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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 10445 images. Further, we present the digitised 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.

The Modern Era Reanalysis (ModE-RA) extends current re-analysis back until the year 1420 CE at monthly resolution. It combines our understanding of physics coming from an ensemble of atmospheric model simulations with all available direct and indirect climate observations of monthly to annual resolution. A 20-member ensemble of atmospheric model simulations (ModE-Sim) driven by an ensemble of external forcings and an ensemble of new sea surface temperature reconstructions serves as a prior estimate of the possible climate states at each assimilation time step. After the entire simulations were completed, we assimilated multiple data sources using an offline Kalman filtering technique. We include up to ~100000 monthly to annual observations per year. These consist of thousands of existing and newly digitised instrumental measurements of temperature, precipitation, wet days per months, and pressure, including measurements made on ships over the ocean and in harbours. Earliest instrumental station data go back to the year 1658. Additionally, we collected and digitised climate information from historical documents, including phenological data. These are especially valuable for the autumn, winter and spring season and go back to the year 1420. Finally, we assimilate annually resolved climate proxies. The vast majority are tree-ring observations, which represent growing season conditions on the continents. In otherwise data sparse regions, we supplemented ice and coral data at high latitudes and the tropical oceans, respectively. ModE-RA offers especially insides into interannual to multidecadal variability such as phases of accelerated warming, monsoon strength or subtropical droughts as well as rare events such as volcanic eruptions.

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.

Upper-air data form the backbone of weather analysis and reanalysis products, particularly in the pre-satellite era. However, they are particularly prone to errors and uncertainties, especially data from the early days of aerology. Information that allows to better characterize the errors of radiosonde data is important. This paper reports on an attempt to collect data from historical upper-air intercomparisons and from historical error assessments reaching back to the 1930s. The digitised numerical data will be made available through Copernicus Climate Change Services; here we publish the full information that includes images, literature, and other metadata that may be relevant and can be used to inform homogenization approaches or reanalysis production. The data collection described in this paper is available on PANGAEA https://www.pangaea.de/tok/9e5b464ba828e931103d3794d9ae1d4576f7a03d (Imfeld et al, in review).

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

European flood frequency and intensity change on a multidecadal scale. Floods were more frequent in the 19th (Central Europe) and early 20th century (Western Europe) than during the mid-20th century and again more frequent since the 1970s. The causes of this variability are not well understood and the relation to climate change is unclear. Palaeoclimate studies from the northern Alps suggest that past flood-rich periods coincided with cold periods. In contrast, some studies suggest that more floods might occur in a future, warming world. Here we reconcile the apparent contradiction by addressing and quantifying the contribution of atmospheric processes to multidecadal flood variability. For this, we use long series of annual peak streamflow, daily weather data, reanalyses, and reconstructions. We show that both changes in atmospheric circulation and moisture content affected multidecadal changes of annual peak streamflow in Central and Western Europe over the past two centuries. We find that during the 19th and early 20th century, atmospheric circulation changes led to high peak values of moisture flux convergence. The circulation was more conducive to strong and long-lasting precipitation events than in the mid-20th century. These changes are also partly reflected in the seasonal mean circulation and reproduced in atmospheric model simulations, pointing to a possible role of oceanic variability. For the period after 1980, increasing moisture content in a warming atmosphere led to extremely high moisture flux convergence. Thus, the main atmospheric driver of flood variability changed from atmospheric circulation variability to water vapour increase.

