bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving seasonal forecasts for the Iberian Peninsula through climate variability modes
Authors: Senande-Rivera Martín , Domínguez-Alonso Marta , Rodríguez-Guisado Esteban
Published: July 2024

Accurate seasonal forecasts can be very useful for taking adaptation and prevention measures to unfavourable weather conditions, such as droughts, heatwaves, extreme precipitation events or high fire risk conditions. In a region with a very marked seasonal cycle and a high inter-annual variability of atmospheric conditions such as the Iberian Peninsula, the demand for improved seasonal forecasting becomes even more compelling. Currently operational seasonal forecasting systems have considerable scope for improvement in their ability to predict mid-latitude temperature or precipitation. However, some of these systems have shown some skill in predicting, months in advance, the phase of some modes of variability such as the North Atlantic Oscillation.Here we show that seasonal forecast of surface variables (such as 2m air temperature or total precipitation) can be improved by taking advantage of the models' skill in predicting the main modes of variability in Europe. First, a quantification of the theoretical potential for improving the forecasts is carried out using an ensemble-member weighting technique that increase the statistical weight of those members whose variability mode configuration is closer to the observed configuration. Then, we assess the actual potential for improving the forecasts by using a two steps prediction: (1) a multi-system forecast of the corresponding variability mode configuration and (2) a seasonal forecast of the surface variables in which we use the first step forecast of the variability mode configuration to weight the ensemble members. Different verification metrics were used to quantify the skill of the forecasts, both deterministic and probabilistic, all defined as in the WMO forecast guidance.The results show that ensemble-member weighting method with information on variability modes is a window of forecast opportunity that is able to improve seasonal forecasts if further research is conducted.
ClimRisk.eu – new climate proofing tool for Central Europe
Authors: Trnka Miroslav , Stepanek Petr , Skalák Petr , Balek Jan , Zahradníček Pavel , Meitner Jan , Farda Aleš
Published: July 2024

ClimRisk.eu is a new web portal about climate change projections and underlying data needed for climate proofing. ClimRisk.eu is designed for investors planning their investments to withstand the future climate conditions. It is intended to help the public organizations to formulate their policies and plan specific adaptation measures. It is also a tool for companies and private entities to comply with the EU requirements to assess the sustainability of investments (EU Regulation 2021/1060, the so-called EU Taxonomy). ClimRisk.eu also serves as the data source for individuals and broad public and their climate-related personal interests.ClimRisk.eu works over two domains: Czech Republic and Central Europe. Information for the Czech Republic is based on more detailed and accurate data inputs, e.g. climate observations and it is derived from high resolution data grid of 0.5 km step. The data on the Central European domain is built on the less detailed data sources and thus the spatial resolution reaches only 10 km. Climate projections are delivered for four selected Shared Socioeconomic Pathways (SSP) scenarios and ensemble of seven Coupled Model Intercomparison Project Phase 6 global climate models (GCMs). The set of seven  GCMs was taken from a larger ensemble consisted of more than 20 CMIP6 GCMs and its choice was done to reduce the effort needed to process all data while keeping the statistical properties of the original ensemble of more than 20 GCMs.ClimRisk.eu offers long-term means of meteorological parameters (air temperature, precipitation, wind speed, humidity, solar radiation) as well as important climate indices including those focused on extremes. Information on the range of uncertainty related to climate projection for a given territory is also included at ClimRisk.eu.Analysis of future climate conditions is based on simulations of the most recent generation of global climate models (GCM). Given the outputs of climate models are associated with systematic errors (due to the necessary simplification of the complex real-world processes), they need to be corrected in order to obtain meaningful results about the simulated properties of the climate system. In case of GCM it is not possible to apply correction methods suitable for RCMs, like quantile mapping. In this case this is made possible by the Advanced Delta Change ("ADC") method.
The Dilemma of Coeval Climate and Demographic Changes: Heat Risk Analysis for the German Hospital Care System
Authors: Geiger Tobias , Potzauf Merle , Detring Igor , Castino Fabiana , Kreienkamp Frank
Published: July 2024

