Abstract The UK Met Office's Daily Weather Reports (DWR) contain extensive logs of UK thunderstorm activity. To date, only a very small fraction of these data have been digitized as part of the MIDAS dataset, and exclusively after 1950. Using the recently‐scanned UK Met Office Monthly Weather Reports (MWR), which are based on a subset of the observations that form the DWR, we here provide digitized data and a summary of thunderdays from 10 long‐running British stations over the period 1884–1993. The data are presented ‘as is’, with no attempt to provide any corrections or calibration. For 4 of the 10 stations, thunderday observations were discontinued at various times between 1949 and 1964, and it is necessary to switch to a neighbouring station in order to continue the series. Approximately half the series exhibit sharp drops in thunderdays at various points between 1960 and 1990, although none are coincident with known station changes. Comparison with nearby MIDAS stations suggests the low thunderdays are the result of changes in observing practice, rather than genuine changes in thunderstorm occurrence. These potential data issues limit interpretation of the long‐term trends. However, it can nevertheless be concluded that none of the stations show the expected increase in thunderdays as a result of the rise in surface temperature over the 20th century. In order to provide more quantitative determination of the long‐term trends in thunderstorm occurrence, we advocate further digitization efforts to recover the data from the numerous stations in the MWRs, and subsequent analysis of the common signals across neighbouring stations.

Abstract Besides cumulative temperature and precipitation, the phase synchronization of temperature and precipitation also helps to regulate vegetation distribution and productivity across global lands. However, the phase synchronization has been rarely considered in previous studies related to climate and biogeography due to a lack of a robust and quantitative approach. In this study, we proposed a synchronization index of temperature and precipitation (SI‐TaP) and then investigated its global spatial distribution, interannual fluctuation, and long‐term trend derived from a global 60‐year dataset of meteorological forcings. Further investigation was conducted to understand the relationship between SI‐TaP and the annually summed Normalized Difference Vegetation Index (NDVI), which could be a proxy of terrestrial vegetation productivity. Results show differences in both spatial patterns and temporal variations between SI‐TaP and air temperature and precipitation, but SI‐TaP may help to explain the distribution and productivity of terrestrial vegetation. About 60% of regions where annually summed NDVI is greater than half of its maximum value overlap regions where SI‐TaP is greater than half of its maximum value. By using SI‐TaP to explain vegetation productivity along with temperature and precipitation, the maximum increase in the coefficient of determination is 0.66 across global lands. Results from this study suggest that the proposed SI‐TaP index is helpful to better understand climate change and its relation to the biota.

Abstract Data and metadata products based on intensive terrestrial gravity measurements covering the Belgian territory have been established compliant to the new ISO19115‐1 profile for gravity‐related data. A short historical introduction on gravity measurements on the Belgian territory is presented, starting with the first gravity measurement in 1892. This paper focuses on gravity data acquired after 1971, which marks the adoption of IGSN71 as a reference for the world network by the International Association of Geodesy (IAG). Next to a complete dataset containing all Belgian gravity data, the available gravity data were reduced to the corresponding measurement campaign, enabling determination of specific metadata (e.g. used gravimeter, tide system parameters, time of acquisition, etc.). New gravity and spirit levelling measurements were executed by the Royal Observatory Belgium and the National Geographic Institute (Federal Mapping Agency) to validate the g‐value at the historical reference base station ‘Carte du Ciel’ in Brussels. Taking into account the instrumental errors and the varying ground water content, the retrieved data are validated and approved for use in further research.

Abstract The recovery of early geomagnetic data is essential to improve knowledge of the evolution of the geomagnetic field. A recently recovered dataset is described that includes mean decadal (10 days) values of the geomagnetic declination and some disperse values of geomagnetic inclination measured at the Astronomical Observatory of Madrid from 1879 to 1901. These recovered data are compared directly with the results obtained from a geomagnetic model. The data have now been made accessible for the scientific community in digital form.Open PracticesThis article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://doi.pangaea.de/10.1594/PANGAEA.885886. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.

Abstract The MaRIUS‐G2G datasets were produced for the MaRIUS (Managing the Risks, Impacts and Uncertainties of drought and water Scarcity) project, using the Grid‐to‐Grid (G2G) national‐scale hydrological model for Great Britain. There are six separate datasets, with each of three combinations of meteorological driving data (two observation‐based and one from climate model ensembles) used to produce two types of outputs (daily time‐series of natural river flow for 260 sites, and monthly 1 × 1 km grids of natural flow and soil moisture in the unsaturated zone). The driving data required by G2G are rainfall and potential evaporation (PE). Two of the datasets from observation‐based driving data use rainfall from CEH‐GEAR (CEH‐Gridded Estimates of Areal Rainfall) with PE from MORECS (Met Office Rainfall and Evaporation Calculation System), and cover the period 1960–2015, while the other two use CEH‐GEAR rainfall with PE derived from 5 km temperature data using the Oudin method, and cover 1891–2015. The two datasets based on driving data (rainfall and PE) from the climate model ensembles cover three periods: Historical Baseline (1900–2006), Near‐Future (2020–2049), and Far‐Future (2070–2099). Data for a 30‐year Baseline period (1975–2004), against which the Near‐Future and Far‐Future periods should be compared, are also available directly. There are 100 members in each ensemble, and the future periods use the RCP8.5 emissions scenario. This paper provides details of the G2G model and the different sets of meteorological driving data, as well as the availability and formatting of the output datasets. It also describes some recent and potential applications of the datasets, which have already been used to support historical and future analyses of low flow and drought characteristics across Britain, and provides some guidance on how the climate model‐driven datasets should (and should not) be used.Open PracticesThis article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at DOI 10.5285/5f3c1a02-d5c4-4faa-9353-e8b68ce2ace2. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.