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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.

Climate risk management has emerged over the last decade as a distinct area of activity within the wider field of climatology. Its focus is on integrating climate and non-climate information in order to enhance the decision-making process in a wide range of climate-sensitive sectors of society, the economy and the environment. Given the burgeoning pure and applied climate science literature that addresses a range of climate risks, the purpose of this progress report is to provide an overview of recent developments in the field of climatology that may contribute to the risk assessment component of climate risk management. Data rescue and climate database construction, hurricanes and droughts as examples of extreme climate events and seasonal climate forecasting are focused on in this report and are privileged over other topics because of either their fundamental importance for establishing event probability or scale of societal impact. The review of the literature finds that historical data rescue, climate reconstruction and the compilation of climate data bases has assisted immensely in understanding past climate events and increasing the information base for managing climate risk. Advances in the scientific understanding of the causes and the characterization of hurricanes and droughts stand to benefit the management of these two extreme events while work focused on unravelling the nature of ocean–atmosphere interactions and associated climate anomalies at the seasonal timescale has provided the basis for the possible seasonal forecasting of a range of climate events. The report also acknowledges that despite the potential of climate information to assist with managing climate risk, its uptake by decision makers should not be automatically assumed by the climatological community.

The extension of spatial and temporal coverage of digital daily maximum and minimum air temperature observations is indispensable for a greater understanding of past climate variability. Long‐term series are fundamental for the assessment of frequency, duration, intensity and geographical distribution of past extreme air temperature events at local and regional scales in Ireland. Raw daily observations from 12 long‐term and 21 short‐term maximum and minimum air temperature series in Ireland, extending from 1831 to 1968, were rescued from multiple archives. Detailed station metadata on instrumentation, site location, observation practices and observer's notes are included in the dataset. Over 970,000 daily maximum and minimum air temperature observations were transcribed from handwritten meteorological registers, publications, newspapers and the Daily Weather Report. The data rescue strategies, sources for data and metadata rescue, and methodologies for double keying are discussed. The Ireland Long‐term Maximum and Minimum Air Temperature dataset (ILMMT) format for daily air temperature and metadata and organization is reviewed. The ILMMT dataset comprises raw observations and detailed station metadata, so data users can apply their selected quality control and homogenization approaches. Raw daily observations from 12 long‐term and 21 short‐term maximum and minimum air temperature series in Ireland, extending from 1831 to 1968, and related metadata are available in the Ireland Long‐term Maximum and Minimum Air Temperature dataset (ILMMT). Open access to unexplored and geographically well‐distributed daily maximum and minimum air temperature series and related detailed metadata through the ILMNT dataset will fill key gaps in climate research for Ireland, Europe and worldwide.

Historical meteorological data are essential for increasing the level of understanding about past, present, and future climates. In the Northern Hemisphere, a significant amount of research has been dedicated to rescuing climate data from historical sources such as ship logbooks (e.g. RECLAIM, CLIWOC, and ICOADS). However, limited research in this field has focused on New Zealand, Southeast Australia, and the Southwest Pacific. Because these regions were colonized recently by Europeans (~200 years ago), only 50–100 years of land‐based meteorological data exist for many locations. However, meteorological information contained in ship logbooks may extend and reinforce the existing historical climate record for these regions. The Log of Logs is a catalogue of ships that visited Australia, New Zealand, and surrounding waters in the th and 20th centuries of the Common Era. These volumes provide a record of the location of ship logbooks. This study extracted information from the Log of Logs for New Zealand, Southeast Australia, and the Southwest Pacific for 1786–1900. The purpose of this was to locate ship logbooks that may contain meteorological data. The next stage of this project is to gather, image, digitize, and to analyse the data from the prioritized logbooks. These data have application for local climate reconstruction, extension of regional circulation indices, and augmentation of the extended reanalysis without radiosondes effort.

Shifting weather, changing settlement patterns and a lack of preparedness mean that dryland areas are most at risk from flooding. Researchers need to focus on data collection, early-warning systems, flood protection and more. Shifting weather, changing settlement patterns and a lack of preparedness mean that dryland areas are most at risk from flooding. Researchers need to focus on data collection, early-warning systems, flood protection and more. Credit: Akhtar Soomro/Reuters A family walks amid flood waters following rains and floods during the monsoon season in Mehar, Pakistan

The rescue, digitization, quality control, preservation, and utilization of long and high quality meteorological and climate records, particularly related to historical marine data, are crucial for advancing our understanding of the Earth’s climate system. In combination with land and air measurements, historical marine records serve as foundational pillars in linking present and past weather and climate information, offering essential insights into natural climate variability, extreme events in marine areas, baseline data for assessing current changes, and inputs for enhancing predictive climate models and reanalyses. This paper provides an overview of rescue activities covering marine weather data over the past centuries and presents and highlights several ongoing projects across the world and how the data are used in an integrative and international framework. Current and future continuous efforts in data rescue, digitization, quality control, and the development of temporally high-resolution meteorological and climatological observations from oceans, will greatly help to further complete our understanding and knowledge of the Earth’s climate system, including extremes, as well as improve the quality of reanalysis.

