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In this study, we assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities in western Europe. The SWG is based on a random sampling of analogs of the geopotential height at 500 hPa (Z500). The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) compared with persistence and climatology forecasts for lead times of 5 and 10 d for different areas in Europe. We find that the low predictability episodes of our model are related to specific weather regimes, depending on the European region. Comparing the SWG forecasts to ECMWF forecasts, we find that the SWG shows a good performance for 5 d. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g., region for analogs) must be tuned for each region in order to optimize its performance. [ABSTRACT FROM AUTHOR]

Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from the European Centre for Medium-range Weather Forecasts (ECMWF) in predicting droughts over the European continent. The Standardized Precipitation Index (SPI- 1) is used to quantify the onset or likely evolution of ongoing droughts for the next month. It can be shown that on average the extended range forecast has greater skill than the seasonal forecast, whilst both outperform climatology. No significant spatial or temporal patterns can be observed, but the scores are improved when focussing on large-scale droughts. In a second step we then analyse several different methods to convert the probabilistic forecasts of SPI into a Boolean drought warning. It can be demonstrated that methodologies which convert low percentiles of the forecasted SPI cumulative distribution function into warnings are superior in comparison with alternatives such as the mean or the median of the ensemble. The paper demonstrates that up to 40% of droughts are correctly forecasted one month in advance. Nevertheless, during false alarms or misses, we did not find significant differences in the distribution of the ensemble members that would allow for a quantitative assessment of the uncertainty. [ABSTRACT FROM AUTHOR]

The acquisition and dissemination of 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 comprise a fundamental prerequisite for societal resilience in the face of climate change. In particular, the detection of trends and seasonality in the abundance of greenhouse gases and short-lived climate-active atmospheric constituents is an important aspect of climate science. Therefore, easy and fast access to reliable, long-term, and high-quality observational environmental data is recognised as fundamental to research and the development of environmental forecasting and assessment services. In our opinion article, we discuss the potential role that environmental research infrastructures in Europe (ENVRI RIs) can play in the context of an integrated global observation system. In particular, we focus on the role of the atmosphere-centred research infrastructures ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure), IAGOS (In-service Aircraft for a Global Observing System), and ICOS (Integrated Carbon Observation System), also referred to as ATMO-RIs, with their capabilities for standardised collection and provision of long-term and high-quality observational data, complemented by rich metadata. The ATMO-RIs provide data through open access and offer data interoperability across different research fields including all fields of environmental sciences and beyond. As a result of these capabilities in data collection and provision, we elaborate on the novel research opportunities in atmospheric sciences which arise from the combination of open-access and interoperable observational data, tools, and technologies offered by data-intensive science and the emerging collaboration platform ENVRI-Hub, hosted by the European Open Science Cloud (EOSC). [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]

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 Europe-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-storm reports. The database is comprised of 62 053 large-hail reports from 40 CE to September 2020, yet its characteristics have not been evaluated. Thus, the purpose of this study is to evaluate hail reports from this database for 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 13:00–19:00 local time, a consistent pattern since 2010. The intensity, as measured by maximum hail size, shows decreasing frequency with increasing hailstone diameter and little change over the 20-year period. The quality of reports by country varies, with the most complete reporting being from central European countries. Thus, results suggest that despite its short record, many indications point to the dataset representing some reliable aspects of the European large-hail climatology, albeit with some limitations. [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]

This paper presents a novel data set of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. The data set provides a unique and physically consistent data set, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100 × 10-year simulations and 25 × 10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. The changes in four climate indices for temperature targets of 1.5 °C and 2.0 °C global warming are quantified: number of days per year with daily mean near-surface apparent temperature of > 28 °C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-yr return period (RI50yr); and the annual Consecutive Dry Days (CDD). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at a policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 °C. [ABSTRACT FROM AUTHOR]

Deriving a parameterisation of ammonia emissions for use in chemistry-transport models (CTMs) is a complex problem as the emission varies locally as a result of local climate and local agricultural management. In current CTMs such factors are generally not taken into account. This paper demonstrates how local climate and local management can be accounted for in CTMs by applying a modular approach for deriving data as input to a dynamic ammonia emission model for Europe. Default data are obtained from information in the RAINS system, and it is demonstrated how this dynamic emission model based on these input data improves the NH3 calculations in a CTM model when the results are compared with calculations obtained by traditional methods in emission handling. It is also shown how input data can be modified over a specific target region resulting in even further improvement in performance over this domain. The model code and the obtained default values for the modelling experiments are available as supplementary information to this article for use by the modelling community on similar terms as the EMEP CTM model: the GPL licencse v3. [ABSTRACT FROM AUTHOR]

