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The “Year Without a Summer” in 1816 was characterized by extraordinarily cold and wet periods in central Europe, and it was associated with severe crop failures, famine, and socio-economic disruptions. From a modern perspective, and beyond its tragic consequences, the summer of 1816 represents a rare opportunity to analyze the adverse weather (and its impacts) after a major volcanic eruption. However, given the distant past, obtaining the high-resolution data needed for such studies is a challenge. In our approach, we use dynamical downscaling, in combination with 3D variational data assimilation of early instrumental observations, for assessing a cold-air outbreak in early June 1816. We find that the cold spell is well represented in the coarse-resolution 20th Century Reanalysis product which is used for initializing the regional Weather Research and Forecasting Model. Our downscaling simulations (including a 19th century land use scheme) reproduce and explain meteorological processes well at regional to local scales, such as a foehn wind situation over the Alps with much lower temperatures on its northern side. Simulated weather variables, such as cloud cover or rainy days, are simulated in good agreement with (eye) observations and (independent) measurements, with small differences between the simulations with and without data assimilation. However, validations with partly independent station data show that simulations with assimilated pressure and temperature measurements are closer to the observations, e.g., regarding temperatures during the coldest night, for which snowfall as low as the Swiss Plateau was reported, followed by a rapid pressure increase thereafter. General improvements from data assimilation are also evident in simple quantitative analyses of temperature and pressure. In turn, data assimilation requires careful selection, preprocessing, and bias-adjustment of the underlying observations. Our findings underline the great value of digitizing efforts of early instrumental data and provide novel opportunities to learn from extreme weather and climate events as far back as 200 years or more.

Abstract To prepare for a hotter future, information on intra‐urban temperature distributions is crucial for cities worldwide. In recent years, different methods to compute high‐resolution temperature datasets have been developed. Such datasets commonly originate from downscaling techniques, which are applied to enhance the spatial resolution of existing data. In this study, we present an approach based on a fine‐scaled low‐cost urban temperature measurement network and a formerly developed land use regression approach. The dataset covers mean nocturnal temperatures of 16 summers (2007–2022) of a medium‐sized urban area with adapted land cover data for each year. It has a high spatial (50 m) and temporal (daily) resolution and performs well in validation (RMSEs of 0.70 and 0.69 K and mean biases of +0.41 and −0.19 K for two validation years). The dataset can be used to examine very detailed statistics in space and time, such as first heatwave per year, cumulative heat risks or inter‐annual variability. Here, we evaluate the dataset with two application cases regarding urban planning and heat risk assessment, which are of high interest for both researchers and practitioners. Due to potential biases of the low‐cost measurement devices during daytime, the dataset is currently limited to night‐time temperatures. With minor adaptions, the presented approach is transferable to cities worldwide in order to set a basis for researchers, city administrations and private stakeholders to address their heat mitigation and adaptation strategies.

The winter of 1739/40 is known as one of the coldest winters in Europe since early instrumental measurements began. Many contemporary sources discuss the cold waves and compare the winter to that of 1708/09. It is less well known that the year 1740 remained cold until August and was again cold in October and that negative temperature anomalies were also found over Eurasia and North America. The 1739/40 cold season over northern mid-latitude land areas was perhaps the coldest in 300 years, and 1740 was the coldest year in central Europe in 600 years. New monthly global climate reconstructions allow us to address this momentous event in greater detail, while daily observations and weather reconstructions give insight into the synoptic situations. Over Europe, we find that the event was initiated by a strong Scandinavian blocking in early January, allowing the advection of continental cold air. From February until June, high pressure dominated over Ireland, arguably associated with frequent eastern Atlantic blocking. This led to cold-air advection from the cold northern North Atlantic. During the summer, cyclonic weather dominated over central Europe, associated with cold and wet air from the Atlantic. The possible role of oceanic influences (El Niño) and external forcings (eruption of Mount Tarumae in 1739) are discussed. While a possible El Niño event might have contributed to the winter cold spells, the eastern Atlantic blocking is arguably unrelated to either El Niño or the volcanic eruption. All in all, the cold year of 1740 marks one of the strongest, arguably unforced excursions in European temperature.

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.