Current climate projections illustrate a drastic increase in the likelihood of heatwaves throughout the century. At the same time, population scenarios for many developed countries indicate a coeval rise in elderly population fraction, in particular, in rural areas. Research demonstrates that failing to consider projected changes in population and demographics leads to a significant underestimation of health burdens [1]. Thus, in addition to the fundamental increase in healthcare demand due to the increased baseline morbidity of older individuals, there are also additional infrastructural requirements to prepare for weather and climate change-related consequences. Incorporating population changes into climate projections is therefore crucial for a comprehensive assessment of heat-related health impacts.Here, we pursue a risk analysis for Germany by mid-century by combining health-relevant heat indicators with demographic scenarios and data on the healthcare provision situation at the county level. Climate indicators are based on latest results from statistically-downscaled CMIP6 climate projections for Germany [2]. Demographic changes are inferred from socioeconomic model simulation conducted for the latest Climate Impact and Vulnerability Analysis by the German Federal Environment Agency, available at the county level until 2045 [3]. Data from the German Federal Statistical Office are used to depict the spatial healthcare provision in terms of the number of spatially-explicit emergency department beds. For the risk analysis selected literature-based heat indicators with demonstrated health impacts are evaluated and the potential consequences for the German hospital emergency care system are discussed. In particular, the unequal spatial distribution of risks for the elderly population as well as the “double exposure” dilemma for certain areas facing large demographic changes and intensified heatwaves are identified.Interpreting the spatial-temporal pattern of German regional exposure of hospitals to future heatwaves and demographic changes will help health care practitioners and political decision makers to develop strategies to reduce heat-related health risks both in the short and long term.References[1] Vanos, J. K., Baldwin, J. W., Jay, O., & Ebi, K. L. (2020). Simplicity lacks robustness when projecting heat-health outcomes in a changing climate. Nature Communications, 11(1), 6079. https://doi.org/10.1038/s41467-020-19994-1[2] Kreienkamp, F., Lorenz, P., & Geiger, T. (2020). Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany. Atmosphere, 11(11), Article 11. https://doi.org/10.3390/atmos11111245[3] Buth, M., Kahlenborn, W., Greiving, S., Fleischhauer, M., Zebisch, M., Schneiderbauer, S., & Schause, I. (2017). Leitfaden für Klimawirkungs- und Vulnerabilitätsanalysen. Umweltbundesamt.
Atmospheric blockings and downstream cyclones in the Euro-Mediterranean sector
Authors: Loizou Pinelopi , Raveh-Rubin Shira
Published: July 2024

Societies across the Mediterranean and wider European region can be significantly affected by weather extremes such as heatwaves, cold spells, windstorms and intense rainfall events. Temperature extremes are connected to atmospheric blocking, while heavy precipitation and windstorms have been associated with Mediterranean Cyclones (MCs). However, the connection between atmospheric blocking and MCs is still understudied, despite evidence suggesting their mutual importance for cyclone development and for exacerbating and synchronizing surface extremes. The aim of the present study is the systematic investigation of the frequency of MC development downstream of atmospheric blocks over the Euro-Atlantic region, and to examine how different cyclone track characteristics along with cyclone-attributed-precipitation might be modulated under such conditions. To this end we employ the combined ‘best tracks’ MCs dataset with objectively identified (using potential vorticity anomalies) blocking features in ERA5 for the 1970-2020 period. We find that in the presence of atmospheric blocks, MCs that develop downstream tend to be more intense and to be associated with increased precipitation compared to other MCs. Moreover, under blocking conditions, the distribution of precipitation varies geographically between the north-west and south-east Euro-Mediterranean region, with moisture transport contributing to this difference. MCs developing under this scenario form particular subsets of MCs, with preferred seasonality and geographical distribution, compared to all MCs. Lastly, certain MCs tend to be more static, while their mobility exhibits variability under blocking conditions. These results have important implications for the predictability of MCs and their various impacts in the region on both weather and climate time scales.
FAIR and reproducible moisture tracking with WAM2layers V3
Authors: Kalverla Peter , Benedict Imme , van der Ent Ruud , Weijenborg Chris , Schilperoort Bart
Published: July 2024

Moisture tracking is a technique to characterize the transport of moisture in gridded atmospheric datasets. It enables users to map source and sink region of (extreme) precipitation or evaporation, respectively. With intensifying wet and dry extremes, the interest in moisture tracking has grown substantially over the past decade.Various models are used for moisture tracking today. They differ in their approach (Lagrangian versus Eulerian), complexity, input data, assumptions, et cetera. The lack of ground truth data makes it challenging to interpret the results, especially where different approaches lead to different moisture tracking results.To facilitate comparison between models, it is helpful if both models and their input/output data adhere to best practices for reusable and reproducible data and software. In the development of WAM2layers V3, we have made good progress in incorporating such (FAIR) standards. For example, the WAM2layers software is easy to find and install, well documented, has a DOI, and follows semantic versioning standards. Input data for example cases is available as well, also with a DOI, and a download utility is part of the software so one can get started in minutes. Similarly, the output data adheres (as much as possible) to CF-conventions and adds all relevant metadata, to maximize the reusability and reproducibility of the results.In May of 2024, users and developers of various moisture tracking models will gather in Leiden to analyse the results of the first community moisture tracking intercomparison study. This is a first step towards better understanding of the effects of certain modelling choices and to address the uncertainty among different models. At EMS, we will showcase our WAM2layers contributions to the Lorentz intercomparison study to demonstrate how the incorporation of data and software best practices in WAM2layers contribute to a FAIR and reproducible moisture tracking workflow.
Assessing the performance of reanalysis models for wind power modelling in Germany 
Authors: Geiger David , Pfennig Maximilian , Callies Doron , Pape Carsten , Pauscher Lukas
Published: July 2024

