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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.

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.

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)

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.

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 …

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

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).

The uncertainty in Extended Reconstructed SST (ERSST) version 4 (v4) is reassessed based upon 1) reconstruction uncertainties and 2) an extended exploration of parametric uncertainties. The reconstruction uncertainty (Ur) results from using a truncated (130) set of empirical orthogonal teleconnection functions (EOTs), which yields an inevitable loss of information content, primarily at a local level. The Ur is assessed based upon 32 ensemble ERSST.v4 analyses with the spatially complete monthly Optimum Interpolation SST product. The parametric uncertainty (Up) results from using different parameter values in quality control, bias adjustments, and EOT definition etc. The Up is assessed using a 1000-member ensemble ERSST.v4 analysis with different combinations of plausible settings of 24 identified internal parameter values. At the scale of an individual grid box, the SST uncertainty varies between 0.3° and 0.7°C and arises from both Ur and Up. On the global scale, the SST uncertainty is substantially smaller (0.03°–0.14°C) and predominantly arises from Up. The SST uncertainties are greatest in periods and locales of data sparseness in the nineteenth century and relatively small after the 1950s. The global uncertainty estimates in ERSST.v4 are broadly consistent with independent estimates arising from the Hadley Centre SST dataset version 3 (HadSST3) and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2). The uncertainty in the internal parameter values in quality control and bias adjustments can impact the SST trends in both the long-term (1901–2014) and “hiatus” (2000–14) periods.

Ear and hearing health services are scarce within Pacific Island countries. Where available, they remain under-resourced, despite there being a high estimated prevalence of otitis media and otitis media-related hearing loss. This study examines the potential for improving ear and hearing health strategies by examining key factors, opportunities and challenges, working with Fiji as a case study.A series of semi-structured interviews were conducted with professionals who had experience in otitis media programme implementation and/or professionals with working experience within the Pacific region and the Fiji health system. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis methods.The three main themes, Identification, Integration, Sustain, highlight the importance of a Pacific-based, locally-driven strategy that builds on existing infrastructure. Three operational themes, Advocacy, Funding and Long-Term Vision, were pivotal to the viability of the main themes.Despite significant challenges, there is potential to develop sustainable otitis media identification, treatment and prevention strategies in Fiji. The sustainability of such a strategy is contingent on a number of key factors, which includes a long-term commitment by stakeholders, be culturally appropriate and responsive to local need, develop close linkages across health, social and educational sectors, and ensure it is embedded within a broader public health framework.

This briefing outlines the evidence from instrumental records that leads to an unequivocal finding that the world has warmed. It then goes on to address the underlying causes, showing that only through invoking the effects of humans can the last 50 years be adequately explained. Finally, it addresses the recent hiatus/pause in warming of global surface temperatures and the implications thereof.

The global tropical cyclone (TC) intensity record, even in modern times, is uncertain because the vast majority of storms are only observed remotely. Forecasters determine the maximum wind speed using a patchwork of sporadic observations and remotely sensed data. A popular tool that aids forecasters is the Dvorak technique—a procedural system that estimates the maximum wind based on cloud features in IR and/or visible satellite imagery. Inherently, the application of the Dvorak procedure is open to subjectivity. Heterogeneities are also introduced into the historical record with the evolution of operational procedures, personnel, and observing platforms. These uncertainties impede our ability to identify the relationship between tropical cyclone intensities and, for example, recent climate change. A global reanalysis of TC intensity using experts is difficult because of the large number of storms. We will show that it is possible to effectively reanalyze the global record using crowdsourcing. Through modifying the Dvorak technique into a series of simple questions that amateurs (“citizen scientists”) can answer on a website, we are working toward developing a new TC dataset that resolves intensity discrepancies in several recent TCs. Preliminary results suggest that the performance of human classifiers in some cases exceeds that of an automated Dvorak technique applied to the same data for times when the storm is transitioning into a hurricane.

Using state-of-the-art observational datasets and results from a large archive of computer model simulations, a consortium of scientists from 12 different institutions has resolved a long-standing conundrum in climate science—the apparent discrepancy between simulated and observed temperature trends in the tropics. Research published by this group indicates that there is no fundamental discrepancy between modeled and observed tropical temperature trends when one accounts for: (1) the (currently large) uncertainties in observations; and (2) the statistical uncertainties in estimating trends from observations. These results refute a recent claim that model and observed tropical temperature trends “disagree to a statistically significant extent”. This claim was based on the application of a flawed statistical test and the use of older observational datasets.

