bluuebunny/crossref_metadata_2025 huggingface dataset:
Intra- and inter-annual monitoring of the Atmospheric Boundary Layer at a semiarid site in the Eastern Ebro basin during the current long-term drought
Authors: Cuxart Joan , Price Jeremy , Martínez-Villagrasa Daniel , Groh Jannis , Martí Belén , Miró Josep Ramon , Wrenger Burkhard , Jiménez Maria Antònia
Published: July 2024

The site of Els Plans is located since June 2021 in a semiarid area protected for steppe-land birds in the Eastern Ebro Basin, in the term of Preixana (Catalonia), close to a wide irrigated area. Its initial installation was as a reference site in the non-irrigated area of the 2021 LIAISE campaign (Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment), mainly intended to study the effect of missing irrigation in numerical models. After the end of the campaign in September 2021, some of the teams involved in Els Plans decided to leave the instruments there for some more time. In particular, the UKMO deployment, which included a 50m-high meteorological tower, stayed until July 2022. The resulting 13-month series includes the start of a drought period after a very moist winter in 2021. It allows to inspect the intra-annual evolution of some ABL characteristics at the semi-arid site, including evaporation, dew and frost, soil moisture, local circulations, fog events or mesoscale circulations. Since July 2022 a Surface Energy Budget station and a weighable lysimeter continue operation allowing to perform the inter-annual monitoring of the aforementioned quantities, including the exceptional drought event in Catalonia that started during 2021 and is still active at the time of writing. The previous long-term drought was in 2008. This information allows to perform an interannual analysis using the three-year period available. The Canal d'Urgell bringing water from the dams in the Pre-Pyrenees was closed for the first time in history in 2023 with a huge impact in the local agriculture and economy. Some substantial rain events have happened since fall 2023, allowing for some greening in the area and the progress of the winter cereals. Long-term observations are necessary to better understand prolonged drought and its impact on terrestrial ecosystem and its feedback mechanism between the different compartments (soil, water, atmosphere, and vegetation).
MET Nordic dataset: post-processing of model output near-surface fields
Authors: Lussana Cristian , Nipen Thomas N. , Seierstad Ivar A. , Båserud Line , Neuville Amélie
Published: July 2024

MET Nordic is a dataset created by the Norwegian Meteorological Institute (MET Norway), providing  near-surface variables for Scandinavia, Finland, and the Baltic countries with a resolution of 1 km. This dataset is available through two distinct production streams: MET Nordic RT designed for real-time data provision to applications, and Met Nordic LTC aimed at supporting applications that require consistent long-term data.The dataset includes the following near-surface variables: temperature at two metres, precipitation, sea-level air pressure, relative humidity, wind speed and direction, solar global radiation, long-wave downwelling radiation, and cloud area fraction.MET Nordic RT provides updated products every hour with a 20 minute delay, and it has an archive that goes back to 2012. The dataset consists of post-processed products that (a) describe the current and past weather (analyses), and (b) give our best estimate of the weather in the future (forecasts). The products integrate output from MetCoOp Ensemble Prediction System (MEPS) as well as measurements from various observational sources, including crowdsourced weather stations. These products are deterministic, that is they contain only a single realisation of the weather. The forecast product forms the basis for the forecasts on Yr (https://www.yr.no). Both analyses and forecasts are freely available for download in NetCDF format.For temperature and precipitation, the model output is combined with unconventional observations, such as data from citizen weather stations. Their inclusion shows a clear improvement to the accuracy of short-term temperature forecasts, especially in areas where existing professional stations are sparse. In this study, we will summarise the results obtained with the post-processing and we will share the main lessons learned, which can also be useful for systems that want to use these observations for data assimilation.The MET Nordic LTC is currently in an experimental phase and undergoing significant modifications, more than the RT stream. The primary goal of LTC is to extend the temporal coverage of the variables provided by RT, possibly back to 1961, with an emphasis on achieving time consistency—a feature not prioritised in the RT dataset. The approach involves applying post-processing techniques similar to those used for RT but with notable distinctions. Firstly, we utilise a reanalysis dataset, such as the 3-km Norwegian Reanalysis (NORA3),  rather than outputs from numerical weather predictions. Secondly, we are developing methods to incorporate observational data in a way that maintains the time consistency of the dataset. This may necessitate using only a selection of high-quality observations that are available over extended periods.
Public Perceptions and Behaviour in Response to Weather Warnings: A UK Perspective
Authors: Jenkins Sarah , Taylor Andrea , Summers Barbara , Roberts Helen
Published: July 2024

