Most disasters triggered by natural hazards and leading to humanitarian crises are caused by extreme weather- and climate-related events. Between 1970 and 2019, there were more than 11’000 disasters attributed to weather, climate and water-related hazards, resulting in over 2 million deaths and 3.64 trillion US dollars in losses. To further reduce the impacts of these hydrometeorological events, it is increasingly crucial that humanitarian organizations take anticipatory action before crises as well as responding during or after the events.

More is to be done to harness reliable and timely hydrometeorological information to support Anticipatory Action and Response. The WMO Coordination Mechanism (WCM) aims is to facilitate the access to authoritative information and expert advice in case of hydromet events to support the humanitarian community and Anticipatory Action. The WCM is an interface between WMO and its Members and the humanitarian community and thus is a key element of the Early Warning for All initiative. Under the coordination of the WMO Secretariat, all National Meterorological and Hydrological Services are invited to contribute to the WCM. Switzerland has replied to that call through a dedicated project, so-called, Weather4UN.

In the project, we develop prototypes for impact estimates based on weather forecasts of WMO Members and WMO Regional Climate Centers to support the decision making of the UN and other humanitarian agencies. This information is tailored to best meet the needs of these humanitarian actors. The HydroMet Impact Outlook estimates the humanitarian impacts of the forecasted hazard events for decision-support of the WCM and other humanitarian initiatives. These estimates are the result of a globally consistent impact modelling framework using hazard, exposure and vulnerability information. The main focus are the hazards tropical cyclone and floods and their humanitarian impacts. The prototype of the tropical cyclone impact estimates works for exposed population and direct economic damage due to the sub hazard wind. It is currently being extended to take into account more sub hazards like precipitation, river floods and surge, and to represent different types of humanitarian impacts. The river flood impact estimates are currently under development and their skill and usability to inform decisions is assessed. We work collaboratively with the end user on the communication of these impact estimates to provide useful, useable and used access to authoritative information of the WMO family.

How to cite: Röösli, T., Probst, P., Riedel, L., Pache, A., Flubacher, M., and Bey, I.: Weather4UN – Supporting humanitarian decision-making with impact estimates , EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-654, https://doi.org/10.5194/ems2023-654, 2023.

Mitigating and adapting to climate change is an urgent and complex issue. This is particularly challenging for those who have to make the actual decisions on how to start and which measures to prioritise. Especially in the public sector, regulations are made at the European or national level, but implementation is the responsibility of local authorities such as cities, villages and municipal companies, which adds a complex task to their scarce financial and human resources. The South German Climate Office at the Karlsruhe Institute of Technology (KIT) aims to provide advice and disseminate knowledge on climate change to society and the public sector through various projects. One current project is the development of a graph database, in which a variety of climate protection measures are implemented. The key elements are: firstly, a proper and neutral assessment of each measure according to scientific standards; secondly, a quantification of different aspects of the measure to facilitate prioritisation; and thirdly, using the nature of the graph database to link different measures to each other and to their climate targets.

The project was prompted by a request from a public service to use scientific expertise to help the public navigate the overwhelming flood of information.  In a joint series of workshops with municipalities, municipal companies and KIT, a number of key issues (energy, mobility, water management) and decision factors (climate impact, social acceptance, financial burden, timeframe) were identified. These define the first framework for the database and the evaluation of climate protection measures. A team of scientists from different institutes (KIT-IIP, KIT-ITAS, KIT-IfV, KIT-IMK, KIT-Division IV) covering the thematic side as well as the social factor evaluates the measures according to their expertise and implements them in the database. Through intensive consultation with the Association of Municipal Companies and partner cities and municipal companies, the database is tested for practical suitability. The aim of the project is to facilitate decision-making in the municipal sector on climate change mitigation measures, thereby reducing the burden on financial and human resources and increasing the proportion of decisions made on the basis of neutral, scientifically sound information

How to cite: Vallon, M.: Local climate protection in science, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-43, https://doi.org/10.5194/ems2023-43, 2023.

