Use imprint of society and history on climate data to inform climate services

Brönnimann, Stefan; Wintzer, Jeannine

doi:10.1038/d41586-018-02201-z

Cited by 6 publications

(2 citation statements)

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“…They were collected not only for scientific but also for practical applications such as agriculture or medicine, and they were measured in colonial or missionary contexts. 62 A huge amount of early instrumental weather reports stem from ship logbooks including temperature, precipitation, air pressure, and winds. While spatial and temporal coverage of these data sources is limited, they nevertheless allow analyzing historical extremes.…”

Section: Historical Instrumental Datamentioning

confidence: 99%

Historical weather data for climate risk assessment

Brönnimann

Martius

Röhr

et al. 2018

Annals of the New York Academy of Sciences

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Weather‐ and climate‐related hazards are responsible for monetary losses, material damages, and societal consequences. Quantifying related risks is, therefore, an important societal task, particularly in view of future climate change. For this task, climate risk assessment increasingly uses model chains, which mainly build on data from the last few decades. The past record of events could play a role in this context. New numerical techniques can make use of historical weather data to simulate impacts quantitatively. However, using historical data for model applications differs from using recent products. Here, we provide an overview of climate risk assessment methodologies and of the properties of historical instrumental and documentary data. Using three examples, we then outline how historical environmental data can be used today in climate risk assessment by (1) developing and validating numerical model chains, (2) providing a large statistical sample which can be directly exploited to estimate hazards and to model present risks, and (3) establishing “worst‐case” events which are relevant references in the present or future. The examples show that, in order to be successful, different sources (reanalyses, digitized instrumental data, and documentary data) and methods (dynamical downscaling and analog methods) need to be combined on a case‐by‐case basis.

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Section: Historical Instrumental Datamentioning

confidence: 99%

Historical weather data for climate risk assessment

Brönnimann

Martius

Röhr

et al. 2018

Annals of the New York Academy of Sciences

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“…Another limitation of station data is their uneven spatial distribution. Instrumental observations have started in Europe in the 17th century and have then spread in the rest of the world following colonization and commercial routes (Brönnimann and Wintzer, ). Even nowadays data coverage depends strongly on socio‐economic factors, meaning that some areas of the world are still poorly monitored.…”

Section: Introductionmentioning

confidence: 97%

The EUSTACE global land station daily air temperature dataset

Brugnara

Good

Squintu

et al. 2019

Geoscience Data Journal

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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 (http://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.

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A roadmap to climate data rescue services

Brönnimann

Brugnara

Allan

et al. 2018

Geoscience Data Journal

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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.

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Use imprint of society and history on climate data to inform climate services

Cited by 6 publications

References 0 publications

Historical weather data for climate risk assessment

Historical weather data for climate risk assessment

The EUSTACE global land station daily air temperature dataset

A roadmap to climate data rescue services

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