Between 2000 and 2019, natural hazards and extreme weather events claimed approximately 1.23 million lives, affected over 4 billion people, and resulted in economic losses of around US$ 2.97 trillion worldwide. With climate change increasing the frequency and severity of severe weather events globally, it is critically important to develop effective strategies for disaster risk communication and reduction. One such strategy is the provision of early warning systems (EWS), shown to significantly reduce mortality and economic losses. To develop and improve EWS, it is critical to understand how (a) the public perceive and respond to risk information and (b) the factors that promote and inhibit risk preparedness.Since 2011, in the UK, the Met Office have been responsible for issuing impact-based weather warnings for rain, thunderstorms, wind, snow, lightning, ice, fog, with extreme heat added in 2021. When amber or red warnings are issued, post event surveys are regularly used to capture public awareness, understanding, perceptions and behavioural responses to the warning. This talk presents the results of analysis of data collected from 32 surveys between 2014 and 2022 (n= 16,276). Here we examine whether perceptions and behavioural responses significantly differ according to the timing of the event, type of event the warning was issued for (e.g., snow, wind, heat etc), warning colour and whether storm naming was used. We also consider whether socio-demographic characteristics such as age, gender and location (urban/rural) predict perceptions and responses. Our results have implications for the communication of impact based weather warnings, and provide insights into how these can be best utilised to promote protective action and preparedness.  
Characteristics of rainfall simulation over South Korea by summertime weather patterns according to the use of cumulus parameterization
Authors: Ahn Joong-Bae , Seo Ga-Young
Published: July 2024

The characteristics of summertime extreme rainfall events (ERE) according to the weather patterns in South Korea and compares the extreme rainfall model simulation skills depending on the use of cumulus parameterization has been studied. The WRF simulations with and without the a cumulus parameterization scheme (Kain–Fritsch cumulus scheme in this study) (CU_ON and CU_OFF experiment, respectively) are conducted over a 3 km high-resolution domain. The ERE-occurring days are clustered into four representative weather patterns (northern cyclonic circulation, frontal pattern between low and high, southwestern extratropical cyclone, and dominant positive geopotential height patterns) according to the 850 hPa geopotential height anomaly. As the occurrence dates of observed Clusters 1 and 2 overlap with a significant portion of the Korean summer monsoon season (Changma period), their rainfall is characterized by continuous low-intensity rainfall. In contrast, relatively high-intensity, short-duration rainfall occurs mainly in Clusters 3 and 4. The WRF experiments generally describe the clustered weather patterns well. For CU_ON, the spatial distribution of the daily rainfall anomaly composite in Clusters 1 and 2 is well depicted, but the overall rainfall intensity is underestimated. CU_ON better reproduces the Clusters 1 and 2 type rainfall characterized by long-duration rainfall than CU_OFF. The observed rainfall events exceeding 20 mm h−1 intensity with a short-duration are reproduced better in CU_OFF than in CU_ON, showing reasonable performance for sub-daily time-scale rainfall in Clusters 3 and 4. Hence, the CU_ON well depicts the continuous low-intensity ERE type, while CU_OFF captures the ERE type where high-intensity rainfall with a short duration occurs relatively frequently.
Analysis of tourists' perceptions regarding the weather influence in Cantabria based on TripAdvisor reviews
Authors: Conde-Oria Francisco , Rasilla Domingo F. , Toribio Pérez Miguel
Published: July 2024

Tourism has grown in recent years and now involves one sixth of the world's population. However, its dynamics are subject to many economic, social, political and environmental factors. Although the role of weather in the tourist experience is widely recognised, this issue has become increasingly important in recent years as the sector faces the impacts of anthropogenic climate change. Tourist perceptions are important in understanding these impacts. Ratings and reviews shared on websites, social networks and tourism comparison platforms are a useful way of understanding the impact of weather and climate on public perceptions and individual satisfaction.The aim of this study is to investigate tourists' weather preferences in three of the most visited tourist facilities in Cantabria (northern Spain). First, all TripAdvisor reviews for these facilities were systematically downloaded using automated web scraping techniques. Next, all comments containing information or references were filtered using libraries of weather and climate terms. Finally, the comments were categorised according to the conditions expressed.Tourists visiting the Cabárceno Nature Park prefer cooler and cloudier days. As most of the activities take place outdoors and usually last several hours, they need to protect themselves from the heat and high levels of radiation. Opinions on rainfall are divided between those who consider it a negative factor and those who appreciate it, while most visitors consider wind to be a negative factor. On the other hand, tourists prefer clear skies at Fuente Dé, as it is a well-known mountain viewpoint. In addition, high temperatures are considered a positive factor almost all year round, while low temperatures are avoided. Rain and wind are generally perceived as negative factors, but usually only for short treks. In Altamira, the influence of the weather is much less obvious, and the only meteor mentioned is rain, but there is no consensus, since for some people rainy days are ideal for visiting the museum, if they don't have to wait in the rain and get wet.The perception of the weather and its influence on tourism is important and must be considered in the coming years. However, the analysis must be carried out separately for each destination and each type of tourism. Nevertheless, it should be borne in mind that opinions may differ and be partial and therefore need to be critically analysed and combined with local surveys and frequentation data.
A new method for retrieving high spatial resolution snowpack height and water equivalent
Authors: Martínez Foronda Alejandro , García Maroto Diego , Durán Montejano Luis
Published: July 2024