Korea Institute of Atmospheric Prediction Systems(KIAPS) is currently developing a numerical weather prediction model, including a data assimilation system, to replace the Unified Model(UM). The KIAPS Integrated Model(KIM) consists of a spectral element-based non-hydrostatic dynamical core using a finite-volume method and physics packages. The data assimilation system adopted a hybrid 4D-EnVAR. 4D-EnVAR means that combined KIM VARiational data assimilation system(KVAR) and Local Ensemble Transform Kalman Filter(LETKF) data assimilation technique. Ensemble members currently uses 50 members. To evaluate the performance of the KIM, it is one of the important factors to understand the performance of system by operating and combining the individually developed systems. KIM Operational System(KOS) constructed a cycle experiments using the cylc meta-scheduler, which is widely used by various operational agencies and research laboratories. The cyclical experiments involves a data assimilation process every 6 hours, including KIM Package for Observation Processing(KPOP). The cyclical experiments were performed DA systems at 4 times a day, and 10-day forecasts are execute at 00 and 12 UTC due to model verification. Each task was written by python scripts and was configured to efficiently parallelize by using the cylc meta scheduler. Each task was configured pre- and post-processing progress and can be executed independently. Post processing contains visualization and remap from cubed-sphere grid to lat/lon grid. In addition, the grid remapped lat/lon grid data is used for visualization and displayed on the monitoring system for performance verification and stable operation of the numerical forecasting system. All of procedure are carried out automatically without any operation by user.

How to cite: lee, Y.: Current status and future plan for KIM operational system, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-183, https://doi.org/10.5194/ems2023-183, 2023.

The 'Network of Experts' (BMDV-Expertennetzwerk) of the German Ministry for Digital and Transport (BMDV) is a network of German government agencies. Their main topic is the future-oriented transition of the transportation infrastructure in Germany. Currently six topic areas are addressed. One of these topic areas (“renewable energies”) has its focus on the assessment of the potential contribution of renewable energies along the transportation infrastructure in Germany (esp. along highways and railways). Germany’s national meteorological service DWD coordinates the topic area and is responsible for provision of climatological data in support of the assessments of the potential energy generation. The assessments also benefit from the expertise of the partners for the specific modes of transport, esp. the Federal Highway Research Institute (Bundesanstalt für Straßenwesen, BASt) and the Federal Railway Authority (Eisenbahn Bundesamt (EBA) / Deutsches Zentrum für Schienenverkehrsforschung (DZSF)).

One option to use renewable energies along the transport infrastructure is the installation of photovoltaic on noise protection facilities. In one prominent study of the BMDV ‘Network of Experts’, the possible yields along already existing noise protection facilities along highways and railways in Germany were calculated on the basis of satellite-derived surface radiation data (dataset: SARAH-2; DOI:10.5676/EUM_SAF_CM/SARAH/V002_01), in combination with temperature and wind speed data of the regional reanalysis COSMO-REA6 (DOI:10.1002/qj.2486). This resulted in a possible installable capacity of approx. 1500 MW_p and a potential annual electricity production of about 1400 GWh, avoiding about 1 million tonnes of CO₂ annually. The area on the noise protection facilities theoretically occupiable with PV modules was conservatively estimated considering statics, noise protection properties, or shading. For vertical noise barriers and steep embankments, the occupiable area is estimated to be approx. ~10 %, while for dyke-like noise barriers with sloping surface (of ~30°) is estimated by the experts to ~50 %. For such noise barriers the largest potential for electricity production is estimated: 80 % of the installable capacity and 85 % of the potential yield can be attributed to these noise barriers. Recent political discussions on energy generation along transport routes have led to repeated media interest in these results. The use case illustrates the benefit of satellite and reanalysis data for large-scale energy studies. Studies on the quality of these climate data sets are also carried out in the Network of Experts.

In addition to the generation of renewable energy through photovoltaics, the installation of small wind turbines and the use of geothermal energy for heat generation on road and rail transport modes were investigated. While geothermal energy is used in some pilot studies and can lead to road salt reduction, small wind turbines can contribute to a continuous year-round energy supply in interaction with photovoltaics on a site-specific basis.

How to cite: Bär, F., Kaspar, F., Auerbach, M., Rieck, D., and Streek, P.: Potential of renewable energies along the German transport infrastructure, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-549, https://doi.org/10.5194/ems2023-549, 2023.

