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This analysis highlights the potential value in reanalysing early national meteorological records from around the world. These were oftentimes measured via techniques that preceded standardisation of instrumentation and methods of observation and thus could be subject to considerable biases and uncertainties. This analysis uses the techniques pioneered by WMO record assessment teams. The highest currently recognised air temperature (33.3 ∘C) ever recorded in the Republic of Ireland was logged at Kilkenny Castle in 1887. The original observational record however no longer exists. Given that Ireland is now the only country in Europe to have a national heat record that was set in the 19th century, a reassessment of the verity of this record is both timely and valuable. The present analysis undertakes a fundamental reassessment of the plausibility of the 1887 temperature record using methods similar to those used to assess various weather extremes under WMO auspices over recent years. Specifically, we undertake an inter-station reassessment using sparse available records and make recourse to the new and improved 20CRv3 sparse-input reanalysis product. Neither surrounding available stations nor the reanalysis offer substantive support for the Kilkenny record of 33.3 ∘C being correct. Moreover, recent data rescue efforts have uncovered several earlier extreme values, one of which exceeds the Kilkenny value (33.5 ∘C on 16 July 1876 recorded at the Phoenix Park). However, the sparsity of early observational networks, a distinct lack of synoptic support from 20CRv3 for many of the extreme heat values, and the fact that these measurements were obtained using non-standard exposures lead us to conclude that there is grossly insufficient evidence to support any of these 19th century extremes as robust national heat record candidates. Data from the early 20th century onwards benefit from a denser network of stations undertaking measurements in a more standardised manner, many under the direct auspices of Met Éireann and its predecessors, adhering to WMO guidance and protocols. This enables more robust cross-checking of records. We argue that the Met Éireann-recognised 20th century heat record from Boora in 1976 is verified as the most plausible robust national temperature record based upon the synoptic situation and comparisons with nearby neighbouring stations. This measurement of 32.5 ∘C thus likely constitutes the highest reliably recorded temperature measurement in the Republic of Ireland. Ultimately, the formal decision on any reassessment and reassignment of the national record rests with the national meteorological service, Met Éireann.

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We base this update on the assessment methods used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report, updating the monitoring datasets and to produce updated estimates for key climate indicators including emissions, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth’s energy imbalance, warming attributed to human activities, the remaining carbon budget and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.7883758, Smith et al., 2023). As they are traceable and consistent with IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human induced warming reached 1.14 [0.9 to 1.4] °C over the 2013–2022 period and 1.26 [1.0 to 1.6] °C in 2022. Human induced warming is increasing at an unprecedented rate of over 0.2 °C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 57 ± 5.6 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there are signs that emission levels are starting to stabilise, and we can hope that a continued series of these annual updates might track a real-world change of direction for the climate over this critical decade.

This study develops an innovative approach to homogenize discontinuities in both mean and variance in global subdaily radiosonde temperature data from 1958 to 2018. First, temperature natural variations and changes are estimated using reanalyses and removed from the radiosonde data to construct monthly and daily difference series. A penalized maximal F test and an improved Kolmogorov–Smirnov test are then applied to the monthly and daily difference series to detect spurious shifts in the mean and variance, respectively. About 60% (40%) of the changepoints appear in the mean (variance), and ~56% of them are confirmed by available metadata. The changepoints display a country-dependent pattern likely due to changes in national radiosonde networks. Mean segment length is 7.2 (14.6) years for the mean (variance)-based detection. A mean (quantile)-matching method using up to 5 years of data from two adjacent mean (variance)-based segments is used to adjust the earlier segments relative to the latest segment. The homogenized series is obtained by adding the two homogenized difference series back to the subtracted reference series. The homogenized data exhibit more spatially coherent trends and temporally consistent variations than the raw data, and lack the spurious tropospheric cooling over North China and Mongolia seen in several reanalyses and raw datasets. The homogenized data clearly show a warming maximum around 300 hPa over 30°S–30°N, consistent with model simulations, in contrast to the raw data. The results suggest that spurious changes are numerous and significant in the radiosonde records and our method can greatly improve their homogeneity.