Numerous historical sources report on hazardous past climate and weather events that had considerable impacts on society. Studying for example mechanisms of such events is however hampered by a lack of spatial weather information. Gridded high-resolution daily data sets mostly cover the past few decades. For Switzerland, Pfister et al. (2019) reconstructed daily fields of precipitation and temperature back to 1864, but the century before this year would be particularly relevant to study the transition from the Little Ice Age climate to the Anthropocene and to analyze the anomalous, volcanically-perturbed climate in the early 19th century and late 18th century. Here we present a daily high-resolution (1x1 km2) reconstruction of temperature and precipitation fields for Switzerland for the years 1763 to 1960 using the analog resampling method (ARM). Together with the present-day meteorological fields, this forms a 250-year data set. The ARM samples temperature and precipitation fields for the historical period from the most similar days in a reference period. These most similar days are selected based on the smallest distance calculated between the observational data in the historical period and the reference period. As observational data, we use temperature, pressure, precipitation, and precipitation occurrence. The resampled fields are then post-processed by assimilating historical temperature measurements and adjusting precipitation fields using isotonic distributional regression. Despite the much scarcer data availability in the period before 1864, evaluation results are promising for the temperature reconstruction with correlation values of on average 0.9 and root mean square errors of on average 1.8°C. For precipitation, the evaluation results are less promising with correlation values of on average 0.7 and root mean square errors of on average 5 mm. Due to its high spatial variability and the small number of records in the historical period, precipitation is more difficult to reconstruct. With the here presented data set, it is now possible to study historical weather and climate events in their spatial extent, such as the warm summer in 1807, and bring it into context leveraging other historical sources. The data set can further be used to calculate impact-relevant indices, for agricultural, phenological, and hydrological modeling.

We describe a global dataset of quality‐controlled in situ daily air temperature ob-servations covering the period 1850–2015, developed in the framework of the EUSTACE (EU Surface Temperature for All Corners of Earth) project (www.eusta cepro ject.org). The dataset includes a total of 35,364 daily series of maximum and minimum temperature obtained from seven different collections. About 97% of the series are publicly available in a common format, while the remaining 3% can be obtained from the original data providers. Unlike other similar products, duplicates have been removed without blending of series, which simplifies data traceability and improves the temporal homogeneity of the individual series at the cost of a smaller average length. Residual artificial signals (breakpoints) in the series caused by sta-tion relocations, changes in instrumentation, etc., have been detected by means of the combination of four breakpoint detection tests, four variables and three temporal aggregations. The combined results give not only the most probable position of the breakpoints, but also a measure of their likelihood. The reliability of the detection was estimated for each year of each target series, based on the number of reference series and on their correlation with the target series. Moreover, its general perfor-mance was evaluated through a benchmark of synthetic series. This product will be combined with datasets of marine and ice in situ air temperature observations and with measurements from satellite to produce the first complete global statistical re-construction of daily near‐surface air temperature.

Meteorological series (daily precipitation, minimum and maximum air temperature) for Asmara (Eritrea) for the last hundred years (1914 – 2015) are analysed. The data were quality controlled and homogenized using publicly available data from surrounding countries as well as newly recovered data from twelve stations in Eritrea. Overall, the Asmara data showed a consistent pattern and there were no outliers outside of four standard deviations from the corresponding reference. Climate indices were calculated using the program RClimDex. Overall, eight indices for description of the air temperature data and ten for precipitation data were calculated. The analyses of averages and indices reveal large climatic variations in the central highlands of Eritrea. The results indicate significant changes in air temperature since 1943, with daily minimum and maximum air temperature increasing at a similar rate of 0.22 and 0.19 °C dec-1, respectively. The diurnal air temperature range shows a non-significant decreasing trend over the study period. No significant variation was found in the annual total and the seasonal precipitation over the last century. Significant trends were detected for some daily precipitation indices, although the lack of reference series prevents an evaluation of their reliability.

We analysed a data set of 18 homogenized daily precipitation series from the southern European Alps, covering approximately the last 150 years. Previously available data from stations in Northern Italy have been extended considerably by recent digitization work, and, for the first time, they have been combined with daily data from Swiss stations on a centennial scale. Precipitation frequency in the southern Alps decreased significantly over the period 1890–2017. We show that this trend is related to a step-like reduction in cyclonic weather types over central Europe that occurred around 1940. This decrease is an example of the large variability that affects precipitation in the region over many different time cales. In particular, strong trends on a decadal scale are related to the Atlantic Multidecadal Oscillation and the North Atlantic Oscillation, although the influence of the latter is present only in the recent decades. Trends in heavy precipitation indices do not show a coherent pattern across the study area. We find a significant increase in heavy precipitation in Switzerland, while a decrease affects the southeastern subset in spring.