The circulation system of the North Atlantic Ocean has weakened and is predicted to weaken further in the coming decades. An analysis suggests that this decline could lead to accelerated global surface warming. The circulation system of the North Atlantic Ocean has weakened and is predicted to weaken further in the coming decades. An analysis suggests that this decline could lead to accelerated global surface warming.

[1] This analysis revisits detection and attribution of free atmosphere temperatures from radiosondes, almost a decade after previous studies. Since that time, data sets have not only become longer, but understanding of observational uncertainty has vastly improved. In addition, a coordinated set of experiments exploring the effects of human and natural forcings on past climate change has been made with a new generation of climate models. These advances allow a much more thorough analysis of the effects of modeling and observational uncertainty on attribution results than previously possible. Observational uncertainty is explored using multiple radiosonde reconstructions including those with ensembles of realizations exploring the effects of processing choices. Modeling uncertainty is explored by calculating multiple fingerprints of natural influence (from changes in solar irradiance and volcanic aerosols) and of human influence (due to greenhouse gases and due to the effects of combined anthropogenic forcings including stratospheric ozone depletion). With increased confidence over previous studies, human influences (both greenhouse gas and other anthropogenic forcings) have been detected in spatiotemporal changes in free atmosphere temperature from 1961 to 2010, irrespective of whether the full atmospheric column (30–850 hPa) is examined or purely the troposphere, with stratospheric ozone depletion dominating the cooling that has been observed in the lower stratosphere. Thus the advances of the last decade yield increased confidence that anthropogenic influences have made a substantial contribution to the evolution of free atmosphere temperatures.

In this chapter, we have surveyed some of the issues relating to the implementation of participatory research for livestock development. Participatory research is only one type of research paradigm, and we do not see it as a replacement for traditional research with a technological focus. However, it can often help to generate research outputs that are much more focused on the needs of end-users in the world’s least developed countries (LDCs). Many of the issues related to applying participatory research to livestock constraints and opportunities are similar to those for other household enterprises. There is, however, a number of unique features of the role that livestock play in households, such as their capacity to support other aspects of the farming system through the provision of draught power and high quality organic matter for soil amendment (manure/compost), as well as their contribution to rolling household incomes. In smallholder farm systems, livestock are normally highly integrated in the system, both deriving and conferring benefits on other enterprises within the system, and their contributions to livelihoods are diverse and not just related to the provision of food. Much has been made of the negative impacts of livestock on environmental parameters in recent years. While effective management of livestock is essential to mitigate against their potentially negative environmental effects, it can be argued that these have often been overstated, and that good livestock management can actively support good environmental outcomes. Livestock populations are likely to increase in LDCs over the coming decades, so well planned research is likely to continue to be very important in supporting this trend by identifying approaches for increasing the productive efficiency of livestock enterprises.

Observations are the foundation for understanding the climate system. Yet, currently available land meteorological data are highly fractured into various global, regional, and national holdings for different variables and time scales, from a variety of sources, and in a mixture of formats. Added to this, many data are still inaccessible for analysis and usage. To meet modern scientific and societal demands as well as emerging needs such as the provision of climate services, it is essential that we improve the management and curation of available land-based meteorological holdings. We need a comprehensive global set of data holdings, of known provenance, that is truly integrated both across essential climate variables (ECVs) and across time scales to meet the broad range of stakeholder needs. These holdings must be easily discoverable, made available in accessible formats, and backed up by multitiered user support. The present paper provides a high-level overview, based upon broad community input, of the steps that are required to bring about this integration. The significant challenge is to find a sustained means to realize this vision. This requires a long-term international program. The database that results will transform our collective ability to provide societally relevant research, analysis, and predictions in many weather- and climate-related application areas across much of the globe.

Climate science has a key role to play in informing strategies for adapting to and mitigating the effects of climate change. However, given the magnitude of the issues involved and their implications, it is imperative that the scientific process is–and is seen to be—rigorous, defensible, and transparent so as to ensure trust in the results. A key element in building such trust is to provide access to underlying data, so that interested parties can check published results and compare with their own analyses. A further priority is to provide data at the fine space and time scales that are relevant for user needs. Until recently, the ability to meet these requirements has been constrained by data-sharing agreements and limitations on digital storage and processing. This is now changing however, thanks to improved global collaboration, communication and computing capability. This article describes current efforts to exploit these opportunities via the International Surface Temperature Initiative, an international and multidisciplinary effort that aims: firstly, to create a single comprehensive global databank of surface meteorological observations at monthly, daily, and sub-daily resolutions; and secondly, to encourage the contribution of multiple independent data products, subject to common performance assessment and benchmarking criteria, thus providing the opportunity for a detailed assessment of uncertainties. The rationale for the initiative is discussed, along with logistical and technical challenges, as well as opportunities for involvement from the statistical and wider scientific and user communities. Copyright © 2012 John Wiley & Sons, Ltd.