Global surface air temperature increased by ca. 0.5 °C from the 1900s to the mid-1940s, also known as Early 20th Century Warming (ETCW). However, the ETCW started from a particularly cold phase, peaking in 1908–1911. The cold phase was global but more pronounced in the Southern Hemisphere than in the Northern Hemisphere and most pronounced in the Southern Ocean, raising the question of whether uncertainties in the data might play a role. Here we analyse this period based on reanalysis data and reconstructions, complemented with newly digitised ship data from 1903–1916, as well as land observations. The cooling is seen consistently in different data sets, though with some differences. Results suggest that the cooling was related to a La-Niña-like pattern in the Pacific, a cold tropical and subtropical South Atlantic, a cold extratropical South Pacific, and cool southern midlatitude land areas. The Southern Annular Mode was positive, with a strengthened Amundsen–Bellingshausen seas low, although the spread of the data products is considerable. All results point to a real climatic phenomenon as the cause of this anomaly and not a data artefact. Atmospheric model simulations are able to reproduce temperature and pressure patterns, consistent with a real and perhaps ocean-forced signal. Together with two volcanic eruptions just before and after the 1908–1911 period, the early 1900s provided a cold start into the ETCW.

In archaeological soil and sediment micromorphology, research is grounded in observations made with petrographic microscopes. These observations are recorded using standardised terms and microphotographs. The two-pillar database system allows a user-friendly recording of these observations with I-GEOARCHrec and the possibility to link these data to field pictures and microphotographs publicly available in I-GEOARCHive.

Historical sources report manifold on hazardous past climate and weather events that had considerable impacts on society. Studying changes in the occurrence or mechanisms behind such events is, however, hampered by a lack of spatially and temporally complete weather data. In particular, the spring season has received less attention in comparison to summer and winter but is nevertheless relevant, since weather conditions in spring can delay vegetation and create substantial damage due to late-frost events. For Switzerland, we created a daily high-resolution (1 × 1 km2) reconstruction of temperature and precipitation fields from 1763 to 1960 that forms, together with present-day meteorological fields, a 258-year-long gridded data set. With this data set, we study changes in long-term climate and historical weather events based on climate and phenological indices focusing on the spring season. Climate and phenological indices show few changes in the mean during the first 200 years compared to the most recent period from 1991 to 2020, where climate change signals clearly emerged in many indices. We evaluate the climate and phenological indices for three cases of extreme spring weather conditions: an unusually warm spring, two late-frost events, and three cold springs. Warm springs are much more frequent in the 21st century, but a very warm and early spring also occurred in 1862. Spring temperatures, however, do not agree on how anomalously warm the spring was when comparing the Swiss temperature reconstruction with reanalyses that extend back to 1868. The three springs of 1785, 1837, and 1853 were particularly cold, with historical sources reporting, for example, prolonged lake freezing and abundant snowfall. Whereas the springs of 1837 and 1853 were characterized by cold and wet conditions, in the spring of 1785 wet days were below average, and frost days reached an all-time maximum, in particular in the Swiss Plateau, indicating inversion conditions. Such conditions are in line with a high occurrence of northeasterly and high-pressure weather types and historical sources describing Bise conditions, a regional wind in the Alpine area related to inversions. Studying such historical events is valuable, since similar atmospheric conditions can lead to cold springs affecting vegetation growth and agricultural production.

Abstract The Modern Era Reanalysis (ModE-RA) is a global monthly paleo-reanalysis covering the period between 1421 and 2008. To reconstruct past climate fields an offline data assimilation approach is used, blending together information from an ensemble of transient atmospheric model simulations and observations. In the early period, ModE-RA utilizes natural proxies and documentary data, while from the 17th century onward instrumental measurements are also assimilated. The impact of each observation on the reconstruction is stored in the observation feedback archive, which provides additional information on the input data such as preprocessing steps and the regression-based forward models. The monthly resolved reconstructions include estimates of the most important climate fields. Furthermore, we provide a reconstruction, ModE-RAclim, which together with ModE-RA and the model simulations allows to disentangle the role of observations and model forcings. ModE-RA is best suited to study intra-annual to multi-decadal climate variability and to analyze the causes and mechanisms of past extreme climate events.

