bluuebunny/crossref_metadata_2025 huggingface dataset:
Modelling Changes in Climate and Land Use in 2100. Importance of climate for business and policy.
Authors: Gutbrod Dr. Karl G. , Schloegl Dr. Sebastian , Wenzel Caspar , Ramshorn Christoph
Published: July 2024

The availability of climate change models and derived values has produced quantifiable indices for temperatures, precipitation, soil moisture and others, with different scenarios of evolution, These have been developed and made available by the public sector and academia, and now largely been validated within the past 20 years.These data can be used to calculate the impact of the changes on the future of key resources, such as agricultural land and productivity, in an effort led both by the public and private sector, as these expected changes are of interest to policymakers, business managers, investors as well as to the public at large in sectors ranging from agriculture to energy and urban development and many others.This paper analyses the potential impact of climate change on the size and productivity of agricultural land until the year 2100 in different regions of the world. It further includes an estimate of world population development and the likely impact on the food demand and supply in different regions of the world.Based on these predictions, the paper addresses the most likely policy and business implications. and the need for the public and private sectors to cooperate in mastering the expected challenges.It concludes by showing the areas in which meteorology has a significant or even essential influence on the decisions to be taken and demonstrates some simple applications to access climate data which can be used for estimating further climate evolution on a local level and quantifying the impact of this evolution on various policy and business decisions.
Weather routing solutions towards decarbonisation of maritime transport
Authors: Zygarlowska Emilia , Dumard Christian , Rochut Basile
Published: July 2024

Approximately 90% of trade occurs through maritime shipping, which makes it a significant contributor to greenhouse gas emissions, accounting for 3% of global emissions [1]. Reducing the carbon footprint of maritime industry is therefore crucial to combat climate change and achieve the UN’s Sustainable Development Goals. One effective approach to decarbonize the industry is through weather routing. It is a cost-effective tool, which allows shipping companies to select the most energy-efficient navigation routes.Adverse weather conditions, such as rough sea state, strong winds and currents can increase voyage time and ship’s resistance, leading to higher fuel consumption rates. Our research is centered on the integration of weather forecasts into voyage planning to identify optimal and most sustainable routes. Effective route optimization depends on the availability of reliable weather forecasts. To this end, we have established collaborative partnerships with several academic institutions to access high-quality model and satellite data. Moreover, we address the inherent uncertainty of weather models by integrating Artificial Intelligence algorithms to enhance accuracy of our analysis.Our results based on numerous case studies demonstrate the benefits of weather routing solutions. We have consistently observed reduction in fuel consumption and travel time by at least 3-10% across various scenarios, which is substantial given the scale of the maritime industry. With shorter voyage times and consequently fuel consumption, our approach leads to decreases in operating costs. This cost efficiency provides an incentive for mariners to adopt more sustainable solutions, which are relatively easy to implement in their daily operations.Our developments and collaborative approach underscore the potential of weather routing as an efficient tool to support climate-neutrality goals within the maritime industry. Substantial fuel savings and reduced greenhouse gas emissions can by achieved with minimal route adjustments, which emphasizes the importance of integrating weather routing strategies into maritime operations to facilitate the industry’s transition towards a more sustainable future.[1] Arslan, O., E. Besikci, and A. Olcer. "Improving energy efficiency of ships through optimisation of ship operations." No. FY2014-3 IAMU (2014).
Subsampling members in a seasonal forecast ensemble
Authors: Pérez Pérez Francisco Javier , Rodríguez Guisado Esteban
Published: July 2024

Climate Services based on seasonal forecasts are a powerful tool for adaptation in a changing climate and they attract growing interest from different sectors. However, operational seasonal forecasts have traditionally been issued following a subjective procedure, combining information from different sources, such as observation, empirical and dynamical models. Although it adds value by incorporating expert knowledge, the subjective procedure usually results in graphic products, with limited traceability, and not suitable for objective skill assessment or coupling sectoral applications. Identifying this issue, WMO encourages Regional Climate Centers and RCOFs to develop an objective procedure. The purpose is to increase the reliability of our results and to provide the basis for future climate services. With that aim, we explored ways of developing an objective approach that adds value to raw model forecasts in the Mediterranean region.As is usually accepted, the starting point is a multimodel ensemble, which in our case combines seven Copernicus seasonal forecast models, hoping to minimize the weaknesses of individual models. The work focuses on looking for ways of subsampling the ensemble data based on comparing observational patterns with the evolution of ensemble members at the beginning of the period. Therefore, we did not use the latest model run, choosing instead earlier initializations and applying techniques such as cluster analysis or subsampling a fixed number of members to select those that were closer to reality.First, we performed a cluster analysis to the ensemble forecast for winter (DJF) 2023-2024 and chose the cluster which best predicted the values of precipitation in October, which would be the last month with complete data when producing the winter seasonal forecast. However, we did not find a significant increase in skill in the Mediterranean region, possibly due to the great differences in cluster population between each year of the hindcast.Then, we tested an alternative method by selecting a fixed number of members for the forecast and each year of the hindcast. We subsampled the group of members which best predicted precipitation in October and found a significant increase in skill in certain areas. However, there were not consistent improvements along the whole region, with some areas showing lower skill.A comparison of the methodology using different model runs was conducted, finding better performance for the September run.
Influence of atmospheric systems on dry conditions in Serbia
Authors: Tošić Ivana , Filipović Lazar , Putniković Suzana
Published: July 2024