Snow plays a fundamental role in the water cycle. In some areas, precipitation in the form of snow and the formation of a seasonal snow cover in the mountains is the main source of freshwater. Snow, accumulated during the winter, supplies drinking water during the spring by quenching the prolonged summer drought of mediterranean regions. There are several methods for measuring snow, some of which are based on measuring the distance from a certain elevation to the ground. This distance can be used to infer the height of the snow cover. To measure this distance, ultrasound or lasers are used. These systems are usually expensive and require infrastructure, both to support the sensors and to be powered by solar panels during the winter. These systems sometimes suffer from operational, structural or energy problems. On the other hand, there are rudimentary manual techniques based on the introduction of probes that determine the distance to the ground in a very robust and simple way. It is also possible to extract samples for density calculation in trenches specifically dug for that purpose. These manual techniques are inexpensive, highly robust, but require human intervention for their operation, therefore, the spatial and temporal density is low. For several years, techniques have been employed to ascertain the height of the snowpack by analyzing temperature data collected at varying heights. These systems, commonly referred to as snow poles, combine the benefits of manual methods, particularly their simplicity, with those of automatic methods, such as their higher temporal resolution. Additionally, they offer further advantages, including their low cost and minimal environmental impact, which allow for a greater density of measurement points to be obtained. This is particularly crucial in complex terrain like mountains. This work presents the methods used to obtain not only the height of the snowpack, but also its equivalent in water using this type of snow pole. Obtaining the water content is especially important and novel in this field. Furthermore, it presents the evolution of the traditional snow pole with several enhancements related to new IoT hardware available. Also, sensitivity tests in the laboratory, network operation and mesh design results will be shown.
Long-Term Trends in Extreme Precipitation Events in Northwest Iran: A Comprehensive Analysis Using ERA5 Reanalysis Data 
Authors: Fakour Poya , Ustrnul Zbigniew
Published: July 2024

This paper aims to evaluate the precipitation extremes over Northwest of Iran based on 5th generation reanalysis precipitation data (ERA5) for long-term historical period from 1941 to 2020. The trend of 10 selected extremes precipitation indices (EPI) were evaluated in monthly, seasonal, annual and decadal time scales. The nonparametric Mann–Kendall test were applied to 960 grid (600,000 km2) with a high spatial resolution of 0.25° × 0.25° to detect the possible temporal trends in all EPI on the significance levels of 1% and 5%, include frequency and intensity indices. Notably, anomalies in precipitation for the recent reference period (1991-2020) compared to a different subperiods (e.g. 1961-1990 and 1941-2020) seeking to distinguish shifts in precipitation patterns. Our findings have uncovered a significant increase in the frequency of daily heavy precipitation events over the studied period, particularly in the latter decades. Trends showing mostly positive patterns, many of which are statistically significant. Moreover, in some areas over the past 30 years, the total rainfall has increased by approximately 25%. Although most events occur during the winter (DJF) and spring (MAM) seasons, the highest number of events belongs to March. The largest positive trend in terms of intensity and frequency is associated with the fall season (SON) and the month of October and November in this region. Also the highest number of grid points with significant positive trends is related to fall in seasonal timescale and November in monthly timescale. Although the results of the annual study indicate that 56.5% of the respected territory have a positive trend which statistically significant in at least one of the indices, this percentage increases to 80.3% on a seasonal scale for (SON). In some cases, negative trends were observed for few number of grids, none of which were statistically significant in annual scale. Also, periodic behavior observed, characterized by oscillations spanning on interdecadal scales.Overall, the combination of the results of the EPI trends, illustrate a considerable changes towards more intense and more frequent precipitation on interannual scales. Consequently, this region can be considered as a high risk area in terms of extreme events and flash floods.
A study of ABL processes that increase the uncertainty in temperature forecasting
Authors: Arrillaga Jon Ander , Gelpi Ivan. R. , Gaztelumendi Santiago
Published: July 2024

