Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5651-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-5651-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 Dec 2022
Data description paper |
| 21 Dec 2022
| 21 Dec 2022
GSDM-WBT: global station-based daily maximum wet-bulb temperature data for 1981–2020
Jianquan Dong
Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
Institute of Geography and Oeschger Centre for Climate Change
Research, University of Bern, Bern, 3012, Switzerland
Stefan Brönnimann
Institute of Geography and Oeschger Centre for Climate Change
Research, University of Bern, Bern, 3012, Switzerland
Tao Hu
Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
Yanxu Liu
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Jian Peng
CORRESPONDING AUTHOR
Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
Related authors
Zhiwei Yang, Jian Peng, Yanxu Liu, Song Jiang, Xueyan Cheng, Xuebang Liu, Jianquan Dong, Tiantian Hua, and Xiaoyu Yu
Earth Syst. Sci. Data, 16, 2407–2424, https://doi.org/10.5194/essd-16-2407-2024, https://doi.org/10.5194/essd-16-2407-2024, 2024
Short summary
Short summary
We produced a monthly Universal Thermal Climate Index dataset (GloUTCI-M) boasting global coverage and an extensive time series spanning March 2000 to October 2022 with a high spatial resolution of 1 km. This dataset is the product of a comprehensive approach leveraging multiple data sources and advanced machine learning models. GloUTCI-M can enhance our capacity to evaluate thermal stress experienced by the human, offering substantial prospects across a wide array of applications.
Nicolás Duque-Gardeazabal, Andrew R. Friedman, and Stefan Brönnimann
Hydrol. Earth Syst. Sci., 29, 3277–3295, https://doi.org/10.5194/hess-29-3277-2025, https://doi.org/10.5194/hess-29-3277-2025, 2025
Short summary
Short summary
Understanding hydrological variability is essential for ecological conservation and sustainable development. Evapotranspiration influences the carbon cycle, and finding what causes its variability is important for ecosystems. This study shows that ENSO (El Niño–Southern Oscillation) influences not only South America’s rainfall, soil moisture, radiation, and evaporation but also other phenomena in the Atlantic Ocean. The impacts change regionally depending on the season analysed and have implications for heat extremes.
Christian Pfister, Stefan Brönnimann, Laurent Litzenburger, Peter Thejll, Andres Altwegg, Rudolf Brázdil, Andrea Kiss, Erich Landsteiner, Fredrik Charpentier Ljungqvist, and Thomas Pliemon
EGUsphere, https://doi.org/10.5194/egusphere-2025-3242, https://doi.org/10.5194/egusphere-2025-3242, 2025
This preprint is open for discussion and under review for Climate of the Past (CP).
Short summary
Short summary
Narrative historical records of wine production in Central Europe date back to 1200. A study of taxes paid to authorities in the French-Luxembourg Moselle region, Germany, and the Swiss Plateau over the last few centuries shows that wine yields provide indirect indications of summer temperatures when the impact of heavy frosts is taken into account. This enables climate reconstructions based on tree rings to be refined and confirmed. Occasionally, poor harvests gave rise to witch hunts.
Noemi Imfeld and Stefan Brönnimann
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-249, https://doi.org/10.5194/essd-2025-249, 2025
Preprint under review for ESSD
Short summary
Short summary
We extend Swiss daily climate reconstructions from 1763 to 2020 to six additional variables at 1×1 km resolution using analogue resampling and data assimilation. Wind and temperature reconstructions show reasonable skill, while humidity and sunshine duration perform less well. Application to historical wild fire events demonstrates the data set’s potential for impact studies. This is the first Swiss data set providing several variables at a high-resolution of 1x1 km and going back to 1763.
Lucas Pfister, Lena Wilhelm, Yuri Brugnara, Noemi Imfeld, and Stefan Brönnimann
Weather Clim. Dynam., 6, 571–594, https://doi.org/10.5194/wcd-6-571-2025, https://doi.org/10.5194/wcd-6-571-2025, 2025
Short summary
Short summary
Our work compares different machine learning approaches for creating long-term classifications of daily atmospheric circulation patterns using input data from surface meteorological observations. Our comparison reveals that a feedforward neural network performs best at this task. Using this model, we present a daily reconstruction of a commonly used weather type classification for central Europe that dates back to 1728.