The study of European and Russian Quaternary glacial-geological evidence during the last 15 years has generated sufficient data to use former glacial extent as a proxy for Last Glacial Maximum (LGM) climate (precipitation and temperature) at a continental scale. Utilisation of such data is relevant for two reasons. First, continental to global scale proxy reconstructions of past climate are an important tool in the assessment of retrospective general circulation model (GCM) simulations. Second, the development of a multiproxy approach will result in a more robust proxy based climate signal. A new and independent dataset of 36 LGM precipitation and temperature relationships derived from European and Russian mountain regions is presented in this paper. A simple glacier-climate model was used to establish the optimum LGM precipitation/temperature conditions for each region from a suite of over 4000 model climates using the principle of zero cumulative mass balance. Clear regional trends are present in the reconstructed LGM precipitation and temperature curves; assuming present precipitation temperature anomalies north of the Alps are 2°C and 5°C larger than those in the western and eastern Mediterranean, respectively. In Russia the model results suggest that the climates in both the Arctic Urals and Puterana Plateau were probably conducive to the existence of small mountain glaciers at the LGM. [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]

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]

This study investigates present and future European heat wave magnitudes, represented by the Heat Wave Magnitude Index-daily (HWMId), for regional climate models (RCMs) and the driving global climate models (GCMs) over Europe. A subset of the large EURO-CORDEX ensemble is employed to study sources of uncertainties related to the choice of GCMs, RCMs, and their combinations. We initially compare the evaluation runs of the RCMs driven by ERA-interim reanalysis to E-OBS (observation-based estimates), finding that the RCMs can capture most of the observed spatial and temporal features of HWMId. With their higher resolution compared to GCMs, RCMs can reveal spatial features of HWMId associated with small-scale processes (e.g., orographic effects); moreover, RCMs represent large-scale features of HWMId satisfactorily (e.g., by reproducing the general pattern revealed by E-OBS with high values at western coastal regions and low values at the eastern part). Our results indicate a clear added value of the RCMs compared to the driving GCMs. Forced with the emission scenario RCP8.5, all the GCM and RCM simulations consistently project a rise in HWMId at an exponential rate. However, the climate change signals projected by the GCMs are generally attenuated when downscaled by the RCMs, with the spatial pattern also altered. The uncertainty in a simulated future change of heat wave magnitudes following global warming can be attributed almost equally to the difference in model physics (as represented by different RCMs) and to the driving data associated with different GCMs. Regarding the uncertainty associated with RCM choice, a major factor is the different representation of the orographic effects. No consistent spatial pattern in the ensemble spread associated with different GCMs is observed between the RCMs, suggesting GCM uncertainties are transformed by RCMs in a complex manner due to the nonlinear nature of model dynamics and physics. In summary, our results support the use of dynamical downscaling for deriving regional climate realization regarding heat wave magnitudes. [ABSTRACT FROM AUTHOR]

Many climate extremes, including heatwaves and heavy precipitation events, are projected to worsen under climate change, with important impacts for society. Future projections required for adaptation are often based on climate model simulations. Given finite resources, trade-offs must be made concerning model resolution, ensemble size, and level of model complexity. Here we focus on the resolution component. A given resolution can be achieved over a region using either global climate models (GCMs) or at lower cost using regional climate models (RCMs) that dynamically downscale coarser GCMs. Both approaches to increasing resolution may better capture small-scale processes and features (downscaling effect), but increased GCM resolution may also improve the representation of the large-scale atmospheric circulation (upscaling effect). The size of this upscaling effect is therefore important for deciding modelling strategies. Here we evaluate the benefits of increased model resolution for both global and regional climate models for simulating temperature, precipitation, and wind extremes over Europe at resolutions that could currently be realistically used for coordinated sets of climate projections at the pan-European scale. First we examine the benefits of regional downscaling by comparing EURO-CORDEX simulations at 12.5 and 50 km resolution to their coarser CMIP5 driving simulations. Secondly, we compare global-scale HadGEM3-A simulations at three resolutions (130, 60, and 25 km). Finally, we separate out resolution-dependent differences for HadGEM3-A into downscaling and upscaling components using a circulation analogue technique. Results suggest limited benefits of increased resolution for heatwaves, except in reducing hot biases over mountainous regions. Precipitation extremes are sensitive to resolution, particularly over complex orography, with larger totals and heavier tails of the distribution at higher resolution, particularly in the CORDEX vs. CMIP5 analysis. CMIP5 models underestimate precipitation extremes, whilst CORDEX simulations overestimate compared to E-OBS, particularly at 12.5 km, but results are sensitive to the observational dataset used, with the MESAN reanalysis giving higher totals and heavier tails than E-OBS. Wind extremes are somewhat stronger and heavier tailed at higher resolution, except in coastal regions where large coastal grid boxes spread strong ocean winds further over land. The circulation analogue analysis suggests that differences with resolution for the HadGEM3-A GCM are primarily due to downscaling effects. [ABSTRACT FROM AUTHOR]

