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In situ data are an essential need in the analyses of past and current climate change. All over the world, different data rescue activities try to extend existing time series and to retrieve data in spatial and temporal data sparse regions. Also in Europe, part of the historic data is still only available in paper archives, without any digital access to these sources. The EUMETNET Data Rescue activity was started in order to investigate the potential for additional long‐term stations, to monitor the progress of data rescue in European countries and to support participants with know‐how exchange and international connections to facilitate data exchange. The activity raised the awareness of the need for data rescue and ongoing activities. This paper will provide information on the data rescue activities of 4 EUMETNET countries focusing on different aspects and in different states of the data rescue process. While Austria, Croatia and Slovenia are connected by a partly common history which is also displayed in the history of their meteorological network, Catalonia has a different historical background. Nevertheless, the problems data rescue is confronted with are similar for all activities. Therefore, the selected countries will provide a good overview of the problems and solutions discussed during the EUMETNET meetings. This paper wants to offer insight into different strategies for data rescue as well as into ongoing activities. Moreover, the importance of cooperation in regard to data rescue is underlined and examples of possible partnerships are provided. [ABSTRACT FROM AUTHOR]

The data set represents the Regional Meteoric Water Line (RMWL) for the Yucatan Peninsula (Mexico), a region with karstic aquifers but little knowledge of the isotopic composition of meteoric water. This data paper comprises the isotopic composition of meteoric water collected from November 2018 to November 2019 as monthly composite samples expressed as weighted monthly mean precipitation from 16 locations across the Yucatan Peninsula. The RMWL was δ2H = 7.803 δ18O + 12.075, with a slope and intercept suggesting precipitation from air that has undergone condensation, air masses with variable moisture or recycled moisture. The average δ18O is −2.57‰ (−12.23 to 1.2‰) and δ2H is −7.94‰ (−83.39 to 18.32‰). This data set was collected by the implementation of an isotopic monitoring network, gathering information useful as a tool for better understanding of the hydrology and hydrogeochemistry of karstic aquifers, and to develop proxies for paleohydrology, ecohydrology, climate change and paleoclimate studies in the Gulf of Mexico and the Great Caribbean Area. [ABSTRACT FROM AUTHOR]

A new data set of Night Marine Air Temperature (NMAT) is presented that builds on the HadNMAT2 data set, which was released in 2013. In a similar manner to HadNMAT2, the new data set (CLASSnmat) provides uninterpolated, monthly global values at a 5° resolution back to 1880. In addition to being extended to the end of 2019, four main developments are made in CLASSnmat: (1) the NMAT values are extracted from the most recent version of the International Comprehensive Ocean‐Atmosphere Data Set (ICOADS Release 3) and a revised method of eliminating duplicated observations is used; (2) values of NMAT are adjusted to 2m and 20m heights in addition to the 10 m height used in HadNMAT2; (3) a refinement is made to the corrections necessary during World War 2, which uses more of the NMAT observations and hence results in a more extensive spatial coverage for this period than was possible in HadNMAT2; (4) an updated gridding method is used that allows for an improved propagation of uncertainty from the individual NMAT values through to the gridded estimates. In this paper, the method used to construct CLASSnmat (version 1.0.0.0) is described. [ABSTRACT FROM AUTHOR]

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 Practices: This 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. [ABSTRACT FROM AUTHOR]

The Loess Plateau of China, located to the west of the Liupan Mountains and north of the Qinling Mountains stretching across the Yellow River, is the main loess deposit area in Northwest China. The loess in the northwest of China has deposits with large thickness and extensive distribution. Scanning electron microscopy (SEM) and energy spectrum analysis of loess have been performed to study the relationship between the loess microstructures and the forming climates Era. The relevant indexes were evaluated including the sand‐dropping speed (Vn), certain sedimentary depths (hn sand particle volume (Vd) and element ratios of Ca/Fe, K/Al, Si/Al and Ca/Mg. The microstructure indexes of loess accumulation and evolution reflect paleoclimate conditions and time scales to a certain extent. The important discovery is the microscopic sand‐dropping speed (Vn) and the sedimentary depth hn) calculation method of different sedimentary ages. These indices are compared with the record of major aeolian‐forming climates from the Guliya ice core, and provide a reliable benchmark for studying climate change It also can be used as important indicators of monsoonal change and environmental evolution reconstruction. The index of sand sedimentation speed (Vn) got from loess microstructure could reflect sand‐dropping speed and loess deposition course. According the article can serve as new indicators of climatic changes of different forming loess layers. It can also be concluded that the climatic indexes obtained from loess microstructure can reflect climate conditions of loess forming. The loess forming climatic parameters are synchronous correspond to Tengger Desert and Guliya ice core for studying climate change, then microscopic parameters can also be used for preliminary analysis of loess climate formation and has be found corresponding evidence, and the loess climatic parameters correspond to the other two indexes. The analysis of loess microstructure indexes is very useful in researching climate change. Loess microstructure indexes can find new indicators and information about the monsoon climate evolution and paleoclimate changes. [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]

