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The El Niño event of 1877–1878 was one of the strongest during the last 150 years and had worldwide impacts, bringing widespread severe droughts, but also unusually wet conditions to some regions. We focus on the effects that this event had on precipitation in South Africa, taking advantage of newly rescued instrumental data, as well as documentary sources. Through the assimilation of the new data into an existing reanalysis, we could improve the synoptic reconstruction of specific weather events. However, much remains to be done in order to achieve a good and consistent reconstruction quality for the region. [ABSTRACT FROM AUTHOR]

Building on previous experience with continental and global data sets, we use a quantile‐matching approach to homogenize temperature and humidity series measured by a network of 220 stations in the United Kingdom (UK). The data set spans 160 years at daily resolution, although data coverage varies greatly in time, space, and across variables. We use the homogenized data to analyse trends of the mean values as well as the lowest and highest quantiles of the distribution over the last 100 and 50 years. For the latter period, we find large regional differences, particularly between the southeastern and the northern part of the UK. The southeast has seen a faster warming, particularly for maximum temperatures in spring and summer, and a reduction of relative humidity; the northern mainland has become more humid and only slightly warmer. These differences become more evident for the highest quantiles and reflect a well‐known pattern of climate change affecting the extra‐tropics. Among the studied variables, the increases of wet bulb temperature and specific humidity are the most spatially homogeneous and are statistically significant for most stations in all seasons except winter. [ABSTRACT FROM AUTHOR]

Central Europe is among the regions with the largest availability of pre-industrial meteorological records. In the Alps, however, such records are relatively rare, especially in the southern slope. We recently found and digitized two new pressure and temperature series for the Alpine cities of Rovereto (1800--1839) and Bolzano/Bozen (1842--1849) covering together the FIrst half of the 19th century, a period characterized by large climate variability and important extreme events. The meteorological record of Rovereto, in particular, is the oldest available for the southeastern Alps. We used the shorter record of Bolzano/Bozen as a testbed for different digitization methods, namely citizen science and machine-learning based Optical Character Recognition. The data are converted to modern units, quality controlled, and homogenized. We also provide daily and monthly means together with an estimation of their uncertainty. [ABSTRACT FROM AUTHOR]

This study explores the teleconnection between the boreal summer precipitation for the central highlands of Eritrea, with respect to global sea‐surface temperature (SST) and atmospheric parameters (winds, pressure, humidity, and vertical velocity) as indirect indicators of atmospheric circulation. The central‐highland summer (Jul–Aug) precipitation (1914–2015) is significantly negatively correlated with the Atlantic Niño (ATL3) index. Similarly, with a 2‐month (May) or 1‐month lead (Jun), SSTs over the equatorial Atlantic (Gulf of Guinea) are inversely correlated with the central‐highland summer precipitation. Moreover, the regional atmospheric circulation reveals that the boreal summer precipitation series has a significant positive correlation with the westerly wind over the Gulf of Guinea at 700 hPa and a negative correlation with the Tropical Easterly Jet (TEJ) at 200 hPa. Overall, these findings depict the African Walker circulation cell, whereby a low‐level westerly wind flows from the Gulf of Guinea across central Africa, coupled with a rising branch over north‐eastern Africa, and reverses as an easterly wind at a higher level. These significant correlations between Atlantic Ocean SST as the precipitation‐driving factor enabled the development and validation of an area‐specific seasonal precipitation prediction model, as precipitation in this region is temporally and spatially highly variable. [ABSTRACT FROM AUTHOR]

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 10.1594/PANGAEA.909141. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Meteorological series (daily precipitation, minimum and maximum air temperature) for Asmara (Eritrea) for the last 100 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 12 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, 8 indices for description of the air temperature data and 10 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/decade, 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. [ABSTRACT FROM AUTHOR]

We describe a global dataset of quality‐controlled in situ daily air temperature observations covering the period 1850–2015, developed in the framework of the EUSTACE (EU Surface Temperature for All Corners of Earth) project (www.eustaceproject.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 station 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 performance 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 reconstruction of daily near‐surface air temperature. [ABSTRACT FROM AUTHOR]

We describe a dataset of recently digitised meteorological observations from 40 locations in today's Switzerland, covering the 18th and 19th century. 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 the data. [ABSTRACT FROM AUTHOR]

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 scales. 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. [ABSTRACT FROM AUTHOR]