In this study, we evaluate a set of high-resolution (25–50 km horizontal grid spacing) global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP), developed as part of the EU-funded PRIMAVERA (Process-based climate simulation: Advances in high resolution modelling and European climate risk assessment) project, and from the EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment) regional climate models (RCMs) (12–50 km horizontal grid spacing) over a European domain. It is the first time that an assessment of regional climate information using ensembles of both GCMs and RCMs at similar horizontal resolutions has been possible. The focus of the evaluation is on the distribution of daily precipitation at a 50 km scale under current climate conditions. Both the GCM and RCM ensembles are evaluated against high-quality gridded observations in terms of spatial resolution and station density. We show that both ensembles outperform GCMs from the 5th Coupled Model Intercomparison Project (CMIP5), which cannot capture the regional-scale precipitation distribution properly because of their coarse resolutions. PRIMAVERA GCMs generally simulate precipitation distributions within the range of EURO-CORDEX RCMs. Both ensembles perform better in summer and autumn in most European regions but tend to overestimate precipitation in winter and spring. PRIMAVERA shows improvements in the latter by reducing moderate-precipitation rate biases over central and western Europe. The spatial distribution of mean precipitation is also improved in PRIMAVERA. Finally, heavy precipitation simulated by PRIMAVERA agrees better with observations in most regions and seasons, while CORDEX overestimates precipitation extremes. However, uncertainty exists in the observations due to a potential undercatch error, especially during heavy-precipitation events. The analyses also confirm previous findings that, although the spatial representation of precipitation is improved, the effect of increasing resolution from 50 to 12 km horizontal grid spacing in EURO-CORDEX daily precipitation distributions is, in comparison, small in most regions and seasons outside mountainous regions and coastal regions. Our results show that both high-resolution GCMs and CORDEX RCMs provide adequate information to end users at a 50 km scale. [ABSTRACT FROM AUTHOR]

Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over poorly gauged regions. This study examines the usefulness of current state-of-the-art precipitation data sets in hydrological modeling. For this purpose, we force a conceptual hydrological model with multiple precipitation data sets in >200 European catchments to obtain runoff and evapotranspiration. We consider a wide range of precipitation products, which are generated via (1) the interpolation of gauge measurements (E-OBS and Global Precipitation Climatology Centre (GPCC) V.2018), (2) data assimilation into reanalysis models (ERA-Interim, ERA5, and Climate Forecast System Reanalysis – CFSR), and (3) a combination of multiple sources (Multi-Source Weighted-Ensemble Precipitation; MSWEP V2). Evaluation is done at the daily and monthly timescales during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs; in contrast, simulated evapotranspiration is generally much less influenced in our comparatively wet study region. We also find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation data sets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the particularly strong agreement, while ERA5, GPCC V.2018, and MSWEP V2 show good performances. We further reveal climate-dependent performance variations of the considered data sets, which can be used to guide their future development. The overall best agreement is achieved when using an ensemble mean generated from all the individual products. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions, such as central Europe, there are increasing implications for evapotranspiration in drier regions. [ABSTRACT FROM AUTHOR]

Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and scepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project the GROW Observatory (GROW; https://growobservatory.org , last access: 30 March 2020). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within 24 high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low-cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias, and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low intersensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes; only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science. [ABSTRACT FROM AUTHOR]