Richard Warren, Niklaus Emanuel Bartlome, Noémie Wellinger, Jörg Franke, Ralf Hand, Stefan Brönnimann, and Heli Huhtamaa
Clim. Past, 20, 2645–2662, https://doi.org/10.5194/cp-20-2645-2024, https://doi.org/10.5194/cp-20-2645-2024, 2024
Short summary
Short summary
This paper introduces the ClimeApp web application. The app provides quick access to the ModE-RA global climate reanalysis. Users can calculate and plot anomalies, composites, correlations, regressions and annual cycles across three different datasets and four climate variables. By re-examining the 1815 Tambora eruption, we demonstrate how combining results from different datasets and sources can help us investigate the historical palaeoclimate and integrate it into human history.
Peter Stucki, Lucas Pfister, Yuri Brugnara, Renate Varga, Chantal Hari, and Stefan Brönnimann
Clim. Past, 20, 2327–2348, https://doi.org/10.5194/cp-20-2327-2024, https://doi.org/10.5194/cp-20-2327-2024, 2024
Short summary
Short summary
In our work, we reconstruct the weather of the extremely cold and wet summer in 1816 using a weather forecasting model to obtain high-resolution, three-dimensional weather simulations. We refine our simulations with surface pressure and temperature observations, representing a novel approach for this period. Our results show that this approach yields detailed and accurate weather reconstructions, opening the door to analyzing past weather events and their impacts in detail.
Stefan Brönnimann, Janusz Filipiak, Siyu Chen, and Lucas Pfister
Clim. Past, 20, 2219–2235, https://doi.org/10.5194/cp-20-2219-2024, https://doi.org/10.5194/cp-20-2219-2024, 2024
Short summary
Short summary
The year 1740 was the coldest in central Europe since at least 1421. New monthly global climate reconstructions, together with daily weather reconstructions, allow a detailed view of this climatic event. Following several severe cold spells in January and February, a persistent circulation pattern with blocking over the British Isles caused northerly flow towards western Europe during a large part of the year. It was one of the strongest, arguably unforced excursions in European temperature.
Christian Pfister, Stefan Brönnimann, Andres Altwegg, Rudolf Brázdil, Laurent Litzenburger, Daniele Lorusso, and Thomas Pliemon
Clim. Past, 20, 1387–1399, https://doi.org/10.5194/cp-20-1387-2024, https://doi.org/10.5194/cp-20-1387-2024, 2024
Short summary
Short summary
This bottle of Riesling from the traditional Bassermann Jordan winery in Deidesheim (Germany) is a relic of the premium wine harvested in 1811. It was named “Comet Wine” after the bright comet that year. The study shows that wine quality can be used to infer summer weather conditions over the past 600 years. After rainy summers with cold winds, wines turned sour, while long periods of high pressure led to excellent qualities. Since 1990, only good wines have been produced due to rapid warming.
Chengcheng Hou, Yan Li, Shan Sang, Xu Zhao, Yanxu Liu, Yinglu Liu, and Fang Zhao
Earth Syst. Sci. Data, 16, 2449–2464, https://doi.org/10.5194/essd-16-2449-2024, https://doi.org/10.5194/essd-16-2449-2024, 2024
Short summary
Short summary
To fill the gap in the gridded industrial water withdrawal (IWW) data in China, we developed the China Industrial Water Withdrawal (CIWW) dataset, which provides monthly IWWs from 1965 to 2020 at a spatial resolution of 0.1°/0.25° and auxiliary data including subsectoral IWW and industrial output value in 2008. This dataset can help understand the human water use dynamics and support studies in hydrology, geography, sustainability sciences, and water resource management and allocation in China.
Zhiwei Yang, Jian Peng, Yanxu Liu, Song Jiang, Xueyan Cheng, Xuebang Liu, Jianquan Dong, Tiantian Hua, and Xiaoyu Yu
Earth Syst. Sci. Data, 16, 2407–2424, https://doi.org/10.5194/essd-16-2407-2024, https://doi.org/10.5194/essd-16-2407-2024, 2024
Short summary
Short summary
We produced a monthly Universal Thermal Climate Index dataset (GloUTCI-M) boasting global coverage and an extensive time series spanning March 2000 to October 2022 with a high spatial resolution of 1 km. This dataset is the product of a comprehensive approach leveraging multiple data sources and advanced machine learning models. GloUTCI-M can enhance our capacity to evaluate thermal stress experienced by the human, offering substantial prospects across a wide array of applications.
Stefan Brönnimann, Yuri Brugnara, and Clive Wilkinson
Clim. Past, 20, 757–767, https://doi.org/10.5194/cp-20-757-2024, https://doi.org/10.5194/cp-20-757-2024, 2024
Short summary
Short summary
The early 20th century warming – the first phase of global warming in the 20th century – started from a peculiar cold state around 1910. We digitised additional ship logbooks for these years to study this specific climate state and found that it is real and likely an overlap of several climatic anomalies, including oceanic variability (La Niña) and volcanic eruptions.
