bluuebunny/crossref_metadata_2025 huggingface dataset:
Global estimates of ambient reactive nitrogen components during 2000–2100 based on the multi-stage model
Authors: Li Rui , Gao Yining , Zhang Lijia , Shen Yubing , Xu Tianzhao , Sun Wenwen , Wang Gehui
Published: July 2024

Abstract. High contents of reactive nitrogen components aggravate air pollution and could also impact ecosystem structures and functioning across the terrestrial–aquatic–marine continuum. However, the long-term historical trends and future predictions of reactive nitrogen components at the global scale still remain highly uncertain. In our study, field observations, satellite products, model outputs, and many other covariates were integrated into the multi-stage machine-learning model to capture the global patterns of reactive nitrogen components during 2000–2019. In order to decrease the estimate uncertainties in the future scenarios, the constructed reactive nitrogen component dataset for the historical period was utilised as the constraint to calibrate the CMIP6 dataset in four scenarios. The results suggested that the cross-validation (CV) R2 values of four species showed satisfying performance (R2>0.55). The concentrations of estimated reactive nitrogen components in China experienced persistent increases during 2000–2013, while they suffered drastic decreases from 2013, except for NH3. This might be associated with the impact of clean-air policies. However, in Europe and the United States, these compounds have remained relatively stable since 2000. In the future scenarios, SSP3-7.0 (traditional-energy scenario) and SSP1-2.6 (carbon neutrality scenario) showed the highest and lowest reactive nitrogen component concentrations, respectively. Although the reactive nitrogen concentrations in some heavy-pollution scenarios (SSP3-7.0) also experienced decreases during 2020–2100, SSP1-2.6 and SSP2-4.5 (middle-emission scenario) still showed more rapidly decreasing trends. Our results emphasise the need for carbon neutrality pathways to reduce global atmospheric N pollution.
The marinada fall wind in the eastern Ebro sub-basin: physical mechanisms and role of the sea, orography and irrigation
Authors: Lunel Tanguy , Jimenez Maria Antonia , Cuxart Joan , Martinez-Villagrasa Daniel , Boone Aaron , Le Moigne Patrick
Published: July 2024

Abstract. During the warm months of the year in Catalonia, the marine air overcomes the coastal mountain range and reaches the eastern Ebro sub-basin. This phenomenon is called marinada and has recently been thoroughly characterized for the first time by Jiménez et al. (2023), based on surface climatological data. However, the main physical mechanisms involved in its arrival and propagation remain to be discovered. This study aims to understand how the marinada is formed and how it interacts with the already developed atmospheric boundary layer. Surface and atmospheric observations are used in combination with the coupled surface–atmosphere model Meso-NH to reveal the mechanisms at play. It is shown that the marinada is generated by the advection of a cool marine air mass over the Catalan Pre-coastal Range by the action of the sea breeze and the upslope wind. This marine air mass then flows into the Ebro basin, creating what is known as the marinada. The characteristics and dynamics of the marinada allow it to be classified as a fall wind. It is also shown that the arrival, propagation and decay of the marinada is strongly dependent on the larger-scale weather situation: westerlies or thermal low. The current study provides a consistent framework for understanding the marinada, paving the way for better modeling and prediction of the phenomenon.
Lagrangian characterization of heat waves: The perspective matters
Authors: Mayer Amelie , Wirth Volkmar
Published: July 2024

Abstract. Although heat waves are one of the most dangerous types of weather-related hazards, their underlying mechanisms are not yet sufficiently understood. Especially, there is still no scientific consensus about the relative importance of the three key processes: horizontal temperature transport, subsidence accompanied by adiabatic heating, and diabatic heating. The current study quantifies these processes using a Eulerian method based on tracer advection, which allows one to extract Lagrangian information. For each grid point at any time, the method yields a decomposition of temperature anomalies into the aforementioned processes, complemented by the contribution of a pre-existing anomaly. Two different approaches for this decomposition are employed. The first approach is based on full (absolute) fields of the respective terms, whereas the second approach is based on anomaly fields of the respective terms, i.e., deviations from their corresponding climatologies. The two approaches offer two distinct perspectives on the same subject matter. By analyzing two recent heat waves, it is shown that the two decompositions yield substantial differences regarding the relative importance of the processes. A statistical analysis indicates that these differences are not coincidental, but characteristic for the respective regions. It is concluded that the Lagrangian characterization of heat waves is a matter of perspective.
Evaluation of deep convective clouds (DCC) detection methods for satellite infrared observations
Authors: Kotarba Andrzej , Wojciechowska Izabela
Published: July 2024