Basel and Geneva have two of the longest meteorological records in Switzerland, covering more than two and a half centuries. The respective monthly temperature series were published over 60 years ago and are part of todays main global temperature data sets. After digitizing the raw sub-daily measurements, we rebuilt the early instrumental part (i.e., before 1864) of the two series at daily resolution using modern methods and additional data sources that were not considered in previous efforts. A comparison with the old series and with other existing recontructions show a generally good agreement only for the last 30 years. Before the 1830s a few systematic differences appear, particularly in summer, suggesting that both new and old versions contain residual inhomogeneities. We use the new series together with other reconstructions to analyze the periods 1791–1807 and 1808–1824, which have been described, respectively, as a warm and cold period in summer in previous studies. Our results suggest that most existing instrumental data sets tend to overestimate summer temperature in Switzerland during the former period, confirming previous results based on proxy records. The overestimation is particularly large (almost 1 °C) in the old Geneva series. On the other hand, we find a probable systematic underestimation of summer temperature in our Basel series. Before the 1780s the agreement between existing reconstructions is poor, so that it is hardly possible to make confident statements about climate variability for the first few decades covered by the series. Nevertheless, the daily resolution of the data allows an insight into individual meteorological events such as cold spells and heat waves.

Background: This study investigated and compared the efficacy, safety, radiation exposure, and financial compensation of two modalities for percutaneous radiologic gastrostomy (PRG): multislice computed tomography biopsy mode (MS-CT BM)-guided and fluoroscopy-guided (FPRG). The aim was to provide insights into optimizing radiologically assisted gastrostomy procedures. Methods: We conducted a retrospective analysis of PRG procedures performed at a single center from January 2018 to January 2024. The procedures were divided into two groups based on the imaging modality used. We compared patient demographics, intervention parameters, complication rates, and procedural times. Financial compensation was evaluated based on the tariff structure for outpatient medical services in Switzerland (TARMED). Statistical differences were determined using Fisher’s exact test and the Mann–Whitney U test. Results: The study cohort included 133 patients: 55 with MS-CT BM-PRG and 78 with FPRG. The cohort comprised 35 women and 98 men, with a mean age of 64.59 years (±11.91). Significant differences were observed between the modalities in effective dose (MS-CT BM-PRG: 10.95 mSv ± 11.43 vs. FPRG: 0.169 mSv ± 0.21, p < 0.001) and procedural times (MS-CT BM-PRG: 41.15 min ± 16.14 vs. FPRG: 28.71 min ± 16.03, p < 0.001). Major complications were significantly more frequent with FPRG (10% vs. 0% in MS-CT BM-PRG, p = 0.039, φ = 0.214). A higher single-digit number of MS-CT BM-guided PRG was required initially to reduce procedure duration by 10 min. Financial comparison revealed that only 4% of MS-CT BM-guided PRGs achieved reimbursement equivalent to the most frequent comparable examination, according to TARMED. Conclusions: Based on our experience from a retrospective, single-center study, the execution of a PRG using MS-CT BM, as opposed to FPRG, is currently justified in challenging cases despite a lower incidence of major complications. However, further well-designed prospective multicenter studies are needed to determine the efficacy, safety, and cost-effectiveness of these two modalities.

We introduce ModE-Sim (Modern Era SIMulations), a medium-sized ensemble of simulations with the atmospheric general circulation model ECHAM6 in its LR (low-resolution) version (T63; approx. 1.8∘ horizontal grid width with 47 vertical levels). At the lower boundary we use prescribed sea surface temperatures and sea ice that reflect observed values while accounting for uncertainties in these. Furthermore we use radiative forcings that also reflect observed values while accounting for uncertainties in the timing and strength of volcanic eruptions. The simulations cover the period from 1420 to 2009. With 60 ensemble members between 1420 and 1850 and 36 ensemble members from 1850 to 2009, ModE-Sim consists of 31 620 simulated years in total. ModE-Sim is suitable for many applications as its various subsets can be used as initial-condition and boundary-condition ensembles to study climate variability. The main intention of this paper is to give a comprehensive description of the experimental setup of ModE-Sim and to provide an evaluation, mainly focusing on the two key variables, 2 m temperature and precipitation. We demonstrate ModE-Sim's ability to represent their mean state, to produce a reasonable response to external forcings, and to sample internal variability. Through the example of heat waves, we show that the ensemble is even capable of capturing certain types of extreme events.

Astronomer and calendar maker Gottfried Kirch was a keen weather observer and made weather notes in his diary starting in 1677 in Leipzig. In parallel, his second wife Maria Margaretha Winkelmann started a weather diary in 1700 in Berlin. The diaries also contain instrumental measurements of temperature and later pressure. After the death of Gottfried in 1710 and Maria Margaretha in 1720, observations were continued by their son Christfried and then for another 44 years by their daughter Christine. The last measurements date to 1774. Together, the diaries span almost a century of weather observations. The instrumental measurements constitute the oldest part of Germany's longest temperature series, which was however only available as monthly means up to now. Here we publish the imaged diaries, together amounting to 10 445 images. Further, we present the digitized instrumental series, which will serve as the starting point for a new, daily Berlin series. By comparing the series to neighbouring records, we show that the pressure data are reliable in a quantitative sense, whereas this is true for the temperature data only in a qualitative sense as the temperature scale was not converted.