Noemi Imfeld, Koen Hufkens, and Stefan Brönnimann
Clim. Past, 20, 659–682, https://doi.org/10.5194/cp-20-659-2024, https://doi.org/10.5194/cp-20-659-2024, 2024
Short summary
Short summary
Climate and weather in spring are important because they can have far-reaching impacts, e.g. on plant growth, due to cold spells. Here, we study changes in climate and phenological indices for the period from 1763 to 2020 based on newly published reconstructed fields of daily temperature and precipitation for Switzerland. We look at three cases of extreme spring conditions, namely a warm spring in 1862, two frost events in 1873 and 1957, and three cold springs in 1785, 1837, and 1852.
Eric Samakinwa, Christoph C. Raible, Ralf Hand, Andrew R. Friedman, and Stefan Brönnimann
Clim. Past Discuss., https://doi.org/10.5194/cp-2023-67, https://doi.org/10.5194/cp-2023-67, 2023
Publication in CP not foreseen
Short summary
Short summary
In this study, we nudged a stand-alone ocean model MPI-OM to proxy-reconstructed SST. Based on these model simulations, we introduce new estimates of the AMOC variations during the period 1450–1780 through a 10-member ensemble simulation with a novel nudging technique. Our approach reaffirms the known mechanisms of AMOC variability and also improves existing knowledge of the interplay between the AMOC and the NAO during the AMOC's weak and strong phases.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
Short summary
Short summary
ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Stefan Brönnimann and Yuri Brugnara
Clim. Past, 19, 1435–1445, https://doi.org/10.5194/cp-19-1435-2023, https://doi.org/10.5194/cp-19-1435-2023, 2023
Short summary
Short summary
We present the weather diaries of the Kirch family from 1677–1774 containing weather observations made in Leipzig and Guben and, from 1701 onward, instrumental observations made in Berlin. We publish the imaged diaries (10 445 images) and the digitized measurements (from 1720 onward). This is one of the oldest and longest meteorological records from Germany. The digitized pressure data show good agreement with neighbouring stations, highlighting their potential for weather reconstruction.
Stefan Brönnimann
Clim. Past, 19, 1345–1357, https://doi.org/10.5194/cp-19-1345-2023, https://doi.org/10.5194/cp-19-1345-2023, 2023
Short summary
Short summary
Weather reconstructions could help us to better understand the mechanisms leading to, and the impacts caused by, climatic changes. This requires daily weather information such as diaries. Here I present the weather diary by Georg Christoph Eimmart from Nuremberg covering the period 1695–1704. This was a particularly cold period in Europe, and the diary helps to better characterize this climatic anomaly.
Noemi Imfeld, Lucas Pfister, Yuri Brugnara, and Stefan Brönnimann
Clim. Past, 19, 703–729, https://doi.org/10.5194/cp-19-703-2023, https://doi.org/10.5194/cp-19-703-2023, 2023
Short summary
Short summary
Climate reconstructions give insights into monthly and seasonal climate variability of the past few hundred years. However, to understand past extreme weather events and to relate them to impacts, for example to periods of extreme floods, reconstructions on a daily timescale are needed. Here, we present a reconstruction of 258 years of high-resolution daily temperature and precipitation fields for Switzerland covering the period 1763 to 2020, which is based on instrumental measurements.
Moritz Buchmann, Gernot Resch, Michael Begert, Stefan Brönnimann, Barbara Chimani, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 17, 653–671, https://doi.org/10.5194/tc-17-653-2023, https://doi.org/10.5194/tc-17-653-2023, 2023
Short summary
Short summary
Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are susceptible to inhomogeneities that can affect the trends and even change the sign. To assess the relevance of homogenisation for daily snow depths, we investigated its impact on trends and changes in extreme values of snow indices between 1961 and 2021 in the Swiss observation network.
Duncan Pappert, Mariano Barriendos, Yuri Brugnara, Noemi Imfeld, Sylvie Jourdain, Rajmund Przybylak, Christian Rohr, and Stefan Brönnimann
Clim. Past, 18, 2545–2565, https://doi.org/10.5194/cp-18-2545-2022, https://doi.org/10.5194/cp-18-2545-2022, 2022
Short summary
Short summary
We present daily temperature and sea level pressure fields for Europe for the severe winter 1788/1789 based on historical meteorological measurements and an analogue reconstruction approach. The resulting reconstruction skilfully reproduces temperature and pressure variations over central and western Europe. We find intense blocking systems over northern Europe and several abrupt, strong cold air outbreaks, demonstrating that quantitative weather reconstruction of past extremes is possible.
