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This research presents a case study of the biases and discontinuities that were introduced in observed long-term mean wind-speed and gust data-series due to anemometer changes in a meteorological station in northern Spain, operated by the Spanish State Meteorological Agency: San Sebastian-Igueldo. Field and wind-tunnel experiments with predefined conditions have been presented in the literature, however this research uses a real case study to assess the impact of anemometer changes on wind speed measurements due to three factors being: (i) the 3-cup anemometer model (SEAC vs. THIES companies); (ii) sensor height (∼19.95 m vs. ∼20.45 m) and (iii) sensor age (20-years old vs. new). Our results show (a) substantial biases in the measured wind speed and daily peak wind gusts, with the new THIES anemometer reporting stronger surface winds than the old SEAC anemometer; (b) opposing biases under weak (negative) and moderate-strong (positive) winds; and (c) significant breakpoints in the long-term wind data-series, which highlight the importance of data homogenization. National Weather Services and climate assessment groups will benefit from these findings since errors in wind speed and gust measurements can be minimized by implementing systematic observation protocols. Robust anemometer observations provide a basis for accurate quantification of the magnitude of changes and the variability of surface winds. This research was funded by the following projects: IBER-STILLING (RTI2018–095749-A-I00, MCIU/AEI/FEDER,UE); VENTS (GVA-AICO/2021/023) and the CSIC Interdisciplinary Thematic Platform (PTI) Clima (PTI-CLIMA).

[EN] Most studies on wind variability have deepened into the stilling vs. reversal phenomena at global to regional scales, while the long-term changes in local-scale winds such as sea-breezes (SB) represent a gap of knowledge in climate research. The state-of-the-art of the wind variability studies suggests a hypothetical reinforcement of SB at coastal stations. We frst developed a robust automated method for the identifcation of SB days. Then, by using homogenized wind observations from 16 stations across Eastern Spain, we identifed 9,349 episodes for analyzing the multidecadal variability and trends in SB speeds, gusts and occurrence for 1961¿2019. The major fnding is the opposite trends and decoupled variability of SB speeds and gusts: the SB speeds declined signifcantly in all seasons (except for winter), and the SB gusts strengthened at the annual scale and in autumn¿winter, being most signifcant in autumn. Our results also show that the SB occurrence has increased across most of Eastern Spain, although presenting contrasting seasonal trends: positive in winter and negative in summer. We found that more frequent anticyclonic conditions, NAOI+and MOI+are positively linked to the increased winter occurrence; however, the causes behind the opposite trends in SB speeds and gusts remain unclear. The SB changes are complex to explain, involving both large-scale circulation and physical-local factors that challenge the understanding of the opposite trends. Further investigation is needed to assess whether these trends are a widespread phenomenon, while climate models could simulate the drivers behind these decoupled SB changes in a warmer climate., We thank AEMET for the observed wind speed data. This research was funded by the following projects: IBER-STILLING (RTI2018-095749-A-I00, MCIU/AEI/FEDER,UE); VENTS (GVA-AICO/2021/023) and the CSIC Interdisciplinary Thematic Platform (PTI) Clima (PTI-CLIMA). C.A.M was granted by Ramon y Cajal fellowship (RYC-2017-22830), and supported by a 2021 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. This study is also supported by Unidad Asociada CSIC-Universidad de Vigo: Grupo de Fisica de la Atmosfera y del Océano. The authors wish to acknowledge the anonymous reviewers for their detailed and helpful comments to the original manuscript.

Trabajo presentado en EMS Annual Meeting, celebrado en Bonn (Alemania) del 04 al 09 de septiembre de 2022., Sea breezes can occur on any coast of the world affecting meteorological variables broadly used to detect sea breeze episodes. To date, there is no universal method to identify sea breezes that works all over the globe. Most existing methodologies develop their own selection methods based on thresholds related to the local characteristics of the study site: e.g. wind direction based on the coastline orientation, cloud cover, precipitation, humidity, land-sea air temperature difference, pressure amplitude, insolation, among others. However, detecting past episodes from well-defined criteria makes sea breeze identification dependent on these criteria. This makes most classified events on the same site differ from each other, as well as making extrapolation to other regions difficult. The scarcity of high-resolution observed historical data over land and sea surfaces has limited the sea breeze understanding in many coastal regions across the world. To address the need of developing a universal method applicable to any coastal region of the globe, here we explore deep learning techniques (e.g. deep convolutional neural networks). We train these models using a time-series of high-resolution physical reanalysis (e.g. ERA-5 Land) gridded and observed data, after identifying 5 years of sea breezes manually for random stations around the globe. Results from this study constitute the first historical sea breeze global database spanning approximately the last 40 years. The ability of machine learning models to detect sea breezes allows the development of a simple and universal approach, which will improve the understanding of sea breeze at spatial scales which has not been addressed before, as far as we know.