Data from weather diaries have long been used to reconstruct past climate. However, they could also be used to reconstruct past weather. Weather reconstructions could help us to better understand the mechanisms behind, and impacts of, climatic changes. However, reconstructing the day-to-day weather requires many diaries from different regions covering the same period, ideally combined with instrumental measurements. In this paper, I describe the weather diary of Georg Christoph Eimmart from Nuremberg, covering the period 1695 to 1704, which was particularly cold in Europe. The diary was imaged from the Russian National Library in St. Petersburg and then digitized. It contains twice-daily weather conditions in symbolic form, wind direction details, and information on precipitation and temperature in text form. Symbols changed during the first 2 years, after which a much-reduced (and stable) set of symbols was used. Re-coding all days according to the later set of symbols, I find no signs of inconsistency over time in symbols, wind direction, and precipitation information extracted from the text. Comparisons with other sources confirm the day-to-day weather information in the diary. For instance, the wind direction in Nuremberg agrees with the daily pressure gradient between Jena and Paris. Three case studies further confirm the meteorological reliability of the information. This is shown on behalf of an 8 d sequence of stormy weather in 1702, a study of the severe winter of 1697/98, and a study of the summer of 1695, which was cold and wet, possibly related to tropical volcanic eruptions. The examples underline the consistency of the weather diary with other information and suggest that weather reconstructions as far back as the late 17th century might become possible. However, the spatial information is limited, and any approach arguably needs to make good use of the temporal sequence of information.

Abstract Documentary climate data describe evidence of past climate arising from predominantly written historical documents such as diaries, chronicles, newspapers, or logbooks. Over the past decades, historians and climatologists have generated numerous document-based time series of local and regional climates. However, a global dataset of documentary climate time series has never been compiled, and documentary data are rarely used in large-scale climate reconstructions. Here, we present the first global multi-variable collection of documentary climate records. The dataset DOCU-CLIM comprises 621 time series (both published and hitherto unpublished) providing information on historical variations in temperature, precipitation, and wind regime. The series are evaluated by formulating proxy forward models (i.e., predicting the documentary observations from climate fields) in an overlapping period. Results show strong correlations, particularly for the temperature-sensitive series. Correlations are somewhat lower for precipitation-sensitive series. Overall, we ascribe considerable potential to documentary records as climate data, especially in regions and seasons not well represented by early instrumental data and palaeoclimate proxies.

Abstract We show that ModE‐Sim, a global ensemble of atmospheric model simulations that uses observed ocean boundary conditions and radiative forcings providing 36 members with daily climate information can be used to in‐depth analyze the known spatial and temporal variability of heatwaves in the Northern Hemisphere and Australia during the past 160 years. It can also be used to study actual past extreme events like heatwaves during the El Nino 1877/1878. To analyze past heatwaves we use a novel approach of a transient baseline climatology and compare to different observational data sets. Furthermore, we analyze sea surface temperature anomalies during the most extreme heatwave summers in North America, Europe and Australia and identify the most prominent anomaly patterns over the Subpolar North Atlantic and in the Central Pacific. Using a large ensemble of forced simulations, like ModE‐Sim can consequently contribute to a better understanding of preindustrial heatwaves, their decadal variability and their driving mechanisms.

Urban areas are particularly affected by heatwaves through the intensification of heat stress by the urban heat island effect. For effective climate change adaptation, information about microscale surface cover, structures, and human activity in cities is needed to depict the underlying causes of urban heat stress. The framework of “Local Climate Zones” (LCZs) classifies and standardizes urban areas based on such characteristics. To date, most LCZ mapping workflows use satellite imagery as input. The resulting maps may lack some important details, and thus benefit from the use of additional geodata. We introduce a novel approach that combines the geodata of urban canopy parameters with the remote sensing-based LCZ map of Bern, Switzerland. City-specific urban canopy parameters are calculated and used to adjust established value ranges, if necessary. The most common misclassification patterns are identified and misclassified pixels are corrected using a decision tree and k-nearest-neighbor algorithm. Results show that the conformity with the urban canopy parameter values markedly increased, especially in the distinction of water surfaces, non-built areas, and building height. However, for high-resolution LCZ maps, this also leads to unnecessary heterogeneity, which may require further postprocessing. Given sufficiently available urban canopy parameter data, the proposed workflow is simple and easily adaptable for other cities. It could prove useful in urban climate studies and city planning to enhance an existing LCZ map in a contextualized manner quickly.

