Ireland’s winters are getting wetter, with more frequent heavy precipitation events increasing flooding risk across the Island. Extreme precipitation is a key driver of flooding in northwestern Europe; however, observational records are relatively short and represent only a single realisation of the climate state. As a result, they are inadequate for sampling low-likelihood, high-impact flood-relevant extreme precipitation events and for quantifying plausible maxima of such extremes. In this study, we quantify plausible maxima for flood-relevant winter precipitation under the current climate. We apply the UNprecedented Simulated Extremes using Ensembles (UNSEEN) approach to the flood-relevant winter precipitation indices (Rx1day, Rx5day, and Rx30days), using daily winter observations, the ECMWF SEAS5 seasonal prediction systems, and the CANARI Single Model Initial-condition Large Ensemble (SMILE) over the Island of Ireland. These indices are consistently derived across observations, pooled SEAS5 winter ensembles (ensemble member x lead times), and the CANARI SMILE. Model fidelity for CANARI and ensemble independence, stability, and fidelity for pooled SEAS5 are assessed to ensure that both models realistically represent extreme precipitation. Preliminary results indicate that both SEAS5 and the CANARI sample the physically plausible Rx1day and Rx5day extremes that exceed the maximum observed in the current climate, while neither system produces UNSEEN values exceeding the observed maximum Rx30day. The CANARI large ensemble passes the fidelity test without bias correction, whereas the SEAS5 passes the fidelity test after applying simple multiplicative mean scaling bias correction. For CANARI, plausible maxima are approximately 18.01% higher for Rx1day and 20.77% higher for Rx5day than observed maxima, while Rx30day plausible maxima are approximately 8.70% lower than the highest observed Rx30day. For SEAS5, plausible maxima exceed observations by approximately 3.05% for Rx1day and 17.68% for Rx5day, while Rx30day plausible maxima are approximately 17.74% lower than the highest observed. These results highlight the limitations of observational records in sampling extreme tails and indicate that CANARI SMILE captures a broader range of internal climate variability than the initialised SEAS5 seasonal prediction system. They also show that UNSEEN ensembles are more effective at sampling short-duration precipitation extremes (Rx1day and Rx5day) than longer-duration accumulation precipitation extremes (Rx30day). Our study highlights the value of combining the UNSEEN approach with both seasonal prediction systems and SMILEs to better understand unprecedented flood-relevant precipitation extremes in the current climate.

Abstract The objective of this paper is to examine transformational change in the New York City water supply system through the use of a framework designed to investigate urban environmental policy transitions. The framework utilises resilience theory and complex system transition theory to examine how a policy system responds to a series of stressors and shocks and then might undergo a broad system-level change — a “critical transition,” which is a non-linear system shift from one state of equilibrium to another. We use the framework to analyse a 1830s critical transition that occurred in the city’s water supply system as it switched from a local private supply system to a regional, publicly financed system. In the discussion, we review how this type of analysis might be useful for understanding the stresses and crises emerging in contemporary urban systems and policy environments as they face the impacts of climate change.

