Instrumental meteorological measurements from periods prior to the start of national weather services are designated “early instrumental data.” They have played an important role in climate research as they allow daily to decadal variability and changes of temperature, pressure, and precipitation, including extremes, to be addressed. Early instrumental data can also help place twenty-first century climatic changes into a historical context such as defining preindustrial climate and its variability. Until recently, the focus was on long, high-quality series, while the large number of shorter series (which together also cover long periods) received little to no attention. The shift in climate and climate impact research from mean climate characteristics toward weather variability and extremes, as well as the success of historical reanalyses that make use of short series, generates a need for locating and exploring further early instrumental measurements. However, information on early instrumental series has never been electronically compiled on a global scale. Here we attempt a worldwide compilation of metadata on early instrumental meteorological records prior to 1850 (1890 for Africa and the Arctic). Our global inventory comprises information on several thousand records, about half of which have not yet been digitized (not even as monthly means), and only approximately 20% of which have made it to global repositories. The inventory will help to prioritize data rescue efforts and can be used to analyze the potential feasibility of historical weather data products. The inventory will be maintained as a living document and is a first, critical, step toward the systematic rescue and reevaluation of these highly valuable early records. Additions to the inventory are welcome.

Abstract. Spatial information on past weather contributes to better understanding the processes behind day-to-day weather variability and to assessing the risks arising from weather extremes. For Switzerland, daily resolved spatial information on meteorological parameters is restricted to the period starting from 1961, whereas prior to that local station observations are the only source of daily long-term weather data. While attempts have been made to reconstruct spatial weather patterns for certain extreme events, the task of creating a continuous spatial weather reconstruction dataset for Switzerland has so far not been addressed. Here, we aim to reconstruct daily high-resolution precipitation and temperature fields for Switzerland back to 1864 with an analogue resampling method (ARM) using station data and a weather type classification. Analogue reconstructions are post-processed with an ensemble Kalman fitting (EnKF) approach and quantile mapping. Results suggest that the presented methods are suitable for daily precipitation and temperature reconstruction. Evaluation experiments reveal excellent skill for temperature and good skill for precipitation. As illustrated with the example of the avalanche winter of 1887/88, these weather reconstructions have great potential for various analyses of past weather and for climate impact modelling.

Abstract. 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).