Country-wide energy systems analysis requires the simulation of wind power feed-in. This is usually derived using wind turbine data and wind speed time series from reanalysis models. As reanalysis models exhibit biases especially in complex terrain, the modelled feed-in deviates from the observed wind energy feed-in. In this work, we assess several state-of-the-art reanalysis models such as REA6, ERA5, CERRA and NEWA by simulating all current German windfarms and comparing the results with country-wide feed-in time series from ENTSO-E or SMARD. In a second step the wind turbine model parameters are calibrated for each reanalysis model to better match the observed capacity factors.
In order to simulate the wind power feed-in, we use a regionally smoothed power curve to model feed-in and an empirical shading curve to account for intra park turbine wakes. The location, hub height, rotor diameter and installed capacity are taken from the Marktstammdatenregister (MaStR) with missing parameters estimated from wind turbines with similar characteristics. The total installed capacity is corrected or scaled using the ENTSO-E and SMARD data. If available, manufacturer power curves are used; otherwise, synthetic power curves are applied. Additionally, other loss factors such as technical availability are included using constant factors.
In the first analysis the feed-in model is parameterized with standard values to assess the general performance of each reanalysis model. The obtained results are analyzed with respect to capacity factors, bias, and correlation coefficients, providing an indication of the overall performance of the reanalysis models in terms of wind energy generation.
In the second step, the analysis framework is used to derive calibration factors for the feed-in model, aiming to achieve more realistic results for each of the reanalysis models. By calibrating the wind turbine model parameters, we aim to reduce the difference between the modeled and the observed capacity factors.
The performed analysis and calibration highlight the need for an improved understanding of the performance of reanalysis data sets for wind energy system modelling and the development of more sophisticated corrections.  
A moisture tracking intercomparison study - Addressing the uncertainty in modelling the origins of precipitation
Authors: Benedict Imme , Weijenborg Chris , van der Ent Ruud , Keune Jessica , Koren Gerbrand , Kalverla Peter
Published: July 2024

To understand the mechanisms behind precipitation extremes, one can determine the origin of this precipitation, i.e. its moisture sources. The temporal and spatial distribution of these sources provide insights into the synoptic situation of the extreme event, and the importance of land-atmosphere interactions and moisture recycling. To determine moisture sources, moisture tracking models are used and forced with gridded atmospheric data to track water vapour in the atmosphere backward in time to its origin. Moisture tracking models have become an increasingly popular tool in scientific studies in recent years, but diversify in their underlying assumptions. Validation of tracking models is difficult due to the scarcity of isotope measurements as a benchmark. Further, structured intercomparisons among different models are lacking.Here, we present our efforts to coordinate a moisture tracking intercomparison study. Therefore, we reached out to many members of the moisture tracking community, and asked them to run their own moisture tracking model for three selected extreme precipitation events that occurred in 2022. Those three selected events cover different precipitation mechanisms: a monsoon event in Pakistan, a convective precipitation case in Australia, and an atmospheric river driven precipitation case over Scotland. We aim to compare the sources for the three cases among the different models during a one-week workshop in May 2024 in Leiden, The Netherlands. To this date, this intercomparison study covers about eight different moisture tracking models, allowing us to address and quantify the uncertainty in the moisture sources. At the EMS Annual meeting, we will present our approach and preliminary results of this intercomparison. This coordinated model intercomparison facilitates the explanation and quantification of uncertainty, acting as a point of reference for future work and literature on moisture tracking.
Aerosol properties retrieval in partially cloud conditions using HDR All-Sky imagery
Authors: Scarlatti Francesco , Gómez-Amo José Luis , C. Valdelomar Pedro , Matos Violeta , Estelles Victor , Utrillas Maria Pilar
Published: July 2024

In the present study we focus our attention on determine aerosol properties from HDR All-Sky calibrated imagery. Even if this methodology is designed to be used regardless of the sky conditions, we especially explore the sensitivity to partially cloud scenarios, which is the main novelty of this work. Our methodology is based on using a small sector of the image that contains the principal plane of the Sun. The RGB principal plane radiances are associated to the Aerosol Optical Depth (AOD) and Ångström exponent (AE) AERONET observations through a Gaussian Process Regression (GPR) machine learning (ML) model. The cloudy points in our working sector are previously identified and then the correspondent free-sky radiances simulated with the aid of the Perez model. Therefore, the entire principal plane signal as if no clouds are present is synthetized and used as the ML model input. As the outputs of the ML model are the AOD and AE, the ML find a hidden relationship between these quantities and provide a useful prediction method. Finally, 2 years dataset has been used to test the method considering different atmospheric conditions related to the presence of clouds and aerosols, according to their amount and type. Since we used a GPR as ML model, we took advantage of the statistically propagated uncertainty the model relates to the outputs to give a proxy of an error of measurement to the predicted quantities. We also used these uncertainties to develop a method that evaluate the quality of predictions themselves. This quality assurance method may be fine-tuned according to the desired accuracy based on the application for which it is intended. Our AOD and AE predictions show an excellent overall agreement with AERONET measurements that substantially improves when our quality assurance method is applied. In that case, we obtain a high degree of correlation (R2 >0.97) and an overall MAE lower than the nominal uncertainty of AERONET measurements (0.006 and 0.05 for AOD and AE, respectively). Moreover, more than 83% and 77% of the predictions fall within the nominal uncertainty associated with AERONET measurements for AOD and AE, respectively. A comprehensive sensitivity analysis of the factors affecting the performance of the proposed methodology confirms that our method is stable and not very sensitive to external and methodological factors, especially when we apply quality assurance criteria. All this supports that our methodology is a reliable alternative to retrieve the optical properties of aerosols independently of the cloud conditions. Our results may contribute to the operational use of all-sky cameras, which may be an interesting complement regarding the study of aerosol-cloud interactions in partially cloud scenarios.
Investigating heat, cold and air pollution effects on mortality in a coastal Mediterranean city 
Authors: Parliari Daphne , Economou Theo , Giannaros Christos , Kushta Jonilda , Melas Dimitris , Matzarakis Andreas , Lelieveld Jos
Published: July 2024