Recent years have seen early instrumental observations play an increasingly important role in climate research, allowing past daily-to-decadal climate variability and weather extremes to be explored in greater detail. The 18th century saw the formation of several short-lived meteorological networks of which the one organised by the Societas Meteorologica Palatina is arguably the most well-known. This network stood out as one of those few that efficiently managed to control its members, integrating, refining and publishing measurements taken from numerous stations around Europe and beyond. Although much has been written about the network in both history, science, and individual prominent series used for climatological studies, the actual measurements have not yet been digitised and published in extenso. This paper represents an important step towards filling this perceived gap in research. Here we provide an inventory listing the availability of observed variables for the 37 stations that belonged to the Society's network and discuss their historical context. Most of these observations have been digitised and a considerable fraction has been converted and formatted. In this paper we focus on the temperature and pressure measurements, which have been corrected and homogenised. We then demonstrate their potential for climate research by analysing two cases of extreme weather. The recovered series will have wide applications and could contribute to a better understanding of the mechanisms behind climatic variations and extremes, as well as the societal reactions to adverse weather. Even the shorter series could be ingested into reanalyses and improve the quality of large scale reconstructions.

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.

The European Alps have experienced remarkable climate changes since the beginning of the Industrial Age. In particular, mean air temperature in the region increased at a greater rate than global temperature, leading to the loss of nearly half of the glaciated area and to important changes in the ecosystems. Spanning 1,200 km in length, with peaks reaching over 4,000 meters above sea level (m asl), the Alps have a critical influence over the weather in most of Europe and separate the colder oceanic/continental climate in the north from the milder Mediterranean climate in the south. The climatic differences between the main slopes are reflected into different climate changes—whereas the northern slope got wetter, the southern slope got drier. The consequences of these climate changes are not confined to the Alpine region. Being located in the center of Europe, the Alps provide water and electricity for over 100 million people. Alpine run-off is a major contributor to the total discharge of several major European rivers such as the Rhine, the Rhône, the Po, and the Danube. Therefore, climate change in the Alps can have significant economic impacts on a continental scale. Their convenient geographical position allowed scientists to study the Alpine climate since the very beginning of the instrumental era. The first instrumental meteorological observations in an Alpine valley were taken as early as the mid-17th century, soon followed by measurements at higher elevations. Continuous records are available since the late 18th century, providing invaluable information on climate variability to modern-day researchers. Although there is overwhelming evidence of a dominant anthropogenic influence on the observed temperature increase, the causes of the changes that affected other variables have, in many cases, not been sufficiently investigated by the scientific community.

We describe a dataset of recently digitised meteorological observations from 40 locations in today's Switzerland, covering the 18th and 19th centuries. Three fundamental variables – temperature, pressure, and precipitation – are provided in a standard format after they have been converted into modern units and quality-controlled. The raw data produced by the digitisation, often including additional variables and annotations, are also provided. Digitisation was performed by manually typing the data from photographs of the original sources, which were in most cases handwritten weather diaries. These observations will be important for studying past climate variability in Central Europe and in the Alps, although the general scarcity of metadata (e.g. detailed information on the instruments and their exposure) implies that some caution is required when using them. The data described in this paper can be found at https://doi.org/10.1594/PANGAEA.909141 (Brugnara, 2020).

