Weather Types Research Articles

High among‐species variability in the context dependence of herbivory across disturbance, weather and topoedaphic gradients

Abstract Species interaction effects on populations can vary in both magnitude (i.e. strong vs. weak) and sign (positive, negative, or no effect). Context‐dependent effects of species interactions occur when the sign or strength of the interaction's effect on population growth rate changes across abiotic gradients. We know that species can vary substantially in the degree of context dependence they exhibit, even across similar abiotic gradients. However, few studies have characterised context dependence of co‐occurring species, limiting our ability to understand the implications of context dependence for species interaction effects on community composition. Using over three decades of data collected for 13 tallgrass prairie forbs at the Konza Prairie Biological Station, we parameterise density structured population models that predict population dynamics as functions of abiotic conditions and bison herbivory. We use these models to estimate the degree of context dependence in responses to bison herbivory for 13 species across three abiotic gradients: weather, fire frequency and soil type. All species showed significant context dependence for fire frequency in the same direction, though with variable magnitude, such that herbivory increased cover with more frequent fires. Context dependence with weather and soil type varied dramatically across species in both direction and magnitude. For example, herbivory effects on 3/13 species were stronger in wet conditions, but herbivory effects on 5/13 species were stronger in dry conditions. Thus, context dependence exhibited by individual species, as opposed to effects of abiotic conditions on the relative abundances of species, could generate much of the weather‐dependent effects of herbivory on community composition. Synthesis: Our work suggests that species can vary dramatically in the presence, direction and magnitude of context dependence, even when occurring in the same community and when considering the same species interaction (i.e. response to a herbivore). In addition, we find that context dependence could drive substantial variation in the effect of species interactions on community characteristics (e.g. composition) across multiple abiotic gradients.

Mediterranean dust outbreaks, dynamic meteorological patterns and weather types

Mediterranean dust outbreaks, dynamic meteorological patterns and weather types

The role of innovations in the development of modern sports games and its impact on physical activity and sports achievements

Scientific and technical progress is rapidly accelerating, therefore innovative technologies penetrate into all aspects of human life, in particular, into the sports sphere. They open new horizons, allow you to achieve positive results and offer high-tech equipment and equipment. The purpose of the article was to analyze the impact of innovations on physical activity in modern society and sports achievements. In the course of the study, it was found that innovations are innovative methods, technologies and approaches that are implemented to achieve new results, improve processes or solve problems in a certain field. Technological innovations such as virtual reality, innovative materials for sports equipment, artificial intelligence, digital technologies have affected all sports. The main examples of these technologies and their advantages of implementation in the sports industry were analyzed. So, for example, it is determined that the use of synthetic materials in poles for athletics increases their catapulting properties and sports achievements of athletes. Virtual reality technologies contribute to the effective training of cyclists, providing the opportunity to reproduce different weather conditions and types of routes, to test different strategies of speed and intensity of riding without any risks. Modern technologies, which allow to objectively evaluate the biomechanical parameters of the athlete's movements and to correct and improve them, contribute to a significant increase in the effectiveness of the athlete training system. The main groups of modern biomechanical technologies implemented in sports are analyzed. Significant technological updates in the field of sports are caused by the active implementation of digital technologies. Digital technologies help improve the national health care system by facilitating the collection, analysis and dissemination of data on health and physical activity. A key innovation in sports is also the use of fitness trackers and mobile applications that provide personalized training programs and nutritional recommendations.

Analysis of suitable regions for microalgae cultivation and harvesting potential for carbon capture: A global feasibility study

Microalgae, have the ability to capture CO2 and the potential for biofuel production. However, scaling up capturing carbon by microalgae is not feasible worldwide because of difference in weather conditions. Hence, identifying favorable regions for capturing carbon by microalgae is the initial step in commercializing this industry. In this research, a global feasibility study has been conducted to analyse the potential of microalgae cultivation and harvesting to capture CO2, based on carbon emissions, weather conditions, and the boundary of countries. A total of 454 cities were selected for this research, and the amount of microalgae productivity and carbon capture rate in each area was calculated according to the optimal environmental conditions for microalgae growth. Methodology and results have been illustrated using Geographical Information System (ArcGIS 10.8) to provide better visual insight for researchers. The results showed that the average microalgae productivity in Open Ponds (OP) and Tubular Photobioreactors (TPBR) is 132.63 T/ha/y and 78.26 kg/m3/y, respectively. Additionally, the average carbon capture rate by microalgae in OP and TPBR is 37.60 T/ha/y and 40.83 kg/m3/y, respectively. The study also revealed that microalgae will have a higher productivity in OP than in TPBR for 158 regions according to the first scenario, while the number of these areas will decrease to 58 and 29 under the second and third scenarios, respectively. Moreover, the global average potential energy content resulting from microalgae cultivation in OP is 4.854*106 MJ/ha/y. Therefore, selecting suitable regions regarding weather condition and type of cultivation system, is the most significant factors in outdoor commercial scaling up of carbon capture by microalgae.

