze the evolution of the plumes and to estimate the amount of smoke aerosols transported to Europe, retrievals from the space-borne lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) were used. The plumes were located with the help of a trajectory analysis, and the mass of smoke aerosols were retrieved from the CALIOP observations. The accuracy of the CALIOP mass retrievals was compared with the accuracy of ground-based lidars/ceilometer near the source in North America and after the long-range transport in Europe. Overall, CALIOP and the ground-based lidars/ceilometer produced comparable results. Over North America the CALIOP layer mean mass was 30 % smaller than the ground-based estimates, whereas over Southern Europe that difference varied between 12 % and 43 %. Finally, the CALIOP mass retrievals were compared with simulated aerosol concentrations from two reanalysis models, MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) and CAMS (Copernicus Atmospheric Monitoring System). The simulated total column aerosol optical depths (AOD) and the total column mass concentration of smoke agreed quite well with CALIOP observations, but the comparison of the layer mass concentration of smoke showed significant discrepancies. The amount of smoke aerosols in the model simulations was consistently smaller than in the CALIOP retrievals. These results highlight the limitations of such models, and more specifically their limitation to reproduce properly the smoke vertical distribution. They indicate that CALIOP is a useful tool monitoring smoke plumes over secluded areas whereas reanalysis models have difficulties in representing the aerosol mass in these plumes. This study shows the advantages of space-borne aerosol lidars, e.g. being of paramount importance to monitor smoke plumes, and reveals the urgent need of future lidar missions in space. [ABSTRACT FROM AUTHOR]

The rural-to-urban transformation (RUT) is the process of turning rural or natural land-surface into urban one which brings important modifications in the surface causing well know effects like the urban heat island (UHI), reduced wind-speeds, increased boundary layer heights and so on. Moreover, with concentrated human activities RUT introduces new emission source which greatly perturbs the local and regional air-pollution. Particulate matter (PM) is one of key pollutants responsible for deterioration of urban air-quality and is still a major issue in European cities with frequent exceedances of limit values. Here we introduce a regional chemistry-climate model (regional climate model RegCM coupled offline to chemistry transport model CAMx) study which quantifies how the process of RUT modified the PM concentrations over central Europe including the underlying controlling mechanisms that contribute to the final PM pollution. Apart from the two most studied ones, i) the urban emissions and ii) the urban canopy meteorological forcing (UCMF, i.e. the impact of modified meteorological conditions on air-quality) we analyze also two less studied contributors to the RUT's impact on air-quality: iii) the impact of modified dry-deposition velocities due to urbanized land-use and iv) the impact of modified biogenic emissions due to urbanization induced vegetation modifications and changes in meteorological conditions which affect these emissions. To calculate the magnitude of each of these RUT contributors, we perform a cascade of simulations were each contributor is added one-by-one to the reference state while focus is given on PM2.5 (particulate matter with diameter less then 2.5 µm). We also look at their primary and secondary components, namely primary elemental carbon (PEC), sulphates (PSO4), nitrates (PNO3), ammonium (PNH4) and secondary organic aerosol (SOA). The validation using surface measurements showed a systematic negative bias for the total PM2.5 which is probably caused by underestimated organic aerosol and partly by the negative bias in sulphates and elemental carbon. For ammonium and nitrate, the underestimation is limited to the warm season while for winter, the model tends to overestimate their concentrations. However, in each case, the annual cycle is reasonably captured. We evaluated the RUT impact on PM2.5 over an ensemble of 19 central European cities and found that the total impact of urbanization is about 2–3 and 1–1.5 µgm−3 in winter and summer, respectively. This is mainly driven by the impact of emissions alone causing a slightly higher impact (1.5–3.5 and 1.2–2 µgm−3 in winter and summer), while the effect of UCMF was a decrease at about 0.2–0.5 µgm−3 (in both seasons) which was mainly controlled by enhanced vertical eddy-diffusion while increases were modelled over rural areas. The transformation of rural land-use into urban one caused an increase of dry-deposition velocities by around 30–50 % which alone resulted in a decrease of PM2.5 by 0.1–0.25 µgm−3 in both seasons. Finally, the impact of biogenic emission modification due to modified land-use and meteorological conditions caused a decrease of summer PM2.5 of about 0.1 µgm−3 while the winter effects were negligible. The total impact of urbanization on aerosol components is modelled to be (values indicate winter and summer averages) 0.4 and 0.3 µgm−3 for PEC, 0.05 and 0.02 µgm−3 for PSO4, 0.1 and 0.08 µgm−3 for PNO3, 0.04 and 0.03 µgm−3 for PNH4 and 0 and 0.05 µgm−3 for SOA. The main contributor of each of these components was the impact of emissions which was usually larger than the total impact due to the fact that UCMF counteracted with a decrease. For each aerosol component the impact of modified DV was a clear decrease of concentration and finally, the modifications of biogenic emissions impacted predominantly SOA causing a summer decrease while a very small secondary effect of secondary inorganic aerosol was modelled too (they increased). In summary, we showed that when analyzing the impact of urbanization on PM pollution, apart from the impact of emissions and the urban canopy meteorological forcing, one has to consider also the effect of modified land-use and its impact on dry-deposition. These were shown to be important in both seasons. For the effect of modified biogenic emissions, our calculations showed that it acts on PM2.5 predominantly trough SOA modifications which turned to be important only during summer. [ABSTRACT FROM AUTHOR]