Daily weather types over Serbia were obtained using the objective weather typing system of Jenkinson and Collison (1977), which is based on the Lamb weather types (Lamb 1972). The daily atmospheric circulation that affects Serbia was characterized using a set of indices associated with the direction and vorticity of the geostrophic flow over an area that covers the majority of Europe and the Mediterranean Sea. To obtain the circulation types, the daily values of the sea level pressure (SLP) and the geopotential height at 500 hPa for the period 1961-2020 were retrieved from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis (Kalnay et al. 1996) on a 2.5° × 2.5° longitude-latitude grid.Composite maps of ten circulation weather types (CWT): northerly (N), northeasterly (NE), easterly (E), southeasterly (SE), southerly (S), southwesterly (SW), westerly (W), northwesterly (NW), cyclonic (C) and anticyclonic (A), were constructed for the period 1961-2020 for all seasons. The C and AC weather types are based on the severity of the geostrophic vorticity. Each of the CWT has a distinct underlying synoptic pattern that produces the expected type and direction of flow over the study area. The relative frequencies of the circulation types were computed. It was found that the AC type with 25.2% was the most common type, followed by the C type with a frequency of 14.6%.Multiple Linear Regression (MLR) models were used to establish the relationship between atmospheric circulation and number of days without precipitation. The models were calibrated for the first 40 years and validated for the remaining 20 years. Using ten circulation weather patterns (eight pure directional, cyclonic, and anticyclonic) as predictor variables, good results were obtained for modelling of number of days without precipitation in Serbia. Almost 52% of the variability could be explained by ten circulation types in Belgrade.ReferencesJenkinson AF, Collison FP (1977) An initial climatology of gales over the North Sea. Synoptic Climatology Branch Memorandum 62, Meteorological Office, Bracknell, UKKalnay E, Kanamitsu M, Collins W, Deaven D, Gandin L, Iredell M, et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–470Lamb HH (1972) British Isles Weather Types and a Register of Daily Sequence of Circulation Patterns, 1861–1971. Geophysical Memoir 116, HMSO, London (UK), 85 pp
Arctic cyclones and their representation in climate models
Authors: Besson Myriam , Rivière Gwendal , Fromang Sébastien
Published: July 2024

Synoptic-scale Arctic cyclones are atmospheric perturbations of thousands of kilometers size associated with a low pressure that form in the Arctic or move towards this region. They live between a few days and more than a month and are characterized by strong surface winds and precipitation. The specificity of Arctic cyclones, in comparison with mid-latitude ones, is that some of them, long-lived ones, have a cold-core vertical structure. Others have a warm-core mid-latitude-like structure. Arctic cyclones form a major hazard if human activities are to be continued, even intensified, in the Arctic region. As they transport heat and humidity, they also represent a key feature to understand Arctic climate, especially in a context of global warming. In order to simulate Arctic faster warming – i.e. polar amplification – in a realistic way, it is crucial to better understand Arctic cyclones life cycles and to improve their representation in the models. The aim of our study is to evaluate this representation in the IPSL climate model by analysing HighResMIP simulations run at four different spatial resolutions (Low (LR), Middle (MR), High (HR) and Very High (VHR)) and comparing with ECMWF-ERA5 reanalysis. To do so, Arctic cyclones are detected and tracked by applying the algorithm TempestExtremes to the mean sea level pressure (MSLP) field. Different cyclones’ characteristics are analysed: their frequency, their lifetime and their intensity. Maps of genesis, lysis and track densities are also analysed. Preliminary results show that the higher the resolution, the higher the number of tracks. The model represents Arctic cyclones quite well in summer but is less skillful in winter with an overrepresentation of the most intense ones, especially above the continents and sea ice. This bias increases with the resolution. The ability of the model to represent interactions of Arctic cyclones with tropopause polar vortices and the transitions from baroclinic structure to axisymmetric cold-core structure is also investigated.
Ocean-wave-atmosphere-coupling in the AROBASE modelling system for coastal wave warning and sensitivity to momentum flux parametrisation
Authors: Nicolay Fleur , Lebeaupin Brossier Cindy , Dalphinet Alice , Bouin Marie-Noëlle , Pianezze Joris , Bouttier François , Beuvier Jonathan
Published: July 2024