Climatological studies of deep convective clouds (DCCs) require the clouds to be detected with equal efficiency during the day and night (to avoid day-night bias). In case of satellite observations it means only that the thermal infrared observations can be adopted. First generation of meteorological satellites only covered two infrared bands: window channel (~11 µm) and water vapour absorption channel (~6.8 µm). The channels are preserved on subsequent generations of meteorological satellites. Consequently, development of any long term (over 30-year) DCC climatology has to rely on these two heritage bands.In this study we evaluated three DCC detection methods commonly used in climatology: single-band infrared window test (IRW), bi-spectral test for difference in brightness temperature between window channel and water vapour channel (BTD), and test for temperature difference between window channel and tropopause temperature (TROPO). The analysis was performed for MODIS/Aqua observations collected in 2007, globally. The source of ‘ground truth’ for DCC was CloudSat-CALIPSO, collocated in space and time with MODIS. Instead of assuming any thresholds for the methods we evaluated a wide range of potential values and sought for the optimal one (i.e. resulting with highest agreement with the reference data).We found that out of three approaches the BTD method performed most accurately, reaching 69.1% overall accuracy (probability of detection: 67.2%, false alarm rate: 30.1%) when the threshold for BTD was set to -0.5K. IRW method with a threshold at 221K resulted with overall accuracy of 68.5% (probability of detection: 62.1%, false alarm rate: 28.7%), a performance very similar to BTD. The final method that referred to tropopause temperature, turned out to be the least reliable: offered an overall accuracy of 53.6% with very low probability of detection (18.8%), and high false alarm rate (38.0%). All methods performed poorly in terms of the kappa coefficient: the statistic only was 37% (IRW), or 38% (BTD), and as little as 8% for TROPO methods (agreement with CloudSat-CALIPSO can be acclaimed almost random).Knowing the optimal set of thresholds for considered methods, we calculated a mean seasonal DCC frequency for SEVIRI/ Meteosat. Resulting maps demonstrated the degree of discrepancy in DCC frequencies among approaches that are most commonly used in DCC climatology.This research was funded by the National Science Centre of Poland, grant no. UMO-2020/39/B/ST10/00850.
Investigating the uncertainty in gridded in situ climate data for the UK
Authors: Hollis Daniel , Kendon Michael , Carlisle Emily
Published: July 2024

The Met Office has been generating gridded climate datasets from UK in situ observations for over 25 years. The current version of these gridded data is known as the HadUK-Grid dataset – it includes monthly, seasonal, annual and long-term average values for 11 variables at 1km resolution. Three of these variables are also available at daily resolution. These datasets have a variety of applications including climate monitoring, model calibration and model validation.Although the software, data sources, file formats, grid resolution and distribution methods have all changed since we first started producing these datasets, the techniques used to create the grids from in situ observations have remained largely the same. The observations are first detrended, either by converting the values to anomalies from the long-term average, or by using regression analysis to model the dependency on geospatial variables such as terrain elevation or proximity to the coast. The anomaly values or regression residuals are then interpolated to the target grid points using inverse-distance weighted averaging.Here we present a review of the uncertainties in our gridded data. The aims of the analysis are threefold – to update the information we provide to users regarding the quality of our gridded products, to better understand the strengths and weaknesses of our gridding methods, and to investigate the efficiency of the quality control tests in our gridding software.A leave-one-out cross-validation analysis has been carried out for the majority of our archive of gridded data. Time series graphs will be presented showing how the RMS error varies through the data record for each variable. These graphs show clear trends, seasonal cycles and outliers. Case studies have been used to understand the causes of some of these features.Based on this analysis we have investigated aspects of our gridding techniques and quality control methods which have the potential to be improved. Preliminary results are presented which show how different gridding methods affect the cross-validation results and we draw some initial conclusions regarding the impact on gridding uncertainties.
How does a dry model with the same wave energy as observation represent atmospheric blocking ?
Authors: Deshmukh Vinita , Riviere Gwendal , Fromang Sebastien
Published: July 2024

Atmospheric blockings are large-scale quasi-stationary anomalies which block the mean westerlies and can trigger extreme temperature events such as heatwaves and cold spells. While the dry dynamics theory has been a key initial framework for understanding blocking mechanisms, recent studies have highlighted the significant role of moist processes in the formation and maintenance of blocking. However, it remains unclear whether moist processes introduce unique characteristics in the blocking life cycle which can not be captured by dry dynamics or they are just a source of extra energy for the atmospheric disturbances forming the blockings. We aim to gain insights into the representation of blocking mechanisms and statistics using the new dynamical core of the General Circulation Model (GCM) developed at IPSL called DYNAMICO. A long-term simulation of a dry GCMwith synoptic eddy energy and mean flow characteristics close to boreal winter climatology of ERA5 reanalysis.  The blocking statistics in the model and ERA5 are then compared using a PV-based blocking detection. Frequencies of occurrence of blocks are found to be 50% less in the model than in ERA5. This is largely due to too strong westerly background flow in the model, in the North Pacific in particular, which prevents the formation of persistent anticyclones. Despite this underestimation in frequencies of occurrence, the main characteristics of the blocks (intensity, size, duration) are similar in the model and ERA5 despite some slight structural differences. By employing Eulerian and backward Lagrangian analysis, we demonstrate how a dry model compensates for moisture deficiency to generate potential vorticity (PV) anomalies comparable in intensity to those in ERA5. This study reveals that despite distinct underlying mechanisms, the essential features of observed blocking events' life cycles can be effectively captured within a dry model framework.
We also conduct a long-term moist simulation, incorporating forced sea surface temperature (SST), to observe how the moist model sensitive to warmer climate reproduces blocking characteristics 
 