Irrigation is a crucial land use practice to adapt agriculture to unsuitable climate and soil conditions. Aiming for improving the growth of plants, irrigation modifies the soil condition, which causes atmospheric effects and feedbacks through land-atmosphere interaction. These effects can be quantified with numerical climate models, as has been done in various studies. It could be shown that irrigation effects, such as air temperature reduction and humidity increase are well understood and should not be neglected on local and regional scales. However, there is a lack of studies including the role of vegetation in the altered land-atmosphere interaction. With the increasing resolution of numerical climate models, these detailed processes have a chance to be better resolved and studied. This study aims for analyzing the effects of irrigation on land-atmosphere interaction, including the effects and feedbacks of vegetation. We developed a new parameterization for irrigation and implemented it into the REgional climate MOdel REMO2020, coupled with the interactive MOsaicbased VEgetation module iMOVE. Following this new approach of a separate irrigated fraction, the parameterization is suitable as a subgrid parameterization for high-resolution studies and resolves irrigation effects on land, atmosphere, and vegetation. Further, the parameterization is designed with three different water application schemes in order to analyze different parameterization approaches and their influence on the representation of irrigation effects. We apply the irrigation parameterization for South-West Europe including the Mediterranean region on 0.11° horizontal resolution for hot extremes. The simulation results are evaluated in terms of the consistency of physical processes. We found direct effects of irrigation, like a changed surface energy balance with increased latent and decreased sensible heat fluxes, and a surface temperature reduction of more than -4 K as mean during the growing season. Further, vegetation reacts to irrigation with direct effects, such as reduced water stress, but also with feedbacks, such as a delayed growing season caused by the reduction of the near-surface temperature. Furthermore, the results were compared to observational data showing a significant bias reduction in the 2 m mean temperature when using the irrigation parameterization. [ABSTRACT FROM AUTHOR]

Coastal flooding is recognized as one of the most devastating natural disasters, resulting in significant economic losses. Therefore, hazard assessment is crucial to support preparedness and response to such disasters. Toward this, flood map databases and catalogues are essential for the analysis of flood scenarios, and furthermore can be integrated into disaster risk reduction studies. In this study and in the context of the ECFAS project (GA 101004211), which aimed to propose a European Copernicus Coastal Flood Awareness System, a catalogue of flood maps was produced. The flood maps were generated from flood models developed with LISFLOOD-FP for defined coastal sectors along the entire European coastline. For each coastal sector, fifteen synthetic scenarios were defined focusing on high-frequency events specific to the local area. These scenarios were constructed based on three distinct storm durations and five different Total Water Level (TWL) peaks incorporating tide, mean sea level, surge and wave set-up components. The flood model method was extensively validated against twelve test cases for which observed data were collated using satellite-derived flood maps and in situ flood markers. Half of the test-cases well represented the flooding with hit scores higher than 80 %. The synthetic scenario approach was assessed by comparing flood maps from real events and their closest identified scenarios, producing a good agreement and global skill scores higher than 70 %. Using the catalogue, flood scenarios across Europe were assessed, and the biggest flooding occurred in well-known low-lying areas. In addition, different sensitivities to the increase of the duration and TWL peak were noted. The storm duration impacts a few limited flood prone areas such as the Dutch coast for which the flooded area increases more than twice between a 12 h and 36 h storm scenarios. The influence of the TWL peak is more global, especially along the Mediterranean coast for which the relative difference between a 2- and 20-year return period storm is around 80 %. Finally, at a European scale, the expansion of flood areas in relation to increases in TWL (Total Water Level) peaks demonstrated both positive and negative correlations with the presence of urban and wetland areas, respectively. This observation supports the concept of storm flood mitigation by wetlands. [ABSTRACT FROM AUTHOR]