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

We provide an updated sea level dataset for Dublin for the period 1938–2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the mean low water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of sea level rise of 1.1 mm yr−1 during 1953–2016 (95 % credible interval from 0.6 to 1.6 mm yr−1), and a rate of 7 mm yr−1 during 1997–2016 (95 % credible interval from 5 to 8.8 mm yr−1). The overall sea level rise is in line with expected trends, but large multidecadal variability has led to higher rates of rise in recent years.

C.R.'s portion of the work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). This work used JASMIN, the UK collaborative data analysis facility. Anthropogenic forcing is driving energy accumulation in the Earth system, including increases in the sensible heat content of the atmosphere, as measured by dry-bulb temperature—the metric that is almost universally used for communications about climate change. The atmosphere is also moistening, though, representing an accumulation of latent heat, which is partly concealed by dry-bulb temperature trends. We highlight that, consistent with basic theory, latent heat gains are outpacing sensible heat gains over about half of the Earth's surface. The difference is largest in the tropics, where global “hotspots” of total heat accumulation are located, and where regional disparities in heating rates are very poorly represented by dry-bulb temperatures. Including latent heat in climate-change metrics captures this heat accumulation and therefore improves adaptation-relevant understanding of the extreme humid heat and precipitation hazards that threaten these latitudes so acutely. For example, irrigation can lower peak dry-bulb temperatures, but amplify latent heat content by a larger margin, intensifying dangerous heat stress. Based on a review of the research literature, our Perspective therefore calls for routine use of equivalent temperature, a measure that expresses the combined sensible and latent heat content of the atmosphere in the familiar units of °C or K. We recognize that dry-bulb air temperature must remain a key indicator of the atmospheric state, not least for the many sectors that are sensitive to sensible heat transfer. However, we assert here that more widespread use of equivalent temperature could improve process understanding, public messaging, and adaptation to climate change. Publisher PDF

The Global Land and Marine Observations Database aims to produce a comprehensive land based meteorological data archive and inventory. This requires the compilation of available land-based station meteorological data information from all known available in-situ meteorological data repositories/sources at multiple timescales (e.g. sub-daily, daily and monthly). During this process the service team members have identified that many of the data sources contained stations with incorrect location coordinates. These stations cannot be included in the processing to be served via the Copernicus Climate Change Service until the issues are satisfactorily resolved. Many of these stations are located in regions of the world where a sparsity of climate data currently exists, such as Southeast Asia and South America. As such, resolving these issues would provide important additional climate data, but this is a very labour-intensive task. Therefore, we have developed the Geo-locate project enrolling the help of undergraduate Geography students at Maynooth University, Ireland, to resolve some of the land-based station geolocation issues. We have successfully run two Geo-locate projects, the first in the second semester of the 2017/18 academic year and the second in the 2018/19 academic year. Both iterations to date have been very successful with 1926 out of 2168 total candidate stations ostensibly resolved, which equates to an 88 % success rate. At the same time, students gained critical skills helping to meet the expected pedagogical outcomes of the second-year curriculum, while producing a lasting scientific legacy. We asked the class of 2018/19 to reflect critically upon the outcomes and present the results herein which provide important feedback on what students felt that they gained from their participation and how we may improve the experience and learning outcomes in future. We will be continuing to run Geo-locate projects over the next few years. Due to the success of the Geo-locate project we encourage other organisations to investigate the potential for engaging university students to help resolve similar data issues while enriching the student experience and aiding the delivery of learning outcomes. This paper provides details of the project, and all supporting information such as project guidelines and templates to enable this.