Unravelling the atmospheric dynamics involved in flash drought development over Spain

AbstractFlash droughts (FDs) are distinguished by a rapid development associated with strong precipitation deficits and/or increases in atmospheric evaporative demand in the short‐term, but little is known about the atmospheric conditions underlying these events. In this study, we analyse for the first time the atmospheric dynamics involved in the development of FDs in Spain over the period 1961–2018. FDs are related to large‐scale atmospheric circulation patterns affecting the region, in particular with the positive phase of the North Atlantic Oscillation (NAO). The NAO is the main atmospheric driver of FDs in winter and autumn, and it is essential in explaining FD development in spring. We also found that FDs are typically linked to strong positive anomalies in 500 hPa geopotential height and sea level pressure over the region during the weeks prior to the onset. At the synoptic scale, the most common weather types (WTs) recorded during the development of FDs are Anticyclonic Western (ANT_W_AD), East (E_AD) and Northeast (NE_AD) advection, and Anticyclonic (ANTICYC). In particular, ANTICYC WT is the main atmospheric driver of FDs in summer. Ridging conditions occur frequently during FDs in all seasons, being the most important factor controlling FD development in spring. Likewise, we noted that some of the FDs recorded in summer are related to and/or exacerbated by Saharan air intrusions associated with pronounced ridges. The results of this research have important implications for the understanding, monitoring and prediction of FDs in Spain, providing a detailed assessment of the main atmospheric dynamics involved in FD triggering at different spatial scales.

MISAO: Ultra-Short-Term Photovoltaic Power Forecasting with Multi-Strategy Improved Snow Ablation Optimizer

The increase in installed PV capacity worldwide and the intermittent nature of solar resources highlight the importance of power prediction for grid integration of this technology. Therefore, there is an urgent need for an effective prediction model, but the choice of model hyperparameters greatly affects the prediction performance. In this paper, a multi-strategy improved snowmelt algorithm (MISAO) is proposed for optimizing intrinsic computing-expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and weighted least squares support vector machine for PV power forecasting. Firstly, a cyclic chaotic mapping initialization strategy is used to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain quickly. Secondly, the Gaussian diffusion strategy enhances the local exploration ability of the intelligences and extends their search in the solution space, effectively preventing them from falling into local optima. Finally, a stochastic follower search strategy is employed to reserve better candidate solutions for the next iteration, thus achieving a robust exploration–exploitation balance. With these strategies, the optimization performance of MISAO is comprehensively improved. In order to comprehensively evaluate the optimization performance of MISAO, a series of numerical optimization experiments were conducted using IEEE CEC2017 and test sets, and the effectiveness of each improvement strategy was verified. In terms of solution accuracy, convergence speed, robustness, and scalability, MISAO was compared with the basic SAO, various state-of-the-art optimizers, and some recently developed improved algorithms. The results showed that the overall optimization performance of MISAO is excellent, with Friedman average rankings of 1.80 and 1.82 in the two comparison experiments. In most of the test cases, MISAO delivered more accurate and reliable solutions than its competitors. In addition, the altered algorithm was applied to the selection of hyperparameters for the ICEEMDAN-WLSSVM PV prediction model, and seven neural network models, including WLSSVM, ICEEMDAN-WLSSVM, and MISAO-ICEEMDAN-WLSSVM, were used to predict the PV power under three different weather types. The results showed that the models have high prediction accuracy and stability. The MAPE, MAE and RMSE of the proposed model were reduced by at least 25.3%, 17.8% and 13.3%, respectively. This method is useful for predicting the output power, which is conducive to the economic dispatch of the grid and the stable operation of the power system.