The AROBASE (AROme-BAsed coupled SystEm) project aims to develop a coupled kilometre-scale numerical forecasting system incorporating ocean, waves, aerosols/chemistry and land surface models around the fine-scale AROME (Application of Research to Operations at MEsoscale) numerical weather prediction model. The AROBASE modelling system aims to improve : 1. our understanding of meteorological processes and interactions between the different environmental components; 2. the realism of the simulations by precisely and consistently representing these phenomena and by taking into account the complexity of the exchange processes between components; 3. the forecast of severe meteorological events by improving the representation of interactions at fine scale.One important step is the evaluation of ocean-atmosphere-waves-coupling in AROBASE in the frame of Météo-France's coastal wave warning system. To assess the ability of AROBASE to better represent meteorological extremes leading to such coastal hazards, various modelling configurations are tested using the SURFEX (SURface EXternalisée) surface model, the MFWAM wave model of Météo-France derived from WAM (WAve Model) and the NEMO (Nucleus for European Modelling of the Ocean) ocean model. Different combinations of these models and different momentum flux parametrisations are used to evaluate the coupling impact and the improvement of both the marine and numerical weather forecasts.The first case study is the Eunice storm, which impacted the British Islands and Northern France on February 18, 2022, causing extensive damages due to strong winds. Eunice is also characterised by a high swell combined with a high tidal coefficient that caused large flooding locally. The modelling results will be compared to satellite and in situ observations, as well as operational outputs of AROME and MFWAM.
Understanding enhanced sub-seasonal predictability of cold spells with nudged experiments.
Authors: Erner Irene , Karpechko Alexey
Published: July 2024

The weather-dependent planning and decision-making benefit greatly from subseasonal to seasonal (S2S) weather predictions made for up to six weeks ahead. At this timescale anomalies in the extratropical stratospheric circulation, which can last for several weeks in the Northern Hemisphere during winter, are known to affect climate at the surface and can extend the predictability of tropospheric weather conditions. Although state-of-the-art models can to some extent capture the surface impacts of such stratospheric variability and have demonstrated enhanced skill at the subseasonal timescales after major stratospheric events, the causal link between the events and the sources of predicted signals in real-time forecasts is difficult to establish.We performed relaxation (nudging) experiments to uncover sources of predicted signals in the forecasts, specifically focusing on forecasts of high impact weather events, such as cold spells in the winter season. Nudging of winds and temperatures towards a reference state, usually towards observations is a technique commonly used to constrain model behavior. In addition to this traditional approach where nudging is done towards observations and climatology, we nudge towards control forecast ensemble members predicting anomalous behavior in the stratosphere. By comparing these nudged experiments with the control forecast ensemble, we identify the cause of predicted signal in real-time situations when observations may not yet be available. Therefore, the main benefit is that the methodology can be applied in operational practice as an additional tool to interpret forecast behavior. This approach also provides more accurate quantification of the signal-to-noise ratio because, when nudging is done towards observations, the predicted signal is artificially enhanced by the elimination of model biases. Understanding the conditions associated with enhanced sub-seasonal predictability allows to distinguish between signals associated with remote sources and cases when the signal has no clear origin.
Integrated nowcasting of convective precipitation with Transformer-based models 
Authors: Küçük Çağlar , Atencia Aitor , Dabernig Markus
Published: July 2024

Precipitation nowcasting remains a challenging topic in weather prediction, particularly in the initial stages of convective activity. Ground-based weather radar observations have generally been used to estimate the motion vectors of precipitation fields, and have a pivotal role in precipitation nowcasting. However, during convection initiation, such data hold limited information, which hampers prediction performance. Nevertheless, different data streams, such as lightning activity and geostationary satellite infrared channels, have demonstrated skill in the early detection of convective activity. Therefore, there is a need to integrate data from various domains to improve nowcasting of convective precipitation, and data-driven approaches offer robust solutions for integrating large volumes of data and extracting the information therein. 

Here, we present a Transformer-based precipitation nowcasting model that integrates data from various sources. To train the model, we created a dataset by harmonising space- and ground-based observations with precipitation reanalysis and convective information data from the Integrated Nowcasting through Comprehensive Analysis (INCA) model over the spatial domain of INCA. Space-based observations include four infrared channels of the Meteosat Second Generation, while ground-based observations comprise lightning and weather radar data with 5-minute temporal resolution and a spatial resolution varying from 1 to 8 kilometres depending on the data source. The data is sampled over 5 years of observations from convective seasons to target convective precipitation events, which are particularly challenging for prediction. Trained on this dataset, our model can nowcast precipitation over the INCA domain for a lead time of 90 minutes. While the model reproduces the shape and location of the fields, the performance in reproducing the structure of the precipitation fields is limited at longer lead times, resulting in blurred predictions.
We will present the model, analyse its performance, present case studies, compare it against the operational INCA predictions, and provide insights through model interpretation experiments. We will also elaborate on the details of developing the dataset, a critical but often underrated step for enhancing model performance. The model offers a novel approach to integrated nowcasting of convective precipitation and motivates further studies with a data-driven perspective. 
The possible change of global and regional tropical cyclone genesis probability in the future as predicted by machine learning models
Authors: Qian Qifeng , Jia Xiaojing , Lin Yanluan
Published: July 2024