The highest currently recognised air temperature (33.3 °C) ever recorded in the Republic of Ireland was logged at Kilkenny Castle in 1887. The original observational record however no longer exists. Given that Ireland is now the only country in Europe to have a national heat record set in the 19th century, a reassessment of the verity of this record is both timely and valuable. The present analysis undertakes a fundamental reassessment of the plausibility of the 1887 temperature record using methods similar to those used to assess various weather extremes under WMO auspices over recent years. Specifically, we undertake an inter-station reassessment using sparse available records and make recourse to the new and improved 20CRv3 sparse-input reanalysis product. Neither surrounding available stations nor the reanalysis offer substantive support for the Kilkenny record of 33.3 °C being correct. Moreover, recent data rescue efforts have uncovered several earlier extreme values, one of which exceeds the Kilkenny value (33.5 °C on 16th July 1876 recorded at the Phoenix Park). However, the sparsity of early observational networks, a distinct lack of synoptic support from 20CRv3 for many of the extreme heat values, and the fact that these measurements were obtained using non-standard exposures leads us to conclude that there is grossly insufficient evidence to support any of these 19th Century extremes as robust national heat record candidates. Data from the early 20th Century onwards benefits from a denser network of stations undertaking measurements in a more standardised manner, many under the direct auspices of Met Éireann and its predecessors, adhering to WMO guidance and protocols. This enables more robust cross-checking of records. We argue that the Met Éireann recognised 20th Century heat record from Boora in 1976 verifies as the most plausible robust national temperature record based upon the synoptic situation and comparisons with nearby neighbouring stations. This measurement of 32.5 °C thus likely constitutes the highest reliably recorded temperature measurement in the Republic of Ireland. Ultimately, the formal decision on any reassessment and reassignment of the national record rests with the national meteorological service, Met Éireann.

We provide an updated sea level dataset for Dublin for the period 1938 to 2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the Mean Low Water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of 1.08 mm/yr sea level rise at Dublin Port between 1953–2016 (95 % CI from 0.62 to 1.55 mm/yr), and a rate of 6.48 mm/yr between 1997–2016 (95 % CI 4.22 to 8.80 mm/yr). Overall sea level rise is in line with expected trends but large multidecadal varaibility has led to higher rates of rise in recent years.

There is considerable import in creating more complete, better understood, holdings of early meteorological data. Such data permit an improved understanding of climate variability and long-term changes. Early records are particularly incomplete in the tropics, with implications for estimates of global and regional temperature. There is also a relatively low level of scientific understanding of how these measurements were made and, as a result, of their homogeneity and comparability to more modern techniques and measurements. Herein we describe and analyse a newly rescued set of long-term, up to six-way parallel measurements, undertaken over 1884–1903 in Mauritius, an island situated in the southern Indian Ocean. Data include: i) measurements from a well-ventilated room, ii) a shaded Thermograph; iii) instruments housed in a manner broadly equivalent to a modern Stevenson Screen; iv) a set of measurements by a Hygrometer mounted in a Stevenson Screen; and for a very much shorter period v) two additional Stevenson Screen configurations. All measurements were undertaken within roughly 80 metre radius. To our knowledge this is the first such multidecadal multi-instrument assessment of meteorological instrument transition impacts ever undertaken, providing potentially unique insights. The intercomparison also considers the impact of different ways of deriving daily and monthly averages. The long-term comparison is sufficient to robustly characterise systematic offsets between all the instruments and seasonally varying impacts. Differences between all techniques range from tenths of a degree Celsius to in excess of a degree Celsius and are considerably larger for maximum and minimum temperatures than for means or averages. Systematic differences of several tenths of a degree also exist for the different ways of deriving average / mean temperatures. All differences bar two average temperature series pairs are significant at the 0.01 level using a paired t-test. Given that all thermometers were regularly calibrated against a primary Kew standard thermometer this analysis highlights significant impacts of instrument exposure, housing, siting and measurement practices in early meteorological records. These results reaffirm the importance of thoroughly assessing the homogeneity of early meteorological records.

The RHARM (Radiosounding HARMonization) algorithm is the first to provide homogenized radiosonde-based records of temperature, relative humidity and wind profiles since 1978, alongside an estimatio...

Future climate projections for the 21st century generally do not include the effects of volcanic eruptions. While some attempt has been made to account for the integrated effect of multiple eruptions by incorporating a small continuous volcanic forcing, a recent study (http://nature.com/articles/doi:10.1038/nclimate3394) has already shown that this approach is insufficient to resolve the increased climate variance caused by individual eruptions, especially on decadal timescales. Increased climate variance exerts stresses on ecosystems and society, thus resolving the impacts of plausible future volcanic eruptions is of importance for certain adaptation and mitigation decisions. While previous work has used a modelling approach to address this problem, in this talk we demonstrate a computationally inexpensive method to incorporate the effects of plausible volcanic eruptions into future climate projections. This method uses stochastic volcanic emulators based on 2,500 years of past volcanic activity and the characterization of the response of the climate system to individual eruptions. We will demonstrate not only this methodology, but also describe the requirements and potential for its application to the wider future projections of CMIP6.