In this study, we analyze the atmospheric boundary layer (ABL) processes contributing to the increase in uncertainty in two-meter temperature forecasting. For temperature prediction, we employ the Weather Research and Forecasting (WRF) mesoscale model with a one-kilometer horizontal resolution, using a numerical configuration calibrated for forecasting temperatures in the Basque Country. This region, located in the north of the Iberian Peninsula, is characterized by complex topography and is highly influenced by the Atlantic Ocean. Specifically, in this contribution, we assess parameterizations (surface layer, boundary layer, microphysics, etc.) and their combinations, as well as the number and distribution of vertical levels and other dynamic options such as 6th-order diffusion, to best replicate the evolution of two-meter temperatures during fair weather and high-pressure conditionsWe conduct statistical analyses for specific periods of interest to understand how temperature bias correlates with variables or factors such as surface thermal profile, mechanical turbulence, surface and 850 hPa winds, cloud cover, and temperature drops associated with sea breeze onset in the warmest months. Depending on variations in error or bias across different time ranges (nighttime, daytime) and meteorological scenarios (e.g., calm winds, clear skies, strong surface heating and cooling), we estimate uncertainty for each temperature predictionOur analysis reveals that high pressure conditions often coincide with significant errors in temperature prediction, primarily attributed to mesoscale model challenges in simulating night time stable conditions, boundary layer morning and evening transitions, and the accurate representation of thermally driven mesoscale flows (such as sea breezes) and their impacts on hourly temperature forecast.
Does AI mean we need to do climate services differently?
Authors: Fung Fai , Mittal Neha
Published: July 2024

The knowledge base to support climate change adaptation is rapidly growing with the latest Intergovernmental Panel on Climate Change’s 2022 report on impacts, adaptation and vulnerability with over 34,000 citations; nearly three times compared to its predecessor published in 2014. This information is complex, requiring specialist knowledge to interpret the science and further expertise to understand how to act upon the information. The small pool of experts across the world presents a challenge with the need of an ever-growing set of decision-makers who need tailored information.To date, most attention has focused on the use of machine learning for climate science rather than the application of AI technologies to enhance climate services provision. With the advent of public access to large language models and more generally, generative AI technologies, we consider here the opportunities that they bring to solve the problems around delivering climate services but also the implications on the pillars of saliency, credibility and legitimacy.We present the findings from the “Towards AI for Climate Services” workshop held on 22 February 2024 in the UK which aimed to understand if and how AI technologies could help to address existing challenges in the sector. Participants represented academic, public and private providers of climate and AI services. Discussions focused on three aspects: challenges in climate services that AI technologies are addressing, emerging risks with employing AI technologies and pathways to mitigating these risks.We will show that while many of the issues raised are not exclusive to using AI technologies, their rapid development and accessibility suggest that the climate services sector need to urgently engage with the debate
The next generation meteogram: enhancing decision making for aviation stakeholders
Authors: Landmann Johannes Marian , Attinger Roman , Aznar Siguan Gabriela , Barras Hélène , Irrgang Melanie , Reiniger Thomas , Wehrli Kathrin , Exterde Szilvia , Jordi Thomas , Stocker Claudia
Published: July 2024

Aviation operations are impossible without reliable weather information. Safe air traffic management requires precise meteorological predictions across various timeframes, ranging from immediate nowcasting (0-3h) to mid-range forecasting (4-30h). Given the inherent uncertainties in predictions, it is crucial to ensure that users can interpret uncertain weather data confidently. This enables effective planning and decision-making within aircraft operations, including setting approach rates and runway configurations.Currently, the aviation meteogram product provided by MeteoSwiss offers deterministic predictions and threshold exceedance probabilities at hourly intervals up to 24 hours in advance. This data is derived both from direct numerical weather model output and statistically post-processed forecasts. However, this approach underutilizes the forecast potential due to (1) possible biases in deterministic predictions, (2) limited granularity in lead time within the nowcasting range, and (3) a lack of uncertainty information passed on to decision makers.To overcome these challenges, we are transitioning from delivering deterministic to probabilistic forecasts by employing machine-learned local predictions instead of relying solely on physical model output. Additionally, we are increasing output granularity in the first three hours. This shift unlocks the full potential of information-based decision-making, leading to smoother and more economically and ecologically sustainable aviation operations. Given that probabilistic data and its implications are largely unfamiliar to our product users, a robust program of frequent training and education is essential.In this contribution, we introduce new machine learning based ensemble predictions for thunderstorms [1], visibility conditions [2], and wind at Swiss airports, focusing on nowcasting and mid-range forecasting. Our emphasis lies in their comprehensive visualization in a web-based meteogram tailored to effectively convey probabilistic information.[1]: abstract Attinger et al., 2024, submitted to EMS 2024.[2]: abstract Wehrli et al., 2024, submitted to EMS 2024.
Addressing climate change in standards
Authors: Schlünzen K. Heinke , Fröhling Catharina
Published: July 2024