Due to a lack of observations and limited understanding of the complex mechanisms of tropical cyclone (TC) genesis, the possible TC activity response to future climate change remains controversial. Previous studies have divergent opinions on how TC activity responds to climate change, as TC activities can be impacted by many environmental variables. One advantage of ML methods is that, compared to traditional analysis methods, they can capture the complex nonlinear relationship between the predictor and predictand; therefore, before the theory of TC genesis is well established, in addition to traditional climate models, ML methods may provide an additional useful tool to predict the possible changes in TC frequency under global warming. Moreover, the ML model may also provide more information about the nonlinear characteristics of TC genesis, which will help to improve our understanding of TC genesis mechanism. In this work, a machine learning model, called the maximum entropy (MaxEnt) model, is established using various environmental variables. The model performs slightly better than the genesis potential index for historical TC activities based on the spatial correlation coefficient. Using coupled model intercomparison project phase 6 model projections, the MaxEnt model predicts a statistically significant decreasing trend of TC genesis probability under all shared socioeconomic pathway scenarios. Further analysis reveals that PI is the most important environmental variable in the MaxEnt model, which provides the most unique and useful information to the model. In addition, our analysis reveals that TC genesis might have a complex nonlinear relationship with potential intensity, which is different from the positive relationship reported in previous studies and might be the key factor leading to the model predicting reduced TC genesis in the future. We further apply principal component analysis to investigate the TC genesis environment in different ocean basins and show that the TC genesis environments are mainly determined by upper and lower level absolute vorticity. The TC genesis environment in basins are classified into three groups and three machine learning (ML) models are build accordingly. These basin-wide models predict a consistent TC genesis trend in each basin under different future scenarios. Further analysis highlight the importance of absolute vorticity for basin-wide TC genesis. A multivariate environmental similarity surface analysis reveals that climate models predict the weakest change in the TC genesis environment in North Atlantic compared to other basins.
Impacts of the changing summer thermal land-sea contrast on the northern hemisphere planetary circulation
Authors: Meroni Agostino N , D’Andrea Fabio , Pasquero Claudia
Published: July 2024

The zonal inhomogeneities of the land surface are known to strongly modulate the large-scale atmospheric circulation, especially in the northern hemisphere. This is related both to the mechanical forcing induced by high orography and to the thermal contrast between water and land. In a warming world, it is known that such a thermal contrast is changing but its impacts on the large-scale atmospheric circulation are poorly constrained. Recently, it has been shown that a reduced winter thermal land-sea contrast is projected to reduce the amplitude of planetary waves, with a predominant control of the Asian-Pacific land-sea contrast at the global scale. Goal of the present study is to investigate the impacts of an enhanced summer thermal land-sea contrast on the planetary circulation. In particular, the role of soil moisture in modulating such contrast is considered with special attention. In fact, despite the importance of the land-atmosphere coupling mechanisms, that involve feedbacks with atmospheric circulation, clouds, precipitation and surface fluxes, there are still fundamental gaps in their understanding. These gaps result in an incorrect representation of the global hydrological cycle in CMIP6 models. For example, CMIP6 models are characterized by large biases in the water vapor trend representation in arid and semi-arid regions, which might be related to a poor representation of soil moisture and its impacts on the overlying and downstream atmospheric dynamics.By analyzing the CMIP6 model spread in soil moisture, surface air temperature and upper-tropospheric geopotential height, we aim to quantify the variability of the planetary circulation in a range of realistic soil moisture configurations. Climate data records of satellite products of surface soil moisture can also be used to constrain the soil moisture variability observed in the last decades. In the second part of the project, numerical simulations with an Earth Model of Intermediate Complexity will shed light on the link between soil moisture distribution, land-sea summer contrast and planetary circulation.
Sources of temperature biases in regional climate simulations over complex terrain
Authors: Zarabara Francesca , Giaiotti Dario
Published: July 2024