The impact of various environmental factors on human health is often assessed in a non-interactive way, yet emerging evidence suggests synergy among multiple risk factors on health outcomes. In Thessaloniki, frequent exposure to air pollution, primarily O3 in summer and PM10 in winter, coupled with elevated air temperatures, has been linked to high mortality risks, exacerbated under the influence of an Urban Heat Island. In the present we apply an innovative statistical framework to investigate the combined effect of thermal stress (maximum apparent temperature, Tappmax) and air quality (PM10, NO2, and O3 concentrations) on daily mortality. We estimate heat and cold effects as the percentage change in all-cause, cardiovascular disease (CVD) and respiratory disease (RD) mortality between a) the 75th and 99th, and b) the 1st and 25th percentiles of Tappmax, respectively, and their variation across low, medium, and high levels of pollutants, defined as the 5th, 50th, and 95th of the sample percentiles.Overall, heat exhibits a more pronounced impact than cold, as exposure to outdoor conditions is limited during winter. High levels of PM10, NO2 and O3 are associated with increases in heat-related all-cause mortality by 43.7%, 37%, and 31%, respectively, while the corresponding increases for cold are 14%, 19%, and 1.7%. A consistent increase in the effect of heat on mortalities is observed, particularly at high pollutant levels. It is worth noting that RD mortality decreases with increasing PM10 and NO2 levels, potentially reflecting protective adaptations among urban residents during adverse conditions. Only the highest pollutant concentrations impact mortalities in the cold part of the Tappmax range, albeit at lower levels than heat. Notably, cold-related RD mortality is prominently affected by high NO2 levels (39%), followed by PM10 (30%), indicating a more pronounced influence of these pollutants on the respiratory system.The present research captures the impact of concurrent exposure to environmental stressors over time, providing insight into the effects of extended periods of poor air quality and heat stress on human health. By understanding these results, public health interventions, such as the enhancement of heat resilience and targeted healthcare services to sensitive populations, can be effectively tailored to address the specific health risks stemming from exposure to extreme temperature conditions and poor air quality.
A Comparative Analysis of Dynamic and Statistical Downscaling Techniques to Bridge the Gap Between the Regional and the Local Scale: Case Study for Nicosia
Authors: Koutroumanou-Kontosi Konstantina , Cartalis Constantinos , Hadjinicolaou Panos
Published: July 2024

The Eastern Mediterranean and Middle East (EMME) region exhibits continuously accelerated warming, posing a significant threat to cities within this area and thus increasing their vulnerability to climate change. Regional Climate Models (RCMs) serve as valuable tools for climate simulations, but when the focus is given to the urban thermal environment the resolution of their output results is not sufficient. Downscaling techniques can be utilized to improve the spatial resolution of the RCMs and, hence, bridge this gap between the regional and the local scale. The downscaling techniques can be divided into two main categories, the dynamical (DD), and the empirical/statistical (ESD). While DD relies on physical schemes, its computational demands pose challenges for long-term simulations contrary to the ESD which is computationally inexpensive. In this study, both the DD and the ESD techniques are applied to downscale the 2m air temperature from the regional to the local scale for the city of Nicosia, the capital of Cyprus. Concerning the DD, the Weather Research and Forecasting (WRF) model is used to downscale  the ERA5 re-analysis in three different domains, over the EMME region, Cyprus, and Nicosia, with a spatial horizontal resolution of 12km×12km, 4km×4km, 1km×1km respectively, over a 5-year historical period (2008-2012). Detailed information on the urban characteristics is incorporated into the WRF model through the coupling of the Single Layer Urban Canopy Model (SLUCM) as well as with the utilization of the state-of-the-art land use/land cover CGLC-MODIS-LCZ dataset. The ESD utilizes the generated database of the WRF model to establish statistical relationships between the regional and the local scale for the same period by employing advanced machine learning techniques, including Artificial Neural Networks (ANNs) and Multiple Linear Regression (MLR). A detailed comparative analysis between the two downscaling techniques as well as an evaluation of the simulation results against observation data from two meteorological stations are performed to assess their accuracy in estimating the air temperature over Nicosia.Key Words: WRF model; dynamical downscaling; statistical downscaling; urban climate modeling; Local Climate Zones
AeDES2.0: An enhanced climate-and-health service for monitoring and forecasting environmental suitability of Aedes-borne disease transmission in hotspots 
Authors: Corvillo Guerra Javier , Torralba Verónica , González Romero Carmen , Llabrés-Brustenga Alba , Riviere-Cinnamond Ana , G. Muñoz Ángel
Published: July 2024