Climate change is one of the greatest challenges of our time. Despite multiple efforts to reduce GHG emissions, reductions are too small to keep global warming well below 2 K, preferably at 1.5 K. Instead, the increase probably reaches 2.9 K by the end of this century [1]. Therefore, adaptation to climate change is essential to reduce the negative consequences.Urban areas with their high population and infrastructure density are specifically vulnerable to extreme weather. The urban fabric increases its vulnerability to (severe) weather events. For example, land-sealing hinders the infiltration of rain water and thus increases the probability for flash floods; three-dimensional buildings with their high heat storage capacity lead to higher temperatures in the city at night compared to surrounding areas. Values might be about 3 K higher in the summer climate average [2] but can reach values of up to 10 K in specific situations [3]. The German Climate Adaptation law states that from 1st of July 2024 adaptation by already occurring or foreseeable climate changes has to be considered in planning and decisions by institutions with public responsibilities. The law names several sectors to act on, for example local heat island effects. The law also states that technical laws or accepted standards are to be taken into account. This requires joint efforts for considering adaptation by the committees working on newly developed or updated standards.The approach followed by VDI and other organisations for standardisation is briefly introduced, and new standards, relevant for assessing climate and its changes (e.g. calculation of climate indicators, how to perform urban climate simulations, heat action plan) are presented.[1] UNEP: Emissions Gap Report 2023. https://www.unep.org/resources/emissions-gap-report-2023, last used 30.03.2024[2] WMO (2023): Guidance on Measuring, Modelling and Monitoring the Canopy Layer Urban Heat Is-land (CL‑UHI). K.H. Schlünzen, S. Grimmond, A. Baklanov (edts.), World Meteorological organisa-tion, WEATHER CLIMATE WATER. 2023 edition. WMO-No. 1292, pp.88. https://library.wmo.int/idurl/4/58410 last used 12.04.2024.[3] Kuttler W.; Weber S. (2023): Characteristics and phenomena of the urban climate. Meteorol. Z. (Contrib. Atm. Sci.), 32, No. 1, 15–47, doi: 10.1127/metz/2023/1153.
TEAMx cold-air pool model intercomparison study
Authors: Lehner Manuela , Goger Brigitta , Quimbayo Duarte Julian , Rodier Quentin , Schmidli Juerg , Sheridan Peter , Staquet Chantal , Wastl Clemens , Westerhuis Stephanie , Wibmer Benedikt , Wieser Hannes , Wittmann Christoph
Published: July 2024

Cold-air pools pose a significant challenge for numerical weather prediction models, primarily because they are often characterized by very stable stratification. Traditional surface-layer parameterizations, which rely on Monin-Obukhov similarity theory, tend to be ineffective in these conditions. Additionally, the processes involved are highly localized and occur on small spatial scales, necessitating models with high horizontal and vertical resolutions. Within the TEAMx research programme, a model intercomparison study is being undertaken. The goal is to assess how well various numerical weather prediction models, each with a horizontal grid spacing of 1 km, can simulate a nocturnal cold-air pool within an Alpine valley. Five models are currently participating in the intercomparison study, including both operational and research models, specifically AROME, ICON, Meso-NH, the Unified Model, and WRF. For the intercomparison, a case study was selected from a multi-day undisturbed period during the PIANO (Penetration and Interruption of Alpine Foehn) field campaign conducted in the Inn Valley, Austria, in fall 2017.The presentation will show first results from the model intercomparison study. An extensive dataset is available for model evaluation from the PIANO measurement campaign, including vertical profiles of wind, temperature, and humidity from multiple Doppler wind lidars and a microwave temperature and humidity profiler, surface observations including surface-energy fluxes from multiple automatic weather stations and eddy-covariance stations, and spatially distributed temperature measurements from a dense network of temperature sensors. The model evaluation and intercomparison covers the entire lifecycle of the cold-air pool, from its initial formation in the afternoon to its dissipation the following day. The analysis addresses specifically the question of how well the simulations represent the cold-air pool's characteristics, such as its intensity, vertical structure, and spatial heterogeneity, and the processes contributing to the temporal evolution and spatial structure of the cold-air pool.
A neural network as observation operator for the assimilation of microwave satellite observations
Authors: Vural Jasmin , Vanderbecken Pierre , Bonan Bertrand , Calvet Jean-Christophe
Published: July 2024