The Land Use and Climate Across Scales Flagship Pilot Study (LUCAS FPS) is a coordinated community effort to improve the integration of land use change (LUC) in regional climate models (RCMs) and to quantify the biogeophysical effects of LUC on local to regional climate in Europe. In the first phase of LUCAS, nine RCMs are used to explore the biogeophysical impacts of re-/afforestation over Europe: two idealized experiments representing respectively a non-forested and a maximally forested Europe are compared in order to quantify spatial and temporal variations in the regional climate sensitivity to forestation. We find some robust features in the simulated response to forestation. In particular, all models indicate a year-round decrease in surface albedo, which is most pronounced in winter and spring at high latitudes. This results in a winter warming effect, with values ranging from +0.2 to +1 K on average over Scandinavia depending on models. However, there are also a number of strongly diverging responses. For instance, there is no agreement on the sign of temperature changes in summer with some RCMs predicting a widespread cooling from forestation (well below -2 K in most regions), a widespread warming (around +2 K or above in most regions) or a mixed response. A large part of the inter-model spread is attributed to the representation of land processes. In particular, differences in the partitioning of sensible and latent heat are identified as a key source of uncertainty in summer. Atmospheric processes, such as changes in incoming radiation due to cloud cover feedbacks, also influence the simulated response in most seasons. In conclusion, the multi-model approach we use here has the potential to deliver more robust and reliable information to stakeholders involved in land use planning, as compared to results based on single models. However, given the contradictory responses identified, our results also show that there are still fundamental uncertainties that need to be tackled to better anticipate the possible intended or unintended consequences of LUC on regional climates. [ABSTRACT FROM AUTHOR]

Size-resolved aerosol trends were investigated based on a 17-year data set (2000–2017) from the rural background site Aspvreten located in southern Sweden (58.8 ∘ N, 17.4 ∘ E). Cluster analysis of the size distributions was performed to aid in the interpretation of the data. The results confirm previous findings of decreasing aerosol mass and number during the last decades as a result of reduced anthropogenic emissions in Europe. We show that both particle modal number concentration and size have substantially been reduced during the last 17 years. Negative trends in particle number concentration of about 10 cm -3 yr -1 are present for nuclei, Aitken, and accumulation modes. In total, integral particle number concentration has decreased by 30 %, from 1860 to ca. 1300 cm -3. The reduction in modal number concentration is accompanied by a decrease in modal size, and this decrease is largest for the accumulation mode (2 nm yr -1 or about 17 % for the whole period). These reductions have resulted in a decrease in submicron particle mass (< 390 nm) by more than 50 % over the period 2000–2017. These decreases are similar to observations found at other stations in northern Europe. Although all size classes show a downward trend as annual averages, we also show that observed trends are not evenly distributed over the year and that a rather complex picture emerges where both sign and magnitude of trends vary with season and size. The strongest negative trends are present during spring (accumulation mode) and autumn (Aitken mode). The strongest positive trends are present during summer months (Aitken mode). The combined trajectory and data analyses do not present evidence for an increase in new particle formation formed locally, although some evidence of increased new particle formation some distance away from the receptor is present. Observed aerosol size distribution data, together with an adiabatic cloud parcel model, were further used to estimate the change in cloud droplet concentration for various assumptions of updraught velocities and aerosol chemical composition. The results indicate a substantial increase in the atmospheric brightening effect due to a reduction in cloud reflectivity corresponding to 10 %–12 % reduction in cloud albedo over the period 2000–2017. [ABSTRACT FROM AUTHOR]

Probabilistic spatial reconstructions of past climate states are valuable to quantitatively study the climate system under different forcing conditions because they combine the information contained in a proxy synthesis into a comprehensible product. Unfortunately, they are subject to a complex uncertainty structure due to complicated proxy–climate relations and sparse data, which makes interpolation between samples difficult. Bayesian hierarchical models feature promising properties to handle these issues, like the possibility to include multiple sources of information and to quantify uncertainties in a statistically rigorous way. We present a Bayesian framework that combines a network of pollen and macrofossil samples with a spatial prior distribution estimated from a multi-model ensemble of climate simulations. The use of climate simulation output aims at a physically reasonable spatial interpolation of proxy data on a regional scale. To transfer the pollen data into (local) climate information, we invert a forward version of the probabilistic indicator taxa model. The Bayesian inference is performed using Markov chain Monte Carlo methods following a Metropolis-within-Gibbs strategy. Different ways to incorporate the climate simulations into the Bayesian framework are compared using identical twin and cross-validation experiments. Then, we reconstruct the mean temperature of the warmest and mean temperature of the coldest month during the mid-Holocene in Europe using a published pollen and macrofossil synthesis in combination with the Paleoclimate Modelling Intercomparison Project Phase III mid-Holocene ensemble. The output of our Bayesian model is a spatially distributed probability distribution that facilitates quantitative analyses that account for uncertainties. [ABSTRACT FROM AUTHOR]