Chantal Camenisch, Fernando Jaume-Santero, Sam White, Qing Pei, Ralf Hand, Christian Rohr, and Stefan Brönnimann
Clim. Past, 18, 2449–2462, https://doi.org/10.5194/cp-18-2449-2022, https://doi.org/10.5194/cp-18-2449-2022, 2022
Short summary
Short summary
We present a novel approach to assimilate climate information contained in chronicles and annals from the 15th century to generate climate reconstructions of the Burgundian Low Countries, taking into account uncertainties associated with the descriptions of narrative sources. Our study aims to be a first step towards a more quantitative use of available information contained in historical texts, showing how Bayesian inference can help the climate community with this endeavor.
Yuri Brugnara, Chantal Hari, Lucas Pfister, Veronika Valler, and Stefan Brönnimann
Clim. Past, 18, 2357–2379, https://doi.org/10.5194/cp-18-2357-2022, https://doi.org/10.5194/cp-18-2357-2022, 2022
Short summary
Short summary
We digitized dozens of weather journals containing temperature measurements from in and around Bern and Zurich. They cover over a century before the creation of a national weather service in Switzerland. With these data we could create daily temperature series for the two cities that span the last 265 years. We found that the pre-industrial climate on the Swiss Plateau was colder than suggested by previously available instrumental data sets and about 2.5 °C colder than the present-day climate.
Gilles Delaygue, Stefan Brönnimann, and Philip D. Jones
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2022-33, https://doi.org/10.5194/wcd-2022-33, 2022
Revised manuscript not accepted
Short summary
Short summary
We test whether any association between solar activity and meteorological conditions in the north Atlantic – European sector could be detected. We find associations consistent with those found by previous studies, with a slightly better statistical significance, and with less methodological biases which have impaired previous studies. Our study should help strengthen the recognition of meteorological impacts of solar activity.
Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 16, 2147–2161, https://doi.org/10.5194/tc-16-2147-2022, https://doi.org/10.5194/tc-16-2147-2022, 2022
Short summary
Short summary
Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.
Stefan Brönnimann, Peter Stucki, Jörg Franke, Veronika Valler, Yuri Brugnara, Ralf Hand, Laura C. Slivinski, Gilbert P. Compo, Prashant D. Sardeshmukh, Michel Lang, and Bettina Schaefli
Clim. Past, 18, 919–933, https://doi.org/10.5194/cp-18-919-2022, https://doi.org/10.5194/cp-18-919-2022, 2022
Short summary
Short summary
Floods in Europe vary on time scales of several decades. Flood-rich and flood-poor periods alternate. Recently floods have again become more frequent. Long time series of peak stream flow, precipitation, and atmospheric variables reveal that until around 1980, these changes were mostly due to changes in atmospheric circulation. However, in recent decades the role of increasing atmospheric moisture due to climate warming has become more important and is now the main driver of flood changes.
Daniel Steinfeld, Adrian Peter, Olivia Martius, and Stefan Brönnimann
EGUsphere, https://doi.org/10.5194/egusphere-2022-92, https://doi.org/10.5194/egusphere-2022-92, 2022
Preprint archived
Short summary
Short summary
We assess the performance of various fire weather indices to predict wildfire occurrence in Northern Switzerland. We find that indices responding readily to weather changes have the best performance during spring; in the summer and autumn seasons, indices that describe persistent hot and dry conditions perform best. We demonstrate that a logistic regression model trained on local historical fire activity can outperform existing fire weather indices.
Duncan Pappert, Yuri Brugnara, Sylvie Jourdain, Aleksandra Pospieszyńska, Rajmund Przybylak, Christian Rohr, and Stefan Brönnimann
Clim. Past, 17, 2361–2379, https://doi.org/10.5194/cp-17-2361-2021, https://doi.org/10.5194/cp-17-2361-2021, 2021
Short summary
Short summary
This paper presents temperature and pressure measurements from the 37 stations of the late 18th century network of the Societas Meteorologica Palatina, in addition to providing an inventory of the available observations, most of which have been digitised. The quality of the recovered series is relatively good, as demonstrated by two case studies. Early instrumental data such as these will help to explore past climate and weather extremes in Europe in greater detail.