Extension and trend of the London urban heat island under Lamb weather types

Understanding and describing how urban heat islands evolve is important, given the noticeable impact they have on people living in cities. This paper considers the London heat island from gridded values with one-arcminute spatial resolution over a 33-year period, from 1990 to 2022. Among the available variables in the database, maximum and minimum air temperatures were used. A cold island was not observed, since temperatures in the city centre were higher than those in the surroundings during the day and at night. However, the urban heat island extension was higher for the maximum temperature, whereas this island was limited to the city centre for the minimum temperature, in line with the area delimited by the congestion charge. Lamb weather types were determined, and it was found that the anticyclonic type prevailed, followed by southwest, west, and cyclonic types. The difference between both temperatures was about 6.8 °C in the city centre, and was particularly defined for anticyclonic and cyclonic types. Moreover, anticyclonic situations were linked with the highest urban heat island intensities for minimum temperature. Finally, the temperature trend was similar for both temperatures –about 0.2–0.3 °C/10 years in the city centre– thereby offering a possible quantification of climate change.

Use of weather types to analyze the simultaneous abundance of ozone, PM2.5 and allergenic tree pollen: focusing on the potential impact on asthma hospitalization in Montreal, Canada

AbstractAir pollution, aeroallergens, and weather conditions can worsen health symptoms such as asthma. While studying the impact of these factors, the use of weather types (WTs) rather than individual meteorological variables (such as temperature, relative humidity, wind, cloudiness, or precipitation) is more appropriate since it is holistic and integrative. Moreover, several studies have shown that the human body responds to WTs, rather than to individual meteorological variables. In this study, the use of Sheridan’s WTs is adopted and compared with a so-called “In-House” WTs. The analysis presented here deals with the links between asthma hospitalization and the synergy among air pollution, birch tree pollen and WTs. Knowing the daily WT in a region can provide valuable information for health planning and management of asthma hospitalization, emergency visits and sub-clinical symptoms in the population. This is because air pollution and birch pollen both occur within only a few specific WTs, such as the TROWAL (trough of warm air aloft) or tropical airmasses. These specific WTs need to be more scrutinized since, in Montreal, these are often linked with higher daily mean hospitalization. The findings of this study emphasize the importance of specific WTs in determining the maximum daily concentrations of ozone, fine particles, Betula pollen concentrations and health effects such as asthma hospitalization. Moreover, the use of data filters in the analysis (for temperature and total count of hospitalization) also reveals new insights in the complex nature of asthma disease and its relationship with environmental factors.

Atmospheric Conditions Related to Extreme Heat and Human Comfort in the City of Rio de Janeiro (Brazil) during the First Quarter of the Year 2024

This study aims to investigate the atmospheric conditions and human thermal comfort related to extreme heat in Rio de Janeiro during the first quarter of 2024. The dataset includes meteorological data from the A636-Jacarepaguá station of INMET and seven stations from the Alerta Rio system. Weather types were classified using principal components analysis (PCA) and cluster analysis (CA). Additionally, three thermal comfort indices were calculated: the heat index (HI), physiologically equivalent temperature (PET), and modified PET (mPET). Five groups of surface weather types were identified, with two being more frequent and associated with extreme heat events. These two groups accounted for over 70% of the days in all months. Critical thermal sensation values were found, particularly at the Guaratiba station, where the daytime HI exceeded 60 °C, and at the Riocentro station, where the nighttime HI surpassed 40 °C. The HI showed a greater range and variability compared with the PET and mPET, highlighting the importance of investigating microclimatic factors which intensify urban heat in central and coastal areas and cause daytime overheating in more distant regions like Guaratiba. This study emphasizes the need for detailed investigation into microclimatic factors and their public health implications, especially in areas with high tourist activity and vulnerable populations.

Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data

Due to its strong dependence on weather conditions, photovoltaic (PV) power is highly intermittent in nature. In light of this, this paper introduces a PV power prediction model based on similar-day clustering and temporal convolutional network (TCN) with bidirectional long short-term memory (BiLSTM) model. Firstly, in the data preprocessing stage, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is used to decompose and denoise the original PV power data to obtain subsequences of different frequencies. Subsequently, the sample entropy (SE) method is incorporated to reconstruct the subsequences and generate low-frequency and high-frequency subsequences with more pronounced temporal features. In addition, a self-organizing map (SOM) with the K-means algorithm is used to categorize historical days into four weather types: sunny, cloudy, rainy, and intermittent. Through grey relational analysis (GRA), meteorological factors significantly affecting PV power prediction are identified to construct a multidimensional feature set. During the model prediction phase, historical PV power data and related meteorological impact factors are input into the TCN-BiLSTM model to perform multi-data-driven PV power prediction. Finally, the predicted results of each subsequence are linearly combined to obtain the ultimate PV power prediction. The proposed model is compared with convolutional neural network (CNN)-LSTM, LSTM, and TCN models, achieving a reduction in the root mean square error (RMSE) of 0.129 kW, 0.238 kW, and 0.257 kW, respectively. This demonstrates that the proposed model exhibits superior overall performance in time series modeling and information capture, enhancing the understanding of seasonal, periodic, and irregular patterns in PV power generation data.

Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE

Precise prediction of PV power in the short term is crucial for maintaining power system stability and balance. However, the performance of conventional time series prediction models on short-term long series prediction is scarcely sufficient because of the stochastic and turbulent character of PV power data. This work suggests a PV short-term power forecast model based on weather type, AHA-VMD-MPE decomposition reconstruction, and improved Informer combination to tackle this issue. Firstly, a SUM-ApEn-K-mean++ multidimensional clustering method to group the dataset by weather conditions. Then an AHA-VMD-MPE decomposition model is proposed to decompose the historical power data Finally the Informer model is improved and the improved model is utilized to predict the PV power under various weather conditions. The model exhibits great accuracy and stability in short-term PV power prediction, as demonstrated by the experimental results, which were validated using measured data from many PV power plants.

An Optimal Weighted Ensemble Machine Learning Approach to Accurate Estimate the Coastal Boundary Layer Height Using ERA5 Multi‐Variables

AbstractThe coastal boundary layer height (CBLH/Coastal‐BLH) is critical in determining the exchange of heat, momentum, and materials between the land and ocean, thereby regulating the local climate and weather change. However, due to the complexity of geographical characteristics and meteorological conditions, accurate estimation of the CBLH remains challenging. Herein, based on continuous high‐resolution measurements of CBL performed from November 2019 to April 2020 in coastal Ningbo city in eastern China, an optimal weighted ensemble model (OWEM) integrating multi‐meteorological variables of the ERA5 reanalysis data sets is constructed and validated to estimate the CBLH. The mean absolute percentage error of the derived CBLH by OWEM is as low as 3%–5%, significantly lower than that of 36%–65% of the ERA5 CBLH products. Furthermore, three categories of different weather scenarios, that is, sunny, cloudy, and rainy, are separately discussed, and OWEM shows greater performance and higher accuracies in comparison with the traditional Least Absolute Shrinkage and Selection Operator, Random Forest, Adaboost, LightGBM, and ensemble model, among which, OWEM under fair weather days behave best, with a robust R2 of 0.97 and a minimum mean absolute error (MAE) of 23 m. Further training results based on wind flow classification, that is, land breeze, sea breeze, and parallel wind, also indicate the outperformance of OWEM than other models, with a relatively large error in parallel wind of 50 m. Subsequent analysis of the Shapley Additive Explanations method strongly correlated with model feature importance, both reveal that thermodynamic factors such as temperature (T2m) and wind velocity (10 m U) are the major factors positively related to estimation accuracy during sunny days. Nevertheless, Relative Humidity dominates on rainy and cloudy days, TP on land breeze days, and dynamic variables like 10 m U and 10 m V on entire types of wind flow weather. In conclusion, the accurate estimation of CBLH from OWEM serves as a feasible and innovative approach, providing technical support for marine meteorology and related engineering applications, for example, onshore wind power, coastal ecological protection, etc.

Assessment of Crop water requirement using FAO CROPWAT 8.0 for Groundnut in South Odisha

Water has a major impact on crop growth, output, and quality and increased production with less water consumption is crucial due to increased demand from domestic and industrial sectors. With evaporative demand-based irrigation scheduling the water consumption gets reduced and improves water productivity. The type of soil, growing season, weather, and crop type affect specific crops' water needs. To compute crop water requirement, irrigation requirement, and its schedule for groundnut cultivated during the rabi season in south Odisha, India, the FAO CROPWAT 8 program was used, with the meteorological data of Centurion University, Paralakhemundi, and NASA power. The water requirement for groundnut was 362.1 mm, the irrigation requirement for groundnut crops was 323.7 mm, and crop evapotranspiration (ETc) was 373.4 mm during rabi season. At 50% critical depletion of soil moisture and soil replenishment to field capacity, the yield did not decrease. These results indicate that irrigation schedules can be practiced for different regions' crops without experimental results by estimating crop water requirements and irrigation schedules obtained through the FAO CROPWAT 8 model.