The link between streamflow extremes and climatology has been widely studied in recent decades. However, a study investigating the effect of large-scale circulation variations on the distribution of seasonal discharge extremes at the European level is missing. Here we fit a climate-informed generalized extreme value (GEV) distribution to about 600 streamflow records in Europe for each of the standard seasons, i.e., to winter, spring, summer and autumn maxima, and compare it with the classical GEV distribution with parameters invariant in time. The study adopts a Bayesian framework and covers the period 1950 to 2016. Five indices with proven influence on the European climate are examined independently as covariates, namely the North Atlantic Oscillation (NAO), the east Atlantic pattern (EA), the east Atlantic–western Russian pattern (EA/WR), the Scandinavia pattern (SCA) and the polar–Eurasian pattern (POL). It is found that for a high percentage of stations the climate-informed model is preferred to the classical model. Particularly for NAO during winter, a strong influence on streamflow extremes is detected for large parts of Europe (preferred to the classical GEV distribution for 46 % of the stations). Climate-informed fits are characterized by spatial coherence and form patterns that resemble relations between the climate indices and seasonal precipitation, suggesting a prominent role of the considered circulation modes for flood generation. For certain regions, such as northwestern Scandinavia and the British Isles, yearly variations of the mean seasonal climate indices result in considerably different extreme value distributions and thus in highly different flood estimates for individual years that can also persist for longer time periods. [ABSTRACT FROM AUTHOR]

The Cloud Climate Change Initiative (Cloud_cci) satellite simulator has been developed to enable comparisons between the Cloud_cci climate data record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth global climate model as well as the Regional Atmospheric Climate Model (RACMO) regional climate model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in liquid water path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces total cloud fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian subcontinent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean but lower than retrieved over the European continent, where in addition the cloud top pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modeled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data. [ABSTRACT FROM AUTHOR]

Causal explanations for the 4.2 ka BP event are based on the amalgamation of seasonal and annual records of climate variability that was manifest across global regions dominated by different climatic regimes. However, instrumental and paleoclimate data indicate that seasonal climate variability is not always sequential in some regions. The present study investigates the spatial manifestation of the 4.2 ka BP event during the boreal winter season in Eurasia, where climate variability is a function of the spatiotemporal dynamics of the westerly winds. We present a multi-proxy reconstruction of winter climate conditions in Europe, west Asia, and northern Africa between 4.3 and 3.8 ka. Our results show that, while winter temperatures were cold throughout the region, precipitation amounts had a heterogeneous distribution, with regionally significant low values in W Asia, SE Europe, and N Europe and local high values in the N Balkan Peninsula, the Carpathian Mountains, and E and NE Europe. Further, strong northerly winds were dominating in the Middle East and E and NE Europe. Analyzing the relationships between these climatic conditions, we hypothesize that in the extratropical Northern Hemisphere, the 4.2 ka BP event was caused by the strengthening and expansion of the Siberian High, which effectively blocked the moisture-carrying westerlies from reaching W Asia and enhanced outbreaks of cold and dry winds in that region. The behavior of the winter and summer monsoons suggests that when parts of Asia and Europe were experiencing winter droughts, SE Asia was experiencing similar summer droughts, resulting from failed and/or reduced monsoons. Thus, while in the extratropical regions of Eurasia the 4.2 ka BP event was a century-scale winter phenomenon, in the monsoon-dominated regions it may have been a feature of summer climate conditions. [ABSTRACT FROM AUTHOR]

Heinrich events are among the dominant modes of glacial climate variability. During these events, massive iceberg armadas were released by the Laurentide Ice Sheet and sailed across the Atlantic where they melted and released freshwater, as well as detritus, that formed characteristic layers on the seafloor. Heinrich events are known for cold climates in the North Atlantic region and global climate changes. We study these events in a fully coupled complex ice sheet–climate model with synchronous coupling between ice sheets and oceans. The ice discharges occur as an internal variability of the model with a recurrence period of 5 kyr, an event duration of 1–1.5 kyr, and a peak discharge rate of about 50 mSv, roughly consistent with reconstructions. The climate response shows a two-stage behavior, with freshwater release effects dominating the surge phase and ice sheet elevation effects dominating the post-surge phase. As a direct response to the freshwater discharge during the surge phase, deepwater formation in the North Atlantic decreases and the North Atlantic deepwater cell weakens by 3.5 Sv. With the reduced oceanic heat transport, the surface temperatures across the North Atlantic decrease, and the associated reduction in evaporation causes a drying in Europe. The ice discharge lowers the surface elevation in the Hudson Bay area and thus leads to increased precipitation and accelerated ice sheet regrowth in the post-surge phase. Furthermore, the jet stream widens to the north, which contributes to a weakening of the subpolar gyre and a continued cooling over Europe even after the ice discharge. This two-stage behavior can explain previously contradicting model results and understandings of Heinrich events. [ABSTRACT FROM AUTHOR]