Accurate, high‐resolution and sector‐specific greenhouse gas emissions information is increasingly needed for the development of local, targeted mitigation policies. We describe a detailed, spatially and temporally resolved CO2 emissions data product, Mahuika‐Auckland, for Auckland, New Zealand, based on Auckland's regional greenhouse gas and air emissions inventories. Emissions are provided at 500 m spatial resolution and at a 1‐hr time step, a level of detail not previously available for any New Zealand city. We divide fossil fuel emissions into six sectors that comprise the majority of Auckland Region's CO2 emissions profile: on‐road transport, industrial non‐point buildings and point sources, commercial non‐point buildings, residential non‐point buildings, air transport and sea transport. We also include separate layers representing biogenic CO2 emissions (primarily waste and wood burning), as these are significant sources in Auckland. We distribute emissions spatially and temporally based on activity data, energy and fuel consumption patterns, and population statistics. The code to generate Mahuika‐Auckland has been designed to be flexible so that updated information and/or data from more recent years can easily be incorporated. This data product improves upon New Zealand's current inventories that are only resolved at the regional and annual scale, providing a new level of detail that can be used as a prior estimate for atmospheric inversions, to inform emissions reduction policies and to guide the development of zero carbon pathways. [ABSTRACT FROM AUTHOR]

Four climate parameters (i.e. maximum, mean and minimum air temperature and precipitation amount) from 10 regional climate models, provided by the EURO‐CORDEX initiative, are adjusted using as reference the ROCADA gridded dataset. The adjustment was performed on a daily temporal resolution for the historical period (1971–2005), as well as for climate change scenarios based on two Representative Concentration Pathways (RCP4.5 and RCP8.5). The most accurately method for bias‐correction (BC) was selected following a 2‐fold cross‐validation approach, which was performed on historical data using two methods: Quantile Mapping (QMAP) and Multivariate Bias Correction with N‐dimensional probability (MBCn). The performances of the two methods are very similar when analysing the frequency distribution of each selected variable, whereas the comparison between (1) the intervariables correlation of the adjusted datasets, and (2) the reference dataset revealed much smaller differences for the dataset adjusted with the multivariate method, hence this was used for producing the BC scenario dataset. Based on the MBCn adjusted dataset, a climate change analysis over Romania was tested at the seasonal and annual scale. Overall, for the multimodel ensemble mean, at the country level, a substantial temperature increase is reported for both scenarios and no significant trend is revealed for precipitation amount. The adjusted RCMs are provided without any restrictions via an open‐access repository in netCDF CF‐1.4‐compliant file format. The BC climate models are archived at the 0.1° spatial resolution, in the WGS‐84 coordinate system, at a daily temporal resolution. [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]

Centennial‐to‐millennial temperature records of the past provide a context for the interpretation of current and future changes in climate. Quaternary climates have been relatively well studied in north‐east North America and the adjacent Atlantic Ocean over the last decades, and new research methods have been developed to improve reconstructions. We present newly inferred reconstructions of sea surface temperature for the north‐western Atlantic region, together with a compilation of published temperature records. The database thus comprises a total of 86 records from both marine and terrestrial sites, including lakes, peatlands, ice and tree rings, each covering at least part of the Holocene. For each record, we present details on seasons covered, chronologies and information on radiocarbon dates and analytical time steps. The 86 records contain a total of 154 reconstructions of temperature and temperature‐related variables. Main proxies include pollen and dinocysts, while summer was the season for which the highest number of reconstructions were available. Many records covered most of the Holocene, but many dinocyst records did not extend to the surface, due to sediment mixing, and dendroclimate records were limited to the last millennium. The database allows for the exploration of linkages between sea ice and climate and may be used in conjunction with other palaeoclimate and palaeoenvironmental records, such as wildfire records and peatland dynamics. This inventory may also aid the identification of gaps in the geographic distribution of past temperature records thus guiding future research efforts. [ABSTRACT FROM AUTHOR]

Two datasets of soil temperature observations collected at Norman, Oklahoma, USA, were analysed to study horizontal and vertical variability in their observations. The first dataset comprised 15-min resolution soil temperature observations from 20 September 2011 to 18 November 2013 in seven plots across a 10-m transect. In each plot, sensors were located at depths of 5, 10, and 30 cm. All seven plots observed fairly consistent maximum soil temperature observations during the spring, fall, and winter months. Starting in late May, the observed spread in soil temperatures across the 10-m transect increased significantly until August when the observed spread in temperatures decreased. The range of observed minimum soil temperature was more consistent year-round at the shallower depths, but showed similar patterns to the maximum soil temperature ranges at deeper depths. The second dataset comprised 15-min resolution soil temperature observations from 20 November 2013 to 1 December 2015 in a single plot at the same location as the first dataset. Soil temperature sensors were placed every 2 cm from the surface down to 40 cm deep to study the vertical variability in soil temperature measurements (focusing on a winter and a summer case). Both winter and summer conditions showed that the temperature differences between depths behaved logarithmically with the shallower depths having larger differences than deeper depths. [ABSTRACT FROM AUTHOR]