Identifying optimal number of driving cycles to represent diverse driving conditions

Driving cycle is one of the main inputs of vehicle emission modeling. However, the variability of driving cycles due to fluctuations in weather conditions is one of the primary sources of uncertainty in vehicle emission estimation. This study aims to identify and determine an optimal number of driving cycles that can correctly represent driving patterns in diverse weather conditions. First, a multivariate multiple regression model is developed to determine the most important weather factors affecting the driving patterns. Then, similar weather conditions are identified according to these factors using unsupervised machine learning. Next, two driving cycles are constructed for diverse weather types, one for weekdays and one for weekends. Afterward, descriptive analysis and a similarity matrix are employed to determine how similar the generated driving cycles are in different weather types. Finally, 15 driving cycles are identified to represent driving patterns in diverse driving conditions.

Influence of gamma-ray irradiation on polystyrene food container weathering

Conventional plastics, such as polystyrene (PS), are widely used due to their high degradation stability, which makes them suitable for various applications. However, the downside of this property is that it results in the persistence of these plastics in the environment, contributing to plastic pollution. This paper investigated the effects of gamma-ray irradiation on the mechanical, optical, structural, thermal, and morphological properties of PS food containers subjected to natural (NW) and artificial weathering (AW). First, the PS films were exposed to a source of 60Co-γ at doses of 0, 10, and 45 kGy, and then they were subjected to NW for 0–190 days and AW for 0–500 h. The mechanical properties of the PS films (tensile strength and elongation at break) were evaluated under NW and AW conditions, revealing an acceleration factor (AF) ranging from 3.8 to 6.2. According to the results, gamma-irradiation had a complex and uneven effect on the physicochemical properties of the weathered PS films. The type of weathering and conditions under which the exposures were performed, in conjunction with the irradiation doses, provided a comprehensive analysis of the effects of gamma-ray irradiation on PS food containers. These results are relevant for extending PS food containers' longevity rather than increasing their degradation.

The signature of the main modes of climatic variability as revealed by the Jenkinson‐Collison classification over Europe

AbstractThe Jenkinson‐Collison Weather Typing (JC‐WT) method uses sea‐level pressure gradients to create 27 types based on the geostrophic flow and vorticity around any extratropical target location. Typically, JC‐WTs are applied over specific locations or limited domains, thus hampering the understanding of the impact of large‐scale mechanisms on regional climate. This study explores the links between regional climate variability, as represented by the JC‐WTs, and large‐scale phenomena, to describe the synoptic‐scale variability in the North Atlantic‐European region and evaluate the JC‐WT methodology. Large‐scale circulation is here characterized by major atmospheric low‐frequency modes, namely the North Atlantic Oscillation, the East Atlantic and the Scandinavian teleconnection indices, and by atmospheric blockings. Results show that JC‐WTs coherently capture the spatial and temporal variability of the large‐scale modes and yields a characteristic response to blocking events. Overall, our results underpin the exploratory potential of this method for the analysis of the near‐surface circulation. These findings endorse the use of JC‐WTs and support the reliability and utility of the JC‐WT classification for process‐based model assessments and model selection, a crucial task for climate impact studies.

Extreme Weather and Firm Market Value: Roles of Corporate Social Responsibility and Stock Analyst Attention

ABSTRACT The outbreak of extreme weather events is increasing in frequency for the last decade, resulting in tremendous human and industrial losses worldwide. Evaluating and managing the effects of such events are highly urgent matters for firms, but the literature on this topic is limited. In this study, the effects of extreme weather events on the stock market performance of firms were estimated to provide a foundation for exploring how to manage such effects. From the perspective of the shareholder view, the heterogeneous effect of corporate social responsibility (CSR) on stock performance associated with extreme weather events were examined. Data on natural disasters that were caused by six types of extreme weather and occurred between 2001 and 2021 in China were used to construct a database of 2,718 Chinese listed manufacturing firms and 23,529 observations regarding the potential effects by extreme weather events. An event analysis indicated that the extreme weather events bring an average of 0.97% drop in the stock performance of potentially affected firms for the month that the event starts and the next month. A subsequent cross-sectional analysis revealed that a high CSR level is associated with low market value during extreme weather events, while a high level of stock analyst attention alleviates the negative effect of CSR on the stock performance of extreme weather events. This study enriches the existing literature and offers practical insights for managers in the fields of CSR and operations management, highlighting the complex dynamics between CSR activities, analyst attention, and stock market performance during extreme weather events.