Aedes-borne diseases, such as dengue, Zika and Chikungunya, pose a grave threat to millions of people worldwide each year. Aware of potential compound effects regarding other important diseases, such as COVID-19, it has become imperative for health authorities to maintain a detailed surveillance of key environmental variables that can trigger epidemic episodes. While disease transmission is generally conditioned by multiple socio-economic factors, the environmental suitability for vectors and viruses to proliferate is a necessary –although not sufficient– condition that needs to be closely monitored and forecasted. As such, a comprehensive service that allows stakeholders to analyse and visualise environmental suitability on affected hotspots is crucial for communities to better prepare in the case of present and future outbreaks. The newest version of the Aedes-borne Diseases Environmental Suitability (AeDES2) climate-and-health service is a next-generation, fully-operational monitoring system that reproduces and improves the previous version (Muñoz et al., 2020), broadening both the temporal and spatial scope while simultaneously enhancing both observational and forecasting quality. With AeDES2, users can consult the historical evolution of the environmental suitability values on any grid point of interest, as well as the expected future evolution up to three seasons in advance. Aside from the environmental suitability values, health authorities can additionally analyse the estimated incidence or percentage of population at risk threshold –a key indicator for governing bodies to trigger the implementation of control measures to reduce the spread of the disease in an affected population. AeDES2 incorporates four state-of-the-art environmental suitability models, considering both epidemiological factors for transmission probability and climate variables such as temperature values. On the monitoring side, AeDES2 provides a continuously updated monthly historical sequence of environmental suitability values by generating an ensemble with multiple observational references, hence providing uncertainty estimates in the monitoring system, an improvement over the previous version. On the prediction side, still under development, AeDES2 builds on its predecessor’s pattern-based multi-model calibration approach, assimilating new Machine Learning calibration methods such as neural networks, aiming to reliably reproduce key non-linear patterns that are used as predictors in the cross-validated forecast system.
Processes governing emissions of HONO at a grassland site
Authors: Di Marco Chiara F. , Twigg Marsailidh M. , Kramer Louisa J. , Crilley Leigh R. , Drewer Julia , Ramsay Robbie , Cowan Nicholas J. , Jones Matthew R. , Leeson Sarah R. , Bloss William J. , Nemitz Eiko
Published: July 2024

Studies have shown that gas-phase nitrous acid (HONO) is a major precursor of hydroxyl (OH) radicals in the boundary layer, which plays an important role in the formation of pollutants such as ozone and secondary aerosol. Despite the many studies undertaken and the development of new techniques to measure HONO, the processes governing formation are not completely understood. As such, current atmospheric models are unable to reproduce daytime HONO concentrations observed by in-situ measurements, suggesting the existence of unknown sources of HONO. Soil has been suggested as a major source of atmospheric HONO under specific conditions. In this study, HONO flux measurements were made on a Scottish grassland using the aerodynamic gradient technique and two Long Path Absorption Spectrometers (LOPAPs) to determine the processes governing the surface-atmosphere exchange of HONO. Fluxes showed a bi-directional behaviour with values between -1.4 ± 0.2 ng N m-2 s-1 and 3.2 ± 0.4 ng N m-2 s-1. Deposition of HONO were observed at night while HONO emissions were observed during the day. In particular, the average flux diurnal pattern showed a distinctive peak around 9 am, suggesting a recurrent release or formation process in the morning.Three potential processes were investigated to explain the observed emissions i) soil microbial activity, ii) photolysis of NO3- and HNO3, iii) absorption/desorption of HONO from water films on vegetation. A simple model was developed to replicate the photolysis of NO3- and HNO3, alongside the absorption/desorption of HONO from water films on vegetation. Whereas lab experiments were undertaken to determine whether microbial activity in soil was a possible source. It was discovered that it was unlikely that microbial activity played a major role in the observed emissions. Instead, a combination of photolysis of HNO3/NO­3- and absorption/desorption of water films at the surface were more likely the processes contributing to the observed emissions. The inclusion of both processes into atmospheric chemistry models may help to improve the agreement between measurements and models in the future.
Improved parameterization for dry deposition of methacrolein, an oxidation product of isoprene
Authors: Medinets Sergiy , Langford Ben , Di Marco Chiara , Vieno Massimo , Nemitz Eikio
Published: July 2024