Evidence from observational records and model simulations suggest that volcanic eruptions can strengthen mid- to high-latitude atmospheric circulation and enhance westerly wind strength, with recent proxy data-model assimilations supporting this. However, assessments of Holocene variability in storminess rarely consider whether major volcanic eruptions could be a possible driver of reconstructed periods of enhanced storminess. This research presents a new reconstruction of past storminess from a coastal peatbog situated in western Ireland spanning the last similar to 7 ka. The record is based on the measurement of the sand content along the core, with XRF core scanning analysis also applied to test whether variations in quartz sand, shell sand and sea spray can be detected by variations in silica, calcium and bromine respectively. Ca measurements were similar to the long-term changes in sand content along the core, however, peaks in sand content were not detected, while Si reflected increases in sand content only within the last millennium when the inorganic content was highest. Br concentrations appear to have been influenced primarily by humification. We also compared sand-based storminess records from northwest Europe. Six multi-decadal to centennial periods with enhanced storminess are common to records from Ireland and Wales during the last 2.5 ka BP, centred at c. 2.25, 2, 1.4, 1.1, 0.5 and 0.2 ka BP, with less agreement between records before this time. The storm periods at 2.8, 2.2-2, 1.1 and 0.5 ka BP are more widespread events and agree with records from Sweden and Scotland. Each of the episodes of increased storminess coincide roughly with major volcanic eruptions during the late Holocene, as well as with periods of enhanced North Atlantic ice-rafting. We hypothesise, therefore, that both enhanced storminess and ice-rafting may have resulted from the climate and environmental impacts of these eruptions, aligning with the findings of recent observational and modelling studies on the climate response to eruptions. Challenges remain, however, in testing this hypothesis, given chronological uncertainties in peatland records and uncertain interpretations of the factors influencing sand deposition. Therefore, to provide an independent assessment of the influence of explosive eruptions on storminess for Ireland's northeast Atlantic position, we draw upon the rich tradition of annalistic record keeping on the island, including many reports of major storms and windy seasons, to develop a windiness index running from the sixth to seventeenth centuries CE. A set of superposed epoch analyses shows that the ice-core-based dates of explosive volcanic eruptions are statistically significantly associated with the dates of documented storms and windy seasons in Ireland, suggesting avenues for future research.

ABSTRACT We present an update of the Irish Hydrometric Reference Network (IHRN) of river gauging stations from across the Republic of Ireland that have been deemed suitable for assessing climate‐driven changes in high, mean and low flows. Selection criteria, analysis of metadata and historical flows, and stakeholder feedback are applied to identify 51 stations for inclusion in the network. Missing daily data were infilled using a conceptual hydrological model and an Artificial Neural Network. As well as providing a dataset for monitoring and detecting the impact of changing climatic conditions on Irish catchments, the updated IHRN offers utility for assessing extremes of flood and drought and for modelling future flow regimes via climate change impact assessments.

Detecting the emergence of anthropogenic climate change signals in precipitation is essential for informing adaptation strategies. This study analyses long-term, quality-assured observations from 36 stations across Ireland (1930-2019) to assess trends and emergence in six seasonal precipitation indices. Using a combination of Mann-Kendall trend testing, Theil-Sen slope estimation, and monthly persistence analysis, robust seasonal changes are identified. Emergence is evaluated by regressing local precipitation indices against global mean surface temperature (GMST), with the resulting signal-to-noise ratio (SNR) classified as normal, unusual, or unfamiliar relative to early industrial (1850-1900) and modern (1950-1980) baselines. The influence of the North Atlantic Oscillation (NAO) is also assessed using commonality analysis. Results show statistically significant intensification of rainfall extremes, particularly in western Ireland during winter and spring, and in the southeast during summer and autumn. Many stations exhibit significant relationships with GMST, with increases in extreme indices (e.g., Rx5day, SDII) ranging from 12% to 27% per degrees C of warming, often exceeding thermodynamic expectations. Emergence of unusual climate conditions is already evident at several stations relative to the early industrial baseline, and many are nearing this threshold for the modern baseline. While NAO variability strongly modulates winter precipitation extremes in the west, significant GMST relationships in the SNR analysis indicate that these are still robust climate change signals. Commonality analysis reveals that GMST and NAO jointly explain variability in winter PRCPTOT and Rx5day at western stations, suggesting that natural modes of variability like the NAO may not be independent noise but rather embedded within a warming climate signal, complicating the separation of anthropogenic and natural drivers in attribution studies. Findings also challenge projections of widespread summer drying with warming, instead revealing intensification of short-duration extremes in the southeast. As Ireland faces increasingly intense and seasonally variable rainfall extremes, regionally tailored adaptation strategies will be essential.