Moritz Buchmann, Michael Begert, Stefan Brönnimann, and Christoph Marty
The Cryosphere, 15, 4625–4636, https://doi.org/10.5194/tc-15-4625-2021, https://doi.org/10.5194/tc-15-4625-2021, 2021
Short summary
Short summary
We investigated the impacts of local-scale variations by analysing snow climate indicators derived from parallel snow measurements. We found the largest relative inter-pair differences for all indicators in spring and the smallest in winter. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and provide, in combination with break-detection methods, the groundwork in view of any homogenization efforts regarding snow time series.
Claudia Timmreck, Matthew Toohey, Davide Zanchettin, Stefan Brönnimann, Elin Lundstad, and Rob Wilson
Clim. Past, 17, 1455–1482, https://doi.org/10.5194/cp-17-1455-2021, https://doi.org/10.5194/cp-17-1455-2021, 2021
Short summary
Short summary
The 1809 eruption is one of the most recent unidentified volcanic eruptions with a global climate impact. We demonstrate that climate model simulations of the 1809 eruption show generally good agreement with many large-scale temperature reconstructions and early instrumental records for a range of radiative forcing estimates. In terms of explaining the spatially heterogeneous and temporally delayed Northern Hemisphere cooling suggested by tree-ring networks, the investigation remains open.
Noemi Imfeld, Leopold Haimberger, Alexander Sterin, Yuri Brugnara, and Stefan Brönnimann
Earth Syst. Sci. Data, 13, 2471–2485, https://doi.org/10.5194/essd-13-2471-2021, https://doi.org/10.5194/essd-13-2471-2021, 2021
Short summary
Short summary
Upper-air data form the backbone of reanalysis products, particularly in the pre-satellite era. However, historical upper-air data are error-prone because measurements at high altitude were especially challenging. Here, we present a collection of data from historical intercomparisons of radiosondes and error assessments reaching back to the 1930s that may allow us to better characterize such errors. The full database, including digitized data, images, and metadata, is made publicly available.
Stefan Brönnimann and Sylvia Nichol
Atmos. Chem. Phys., 20, 14333–14346, https://doi.org/10.5194/acp-20-14333-2020, https://doi.org/10.5194/acp-20-14333-2020, 2020
Short summary
Short summary
Historical column ozone data from New Zealand and the UK from the 1950s are digitised and re-evaluated. They allow studying the ozone layer prior to the era of ozone depletion. Day-to-day changes are addressed, which reflect the flow near the tropopause and hence may serve as a diagnostic for atmospheric circulation in a time and region of sparse radiosondes. A long-term comparison shows the amount of ozone depletion at southern mid-latitudes and indicates how far we are from full recovery.
Stefan Brönnimann
Clim. Past, 16, 1937–1952, https://doi.org/10.5194/cp-16-1937-2020, https://doi.org/10.5194/cp-16-1937-2020, 2020
Short summary
Short summary
Scientists often reconstruct climate from proxy data such as tree rings or historical documents. Here, I do the reverse and produce a weather diary from historical numerical weather data. Such "synthetic weather diaries" may be useful for historians, e.g. to compare with other sources or to study the weather experienced during a journey or a military operation. They could also help train machine-learning approaches, which could then be used to reconstruct weather from historical diaries.
Cited articles
Ahmadalipour, A. and Moradkhani, H.: Escalating heat-stress mortality risk
due to global warming in the Middle East and North Africa (MENA), Environ.
Int., 117, 215–225, https://doi.org/10.1016/j.envint.2018.05.014, 2018.
Alexandersson, H.: A homogeneity test applied to precipitation data, J.
Climatol., 6, 661–675, https://doi.org/10.1002/joc.3370060607, 1986.
Azorin-Molina, C., Guijarro, J.-A., McVicar, T. R., Vicente-Serrano, S. M.,
Chen, D., Jerez, S., and Espírito-Santo, F.: Trends of daily peak wind
gusts in Spain and Portugal, 1961–2014, J. Geophys. Res.-Atmos., 121,
1059–1078, https://doi.org/10.1002/2015JD024485, 2016.
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A.,
and Wood, E. F.: Present and future Köppen-Geiger climate classification
maps at 1-km resolution, Sci. Data, 5, 180214,
https://doi.org/10.1038/sdata.2018.214, 2018.
Bolton, D.: The computation of equivalent potential temperature, Mon. Weather
Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2, 1980.
Brugnara, Y., Good, E., Squintu, A. A., van der Schrier, G., and
Brönnimann, S.: The EUSTACE global land station daily air temperature
dataset, Geosci. Data J., 6, 189–204, https://doi.org/10.1002/gdj3.81, 2019.