Volatile organic compounds (VOCs) and nitrogen oxides are significant contributors to ground-level ozone and PM2.5 pollution via secondary organic aerosols (SOA), with detrimental effects on human health and the environment. While previous research has predominantly focused on quantifying VOC emissions from plant canopies and their drivers, less attention has been given to measuring the dry deposition of primary and secondary VOCs onto surfaces. As a result, dry deposition processes are associated with large uncertainties in atmospheric models. To address this gap, we conducted a measurement-based investigation into VOC dry deposition under the ‘Dry Deposition Processes of VOCs’ project funded by the Natural Environment Research Council. Our primary aim was to refine atmospheric chemistry models by improving parameterisations for the VOC dry deposition. Here, we present the findings of our laboratory study on plant exposure to methacrolein (MACR), among other selected VOCs.An automated dynamic gas-exchange chamber system was developed to fumigate test plants with a range of VOCs concentrations. Five plant species were subjected to each experiment spanning four days: one day to observe background emissions and three days of VOC exposure at 20, 15 and 10 °C. Three levels of relative humidity (RH) were maintained over light and dark conditions, being fumigated with four concentrations of VOCs within each RH level. In total, eleven VOCs were chosen for exposure: water-insoluble (isoprene, benzene, toluene, xylene, a-pinene) and water-soluble (methanol, acetonitrile, acetaldehyde, acetone, acetic acid and MACR). VOCs were monitored using a proton transfer reaction instrument equipped with time-of-flight mass spectrometer (PTR-Qi-TOF). Fluxes were computed based on the concentration difference between reference and measurement chambers, subsequently normalized by the corresponding plant leaf area indices.MACR, together with methyl-vinyl ketone, is the main product of isoprene oxidation (under NOx conditions) and ozonolysis, playing an important role in air pollution through SOA formation. The observed MACR deposition rate was larger at higher fumigating concentrations. We revealed that MACR deposition velocity (Vd) increased with increasing RH under light conditions; no similar pattern was observed for dark periods with much lower Vd magnitudes. This humidity-dependent Vd variation indicates a strong stomatal control (see Morumoto et al., 2015) and a possible contribution of on-leaf heterogenic chemistry under daylight conditions. At all times, MACR compensation points were found to be negligible, suggesting minor or no impact on deposition rates.These findings will benefit atmospheric chemistry models, providing an improved parameterization for MACR dry deposition to typical coniferous, deciduous, and evergreen temperate climate plant species.
Evaluation of reanalyses for onshore wind energy applications in Germany
Authors: Bär Franziska , Borsche Michael , Geiger David , Kaspar Frank , Kelbch Alexander , Pauscher Lukas , Spangehl Thomas , Weber Helga , Yuan Dehong
Published: July 2024

In 2023, renewable energies contributed to 56% of the total electricity production in Germany. A further increasing of this percentage is expected in the near future due to rising number of wind and solar power installations. Their production is closely intertwined with climatological conditions and tied to local meteorological conditions.In order to estimate and investigate potentials for the generation of these renewable energies’, consistent datasets on a climatological scale are required. In recent years, reanalyses have proven to be a reliable source providing long-term consistent meteorological data sets for use in the energy sector. For such applications Germany's National Meteorological Service (Deutscher Wetterdienst, DWD) currently produces a new regional reanalysis product, the “COSMO-REA6 Generation 2” (COSMO-R6G2). The new data set is a successor of the well-known COSMO-REA6 reanalysis (developed by DWD in cooperation with the universities of Bonn and Cologne within the Hans-Ertel-Centre for Weather Research of DWD), but using ERA5 instead of ERA-Interim as boundary conditions. COSMO-R6G2 will cover the period from 1995 to present, retaining the spatial and temporal resolution of COSMO-REA6.A systematic quality assessment is required before the data set is applied in energy-related projects of the DWD and released to the public.For example, DWD is involved in the ‘Network of Experts’ of the German Ministry of Digital and Transport (BMDV), where the energy potential in the German transport infrastructure is analysed. Another example is the project MEDAILLON (supported by the Ministry of Economic Affairs and Climate Action (BMWK)), which aims to provide datasets for energy system research).This contribution focuses on the evaluation of wind speed and direction at typical hub height of wind turbines of about 100 m a.s.l. over the German land area. The quality of different reanalyses and derived data sets for national offshore wind applications has recently been demonstrated by Spangehl et al., 2023 (https://doi.org/10.5194/asr-20-109-2023). To assess the usability for onshore wind energy applications we compare R6G2 wind fields to ground-based LIDAR measurements and wind measurement masts in Germany. Moreover, we compare the performance of COSMO-R6G2 compared to other reanalysis data sets, e.g. ERA5, COSMO-REA6 to identify potential improvements such as the more realistic representation of the diurnal cycle.
Understanding the impact of human activities on the terrestrial hydrological cycle in a semi-arid bread-basket region
Authors: Boone Aaron , Bellvert Joaquim , Best Martin , Brooke Jennifer , Canut-Rocafort Guylaine , Cuxart Joan , Hartogensis Oscar , Le Moigne Patrick , Mangan Mary Rose , Martí Belén , Ramon Miró Josep , Polcher Jan , Price Jeremy , Quintana Seguí Pere , Villagrassa Daniel
Published: July 2024