The performance of a new historical reanalysis, the NOAA–CIRES–DOE Twentieth Century Reanalysis version 3 (20CRv3), is evaluated via comparisons with other reanalyses and independent observations. This dataset provides global, 3-hourly estimates of the atmosphere from 1806 to 2015 by assimilating only surface pressure observations and prescribing sea surface temperature, sea ice concentration, and radiative forcings. Comparisons with independent observations, other reanalyses, and satellite products suggest that 20CRv3 can reliably produce atmospheric estimates on scales ranging from weather events to long-term climatic trends. Not only does 20CRv3 recreate a “best estimate” of the weather, including extreme events, it also provides an estimate of its confidence through the use of an ensemble. Surface pressure statistics suggest that these confidence estimates are reliable. Comparisons with independent upper-air observations in the Northern Hemisphere demonstrate that 20CRv3 has skill throughout the twentieth century. Upper-air fields from 20CRv3 in the late twentieth century and early twenty-first century correlate well with full-input reanalyses, and the correlation is predicted by the confidence fields from 20CRv3. The skill of analyzed 500-hPa geopotential heights from 20CRv3 for 1979–2015 is comparable to that of modern operational 3–4-day forecasts. Finally, 20CRv3 performs well on climate time scales. Long time series and multidecadal averages of mass, circulation, and precipitation fields agree well with modern reanalyses and station- and satellite-based products. 20CRv3 is also able to capture trends in tropospheric-layer temperatures that correlate well with independent products in the twentieth century, placing recent trends in a longer historical context.

Trabajo presentado en EGU General Assembly, celebrado en Vienna (Austria) del 23 al 27 de mayo de 2022., Sudden Stratospheric Warmings (SSW) are known to have impacts on the tropospheric atmospheric circulation that can persist up to 60 days. The aim of this work is to evaluate the influence of SSW on both observed mean wind speed and daily peak wind gusts across the Northern Hemisphere for 1961-2021. A set of 26 SSW with tropospheric response, including split (12) and displaced (14) events, are chosen for this matter. Daily wind speed means and peak gust data are retrieved from the quality-controlled and sub-daily station dataset: HadISD. The ultimate goal will be to prove the ability of SSW as possible source of predictability in the medium term for surface wind speed across the Northern Hemisphere, which would have direct applications in areas such as: wind-power generation, agriculture and air quality, among many other socioeconomic and environmental issues.

Climate change may affect sea breezes in their magnitude and occurrence, having direct implications for the hydrologic cycle and desertification (i.e., development of sea breeze thunderstorms), air pollution dispersal, wind energy production, to name but a few. To date, trends and multidecadal variability of sea breezes have been barely quantified because of the scarcity of long-term series, the low spatial-temporal resolution and the unreliability of observations over land-sea surfaces. Recent studies showed an increase in the occurrence of sea breeze days for the Eastern Spain, as well as opposite trends between the mean speed and gusts. The causes behind these opposite trends remain unknown because of the complexity of thermally driven coastal wind systems. The aim of this study is to advance in the knowledge of the observed changes in sea breezes over the Western Mediterranean area for 1961-2020, and their likely causes. To do so, we will first apply a robust automated algorithm based on alternative criteria to detect potential sea breeze events. Then, we will use homogenized wind speed and gusts data from sub-daily observations across the Mediterranean region to quantify the magnitude and significance of changes in sea breezes for 1961-2020. Finally, we will estimate the relationship with large-scale circulation (e.g., modes of variability, weather types and mean layer vector wind) and physical-local factors (e.g., land use changes and land-sea air temperature gradient) from ERA5 reanalysis to better understand the likely causes behind the observed changes in sea breezes.

This study analyses quality controlled and homogenized near-surface wind speed series (SWS) at 87 meteorological stations distributed across Spain and Portugal for 1961–2019. Multidecadal variability analysis of both mean and gusts SWS confirms for the first time in the region the cessation of the stilling (decline of SWS) and a possible weak reversal phenomenon (increase in SWS) in the last decades, varying its onset from 1999 to 2018, depending on the season and variable. Different atmospheric circulation drivers are evaluated to explain the stilling and reversal phenomena. Among the chosen teleconnection indices, the Western Mediterranean Oscillation index (WeMOi) is the one that presents the highest (positive) correlation with SWS, although the correlation (negative) with the North Atlantic Oscillation index and the Mediterranean Oscillation index in winter is also statistically significant (p < 0.05). Results show that the interannual variability of the WeMOi could exert a strong influence on both the stilling and reversal phenomena, as it displays statistically significant negative trends for 1961–2010 and positive ones (non-significant) for 2010–2019, in agreement with the observed SWS trends. The use of the Jenkinson and Collison weather type classification reveals the marked influence of certain weather types in modulating SWS changes (i.e., west, northwest, and anticyclonic types). We proposed the increase in atmospheric thermal stability and the northward shift of the jet stream as principal causes of the stilling phenomenon in this region. Our results improve our understanding of wind changes, and highlight the importance of regional assessments to discern their socioeconomic and environmental impacts., E.U.-C. was supported by the FPI fellowship (PRE2019-090148), and C.A.-M. by the Ramon y Cajal fellowship (RYC-2017-22830). This study was conducted in the framework of the IBER-STILLING project RTI2018-095749-A-100 (MCIU/AEI/FEDER,UE), the VENTS project AICO/2021/023 (GVA) and the CSIC Interdisciplinary Thematic Platform (PTI) Clima. (PTI-CLIMA).