Future flood dynamics in a lowland karst catchment draining into Galway Bay, Ireland, have been assessed under climate-change scenarios. Bayesian neural network model (BNN) was calibrated on 1980-2015 observations of rainfall, tides, and turlough flood volumes, yielding correlations of R = 0.95 (training) and R = 0.78 (overall). Projections driven by CORDEX under RCP 4.5 and 8.5 for 2016-2100 reveal ensemble-mean rainfall increases of 1.2 mm decade-1 and 2.5 mm decade-1, respectively, corresponding to flood-volume growth rates of 5 × 10⁶ m3 decade-1 and 1.1 × 107 m3 decade-1. Wavelet coherence indicated high-frequency coupling (> 0.7) between rainfall and floods under RCP 8.5 versus < 0.5 under RCP 4.5. Extreme-event analysis showed a 40% rise in joint 95th -percentile rainfall and flood-volume events under RCP 8.5 (p < 0.05). Generalized extreme-value fits to annual maxima for 2018-2037 versus 2080-2099 project that a historical 100-year storm becomes 1-in-16-year event under RCP 8.5. 10-year rolling 90th -percentile analysis revealed rapid intensification of upper-tail floods under RCP 8.5 than RCP 4.5. These findings demonstrated that high-emission pathways substantially amplify flood magnitudes and frequencies and underscores the utility of integrated statistical and machine-learning frameworks for robust flood-risk assessment and climate-adaptation planning.

This study presents a chronology of fatal lightning strikes in Ireland (1900-2024) derived from digitised newspaper archives. Analysis provides insights into the seasonality, location, victim activity and synoptic conditions (from Lamb weather types) of 94 fatal events causing 113 deaths. Fatalities are most common in summer, especially June and July, during outdoor work or gatherings, and geographically widespread but concentrated in the west and south. Cyclonic weather dominates fatal days (47%). A sharp reduction in fatalities is observed in recent decades, reflecting societal change, improved forecasting and awareness.

Climate change is already impacting Ireland, through rising temperatures, more frequent extreme weather events, and increasing risks such as coastal erosion and flooding. Volume 3 of the Irish Climate Change Assessment Report (ICCA), launched in 2024, synthesises extensive research on past and projected climate-change impacts and provides a roadmap for being prepared for Ireland’s future climate. While climate action nationally has been focused on reducing greenhouse gas emissions, adaptation is an equally pressing concern as climate risks escalate. Drawing on the ICCA, this paper examines how adaptation and resilience are framed in Irish climate policy, and highlights key challenges in implementation. The findings emphasise the need for a systematic, well-resourced, and socially inclusive approach to adaptation. National evaluations indicate slow progress in adaptation, with significant gaps in cross-sectoral coordination, financial investment and community engagement. The authors highlight key opportunities to enhance adaptation efforts, including: setting clear goals and targets; recognising cascading and transboundary risks; integrating people-centred approaches; decision-making under uncertainty; widening the solution space beyond technical interventions; better monitoring and evaluation of adaptation outcomes; and pursuing climate-resilient development. Without substantial improvements in adaptation planning, Ireland risks unplanned, crisis-driven transformations in response to escalating climate shocks. Strengthening governance, deepening public engagement, and embedding adaptation into all aspects of policy and planning will be critical to achieving a climate-resilient Ireland.