Data assimilation approaches such as the ensemble Kalman filter method have become an important technique for paleoclimatological reconstructions and reanalysis. Different sources of information from proxy records and documentary data to instrumental measurements were assimilated in previous studies to reconstruct past climate fields. However, precipitation reconstructions are often based on indirect sources (e.g., proxy records). Assimilating precipitation measurements is a challenging task because they have high uncertainties, often represent only a small region and generally do not follow Gaussian distribution. In this paper, a set of experiments are conducted to test the possibility of using information about precipitation in climate reconstruction with monthly resolution by assimilating monthly instrumental precipitation amounts or the number of wet days per month, solely or in addition to other climate variables such as temperature and sea level pressure, into an ensemble of climate model simulations. The skill of all variables (temperature, precipitation, sea-level pressure) improved over the pure model simulations when only monthly precipitation amounts were assimilated. Assimilating the number of wet days resulted in similar or better skill compared to assimilating precipitation amount. The experiments with different types of instrumental observations being assimilated indicate that precipitation data can be useful, particularly if no other variable is available from a given region. Overall the experiments show promising results because with the assimilation of precipitation information a new data source can be exploited for climate reconstructions. Especially the wet day records can become an important data source in future climate reconstructions because many existing records date several centuries back in time and are not limited by the availability of meteorological instruments.

Phenological data have become increasingly important as indicators of long-term climate change. Consequently, long-term homogeneity of the records is an important aspect. In this paper, we apply a breakpoint detection algorithm to the phenological series from the Swiss Phenology Network (SPN). A combination of three statistical tests is applied and different constraints are tested with respect to the choice of reference series. Breakpoint detection is only possible for a fraction of the series due to the shortness of some series and the lack of suitable reference series. Spring phases are more likely to be suitable than fall phases because of their higher spatial correlation. Out of nearly 3000 phenological series with at least 20 data points, only about 5% were found to be significantly inhomogeneous, although a visual validation indicates that many mid-sized breakpoints remained undetected. The detected breakpoints were compared with metadata and more than half of them could be attributed to a change of observer.

In 1970, the Institute of Geography of the University of Bern initiated the phenological observation network BernClim. Seasonality information from plants, fog and snow was originally available for applications in urban and regional planning and agricultural and touristic suitability and is now a valuable data set for climate change impact studies. Covering the growing season, volunteer observers record the dates of key development stages of hazel (Corylus avellana), dandelion (Taraxacum officinale), apple tree (Pyrus malus) and beech (Fagus sylvatica). All observations consist of detailed site information, including location, altitude, exposition (aspect) and inclination, that makes BernClim unique in its richness in detail on decadal timescales. Quality control (QC) by experts and statistical analyses of the data have been performed to flag impossible dates, dates outside the biologically plausible range, repeated dates in the same year, stretches of consecutive identical dates and statistically inconsistent dates (outliers in time or in space). Here, we report BernClim data of 7414 plant phenological observations from 1970 to 2018 from 1304 sites at 110 stations, the QC procedure and selected applications (Rutishauser et al., 2019: https://doi.org/10.1594/PANGAEA.900102). The QC points to very good internal consistency (only 0.2 % were flagged as internally inconsistent) and likely high quality of the data. BernClim data indicate a trend towards an extended growing season. They also track the regime shift in the late 1980s well to pronounced earlier dates like numerous other phenological records across the Northern Hemisphere.

Decadal variability of weather and its extremes is still poorly understood. This is partly due to the shortness of records, which, for many parts of the world, only allow studies of 20th century weather. However, the 18th and early 19th century have seen some pronounced climatic variations, with equally pronounced impacts on the environment and society. Considerable amounts of weather data are available even for that time, but have not yet been digitised. Given the recent progress in quantitative reconstruction of sub-daily weather, such data could form the basis of weather reconstructions. In Switzerland, measurements before 1864 (the start of the national network) have never been systematically compiled except for three prominent series (Geneva, Basel, Great St. Bernard Pass). Here we provide an overview of early instrumental meteorological measurements in Switzerland resulting from an archive survey. Our inventory encompasses 335 entries from 206 locations, providing an estimated 3640 station years and reaching back to the early 18th century. Most of the data sheets have been photographed and a considerable fraction is undergoing digitisation. This paper accompanies the online publication of the imaged data series and metadata. We provide a detailed inventory of the series, discuss their historical context and provide the photographed data sheets. We demonstrate their usefulness on behalf of two historical cases and show how they complement the existing series in Europe. If similar searches in other countries yield similarly rich results, an extension of daily weather reconstructions for Europe back to the 1760s is possible.