Understanding the impact of human activities on the terrestrial hydrological cycle and water resources is crucial for sustainable management, particularly in regions like the Mediterranean basin which is facing increasing challenges owing to climate change. The international Global Energy and Water Exchanges (GEWEX)-supported Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) project seeks to enhance comprehension of land-atmosphere-hydrology interactions in semi-arid regions characterized by diverse surface heterogeneity. Situated in the Ebro basin of northeastern Spain, the study area exhibits a pronounced contrast between extensively irrigated regions and natural landscapes. Somewhat limited observations have hindered understanding of anthropogenic influences and their representation in models. Recently, advancements in land surface and atmospheric observation capabilities, the representation of irrigation in models, coupled with expanding irrigated surfaces, have further enhanced need for dedicated field campaigns in contrasting climate regions. Given the economic importance of expanding irrigated areas, coupled with projections of significant warming and drying in many such regions, accurate prediction of regional water cycle changes is imperative. This presentation provides a succinct overview of LIAISE's intensive field campaign and an overview of key results obtained thus far. This research underscores the potential impact of irrigation on local atmospheric boundary layer dynamics, affecting near-surface atmospheric conditions. In addition, central to effective water resource management is the accurate estimation of water use efficiency, which hinges on precise quantification of evapotranspiration (ET). An improved understanding of ET processes caters to diverse research interests ranging from Earth system modeling to practical hydrological and agricultural applications like reservoir management and crop irrigation. A summary of ongoing studies and prospectives for planned future LIAISE community projects will be given. 
Machine Learning Approaches for High-Resolution Wind Climatology Mapping in Switzerland
Authors: Lloréns Jover Icíar , Zanetta Francesco , Isotta Francesco , Nerini Daniele , Grams Christian M. , Schwierz Cornelia
Published: July 2024

This talk addresses the challenge of generating high-resolution wind climatology maps for Switzerland, a region characterized by sparse measurement stations and complex mountainous terrain. Accurately mapping wind patterns in such areas is inherently difficult due to the nonlinear and rapidly changing nature of wind flow, compounded by diverse and abrupt relief. Existing numerical models also lack the spatial granularity and temporal resolution necessary for comprehensive wind mapping.Our objective is twofold. Firstly, we aim to create more detailed wind maps that closely align with observations. Leveraging comprehensive orography maps, topographic descriptors, and numerical model outputs, we seek to downscale wind maps to achieve finer spatial resolution. Secondly, we intend to utilize these downscaled wind maps to compute wind climatology maps, focusing on maximum wind gusts and mean wind velocity, both hourly and daily. These wind climatology maps are invaluable for a plethora of stakeholders such as renewable energy planning, infrastructure development, and environmental monitoring, aiding in informed resource allocation and decision-making.We propose employing machine learning techniques, specifically Gaussian Processes (GPs) and Neural Processes (NPs), to address these challenges. Both present a compelling approach for wind map downscaling as both learn from small datasets and sparse observations to interpolate wind data and generalize to unseen locations. GPs offer a probabilistic framework to model complex relationships between inputs and outputs. By pairing sparse observations, high-resolution topographic descriptors and knowledge of the dynamics via a numerical model output, GPs can infer wind patterns across the territory with improved accuracy and spatial detail. Additionally, GPs inherently provide uncertainty estimates crucial for determining confidence in predictions. Finally, GPs rely on explicit prior knowledge and modeling assumptions, making their predictions interpretable. NPs, on the other hand, leverage neural networks to learn complex, non-linear relationships between inputs and outputs. Furthermore, NPs are scalable and highly efficient in handling large-scale datasets, and their model-less nature allow for more flexibility in modelling complex distributions.Integration of these machine learning techniques with domain-specific knowledge and data sources will enable the development of robust and accurate models for generating high-resolution wind climatology maps. These maps will closely align with observational data, advancing our understanding of local wind patterns and their impact on various applications. This approach aims to foster interdisciplinary collaboration and innovation in this field.Note: Drafting the initial version of this abstract has been aided by AI tools.
Satellite Observations of Organizational Morphology Associated with the Extreme-rain-producing Mesoscale Convective Systems over Northwestern China
Authors: Shou Yixuan , Lu Feng
Published: July 2024

