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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 Additionally, we collected and digitised climate information from historical documents, including phenological data.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.

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.