Climate change is likely to add further pressures to water quality degradation across the globe. The development of robust climate-smart mitigation measures necessitates understanding the impact of extreme hydrological events on catchment hydrology and nutrient losses. Here, empirical modelling (EM) was applied on 14 years of sub-hourly water quality and weather data from six hydrologically diverse agricultural catchments in Ireland to understand the climatic factors that trigger an increase in phosphorus (P) losses [manifested as increase of 0.01 mg L- 1 in total phosphorus (TP) and increase of 0.005 mg L- 1 in total reactive phosphorus (TRP) over one day]. Plausible future P-loss due to extreme weather events was then modelled using climate change scenarios (from 2010 to 2100) for medium and high emission pathways, i.e. Representative concentration pathways (RCP) 4.5 and RCP8.5, respectively. EM identified three climatic conditions that trigger TP and TRP losses across all study catchments, namely: (i) cumulative effective rainfall > 5 mm over five days followed by effective rainfall > 5 mm in one day; (ii) effective rainfall > 5 mm in one day, and; (iii) effective rainfall over ten mm in one day. Together, these criteria captured up to 80% of the events across all catchments despite their different characteristics. From the projected climate change scenarios, the frequency of triggering events and their associated discharge rates, increases significantly towards the end of the century in all catchments, especially under RCP8.5. The sensitivity of catchment response to the changing weather patterns and the monthly trend of precipitation throughout the century strongly depended on catchment characteristics. The hydrologically flashy catchments in the dataset tend to be most sensitive to climate driven changes, returning the highest percentage increase of annual P-loss events in both RCPs. Considering far-future scenario, there would be 10-66% increase in the number of P-loss events under RCP4.5, and 28-67% under RCP8.5, taking into account the potential underestimation of projected precipitation probability. Assuming no changes in P-inputs in the future scenarios, the projections also indicated average discharge of up to 8.5 mm per a single triggering event that would directly contribute to increases in P-concentrations and mass loads leaving the catchments. Changes in climate are likely to compound already significant challenges in improving/ maintaining good water quality. It is therefore critical to incorporate the influences of climate change on nutrient losses in developing mitigation/adaptation strategies that are tailored to catchment-specific characteristics.

Feeding the large future population is associated with severe environmental challenges to which climate change is adding further complications and stress to the global food supply system. The strategies to the challenges posed on ecosystem conservation and climate neutrality would be best achieved by integrating the most current scientific findings in ‘best practice’ policies and their implementation. This paper presents the outcomes from the fourth International Catchment Science Conference in Ireland, a three-day multi-actor conference, and calls for action to improve soil fertility, reduce GHG emissions, increase carbon sequestration, and reduce pollution loss to waters. It was concluded that an accountable management of the agricultural landscape requires a multi-actor, multidisciplinary and multi-scale approach with collaboration between the scientific community, policy makers and farmers. Importantly there should be a focus on linking research, technology, education, information, engagement and innovation. Following needed requirements were identified: (i) long-term monitoring, high-temporal and high-spatial resolution data collection, (ii) combining temporarily and spatially rich datasets, (iii) long-term planning horizons to be adopted by key institutional stakeholders, (iv) mitigation strategies to adapt to changing climate and agricultural practices, and (v) an adequate advisory support and training for farmers. Some progress has been achieved to a situation where it is possible to counter or mitigate some of the more urgent issues in the food systems under consideration in the review.

This study analyses the evolution of annual streamflow across Europe between 1962 and 2017, focusing on the connection of streamflow trends with climate dynamics and physiographic and land cover characteristics and changes. The spatial pattern of trends in streamflow shows strong agreement with the spatial patterns of climate trends, suggesting a climate control of these trends. However, analysing temporal evolution at the basin scale shows that the strong decrease in streamflow in southern Europe cannot be directly associated with climate dynamic. In fact, a negative trend related to non-climate factors clearly emerges. Rather, we show that forest growth and irrigated agriculture are the leading drivers of negative streamflow trends in southern Europe, particularly during dry years due to the greater proportion of green water consumption compared to blue water generation. These findings have significant implications, particularly in the context of widely embraced nature-based solutions for mitigating climate change, including carbon sequestration through forests and the planned expansion of irrigated agricultural lands in central and northern European countries as a response to rising crop water demands. These developments could potentially diminish water resources availability, leading to an increased occurrence and severity of low flow periods.