There is growing awareness among governments, businesses, and the general public of risks arising from changes to our climate on time scales from months through to decades. Some climatic changes could be unprecedented in their harmful socioeconomic impacts, while others with adequate forewarning and planning could offer benefits. There is therefore a pressing need for decision-makers, including policy-makers, to have access to and to use high-quality, accessible, relevant, and credible climate information about the past, present, and future to help make better-informed decisions and policies. We refer to the provision and use of such information as climate services. Established programs of research and operational activities are improving observations and climate monitoring, our understanding of climate processes, climate variability and change, and predictions and projections of the future climate. Delivering climate information (including data and knowledge) in a way that is usable and useful for decision-makers has had less attention, and society has yet to optimally benefit from the available information. While weather services routinely help weather-sensitive decision-making, similar services for decisions on longer time scales are less well established. Many organizations are now actively developing climate services, and a growing number of decision-makers are keen to benefit from such services. This article describes progress made over the past decade developing, delivering, and using climate services, in particular from the worldwide effort galvanizing around the Global Framework for Climate Services under the coordination of UN agencies. The article highlights challenges in making further progress and proposes potential new directions to address such challenges.

Two reports of Antarctic region potential new record high temperature observations (18.3°C, 6 February 2020 at Esperanza station and 20.8°C, 9 February 2020 at a Brazilian automated permafrost monitoring station on Seymour Island) were evaluated by a World Meteorological Organization (WMO) panel of atmospheric scientists. The latter figure was reported as 20.75°C in the media. The panel considered the synoptic situation and instrumental setups. It determined that a large high pressure system over the area created föhn conditions and resulted in local warming for both situations. Examination of the data and metadata of the Esperanza station observation revealed no major concerns. However, analysis of data and metadata of the Seymour Island permafrost monitoring station indicated that an improvised radiation shield led to a demonstrable thermal bias error for the temperature sensor. Consequently, the WMO has accepted the 18.3°C value for 1200 LST 6 February 2020 (1500 UTC 6 February 2020) at the Argentine Esperanza station as the new “Antarctic region (continental, including mainland and surrounding islands) highest temperature recorded observation” but rejected the 20.8°C observation at the Brazilian automated Seymour Island permafrost monitoring station as biased. The committee strongly emphasizes the permafrost monitoring station was not badly designed for its purpose, but the project investigators were forced to improvise a nonoptimal radiation shield after losing the original covering. Second, with regard to media dissemination of this type of information, the committee urges increased caution in early announcements as many media outlets often tend to sensationalize and mischaracterize potential records.

A World Meteorological Organization (WMO) Extremes Evaluation Committee investigated an observation of −69.6 °C by Klinck Automatic Weather Station (AWS) in Greenland on 22 December 1991 as the lowest temperature observed in Greenland, thereby making it the lowest recorded near‐surface air temperature for the Northern and Western Hemispheres and for WMO Region VI. The committee examined the metadata and observations of the station as well as the regional synoptic circulation. The committee concluded that the observation is credible in terms of instrument calibration, monitoring of the station and the synoptic situation. Consequently, the WMO Rapporteur accepted the observation as the officially lowest observed near‐surface air temperature for Greenland, the Northern and Western Hemisphere and for WMO Region VI. As a supplement to this investigation, the committee also recommends that opportunities be investigated such that AWS data from Greenland can be efficiently incorporated into real‐time weather forecasts and hence into reanalysis datasets.

The aim of time series homogenization is to remove nonclimatic effects, such as changes in station location, instrumentation, observation practices, and so on, from observed data. Statistical homogenization usually reduces the nonclimatic effects but does not remove them completely. In the Spanish ‘‘MULTITEST’’ project, the efficiencies of automatic homogenization methods were tested on large benchmark datasets of a wide range of statistical properties. In this study, test results for nine versions, based on five homogenization methods—the adapted Caussinus-Mestre algorithm for the homogenization of networks of climatic time series (ACMANT), ‘‘Climatol,’’ multiple analysis of series for homogenization (MASH), the pairwise homogenization algorithm (PHA), and ‘‘RHtests’’—are presented and evaluated. The tests were executed with 12 synthetic/surrogate monthly temperature test datasets containing 100–500 networks with 5–40 time series in each. Residual centered root-mean-square errors and residual trend biases were calculated both for individual station series and for network mean series. The results show that a larger fraction of the nonclimatic biases can be removed from station series than from network-mean series. The largest error reduction is found for the long-term linear trends of individual time series in datasets with a high signal-to-noise ratio (SNR), where the mean residual error is only 14%–36% of the raw data error. When the SNR is low, most of the results still indicate error reductions, although with smaller ratios than for large SNR. In general, ACMANT gave the most accurate homogenization results. In the accuracy of individual time series ACMANT is closely followed by Climatol, and for the accurate calculation of mean climatic trends over large geographical regions both PHA and ACMANT are recommended. This research was funded by the Spanish MULTITESTproject (Ministry of Economics and Competitiveness, CGL2014-52901-P).