In a fast-changing global economy, possessing knowledge and skills plays a crucial role in determining individual, institutional, and societal competitiveness and the capacity to drive innovation. The FAIR Micromet Portal FMP2.0  (Roantree et al., 2023;  denoted as FKP) and CA20108 FAIRNESS network aim to enhance transferable skills in measurement planning and implementation challenges (creative thinking and problem-solving) and interdisciplinary approaches (the ability to combine work across different fields), with the expectation of improved outcomes.

These skills are highly valued by employers and in great demand in the labor market, making them important drivers of individual career development. However, there often exists a significant diversity among Ph.D. students and new employees (young researchers) regarding functional knowledge and skills, which may result in certain gaps. Identifying whether these gaps relate to soft, hard, or transferable knowledge and skills is essential. A lack of transferable skills can significantly hinder further career development. To address this issue, we have designed a transferrable skills questionnaire to help young researchers and experts assess their transferable skills related to micrometeorological measurements and decide which ones to enhance. The selected skills include micrometeorological instrumentation (principles of work, selection and installation), experiment design (designing micrometeorological measurements in rural and urban areas; anticipating and overcoming the most frequent issues; data and metadata selection), data assimilation (managing different data formats and units), critical control (quality control; identifying different data gaps in micrometeorological data and metadata), and gap filling (different methodologies in filling gaps in data and metadata).

In this study, we present the results of the self-assessment of the first hundred participants.

Literature

Roantree, M., Lalic, B., Savic, S., Milosevic, D., and Scriney, M., 2023: Constructing a Searchable Knowledge Repository for FAIR Climate Data, EGU General Assembly2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7786. https://doi.org/10.5194/egusphere-egu23-7786.

How to cite: Lalic, B. and Firanj Sremac, A.: Micrometeorological measurements and data management as transferrable skills, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-569, https://doi.org/10.5194/ems2023-569, 2023.

Thermal indices are valuable tools for assessing thermal environments linked to applications related to architecture, planning, tourism, energy conservation and health. They combine meteorological variables into a single value which can be assigned to a category of an assessment scale expressing the predicted degree of human thermal discomfort, sensation or stress. The assessment scales include one indifference (neutral) category and negative and/or positive categories of increasing intensity of thermal feeling. The numerous indices that have been developed makes the selection of a suitable one for a specific application a complex issue. The aim of this study was to assess the effectiveness of several commonly used thermal indices for their operational use in weather applications in the Mediterranean climate of Greece. Hourly data (2010-2021) of air temperature (Tair, ^oC), relative humidity (Rh, %), wind speed (WS, m/s) and global solar radiation (SR, W/m²) recorded in 15 weather stations across the Athens metropolitan area of the Automatic Weather Stations Network of the National Observatory of Athens were used to calculate the indices. The indices should follow the following criteria: (a) be currently in common use, (b) be easily estimated operationally, i.e., short estimation time using Tair, Rh, WS and SR, and (c) provide exploitable results. Apparent temperature (AT), Heat Index (HI), Humidex (HU), Normal Effective Temperature (NET), Physiologically Equivalent Temperature (PET), Universal Thermal Climate Index (UTCI), Wet-Bulb Globe Temperature (WBGT) and Wind Chill Index (WCI) were identified as the most commonly used thermal indices in research and by weather services around the world. PET showed the highest computational demand compared to the other indices. At the first step, NET, PET and UTCI were assessed as more suitable for the climate of Athens (compared to AT, HI, HU, WBGT and WCI), extending their predictions to the entire range of their assessment scales. NET and PET tended to classify more often than UTCI thermal conditions in the negative categories of their assessment scales (67.8%-NET and 62.1%-PET versus 36.8%-UTCI; p<0.001) while most of UTCI values were classified in the neutral category (48.9%-UTCI versus 15.6%-NET and 15.1%-PET; p<0.001). Finally, more heat wave days were classified in accordance to Tair by NET (86.8%) and UTCI (84.5%) compared to PET (70.5%; p=0.014). According to the results of this study, NET and UTCI could be suggested as the best candidate indices for operational use in the climate of Athens, Greece.