The mesoscale convection system (MCS) is known as a substantial contributor to flash flooding, hailstorms, wind gusts, and tornadoes. The organizational morphology of mesoscale convection systems (MCSs) can reflect the initiation, development, and propagation processes of convective cells in the system, which are the keys to determining whether heavy precipitation can be produced. So far, the organizational modes for the MCSs over mountainous areas or areas with sparse radar stations often fail to be wholly understood due to geographical conditions that restrict radar observation. This paper proposes a method for making an objective MCS classification and segmentation based on geostationary satellite observations. In this study, the algorithm is developed using Himawari/AHI multiple infrared and water vapor channel observations, which have a 2 km and 10-min spatial-temporal resolution. Validation of the product relative to the classification in radar composite reflectivity imagery indicates that the algorithm offers skill for discriminating between convective and stratiform areas and matches 65% of convective area identifications in radar imagery with a false alarm rate of 39% and an accuracy of 94%. A quantitative evaluation of the similarity between 50 given MCSs' structures derived from satellite and radar observations shows that the similarity between the two is as high as 60%. For further testing, organizational modes of the MCS that induced the heavy precipitation in northwest China on 21 August 2016 (hereafter known as the "0821" rainstorm) are identified. It is found the MCS accompanied by the "0821" rainstorm successively exhibits modes of the isolated cell (IC), squall line with parallel stratiform rain (PS), and nonlinear system (NL) during its lifecycle. In the developing stage of the MCS, the system is characterized by PS and NL structures. As compared to precipitation intensity, the PS mode might play a key role in producing this flooding. These findings suggest that the PS-organized MCS may be an essential structural feature of storms that triggers extreme heavy rainfall in the semi-arid region of northwest China.
Direct observational evidence of amplified nitrate formation by the coupled chemical–boundary layer interactions in a Northern China valley
Authors: jiayun Li
Published: July 2024

The interactions of aerosols and planetary boundary layer and its critical impacts on environment, weather and climate is a hot issue in the atmospheric environment field at present. The coupled chemical–boundary interactions that drive severe haze events are less understood due to the limited comprehensive vertical measurements，especially for valley cities, in which the formation and development of haze pollution are more complicated and unique. High ozone concentrations promote the formation of nitrate in the nocturnal residual layer, but this phenomenon has not been confirmed and quantified by direct observation. Here, gaseous pollutants, water-soluble ions in PM2.5 and meteorological factors were simultaneously observed in the stable boundary layer (SBL), residual layer (RL) and mixing layer (ML) in a typical valley city in Northern China. Strong photochemical formation was found in the upper ML during the daytime. At night, nitrate formation was weak due to poor reaction conditions with low ozone and relative humidity (RH) in the SBL near ground level. However, enhanced reaction conditions were observed in the RL, which was associated with the increased precursors transported by the upward valley breeze during the daytime and the increased humidity caused by the temperature reduction; the increased relative humidity and precursors in turn promoted nitrate formation. The mixing down of nitrate from the RL contributes 70% of the surface-level nitrate in the next morning. This vertical feedback, together with the pollutant-trapped terrain, constitutes a key mechanism that links boundary evolution, mountain-valley breeze and nitrate formation in the valley city. This mechanism is self-amplifying, leading to faster chemical production, accumulation, and more severe haze pollution，which was significant to air quality in the valley or basin cities in the world.
Tackling Drizzle Bias in the Euro-Mediterranean region: The Multivariate Adjustment Solution
Authors: Lazoglou Georgia , Economou Theo , Anagnostopoulou Christina , Zittis George , Tzyrkalli Anna , Georgiades Pantelis , Lelieveld Jos
Published: July 2024

Precipitation plays a pivotal role as a climatic indicator across various domains, particularly in the context of climate change studies. However, its accurate simulation and projection remain challenging due to its inherent stochastic nature. Climate models frequently exhibit a tendency to overestimate the frequency of light precipitation events and underestimate the magnitude of extreme precipitation totals, a phenomenon commonly referred to as the 'drizzle bias'. Consequently, while the total precipitation amounts may be adequately represented, discrepancies often emerge in the frequency distribution of rainy days. This discrepancy challenges the model’s ability to capture the precipitation patterns, impacting climate related assessments and predictions.This study seeks to mitigate the 'drizzle bias' in simulated precipitation, by introducing and implementing two distinct statistical methodologies aimed at improving the precision of simulated and projected rainy-day counts within the broader Euro-Mediterranean region. The first approach, which mimics the convention, involves adjusting the number of rainy days based on the assumption that the relationship between observed and simulated rainy days remains constant over time (thresholding). In contrast, the second approach employs a machine learning model specifically Random Forests (RF), to statistically minimize the drizzle bias based on a function of several simulated climate variables.The findings suggest that utilizing a multivariate approach yields outcomes comparable to traditional thresholding techniques when adjusting for sub-periods characterized by similar climatic patterns. However, the efficacy of the RF method becomes apparent when addressing periods marked by extreme bias, distinguished by substantially different frequencies of rainy days. These notable deviations predominantly manifest within the Mediterranean domain, particularly in the extremely arid regions. Specifically, within the Mediterranean area, simulated rainy-day counts exceed 100% (relative proportion), with percentages in the African segment of Mediterranean reaching up to 200%.Furthermore, the study reveals that while the prevalence of the thresholding method is prominent in Eastern Europe and select Mediterranean locations, the RF method demonstrates superior performance for stations exhibiting significant disparities between the two methodologies, notably in the Balkan Peninsula.Concluding, by employing innovative statistical techniques, this study enhances our understanding of precipitation modelling and highlights the importance of tailored methodologies, particularly in regions characterized by distinct climatic characteristics, such as the Mediterranean.