Trabajo presentado en EGU General Assembly, celebrada en modalidad virtual del 4 al 8 de mayo de 2020., In a context of global climate change, the scientific community has evidenced a significant decrease in wind speed, a phenomenon known as «stilling». This climate trend has mainly been observed over mid-latitude continental surfaces since the 1980s. On the contrary, other studies have detected an increase in wind speed over ocean surfaces; and there is little conclusive scientific evidence on trends in wind speed across the troposphere. Furthermore, a reversal in global terrestrial stilling has recently been documented in few regional and global studies since the 2010s. The causes associated with the climate variability of wind speed have not yet been resolved and there are many uncertainties behind the «stilling» and «recovery» phenomenon because neither the quantity nor the quality of wind speed observations is adequate. This contribution shows an overview of the IBER-STILLING project (RTI2018-095749-A-I00) funded by the Spanish Ministry of Science, Innovation and Universities. This project aims to move forward on the assessment of wind speed and wind gusts variability and underlying causes globally, with emphasis on the Spanish territory and surrounding ocean (Atlantic) and sea (Mediterranean) surfaces. The IBER-STILLING project will collect and generate climate information of wind speed from different data sources; climate data will be subject to a comprehensive protocol for quality control and homogenization. The statistical analysis of these climate databases will allow characterizing trends and climatic cycles of wind speed, allowing a pioneering global analysis of wind speed over continental and ocean surfaces, and across the boundary layer and the entire troposphere. The project will also conduct wind-tunnel experiments to quantify biases introduced by anemometers devices.

In recognition of the need for data used in climate-related activities to be reliably and transparently managed, the World Meteorological Organization (WMO[1]) Congress adopted a High-Quality Global Data Management Framework for Climate (HQ-GDMFC) at its eighteenth session in June 2019. The HQ-GDMFC enables effective standards-based development and exchange of high-quality climate data. The scope of the HQ-GDMFC includes all of the Essential Climate Variables under WMO auspices, as described in WMO Resolution 60 (Cg-17). This includes observational data as well as data derived from climate analysis, reanalysis, predictions and projections. The framework of collaboration incorporates the National Meteorological and Hydrological Services’ Data Management units, Regional Climate Centers, international data centers, climate research bodies, certain Government agencies, academia and any other institution dealing with climate data archival, management, analysis and exchange. An International Expert Group on Climate Data Modernization (IEG-CDM2) was established in 20183, involving subject matter experts from several WMO programs and international data centers to guide the development of practical tools required for assessing data maturity for climate purposes.We present here the structure, elements and associated guidance and tools of the HQ-GDFMC. The essential components are: (1) The standards and recommended best practices for climate data management and stewardship are encapsulated in a regulatory manual called the Manual on HQ-GDMFC (WMO-No 1238). (2) A guidance booklet provides guidance on maturity assessment of climate datasets that contribute to the computation and analysis of climate indicators supporting climate policy-relevant information. (3) A climate data catalogue in support of climate change monitoring has been established, with the aim of providing a living list of datasets, with a primary focus on climate indicators. It is recommended that the maturity of such datasets be assessed; a maturity rating provides users with information on the level of maturity in documentation, archival, access, data quality assurance, data integrity and more, for each of the datasets. [1] The World Meteorological Organization (WMO) is a United Nations’ specialized agency in the field of weather, water and climate. As part of its activities, WMO fosters international collaboration to develop technical guidance and standards for the collection, processing, and management of data and forecast products. 2 The International Expert Group on Climate Data Modernization (IEG-CDM) is an Ad-Hoc group run with the support of the World Meteorological Organization (WMO). 3 The participants of the 2018 workshop implicitly formed the membership of the Ad-Hoc IEG-CDM team. (Reference report)

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org. Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.

An extreme (weather and climate) event does not only mean that an extreme occurs at a location, but more generally it can impact a certain area and last a certain period of time, which is defined as a regional extreme event (REE) with a certain impacted area and duration. The concept of REE has been defined to allow mainly objective assessment of the events without a pre-determined boundary and duration. This paper reviews the studies on REEs published during the past 20 years, especially recent years. Mainly in view of methodology, these studies can be divided into three types: studies focusing on spatial simultaneity, studies focusing on temporal persistence, and studies identifying REEs. The methods identifying REEs include two kinds, e.g., type-I methods stressing REE's temporal persistence within a relatively certain area and type-II methods focusing on catching a complete REE. Identification methods proposed in this paper could provide valuable information for various purposes, such as real-time monitoring, estimating long-term changes, mechanism diagnosis, forecasting study and even attribution analysis. Research on REEs is important for objectively defining extreme weather and climate events, which depends on the spatial and temporal scales of interest. Such an objective definition will support ongoing climate monitoring and improve the assessment of how regional extreme events have changed over time. Keywords: Extreme (weather and climate) events, Regional extreme events, Research progress, Review