The likelihood of exceeding 1.5 °C of global warming relative to preindustrial depends on the warming observed so far, anthropogenic warming that may occur in the future, and the degree to which internal variability will either temporarily amplify or attenuate future anthropogenic warming. Here, we introduce a new framework that estimates the likelihood of exceeding 1.5 °C of global warming wherein uncertainties in each one of these factors is explicitly accounted for.In this new framework, we estimate the historical warming, and its uncertainty, from preindustrial to present using the recently-minted HadCRUT5 dataset. Future anthropogenic warming is estimated using an energy balance model tuned to an assessed range of climate sensitivity and applied to each of the core emissions scenarios (i.e. SSPs) underlying the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). Finally, we estimate the influence of internal variability using a large ensemble of initial condition simulations. On this basis, we find that the largest uncertainty in estimates of the likelihood of exceeding 1.5°C of global warming is due to model-to-model differences in estimates of future anthropogenic warming, followed by historical warming uncertainty, and then uncertainty due to internal variability.Based on our analysis, we find that the earliest time for crossing 1.5 °C of global warming, here defined as the 5% likelihood, is approximately emissions-scenario independent. We define the 1.5 °C threshold without any overshoot: if a time series warms by more than 1.5 °C during any 20-year period before 2100, it is counted as having crossed 1.5 °C. In each considered scenario except SSP5-8.5, the 20-year average period that crosses the 1.5 °C threshold with a 5% likelihood is 2013 to 2032. On the other hand, the 50% likelihood does depend on the scenario, with the SSP5-8.5 crossing occurring in 2018 to 2037 and SSP1-1.9 crossing in 2022 to 2041. All scenarios except SSP1-1.9 have a likelihood close to 100% to cross 1.5 °C global warming before 2100. Even in SSP1-1.9, the scenario with the strongest emission reductions, there is a 71% likelihood to cross 1.5 °C by the end of this century. This implies that even in SSP1-1.9, the world may stay below 1.5 °C only if both climate sensitivity and historical warming are near the lower end of their respective distributions.These estimates, with their associated uncertainties, may have major implications for policy decisions.

The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) concluded that warming of the climate system since the start of the instrumental record was unequivocal. While this conclusion does not solely rest upon the surface temperature record, this record is a key aspect underpinning this finding. In this chapter, the basis for the observational evidence for increases in global surface temperatures, since the start of the instrumental record, is discussed. The chapter begins with a discussion on how the measurements have been taken over time. This is followed by a description of the necessary efforts required to adjust the records to account for time varying biases. The resulting global estimates and attempts at quantifying uncertainties are then discussed. The changes in temperature extremes are briefly highlighted and the chapter closes with a view to future research directions and innovations.

Observations from the historical meteorological observing network contain many artefacts of non-climatic origin which must be accounted for prior to using these data in climate applications. State-of-the-art homogenisation approaches use various flavours of pairwise comparison between a target station and candidate neighbour station series. Such approaches require an adequate number of neighbours of sufficient quality and comparability - a condition that is met for most station series since the mid-20th Century. However, pairwise approaches have challenges where suitable neighbouring stations are sparse, as remains the case in vast regions of the globe and is common almost everywhere prior to the early 20th Century. Modern sparse-input centennial reanalysis products continue to improve and offer a potential alternative to pairwise comparison, particularly where and when observations are sparse. They do not directly ingest or use land-based surface temperature observations, so they are a formally independent estimate. This may be particularly helpful in cases where structurally similar changes exist across broad networks, which challenges current techniques in the absence of metadata. They also potentially offer a valuable methodologically distinct method, which would help explore structural uncertainty in homogenisation techniques. The present study compares the potential of spatially-interpolated sparse-input reanalysis products to neighbour-based approaches to perform homogenisation of global monthly land surface air temperature records back to 1850 based upon the statistical properties of station-minus-reanalysis and station-minus-neighbour series. This shows that neighbour-based approaches likely remain preferable in data dense regions and epochs. However, the most recent reanalysis product, NOAA-CIRES-DOE 20CRv3, is potentially preferable in cases where insufficient neighbours are available. This may in particular affect long-term global average estimates where a small number of long-term stations in data sparse regions will make substantial contributions to global estimates and may contain missed data artefacts in present homogenisation approaches.