ABSTRACT This paper details the compilation of data and application of quality assurance procedures for constructing a 157‐year snow and sleet series for the Greater Dublin Area, Ireland. Snowfall is particularly sensitive to climate variability in temperate regions, and long‐term records are essential for understanding changes in winter weather extremes over time. The dataset integrates observations from six sites and provides a regional snow and sleet frequency dataset at monthly, seasonal (October–May) and annual resolutions. Data sources include archived meteorological records, digitised station logs and synoptic weather reports. A brief analysis offers insights into long‐term snowfall climatology in the Greater Dublin region from 1867 to 2024, revealing substantial interannual and decadal variability, as well as notable reductions in snow frequency in recent decades. This dataset provides a valuable baseline for assessing historical trends in snowfall and contributes to broader efforts in climate reconstruction and climate change impact studies in Ireland and beyond.

This research examines the changes in annual streamflow across Europe from 1962 to 2017, with a specific focus on the correlation between streamflow trends and climate dynamics, as well as physiographic and land cover characteristics. The spatial distribution of streamflow trends aligns closely with climate patterns, suggesting a climate-related influence. However, a detailed analysis at the basin scale reveals that the significant decline in streamflow in southern Europe cannot be solely attributed to climate dynamics. Instead, a discernible negative trend linked to non-climate factors becomes apparent. Specifically, our study indicates that the primary drivers of negative streamflow trends in southern Europe, especially during dry years, are forest growth and irrigated agriculture. This is attributed to the higher proportion of green water consumption compared to blue water generation. These findings hold substantial implications, particularly in the context of widely adopted nature-based solutions for addressing climate change. This includes concerns about carbon sequestration through forests and the planned expansion of irrigated agricultural lands in central and northern European countries to meet growing crop water demands. Such developments may potentially reduce the availability of water resources, leading to an increased frequency and severity of low flow periods.

We assess the value of calibrating forecast models for significant wave height HS, wind speed W and mean spectral wave period T-m for forecast horizons between zero and 168 h from a commercial forecast provider, to improve forecast performance for a location in the central North Sea. We consider two straightforward calibration models, linear regression (LR) and non-homogeneous Gaussian regression (NHGR), incorporating deterministic, control and ensemble mean forecast covariates. We show that relatively simple calibration models (with at most three covariates) provide good calibration and that addition of further covariates cannot be justified. Optimal calibration models (for the forecast mean of a physical quantity) always make use of the deterministic forecast and ensemble mean forecast for the same quantity, together with a covariate associated with a different physical quantity. The selection of optimal covariates is performed independently per forecast horizon, and the set of optimal covariates shows a large degree of consistency across forecast horizons. As a result, it is possible to specify a consistent model to calibrate a given physical quantity, incorporating a common set of three covariates for all horizons. For NHGR models of a given physical quantity, the ensemble forecast standard deviation for that quantity is skilful in predicting forecast error standard deviation, strikingly so for HS. We show that the consistent LR and NHGR calibration models facilitate reduction in forecast bias to near zero for all of HS, W and T-m, and that there is little difference between LR and NHGR calibration for the mean. Both LR and NHGR models facilitate reduction in forecast error standard deviation relative to naive adoption of the (uncalibrated) deterministic forecast, with NHGR providing somewhat better performance. Distributions of standardised residuals from NHGR are generally more similar to a standard Gaussian than those from LR.