A World Meteorological Organization weather and climate extremes committee has judged that the world’s longest reported distance for a single lightning flash occurred with a horizontal distance of 321 km (199.5 mi) over Oklahoma in 2007, while the world’s longest reported duration for a single lightning flash is an event that lasted continuously for 7.74 seconds over southern France in 2012. In addition, the committee has unanimously recommended amendment of the AMS Glossary of Meteorology definition of lightning discharge as a “series of electrical processes taking place within 1 second” by removing the phrase “within one second” and replacing with “continuously.” Validation of these new world extremes (a) demonstrates the recent and on-going dramatic augmentations and improvements to regional lightning detection and measurement networks, (b) provides reinforcement regarding the dangers of lightning, and (c) provides new information for lightning engineering concerns. Fil: Lang, Timothy J.. NASA Marshall Space Flight Center; Estados Unidos Fil: Pédeboy, Stéphane. Météorage; Francia Fil: Rison, Willlliam. Institute of Mining and Technology, Department of Electrical Engineering; México Fil: Cerveny, Randallll S.. Arizona State Universit, School of Geographical Sciences and Urban Planning; Estados Unidos Fil: Montanyà, Joan. Universidad Politécnica de Catalunya; España Fil: Chauzy, Serge. University of Toulouse/CNRS, Laboratoire d’Aérologie; Francia Fil: MacGorman, Donald R.. NOAA/National Severe Storms Laboratory; Estados Unidos Fil: Holle, Ronald L.. Vaisala, Inc.; Estados Unidos Fil: Avila, Eldo Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina Fil: Zhang, Yijun. Chinese Academy of Meteorological Sciences, Laboratory of Lightning Physics and Protection Engineering; China Fil: Carbin, Gregory. NOAA/Storm Prediction Center; Estados Unidos Fil: Mansell, Edward R.. NOAA/National Severe Storms Laboratory; Estados Unidos Fil: Kuleshov, Yuriy. Royal Melbourne Institute of Technology University, Bureau of Meteorology and School of Mathematical and Geospatial Sciences; Australia Fil: Petersrson, Thomas C.. World Meteorological Organization, Commission for Climatology; Estados Unidos Fil: Brunet, Manola. University of East Anglia, School of Environmental Sciences,Climatic Research Unit; Reino Unido. Universitat Rovira I Virgili; España Fil: Driouech, Fatima. Direction de la Météorologie Nationale, Climate Studies Service; Marruecos Fil: Krahenbuhl, Daniel S.. Arizona State University, School of Geographical Sciences and Urban Planning; Estados Unidos

Historical reanalyses that span more than a century are needed for a wide range of studies, from understanding large‐scale climate trends to diagnosing the impacts of individual historical extreme weather events. The Twentieth Century Reanalysis (20CR) Project is an effort to fill this need. It is supported by the National Oceanic and Atmospheric Administration (NOAA), the Cooperative Institute for Research in Environmental Sciences (CIRES), and the U.S. Department of Energy (DOE), and is facilitated by collaboration with the international Atmospheric Circulation Reconstructions over the Earth initiative. 20CR is the first ensemble of sub‐daily global atmospheric conditions spanning over 100 years. This provides a best estimate of the weather at any given place and time as well as an estimate of its confidence and uncertainty. While extremely useful, version 2c of this dataset (20CRv2c) has several significant issues, including inaccurate estimates of confidence and a global sea level pressure bias in the mid‐19th century. These and other issues can reduce its effectiveness for studies at many spatial and temporal scales. Therefore, the 20CR system underwent a series of developments to generate a significant new version of the reanalysis. The version 3 system (NOAA‐CIRES‐DOE 20CRv3) uses upgraded data assimilation methods including an adaptive inflation algorithm; has a newer, higher‐resolution forecast model that specifies dry air mass; and assimilates a larger set of pressure observations. These changes have improved the ensemble‐based estimates of confidence, removed spin‐up effects in the precipitation fields, and diminished the sea‐level pressure bias. Other improvements include more accurate representations of storm intensity, smaller errors, and large‐scale reductions in model bias. The 20CRv3 system is comprehensively reviewed, focusing on the aspects that have ameliorated issues in 20CRv2c. Despite the many improvements, some challenges remain, including a systematic bias in tropical precipitation and time‐varying biases in southern high‐latitude pressure fields.

The study describes significant outcomes of the Metrology for Meteorology' project, MeteoMet, which is an attempt to bridge the meteorological and metrological communities. The concept of traceability, an idea used in both fields but with a subtle difference in meaning, is at the heart of the project. For meteorology, a traceable measurement is the one that can be traced back to a particular instrument, time and location. From a metrological perspective, traceability further implies that the measurement can be traced back to a primary realization of the quantity being measured in terms of the base units of the International System of Units, the SI. These two perspectives reflect long-standing differences in culture and practice and this project - and this study - represents only the first step towards better communication between the two communities. The 3 year MeteoMet project was funded by the European Metrology Research Program (EMRP) and involved 18 European National Metrological Institutes, 3 universities and 35 collaborating stakeholders including national meteorology organizations, research institutes, universities, associations and instrument companies. The project brought a metrological perspective to several long-standing measurement problems in meteorology and climatology, varying from conventional ground-based measurements to those made in the upper atmosphere. It included development and testing of novel instrumentation as well as improved calibration procedures and facilities, instrument intercomparison under realistic conditions and best practice dissemination. Additionally, the validation of historical temperature data series with respect to measurement uncertainties and a methodology for recalculation of the values were included.

The International Surface Pressure Databank (ISPD) is the world's largest collection of global surface and sea-level pressure observations. It was developed by extracting observations from established international archives, through international cooperation with data recovery facilitated by the Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative, and directly by contributing universities, organizations, and countries. The dataset period is currently 1768–2012 and consists of three data components: observations from land stations, marine observing systems, and tropical cyclone best track pressure reports. Version 2 of the ISPD (ISPDv2) was created to be observational input for the Twentieth Century Reanalysis Project (20CR) and contains the quality control and assimilation feedback metadata from the 20CR. Since then, it has been used for various general climate and weather studies, and an updated version 3 (ISPDv3) has been used in the ERA-20C reanalysis in connection with the European Reanalysis of Global Climate Observations project (ERA-CLIM). The focus of this paper is on the ISPDv2 and the inclusion of the 20CR feedback metadata. The Research Data Archive at the National Center for Atmospheric Research provides data collection and access for the ISPDv2, and will provide access to future versions.