In the framework of the EU-project CORSO, a multitude of in-situ and remote-sensing observations are exploited to better quantify the anthropogenic part of the CO2 emissions. Here, we use microwave satellite observations from the SMAP and AMSR2 instruments to improve the estimation of the state of carbon cycle variables.We employ the LDAS-Monde system using a simplified extended Kalman filter and the ISBA land surface model within the SURFEX modelling platform to assimilate both H and V polarisation of the brightness temperatures in different microwave bands provided by the respective satellite observations. As the classical approach of using a radiative transfer model as a forward operator is often computationally very expensive, artificial neural networks are a promising method to transform the model variables into observation space consuming only a relatively small amount of computing resources during the assimilation.In our study, we train a feedforward neural network on predictors extracted from the open loop run of LDAS-monde on the European and the global domain, respectively, employing models with different grid sampling. We perform tests using different setups of hyperparameters in the neural network and different combinations of predictors using not only model variables but also AVHRR LAI (leaf area index) observations provided by THEIA. To assess the relative importance of the employed predictors, we performed sensitivity analyses on the training results. We found that the temperature and moisture of the upper soil layer as well as the LAI play a major role but useful information can also be extracted from static features such as the latitude and different topographic measures. Special care has to be given to using coordinates as predictors to avoid overfitting.We implement the weights found with our best setup for each instrument into the LDAS-Monde data assimilation system. Eventually, we verify the effect of the assimilation on LAI analyses on both European and global domains against LAI observations and evaluate the performance of the system with regard to different land covers.
Hot, dry and compound hot-dry extremes in decadal predictions
Authors: Aranyossy Alvise , Donat Markus , De Luca Paolo , Delgado-Torres Carlos , Solaraju-Murali Balakrishnan
Published: July 2024

We investigate the representation of hot-dry compound extremes in decadal predictions and their correlations with the corresponding univariate extremes. We use a multi-model ensemble (MME) of 125 members from the CMIP6 Decadal Climate Prediction Project (DCPP) hindcast simulations and compare it with different observational references. Our analysis focuses on the average of forecast years 2 to 5, with the different forecasts initialised every year from 1960 to 2014. We analyse the skill of predicting hot, dry and compound hot-dry events in the MME. Specifically, we select the days above the 90th percentile of the daily maximum temperature for hot events (tx90p). For dry events, we use two indicators, based on the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI), with accumulation periods of 3, 6 and 12 months, and we consider a dry event a month when the SPI or SPEI value ≤ -1. Finally, we identify days that present both hot and dry conditions according to these criteria as compound hot-dry days (HDSPEI3,-6,-12 and HDSPI3,-6,-12).For the univariate extremes, results show strong skill for hot extremes, to a large extent driven by the warming trend. On the contrary, dry extremes show less uniform skill, with isolated areas of significant correlations. We find regional skill in some areas of the globe for hot-dry compounds. Through a comparison with historical simulations, we find that initialisation of the predictions leads to additional skill in localised regions, with most areas showing non-significant differences. However, the models still appear to underestimate the connections between compound and univariate extremes, especially between hot-dry compounds and dry conditions. These results show the potential and the limitations of predicting compound events in decadal forecasts, highlighting the pivotal role of dry conditions in assuring a skilful prediction. 
Identifying Mediterranean cyclones in GCM simulations – validation and future predictions
Authors: Dolgos Emília , Pongrácz Rita , Bartholy Judit
Published: July 2024

Mid-latitude cyclones play a significant role in shaping the weather and climate of Europe. Cyclones forming in the Mediterranean region and its neighbouring areas – known as Mediterranean cyclones –, typically have shorter life spans and lower pressure at their centres than their Atlantic counterparts. This study uses ERA5 reanalysis data and general circulation model (GCM) outputs of the CMIP6 project to identify potential cyclone centres during the period of 1950–2014. First, the identification algorithm locates local minima in the mean sea level pressure values, which are regridded to a spatial resolution of 1°, and are available in the main synoptic times (6-hour temporal frequency). The potential cyclone centres are then connected through subsequent timesteps to produce cyclone trajectories. The frequency of cyclones and the characteristics of these trajectories are analysed and are validated by comparing the results of reanalysis and CMIP6-simulation data. For the best performing models future trends are assessed across different Shared Socioeconomic Pathway (SSP) scenarios – representing different anthropogenic impacts and mitigation efforts. Mediterranean cyclones affect other parts of Europe, including Hungary and its vicinity. Our ultimate goal is to analyse what portion of the annual precipitation is connected to Mediterranean cyclones and their frontal systems, and how it will change in the future. For this purpose, we use precipitation data from the above-mentioned databases. The outcome of this study is important for future impact assessments. Results can serve as input for decision-makers and stakeholders as they formulate long-term strategies for their respective sectors.Acknowledgements: The study contributes to the COST CA19109 action (MEDCYCLONES). Research leading to this study has been supported by the following sources: the Hungarian National Research, Development and Innovation Fund (under grant K-129162), and the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014).
Valley wind dynamics and large-scale airflow interaction within the Cerdanya valley (eastern Pyrenees)
Authors: Martínez-Villagrasa Daniel , Cuxart Joan , Jiménez Maria Antònia , Conangla Laura , Miró Josep Ramón
Published: July 2024