In the frame of the worrying pace and effects of human induced climate change, mountainous regions are warming about twice as fast as the global average. Modeling climate and climate change scenarios over highly complex topography regions like the Alps still represents a great challenge to regional climate modeling: better characterizing the sources of models biases remains a major issue. We analyze the sources of the bias affecting the near surface temperature (TAS) of regional climate simulations, focusing on a region with complex orography, namely, the Friulian Alps. The temperature measurements consist of weather station records, while model TAS outputs belong to an ensemble of EURO-CORDEX models.Starting from the vertical structure of the atmospheric thermal profiles, we provide a description of the origin of TAS biases in climate simulations and suggest the presence of three main bias components. The first is linked to the driving GCM ability to reproduce the free atmosphere temperatures at a given level (e.g. 500 hPa); the second bias component depends on the models performance in reproducing the thermal structure between the free atmosphere and the boundary layer top: we show that, under the environmental lapse rate approximation, this in turn depends on the orographic error and on the error in the boundary layer thickness. The last bias component accounts for the poor performance of the boundary layers parameterization schemes in reproducing land-atmosphere processes.Applying this bias analysis to the temperature measurements and EURO-CORDEX simulations that are available for the study area, we evaluated the goodness of the common practice to remove the effects of orographic discrepancies, by means of the environmental lapse rate approximation, frequently adopted in climate projections and model evaluation studies. We show that, after the orographic correction of GCM-RCM TAS outputs, substantial biases still persist, especially during cold months. According to the existing literature, we argue that CMIP5 might contribute to the overall TAS bias, with a cold bias of about 2 degrees, explaining the free atmosphere level component of the bias, and we discuss the influence of the errors in reproducing the boundary layer thickness.
Integrating the ParFlow hydrological model into ICON
Authors: Weinkaemmerer Jan , Schnur Reiner , Goergen Klaus , Kollet Stefan
Published: July 2024

In order to further develop an ICON-based, storm-resolving earth-system model, the ParFlow hydrological model has been coupled to the ICON land component, which is based on the JSBACH land surface model. The overall goal is to better capture land-atmosphere interactions ensuring balanced water and energy budgets in global, kilometer-scale simulations on the climate time scale. Compared to the JSBACH hydrology, ParFlow allows a variable subsurface thickness and 3D groundwater dynamics in connection with resolved overland flow in a continuum approach. In combination with the two-way feedback between atmospheric and hydrological processes provided by the coupling, this is expected to resolve hydrological processes more realistically in space and time. Technically, the YAC coupling library has been used to account for horizontal grid differences and different spatial and temporal resolutions in ICON and ParFlow.In the proposed global setup, ParFlow has been implemented over a European domain with a subsurface depth of 60 m to account for deeper aquifers. Thus, 3D groundwater dynamics and two-way-coupling with the land surface and atmosphere is active only over that specific region of interest while the rest of the global land surface relies on the default hydrological model of JSBACH. This way, no atmospheric boundary data needs to be provided in contrast to regional weather and climate model setups so that a closed representation of the global water cycle can be established including regional feedbacks with groundwater. In addition to a general proof-of-concept, our presentation provides a first analysis of moisture distribution and land-atmosphere fluxes both inside and outside the coupling region as well as potential feedbacks of groundwater on the atmospheric circulation and large-scale weather patterns beyond the regional scale. 
Towards a new generation of regional reanalyses for Europe
Authors: Valmassoi Arianna , Keller Jan D. , Potthast Roland , Anlauf Harald , Cress Alexander
Published: July 2024

Regional reanalysis data sets are becoming more used and requested for a broad spectrum of users, from climate adaptation and mitigation, to agricultural and economical applications (e.g. energy meteorology). While the development of a reanalysis system mainly relies on an existing numerical weather prediction (NWP) model and corresponding data assimilation scheme, it involves a large amount of testing from both a computational and technical perspective.The talk will present preliminary results of the development for a new pan-European regional reanalysis at the Deutscher Wetterdienst (DWD). The framework involves the ICON NWP model in its global configuration with a two-way coupled nest over Europe and its operational data assimilation method run at 3-hours intervals, namely EnVar for the deterministic run at 13 km global resolution (6.5 km over Europe) and Localized Ensemble Transform Kalman Filter (LETKF) for the 20 ensemble members at 40 km global resolution (20 km over Europe). The reanalyses cover the 2010-2022 period, extended to current, and it is run split in 3 streams (June 2009, January 2015, and January 2018) with forward overlap. The first stream start date includes a 6-months initial spin-up, that in the other two streams is dealt through the forward overlap. The spin-up phase is necessary for the soil to reach the equilibrium, and in our case is shorter than what usually seen in free-running simulations because our system performs a surface analysis for soil-moisture every day at 00UTC.First of all, we present that the number of active observations has a similar magnitude to what shown for ERA5. Then we proceed showing the evolution of the profiles averaged over Europe of first-guess and analyses departures w.r.t. observations. Preliminary results of the first year of the 2018 stream are evaluated in observation space on a 6-day average over the European domain. First we see that first-guess bias for the radiosondes and dropsondes (TEMP) temperature of at most -0.12K below 500 hPa and +0.3 K above it. The bias magnitude is reduced by at least half when the analysis is used.  The SYNOP biases for the 2-meter temperature and relative humidity are on average zero, with spatial variability maxima of 0.2 K/0.01 (first guess) and 0.1 K/0.005 (analysis).
Ten years monitoring crops with citizen science
Authors: Busto Montserrat , Cunillera Jordi , de Yzaguirre Xavier
Published: July 2024