Acquiring and distributing essential information for understanding global biogeochemical interactions between the atmosphere and ecosystems, and how climate-ecosystem feedback loops may change atmospheric composition in the future is a fundamental pre-requisite for societal resilience in view of climate change. Particularly, the detection of trends and periodicity in the presence of greenhouse gases and short-lived climate-active atmospheric constituents is an important aspect of climate science. Thus, the availability of an easy and fast access to reliable, long-term, and high-quality environmental data is recognized as fundamental for research and for developing environmental prediction and assessment services. In our Opinion Article, we develop the role environmental research infrastructures in Europe (ENVRI RIs) and particularly the atmosphere-centred research infrastructures ACTRIS, IAGOS and ICOS can assume with their capacities for standardised acquisition and reporting of long-term and high-quality observational data, complemented by rich metadata, for the provision of data by open access, and for data interoperability across different research fields including all fields of environmental sciences and beyond. Resulting from these capacities in data collection and provision, we elaborate on the novel research opportunities in atmospheric sciences which evolve from the combination of open-access and interoperable observational data, tools and technologies offered by data-intensive science, and the emerging service ecosystem of the collaboration platform ENVRI-Hub, hosted by the European Open Science Cloud. [ABSTRACT FROM AUTHOR]

In the 2022 summer, West-Central Europe and several other northern-hemisphere mid-latitude regions experienced substantial soil moisture deficits in the wake of precipitation shortages and elevated temperatures. Much of Europe has not witnessed a more severe soil drought since at least the mid-20th century, raising the question whether this is a manifestation of our warming climate. Here, we employ a well-established statistical approach to attribute the low 2022 summer soil moisture to human-induced climate change, using observation-driven soil moisture estimates and climate models. We find that in West-Central Europe, a June-August root-zone soil moisture drought such as in 2022 is expected to occur once in 20 years in the present climate, but would have occurred only about once per century during pre-industrial times. The entire northern extratropics show an even stronger global warming imprint with a 20-fold soil drought probability increase or higher, but we note that the underlying uncertainty is large. Reasons are manifold, but include the lack of direct soil moisture observations at the required spatiotemporal scales, the limitations of remotely sensed estimates, and the resulting need to simulate soil moisture with land surface models driven by meteorological data. Nevertheless, observationbased products indicate long-term declining summer soil moisture for both regions, and this tendency is likely fueled by regional warming, while no clear trends emerge for precipitation. Finally, our climate model analysis suggests that in a 2 °C world, 2022-like soil drought conditions would become twice as likely for West-Central Europe compared to today, and would take place nearly every year across the northern extratropics. [ABSTRACT FROM AUTHOR]