Observational records are essential for assessing long-term changes in our climate. However, these records are more often than not influenced by residual non‐climatic factors which must be detected and adjusted prior to their usage. Ideally, measurement uncertainties should be properly quantified and validated. In the context of the Copernicus Climate Change Service (C3S), a novel approach, named RHARM (Radiosounding HARMonization), has been developed to provide a harmonized dataset of temperature, humidity and wind profiles along with an estimation of the measurement uncertainties for about 650 radiosounding stations globally. The RHARM method has been applied to IGRA daily (0000 and 1200 UTC) radiosonde data holdings on 16 standard pressure levels (from 1000 to 10 hPa) from 1978 to present. Relative humidity adjustment and data provision has been limited to 250 hPa owing to pervasive issues on sensors' performance in the upper troposphere and lower stratosphere. The applied adjustments are interpolated to all reported significant levels to retain information content contained within each individual ascent profile. Each historical station time series is harmonized using two distinct methods. Firstly, the most recent period of the records when modern radiosonde models have been in operation at each station (typically starting between 2004 and 2010 but varying on a station-by-station basis) are post-processed and adjusted using reference datasets from the GCOS Reference Upper Air Network (GRUAN) and from the 2010 WMO/CIMO (World Meteorological Organization/Commission for Instruments and Methods of Observation) radiosonde intercomparison. Subsequently, at each mandatory pressure level, the remaining historical data are scanned backward in time to detect structural breaks due to prolonged systematic effects in the measurements and then adjusted to homogenize the time series. This paper describes the dataset portion related to the adjustment of post-2004 measurements only. A step-by-step description of the algorithm is reported and comparisons with GRUAN and atmospheric reanalysis data for temperature and relative humidity data are discussed. The evaluation shows that the strongest benefit of RHARM compared to existing products is related to the substantive adjustments applied to relative humidity time series for values below 15 % and above 55 % as well as to the provision of the uncertainties for all variables. Uncertainties have been validated using the ECMWF reanalysis short-range forecast outputs. The RHARM algorithm is the first to provide homogenized time series of temperature, relative humidity and wind profiles alongside an estimation of the observational uncertainty for each single observation at each pressure level. A subset of RHARM dataisavailable at https://doi.org/10.5281/zenodo.3973353 (Madonnaet al., 2020a)

This paper describes the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) approach to managing the transition from the Vaisala RS92 to the Vaisala RS41 as the operational radiosonde. The goal of GRUAN is to provide long-term high-quality reference observations of upper-air essential climate variables (ECVs) such as temperature and water vapor. With GRUAN data being used for climate monitoring, it is vital that the change of measurement system does not introduce inhomogeneities to the data record. The majority of the 27 GRUAN sites were launching the RS92 as their operational radiosonde, and following the end of production of the RS92 in the last quarter of 2017, most of these sites have now switched to the RS41. Such a large-scale change in instrumentation is unprecedented in the history of GRUAN and poses a challenge for the network. Several measurement programs have been initiated to characterize differences in biases, uncertainties, and noise between the two radiosonde types. These include laboratory characterization of measurement errors, extensive twin sounding studies with RS92 and RS41 on the same balloon, and comparison with ancillary data. This integrated approach is commensurate with the GRUAN principles of traceability and deliberate redundancy. A 2-year period of regular twin soundings is recommended, and for sites that are not able to implement this, burden-sharing is employed such that measurements at a certain site are considered representative of other sites with similar climatological characteristics. All data relevant to the RS92–RS41 transition are archived in a database that will be accessible to the scientific community for external scrutiny. Furthermore, the knowledge and experience gained regarding GRUAN's RS92–RS41 transition will be extensively documented to ensure traceability of the process. This documentation will benefit other networks in managing changes in their operational radiosonde systems. Preliminary analysis of the laboratory experiments indicates that the manufacturer's calibration of the RS41 temperature and humidity sensors is more accurate than for the RS92, with uncertainties of  K for the temperature and  % RH (RH: relative humidity) for the humidity sensor. A first analysis of 224 RS92–RS41 twin soundings at Lindenberg Observatory shows nighttime temperature differences  K between the Vaisala-processed temperature data for the RS41 (TRS41) and the GRUAN data product for the RS92 (TRS92-GDP.2). However, daytime temperature differences in the stratosphere increase steadily with altitude, with TRS92-GDP.2 up to 0.6 K higher than TRS41 at 35 km. RHRS41 values are up to 8 % higher, which is consistent with the analysis of satellite–radiosonde collocations.