Climate reconstructions give insights in monthly and seasonal climate variability in the past few hundred years. However, for understanding past extreme weather events and for relating them to impacts, for example through crop yield simulations or hydrological modelling, reconstructions on a weather timescale are needed. Here, we present a data set of 258 years of daily temperature and precipitation fields for Switzerland from 1763 to 2020. The data set was reconstructed with the analogue resampling method, which resamples meteorological fields for a historical period based on the most similar day in a reference period. These fields are subsequently improved with data assimilation for temperature and bias correction for precipitation. Even for an early period prior to 1800 with scarce data availability, we found good validation results for the temperature reconstruction especially in the Swiss Plateau. For the precipitation reconstruction, skills are considerably lower, which can be related to the few precipitation measurements available and the heterogeneous nature of precipitation. By means of a case study of the wet and cold years from 1769 to 1772, which triggered widespread famine across Europe, we show that this data set allows more detailed analyses than hitherto possible.

Abstract Island nations may have potential long-term survival value for humanity in global catastrophes such as sun-blocking catastrophes from nuclear winter and large magnitude volcanic eruptions. One way to explore this issue further is to understand the impact on islands after the largest historically observed volcanic eruption: that of Mt Tambora in 1815. For each of the 31 large, populated islands selected, we conducted literature searches for relevant historical and palaeoclimate studies. We also analysed results from a reconstruction (EKF400v2), which uses atmospheric-only general circulation model simulations with assimilated observational and proxy data. From the literature review, there was widespread evidence for weather/climate anomalies in 1815–1817 for these islands (29/29 for those with data). But missing data was an issue for other dimensions such as impaired food production (seen in 8 islands out of only 12 with data). Based on the EKF400v2 reconstruction for temperature anomalies (compared to the relatively “non-volcanic” reference period of 1779 to 1808), the islands had lower temperature anomalies in the 1815–1818 period than latitudinally equivalent continental sites (at 100 km and 1000 km inland). This was statistically significant for the great majority of the comparisons for group analyses by hemisphere, oceans, and temperate/tropical zone. When considering just the islands, all but four showed statistically anomalous temperature reductions in the 1816–1817 period (for most p

The rescue, digitization, quality control, preservation, and utilization of long and high quality meteorological and climate records, particularly related to historical marine data, are crucial for advancing our understanding of the Earth’s climate system. In combination with land and air measurements, historical marine records serve as foundational pillars in linking present and past weather and climate information, offering essential insights into natural climate variability, extreme events in marine areas, baseline data for assessing current changes, and inputs for enhancing predictive climate models and reanalyses. This paper provides an overview of rescue activities covering marine weather data over the past centuries and presents and highlights several ongoing projects across the world and how the data are used in an integrative and international framework. Current and future continuous efforts in data rescue, digitization, quality control, and the development of temporally high-resolution meteorological and climatological observations from oceans, will greatly help to further complete our understanding and knowledge of the Earth’s climate system, including extremes, as well as improve the quality of reanalysis.

Abdominoscrotal hydrocele (ASH) is a rare condition characterized by a large scrotal and abdominal fluid-filled sac. An inguinal surgical approach is generally described in literature.We report the case of a 7-month-old child who underwent surgical repair of bilateral ASH through bilateral transverse scrotal incisions. The scrotal approach enabled optimal visibility and access to the hydrocele sacs. Separation of the sac from the testicular pedicle was possible with excellent control. Complete excision of the sac was performed. The postoperative course was uneventful. Follow-up after three years shows an excellent result.We recommend ASH repair through a transverse scrotal incision.

Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are prone to inhomogeneities that can influence and even change trends if not taken into account. In order to assess the relevance of homogenisation for time-series analysis of daily snow depths, we investigated the effects of adjusting inhomogeneities in the extensive network of Swiss snow depth observations for trends and changes in extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depths in the period 1961–2021. Three homogenisation methods were compared for this task: Climatol and HOMER, which apply median-based adjustments, and the quantile-based interpQM. All three were run using the same input data with identical break points. We found that they agree well on trends of seasonal average snow depth, while differences are detectable for seasonal maxima and the corresponding extreme values. Differences between homogenised and non-homogenised series result mainly from the approach for generating reference series. The comparison of homogenised and original values for the 50-year return level of seasonal maximum snow depth showed that the quantile-based method had the smallest number of stations outside the 95 % confidence interval. Using a multiple-criteria approach, e.g. thresholds for series correlation (>0.7) as well as for vertical ( m) and horizontal ( km) distances, proved to be better suited than using correlation or distances alone. Overall, the homogenisation of snow depth series changed all positive trends for derived series of snow days to either no trend or negative trends and amplifying the negative mean trend, especially for stations >1500 m. The number of stations with a significant negative trend increased between 7 % and 21 % depending on the method, with the strongest changes occurring at high snow depths. The reduction in the 95 % confidence intervals of the absolute maximum snow depth of each station indicates a decrease in variation and an increase in confidence in the results.

Measurement(s) temperature of air Technology Type(s) weather stations Sample Characteristic - Environment Air - temperature, precipitation and pressre Sample Characteristic - Location Global Dataset

The wet-bulb temperature (WBT; TW) comprehensively characterizes the temperature and humidity of the thermal environment and is a relevant variable to describe the energy regulation of the human body. The daily maximum TW can be effectively used in monitoring humid heat waves and their effects on health. Because meteorological stations differ in temporal resolution and are susceptible to non-climatic influences, it is difficult to provide complete and homogeneous long-term series. In this study, based on the sub-daily station-based HadISD (Met Office Hadley Centre Integrated Surface Database) dataset and integrating the NCEP-DOE reanalysis dataset, the daily maximum TW series of 1834 stations that have passed quality control were homogenized and reconstructed using the method of Climatol. These stations form a new dataset of global station-based daily maximum TW (GSDM-WBT) from 1981 to 2020. Compared with other station-based and reanalysis-based datasets of TW, the average bias was −0.48 and 0.34 ∘C, respectively. The GSDM-WBT dataset handles stations with many missing values and possible inhomogeneities, and also avoids the underestimation of the TW calculated from reanalysis data. The GSDM-WBT dataset can effectively support the research on global or regional extreme heat events and humid heat waves. The dataset is available at https://doi.org/10.5281/zenodo.7014332 (Dong et al., 2022).

Availability of long-term and spatially high-resolution precipitation data is important for sustainable water resource management, drought monitoring and hydrological analysis. In Eritrea, in‑situ precipitation measurements are too sparse to represent the highly variable rainfall on the ground. Thus, as alternative data sources, satellite-based (CHIRPS and GPM) and reanalysis (ERA5-Land) products were evaluated against 40 stations within the study period (1992–2018). The performance evaluation was made for monthly precipitation totals along the different elevations, agroecological zones and seasons. Overall, these products captured the seasonality of precipitation over the country's different zones. GPM performed well in all zones of the country (rs>0.42$r_{s}>\nobreak 0.42$, RMSE = 44.0 mm), except for the arid highlands (rs=0.19$r_{s}=\nobreak 0.19$, RMSE = 56.4 mm) during the summer season. The performance of GPM did not exhibit a distinct pattern with respect to elevation, amount, and nature of rainfall. CHIRPS demonstrated better estimates over areas where rainfall is of a convective nature during the summer period. These areas include the moist highlands (rs=0.4$r_{s}=\nobreak 0.4$, RMSE = 47.1), arid lowlands (rs=0.48$r_{s}=\nobreak 0.48$, RMSE = 39.0), arid highlands (rs=0.31$r_{s}=\nobreak 0.31$, RMSE = 48.1) and semi-desert (rs=0.38$r_{s}=\nobreak 0.38$, RMSE = 20.1) regions. However, there were no significant correlations over the sub-humid regions in the winter period (rs=0.19$r_{s}=\nobreak 0.19$, RMSE = 25.8), as rains originate from “warm” clouds. ERA5-Land highly underestimated the summer and spring rainfalls in all parts of the country. The summer percentage bias (PBIAS) of this product ranged between −22.6 and −80.1. Therefore, GPM could primarily be considered as an alternative rainfall data source for the summer and spring seasons over Eritrea. CHIRPS can also be used as a potential data source specifically for the areas with convective rainfall during the summer and spring seasons. However, ERA5-Land needs further improvement to represent precipitation in Eritrea.