Shipping contributes significantly to air pollutant emissions and atmospheric particulate matter (PM) concentrations. At the same time worldwide maritime transport volumes are expected to continue to rise in the future. The Mediterranean Sea is a major short-sea shipping route within Europe, as well as the main shipping route between Europe and East Asia. As a result, it is a heavily trafficked shipping area, and air quality monitoring stations in numerous cities along the Mediterranean coast have detected high levels of air pollutants originating from shipping emissions. The current study is a part of the EU Horizon 2020 project SCIPPER (Shipping contribution to Inland Pollution - Push for the Enforcement of Regulations) which intends to investigate how existing restrictions on shipping-related emissions to the atmosphere ensure compliance with legislation. To demonstrate the impact of ships on relatively large scales, the potential shipping impacts on various air pollutants can be simulated with chemistry transport models. To determine formation, transport, chemical transformation and fate of PM2.5 in the Mediterranean Sea in 2015, five different regional chemistry transport models (CAMx – Comprehensive Air Quality Model with Extensions, CHIMERE, CMAQ – Community Multiscale Air Quality model, EMEP – European Monitoring and Evaluation Programme model, LOTOS-EUROS) were applied. Furthermore, PM2.5 precursors (NH3, SO2, HNO3) and inorganic particle species (SO42−, NH4+, NO3−) were studied, as they are important for explaining differences among the models. STEAM version 3.3.0 was used to compute shipping emissions, and the CAMS-REG v2.2.1 dataset was used to calculate land-based emissions for an area encompassing the Mediterranean Sea at a resolution of 12 × 12 km2 (or 0.1° × 0.1°). For additional input, like meteorological fields and boundary conditions, all models utilized their regular configuration. The zero-out approach was used to quantify the potential impact of ship emissions on PM2.5 concentrations. The model results were compared to observed background data from monitoring sites. Four of the five models underestimated the actual measured PM2.5 concentrations. These underestimations are linked to model-specific mechanisms or underpredictions of particle precursors. The potential impact of ships on the PM2.5 concentration is between 15 % and 25 % at the main shipping routes. Regarding particle species, SO42− is main contributor to the absolute ship-related PM2.5 and also to total PM2.5 concentrations. In the ship-related PM2.5, a higher share of inorganic particle species can be found when compared to the total PM2.5. The seasonal variabilities in particle species show that NO3− is higher in winter and spring, while the NH4+ concentrations displayed no clear seasonal pattern in any models. In most cases with high concentrations of both NH4+ and NO3−, lower SO42− concentrations are simulated. Differences among the simulated particle species distributions might be traced back to the aerosol size distribution and how models distribute among the coarse and fine mode (PM2.5 and PM10). The seasonality of wet deposition follows the seasonality of the precipitation, displaying that precipitation predominates the wet deposition. [ABSTRACT FROM AUTHOR]

Global climate change is increasingly associated with the increase and/or the intensification of extreme weather such as heat waves, droughts, or heavy precipitation events. However, the characteristics and severity of these changes can vary considerably by region and season. This study focuses on heavy and extreme precipitation events over Europe for the time period 1951–2099. The main objective is to identify regions which show a robust and therefore reliable change in such events with ongoing climate change. The study is based on daily precipitation values from 40 regional climate simulations of the EURO-CORDEX ensemble with a spatial resolution of 12 km (EUR-11). Future changes were investigated using four different metrics, which are sensitive to alterations in the number and intensity of the detected events and consider an accumulated precipitation amount over a selected threshold and two return values for a 10- and a 100-year return period. Differences were detected between the climate scenarios RCP4.5 and RCP8.5, between summer and winter half-year, and between three different methods used to identify and quantify temporal changes from a reference period (1951–1980) to a future climate period (2070–2099). Furthermore, two criteria characterizing the robustness of the changes were used, i.e. a dominant agreement on the sign and a majority of significant changes within the full simulation ensemble. The analysis provided relative and absolute changes of the four metrics being used and the area fractions that exhibited a robust change. With all methods applied, our study clearly confirms a significant increase in heavy and extreme precipitation in northern, central, and eastern Europe and a robust decrease in heavy, but not very extreme events in the southwest of the Euro-CORDEX domain associated with projected future climate change. For large parts of Southern Europe and the Mediterranean, a tendency towards decreasing intensities (down to -11 mm/year for the accumulated threshold exceedance) become visible but without the evidence of robustness. Both, the intensity and the area with robust changes in heavy and extreme events prove to be significantly stronger in the RCP8.5 than in the RCP4.5 scenario. For Central Europe, for example, the accumulated threshold exceedance increases from 15 mm/year to 24 mm/year and the 100-year return value from 21 mm/day to 30 mm/day. The related robust area fractions extend from 31 % to 99 % and from 61 % to 95 %, respectively. The relative changes are substantial, even averaged over larger areas, with values greater than 100 % (50 %) for the severe events and up to 40 % (30 %) for the very extreme events in the RCP8.5 (RCP4.5) scenario. Heavy events increase relatively more in winter (for Central Europe +117 % for the accumulated threshold exceedance in RCP 8.5) than in summer (+81 %). The opposite is true for the extreme events with a weaker increase in winter than in summer (e.g. for Central Europe +26 % for the 100-year return value in winter and +43 % in summer). [ABSTRACT FROM AUTHOR]