Brugnara, Y., Auchmann, R., Rutishauser, T., Gehrig, R., Pietragalla, B.,
Begert, M., Sigg, C., Knechtl, V., Konzelmann, T., Calpini, B., and
Brönnimann, S.: Homogeneity assessment of phenological records from the
Swiss Phenology Network, Int. J. Biometeorol., 64, 71–81,
https://doi.org/10.1007/s00484-019-01794-y, 2020.
Buzan, J. R. and Huber, M.: Moist heat stress on a hotter earth, Annu. Rev.
Earth Planet. Sci., 48, 623–655,
https://doi.org/10.1146/annurev-earth-053018-060100, 2020.
Buzan, J. R., Oleson, K., and Huber, M.: Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5, Geosci. Model Dev., 8, 151–170, https://doi.org/10.5194/gmd-8-151-2015, 2015.
Coffel, E. D., Horton, R. M., and de Sherbinin, A.: Temperature and humidity
based projections of a rapid rise in global heat stress exposure during the
21st century, Environ. Res. Lett., 13, 014001,
https://doi.org/10.1088/1748-9326/aaa00e, 2018.
Coll, J., Domonkos, P., Guijarro, J., Curley, M., Rustemeier, E., Aguilar,
E., Walsh, S., and Sweeney, J.: Application of homogenization methods for
Ireland's monthly precipitation records: Comparison of break detection
results, Int. J. Climatol., 40, 6169–6188, https://doi.org/10.1002/joc.6575,
2020.
Davies-Jones, R.: An efficient and accurate method for computing the
wet-bulb temperature along pseudoadiabats, Mon. Weather Rev., 136, 2764–2785,
https://doi.org/10.1175/2007MWR2224.1, 2008.
Di Napoli, C., Pappenberger, F., and Cloke, H. L.: Assessing heat-related
health risk in Europe via the Universal Thermal Climate Index (UTCI), Int.
J. Biometeorol., 62, 1155–1165, https://doi.org/10.1007/s00484-018-1518-2,
2018.
Dong, J., Peng, J., He, X., Corcoran, J., Qiu, S., and Wang, X.:
Heatwave-induced human health risk assessment in megacities based on heat
stress-social vulnerability-human exposure framework, Landsc. Urban Plan.,
203, 103907, https://doi.org/10.1016/j.landurbplan.2020.103907, 2020.
Dong, J., Brönnimann, S., Hu, T., Liu, Y., and Peng, J.: GSDM-WBT: Global
station-based daily maximum wet-bulb temperature data for 1981–2020, Zenodo
[data set], https://doi.org/10.5281/zenodo.7014332, 2022.
Dong, Z., Wang, L., Sun, Y., Hu, T., Limsakul, A., Singhruck, P., and
Pimonsree, S.: Heatwaves in Southeast Asia and their changes in a warmer
world, Earths Future, 9, e2021EF001992,
https://doi.org/10.1029/2021EF001992, 2021.
Doutreloup, S., Fettweis, X., Rahif, R., Elnagar, E., Pourkiaei, M. S., Amaripadath, D., and Attia, S.: Historical and future weather data for dynamic building simulations in Belgium using the regional climate model MAR: typical and extreme meteorological year and heatwaves, Earth Syst. Sci. Data, 14, 3039–3051, https://doi.org/10.5194/essd-14-3039-2022, 2022.
Dumitrescu, A., Cheval, S., and Guijarro, J. A.: Homogenization of a
combined hourly air temperature dataset over Romania, Int. J. Climatol., 40,
2599–2608, https://doi.org/10.1002/joc.6353, 2020.
Dunn, R. J. H., Willett, K. M., Morice, C. P., and Parker, D. E.: Pairwise homogeneity assessment of HadISD, Clim. Past, 10, 1501–1522, https://doi.org/10.5194/cp-10-1501-2014, 2014.
Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473–491, https://doi.org/10.5194/gi-5-473-2016, 2016.
Fang, S., Mao, K., Xia, X., Wang, P., Shi, J., Bateni, S. M., Xu, T., Cao, M., Heggy, E., and Qin, Z.: Dataset of daily near-surface air temperature in China from 1979 to 2018, Earth Syst. Sci. Data, 14, 1413–1432, https://doi.org/10.5194/essd-14-1413-2022, 2022.
Fioravanti, G., Piervitali, E., and Desiato, F.: A new homogenized daily
data set for temperature variability assessment in Italy, Int. J. Climatol.,
39, 5635–5654, https://doi.org/10.1002/joc.6177, 2019.