We describe the recovery of three daily meteorological records for the southern Alps (Domodossola, Riva del Garda, and Rovereto), all starting in the second half of the nineteenth century. We use these new data, along with additional records, to study regional changes in the mean temperature and extreme indices of heat waves and cold spells frequency and duration over the period 1874–2015. The records are homogenized using subdaily cloud cover observations as a constraint for the statistical model, an approach that has never been applied before in the literature. A case study based on a record of parallel observations between a traditional meteorological window and a modern screen shows that the use of cloud cover can reduce the root-mean-square error of the homogenization by up to 30% in comparison to an unaided statistical correction. We find that mean temperature in the southern Alps has increased by 1.4°C per century over the analyzed period, with larger increases in daily minimum temperatures than maximum temperatures. The number of hot days in summer has more than tripled, and a similar increase is observed in duration of heat waves. Cold days in winter have dropped at a similar rate. These trends are mainly caused by climate change over the last few decades.

Land and water management in semi-arid regions requires detailed information on precipitation distribution, including extremes, and changes therein. Such information is often lacking. This paper describes statistics of mean and extreme precipitation in a unique data set from the Mount Kenya region, encompassing around 50 stations with at least 30 years of data. We describe the data set, including quality control procedures and statistical break detection. Trends in mean precipitation and extreme indices calculated from these data for individual rainy seasons are compared with corresponding trends in reanalysis products. From 1979 to 2011, mean precipitation decreased at 75% of the stations during the ‘long rains’ (March to May) and increased at 70% of the stations during the ‘short rains’ (October to December). Corresponding trends are found in the number of heavy precipitation days, and maximum of consecutive 5-day precipitation. Conversely, an increase in consecutive dry days within both main rainy seasons is found. However, trends are only statistically significant in very few cases. Reanalysis data sets agree with observations with respect to interannual variability, while correlations are considerably lower for monthly deviations (ratios) from the mean annual cycle. While some products well reproduce the rainfall climatology and some the spatial trend pattern, no product reproduces both.

The eruption of Mount Tambora (Indonesia) in April 1815 is the largest documented volcanic eruption in history. It is associated with a large global cooling during the following year, felt particularly in parts of Europe and North America, where the year 1816 became known as the "year without a summer". This paper describes an effort made to collect surface meteorological observations from the early instrumental period, with a focus on the years of and immediately following the eruption (1815–1817). Although the collection aimed in particular at pressure observations, correspondent temperature observations were also recovered. Some of the series had already been described in the literature, but a large part of the data, recently digitised from original weather diaries and contemporary magazines and newspapers, is presented here for the first time. The collection puts together more than 50 sub-daily series from land observatories in Europe and North America and from ships in the tropics. The pressure observations have been corrected for temperature and gravity and reduced to mean sea level. Moreover, an additional statistical correction was applied to take into account common error sources in mercury barometers. To assess the reliability of the corrected data set, the variance in the pressure observations is compared with modern climatologies, and single observations are used for synoptic analyses of three case studies in Europe. All raw observations will be made available to the scientific community in the International Surface Pressure Databank.

An important key for the understanding of the dynamic response to large tropical volcanic eruptions is the warming of the tropical lower stratosphere and the concomitant intensification of the polar vortices. Although this mechanism is reproduced by most general circulation models today, most models still fail in producing an appropriate winter warming pattern in the Northern Hemisphere. In this study ensemble sensitivity experiments were carried out with a coupled atmosphere-ocean model to assess the influence of different ozone climatologies on the atmospheric dynamics and in particular on the northern hemispheric winter warming. The ensemble experiments were perturbed by a single Tambora-like eruption. Larger meridional gradients in the lower stratospheric ozone favor the coupling of zonal wind anomalies between the stratosphere and the troposphere after the eruption. The associated sea level pressure, temperature, and precipitation patterns are more pronounced and the northern hemispheric winter warming is highly significant. Conversely, weaker meridional ozone gradients lead to a weaker response of the winter warming and the associated patterns. The differences in the number of stratosphere-troposphere coupling events between the ensembles experiments indicate a nonlinear response behavior of the dynamics with respect to the ozone and the volcanic forcing.