How to cite: Pantavou, K., Lagouvardos, K., and Kotroni, V.: Assessment of thermal indices for the operational evaluation of thermal conditions in Athens, Greece, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-48, https://doi.org/10.5194/ems2023-48, 2023.

The latest Intergovernmental Panel on Climate Change (IPCC) report estimates that the global mean temperature increase will be up to 5.4°C. One of the most affected areas globally is the Eastern Mediterranean and Middle East (EMME), a wide and highly diverse region, identified as a hotspot that is warming twice as fast compared to the global increase. Climate change, in conjunction with poor air quality are two key factors impacting human health. In this work, we use flexible statistical modeling approaches to quantify the joint effect of temperature, humidity and air quality on human mortality for the city of Thessaloniki in Greece. In this work we briefly expose the statistical methodology we propose, and how it was used to quantify the joint effects of temperature, humidity and air quality over prolonged periods of exposure, on human mortality.

More specifically, we utilize data on all-cause but also cause-specific daily mortality and model this as a function of temperature, humidity, ozone, PM₁₀ and nitrogen dioxide – all measured from monitoring stations in Thessaloniki. We implement the well-established framework of Distributed Lag Models (DLMs) as Generalized Additive Models (GAMs), to capture the complex interactions between the aforementioned exposures on the risk of mortality. Such models capture the exposure effect through time and thus enable understanding into how prolonged periods of poor air quality and heat stress affect human health. Results confirm the intuition that exposure to extreme heat and humidity in conjunction with poor air quality significantly increases the risk of mortality. This increase in risk varies considerably by case-of-death and also by age group. We show how the type of air pollutant results in different risk profiles, but also that there are correlations across the pollutants that affect the risks.

How to cite: Economou, T., Parliari, D., and Kushta, J.: Using statistical approaches to quantify the synergy of heat stress and air pollution on human mortality for a Mediterranean city, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-69, https://doi.org/10.5194/ems2023-69, 2023.

A more accurate understanding of climate and its changes requires temporally and spatially representative climate databases. However, measurement conditions change frequently: relocation of stations, instrument changes, changes in measurement time, changes in environmental conditions can all cause inhomogeneities in the data series, and therefore homogenization is needed.

For homogenization of data series, quality control and filling in the missing values we use the MASH (Multiple Analysis of Series for Homogenization) procedure (MASHv3.03 software) at the Climate Department of the Hungarian Meteorological Service (OMSZ). Inhomogeneities are estimated using monthly data series. Monthly, seasonal and annual inhomogeneities are harmonized in all MASH systems, constructed for homogenization of various station systems which consist of stations with different length of data. After homogenization, we have temporally representative data series.

However, weather stations are not evenly distributed, the station network consists of both densely and sparsely covered subregions. In order to estimate the values of meteorological variables at points where no measurements are available, a spatial interpolation method must be used. Our gridded climate datasets are generated using the MISH method (MISHv1.03 software). After interpolation, we have spatially representative climate database.

Currently, the start of the Hungarian precipitation climate database is 1901, but the beginning of regular precipitation measurements started decades earlier, so it is possible to extend the precipitation database in time. In addition, the 131 datasets from the first half of the 20th century that are currently used can be significantly extended, as there are still many undigitized datasets before the 1950s. The collection of monthly precipitation data stored still on paper made it possible to use many more stations from the first half of the 20th century than before, and thus, the precipitation patterns in Hungary in the second half of the 19th century can be analyzed.

In this poster presentation, we will present the new precipitation station systems used for homogenization, the most important verification statistics of the homogenization of precipitation data series, and analyze the gridded spatial means (national averages for Hungary) from the beginning of the measurements to the present.

Acknowledgements:

The research presented was carried out within the framework of the Széchenyi Plan Plus program with the support RRF 2.3.1 21 2022 00008 project.

How to cite: Szentes, O., Lakatos, M., and Pongrácz, R.: Long-term homogenized and gridded precipitation data for Hungary, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-376, https://doi.org/10.5194/ems2023-376, 2023.

The recurrence of upper-level synoptic-scale Rossby wave packets (RRWPs) at a location over a short period can lead to persistent surface weather that may drive extreme weather events. RRWPs were observed during several extreme weather events, for example, the 2010 heatwave over Russia, the 2004 and 2009 south-eastern Australian heatwaves, and the anomalous western European precipitation of 1983. RRWPs have been shown to lengthen hot, cold, dry and wet spells across the globe.