The winter 1788/89 was one of the coldest winters Europe had witnessed in the past 300 years. Fortunately, for historical climatologists, this extreme event occurred at a time when many stations across Europe, both private and as part of coordinated networks, were making quantitative observations of the weather. This means that several dozen early instrumental series are available to carry out an in-depth study of this severe cold spell. While there have been attempts to present daily spatial information for this winter, there is more to be done to understand the weather variability and day-to-day processes that characterised this weather extreme. In this study, we seek to reconstruct daily spatial high-resolution temperature and sea level pressure fields of the winter 1788/89 in Europe from November through February. The reconstruction is performed with an analogue resampling method (ARM) that uses both historical instrumental data and a weather type classification. Analogue reconstructions are then post-processed through an ensemble Kalman fitting (EnKF) technique. Validation experiments show good skill for both reconstructed variables, which manage to capture the dynamics of the extreme in relation to the large-scale circulation. These results are promising for more such studies to be undertaken, focusing on different extreme events and other regions in Europe and perhaps even further back in time. The dataset presented in this study may be of sufficient quality to allow historians to better assess the environmental and social impacts of the harsh weather.

Although collaborative efforts have been made to retrieve climate data from instrumental observations and paleoclimate records, there is still a large amount of valuable information in historical archives that has not been utilized for climate reconstruction. Due to the qualitative nature of these datasets, historical texts have been compiled and studied by historians aiming to describe the climate impact in socioeconomic aspects of human societies, but the inclusion of this information in past climate reconstructions remains fairly unexplored. Within this context, we present a novel approach to assimilate climate information contained in chronicles and annals from the 15th century to generate robust temperature and precipitation reconstructions of the Burgundian Low Countries, taking into account uncertainties associated with the descriptions of narrative sources. After data assimilation, our reconstructions present a high seasonal temperature correlation of ∼0.8 independently of the climate model employed to estimate the background state of the atmosphere. Our study aims to be a first step towards a more quantitative use of available information contained in historical texts, showing how Bayesian inference can help the climate community with this endeavor.

We describe the compilation of two early instrumental daily temperature series from Bern and Zurich, Switzerland, starting from 1760 and 1756, respectively. The series are a combination of numerous small segments from different observers at different locations within and outside the two cities that are converted to modern units and homogenized. In addition, we introduce a methodology to estimate the errors affecting daily and monthly mean values derived from early instrumental observations. Given the frequent small data gaps, we merge the two daily series into a more complete series representing the central Swiss Plateau. We finally compare the homogenized monthly series with other temperature reconstructions for Switzerland. We find significant differences before 1860, pointing to biases that might affect some of the most widely used instrumental data sets. In general, the homogenization of temperature measurements at the transition between the early instrumental and national weather service eras remains a problematic issue in historical climatology and has significant implications for other fields of climate research.

The global health burden associated with exposure to heat is a grave concern and is projected to further increase under climate change. While physiological studies have demonstrated the role of humidity alongside temperature in exacerbating heat stress for humans, epidemiological findings remain conflicted. Understanding the intricate relationships between heat, humidity, and health outcomes is crucial to inform adaptation and drive increased global climate change mitigation efforts. This article introduces ‘directed acyclic graphs’ (DAGs) as causal models to elucidate the analytical complexity in observational epidemiological studies that focus on humid-heat-related health impacts. DAGs are employed to delineate implicit assumptions often overlooked in such studies, depicting humidity as a confounder, mediator, or an effect modifier. We also discuss complexities arising from using composite indices, such as wet-bulb temperature. DAGs representing the health impacts associated with wet-bulb temperature help to understand the limitations in separating the individual effect of humidity from the perceived effect of wet-bulb temperature on health. General examples for regression models corresponding to each of the causal assumptions are also discussed. Our goal is not to prioritize one causal model but to discuss the causal models suitable for representing humid-heat health impacts and highlight the implications of selecting one model over another. We anticipate that the article will pave the way for future quantitative studies on the topic and motivate researchers to explicitly characterize the assumptions underlying their models with DAGs, facilitating accurate interpretations of the findings. This methodology is applicable to similarly complex compound events.

Analyses of a newly constructed record of column ozone together with an existing record suggest that two thirds of the ozone recovery due to the reduction of ozone depleting substances over the past 25 years is compensated by a dynamically-caused ozone loss.