Fischer, E. M., Sippel, S., and Knutti, R.: Increasing probability of
record-shattering climate extremes, Nat. Clim. Change, 11, 689–695,
https://doi.org/10.1038/s41558-021-01092-9, 2021.
Freychet, N., Tett, S. F. B., Yan, Z., and Li, Z.: Underestimated change of
wet-bulb temperatures over east and south China, Geophys. Res. Lett., 47,
e2019GL086140, https://doi.org/10.1029/2019GL086140, 2020.
Gubler, S., Hunziker, S., Begert, M., Croci-Maspoli, M., Konzelmann, T.,
Brönnimann, S., Schwierz, C., Oria, C., and Rosas, G.: The influence of
station density on climate data homogenization, Int. J. Climatol., 37,
4670–4683, https://doi.org/10.1002/joc.5114, 2017.
Guo, Y., Huang, Y., and Fu, Z.: Regional compound humidity-heat extremes in
the mid-lower reaches of the Yangtze River: a dynamical systems perspective,
Environ. Res. Lett., 17, 064032, https://doi.org/10.1088/1748-9326/ac715f,
2022.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. R. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Ho, H. C., Knudby, A., Walker, B. B., and Henderson, S. B.: Delineation of
spatial variability in the temperature-mortality relationship on extremely
hot days in greater Vancouver, Canada, Environ. Health Perspect., 125,
66–75, https://doi.org/10.1289/EHP224, 2017.
Hu, L. and Li, Q.: Greenspace, bluespace, and their interactive influence on
urban thermal environments, Environ. Res. Lett., 15, 034041,
https://doi.org/10.1088/1748-9326/ab6c30, 2020.
Im, E.-S., Pal, J. S., and Eltahir, E. A. B.: Deadly heat waves projected in
the densely populated agricultural regions of South Asia, Sci. Adv., 3,
e1603322, https://doi.org/10.1126/sciadv.1603322, 2017.
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J. J.,
Fiorino, M., and Potter, G. L.: NCEP–DOE AMIP-II Reanalysis (R-2), B.
Am. Meteorol. Soc., 83, 1631–1644, https://doi.org/10.1175/BAMS-83-11-1631,
2002.
Kang, S. and Eltahir, E. A. B.: North China Plain threatened by deadly
heatwaves due to climate change and irrigation, Nat. Commun., 9, 2894,
https://doi.org/10.1038/s41467-018-05252-y, 2018.
Kopp, B.: WetBulb.m, GitHub [code], https://github.com/bobkopp/WetBulb.m (last access: 14 November 2022),
2020.
Li, Z., Yan, Z., Zhu, Y., Freychet, N., and Tett, S.: Homogenized Daily
Relative Humidity Series in China during 1960–2017, Adv. Atmos. Sci., 37,
318–327, https://doi.org/10.1007/s00376-020-9180-0, 2020.
Mamara, A., Argiriou, A. A., and Anadranistakis, M.: Homogenization of mean
monthly temperature time series of Greece, Int. J. Climatol., 33, 2649–2666,
https://doi.org/10.1002/joc.3614, 2013.
Mazdiyasni, O., AghaKouchak, A., Davis, S. J., Madadgar, S., Mehran, A.,
Ragno, E., Sadegh, M., Sengupta, A., Ghosh, S., Dhanya, C. T., and Niknejad,
M.: Increasing probability of mortality during Indian heat waves, Sci. Adv.,
3, e1700066, https://doi.org/10.1126/sciadv.1700066, 2017.
McKinnon, K. A. and Poppick, A.: Estimating changes in the observed
relationship between humidity and temperature using noncrossing quantile
smoothing splines, J. Agr. Biol. Envir. St., 25, 292–314,
https://doi.org/10.1007/s13253-020-00393-4, 2020.
Menne, M. J. and Williams, C. N.: Homogenization of temperature series via
pairwise comparisons, J. Climate, 22, 1700–1717,
https://doi.org/10.1175/2008JCLI2263.1, 2009.
Mora, C., Dousset, B., Caldwell, I. R., Powell, F. E., Geronimo, R. C.,
Bielecki, C. R., Counsell, C. W. W., Dietrich, B. S., Johnston, E. T.,
Louis, L. V., Lucas, M. P., McKenzie, M. M., Shea, A. G., Tseng, H.,
Giambelluca, T. W., Leon, L. R., Hawkins, E., and Trauernicht, C.: Global
risk of deadly heat, Nat. Clim. Change, 7, 501–506,
https://doi.org/10.1038/nclimate3322, 2017.