The newly developed atmosphere–ocean-chemistry-climate model SOCOL-MPIOM is presented by demonstrating the influence of the interactive chemistry module on the climate state and the variability. Therefore, we compare pre-industrial control simulations with (CHEM) and without (NOCHEM) interactive chemistry. In general, the influence of the chemistry on the mean state and the variability is small and mainly restricted to the stratosphere and mesosphere. The largest differences are found for the atmospheric dynamics in the polar regions, with slightly stronger northern and southern winter polar vortices in CHEM. The strengthening of the vortex is related to larger stratospheric temperature gradients, which are attributed to a parametrization of the absorption of ozone and oxygen in the Lyman-alpha, Schumann–Runge, Hartley, and Higgins bands. This effect is parametrized in the version with interactive chemistry only. A second reason for the temperature differences between CHEM and NOCHEM is related to diurnal variations in the ozone concentrations in the higher atmosphere, which are missing in NOCHEM. Furthermore, stratospheric water vapour concentrations differ substantially between the two experiments, but their effect on the temperatures is small. In both setups, the simulated intensity and variability of the northern polar vortex is inside the range of present day observations. Sudden stratospheric warming events are well reproduced in terms of their frequency, but the distribution amongst the winter months is too uniform. Additionally, the performance of SOCOL-MPIOM under changing external forcings is assessed for the period 1600–2000 using an ensemble of simulations driven by a spectral solar forcing reconstruction. The amplitude of the reconstruction is large in comparison to other state-of-the-art reconstructions, providing an upper limit for the importance of the solar signal. In the pre-industrial period (1600–1850) the simulated surface temperature trends are in reasonable agreement with temperature reconstructions, although the multi-decadal variability is more pronounced. This enhanced variability can be attributed to the variability in the solar forcing. The simulated temperature reductions during the Maunder Minimum are in the lowest probability range of the proxy records. During the Dalton Minimum, when also volcanic forcing is an important driver of temperature variations, the agreement is better. In the industrial period from 1850 onward SOCOL-MPIOM overestimates the temperature increase in comparison to observational data sets. Sensitivity simulations show that this overestimation can be attributed to the increasing trend in the solar forcing reconstruction that is used in this study and an additional warming induced by the simulated ozone changes.

Here we present a study of the 11 yr sunspot cycle's imprint on the Northern Hemisphere atmospheric circulation, using three recently developed gridded upper-air data sets that extend back to the early twentieth century. We find a robust response of the tropospheric late-wintertime circulation to the sunspot cycle, independent from the data set. This response is particularly significant over Europe, although results show that it is not directly related to a North Atlantic Oscillation (NAO) modulation; instead, it reveals a significant connection to the more meridional Eurasian pattern (EU). The magnitude of mean seasonal temperature changes over the European land areas locally exceeds 1 K in the lower troposphere over a sunspot cycle. We also analyse surface data to address the question whether the solar signal over Europe is temporally stable for a longer 250 yr period. The results increase our confidence in the existence of an influence of the 11 yr cycle on the European climate, but the signal is much weaker in the first half of the period compared to the second half. The last solar minimum (2005 to 2010), which was not included in our analysis, shows anomalies that are consistent with our statistical results for earlier solar minima.

Climatic variations at decadal scales such as phases of accelerated warming or weak monsoons have profound effects on society and economy. Studying these variations requires insights from the past. However, most current reconstructions provide either time series or fields of regional surface climate, which limit our understanding of the underlying dynamics. Here, we present the first monthly paleo-reanalysis covering the period 1600 to 2005. Over land, instrumental temperature and surface pressure observations, temperature indices derived from historical documents and climate sensitive tree-ring measurements were assimilated into an atmospheric general circulation model ensemble using a Kalman filtering technique. This data set combines the advantage of traditional reconstruction methods of being as close as possible to observations with the advantage of climate models of being physically consistent and having 3-dimensional information about the state of the atmosphere for various variables and at all points in time. In contrast to most statistical reconstructions, centennial variability stems from the climate model and its forcings, no stationarity assumptions are made and error estimates are provided.