Motivated by the importance of RRWPs, this work investigates the causal drivers of RRWPs in the North Atlantic basin for summer and winter in a two-step approach. First, composite maps of top-30 RRWP events during 1979 – 2018 are used to study the characteristics of RRWP events and identify potential drivers using ERA-5 reanalysis data. The potential drivers are shortlisted following statistical significance in a bootstrapping approach. Subsequently, the causality of the shortlisted drivers is assessed using a Bayesian causal network (CN) framework for time series.

Composite maps reveal that RRWP events have a preferred seasonal phase configuration despite not having an explicit condition for phasing in the event selection. In winter, wavenumbers 3, 4 and 5 dominate with a hemisphere-wide wave pattern, whereas wavenumbers 5, 6 and 7 dominate in summer without a hemisphere-wide wave pattern. The causal networks reveal that local changes in atmospheric blocking and low wavenumber flow, termed background flow, are major drivers of RRWPs. RRWPs also have a feedback effect on background flow and blocks. The tropical-extratropical causal link only exists in winter and is indirect, mediated by the changes in the background flow over the Pacific. The causal drivers outlined in this study help to further the understanding of RRWPs.

How to cite: Ali, M., Martius, O., Röthlisberger, M., Methven, J., and Zscheischler, J.: Causal drivers of Recurrent Rossby Wave Packets, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-189, https://doi.org/10.5194/ems2023-189, 2023.

This study utilizes an optical-type disdrometer, called High-speed Optical Disdrometer (HOD), that we recently developed for precipitation microphysics observations and investigates raindrop collisions through HOD’s high-speed video observations during rainfall events.  HOD’s innovative technology enables capturing high-resolution sequential images of the same hydrometeor multiple times as it passes through the measuring volume.  Hydrometeor characteristics are then accurately measured via digital processing of the recorded images.  HOD offers unique observational capabilities such as, for the case of raindrops as hydrometeors, observations of raindrop oscillations and collisions as well as high-accuracy measurements of relevant characteristics.  This study focuses mostly on raindrop collisions observed during rainfall events using HOD.  Raindrop collision rates and outcomes are important quantities for applications such as raindrop size distribution (DSD) modeling in hydrological and meteorological models.  Direct field observations of these quantities have not been available due to the technological limitations of the existing disdrometer technologies, and HOD’s measurement capabilities provided us the opportunity to investigate these quantities through field observations.  Rainfall events considered in this study were observed during a 3-year long field campaign conducted at our outdoor rainfall laboratory located on the West campus of the University of Texas at San Antonio, Texas, USA.  This field campaign provided a dataset on collision observations that extended the small number of raindrop collision observations that we had previously reported as the first-time raindrop collision observations to visually demonstrate the presence of raindrop collisions in rainfall events.  We will provide an overview of this high-resolution disdrometer technology and present our observations on raindrop collisions with discussions on our findings and their applications.  This material is based upon work supported by the National Science Foundation under Grants No. AGS-1741250.

How to cite: Testik, F. and Saha, R.: High-speed Video Observations for Precipitation Microphysics:  Case of Raindrop Collisions, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-621, https://doi.org/10.5194/ems2023-621, 2023.

Skillful location-specific weather forecasts are invaluable for the public and concerned communities in planning daily activities and preparedness actions for weather-related impacts. Despite continuous enhancements in numerical weather prediction (NWP) models, it remains challenging to capture the intricate variations in meteorological conditions within a city with highly heterogeneous landscape and diverse urban environment such as Hong Kong. In support of regional weather forecasting services, the Hong Kong Observatory (HKO) has developed the Objective Consensus Forecast (OCF) system for various weather elements, including a 9-day automatic forecast in daily maximum and minimum air temperatures (T_max, T_min). OCF is a past performance-weighted multi-model consensus forecast which employs Kalman Filter (KF) as an adaptive post-processing method for NWP forecasts at different weather stations. Though the performance of OCF has been largely satisfactory in the past decade, recent trends of machine learning (ML) methods in weather forecasting applications have driven efforts to improve the post-processing of NWP forecasts.