We review the scientific literature since the 1960s to examine the evolution of modeling tools and observations that have advanced understanding of global stratospheric temperature changes. Observations show overall cooling of the stratosphere during the period for which they are available (since the late 1950s and late 1970s from radiosondes and satellites, respectively), interrupted by episodes of warming associated with volcanic eruptions, and superimposed on variations associated with the solar cycle. There has been little global mean temperature change since about 1995. The temporal and vertical structure of these variations are reasonably well explained bymodels that include changes in greenhouse gases, ozone, volcanic aerosols, and solar output, although there are significant uncertainties in the temperature observations and regarding the nature and influence of past changes in stratospheric water vapor. As a companion to a recent WIREs review of tropospheric temperature trends, this article identifies areas of commonality and contrast between the tropospheric and stratospheric trend literature. For example, the increased attention over time to radiosonde and satellite data quality has contributed to better characterization of uncertainty in observed trends both in the troposphere and in the lower stratosphere, and has highlighted the relative deficiency of attention to observations in the middle and upper stratosphere. In contrast to the relatively unchanging expectations of surface and tropospheric warming primarily induced by greenhouse gas increases, stratospheric temperature change expectations have arisen from experiments with a wider variety of model types, showingmore complex trend patterns associated with a greater diversity of forcing agents.

Radiosonde humidity records represent the only in situ observations of tropospheric water vapor content with multidecadal length and quasi-global coverage. However, their use has been hampered by ubiquitous and large discontinuities resulting from changes to instrumentation and observing practices. Here a new approach is developed to homogenize historical records of tropospheric (up to 100 hPa) dewpoint depression (DPD), the archived radiosonde humidity parameter. Two statistical tests are used to detect changepoints, which are most apparent in histograms and occurrence frequencies of the daily DPD: a variant of the Kolmogorov–Smirnov (K–S) test for changes in distributions and the penalized maximal F test (PMFred) for mean shifts in the occurrence frequency for different bins of DPD. These tests capture most of the apparent discontinuities in the daily DPD data, with an average of 8.6 changepoints (∼1 changepoint per 5 yr) in each of the analyzed radiosonde records, which begin as early as the 1950s and ended in March 2009. Before applying breakpoint adjustments, artificial sampling effects are first adjusted by estimating missing DPD reports for cold (T < −30°C) and dry (DPD artificially set to 30°C) conditions using empirical relationships at each station between the anomalies of air temperature and vapor pressure derived from recent observations when DPD reports are available under these conditions. Next, the sampling-adjusted DPD is detrended separately for each of the 4–10 quantile categories and then adjusted using a quantile-matching algorithm so that the earlier segments have histograms comparable to that of the latest segment. Neither the changepoint detection nor the adjustment uses a reference series given the stability of the DPD series. Using this new approach, a homogenized global, twice-daily DPD dataset (available online at www.cgd.ucar.edu/cas/catalog/) is created for climate and other applications based on the Integrated Global Radiosonde Archive (IGRA) and two other data sources. The adjusted-daily DPD has much smaller and spatially more coherent trends during 1973–2008 than the raw data. It implies only small changes in relative humidity in the lower and middle troposphere. When combined with homogenized radiosonde temperature, other atmospheric humidity variables can be calculated, and these exhibit spatially more coherent trends than without the DPD homogenization. The DPD adjustment yields a different pattern of change in humidity parameters compared to the apparent trends from the raw data. The adjusted estimates show an increase in tropospheric water vapor globally.

Reanalyses are, by a substantial margin, the most utilized climate data products, and they are applied in a myriad of different contexts. Despite their popularity, there are substantial concerns about their suitability for the monitoring of long-term climate trends. This has led to calls for a truly “climate quality” reanalysis that retains long-term trend fidelity. The authors contend that for such a reanalysis to be achieved, a substantial rethinking of the current strategy for producing reanalysis products is required. First, the problem must be defined clearly. Second, the methodology that is employed must be reconsidered so as to minimize potential nonclimatic artifacts and robustly ascertain the inevitable residual uncertainty. Finally, a set of validation data and metrics must be constructed that the community can use to compare and unambiguously assess the claims of climate quality. The purpose of this essay is very much to initiate discussions to this end rather than to prescribe solutions.

This report describes observations of precipitation, temperature, and other climatology metrics from different global regions.