AbstractUnderstanding of past droughts has been mostly shaped by meteorological data, with relatively less known about the human aspects of droughts, their socio‐economic impacts, as well as choices people make in response to droughts in different environmental and socio‐political contexts. The lack of data that systematically record and categorize drought impacts is an important reason for this disparity. In this paper, we present an Irish drought impacts database (IDID) containing 6094 newspaper reports and 11,351 individual impact records for the island of Ireland, covering the period 1733–2019. Relevant articles were identified through systematic searching of the Irish Newspaper Archives, and recorded impacts were categorized using a modified version of the classification scheme employed by the European drought impact inventory (EDII). Drawing on the wealth and diversity of content provided by the newspapers, the IDID database provides information on the documented temporal and geographical extent of drought events, their socio‐economic and political contexts, their consequences, mitigation strategies employed and their change over time. The IDID also facilitates analysis of long‐term patterns in drought incidence, individual impact categories, as well as detailed insight into the impacts of individual drought events over nearly three centuries of Ireland's history. In addition, by allowing an examination of the coherence between meteorological records and identified impacts, it advances our understanding of the influences that contemporary economic, political, environmental and societal events had on the human experience, perception and impact of droughts. This new open‐access database, therefore, provides opportunities for improving understanding of drought vulnerability and is an important step in developing greater capacity to cope with and respond to future droughts on the island of Ireland.

Threshold selection is a fundamental problem in any threshold-based extreme value analysis. While models are asymptotically motivated, selecting an appropriate threshold for finite samples is difficult and highly subjective through standard methods. Inference for high quantiles can also be highly sensitive to the choice of threshold. Too low a threshold choice leads to bias in the fit of the extreme value model, while too high a choice leads to unnecessary additional uncertainty in the estimation of model parameters. We develop a novel methodology for automated threshold selection that directly tackles this bias-variance trade-off. We also develop a method to account for the uncertainty in the threshold estimation and propagate this uncertainty through to high quantile inference. Through a simulation study, we demonstrate the effectiveness of our method for threshold selection and subsequent extreme quantile estimation, relative to the leading existing methods, and show how the method's effectiveness is not sensitive to the tuning parameters. We apply our method to the well-known, troublesome example of the River Nidd dataset.

Understanding temporal trends in low streamflows is important for water management and ecosystems. This work focuses on trends in the occurrence rate of extreme low-flow events (5- to 100-year return periods) for pooled groups of stations. We use data from 1,184 minimally altered catchments in Europe, North and South America, and Australia to discern historical climate-driven trends in extreme low flows (1976-2015 and 1946-2015). The understanding of low streamflows is complicated by different hydrological regimes in cold, transitional, and warm regions. We use a novel classification to define low-flow regimes using air temperature and monthly low-flow frequency. Trends in the annual occurrence rate of extreme low-flow events (proportion of pooled stations each year) were assessed for each regime. Most regimes on multiple continents did not have significant (p < 0.05) trends in the occurrence rate of extreme low streamflows from 1976 to 2015; however, occurrence rates for the cold-season low-flow regime in North America were found to be significantly decreasing for low return-period events. In contrast, there were statistically significant increases for this period in warm regions of NA which were associated with the variation in the Pacific Decadal Oscillation. Significant decreases in extreme low-flow occurrence rates were dominant from 1946 to 2015 in Europe and NA for both cold- and warm-season low-flow regimes; there were also some non-significant trends. The difference in the results between the shorter (40-year) and longer (70-year) records and between low-flow regimes highlights the complexities of low-flow response to changing climatic conditions.

Climate change is likely to exacerbate land to water phosphorus (P) transfers, causing a degradation of water quality in freshwater bodies in Northwestern Europe. Planning for mitigation measures requires an understanding of P loss processes under such conditions. This study assesses how climate induced changes to hydrology will likely influence the P transfer continuum in six contrasting river catchments using Irish national observatories as exemplars. Changes or stability of total P (TP) and total reactive P (TRP) transfer processes were estimated using far-future scenarios (RCP4.5 and RCP8.5) of modelled river discharge under climate change and observed links between hydrological regimes (baseflow and flashiness indices) and transfer processes (mobilisation and delivery indices). While there were no differences in P mobilisation between RCP4.5 and RCP8.5, both mobilisation and delivery were higher for TP. Comparing data from 2080 (2070–2099) with 2020 (2010–2039), suggests that P mobilisation is expected to be relatively stable for the different catchments. While P delivery is highest in hydrologically flashy catchments, the largest increases were in groundwater-fed catchments in RCP8.5 (+ 22