In this study, two ML-based approaches to improve location-specific daily T_max and T_min are presented. The first (OCF-CB) makes use of CatBoost, a ML technique based on gradient boosting on decision trees, to adjust the current OCF outputs. Besides the time-varied temperature prediction series and past errors against observations of an ensemble of global NWP models, other predictors that affect the diurnal variation of temperature (i.e. relative humidity, cloud cover, amount of rainfall, wind speed and direction) are used as model training data. The second (OCF-RF) aims to consider the effects of surface cover, urban geometry, and demographics to the screen-level temperature at each weather station. It uses a random forest model trained with different station environmental parameters, alongside other forecasted weather elements, to predict and correct the forecast errors of each NWP model. Biases of the corrected outputs are further reduced by a KF and multi-model consensus approach, similar to the current OCF system.

The daily T_max and T_min forecasted by both ML-based approaches are generally found to outperform the OCF for the verification period (2020-2022). OCF-CB shows more appreciable improvement in forecast performance in spring, as it can quickly readjust based on recent forecast errors and capture the changeable weather due to competence between the northeast monsoon and southerly airstream. On the other hand, using urban parameters allows OCF-RF to better represent the higher urban temperatures in spring and summer, and the lower rural T_min in winter due to radiation cooling. Further work is underway to integrate the proposed ML-based temperature forecast approaches into the operational system for improving weather forecasting services. Applications in urban climate studies and gridded forecasts for urban heat risk assessments at refined spatial resolutions would also be explored.

How to cite: Kwok, Y., Mok, W., Lam, M., and Wong, W.: Comparison of Daily Urban Temperature Forecast Performance by Traditional and Machine Learning-based Approaches, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-495, https://doi.org/10.5194/ems2023-495, 2023.

Droughts and floods are the most hazardous disasters to affect the landscape and human communities. Climate change is increasing their frequency and intensity all around the world. In this research, we study the impact that climate change in central Poland (Toruń) is having on the frequency of meteorological droughts and risk of floods occurrence in the region, based on the standardised precipitation index (SPI) as one of the most common and effective indices. Although the SPI was developed for monitoring droughts, previous studies have shown that it can also be applied to recognise wet and normal conditions. Therefore, in this study, we attempt to determine the relationship between SPI values and high flood risk. First, by an average mean ensemble of several general circulation models (GCMs), precipitation for a future period (2026–2100) in Toruń was projected under two climate change socio-economic pathway scenarios (SSPs), SSP1-2.6 and SSP5-8.5. Then, based on the projected precipitation for the future period, the SPI values were calculated. The results indicated that, in general, the precipitation in the study area will increase for scenarios SSP1-2.6 and SSP5-8.5.

Estimation of future meteorological drought based on SPI calculation showed that the frequency of the “Extremely dry” (SPI ≤ ˗2.0) and “Severely dry” (˗1.50 ≤ SPI < ˗2.0) categories will decrease under scenarios SSP1-2.6 and SSP5-8.5, while the frequency of the “Moderately dry” category (˗1.0 ≤ SPI < ˗1.50) will increase for scenarios SSP1-2.6 and SSP5-8.5 relative to the historical reference period (1991–2014).

Estimation of flood risk occurrences in the future period based on SPI values showed that the frequency of “Extremely wet” (SPI ≥ 2.0) will increase for SSP1-2.6 and SSP5-8.5, excluding the future periods 2076–2100 in SSP1-2.6 and 2026–2050 for SSP5-8.5. The frequency of “Very wet” (1.50 ≤ SPI < 2.00) will increase for both scenarios. It is also expected that frequencies of other categories including “Moderately wet” (1.00 ≤ SPI < 1.50), and “Near normal” (˗0.99 ≤ SPI <1) will slightly decrease for both scenarios.

To conclude, a decrease in the frequency of “Extremely dry” and “Severely dry” categories and an increase in the frequency of “Extremely wet” and “Very wet” showed that the occurrence of floods is more probable than droughts in the study area in the future period.

The work was supported by the National Science Centre, Poland project No. 2020/37/B/ST10/00710.