Societal dependence on, and commercial and scientific exploitation of Earth-Oriented remote sensing from satellites is growing at an exponential rate. The comprehensive EU Copernicus programme provides a major contribution to the global effort, but even so, to achieve the necessary global and temporal coverage requires synergistic cooperation and associated interoperability of the Worlds sensors. For a user to exploit Earth Observation (EO) data there must exist confidence in data characteristics, quality and reliable delivery. Although long-term data records for climate may be the most demanding in nature, generation of analysis-ready operational data sets for applications, as diverse as food security to pollution monitoring, all require the user to have some quantitative level of confidence in the data and derived information. A long-term Calibration/Validation (Cal/Val) vision necessitates clear ownership and long-term funding. Delineating the roles of the European Commission (EC), space agencies and member states in long-term Cal/Val would provide clarity. It is clear that the space agencies have the responsibility to meet the mission requirement of their spaceborne instruments but long-term validation is often entrusted to interested parties bringing their own resources to the task. Furthermore, there is a critical need for Fiducial Reference Measurements (FRMs), acquired in operational mode, and comprehensive in coverage both spatially and temporally, to assure that the satellite product accuracies are met. This paper discusses the current status, gaps and challenges regarding long-term Cal/Val of EO satellites and recommends the creation of a European coordinating entity for satellite product calibration and validation. The proposed entity would be an integrative organization coordinating the European Cal/Val activities in partnership with the member states and the space agencies and working together with existing data providers to secure access to satellite and in-situ data of traceable FRM standards.

In the last two decades, technological progress has not only seen improvements to the quality of atmospheric upper-air observations but also provided the opportunity to design and implement automated systems able to replace measurement procedures typically performed manually. Radiosoundings, which remain one of the primary data sources for weather and climate applications, are still largely performed around the world manually, although increasingly fully automated upper-air observations are used, from urban areas to the remotest locations, which minimize operating costs and challenges in performing radiosounding launches. This analysis presents a first step to demonstrating the reliability of the automatic radiosonde launchers (ARLs) provided by Vaisala, Meteomodem and Meisei. The metadata and datasets collected by a few existing ARLs operated by the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) certified or candidate sites (Sodankylä, Payerne, Trappes, Potenza) have been investigated and a comparative analysis of the technical performance (i.e. manual versus ARL) is reported. The performance of ARLs is evaluated as being similar or superior to those achieved with the traditional manual launches in terms of percentage of successful launches, balloon burst and ascent speed. For both temperature and relative humidity, the ground-check comparisons showed a negative bias of a few tenths of a degree and % RH, respectively. Two datasets of parallel soundings between manual and ARL-based measurements, using identical sonde models, provided by Sodankylä and Faa'a stations, showed mean differences between the ARL and manual launches smaller than ±0.2 K up to 10 hPa for the temperature profiles. For relative humidity, differences were smaller than 1 % RH for the Sodankylä dataset up to 300 hPa, while they were smaller than 0.7 % RH for Faa'a station. Finally, the observation-minus-background (O–B) mean and root mean square (rms) statistics for German RS92 and RS41 stations, which operate a mix of manual and ARL launch protocols, calculated using the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast model, are very similar, although RS41 shows larger rms(O–B) differences for ARL stations, in particular for temperature and wind. A discussion of the potential next steps proposed by GRUAN community and other parties is provided, with the aim to lay the basis for the elaboration of a strategy to fully demonstrate the value of ARLs and guarantee that the provided products are traceable and suitable for the creation of GRUAN data products.