Acknowledgement: This Project of a part of the EU International Training Network (ITN). EDIPI (European Extreme weather; Drivers, predictability and impacts). EDIPI is funded under the European Commission’s H2020-MSCA-ITN programme.
 
A framework for triggering forecast-based action and financing using impact-based forecasts of extreme weather events
Authors: Röösli Thomas , Probst Pamela , Riedel Lukas
Published: July 2024

Extreme weather events affect millions of lives each year. The impacts include displaced and injured population, casualties, as well as interruptions of access to public services and critical infrastructure. Humanitarian organizations support affected populations all over the globe and do so increasingly with anticipatory action to mitigate the impacts before they happen. Anticipatory actions may be contractually triggered by impact-based forecasts. One key challenge in implementing forecast-based action and financing is the timely availability and the contextual understanding of forecasts that provide the basis for anticipatory action.
We present an integrated framework for the creation and analysis of trigger mechanisms suitable for forecast-based financing and action. The core is an impact model using hazard, exposure and vulnerability to create globally consistent impact estimates. Using forecasts of extreme weather events as inputs, impact-based forecasts for past events are calculated using this impact model. The behavior of any contractual trigger can be modelled for the past events. Employing uncertainty and sensitivity analysis in a comprehensible way will help practitioners assess the behavior of such a trigger for their use case.
We exemplify the application of the framework globally. We estimate the skill metrics of the impact-based forecasts using globally available observations of past impacts, additionally we derive a simplified set of requirements for the trigger mechanism using current examples of forecast-based financing and action. These assumptions can be used to show if the forecast skill allows to take early action under such a simplified set of requirements for any location globally. In locations where the requirements for such a trigger mechanism are not yet met, we aim to provide a reasoning on the cause and possible suggestions for improving the skill.
We hope this framework is helping decision makers in the forecast-based financing and action space assess the expectations in trigger setups.
To be or not to be: The operationalisation of climate projections
Authors: Doblas-Reyes Francisco J.
Published: July 2024

Climate projections are one of the few sources of climate information that does not have an operational status at the WMO level. This might look surprising given the key role that both global and regional climate projections play in many domains, from climate risk assessments to climate adaptation plans and mitigation policies. The efforts made by the CMIP and CORDEX initiatives are invaluable, but are often not secured at the individual institution level nor benefit from the support and funding lines that operational activities typically experience.This situation is the result of the infrastructure, models, and experimental design of the climate projections to have developed in the research domain, even when performed by weather services, over the last two decades. The models and the simulations have been developed by an army of researchers, many of them young postdocs, using research funding and computational resources, and relying on best efforts to document and disseminate the resulting data. This is far from what is understood as an operational system, where continuity, service reliability, flexibility, and timely response are essential elements.Discussions about how to evolve climate projections to an operational setting that is compatible with the necessary research-based developments have recently started. The approach adopted by CMIP to define the CMIP7 fast track as a way to produce quicker climate projections that respond to specific requirements is a good example. But the needs go beyond CMIP. Recent literature is illustrating the policy and socioeconomic context that requires a more flexible production of the climate projections. They illustrate the need to include the user requirements at the beginning of the process (both to get robust regional climate information for adaptation and to explore relevant mitigation trajectories), have available a software and hardware infrastructure that can respond to these requirements, and count on well-defined long-term procedures and governance to coordinate the efforts at the global level. Some of these discussions have taken place as part of the Earth Virtualization Engines (EVE) forum and are being demonstrated in the Destination Earth Digital Twin for Climate Adaptation (Climate DT).This presentation will illustrate the requirements for the operationalisation of the climate projections, underpinned by a solid research effort, and the initial steps in this direction by Climate DT, including the role of the fast developments based on machine learning techniques. It will also discuss what it would take to complete this operationalisation challenge and how it could enter the WMO sphere, through both the WMO Integrated Processing and Prediction System (WIPPS) and the initiative to develop WMO guidelines on the use and interpretation of climate change projections.
About the impact of using weather forecasts in statistical weatherrouting studies
Authors: Michel Sixtine , Dupuy Maxime
Published: July 2024