Pal, J. S. and Eltahir, E. A. B.: Future temperature in southwest Asia
projected to exceed a threshold for human adaptability, Nat. Clim. Change,
6, 197–200, https://doi.org/10.1038/nclimate2833, 2016.
Paulhus, J. L. H. and Kohler, M. A.: Interpolation of missing precipitation
records, Mon. Weather Rev., 80, 129–133,
https://doi.org/10.1175/1520-0493(1952)080<0129:IOMPR>2.0.CO;2, 1952.
Perkins-Kirkpatrick, S. E. and Lewis, S. C.: Increasing trends in regional
heatwaves, Nat. Commun., 11, 3357, https://doi.org/10.1038/s41467-020-16970-7,
2020.
Poppick, A. and Mckinnon, K. A.: Observation-based simulations of humidity
and temperature using quantile regression, J Climate, 33, 16,
https://doi.org/10.1175/JCLI-D-20-0403.1, 2020.
Raymond, C., Matthews, T., and Horton, R. M.: The emergence of heat and
humidity too severe for human tolerance, Sci. Adv., 6, eaaw1838,
https://doi.org/10.1126/sciadv.aaw1838, 2020.
Rogers, C. D. W., Ting, M., Li, C., Kornhuber, K., Coffel, E. D., Horton, R.
M., Raymond, C., and Singh, D.: Recent increases in exposure to extreme
humid-heat events disproportionately affect populated regions, Geophys. Res.
Lett., 48, e2021GL094183, https://doi.org/10.1029/2021GL094183, 2021.
Shrestha, M., Acharya, S. C., and Shrestha, P. K.: Bias correction of
climate models for hydrological modelling – are simple methods still
useful?, Meteorol. Appl., 24, 531–539, https://doi.org/10.1002/met.1655,
2017.
Smith, A., Lott, N., and Vose, R.: The Integrated Surface Database: Recent
Developments and Partnerships, B. Am. Meteorol. Soc., 92, 704–708,
https://doi.org/10.1175/2011BAMS3015.1, 2011.
Speizer, S., Raymond, C., Ivanovich, C., and Horton, R. M.: Concentrated and
Intensifying Humid Heat Extremes in the IPCC AR6 Regions, Geophys. Res.
Lett., 49, e2021GL097261, https://doi.org/10.1029/2021GL097261, 2022.
Sun, Y., Zhang, X., Zwiers, F. W., Song, L., Wan, H., Hu, T., Yin, H., and
Ren, G.: Rapid increase in the risk of extreme summer heat in Eastern China,
Nat. Clim. Change, 4, 1082–1085, https://doi.org/10.1038/nclimate2410,
2014.
Wang, B. and Yi, Y. K.: Developing an adapted UTCI (Universal Thermal
Climate Index) for the elderly population in China's severe cold climate
region, Sustain. Cities Soc., 69, 102813,
https://doi.org/10.1016/j.scs.2021.102813, 2021.
Yan, Y., You, Q., Wu, F., Pepin, N., and Kang, S.: Surface mean temperature
from the observational stations and multiple reanalyses over the Tibetan
Plateau, Clim. Dynam., 55, 2405–2419,
https://doi.org/10.1007/s00382-020-05386-0, 2020.
Yan, Y., Xu, Y., and Yue, S.: A high-spatial-resolution dataset of human
thermal stress indices over South and East Asia, Sci. Data, 8, 229,
https://doi.org/10.1038/s41597-021-01010-w, 2021.
Yu, S., Tett, S. F. B., Freychet, N., and Yan, Z.: Changes in regional wet
heatwave in Eurasia during summer (1979–2017), Environ. Res. Lett., 16,
064094, https://doi.org/10.1088/1748-9326/ac0745, 2021.
Zhang, Y., Held, I., and Fueglistaler, S.: Projections of tropical heat
stress constrained by atmospheric dynamics, Nat. Geosci., 14, 133–137,
https://doi.org/10.1038/s41561-021-00695-3, 2021.
Short summary
We produced a new dataset of global station-based daily maximum wet-bulb temperature (GSDM-WBT) through the calculation of wet-bulb temperature, data quality control, infilling missing values, and homogenization. The GSDM-WBT covers the complete daily series of 1834 stations from 1981 to 2020. The GSDM-WBT dataset handles stations with many missing values and possible inhomogeneities, which could better support the studies on global and regional humid heat events.
We produced a new dataset of global station-based daily maximum wet-bulb temperature (GSDM-WBT)...
Altmetrics
Final-revised paper
Preprint