How to cite: Ghazi, B., Przybylak, R., and Pospieszyńska, A.: Estimation of droughts and floods occurrences in central Poland under climate change scenarios, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-91, https://doi.org/10.5194/ems2023-91, 2023.

In recent decades, the Alpine regions have been affected by several heavy precipitation events occasionally associated with storms causing landslides, debris flows, forest damage or flooding with cascading impacts to socio-economic systems. There is scientific evidence that a warming climate is likely to induce changes in the intensity and occurrence of precipitation extremes, even though extracting a long-term changing signal on regional and local scale is still challenging and requires an adequate observation coverage.

Some studies on extreme precipitation observed in the Alpine region exist, but only a few focus more in detail on the Trentino-Alto Adige area (north-eastern Italian Alps). In this context, the intensity and frequency of heavy precipitation, their spatial variability and trends over 1956-2020 are analysed for the region based on an archive of quality-checked daily historical records from more than 60 rain gauges.

The annual and seasonal analyses of maxima and exceedances of the 97^th percentile show distinctive spatial patterns in both magnitude and seasonality. Maxima primarily occur during summer in the northern and more alpine part of the region, and during autumn in the south where the influence of the Mediterranean climate is more pronounced. Almost two thirds of the analysed stations reveal an increasing tendency in either the intensity or the frequency of heavy precipitation, especially in the north, even though trends are statistically significant only for a limited number of stations (e.g. less than 10% for annual maxima). The 1956-2020 intense precipitation observations are also scaled with global and regional temperatures and results are compared with previous studies in surrounding countries in order to assess global and regional warming effects on extreme changes. Finally, the extreme value distributions based on the 65-year records and two subsequent subperiods (1961-1990 and 1991-2020) provide a first evaluation of changes in statistical properties and precipitation amounts associated to different return periods. This is also used to characterize the local precipitation intensities during the exceptional event which hit north-eastern Alps in 2018, named storm Vaia, with intense precipitation together with extreme wind speed conditions.

This contribution shows and discusses the main results and provides an outlook towards the assessment of climate drivers of precipitation events in the region, the occurrence of compound extremes and links to subsequent impacts.

The research leading to these results has received funding from Interreg Alpine Space Program 2021-27 under the project number ASP0100101, “How to adapt to changing weather eXtremes and associated compound and cascading RISKs in the context of Climate Change” (X-RISK-CC).

How to cite: Maines, E., Crespi, A., Steger, S., and Zebisch, M.: Assessment of variability and trends of heavy precipitation in Trentino – South Tyrol (north-eastern Italian Alps) based on rain-gauge records (1956-2020), EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-249, https://doi.org/10.5194/ems2023-249, 2023.

Monitoring near-surface winds near coastal areas is really important since it affects not only the population living nearby but also the energetic sector, such as oil and wind power industries. As an effort to understand the future wind trends in a changing climate, this study presents results from the project Western South Atlantic Climate Experiment for the middle of the century (2031 to 2060); here, future projections of near-surface winds and their extremes over the western South Atlantic are evaluated from regional dynamic downscaling by using two distinct models (WRF and RegCM4) forced by two global climate models (HadGEM2-ES and MPI-ESM-MR) under the worse RCP8.5 warming scenario. In general, the trends show a slight decrease of 0.1 to 0.3 m/s in the mean wind speed for both models over the southwestern South Atlantic adjacent to the continent (between 25º and 35ºS in RegCM4 and 15º and 25ºS in WRF). For the extremes the decrease is amplified, reaching 0.5 m/s; the only exception occurs for WRF forced by HadGEM2, in which there is no trend signal southern 25ºS. For the present climate, WRF and RegCM4 have opposite wind speed biases over the South Atlantic, which propagates to the ocean waves simulation, especially for the extremes. Therefore, in order to reduce the bias propagation, an adjustment of the upper-quantiles of the wind speed (i.e. the extremes) was applied to the present climate simulated winds, which showed an improvement for intense wind speeds. After bias corrections, the future trends of wind speed are also explored.

How to cite: Machado Crespo, N., Pillar da Silva, N., de Camargo, R., and Porfírio da Rocha, R.: Future trends of near-surface winds over the southwestern South Atlantic in two regional climate models, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-469, https://doi.org/10.5194/ems2023-469, 2023.