The carbon dioxide (CO2/ exchange of five different peatland systems across Europe with a wide gradient in land use intensity, water table depth, soil fertility and climate was simulated with the process oriented CoupModel. The aim of the study was to find out whether CO2 fluxes, measured at different sites, can be explained by common processes and parameters or to what extend a site specific con- figuration is needed. The model was calibrated to fit measured CO2 fluxes, soil temperature, snow depth and leaf area index (LAI) and resulting differences in model parameters were analyzed. Finding site independent model parameters would mean that differences in the measured fluxes could be explained solely by model input data: water table, meteorological data, management and soil inventory data. Seasonal variability in the major fluxes was well captured, when a site independent configuration was utilized for most of the parameters. Parameters that differed between sites included the rate of soil organic decomposition, photosynthetic efficiency, and regulation of the mobile carbon (C) pool from senescence to shooting in the next year. The largest difference between sites was the rate coefficient for heterotrophic respiration. Setting it to a common value would lead to underestimation of mean total respiration by a factor of 2.8 up to an overestimation by a factor of 4. Despite testing a wide range of different responses to soil water and temperature, rate coefficients for heterotrophic respiration were consistently the lowest on formerly drained sites and the highest on the managed sites. Substrate decomposability, pH and vegetation characteristics are possible explanations for the differences in decomposition rates. Specific parameter values for the timing of plant shooting and senescence, the photosynthesis response to temperature, litter fall and plant respiration rates, leaf morphology and allocation fractions of new assimilates, were not needed, even though the gradient in site latitude ranged from 48 ° N (southern Germany) to 68 ° N (northern Finland) differed largely in their vegetation. This was also true for common parameters defining the moisture and temperature response for decomposition, leading to the conclusion that a site specific interpretation of these processes is not necessary. In contrast, the rate of soil organic decomposition, photosynthetic efficiency, and the regulation of the mobile carbon pool need to be estimated from available information on specific soil conditions, vegetation and management of the ecosystems, to be able to describe CO2 fluxes under different conditions. [ABSTRACT FROM AUTHOR]

Estimates of climate conditions before the 19th century are based on proxy data reconstructions or sparse meteorological measurements. The reconstruction of the atmospheric circulation that prevailed during the European Little Ice Age (1500-1850) has fostered many efforts. This study illustrates a methodology, combining historical proxies and modern datasets to obtain detailed information on the atmospheric circulation that prevailed over the North Atlantic region during the Little Ice Age. We used reconstructions of temperature gradients over France based on grape harvest dates to infer the atmospheric circulation. We found that blocking situations were more likely in summer, inducing a continental atmospheric flow. This study advocates that the reconstructions of the past atmospheric circulation should take this regime into account. [ABSTRACT FROM AUTHOR]

In this work we asses the benefits of removing bias in climate forcing data used for hydrological climate change impact assessment at pan-European scale, with emphasis on foods. Climate simulations from the HIRHAM5-ECHAM5 model driven by the SRES-A1B emission scenario are corrected for bias using a histogram equalization method. As target for the bias correction we employ gridded interpolated observations of precipitation, average, minimum, and maximum temperature from the E-OBS data set. Bias removal transfer functions are derived for the control period 1961-1990. These are subsequently used to correct the climate simulations for the control period, and, under the assumption of a stationary error model, for the future time window 2071-2100. Validation against E-OBS climatology in the control period shows that the correction method performs successfully in removing bias in average and extreme statistics relevant for food simulation over the majority of the European domain in all seasons. This translates into considerably improved simulations with the hydrological model of observed average and extreme river discharges at a majority of 554 validation river stations across Europe. Probabilities of extreme events derived employing extreme value techniques are also more closely reproduced. Results indicate that projections of future food hazard in Europe based on uncorrected climate simulations, both in terms of their magnitude and recurrence interval, are likely subject to large errors. Notwithstanding the inherent limitations of the large-scale approach used herein, this study strongly advocates the removal of bias in climate simulations prior to their use in hydrological impact assessment. [ABSTRACT FROM AUTHOR]