Ship weather routing is particularly at stake in the shipping industry to meet decarbonization objectives. Besides, as wind propulsion is one of the solutions for effective decarbonization, new applications of weather routing appear. First of all, technically, few algorithms are versatile enough to deal with any kind of propulsion (sail, motor, and hybrid). In this context, D-ICE developed an efficient and robust multi-objective weather routing algorithm that reaches this goal and is embedded onboard Canopée, the first modern sail-assisted cargo. Then, as hybrid-propelled ship performances are directly linked to weather routing, route optimization has to be integrated directly during the design phase, to accurately assess the viability of a project and quantify the associated return on investment. To do so, D-ICE is often acting as a third party for ship owners to compute statistical weather routing studies, meaning simulating hundreds of voyages in the past years, using ECMWF reanalysis weather datasets.However, a potential bias in those statistical studies is the computation of optimal paths based on historical weather data, meaning that the route is optimized knowing in advance the weather evolution, which is not the case in operation as only forecasts are available. This is a critical issue for the relevancy of such studies in the decision-making process of ship-owners.Here we present a study whose objective is to quantify this bias in statistical routing studies. To do so we access ECMWF archives of forecasts and simulate hundreds of past voyages following an operational strategy: meaning a daily weather routing, computed with available weather forecasts at that time. The savings associated with this route optimization strategy are then compared to the results given based on the historical weather data. Some insight about weather routing robustness and weather conditions that could lead to uncertainties will be highlighted, and a compromise will be discussed between computing time (drastically increased with such procedure) and results accuracy.Keywords: weather routing, wind propulsion, multi-objective shortest path algorithms, voyage optimization, historical reanalysis dataset, operational archives
Floods in Southern Brazil: trends and hydroclimate changes
Authors: Redin Vestena Leandro , Acquaotta Fiorella
Published: July 2024

Climate change has become one of the most significant challenges of the 21st century and although there is uncertainty surrounding the scale, scope and, pace of climate change, it is safe to say that all people are exposed. This climate crisis has a strong effect on weather patterns, such as an increase in the frequency and magnitude of adverse events, such as floods and intense rainfall. The number of people affected by these events has grown exponentially over the years and forecasts indicate that the damage caused will increase significantly in the coming years. In southern Brazil, the disasters that cause the most deaths are those of hydrometeorological origin, for example, the event that occurred in May 2024, which affected another 1.4 million people and caused 95 deaths. The lack of planning and the occupation of areas at risk of flooding end up increasing disasters, especially in cities, where more than 85% of the population lives in the South of Brazil, the most populated area. In this context, the study proposes to investigate the incidence, magnitude and, trend of occurrence of disasters associated with floods and intense and extreme rains in the South of Brazil, more specifically in the states of Rio Grande do Sul, Santa Catarina and, Paraná. The methodological procedures included the survey and analysis of spatial and temporal data on hydroclimatic disasters, associated with flooding and extreme precipitation, from the Integrated Disaster Information System S2iD, a platform of the National System and Protection and Civil Defense, of the Ministry of Integration and Regional Development of Brazil, from January 1991 to December 2023. The research results indicated the most affected cities, number of people affected, damages, estimated economic losses and, the trend shown in the evolution of such disasters. Research knowledge provides support for decision-making and management and planning actions, in order to prevent and mitigate the occurrence of disasters, in the search for sustainable and resilient cities.
Innovative Public-Private-Academic Collaboration for Climate Resilience: The Deployment of a Sustainable Weather Radar in Tonga
Authors: Najman Michal
Published: July 2024

The escalating challenges posed by climate change necessitate the advancement of meteorological services to enhance early warning systems and climate research. This presentation showcases a groundbreaking initiative undertaken by Meteopress in collaboration with Metservice New Zealand and the Tongan Met service, aimed at deploying a state-of-the-art, sustainable weather radar in Tonga. This project epitomizes the synergy between public, private, and academic sectors in leveraging technology for climate resilience and compliance with Environmental, Social, and Governance (ESG) regulations.The newly installed radar system is pivotal for the Tongan Met service, offering enhanced capabilities for early warning and accurate weather forecasting. Unlike conventional systems, this radar does not merely supply processed data; it also provides open access to raw data for researchers, thereby fostering scientific investigation and innovation. The radar's construction embraces open-source principles, with software written in Python and C, which facilitates ongoing development, upgrades, and scientific exploration.This initiative aligns with the session's theme of Public-Academic-Private Collaboration to Support Climate Neutrality Goals by showcasing a successful model of collaboration that merges technological innovation with environmental stewardship. It demonstrates how open-source technology can serve as a foundation for sustainable development and how public-private-academic partnerships can play a crucial role in advancing climate neutrality objectives.The project sets a precedent for future endeavors in the meteorological community, highlighting the importance of data sharing, technological innovation, and collaborative efforts in tackling the multifaceted challenges of climate change. Through this deployment, the partnership has not only contributed to the immediate enhancement of Tonga's meteorological services but also to the broader goal of building a climate-resilient society.
Skillful Extended-Range Forecasts of Standardized Precipitation Indices for Drought Early Warning in Switzerland
Authors: Imamovic Adel , Büeler Dominik , Pyrina Maria , Humphrey Vincent , Spirig Christoph , Domeisen Daniela
Published: July 2024