Comprehensive evaluation methods for photovoltaic output anomalies based on weather classification

This article analyzes the relationship between abnormal photovoltaic output events and different weather types based on the output data of distributed photovoltaic stations and meteorological reanalysis data of corresponding time periods using methods such as K-means clustering. The analysis finds that abnormal high output events in photovoltaics are related to high-temperature clear weather brought by stable low-pressure systems, and clear windy cooling weather processes controlled by high pressure. The abnormal low output events of photovoltaics are related to transitional weather processes such as cold waves, cloudy and precipitation free weather, and cloudy and rainy weather processes in low-pressure systems. Building upon this analysis, the study constructs a simple extreme output prediction model and examines the atmospheric circulation anomalies corresponding to extreme output events. Using a January 2023 photovoltaic low-output event as an example, the study validates both the extreme output model based on weather type classification and the subjective weather forecast method based on atmospheric circulation patterns, both of which show potential for improving photovoltaic extreme output predictions. The combination of the two can be used to comprehensively evaluate the photovoltaic extreme output events.

Climate Communication with Chinese Youth by Chinese Nongovernmental Organizations: A Case Study of Chinese Weather Enthusiasts on Bilibili

Abstract Nongovernmental organizations (NGOs) have increasingly played pivotal roles in shaping climate agendas and mobilizing individuals to engage in environmental initiatives. However, the nature of NGOs’ online interaction with users, especially in developing countries, remains largely unexplored. This study focused on the dynamics of engagement between a Chinese NGO, Chinese Weather Enthusiasts (CWE), and Chinese youth on the social media platform of Bilibili. The research comprised two main components. First, named entity recognition was employed to analyze weather-related terms in CWE’s posts on Bilibili and dynamic topic modeling was utilized to uncover shifts in thematic focus. Subsequently, descriptive analysis and negative binomial regression were employed to investigate the correlation between weather types and user engagement metrics. The study unveiled two noteworthy findings: first, CWE posts are closely linked to short-term weather, providing timely content that may meet the public’s demand for climate information. Second, the engagement of Chinese youth users is not affected by extreme weather types. Future research should continue to elucidate strategies that NGOs can employ to enhance online engagement among youth users. Significance Statement This study seeks to contribute to the current literature of climate communication by investigating how NGOs engage with Chinese youth on social media, an area that has received scant attention thus far. Focusing on an influential Chinese climate NGO, CWE, and its interactions with Chinese youth on the social media platform of Bilibili, this research sheds lights on strategies to communicate information related to extreme weather to this demographic. Examining factors that influence online user engagement offers both theoretical insights about the mechanisms of climate communication and practical implications for NGOs and policymakers to mobilize youth for environmental initiatives. The findings also underscore the importance of tailoring climate communication to align with the daily experiences of the target audience and public-centric approaches in climate communication strategies.

600 years of wine must quality and April to August temperatures in western Europe 1420–2019

Abstract. This study investigates the validity of wine must quality as an April-to-August temperature proxy between 1420 and 2019 based on expert ratings and quality measurements from Germany, Luxembourg, eastern France, and the Swiss Plateau. This is highly relevant as uncertainties remain on past climate variations during this period. The evidence was reviewed according to the best practice of historical climatology. Expert ratings tended to agree with Oechsle density measurements that gradually replaced them from the 1840s. A statistical model calibrated to predict wine must quality from climate data explains 75 % of the variance, underlining the potential value of wine must quality as a climate proxy. Premium crops were collected in years of early harvest involving high insolation during maturation, while poor crops resulted from very late harvests in cold and wet summers. An analysis of daily weather types for high- and low-quality years after 1763 shows marked differences. On a decadal timescale, the average quality was highest from 1470 to 1479, from 1536 to 1545, and from 1945 to 1954. Poor crops were collected in periods with prevailing cold and wet summers such as 1453 to 1466, 1485 to 1494, 1585 to 1614, 1685 to 1703, 1812 to 1821, and 1876 to 1936. In the period of enhanced warming after 1990, high quality became the rule.