The robustness and reliability of any climatic assessment, especially those analyzing climate variability and change, depend crucially on the soundness of data and uncertainty associated with measurements. The metrological traceability to national standards of the measurements involved in climate change is a necessary step. This work describes the first attempt to define a fully traceable procedure for the calibration of air-temperature sensors in a special chamber manufactured for this purpose. The facility was designed to calibrate automatic weather stations by comparison with embedded reference standards traceable to temperature and pressure primary standards as maintained in a national metrology institute. The results shown in this paper were obtained through a prototype of the final calibration chamber. An application example of the calibration process is described for the temperature sensor working in the Meteorological Observatory of Moncalieri (Italy) of the Societa Meteorologica Italiana where a continuous climate record has been kept since 1865. As result, the data of the historical data series have now associated calibration uncertainties as contributions from the instrument capability, resolution, and calibration procedure adopted. Finally, the possibility of improving climatic analysis, for evaluating climatic trend, by applying calibration corrections to data recorded has been evaluated. The in situ calibration campaign and the resulting differences between the corrected and uncorrected data for the most important quantities considered in the climate analysis, the maximum, minimum, and mean temperatures, are reported. The analysis carried out on a one-year-long observation period shows that the application of the calibration function has an impact on the recorded data. The differences between corrected and uncorrected data are outside the expanded calibration uncertainty, and in a winter season, this effect is more evident.

A World Meteorological Organization (WMO) committee evaluated the record sea-level pressure (SLP) measurement of 1089.4 hPa on 30 December 2004 in Tosontsengel, Mongolia (1724.6 m). Although instrumentation and data collection procedures were properly followed according to the assessment of the committee, concern was raised regarding the reliability of SLP adjustment from such a high-elevation station. This paper addresses this concern with a number of analyses that look at relationships between SLP extremes and corresponding station elevation and temperature. First, we selected data from stations extracted from the Integrated Surface Database (ISD-Lite) of NOAA's National Climate Data Center. A spatial analysis indicates that elevation shows little to no association (R2 values essentially zero) to extreme SLP. However, a second analysis between extreme SLP and air temperature indicates that high regionalism exists in spatial correlations (local R2) between those two variables. This relationship to temperature is likely the result of differences in SLP adjustment formulae used around the world. Based on this analysis, on the need to differentiate the SLP values adjusted using extremely cold temperatures (and generally high elevation), and following past WMO SLP guidelines, the WMO Rapporteurs for Climate and Weather Extremes therefore have created two distinct SLP records: (a) highest adjusted SLP (below 750 m), currently 1083.3 hPa recorded on 31 December 1968 at Agata, Evenhiyskiy, Russia; and (b) highest adjusted SLP (above 750 m), currently 1089.4 hPa (by Russian method; 1089.1 hPa by WMO formula) on 30 December 2004 in Tosontsengel, Mongolia. Future WMO guidance regarding SLP adjustment may lead to re-evaluation of this and other SLP records.

Historical climatic data from station observations taken in North African and Middle East Mediterranean countries since the second half of the 19th century have been digitized and quality-controlled in the framework of the EU-funded European Reanalysis and Observations for Monitoring (EURO4M) project. Daily maximum and minimum temperatures and precipitation totals, along with sub-daily data for surface air pressure have been recovered by using historical data sources involving book/logbook collections archived in national and international data centres. The new dataset produced comprises climatic time series for 79 stations that have operated in southern and eastern Mediterranean countries. While the developed time series have data gaps, every effort has been made to infill these gaps, to improve assessments of the long-term changes in climate variability in the region.

A workshop was held in Casablanca, Morocco, in March 2012, to enhance knowledge of climate extremes and their changes in the Arab region. This workshop initiated intensive data compilation activities of daily observational weather station data from the Arab region. After conducting careful control processes to ensure the quality and homogeneity of the data, climate indices for extreme temperatures and precipitation were calculated. This study examines the temporal changes in climate extremes in the Arab region with regard to long-term trends and natural variability related to ENSO and NAO. We find consistent warming trends since the middle of the 20th Century across the region. This is evident in the increased frequencies of warm days and warm nights, higher extreme temperature values, fewer cold days and cold nights and shorter cold spell durations. The warming trends seem to be particularly strong since the early 1970s. Changes in precipitation are generally less consistent and characterised by a higher spatial and temporal variability; the trends are generally less significant. However, in the western part of the Arab region, there is a tendency towards wetter conditions. In contrast, in the eastern part, there are more drying trends, although, these are of low significance. We also find some relationships between climate extremes in the Arab region and certain prominent modes of variability, in particular El Nino-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). The relationships of the climate extremes with NAO are stronger, in general, than those with ENSO, and are particularly strong in the western part of the Arab region (closer to the Atlantic Ocean). The relationships with ENSO are found to be more significant towards the eastern part of the area of study. © 2013 Royal Meteorological Society

10.1002/gdj3.4 Availability of long-term and high-quality instrumental climate records is still insufficient and the rich heritage of meteorological surface observations is largely underexploited in many parts of the world. This is particularly striking over the Greater Mediterranean region (GMR), where meteorological observations have been taken since the 18th century at some locations. The lack of high quality and long series here is despite this region being regarded as a climate change hot spot. This article mainly assesses relevant sources containing Mediterranean historical climate data and metadata either from online repositories worldwide or physical archives, with the emphasis here on the rich holdings kept at French archives. A particular case study is the data rescue (DARE) program undertaken by the Algerian National Meteorological Service, as well as some of the past and ongoing projects and initiatives aimed at enhancing climate data availability and accessibility over the GMR. Our findings point to the high potential for undertaking DARE activities over the GMR and the need for bringing longer and higher quality climate time series to support a diverse number of scientific and technical assessments and policies.