The Meteorological Service of Catalonia (SMC) has been carrying out phenological monitoring of crops since 2013 to study the impact of climate change on the ecosystem. For this purpose, it was created the Phenological Network of Catalonia (Fenocat), which is made up of eighty observers who monitor the main phenophases of fruit trees such as almond, cherry, olive, apple, orange, or vine, as well as annual winter crops as wheat or barley, annual summer crops as maize or sunflower, and forage herbs such as alfalfa. In addition, 14 birds and 6 butterflies are also monitored.The Fenocat is a citizen science network whose observers report data two or three times per week, carrying out a periodic assessment of the status of the phenophases for a plant, rather than simply recording the date of an event. This observation system allows the recording of second occurrences in the same season or year (second flowering, second leaf sprouting, second fruit formation), events that happen more frequently due to climate change. The observers insert the information directly into the database using a web application in situ; the web application lets them compare their observations with those produced by other observers.Fenocat uses BBCH codification and is a data provider of the Pan-European Phenology Database (PEP725).As genetic factors determine the phenological response, it was carried out a cultivar identification using genetic markers (DNA extraction, Genotyping using molecular markers -SSRs or SNPs- and a calculation of the similarity between the analysed samples and the database information of the Centre for Research in Agricultural Genomics).After ten years of observations and more than 1,150,000 data collected, we observe a general advance in phenophases such as bud burst, full flowering and maturation, and a delay in the leave fall. For example, in the specific case of the almond tree, the sprouting of the first leaves has advanced 13 days in 10 years, full flowering has advanced by 9 days in 10 years, and the maturation of the almond happens 23 days earlier than 10 years ago (average data for all of Catalonia).
A Causality Perspective on the Impact of Hydroclimatic Extremes on Crop Yields
Authors: Baydaroğlu Özlem , Yeşilköy Serhan , Demir Ibrahim
Published: July 2024

The complex and nonlinear characteristics of atmospheric dynamics, along with their high dimensionality and interconnections across several spatial and temporal factors, pose significant challenges in comprehending, modeling, and predicting hydroclimatic extreme events. Understanding complex systems such as the atmosphere requires establishing causal relationships; therefore, this study focuses on hydroclimatic extremes, including heat waves, droughts, and extreme precipitation, which continue to be emphasized in climate change projections. Hydroclimatic extreme events pose a significant threat to agricultural sustainability, impacting crop yields, public health, migration patterns and ecological balance. The present research delves into the spatial and temporal dynamics of extreme precipitation, droughts, and heatwaves, specifically analyzing their influence on corn and soybean yields. Moreover, we aim to discover the causal relationships between these extreme events and crop yields by employing cross convergent mapping (CCM). CCM is a technique that relies on nonlinear state space reconstruction and is capable of distinguishing between causality and correlation. In accordance with Takens’ embedding theory, the attractor is reconstructed by CCM using time series data. This reconstruction allows for the identification and measurement of causation through cross-mapping prediction. The study uses the Evaporative Demand Drought Index (EDDI) as a metric of agricultural drought, the Palmer Drought Severity Index (PDSI) as an indicator of hydrological drought, together with maximum air temperature, extreme precipitation, and data on corn and soybean yields. Beyond that, this investigation is carried out on rainfed agricultural lands in Iowa with the specific aim of elucidating the impacts of hydroclimatic extreme events. Agricultural sustainability research and the quantification of economic consequences and impacts of extreme events on agriculture will benefit significantly from the findings of this investigation.
Historical Meteorological Open Data Repository at Nicolaus Copernicus University Centre for Climate Change Research
Authors: Wyszyński Przemysław , Przybylak Rajmund
Published: July 2024