Knowing that internal climate variability is the principal driver of Mediterranean droughts during the last millennium, in this study, we investigate circulation patterns in the Euro-Atlantic domain associated with multi-year droughts over the Mediterranean region in CMIP5-PMIP3 and CESM-LME simulations. The focus is on the natural variability of droughts during 850–2005, thus excluding the anthropogenic trends from 1850 CE onward. The results re-confirm that Mediterranean drought occurrence during the last millennium is associated with internal variability. In terms of the temporal variability, all climate models exhibit a multi-decadal anti-phase occurrence of droughts between the western and eastern Mediterranean agreeing with some proxy records. This anti-phase occurrence of droughts can be explained by the dominant circulation patterns in each region: western Mediterranean droughts are dominated by a high-pressure system and ridge over central Europe and an NAO-like pattern, while eastern Mediterranean droughts are linked to positive pressure anomalies in the southern and eastern Mediterranean, negative NAO, negative EA and EA-WR-like patterns. However, these modes of climate variability are strongly model-dependent, i.e. each model has its preferred circulation patterns that occur more frequently during droughts, suggesting that the main drivers of droughts differ between the models. The models' preference is therefore a potential source of uncertainties in Mediterranean droughts in model-proxy comparison and may have implications for future climate projections. [ABSTRACT FROM AUTHOR]

Large hail (greater than 2 cm in diameter) can cause devastating damage to crops and property, and can even cause loss of life. Because hail reports are often collected by individual countries, constructing a European-wide large-hail climatology has been challenging to date. However, the European Severe Storm Laboratory's European Severe Weather Database provides the only pan-European dataset for severe convective storms. The database is comprised of 62,053 large-hail reports from 40 C.E. to September 2020, yet its characteristics have not been evaluated. Thus, the purpose of this study is to evaluate this database for the purposes of constructing a climatology of large hail. For the period 2000–2020, large-hail reports are most prominent in June, whereas large-hail days are most common in July. Large hail is mostly reported between 1300–1900 local time, a consistent pattern since 2010. The intensity, as measured by maximum hail size, shows decreasing frequency with increasing hailstone diameter, and no change over the 20-year period. The quality of reports by country varies, with the most complete reporting being from central European countries. These results suggest that despite its short record, many indications are that the dataset represents some reliable aspects of European large-hail climatology, albeit with some limitations. [ABSTRACT FROM AUTHOR]

Winter windstorms are among the most significant natural hazards in Europe linked to fatalities and substantial economic damages. However, projections of windstorm impact in Europe under climate change are highly uncertain. This study combines climate projections from 30 general circulation models participating in CMIP6 with the climate-risk assessment model CLIMADA to obtain projections of future change in windstorm-induced damages over Europe. We conduct an uncertainty-sensitivity analysis, and find large uncertainties in the projected changes in the damages, with climate model uncertainty being the dominant factor of uncertainty in the projections. We investigate spatial patterns of the future changes in windstorm damages and find an increase in the damages in northwestern and northern-central Europe, and a decrease over the rest of Europe, in agreement with an eastward extension of the North Atlantic storm track into Europe. We combine all 30 available climate models in an ensemble of opportunity approach and find evidence for an intensification of future windstorm damages, with damages with return periods of 100 years under current climate conditions becoming damages with return periods of 28 years under future SSP585 climate scenarios. Our findings demonstrate the importance of climate model uncertainty for the CMIP6 projections of windstorms in Europe, and emphasize the increasing need for risk mitigation due to extreme weather in the future. [ABSTRACT FROM AUTHOR]