On 13 September 1922, a temperature of 58°C (136.4°F) was purportedly recorded at El Azizia (approximately 40 km south-southwest of Tripoli) in what is now modern-day Libya. That temperature record of 58°C has been cited by numerous world-record sources as the highest recorded temperature for the planet. During 2010–11, a World Meteorological Organization (WMO) Commission of Climatology (CCl) special international panel of meteorological experts conducted an in-depth investigation of this record temperature for the WMO World Archive of Weather and Climate Extremes (http://wmo.asu.edu/). This committee identified five major concerns with the 1922 El Azizia temperature extreme record, specifically 1) potentially problematical instrumentation, 2) a probable new and inexperienced observer at the time of observation, 3) unrepresentative microclimate of the observation site, 4) poor correspondence of the extreme to other locations, and 5) poor comparison to subsequent temperature values recorded at the site. Based on these concerns, the WMO World Archive of Weather and Climate Extremes rejected this temperature extreme of 58°C as the highest temperature officially recorded on the planet. The WMO assessment is that the highest recorded surface temperature of 56.7°C (134°F) was measured on 10 July 1913 at Greenland Ranch (Death Valley), California.

Una de las principales novedades que aportará el proyecto MULTITEST consiste en la comparación sistemática de procedimientos automáticos de homogenización aplicados a redes de series de precipitación representativas de climas muy diferentes, algo que no se había hecho hasta ahora. Para la generación de series de temperatura con inhomogeneidades prescritas se han desarrollado una cantidad importante de metodologías en los proyectos previos de intercomparación de procedimientos de homogeneización, como en la pasada Acción COST ES0601. Pero con la precipitación surgen complicaciones evidentes: la distribución (salvo en determinados climas y/o épocas del año) no es normal, sino que tiene habitualmente un sesgo positivo que puede llegar a ser muy pronunciado en climas secos; en estos climas es frecuente la presencia de numerosos ceros de precipitación mensual lo que requiere un tratamiento especial. La ausencia de normalidad complica además la deseable replicación en la red simulada de la estructura de covarianza espacial de los datos observados. En este trabajo presentamos una nueva metodología que consigue, en principio, por una parte reproducir en la red de puntos simulados las características univariantes de las series que cabría esperar a partir de la climatología observada, y por otra conservar la estructura de dependencia espacial. Se muestran y analizan resultados de tres redes simuladas de clima mediterráneo, atlántico y monzónico. One of the main innovations that MULTITEST introduces is that it will allow the systematic comparison of automatic homogenization procedures over networks representative of very disparate climates, something undone up to now. For temperature series generation with inhomogeneities added several methods have been developed under the past COST Action ES0601. But with precipitation there appear a number of complications: non-normality of the underlying distribution (except for some climates and/or year times) but instead it has a positive bias that can be quite great in dry climates; in these last climates it is also fairly common that one encounters null values which requires a special treatment. The absence of normality also disturbs the desirable replication of the spatial covariance structure of the observations. In this work a novel methodology is presented that achieves in principle both the reproduction on simulated points of the univariate characteristics according to the observed climatology and to conserve the spatial structure of dependence. Results of three simulated networks with Mediterranean, Atlantic and monsoon climates are presented and discussed. El proyecto MULTITEST está financiado por el Ministerio de Economía y Competitividad (CGL2014-52901-P). Enric Aguilar y Manola Brunet agradecen el apoyo del proyecto europeo titulado: Uncertainties in Ensembles of Regional Reanalyses (UERRA. FP7-SPACE-2013-1, No. de referencia: 607193).

The currently limited availability of long and high-quality surface instrumental climate records continues to hamper our ability to carry out more robust assessments of the climate. Such assessments are needed to better understand, detect, predict and respond to global climate variabil- ity and change. Despite the wealthy heritage of past climate data and recent efforts to improve data availability and accessibility, much more surface data could be digitised. Additionally, some long records are not of the quality needed for more confidently supporting any climate assessment, ser- vice, or application. The present paper discusses the usefulness of undertaking integrated data res- cue (DARE) activities by showing several climate assessments as examples. It describes emerging DARE activities worldwide, with a focus on the World Meteorological Organization Mediterranean Data Rescue (MEDARE) and the Atmospheric Circulation Reconstructions over the Earth (ACRE) initiatives to assess the benefits historical instrumental climate data can bring to studies of climate variability and change that consider the 21st century.