Being able to predict meteorological droughts several weeks ahead would add value to many sectors including agriculture, river shipping as well as water and energy management. A commonly used meteorological drought index is the standardized precipitation index SPI-N, which puts precipitation anomalies of the past N months into a climatological perspective. The SPI correlates with anomalies of soil-moisture, streamflow or groundwater storage, and thus serves as an inexpensive and attractive hydrological proxy. In this study we quantify how well the SPI-N can be skillfully forecasted in Switzerland. Using ECMWF IFS extended-range forecasts quantile mapped from its native 36 km to a 2 km grid, we produce ensembles of SPI-N forecasts for the Swiss drought warning regions. While previous research has underlined the challenges faced by ensemble forecasting systems in accurately predicting daily precipitation in Europe beyond lead week 1, our analysis reveals that the skill of SPI-1, SPI-3, and SPI-6 forecasts extends into weeks 3 and 4. It generally holds that skill SPI-6 > skill SPI-3 > skill SPI-1. For example, we find that the skill of an SPI-3 forecast for week 4 is comparable to the skill of an SPI-1 forecast for week 2. Overall, the results indicate the potential for skillful prediction of meteorological drought on sub-seasonal timescales. We link the extended predictability horizon to the inherent characteristics of the SPI being a temporal aggregate: the SPI is less sensitive to the exact timing of precipitation events, while also retaining “memory” of past precipitation. The latter manifests in larger skill for longer accumulation time N, in which more observation are weighted into the forecasted SPI. Finally, we show how SPI forecasts and hydrological forecasts are devised as factors for the combined drought indicator, which forms the numerical basis of the new Swiss drought early warning system.  
High carbon emissions from thermokarst lakes and their determinants in the Tibet Plateau
Authors: Mu Cuicui , Mu Mei , Wu Xiaodong , Jia Lin , Fan Chengyan , Peng Xiaoqing , Ping Chien-Lu , Wu Qingbai , Xiao Cunde , Liu Jianbao
Published: July 2024

Thermokarst lake development significantly affects hydrologic systems, infrastructure stability, and greenhouse gas emissions. However,  the spatial and temporal changes, carbon emissions patterns, and motivators in thermokarst lakes remain largely unknown. In this study, we created a thermokarst lake dataset on the Qinghai-Tibet Plateau (QTP) using a threshold-based mapping method based on Google Earth Engine (GEE) data associated with visual inspection. In addition, we measured CH4 and CO2 diffusive fluxes in 163 thermokarst lakes in the QTP over 3 years from May to October. Our results showed that thermokarst lakes on the QTP permafrost region covered a total area of 1572 ± 184 km2, with most of the thermokarst lakes
High-Resolution Modelling of the Boundary Layer over complex Terrain
Authors: Littmann Daniela , de Lozar Alberto , Marsigli Chiara , Schlemmer Linda
Published: July 2024

The exchange of momentum, heat and mass in the atmospheric boundary layer is primarily governed by thermally and dynamically driven processes that occur on a wide range of scales. This can lead to challenges in accurately describing these processes in Numerical Weather Prediction (NWP) models, especially in mountainous regions. With the ability to use finer grid spacings, more processes can be partially resolved. As a result, some of the commonly used parametrisations are no longer appropriate and need to be adjusted or even deactivated.The ICOsahedral Non-hydrostatic model is used in Limited Area Mode (ICON-LAM) with the aim of providing a skillful forecast of the Alpine area at 500 m in the framework of GLORI (GLObal to Regional ICON digital twin). The model performs simulations in two-way nesting mode with the operational ICON-D2, including a nest with a horizontal grid spacing of 1 km. This domain covers the entire Alpine region and its horizontal resolution lies within the so-called “grey zone”, where turbulence, drag and convection are neither fully resolved by large-scale processes nor can they be modelled as a small-scale, sub-grid process.Deterministic experiments are performed for the month of May 2022, as strong convective events have been observed in the Alps during this period. The simulation results are compared with various meteorological stations located at different altitudes within the complex terrain to study the effect of increasing the horizontal resolution and the performance of the model physics on the boundary layer. Small modifications in the grey zone related parametrisations, such as e.g. the turbulent kinetic energy scheme, show that the bias of near-surface variables like temperature and wind speed can be reduced. However, a more sophisticated approach is required for the higher resolution model to represent the complex interactions in the boundary layer.
New Urban Flash Flood Warning System Combines Radar-Based Nowcasts and Surface Flow Model
Authors: Perttula Tuuli , Pulkkinen Seppo , Huokuna Mikko , Niemi Tero
Published: July 2024