We present a WRF-Chem simulation over central Europe with a high spatial resolution of 3 km × 3 km and a focus on nitrogen dioxide (NO₂). A regional emission inventory, issued by the German Environmental Agency, with a spatial resolution of 1 km × 1 km is used. We demonstrate, that by precise temporal modulation of the emission data (use of 'temporal profiles'), significant improvement in model accuracy over existing simulations is achieved. Simulated NO₂ surface concentrations are compared to measurements from a total of 275 in-situ measurement stations in Germany, where the model was able to reproduce average noontime NO₂ concentrations with a bias of +0.9 % and R = 0.76. A comparison between modelled NO₂ vertical column densities (VCDs) and satellite observations from TROPOMI (TROPOspheric Monitoring Instrument) is conducted, where crucial aspects of the observation process, such as altitude-dependent NO₂ sensitivity as well as the influence of clouds and a priori assumptions of the retrieval, are taken into account. Simulations and satellite observations are shown to agree with a model bias of −6.6 % and R = 0.84 for monthly means. Lastly, simulated NO₂ concentration profiles are compared to profiles obtained from Multiaxis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements of five European ground stations using the profile retrieval algorithms from the Mexican MAX-DOAS fit (MMF) and the Mainz Profile Algorithm (MAPA). For stations within Germany, biases of −5.9 % to +50.3 % were obtained when comparing average noontime NO₂ concentrations at different altitudes. Outside of Germany, where lower resolution emission data was used, biases of up to +78.6 % were observed. Overall, the study demonstrates that temporal modulation of emission data is crucial for modelling tropospheric NO₂ realistically. [ABSTRACT FROM AUTHOR]

Heavy precipitation can lead to floods and landslides, resulting in widespread damage and significant casualties. Some of its impacts can be mitigated if reliable forecasts and warnings are available. Of particular interest is the subseasonal to seasonal (S2S) prediction timescale. The S2S prediction timescale has received increasing attention in the research community because of its importance for many sectors. However, very few forecast skill assessments of precipitation extremes in S2S forecast data have been conducted. The goal of this article is to introduce a new methodology to assess the skill of rare events, here extreme precipitation, in S2S forecasts. We verify extreme precipitation events over Europe in the S2S forecast model from the European Centre for Medium-Range Weather Forecasts. The verification is conducted against ERA5 reanalysis precipitation. Extreme precipitation is defined as daily precipitation accumulations exceeding the seasonal 95th percentile. In addition to the classical Brier score, we use a binary loss index to assess skill. The binary loss index is tailored to assess the skill of rare events. We analyse daily events locally and spatially aggregated, as well as 7-day extreme event counts. Results consistently show a higher skill in winter compared to summer. The regions showing the highest skill are Norway, Portugal and the south of the Alps. Skill increases when aggregating the extremes spatially or temporally. The verification methodology can be adapted and applied to other variables, e.g. temperature extremes or river discharge. [ABSTRACT FROM AUTHOR]

Climate reconstructions give insights into monthly and seasonal climate variability of the past few hundred years. However, for understanding past extreme weather events and for relating them to impacts, for example to periods of extreme floods or to yield losses, reconstructions on a daily time scale are needed. Here, we present a data set of 250 years of daily temperature and precipitation fields for Switzerland from 1763 to 2020, which has been created using early instrumental data. The temperature reconstruction shows even for an early period before 1800 with scarce data availability good results, especially in the Swiss Plateau. For the precipitation reconstruction, skills are considerably lower, which can be related to the few precipitation measurements available and the heterogeneous nature of precipitation. By means of a case study on the wet and cold years from 1769 to 1772, which triggered wide-spread famine across Europe, we show that this dataset allows more detailed analyses than hitherto possible. [ABSTRACT FROM AUTHOR]

Aircrafts emit exhaust gases and soot into the atmosphere, which may increase global cirrus cloudiness and as well change the properties of already existing cirrus. In the first COVID-19 lockdown in Europe, changes in cirrus cloud occurrence and properties were detected with the lidar measurements of CALIPSO, which is supposed to be caused by the reduction in civil aviation accordingly. In the last 10 years before COVID, however, aviation grew strongly in terms of CO2 emissions and flight densities in Europe. In the current study, 10-year lidar measurements of cirrus clouds with CALIPSO are analyzed. The Linear contrails and contrail cirrus induced by global aviation have long been known to contribute to climate change by warming the atmosphere. Besides increasing global cirrus cloudiness, aviation may change the properties of the natural cirrus clouds by soot emissions which leads to increased heterogeneous freezing. In the first COVID-19 lockdown in Europe, changes in cirrus cloud properties and occurrence were detected with the lidar measurements of CALIPSO, which is supposed to be caused by the reduction in civil aviation accordingly. In the last 10 years before COVID, however, aviation grew strongly in terms of CO2 emissions and flight densities in Europe. In this study, 10-year lidar measurements of cirrus clouds with CALIPSO are analyzed to determine the seasonality and long-term trends in cirrus clouds as well as their correlations with the ambient temperatures and air traffic. Cirrus clouds follow a distinct seasonal cycle in their occurrence rate (OR) and particle linear depolarization ratio (PLDR) δ p. Cirrus clouds appear within a broader altitude range in winter than in summer and they are characterized by larger OR and δ p values in winter than in summer. The monthly medians of δ p as well as the deseasonalized time series of them in the 10-year period before COVID show both positive trends which are statistically significant according to the Mann-Kendall (MK) significance test. However, the cirrus occurrence shows a negative trend, which might be connected with the background meteorological conditions. Since the cirrus δ p strongly depends on the ambient temperatures in cirrus, we further remove the contributions induced by temperatures from the cirrus δ p with a simple linear regression model. The derived residuals show significant positive trends with the MK test. To compare the cirrus δ p and the air traffic densities, the deseasonalization of the data have previously been conducted since the seasonal cycles in both are not consistent. The deseasonalized time series of the cirrus δ p and CO2 emissions from aviation both show an increasing trend and their correlation coefficients are r = 0.54 at the confidence level above 99.5 %. Finally, the comparisons between the cirrus δ p and aviation in every season were carried out and revealed a strong correlation in other seasons than in summer. [ABSTRACT FROM AUTHOR]