Globally, few precipitation records extend to the 18th century. The England Wales Precipitation (EWP) series is a notable exception with continuous monthly records from 1766. EWP has found widespread use across diverse fields of research including trend detection, evaluation of climate model simulations, as a proxy for mid‐latitude atmospheric circulation, a predictor in long‐term European gridded precipitation data sets, the assessment of drought and extremes, tree‐ring reconstructions and as a benchmark for other regional series. A key finding from EWP has been the multi‐centennial trends towards wetter winters and drier summers. We statistically reconstruct seasonal EWP using independent, quality‐assured temperature, pressure and circulation indices. Using a sleet and snow series for the UK derived by Profs. Gordon Manley and Elizabeth Shaw to examine winter reconstructions, we show that precipitation totals for pre‐1870 winters are likely biased low due to gauge under‐catch of snowfall and a higher incidence of snowfall during this period. When these factors are accounted for in our reconstructions, the observed trend to wetter winters in EWP is no longer evident. For summer, we find that pre‐1820 precipitation totals are too high, likely due to decreasing network density and less certain data at key stations. A significant trend to drier summers is not robustly present in our reconstructions of the EWP series. While our findings are more certain for winter than summer, we highlight (a) that extreme caution should be exercised when using EWP to make inferences about multi‐centennial trends, and; (b) that assessments of 18th and 19th Century winter precipitation should be aware of potential snow biases in early records. Our findings underline the importance of continual re‐appraisal of established long‐term climate data sets as new evidence becomes available. It is also likely that the identified biases in winter EWP have distorted many other long‐term European precipitation series., The trend towards wetter winters and drier summers in EWP can be explained by changes in measurement practice and data biases. For winter (Figure below), the timing of divergence aligns with the introduction of the Snowdon Pattern Rain Gauge in the UK around 1863. Given the widespread use of EWP across environmental and climate research, we expect that our findings will challenge understanding of multi‐centennial trends in precipitation in the UK. These changes in measurement practice are also likely to affect other long‐term precipitation records in north‐west Europe.

Over much of the globe, the temporal extent of meteorological records is limited, yet a wealth of data remains in paper or image form in numerous archives. To date, little attention has been given to the role that students might play in efforts to rescue these data. Here we summarize an ambitious research-led, accredited teaching experiment in which undergraduate students successfully transcribed more than 1,300 station years of daily precipitation data and associated metadata across Ireland over the period 1860–1939. We explore i) the potential for integrating data rescue activities into the classroom, ii) the ability of students to produce reliable transcriptions and, iii) the learning outcomes for students. Data previously transcribed by Met Éireann (Ireland’s National Meteorological Service) were used as a benchmark against which it was ascertained that students were as accurate as the professionals. Details on the assignment, its planning and execution, and student-aids used are provided. The experience highlights the benefits that can accrue for data rescue through innovative collaboration between national meteorological services and academic institutions. At the same time, students have gained valuable learning outcomes and firsthand understanding of the processes that underpin data rescue and analysis. The success of the project demonstrates the potential to extend data rescue in the classroom to other universities, thus providing both an enriched learning experience for the students and a lasting legacy to the scientific community.