With the changing climate, urban flash floods are likely to become more common and societies should be prepared for these events more than ever before. Heavy rainfall and insufficient drainage systems are the main contributors to the overflow of water on streets. Thus, information of meteorological and hydrological conditions are needed to be able to alert the public of urban flood events beforehand.A new urban flash flood warning system was developed and piloted real-time in the framework of project HULEHENRI. The system consists of three main parts: 1) Weather radar based precipitation nowcasts are used as an input to 2) urban rainfall-runoff model, which simulates the changing water level on city streets. These flood forecasts are then 3) delivered to customers via web interface.Good accuracy of radar based precipitation nowcasts is a key to accurate flood forecasts. To achieve best possible results multiple improvements were made to algorithms used to calculate the radar-based quantitative precipitation estimation (QPE) during this project. These improved both the reliability and time resolution of the estimates and included for example rain-gauge correction, attenuation correction and advection correction. For nowcasting we chose a model specifically tailored for urban scale precipitation nowcasting, the LINDA model, which is available via the Pysteps Python library.The flood caused by rain has been calculated on a 2-meter grid using a surface flow model. Rain observations and forecasts based on weather radar data are available every five minutes in a 500 meter grid. This rainfall data is converted to a 2 m grid for use in the surface flow model. In addition to the digital elevation model (DEM), land use and soil data are used in the calculation. Infiltration into the soil is calculated from the soil data using the Green-Ampt method, and accurate land use data (2x2m grid) is used to determine the friction coefficient (Manning n). The main water drainage pipes are included in the model, but not the entire drainage system. Road network data is used to define road sections that may be flooded during the forecast period.Development of the urban flood warning system was user-centered. Pilot customers, including for example public emergency services and logistics companies, were involved in the development process throughout the project. In this work we discuss the customers’ input and needs and the way we addressed them and the development that was made to each three parts of the system. We also show verification results during urban flash flood events that occurred during the pilot period and discuss possible changes that could be made to improve the warning system even further.
Multi-model Physically-based constrained  decadal predictions of precipitation over Europe 
Authors: Martin-Gomez Veronica , Ortega Pablo
Published: July 2024

Decadal climate predictions are our most reliable source of climate information for the near-term climate change. They can be very useful to develop climate services to users from sectors such as agriculture, industry, energy, water management, health, etc. However, before developing these climate services, an assessment of the forecast quality for the variables and regions of interest is needed. In this work, we consider the available retrospective decadal predictions (DCPP-A) contributing to phase 6 of the Coupled model Intercomparison Project (CMIP6) and analyze their skill to predict rainfall over Europe. 
Rainfall variability over Europe is strongly linked to variations in the North Atlantic jet stream. At multidecadal time scales, some studies have shown that the variability of the North Atlantic jet stream could be driven by low-frequency changes in the sea surface temperature over the north Atlantic associated with the Atlantic Multidecadal Variability. Retrospective decadal predictions present skill to predict the low-frequency variability of the SST associated with Atlantic Multidecadal Variability. However, they have difficulties in predicting precipitation variability over Europe. Here, we analyze the skill of DCPP-A models to predict precipitation over Europe, exploring the multimodel ensemble to identify the models with enhanced skill, investigating as well the underlying reasons. In particular, we look at the link between the local precipitation skill and the representation of key teleconnection mechanisms that connect European precipitation to other remote highly-predictable regions.
Results from this study will contribute to a better understanding of the multidecadal European precipitation variability and will define benchmarks for improving its prediction in decadal forecast systems.
Extreme heatwave over Eastern China in summer 2022: the role of three oceans and local soil moisture feedback
Authors: Jiang Jilan , Liu Yimin , Mao Jiangyu , Wu Guoxiong
Published: July 2024

Eastern China experienced persistent regional extreme heatwaves in the summer of 2022, with disparate spatial features and formation mechanisms in different months. We quantitatively assessed the relative contributions of three oceans, i.e., tropical Indian Ocean and Pacific and North Atlantic, and the local soil moisture–temperature feedback using linear regression. The results showed that the monthly mean atmospheric circulation anomalies failed to explain the extreme heatwave in June 2022. The combined contribution of the tropical Indo-Pacific and North Atlantic sea surface temperature anomalies (SSTAs), together with the local soil moisture–temperature feedback, explaining approximately 10% of the temperature anomalies. In July, the tropical Indo-Pacific SSTAs promoted anomalous atmospheric circulation and extreme heat via meridional circulation originating in the Maritime Continent, accounting for approximately 10% of the temperature anomalies, with North Atlantic SSTAs contributing the same percentage by a mid-latitude steady Rossby wave. Local soil moisture–temperature feedback accounted for 42% of the anomalies. The tropical Indo-Pacific SSTAs produced a strong western North Pacific anticyclone in August, but their direct contribution to the temperature anomalies was negligible. The North Atlantic SSTAs contributed 9% of the total via the mid-latitude steady Rossby wave. Local soil moisture–temperature feedback contributed 66%, suggesting that the July heatwave and drought exerted a significant impact on the subsequent August extreme heatwave. Global warming has greatly facilitated extreme heatwaves, accounting for about 30%–40% of these events in summer 2022. These results also suggest that the climatic effects of tropical Indo-Pacific and North Atlantic SSTAs on Eastern China are evident in the month-to-month variation in summer. Our results thus contribute to the understanding and prediction of extreme heatwaves in Eastern China.
The ENSO  variations and their link with solar and volcanic activity 
Authors: Zharkova Valentina , Vasilieva Irina
Published: July 2024