At high concentration, tropospheric O3 deteriorates air quality, inducing adverse effects on human and ecosystem health. Meteorological conditions are key to understand the variability of O3 concentration, especially during extreme weather events. They modify the photochemistry activity and the vegetation state. An important source of uncertainties and inaccuracy in simulating surface O3 during droughts and heatwaves is the lack of interactions between the biosphere and the troposphere. Based on the biogenic emission modelMEGANv2.1 and the chemistry-transport model CHIMERE v2020r1, the first objective of this study is to assess the sensitivity of biogenic emissions, O3 dry deposition and surface O3 to biomass decrease and soil dryness effect (using several configurations) during the extremely dry summer 2012. Secondly, this research aims at quantifying the variation of observed (EEA's air quality database, 2000-2016) and simulated (CHIMERE, 2012-2014) surface O3 during summer heatwaves and agricultural droughts that have been identified using the Percentile Limit Anomalies (PLA) method. Our sensitivity analysis shows that soil dryness is a key factor during drought events, decreasing considerably the C5H8 emissions and O3 dry deposition velocity. This effect has a larger impact than the biomass decrease. However, the resulting effect on surface O3 remains limited. Based on a cluster approach using the PLA indicator, we show that observed O3 concentration is on average significantly higher during heatwaves (+18µg/m³ in daily maximum) and droughts (+9µg/m³) compared to normal conditions. Despite a difference of several µg/m³, CHIMERE correctly simulates the variations of O3 concentration between the clusters of extreme events. The overall increase of surface O3 during both heatwaves and droughts would be explained by O3 precursor emission enhancement (in agreement with HCHO satellite observations), O3 dry deposition decrease and favourable weather conditions. However, we simulated a decrease of C5H8 emissions (in agreement with HCHO observations) during droughts not accompanied by a heatwave, resulting in a non-significant difference of surface O3 compared to normal conditions (from both observations and simulations). [ABSTRACT FROM AUTHOR]

The Earth system is a complex non-linear dynamical system. Despite decades of research, many processes and relations between Earth system variables are still poorly understood. Current approaches for studying relations in the Earth system may be broadly divided into approaches based on numerical simulations and statistical approaches. However, there are several inherent limitations to current approaches that are, for example, high computational costs, reliance on the correct representation of relations in numerical models, strong assumptions related to linearity or locality, and the fallacy of correlation and causality. Here, we propose a novel methodology combining deep learning (DL) and principles of causality research in an attempt to overcome these limitations. The methodology combines the recent idea of training and analyzing DL models to gain new scientific insights in the relations between input and target variables with a theorem from causality research. This theorem states that a statistical model may learn the causal impact of an input variable on a target variable if suitable additional input variables are included. As an illustrative example, we apply the methodology to study soil moisture-precipitation coupling in ERA5 climate reanalysis data across Europe. We demonstrate that, harnessing the great power and flexibility of DL models, the proposed methodology may yield new scientific insights into complex, nonlinear and non-local coupling mechanisms in the Earth system. [ABSTRACT FROM AUTHOR]