Extreme wind speeds and related storm loss potential in Europe have been investigated using multi-model simulations from global (GCM) and regional (RCM) climate models. Potential future changes due to anthropogenic climate change have been analysed from these simulations following the IPCC SRES A IB scenario. The large number of available simulations allows an estimation of the robustness of detected future changes. All the climate models reproduced the observed spatial patterns of wind speeds, although some models displayed systematic biases. A storm loss model was applied to the GCM and RCM simulated wind speeds, resulting in realistic mean loss amounts calculated from 20th century climate simulations, although the inter-annual variability of losses is generally underestimated. In future climate simulations, enhanced extreme wind speeds were found over northern parts of Central and Western Europe in most simulations and in the ensemble mean (up to 5%). As a consequence, the loss potential is also higher in these regions, particularly in Central Europe. Conversely, a decrease in extreme wind speeds was found in Southern Europe, as was an associated reduction in loss potential. There was considerable spread in the projected changes of individual ensemble members, with some indicating an opposite signature to the ensemble mean. Downscaling of the large-scale simulations with RCMs has been shown to be an important source of uncertainty. Even RCMs with identical boundary forcings can show a wide range of potential changes. The robustness of the projected changes was estimated using two different measures. First, the inter-model standard deviation was calculated; however, it is sensitive to outliers and thus displayed large uncertainty ranges. Second, a multimodel combinatorics approach considered all possible subensembles from GCMs and RCMs, hence taking into account the arbitrariness of model selection for multi-model studies. Based on all available GCM and RCM simulations, for example, a 25% mean increase in risk of loss for Germany has been estimated for the end of the 21st century, with a 90% confidence range of +15 to +35%. [ABSTRACT FROM AUTHOR]

The article discusses the European network of meteorological services (EUMETNET) project Showcase EUROGRID (S- EUROGRID) that will provide gridded climate data, products, and services from European resources. The creation of Open Geospatial Consortium (OGC)-protocol leads to seamless integration of distributed information sources into the S-EUROGRID portal and in return the dissemination of the maps and data to the test-user community.

Understanding past climates using GCM models is critical to confidently predicting future climate change. Although previous analysis of GCM simulations have shown them to under calculate European glacial temperature anomalies (the difference between modern and glacial temperatures) such analyses have focused primarily on results from glacial simulations alone. Here we compare glacial maximum GCM results with the palaeoenvironment derived from glacier-climate modelling. The comparison confirms that GCM anomalies are not large enough, and that this is due to modern conditions that are modelled too cold and glacial temperatures that are too warm. The result is that GCM results, if applied to a glacier mass balance model, over predict the extent of glaciers today, and under calculate their extent at the last glacial (as depicted in glacial geological reconstructions). Effects such as seasonality and model parameterisation change the magnitude of the under calculation but still fail to match expected glacial conditions. [ABSTRACT FROM AUTHOR]

The mountain environments of mid-latitude Europe and Arctic Russia contain widespread evidence of Late- Quaternary glaciers that have been attributed to the Last Glacial Maximum (LGM). This glacial-geological record has yet to be used to quantitatively reconstruct the LGM climate of these regions. Here we describe a simple glacier-climate model that can be used to derive regional temperature and precipitation information from a known glacier distribution. The model was tested against the present day distribution of glaciers in Europe. The model is capable of adequately predicting the spatial distribution, snowline and equilibrium line altitude climate of glaciers in the Alps, Scandinavia, Caucasus and Pyrenees Mountains. This verification demonstrated that the model can be used to investigate former climates such as the LGM. Reconstructions of LGM climates from proxy evidence are an important method of assessing retrospective general circulation model (GCM) simulations. LGM palaeoclimate reconstructions from glacial-geological evidence would be of particular benefit to investigations in Europe and Russia, where to date only fossil pollen data have been used to assess continental-scale GCM simulations. [ABSTRACT FROM AUTHOR]

Natural ecosystems respond to, and may affect climate change through uptake and storage of atmospheric CO2. Here we use the land-surface and carbon cycle model JULES to simulate the contemporary European carbon balance and its sensitivity to rising CO2 and changes in climate. We find that the impact of climate change is to decrease the ability of Europe to store carbon by 97 TgC yr-1. In contrast, the effect of rising atmospheric CO2 has been to stimulate increased uptake and storage. The CO2 effect is currently dominant leading to a net increase of 114 TgC yr-1. Our simulations do not at present include other important factors such as land use and management, the effects of forest age classes and nitrogen deposition. Understanding this balance and its implications for mitigation policies is becoming increasingly important. [ABSTRACT FROM AUTHOR]