We investigate the Oceanic Niño Index (ONI), for simplicity called in this paper an El Nino Southern Oscillation (ENSO) index in 1950-2023  by applying  the wavelet spectral transform and the IBM SPSS correlations analysis.  ONI follows the three months current measurements of an average temperature of the sea surface  in the East-Central tropical part of the Pacific ocean nearby the international line of the date change over the average sea surface temperature over the past 30 years. The ENSO index is found to have a strong  ($>$0.87) correlation with the Global Land-Ocean Temperature (GLOT). The scatter plots  of the  ENSO-GLOT correlation with the linear and cubic fits have shown that the ENSO index is better fit by the cubic polynomial increasing proportionally to a cubic power of the GLOT variations.  The  wavelet analysis  allowed us to detect the two key periods in the ENSO (ONI) index: 4-5 year and 12 years.  The smaller period of 4.5 years can be linked to the motion of tectonic plates while the larger period of 12 years is shown to have a noticeable correlation of 0.25 with  frequencies of the under-water (submarine) volcanic eruptions in  the areas with ENSO occurrences.  Not withstanding any local terrestrial factors considered to contribute to the ENSO  occurrences, we investigated the possibility of these volcanic eruptions to be also induced by tidal forces of the Jupiter and Sun showing the correlation of  the under-water volcanic eruption frequency to be 0.12 with the Jupiter-Earth distances and 0.15 with the Sun-Earth distances in January, induced by the solar inertial motion, when the Earth is turned to the Sun with the southern hemisphere where the ENSO occurs.  Hence,  the underwater volcanic eruptions induced by tidal forces  of Jupiter and Sun can be the essential additional factors imposing this 12 year period of the ENSO (ONI) index variations. 
Comparison of AERONET and Skynet retrievals under different anemological conditions in the urban site of Rome (Italy)
Authors: Di Bernardino Annalisa , Campanelli Monica , Iannarelli Annamaria , Casadio Stefano
Published: July 2024

In this contribution, the aerosol optical depth (AOD), the single scattering albedo (SSA) and the refractive index (RI) simultaneously retrieved by measurements from a SKYNET PREDE Sun/Moon photometer (Nakajima et al., 2020) and an AERONET/PHOTONS CIMEL sunphotometer (Giles et al., 2019) in Rome (Italy) over the period 2018-2022 are compared to highlight the effect of different anemological patterns on aerosol columnar optical properties in the urban environment at different wavelengths.The k-means clustering algorithm (Hartigan and Wong, 1979) is applied to the hourly-averaged measurements of wind intensity and direction collected by seven surface meteorological stations, permitting to discern the atmospheric circulation patterns on both local and synoptic scales based on a set of seven parameters, including the wind components during the typical time frame of onset/offset of the sea breeze. Several internal cluster validation methods are used to verify the correctness of the methodology. Four clusters are found, identifying the sea/land breeze regime with sea breeze blowing from the South-West and South, persistent north-east and South-East synoptic wind conditions, respectively. The aerosol optical properties are provided by instruments co-located at the urban site of the Boundary-layer Air Quality-analysis Using Network of Instruments (BAQUNIN, 41.90° N, 12.52° E, https://www.baqunin.eu/, Iannarelli et al., 2021) atmospheric observatory. The BAQUNIN site is particularly relevant for studies on aerosols properties as it hosts instruments from different international networks (e.g., AERONET, SKYNET, PANDONIA, EUBREWnet).The dataset used for the SKYNET/AERONET comparison is obtained by selecting only the measurements carried out by the two instruments in a time frame in ±1min (i.e., simultaneous) and only for the days in which the daily-averaged AOD value (associated with the daily anemological pattern) can be assumed as representative of the entire day. This latter condition is valid if there is at least one measurement for each of the time slots identified by the typical AOD daily cycle (14 UTC).Preliminary results show a great correlation between AOD measurements for each cluster and for each wavelength. Specifically, the AOD from CIMEL is on average slightly greater than the AOD from PREDE at all wavelengths, with the exception of 1020 nm, although the difference is always
Climate Change Attribution of Extreme Events Using AI-based Weather Models
Authors: Jiménez-Esteve Bernat , Barriopedro David , Johnson Juan Emmanuel , García-Herrera Ricardo
Published: July 2024