The Twentieth Century Reanalysis (20CR) project is an international effort to produce a comprehensive global atmospheric circulation dataset spanning the twentieth century, assimilating only surface pressure reports and using observed monthly sea-surface temperature and sea-ice distributions as boundary conditions. It is chiefly motivated by a need to provide an observational dataset with quantified uncertainties for validations of climate model simulations of the twentieth century on all time-scales, with emphasis on the statistics of daily weather. It uses an Ensemble Kalman Filter data assimilation method with background ‘first guess’ fields supplied by an ensemble of forecasts from a global numerical weather prediction model. This directly yields a global analysis every 6 hours as the most likely state of the atmosphere, and also an uncertainty estimate of that analysis. The 20CR dataset provides the first estimates of global tropospheric variability, and of the dataset's time-varying quality, from 1871 to the present at 6-hourly temporal and 2° spatial resolutions. Intercomparisons with independent radiosonde data indicate that the reanalyses are generally of high quality. The quality in the extratropical Northern Hemisphere throughout the century is similar to that of current three-day operational NWP forecasts. Intercomparisons over the second half-century of these surface-based reanalyses with other reanalyses that also make use of upper-air and satellite data are equally encouraging. It is anticipated that the 20CR dataset will be a valuable resource to the climate research community for both model validations and diagnostic studies. Some surprising results are already evident. For instance, the long-term trends of indices representing the North Atlantic Oscillation, the tropical Pacific Walker Circulation, and the Pacific–North American pattern are weak or non-existent over the full period of record. The long-term trends of zonally averaged precipitation minus evaporation also differ in character from those in climate model simulations of the twentieth century. Copyright © 2011 Royal Meteorological Society and Crown Copyright.

Here we present an exploratory statistical analysis aimed at the minimization of the ‘screen bias’ from affected ancient air temperature time series over the Western Mediterranean. Our approach lies in the statistical analysis of about 6 years of daily paired temperature observations taken using the ancient Montsouri shelter and the modern Stevenson screen for daily maximum (Tx) and minimum (Tn) temperature data recorded at two experimental sites: the meteorological gardens of La Coruna and Murcia, Spain (locations under the influence of the Oceanic/Atlantic/Galician and Mediterranean arid and semi-arid climate types, respectively), where ongoing field trials have been carried out. Descriptive statistical analysis of the paired series shows pre-sheltered temperatures tended to induce a strong warm bias in Tx data (of about 1 °C at the annual scale but with a clear seasonal cycle with higher values in summer and lower in winter), while Tn readings have a small (∼0.2 °C, and sustained all year round) cold bias compared to the modern period. Statistical relationships between the screen bias and other related meteorological variables show the highest correlation coefficients between the ‘screen bias’ and Tx, Tn and the diurnal temperature range (DTR) recorded under the replicated ancient shelters at both locations and point to the reliability of these variables as potential predictors of the ΔTx. We generate a parsimonious regression model based on the data from both experimental sites, which takes into account polynomial terms of lower order for the predictor variables (Tx and DTR recorded under the ancient shelter) and harmonic terms, in order to represent the seasonal cycle of the screen bias. The goodness-of-fit of the model is satisfactory, as it explains up to 51.7% of the additional ΔTx variability. Copyright © 2010 Royal Meteorological Society

Present-day habitats of the Ebro Delta, NE Iberian Peninsula, have been ecologically altered as a consequence of intensive human impacts in the last two centuries (especially rice farming). Benthic foraminiferal palaeoassemblages and sediment characteristics of five short cores were used to reconstruct past wetland habitats, through application of multivariate DCA and CONISS techniques, and dissimilarity coefficients (SCD). The timing of environmental changes was compared to known natural and anthropogenic events in order to identify their possible relationships. In deltaic wetlands under altered hydrological conditions, we found a decrease in species diversity and calcareous-dominated assemblages, and a significant positive correlation between microfaunal changes and organic matter content. Modern analogues supported palaeoenvironmental interpretation of the recent evolution of the Delta wetlands. This research provides the first recent reconstruction of change in the Ebro Delta wetlands, and also illustrates the importance of benthic foraminifera for biomonitoring present and future conditions in Mediterranean deltas.

Present-day habitats of the Ebro Delta, NE Iberian Peninsula, have been ecologically altered as a consequence of intensive human impacts in the last two centuries (especially rice farming). Benthic foraminiferal palaeoassemblages and sediment characteristics of five short cores were used to reconstruct past wetland habitats, through application of multivariate DCA and CONISS techniques, and dissimilarity coefficients (SCD). The timing of environmental changes was compared to known natural and anthropogenic events in order to identify their possible relationships. In deltaic wetlands under altered hydrological conditions, we found a decrease in species diversity and calcareous-dominated assemblages, and a significant positive correlation between microfaunal changes and organic matter content. Modern analogues supported palaeoenvironmental interpretation of the recent evolution of the Delta wetlands. This research provides the first recent reconstruction of change in the Ebro Delta wetlands, and also illustrates the importance of benthic foraminifera for biomonitoring present and future conditions in Mediterranean deltas.

Described herein is the first version release of monthly temperature holdings of a new Global Land Surface Meteorological Databank. Organized under the auspices of the International Surface Temperature Initiative (ISTI), an international group of scientists have spent three years collating and merging data from numerous sources to create a merged holding. This release in its recommended form consists of over 30 000 individual station records, some of which extend over the past 300 years. This article describes the sources, the chosen merge methodology, and the resulting databank characteristics. Several variants of the databank have also been released that reflect the structural uncertainty in merging datasets. Variants differ in, for example, the order in which sources are considered and the degree of congruence required in station geolocation for consideration as a merged or unique record. Also described is a version control protocol that will be applied in the event of updates. Future updates are envisaged with the addition of new data sources, and with changes in processing, where public feedback is always welcomed. Major updates, when necessary, will always be accompanied by a new journal paper. This databank release forms the foundation for the construction of new global land surface air temperature analyses by the global research community and their assessment by the ISTI's benchmarking and assessment working group.