The Cerdanya Cold Pool Experiment 2015 (CCP15) aimed to study the air thermal structure and wind circulation within the Cerdanya valley (eastern Pyrenees, Catalonia) under fair-weather conditions. The field campaign took place between 6 and 15 October 2015, focusing on the vertical column of the first hundred meters above the valley together with the surface energy balance at its centre. The Cerdanya valley, east-west oriented, is the largest of the Pyrenees mountain range with a wide bottom at 1000 m above sea level (asl) and bounded by high mountain ranges both to the north and south (peaks above 2500 m asl). Its topographical configuration favours the development of a diurnal valley wind system with the formation of a cold-air pool at night. During the campaign, four of a total of six intensive observation periods (IOPs) were affected by the intrusion of a strong wind channelised down valley during the afternoon and evening periods, followed by the development of a strong surface-based thermal inversion at night. The analysis of the observations leads to the following findings, also supported by high-resolution numerical simulations:The wind channelling observed in the valley centre requires the presence of a large pressure gradient in the north-south direction, i.e. perpendicular to the mountain range (orographic dipole).
The wind channelling has a diurnal evolution, occurring during the afternoon and evening periods. The exact onset time and endurance depends on the particular synoptical configuration and on the valley wind dynamics.
The presence of the wind channelling affects the evolution of the thermodynamic variables at the surface layer and the exchange fluxes at the surface interface, modifying the initial conditions of the nocturnal boundary layer within the valley.
A strong surface-based thermal inversion develops over the valley under both fair-weather conditions or the presence of such channelled wind. The latter fosters the vertical growth of such inversion and dumps its intensity. The thermal inversion separates the lowest levels close to the ground from the wind channelling flow influence.
Airborne wind energy resources: key advantages for the energy transition in Spain
Authors: Gaertner Miguel Angel , López-Franca Noelia , Ortega María
Published: July 2024

In the way towards fully renewable power systems, the variability of conventional renewables represents a fundamental challenge. One of the options to tackle this issue is to exploit the complementarity between different renewable energy sources and technologies. Airborne wind energy (AWE) technology can tap the energy of winds at several hundred meters height, where winds are stronger and less variable than at the height of conventional wind turbines. The deployment of AWE systems is being hindered by their higher present costs, associated to their early development phase, but their large potential advantages point to future highly competitive costs if there are early adoption opportunities that accelerate the progress of this technology.The aim of this study is to analyse the potential added value of AWE resources over Spain, in terms of temporal complementarity to mainstream renewables (onshore wind and photovoltaic energies) and adaptation to demand. Using data from a high resolution reanalysis (CERRA, Copernicus European Regional Reanalysis) and the AWERA tool [1] for analysing AWE resources, complementarity between AWE resources and conventional renewables is evaluated using actual PV and wind energy production data from the Spanish Transmission System Operator (Red Eléctrica Española), while demand data from the same source are used to explore adaptation to demand.We focus on the summer season. This season is associated to distinct problems for a system based on conventional renewables, due to the strong seasonal decrease of conventional wind energy and the simultaneous increase in power demand for air cooling. Summer power demand may even exceed winter demand in the future, due to climate change. Despite its summer production peak, PV energy covers only part of the day. The high summer temperatures in Spain are associated with a particular seasonal low pressure system over the Iberian Peninsula, the Iberian thermal low [2]. The corresponding wind fields at heights of several hundred meters above the surface show a very interesting daily cycle over certain areas of Spain, with strong complementarity to PV generation, while the vertical wind variations reveal substantial advantages with respect to conventional wind energy. Consequently, AWE resources could make a significant contribution to the energy transition in Spain, filling some of the gaps of mainstream renewables.References[1] Thimm, L., Schelbergen, M., Bechtle, P., Schmehl, R.: The Airborne Wind Energy Resource Analysis Tool AWERA. 9th International Airborne Wind Energy Conference (AWEC 2021). Available at: http://resolver.tudelft.nl/uuid:ba0c7fb2-baff-4110-9a51-c27a8498663b[2] Hoinka, K. P., Castro, M. D. (2003). The Iberian peninsula thermal low. Quarterly Journal of the Royal Meteorological Society, 129, 1491-1511.
Zonal wavenumber characterization of the North Atlantic Oscillation-associated anomalies
Authors: Brotons Marta , García-Serrano Javier , Haarsma Rein
Published: July 2024