The demand for intercontinental transportation is increasing and people are requesting short travel times, which supersonic air transportation would enable. However, besides noise and sonic boom issues, which we are not referring to in this investigation, emissions from supersonic aircraft are known to alter the atmospheric composition, in particular the ozone layer, and hence affect climate significantly more than subsonic aircraft. Here, we suggest a metric to quantitatively assess different options for supersonic transport with regard to the potential destruction of the ozone layer and climate impacts. Options for fleet size, engine technology (nitrogen oxide emission level), cruising speed, range, and cruising altitude, are analyzed, based on SCENIC emission scenarios for 2050, which underlay the requirements to be as realistic as possible in terms of e.g., economic markets and profitable market penetration. This methodology is based on a number of atmosphere-chemistry and climate models to reduce model dependencies. The model results differ significantly in terms of the response to a replacement of subsonic aircraft by supersonic aircraft, e.g., concerning the ozone impact. However, model differences are smaller when comparing the different options for a supersonic fleet. Those uncertainties were taken into account to make sure that our findings are robust. The base case scenario, where supersonic aircraft get in service in 2015, a first fleet fully operational in 2025 and a second in 2050, leads in our simulations to a near surface temperature increase in 2050 of around 7mK and with constant emissions afterwards to around 21mK in 2100. The related total radiative forcing amounts to 22 Mw/m² in 2050, with an uncertainty between 9 and 29 mW/m². A reduced supersonic cruise altitude or speed (from Mach 2 to Mach 1.6) reduces both, climate impact and ozone destruction, by around 40%. An increase in the range of the supersonic aircraft leads to more emissions at lower latitudes since more routes to SE Asia are taken into account, which increases ozone depletion, but reduces climate impact compared to the base case. [ABSTRACT FROM AUTHOR]

Local-scale climate information is increasingly needed for the study of past, present and future climate changes. In this study we develop a non-linear statistical downscaling method to generate local temperatures and precipitation values from large-scale variables of a Earth System Model of Intermediate Complexity (here CLIMBER). Our statistical downscaling scheme is based on the concept of Generalized Additive Models (GAMs), capturing nonlinearities via non-parametric techniques. Our GAMs are calibrated on the present Western Europe climate. For this region, annual GAMs (i.e. models based on 12 monthly values per location) are fitted by combining two types of largescale explanatory variables: geographical (e.g. topographical information) and physical (i.e. entirely simulated by the CLIMBER model). To evaluate the adequacy of the non-linear transfer functions fitted on the present Western European climate, they are applied to different spatial and temporal large-scale conditions. Local projections for present North America and Northern Europe climates are obtained and compared to local observations. This partially addresses the issue of spatial robustness of our transfer functions by answering the question "does our statistical model remain valid when applied to large-scale climate conditions from a region different from the one used for calibration?". To asses their temporal performances, local projections for the Last Glacial Maximum period are derived and compared to local reconstructions and General Circulation Model outputs. Our downscaling methodology performs adequately for the Western Europe climate. Concerning the spatial and temporal evaluations, it does not behave as well for Northern America and Northern Europe climates because the calibration domain may be too different from the targeted regions. The physical explanatory variables alone are not capable of downscaling realistic values. However, the inclusion of geographical-type variables - such as altitude, advective continentality and moutains effect on wind (W-slope) - as GAM explanatory variables clearly improves our local projections. [ABSTRACT FROM AUTHOR]

The extraordinarily warm and dry summer experienced in SW Europe in 2003 provides an interesting case study for the analysis of the response of regional-scale vegetation variables to drier and hotter conditions. SW Europe includes a boundary between phytogeographic and climatic regions (Oceanic and Mediterranean) that differ in terms of summer precipitation, which let us compare the response of different vegetation types. In addition, some scenarios predict summer conditions such as those of 2003 to be common by the latter decades of the 21st century in this region. We have analyzed a monthly series of regional fields of normalized difference vegetation index from the VEGETATION-SPOT5 instrument, from 1999 to 2003. We show that negative anomalies of vegetation index in summer 2003 were larger for herbaceous vegetation of the Oceanic climate region and for deciduous forests. The vegetation index of August 2003 in the Mediterranean climate region was also significantly lower than normal values in August 1999-2002, albeit the anomalies were small in absolute value. We compared August NDVI, as a measure of the vegetation response, to the difference between total summer precipitation and total summer potential evapotranspiration, as a measure of atmospheric water stress. Our results indicate that water stress is a major factor structuring the geographic variability of NDVI in this region. In accordance with these results, the analysis of the data of 2003 indicate that the increased water stress was a key factor of the observed anomalies of vegetation index. [ABSTRACT FROM AUTHOR]