The Nicolaus Copernicus Centre for Climate Change Research (CCCR) was established in 2019, as a university interdisciplinary unit. It currently consists of fifteen researchers from four faculties representing five separate scientific disciplines (Mathematics and Computer Science; Biological and Veterinary Sciences; Historical Sciences; and Earth Sciences and Spatial Management). Researchers from Poland and abroad are kindly invited to work at or with the CCCR.The main goal of the CCCR is to research climate changes over the last millennium in Poland, Central Europe and polar areas (mainly the Arctic). This goal can be achieved primarily by collecting historical meteorological data in archives and scientific libraries around the world. Our research is part of a worldwide initiative (Brönnimann et al. 2019) to inventory and rescue meteorological data for historical times.The CCCR repository has so far included historical data gathered during three scientific projects, i.e. CEArc, ExtremeWeather, and MORCLIM for Poland and Arctic areas from the Middle Ages (as indices based on descriptive sources) until Early Twentieth Century Warming (1920-1950). Here we present the method of data collection, their digitization, quality control (QC), post-processing and the structure of our repository according to FAIR (Findability, Accessibility, Interoperability, and Reuse) principles.The work was supported by the National Science Centre, Poland, projects No. 2020/37/B/ST10/00710 and No. 2020/39/B/ST10/00653References:Brönnimann S., Allan R., Ashcroft L., Baer S., Barriendos M., Brázdil R., Brugnara Y., Brunet M., Brunetti M., Chimani B., Cornes R., Domínguez-Castro F., Filipiak J., Founda D., Gergis J., Grab S., Hannak L., García Herrera R., Huhtamaa H., Jacobsen K. S., Jones P., Jourdain S., Kiss A., Lin K. E., Lorrey A., Lundstad E., Luterbacher J., Moberg A., Mauelshagen F., Maugeri M., Maughan N., Neukom R., Nicholson S., Noone S., Nordli Ø., Ólafsdóttir K. B., Pearce P. R., Pfister L., Pribyl K., Przybylak R., Pudmenzky C., Rasol D., Reichenbach D., Řezníčková L., Rodrigo F. S., Rohde R., Rohr C., Skrynyk O., Slonosky V., Thorne P., Valente M. A., Vaquero J. M. Westcottt N. E., Williamson F., Wyszyński P. 2019: Unlocking pre-1850 instrumental meteorological records: A global inventory. Bulletin of the American Meteorological Society 100: ES389-ES413. doi: https://doi.org/10.1175/BAMS-D-19-0040.
Multi-model Ensemble Prediction of Summer Precipitation in China Based on Machine Learning Algorithms
Authors: Yang Jie
Published: July 2024

With the development of machine learning (ML), it provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, JMA CPS2 model data, we conducted the multi-model ensemble (MME) prediction experiment using three tree-based ML algorithms,, the decision tree (DT), the random forest (RF), and the adaptive boosting (AB) algorithm. On this basis, we explored the applicability of ML algorithms to ensemble prediction of seasonal precipitation in China, as well as the impact of different hyperparameters on prediction accuracy.  Then, the MME predictions based on optimal hyperparameters were constructed for different regions of China. The results show that all three ML algorithms have an optimal maximum depth less than 2, which means that based on the current amount of data, the three algorithms can only predict positive or negative precipitation anomalies, and extreme precipitation is hard to predict. The importance of each model in the ML-based MME is quantitatively evaluated. The result shows that NCEP CFSv2 and JMA CPS2 have a higher importance in MME for eastern part of China. Finally, summer precipitation in China was predicted and tested from 2019 to 2021. According to the results, the method provides a more accurate prediction of the main rainband of summer precipitation in China. ML-based MME has a mean ACC of 0.3, an improvement of 0.09 over the weighted average MME of 0.21 for 2019-2021, which exhibits a significant improvement over other methods. It shows that ML methods have great potential in improving short-term climate prediction. These results provide an important reference for short-term climate prediction in China. ML-based MME has the potential to accurately forecast the main rainbands of summer precipitation in China.
ENSO impacts on summer monsoon seasonal aggregate and daily rainfall characteristics in southern Africa
Authors: Silverio Kenedy , Ambrizzi Tercio
Published: July 2024

The rainy season in most of southern Africa (SAF), defined here as Africa subcontinent poleward of 10°S, is associated with the summer monsoon regime, occurring in the region between October and March with seasonal peak during December-January-February (DJF) months. The quality of this season, particularly the peak monsoon (DJF), is of great importance as it rains little during the rest of the year over most SAF. In this study, the influence of El Niño-Southern Oscillation (ENSO) episodes, El Niño (EN) and La Niña (LN), on the summer monsoon circulation, rainfall and the frequency of extreme rainfall events over SAF is analyzed with seasonal and monthly resolution, using data from monthly GPCC products version 2022 and three different daily datasets (CPC version 1.0, CHIRPS version 2.0 and GPCP version 2022) for the 1982-2019 period. Extreme events are defined as 3-day mean precipitation above the 90th percentile. The mean frequencies of extreme events are determined for each month and season, and for each ENSO category episodes (EN, LN, and neutral), and the differences between EN and neutral years and LN and neutral years are computed through anomaly composites superimposed with either outgoing long-wave radiation (OLR) or eddy streamfunction at either low or upper levels, to understand the circulation mechanisms associated with ENSO episodes anomalies. Although some little differences exists, the pattern of significant signals in the frequency of extreme events is found to be well coherent with that of seasonal total rainfall anomalies in both the EN and LN episodes. The analysis also shows that some anomalies, which are consistent and important during part of the season, are smoothed out in a seasonal analysis. All daily datasets considered here show similar results, but that of CHIRPS seems to outperform better the well known and expected pattern, probably due to its high resolution. There are changes of anomalies within the summer monsoon season, with positive (negative) anomalies associated with EN episodes in spring (summer) over almost all SAF, associated with a low-level cyclonic (anticyclonic) anomalies to the north-eastern (southwestern) SAF, connected with a Rossby wave train generated in central south Pacific, which passing over south America/south atlantic ocean suffer from it less interference in comparison with the same wave in late summer. The opposite is almost true for LN events. To our knowledge, such results have never been reported for SAF elsewhere before, and those suggest the prevalence of likely regional processes over remote influences during part of the season. Similar results has been found early for South America. These results have important implications for seasonal predictions improvement, and rain dependent activities.
Co-development of weather, early warning, and climate services in Ukraine
Authors: Hirsikko Anne , Skoropad Oleg , Bilozerova Alla , Pietarila Harri
Published: July 2024