In the last decades, there has been an ongoing discussion about whether the winter NAO has a deep barotropic and annular signature, being the stratosphere a key player of this variability, or, on the contrary, the NAO presents a wave-like behavior with its perturbations originating in the troposphere and propagating upward and downstream.In our work we tackle this discussion through zonal wavenumber decomposition of the NAO-related anomalies in both reanalysis (ERA5 and NCEP-NCAR) and climate models of different complexity and resolution (SPEEDO/L8, CAM5.3/L46 and EC-EARTH3.3/L91). We find that the NAO exhibits a non-annular, wave-like structure in all levels, particularly in the upper troposphere (300 hPa) but also in the middle stratosphere (30 hPa). Indeed, we identify that the troposphere-stratosphere coupling associated with the NAO includes a wavenumber 2 structure tilting westward and increasing in amplitude with height, indicating upward propagation and forcing of the polar vortex. This recurrent NAO-related wavenumber 2 anomaly could contribute to the characteristic wavenumber 2 pattern of the climatological stratospheric circulation. Wavenumber 1, on the other hand, shows no statistical significance in the stratosphere; while in the troposphere it appears to be an artifact of the method, reflecting the prominence of regional dynamics in the North Atlantic. At upper-tropospheric levels, wavenumber 3 seems to dominate the wave-like structure of the total field at mid/high latitudes, whereas wavenumber 5 is more prominent at lower latitudes. Finally, wavenumber 4 does not significantly contribute to the NAO-related anomalies. These results are robust in reanalysis and in the climate models, although some differences are noticeable, likely linked to model biases in the mean flow.To further interpret the vertical propagation of long waves (wavenumbers 1 and 2) and the horizontal propagation of small waves (wavenumber 3 and 5), additional diagnostics such as eddy heat flux and wave activity flux will be analyzed.
Significance of the WPL correction for the latent heat flux in semi-arid conditions
Authors: Martínez-Villagrasa Daniel , Martí Belén , Groh Jannis , Price Jeremy , Wrenger Burkhard , Cuxart Joan
Published: July 2024

The estimation of surface turbulent fluxes with the eddy-covariance (EC) technique is done through instrumentation that accounts for the fast response of primary meteorological variables measured at a certain distance from the surface. In practice, these measurements are typically subjected to additional corrections to mitigate the source of errors related to known micrometeorological effects. One of these effects is related to density fluctuations produced by temperature and humidity variations, a process that has to be considered when determining the flux of any air constituent. In consequence, this correction (called WPL after Webb, Pearman and Leuning work in 1980) affects the estimation of CO2 and water vapour fluxes. However, when comparing the impact of WPL correction respect to other error sources, it is usually considered relevant for CO2 but rather small on water vapour.In the current study, the impact of the WPL correction on the latent heat flux has been analysed in semi-arid conditions where the water availability is scarce in Summer. Experimental data is taken from Els Plans site, an instrumental facility deployed in the framework of the 2021 LIAISE field campaign (Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment), located over a rainfed field close to a wide irrigated area in the Eastern Ebro basin, Catalonia.The characterization of the latent heat flux through the EC systems has been evaluated for 13 months and their results are compared against the observations from a smart-field-lysimeter. In Summer, the WPL correction in daytime accounts for a couple of tens of W/m2 and doubles the initial value provided by the EC system, getting closer to the the lysimeter. The WPL correction is less important after a rain event, when the latent heat flux is larger due to enough water availability. When analysing the entire yearly cycle, the relative importance of WPL correction respect to the total water vapour flux increases with Bowen ratio. These results suggest that WPL influence cannot be neglected when dealing with arid and dry conditions.
Predicting the 2023/24 El Niño from a multi-scale and global perspective
Authors: Lian Tao , Liu Ting , Hu Ruikun , Wang Jie , Song Xunshu , Chen Dake
Published: July 2024

The 2023/24 El Niño ranks as the 2nd strongest El Niño in the 21st century thus far. The intensity of the event was successfully predicted as early as March 2023, based on the buildup of upper ocean heat content in the western equatorial Pacific. Nevertheless, the unusual evolution pattern of this event, including the two-step warming tendency and two warming centers during the 2023/24 El Niño, and the influence from outside of the equatorial Pacific, remain to be explained. Here we show that the 2023/24 strong El Niño was mainly contributed to by three factors. Firstly, the buildup of heat content in the western equatorial Pacific in the early months of 2023 fostered a strong canonical El Niño, characterized by a steady increase in SST anomalies across the central-eastern equatorial Pacific, peaking in late 2023. Secondly, the intense sea surface temperature warming outside of the equatorial Pacific suppressed the El Niño and confined the warming mainly to the eastern basin. Lastly, the westerly wind bursts that occurred in the western-central equatorial Pacific in autumn induced another relatively weak warm center in the central equatorial Pacific toward the end of 2023. While the latter two factors coincidentally offset each other, leaving the buildup of heat content appearing as the primary cause of the strong 2023/24 El Niño, they explained the detailed structure of this El Niño.  Our results not only confirm the essential role of equatorial ocean heat recharge for El Niño development, but also demonstrate the necessity of accounting for multi-scale interactions from a global perspective to understand and predict El Niño.