This paper describes the GRUAN-wide approach to manage the transition from the Vaisala RS92 to the Vaisala RS41 as the operational radiosonde. The goal of the GCOS Reference Upper-Air Network (GRUAN) is to provide long-term high-quality reference observations of upper air Essential Climate Variables (ECVs) such as temperature and water vapor. With GRUAN data being used for climate monitoring, it is vital that the change of measurement system does not introduce inhomogeneities in to the data record. The majority of the 27 GRUAN sites were launching the RS92 as their operational radiosonde, and following the end of production of the RS92 in the last quarter of 2017, most of these sites have now switched to the RS41. Such a large-scale change in instrumentation is unprecedented in the history of GRUAN and poses a challenge for the network. Several measurement programmes have been initiated to characterize differences in biases, uncertainties and noise between the two radiosonde types. These include laboratory characterization of measurement errors, extensive twin sounding studies with RS92 and RS41 on the same balloon, and comparison with ancillary data. This integrated approach is commensurate with the GRUAN principles of traceability and deliberate redundancy. A two-year period of regular twin soundings is recommended, and for sites that are not able to implement this burden sharing is employed, such that measurements at a certain site are considered representative of other sites with similar climatological characteristics. All data relevant to the RS92-RS41 transition are archived in a database that will be accessible to the scientific community for external scrutiny. Furthermore, the knowledge and experience gained about GRUAN's RS92-RS41 transition will be extensively documented to ensure traceability of the process. This documentation will benefit other networks in managing changes in their operational radiosonde systems. Preliminary analysis of the laboratory experiments indicates that the manufacturer's calibration of the RS41's temperature and humidity sensors is more accurate than for the RS92; with uncertainties of

We explore the extent to which internal variability can reconcile discrepancies between observed and simulated warming in the upper tropical troposphere. We compare all extant radiosondebased estimates for the period 1958–2014 to simulations from the Coupled Model Intercomparison Project phase 5 multimodel ensemble and the 100 realization Max Planck Institute large ensemble. We consider annual mean temperatures and all available 30-and 15-year trends. Most observed trends fall within the ensemble spread for most of the record, and trends calculated over 15-year periods show better agreement than 30-year trends, with generally larger discrepancies for the older observational products. The simulated amplification of surface warming aloft in the troposphere is consistent with observations, and the linear correlation between surface and simultaneous tropospheric warming trends decreases with trend length. We conclude that trend differences between observations and simulations of tropical tropospheric temperatures are dominated by observational uncertainty and chaotic internal variability rather than by systematic errors in model performance.

Case Description A 61-year-old man presented, 7 months after deceased donor kidney transplantation, with 4 days of fever, hematuria, and dysuria. His immunosuppression regimen included tacrolimus 1.5 mg twice daily, prednisone 5 mg daily, and mycophenolate mofetil 500 mg twice daily. He was febrile to 101.6 and had tenderness to palpation in the right lower quadrant over his graft site. His serum creatinine on admission was 1.4 mg/dl from a baseline of 1.0 mg/dl. White blood cell count was 4.4×103/ µ l with 85.9% neutrophils. Tacrolimus trough level was 13.3 ng/ml. Urinalysis demonstrated 100 mg/dl protein, 109 white blood cells per high-power field, 344 isomorphic red blood cells per high-power field, and hyaline casts. Urine and blood …

It has been a decade since changes in diurnal temperature range (DTR) globally have been assessed in a stand-alone data analysis. The present study takes advantage of substantively improved basic data holdings arising from the International Surface Temperature Initiative’s databank effort and applies the National Centers for Environmental Information’s automated pairwise homogeneity assessment algorithm to reassess DTR records. It is found that breakpoints are more prevalent in DTR than other temperature elements and that the resulting adjustments have a broader distribution. This strongly implies that there is an overarching tendency, across the global meteorological networks, for nonclimatic artifacts to impart either random or anticorrelated rather than correlated biases in maximum and minimum temperature series. Future homogenization efforts would likely benefit from simultaneous consideration of DTR and maximum and minimum temperatures, in addition to average temperatures. Estimates of change in DTR are relatively insensitive to whether adjustments are calculated directly or inferred from adjustments returned for the maximum and minimum temperature series. The homogenized series exhibit a reduction in DTR since the midtwentieth century globally (-0.044 K/decade). Adjustments serve to approximately halve the long-term global reduction in DTR in the basic “raw” data. Most of the estimated DTR reduction occurred over 1960–1980. In several regions DTR has apparently increased over 1979–2012, while globally it has exhibited very little change (-0.016 K/decade). Estimated changes in DTR are an order of magnitude smaller than in maximum and minimum temperatures, which have both been increasing rapidly on multidecadal timescales (0.186 K/decade and 0.236 K/decade, respectively, since the midtwentieth century).