Tropical cyclone (TC) activities over the western North Pacific (WNP) and TC landfall in Japan are investigated by collecting historical TC track data and meteorological observation data starting from the mid-nineteenth century. Historical TC track data and TC best track data are merged over the WNP from 1884 to 2018. The quality of historical TC data is not sufficient to count the TC numbers over the WNP due to the lack of spatial coverage and different TC criteria before the 1950s. We focus on TC landfall in Japan using a combination of TC track data and meteorological data observed at weather stations and lighthouses from 1877 to 2019. A unified TC definition is applied to obtain equivalent quality during the whole analysis period. We identify lower annual TC landfall numbers during the 1970s to the 2000s and find other periods have more TC landfall numbers including the nineteenth century. No trend in TC landfall number is detected. TC intensity is estimated by an annual power dissipation index (APDI). High APDI periods are found to be around 1900, in the 1910s, from the 1930s to 1960s, and after the 1990s. When we focus on the period from 1977 to 2019, a significant increasing trend of ADPI is seen, and significant northeastward shift of TC landfall location is detected. On the other hand, TC landfall location shifts northeastward and then southwestward in about 100-year interval. European and US ships sailed through East and Southeast Asian waters before the weather station network was established in the late nineteenth century. Then, we focus on TC events in July 1853 observed by the US Naval Japan Expedition of Perry’s fleet and August 1863 by a UK Navy ship that participated in two wars in Japan. A TC moved slowly westward over the East China Sea south of the Okinawa Islands from 21 to 25 July 1853. Another TC was detected in the East China Sea on 15–16 August 1863 during the bombardment of Kagoshima in southern Japan. Pressure data are evaluated by comparing the observations made by 10 naval ships in Yokohama, central Japan during 1863–1864. The deviation of each ship pressure data from the 10 ships mean is about 2.7–2.8 hPa.

Whenever natural and human-made disasters strike, the proper response of the concerned authorities often relies on search and rescue services. Search and rescue services are complex multidisciplinary processes that involve several degrees of interdependent assignments. To handle such complexity, decision support systems are used for decision-making and execution of plans within search and rescue operations. Advances in data management solutions and artificial intelligence technologies have provided better opportunities to make more efficient and effective decisions that can lead to improved search and rescue operations. This paper provides findings from a bibliometric mapping and a systematic literature review performed to: (1) identify existing search and rescue processes that use decision support systems, data management solutions, and artificial intelligence technologies; (2) do a comprehensive analysis of existing solutions in terms of their research contributions to the investigated domain; and (3) investigate the potential for knowledge transfer between application areas. The main findings of this review are that non-conventional data management solutions are commonly used in land rescue operations and that geographical information systems have been integrated with various machine learning approaches for land rescue. However, there is a gap in the existing research on search and rescue decision support at sea, which can motivate future studies within this specific application area.

Large-scale data from digital infrastructure, like mobile phone networks, provides rich information on the behavior of millions of people in areas affected by climate stress. Using anonymized data on mobility and calling behavior from 5.1 million Grameenphone users in Barisal Division and Chittagong District, Bangladesh, we investigate the effect of Cyclone Mahasen, which struck Barisal and Chittagong in May 2013. We characterize spatiotemporal patterns and anomalies in calling frequency, mobile recharges, and population movements before, during and after the cyclone. While it was originally anticipated that the analysis might detect mass evacuations and displacement from coastal areas in the weeks following the storm, no evidence was found to suggest any permanent changes in population distributions. We detect anomalous patterns of mobility both around the time of early warning messages and the storm’s landfall, showing where and when mobility occurred as well as its characteristics. We find that anomalous patterns of mobility and calling frequency correlate with rainfall intensity ( r  = .75, p  < 0.05) and use calling frequency to construct a spatiotemporal distribution of cyclone impact as the storm moves across the affected region. Likewise, from mobile recharge purchases we show the spatiotemporal patterns in people’s preparation for the storm in vulnerable areas. In addition to demonstrating how anomaly detection can be useful for modeling human adaptation to climate extremes, we also identify several promising avenues for future improvement of disaster planning and response activities.

The majority of available climate data in global digital archives consist of data only from the 1940s or 1950s onwards, and many of these series have gaps and/or are available for only a subset of the variables which were actually observed. However, there exist billions of historical weather observations from the 1700s, 1800s, and early 1900s that are still in hard‐copy form and are at risk of being lost forever due to deterioration. An assessment of changes in climate extremes in several IPCC regions was not possible in IPCC AR6 WGI owing, in many cases, to the lack of available data. One such region is Africa, where the climate impact research and the ability to predict climate change impacts are hindered by the paucity of access to consistent good‐quality historical observational data. The aim of this innovative project was to use classroom‐based participatory learning to help transcribe some of the many meteorological observations from Africa that are thus far unavailable to researchers. This project transcribed quickly and effectively station series by enrolling the help of second‐year undergraduate students at Maynooth University in Ireland. The newly digitized African data will increase the temporal and spatial coverage of data in this important data‐sparse region. Students gained new skills while helping the global scientific community unearth new insight into past African climate. The project managed to transcribe 79 months of data at Andapa in Madagascar and 56 months of data for Macenta in Guinea. The digitized data will be openly and freely shared with the scientific and wider community via the Pangaea data repository, the C3S Climate Data Store, and the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI) data centre in the US. The project model has the potential for a broader roll‐out to other educational contexts and there is no shortage of data to be rescued. This paper provides details of the project, and all supporting information such as project guidelines and templates to enable other organizations to instigate similar programs. The CliDaR‐Africa project worked with second‐year undergraduate Geography students to successfully transcribe over 6 years of climate observations at Andapa in Guinea and over 5 years of climate observations at Macenta in Madagascar. The newly digitized African data for Madagascar and Guinea will increase the existing temporal and spatial coverage of data in this important data‐sparse region, where climate change impact studies are crucial. The data will allow the wider scientific community to conduct more robust climate extremes assessments. The data will also provide a basis for verifying future projections, allowing for more appropriate adaptation plans to be implemented.