While the global upper-air observing network has provided useful observations for operational weather forecasting for decades, its measurements lack the accuracy and long-term continuity needed for understanding climate change. Consequently, the scientific community faces uncertainty on key climate issues, such as the nature of temperature trends in the troposphere and stratosphere; the climatology, radiative effects, and hydrological role of water vapor in the upper troposphere and stratosphere; and the vertical profile of changes in atmospheric ozone, aerosols, and other trace constituents. Radiosonde data provide adequate vertical resolution to address these issues, but they have questionable accuracy and time-varying biases due to changing instrumentation and techniques. Although satellite systems provide global coverage, their vertical resolution is sometimes inadequate and they require independent reference observations for sensor and data product validation, and for merging observations from different platforms into homogeneous climate records. To address these shortcomings, and to ensure that future climate records will be more useful than the records to date, the Global Climate Observing System (GCOS) program is initiating a GCOS Reference Upper-Air Network (GRUAN) to provide high-quality observations using specialized radiosondes and complementary remote sensing profiling instrumentation that can be used for validation. This paper outlines the scientific rationale for GRUAN, its role in the Global Earth Observation System of Systems, network requirements and likely instrumentation, management structure, current status, and future plans. It also illustrates the value of prototype reference upper-air observations in constructing climate records and their potential contribution to the Global Space-Based Inter-Calibration System. We invite constructive feedback on the GRUAN concept and the engagement of the scientific community.

Estimates of past climate change often involve teasing small signals from imperfect instrumental or proxy records. Success is often evaluated on the basis of the spatial or temporal consistency of the resulting reconstruction, or on the apparent prediction error on small space and time scales. However, inherent methodological trade-offs illustrated here can cause climate signal accuracy to be unrelated, or even inversely related, to such performance measures. This is a form of the classic conflict in statistics between minimum variance and unbiased estimators. Comprehensive statistical simulations based on climate model output are probably the best way to reliably assess whether methods of reconstructing climate from sparse records, such as radiosondes or paleoclimate proxies, actually work on longer time scales. Copyright © 2008 Royal Meteorological Society

Uncertainties in observed records of atmospheric temperature aloft remain poorly quantified. This has resulted in considerable controversy regarding signals of climate change over recent decades from temperature records of radiosondes and satellites. This work revisits the problems associated with the removal of inhomogeneities from the historical radiosonde temperature records, and provides a method for quantifying uncertainty in an adjusted radiosonde climate record due to the subjective choices made during the data homogenization. This paper presents an automated homogenization method designed to replicate the decisions made by manual judgment in the generation of an earlier radiosonde dataset [i.e., the Hadley Centre radiosonde temperature dataset (HadAT)]. A number of validation experiments have been conducted to test the system performance and impact on linear trends. Using climate model data to simulate biased radiosonde data, the authors show that limitations in the homogenization method are sufficiently large to explain much of the tropical trend discrepancy between HadAT and estimates from satellite platforms and climate models. This situation arises from the combination of systematic (unknown magnitude) and random uncertainties (of order 0.05 K decade−1) in the radiosonde data. Previous assessment of trends and uncertainty in HadAT is likely to have underestimated the systematic bias in tropical mean temperature trends. This objective assessment of radiosonde homogenization supports the conclusions of the synthesis report of the U.S. Climate Change Science Program (CCSP), and associated research, regarding potential bias in tropospheric temperature records from radiosondes.

Reanalyses are used primarily for atmospheric model validation. However, reanalyses suffer from a number of limitations that unfortunately restrict their general use. A reanalysis workshop held recently at the University of Reading, United Kingdom, highlighted a number of central issues in climate research. Attendees discussed how these research needs may benefit from a dedicated reanalysis. Specific topics covered included a more in-depth understanding of the general circulation of the atmosphere and a more reliable assessment of climate trends, the hydrological cycle and the calculation of energy fluxes over the oceans.

The paper proposes that there are a number of low-probability, but not insignificant, physical threat scenarios relating to the office environment that warrant an initial response capability (in addition to fire evacuation drill). Explosive, contamination and intruder scenarios are included in the threats' scope. The term 'initial response' essentially refers to first actions relating to how and where to direct people to preserve life safety. Given the likelihood that an organisation would not have any emergency services support in the early stages of an incident, it follows that it is likely that the organisation would be 'on its own' to assess, decide and execute the initial response. Aside from any moral obligations, the paper proposes that there are potential legal obligations to compel an organisation to be so prepared. An important consideration explored is the feasibility of having an effective initial response capability. The relative simplicity and low cost of implementation and maintenance of a response is described. Lastly, the point is made that, aside from fire evacuation (which is a drill), all the other threat scenarios discussed require some decision making. It is not possible to be 100 per cent correct 100 per cent of the time. A decision support matrix is described with the objective of increasing the percentage chance of making the most appropriate decision most of the time. In particular, this is seen as an area for further discussion.