Finnish Meteorological Institute (FMI) and Ukrainian Hydrometeorological Center (UHMC) have together worked towards enhancing weather, early warning, and climate services in Ukraine since January 2022. The co-development includes automating the whole value chain of service provision from observation station to customer interface. Additionally, the impact of war on air quality and hydrometeorological observation infrastructure have been analyzed.   In addition to developing weather and early warning information exchange with general public, national stakeholders and customers through enhancing service provision, UHMC aims at exchanging information with European meteorological services. As a result of development collaboration with FMI, UHMC publishes operationally land and marine area early warnings in CAP format through Meteo Alarm programme. In return, UHMC is provided with weather forecast data from the European Center for Medium Range Weather Forecast and South-East European platform, as well as European weather radar data from EUMETNET OPERA programme.Enhancing the visbility and creditability of UHMC is a measure of success for the collaboration project. Thus, the next steps of FMI-UHMC collaboration will include enhancing web page platform to effectively share new products and the implementation of mobile phone application to ensure an easy access to weather and early warning information as well as to reach people in the most vulnerable situation with the best effort basis.      The bilateral collaboration project between Finland and Ukraine in the field of meteorology has been funded by the Ministry for Foreign Affairs of Finland. During the 2024 annual meeting of the European Meteorological Society the key results of the project as well as identified best practices in successful peer-to-peer learning will be presented.
Atmospheric circulation associated with 3D heat wave types in Middle Europe
Authors: Bestakova Zuzana , Lhotka Ondrej , Stryhal Jan , Kysely Jan
Published: July 2024

Heat waves are phenomena associated with large negative impacts on society and environment. We evaluate links between atmospheric circulation and three-dimensional (3D) heat wave types in Middle Europe (one of the PRUDENCE regions) over 1979–2022. Heat waves are classified according to their 3D structure of positive temperature anomalies in ERA5 into near-surface (HWG), lower-tropospheric (HWL), higher-tropospheric (HWH), and omnipresent (HWO) types. The Jenkinson–Collison classification is used to identify circulation types (CTs) with increased frequency during the individual heat wave types, and their climatological characteristics (including mean temperature anomaly, precipitation, and Climatic Water Balance index defined as potential evapotranspiration minus precipitation) are studied in station-based E-OBS data. We show that the surface temperature anomalies are largest during HWG (+7.1 °C), while HWL are the driest heat wave type according to mean daily precipitation (0.4 mm). At the same time, HWG types show the driest conditions before HW onset. In all heat wave types there is a large increase in the frequency of southern CTs compared to the June–September climatology, but differences among the heat wave types are found for other CTs. In HWG, the most frequent CT is U (undefined), corresponding to little pronounced pressure pattern over Middle Europe with no clear role of anticyclonic circulation or flow direction. The expected pattern of increased anticyclonic and decreased cyclonic flow is clearly manifested only for HWH and to a lesser degree for HWO and HWL types, while it is reversed for HWG. The analysis contributes to a better understanding of the interrelationships between heat waves, atmospheric circulation, and other driving mechanisms.
Observations and quantification of fog droplet settling made during the SOFOG3D campaign
Authors: Price Jeremy
Published: July 2024

This paper presents observations collected during the SOFOG3D campaign in South Western France during 2019-2020 to elucidate the processes involved in the deposition of fog droplets to the surface, a process that is often modelled in numerical weather prediction models, and forms an important component of the water budget within fog. The study suggests three main mechanisms cause deposition: turbulent, whereby water drops are transported to the surface in turbulent eddies; aerodynamic, whereby the mean horizontal wind deposits droplets directly onto surface canopy elements that protrude into the airflow, and gravitational, whereby droplets fall under the action of gravity directly onto the canopy. Results indicate that the gravitational settling is on average a minor component of the deposition, amounting to around 14% of the total figure. The study also identified that this proportion is relatively larger at 22%, when the turbulence intensity levels are low (vertical velocity variance < 0.003 m2s2). Consequently, for greater values of turbulence intensity the proportion is smaller at 11%. All three deposition processes are studied further using a multiple linear regression of terms against observed water deposition rates, in an attempt to evaluate various methods to estimate the deposition. Results indicate that a simple regression of liquid water content against observed deposition has some predictive skill, but that a multiple regression including all three terms produces better results with smaller regression errors. A simpler multiple regression, that merged turbulent and aerodynamic terms into a single ‘dynamic’ term produced results almost identical to the three-term multiple regression. Consequences for parametrization of liquid water deposition in fog are discussed.