Climate change is altering the frequency and intensity of extreme events globally, such as heat waves, droughts, flooding, and tropical storms. It is, therefore, of primary importance to quantify the influence of climate change on the properties of specific extreme events. The scientific community has developed different methodologies to answer that question. One extended approach, commonly known as the pseudo-global-warming (PWG) approach, consists of removing the anthropogenic climate change signal from event-constrained initial conditions of a physics-based weather model. However, performing state-of-the-art atmospheric model simulations of every single extreme event requires significant computational resources. Artificial Intelligence (AI)-based weather models offer a chance to speed up this process due to its much lower computational cost.In this study, we apply the PGW approach to the AI-based FourCastNet model forecasts of a selection of extreme events. To account for the uncertainty associated with the influence of climate change, we remove the signal in thermodynamic variables from several CMIP6 models independently from the AI model's initial conditions. At the same time, we also test the sensitivity to the initialisation date. Using this approach, we quantify the impact of climate change on a heatwave event and tropical storm. Our results indicate that climate change significantly contributed to the intensity and characteristics of these two extreme events. We also discuss the strengths as well as limitations of the AI-based model in the simulation and attribution of extreme events by comparing our results to reanalysis and to more classical attribution methods. This research contributes to our understanding of the impacts of climate change on extreme events and highlights the potential of AI-based models for climate attribution studies.
Low-frequency features during the two typical extreme cold events in China
Authors: Li Xu , Li Yan , Zhang Jinyu
Published: July 2024

Keeping oscillation of low frequency of 30~60 days, Butterworth band-pass filter method was used to process the NCEP/NCAR reanalysis data. Based on the application of the low-frequency synoptic map, low frequency features of the two extreme low temperature events were analyzed in order to reveal the characteristics of the low frequency systems during these two events. The results show that in early 2008, large-scale atmospheric systems including blocking-high and upper-level jet stream all featured a distinct 30-60-day oscillation. The positive (negative) anomaly of geopotential height was closely coincided with the low frequency high (low) pressure of the low frequency systems, and the center of positive zonal wind anomaly was consistent with the high value center of low frequency zonal wind. Meanwhile, the positive phase of the AO favored the strengthening of the Middle East jet and the maintenance of the blocking high, resulting in durative low temperature in south China. The 30-60-day oscillation features of the weather systems including upper-level jet and blocking high were not so obvious during “overlord”-level cold wave in 2016. However, the low pressure of low frequency can describe the generating and developing of the polar vortex. Under north air stream at the front of blocking high ridge guidance, the rapid invasion of strong cold air in the middle of polar vortex caused temperature in China drop fast. The low-frequency synoptic map reflected the phase transition of AO before and after the cold wave. The phase of AO was positive in later December 2015 while negative in early January 2016. Then the polar cold air invaded southern China, which can be conclude as the main cause of the sharp drop in temperature. The low-frequency flow field showed the phase transition of AO lagging behind the synoptic flow field about two days during the two events. 
Climate projections of UTCI in the Czech Republic
Authors: Hynčica Martin , Novák Martin , Procházková Simona
Published: July 2024

A thermal stress on human is determinated by a combination of various meteorological variables, such as temperature, humidity, etc. Many indices have been created for the determination of level of thermal stress. Here, we select the universal thermal climate index (UTCI), which is created with the use of temperature, humidity, wind speed, and mean radiant temperature. The UTCI index can be acquired from observed data, however, the calculation of UTCI is also incorporated in some reanalyses and climate models too. Recently, it has been demonstrated that UTCI in central Europe increased in the last 30 years, with the largest increase detected in both summer and winter after 1990. While rising UTCI in winter reflects decreasing thermal stress on human, on the contrary, the rapid increase observed in summer indicates the opposite. The combination of rising temperature and humidity is the main cause behind the increase in the index. In this contribution, we bring the topic to the next level as we analyze the changes in UTCI in the Czech Republic for the entire 21st century. The future evolution of several meteorological variables, including the UTCI index, is incorporated in the Aladin-CLIMATE/CZ model, the domain of which covers the broad area of central Europe. Two scenarios are analyzed: SSP2-4.5 (the intermediate scenario) and SSP5-8.5 (the high scenario). It is evident that UTCI will rise in both annually and seasonally step over almost the whole Czechia, the largest increase being found in the lowlands regions of the country. The substantial increase in thermal stress arising from the changing meteorological conditions in the future is a concerning message, influencing lives of people, mainly in the urban areas.