Abstract Data assimilation techniques are becoming increasingly popular for climate reconstruction. They benefit from estimating past climate states from both observation information and from model simulations. The first monthly global paleo‐reanalysis (EKF400) was generated over the 1600 and 2005 time period, and it provides estimates of several atmospheric fields. Here we present a new, considerably improved version of EKF400 (EKF400v2). EKF400v2 uses atmospheric‐only general circulation model simulations with a greatly extended observational network of early instrumental temperature and pressure data, documentary evidences and tree‐ring width and density proxy records. Furthermore, new observation types such as monthly precipitation amounts, number of wet days and coral proxy records were also included in the assimilation. In the version 2 system, the assimilation process has undergone methodological improvements such as the background‐error covariance matrix is estimated with a blending technique of a time‐dependent and a climatological covariance matrices. In general, the applied modifications resulted in enhanced reconstruction skill compared to version 1, especially in precipitation, sea‐level pressure and other variables beside the mostly assimilated temperature data, which already had high quality in the previous version. Additionally, two case studies are presented to demonstrate the applicability of EKF400v2 to analyse past climate variations and extreme events, as well as to investigate large‐scale climate dynamics.

Measurements of urban air temperatures (Tair$T_{\text{air}}$) are vital to successful adaptation and mitigation policies to increasing urban heat stress. However, in-situ measurements in cities are often scarce and costly, and therefore low-cost approaches are increasingly used to study urban Tair$T_{\text{air}}$. This allows for inexpensive, yet still highly spatially and temporally resolved observations of urban Tair$T_{\text{air}}$. Despite their merits, a common issue of such low-cost approaches is lacking data quality and potential measurement errors. In this case study, we compare three low-cost measurement approaches regarding their ability to capture intra-urban variability of Tair$T_{\text{air}}$ over a period of 24 hours in Bern, Switzerland: a) A network of 79 low-cost measurement devices (LCD), b) bicycle mounted mobile measurements (BCY), and c) 581 Netatmo citizen weather stations (CWS). As the BCY sensor is actively ventilated, it is used as the reference for intercomparisons with LCD and CWS. Compared to the BCY, the median difference of Tair$T_{\text{air}}$ for LCD is found to be slightly negative over the entire study period (−0.08 K) as well as during night-time (−0.10 K), and positive during daytime (0.05 K). As the LCD are known to exhibit a positive bias during the daytime, the good agreement of BCY and LCD is speculated to indicate a positive daytime bias in BCY as well. The CWS show a positive median difference of 0.67 K over the entire study period, 0.98 K during night-time, and a negative difference of −0.23 K during daytime. It is hypothesized that these biases result from incorrect siting of the CWS by their owners installing CWS to close to buildings or walls. At night, these emit thermal radiation which could lead to the positive bias whilst the negative bias during daytime might result from buildings shading the CWS. BCY and LCD both show a distinct pattern of nocturnal intra-urban Tair$T_{\text{air}}$ variability, which is less pronounced in the CWS measurements. Furthermore, the intercomparison of the three approaches across local climate zones reveals that CWS do not well represent forested areas. Whilst the bias sources of the individual approaches require in-depth investigation in future studies (e.g., external heat sources and measurement height for BCY, daytime short-wave radiation errors for LCD, and nocturnal thermal heating by nearby buildings for CWS), we conclude that combining the three measurement approaches can allow to reduce the shortcomings of each approach regarding spatial and temporal resolution or correct biases inherent to one approach.

Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist but have rarely been applied to snow-cover-related time series. We undertook a homogeneity assessment of Swiss monthly snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT – Adapted Caussinus-Mestre Algorithm for Networks of Temperature series, Climatol – Climate Tools, and HOMER – HOMogenizaton softwarE in R). The multi-method approach allowed us to compare the different methods and to establish more robust results using a consensus of at least two change points in close proximity to each other. We investigated 184 series of various lengths between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points in 41 of the 184 series investigated, of which 71 % could be attributed to relocations or observer changes. Metadata are helpful but not sufficient for break point verification as more than 90 % of recorded events (relocation or observer change) did not lead to valid break points. Using a combined approach (two out of three methods) is highly beneficial as it increases the confidence in identified break points in contrast to any single method, with or without metadata.

A cold season climate reconstruction for the northern midlatitudes based on freezing and thawing dates of rivers shows a cold spell 1808/9-1815/6 affecting Eurasia. In addition to two volcanic eruptions, increased snow cover played an important role.