Quantitative approaches to climate risk management such as mapping or impact modelling rely on past meteorological data with daily or sub‐daily resolution, a large fraction of which have not yet been digitized. Over the last decade or so, a number of projects have contributed to the rescue of some of these data. Here we provide a summary of a survey we have undertaken of several meteorological and climate data rescue projects, in order to identify the needs of climate data rescue services. To make these efforts more sustainable, additional integrated activities are needed. We argue that meteorological and climate data rescue must be seen as a continuous, coordinated long‐term effort. Technical developments (e.g. data assimilation), new scientific questions (e.g. process understanding of extreme events) and new social (e.g. risk assessment, health) or economic (e.g. new renewable energy sources, agriculture and forestry, tourism, infrastructure, etc.) services are highlighting the immense value of data previously neglected or never considered. This continuous effort is currently undertaken by projects of various sizes, structure, funding and staffing, as well as by dedicated programmes, ranging from those within many national weather services down to "grassroots" initiatives. These activities are often not sufficiently coordinated, staffed, or funded at an international level and will benefit considerably from climate data rescue services being established within the Copernicus Climate Change Service (C3S) (https://climate.copernicus.eu/). Open Practices: This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The daily maximum and minimum temperature series of the European Climate Assessment & Dataset are homogenized using the quantile matching approach. As the dataset is large and the detail of metadata is generally missing, an automated method locates breaks in the series based on a comparison with surrounding series and applies adjustments which are estimated using homogeneous segments of surrounding series as reference. A total of 6,500 series have been processed and after removing duplicates and short series, about 2,100 series have been adjusted. Finally, the effect of the homogenization of daily maximum and minimum temperature on trend estimation is shown to produce a much more spatially homogeneous and then plausible picture. Changes in measurement routines affect the temperature records, inserting biases in the series and compromising the climatological analyses. A homogenization procedure, based on statistical approaches such as the quantile matching, is developed and applied to the European Climate Assessment & Dataset, which collects data for all over Europe. Effect are shown first focusing on representative case studies and then on the geographical consistency of the trends in temperatures in the entire dataset.Example of quantile matching for the month of May on the break of 1952 in the station of Bamberg, Germany. Shaded curves represent the original probability density function, solid lines represent the adjusted ones. [ABSTRACT FROM AUTHOR]

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 us 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 digitized 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: 10.1594/PANGAEA.925860 (Imfeld et al., 2021). [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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: 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. [ABSTRACT FROM AUTHOR]

In 1970, the Institute of Geography of the University of Berne initiated the phenological observation network BernClim. Seasonality information from plants, fog and snow originally served for applications in urban and regional planning, agricultural and touristic suitability and are now a valuable data set for climate change impacts studies. Covering the growing season volunteer observers record the dates of key development stages of hazel (Coryllus avellana), dandelion (Taraxacum officinale), apple tree (Pyrus malus) and beech (Fagus sylvatica). All observations consist of detailed site information including location, altitude, exposition and inclination that make BernClim unique in detail-richness on decadal time-scales. Quality control (QC) by experts and statistical analyses of the data has 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.900103). The QC points to a very good internal consistency (only 0.2 % were flagged as internally inconsistent) and likely a high quality of the data. BernClim data indicate a trend towards an extended growing season. They also well track the regime shift in the late 1980s. [ABSTRACT FROM AUTHOR]

Weather type classifications (WTCs) are a simple tool to analyse variations in weather patterns. Long series of WTCs could be used to address decadal changes in weather as a basis for studying changes in variability or extremes or for addressing contributions of sea-surface temperature or external forcings using climate models. However, there is no long series of daily objective weather types (WTs). A new method (Shortest Mahalanobis Distance, SMD) using daily European weather data is developed to reconstruct WTCs back in time. Here the SMD method is applied on the Cluster Analysis of Principal Components (CAP9) classification used by MeteoSwiss. The CAP9 daily WT time series (computed with ERA-40) is used as reference over the 1958-1998 period. Daily data (temperature, mean sea level pressure and pressure tendency) from 13 European stations covering the period 1763-2009 are used for the reconstruction. The reference CAP9 is reduced from nine to seven types so the new daily WTC is called CAP7. As an assessment, CAP7 is compared to the original classification CAP9 and to the same WTs computed with the Twentieth Century Reanalysis (20CR and 20CRv2c). Over the reference period up to 90% of all the daily WTs can be correctly reproduced in the new WTC compared to the original series, with higher reliability in winter than in summer. In addition, the reliability of the classification is increasing from 1763 onward. The annual occurrence of each type reveals some trends, mostly a decrease in the number of cyclonic days and an increase of cyclonic days. [ABSTRACT FROM AUTHOR]

ABSTRACT Weather type classifications ( WTCs) are a simple tool to analyse variations in weather patterns. Long series of WTCs could be used to address decadal changes in weather as a basis for studying changes in variability or extremes or for addressing contributions of sea-surface temperature or external forcings using climate models. However, there is no long series of daily objective weather types ( WTs). A new method (Shortest Mahalanobis Distance, SMD) using daily European weather data is developed to reconstruct WTCs back in time. Here the SMD method is applied on the Cluster Analysis of Principal Components ( CAP9) classification used by MeteoSwiss. The CAP9 daily WT time series (computed with ERA-40) is used as reference over the 1958-1998 period. Daily data (temperature, mean sea level pressure and pressure tendency) from 13 European stations covering the period 1763-2009 are used for the reconstruction. The reference CAP9 is reduced from nine to seven types so the new daily WTC is called CAP7. As an assessment, CAP7 is compared to the original classification CAP9 and to the same WTs computed with the Twentieth Century Reanalysis ( 20CR and 20CRv2c). Over the reference period up to 90% of all the daily WTs can be correctly reproduced in the new WTC compared to the original series, with higher reliability in winter than in summer. In addition, the reliability of the classification is increasing from 1763 onward. The annual occurrence of each type reveals some trends, mostly a decrease in the number of cyclonic days and an increase of cyclonic days. [ABSTRACT FROM AUTHOR]