Two hundred years ago a very cold and wet summer devastated agriculture in Europe and North America, causing widespread food shortages, unrest and suffering – the "year without a summer". This is usually blamed on the eruption of Mount Tambora, in Indonesia, the previous April, but making a link between these two events has proven difficult, as the major impacts were at smaller space and time-scales than we can reconstruct with tree-ring observations and climate model simulations. Here we show that the very limited network of station barometer observations for the period is nevertheless enough to enable a dynamical atmospheric reanalysis to reconstruct the daily weather of summer 1816, over much of Europe. Adding stratospheric aerosol from the Tambora eruption to the reanalysis improves its reconstruction, explicitly linking the volcano to the weather impacts.

We describe the recovery of three daily meteorological records for the southern Alps (Domodossola, Riva del Garda, and Rovereto), all starting in the second half of the nineteenth century. We use these new data, along with additional records, to study regional changes in the mean temperature and extreme indices of heat waves and cold spells frequency and duration over the period 1874–2015. The records are homogenized using subdaily cloud cover observations as a constraint for the statistical model, an approach that has never been applied before in the literature. A case study based on a record of parallel observations between a traditional meteorological window and a modern screen shows that the use of cloud cover can reduce the root-mean-square error of the homogenization by up to 30% in comparison to an unaided statistical correction. We find that mean temperature in the southern Alps has increased by 1.4°C per century over the analyzed period, with larger increases in daily minimum temperatures than maximum temperatures. The number of hot days in summer has more than tripled, and a similar increase is observed in duration of heat waves. Cold days in winter have dropped at a similar rate. These trends are mainly caused by climate change over the last few decades.

The aim of this work is to elucidate the impact of changes in solar irradiance and energetic particles versus volcanic eruptions on tropospheric global climate during the Dalton Minimum (DM, AD 1780–1840). Separate variations in the (i) solar irradiance in the UV-C with wavelengths λ < 250 nm, (ii) irradiance at wavelengths λ > 250 nm, (iii) in energetic particle spectrum, and (iv) volcanic aerosol forcing were analyzed separately, and (v) in combination, by means of small ensemble calculations using a coupled atmosphere–ocean chemistry–climate model. Global and hemispheric mean surface temperatures show a significant dependence on solar irradiance at λ > 250 nm. Also, powerful volcanic eruptions in 1809, 1815, 1831 and 1835 significantly decreased global mean temperature by up to 0.5 K for 2–3 years after the eruption. However, while the volcanic effect is clearly discernible in the Southern Hemispheric mean temperature, it is less significant in the Northern Hemisphere, partly because the two largest volcanic eruptions occurred in the SH tropics and during seasons when the aerosols were mainly transported southward, partly because of the higher northern internal variability. In the simulation including all forcings, temperatures are in reasonable agreement with the tree ring-based temperature anomalies of the Northern Hemisphere. Interestingly, the model suggests that solar irradiance changes at λ < 250 nm and in energetic particle spectra have only an insignificant impact on the climate during the Dalton Minimum. This downscales the importance of top–down processes (stemming from changes at λ < 250 nm) relative to bottom–up processes (from λ > 250 nm). Reduction of irradiance at λ > 250 nm leads to a significant (up to 2%) decrease in the ocean heat content (OHC) between 0 and 300 m in depth, whereas the changes in irradiance at λ < 250 nm or in energetic particles have virtually no effect. Also, volcanic aerosol yields a very strong response, reducing the OHC of the upper ocean by up to 1.5%. In the simulation with all forcings, the OHC of the uppermost levels recovers after 8–15 years after volcanic eruption, while the solar signal and the different volcanic eruptions dominate the OHC changes in the deeper ocean and prevent its recovery during the DM. Finally, the simulations suggest that the volcanic eruptions during the DM had a significant impact on the precipitation patterns caused by a widening of the Hadley cell and a shift in the intertropical convergence zone.