Climate Services Research Articles (Page 1)

Purpose This study investigates the role of customer value-creating practices in (re)formation of service ecosystem well-being within e-commerce platforms, focusing on the platform co-creation processes facilitated by the DART (Dialogue, Access, Risk Assessment, Transparency) framework. Design/methodology/approach Data were gathered from major e-commerce platforms in Vietnam, with a cross-sectional design. The whole of 260 customer surveys was used to validate a research model using PLS-SEM. Findings Results highlights that the DART dimensions significantly enhance customer role readiness, which positively affects task support practices (both direct and vicarious), and subsequently, perceived service climate. A supportive service climate further promotes governance well-being and customer learning desire, underscoring the role of DART as the platform readiness for system-wide co-created values. Originality/value Grounded in service-dominant logic and structuration theory, this study is one of the first to posit that both platform readiness and customer role readiness act as important determinants for customer co-creation practices. Moreover, this is among the first to quantitatively investigate the well-being outcomes across the three levels of service ecosystems, which are customer learning (micro-), service climate (meso-) and governance well-being (macro level). Finally, the paper may be a pioneering work to validate the full interplay among structures (e.g. DART), actions (i.e. task support) and structures (i.e. service climate) within e-commerce platforms.

The service industry has always been relationship-oriented, where the interaction between service providers and their clients is a core component of service sustainability. The highly frequent interaction between service providers and clients has increased concerns about the quality of life for service providers in the service context. Managing the emotional experiences of service providers is therefore essential to maintaining their health, effectiveness, and the quality of the service they deliver. This study aims to extend the knowledge of service provider-client interactions in beauty salon services, which are characterized as high-contact services. Specifically, it investigates the impact of active empathetic listening on emotional exhaustion. It additionally examines the moderating roles of emotional intelligence (trained skill) and self-emotion regulation (biological mechanism) in this relationship. Data were collected from 204 beauticians in Cyprus and analyzed using PLS-SEM. Results revealed a positive relationship between active empathetic listening and emotional exhaustion. Emotional intelligence and self-emotion regulation both moderate the relationship, with self-emotion regulation showing a stronger influence. Despite limitations, the findings contribute to the literature on emotional labor, service climate, and occupational well-being and provide practical implications for enhancing emotional skills and resilience in service roles.

Abstract Integrating Large Language Models (LLMs) with climate model data, scientific literature, and unstructured text enables a new generation of climate information systems that deliver accurate, localized, and context-aware insights. Our primary objective is to develop and evaluate ClimSight, a scalable platform that turns complex heterogeneous data into actionable information. We augment LLMs with Retrieval Augmented Generation, a method that retrieves relevant climate models and reports at query time to ground responses. An agent-based architecture orchestrates specialized modules that route and process user queries with task-specific tools. Real-world evaluations compare multiple LLM configurations and analyze trade-offs between speed, cost, and accuracy. Results show improved scalability and precision in climate assessments, democratizing access to localized information. This paradigm shift equips stakeholders in agriculture, urban planning, disaster management, and policy with effective tools for forward planning and risk management.

Mounting water scarcity: A complex challenge requiring nuanced solutions Addressing increasing water scarcity is a complex challenge that requires nuanced solutions, according to Peter Greve from the Climate Service Center Germany. A sufficient freshwater supply is essential to ecosystems, societies, and economies. Given the continuous increase in global population, improved living standards, and rampant economic growth, there is an ever-growing demand for water. The increasing global food demand can often only be met through massive increases in irrigation activities (e.g., Kummu et al., 2016).

Abstract This study introduces the first high-resolution hazard probability maps of extreme precipitation for Spain, marking a significant step toward a national climate service for hydrometeorological extremes. Using long-term daily precipitation records from a dense network of stations and incorporating topographic data, the methodology combines the Generalized Pareto distribution with universal kriging to spatially interpolate distribution parameters. These maps offer reliable estimates of extreme precipitation quantiles, validated against station-level observations, and are based on a stationary modelling framework—an approach supported by recent findings showing the temporal stability of such extremes in Spain and considered more robust than non-stationary alternatives. Distinct spatial patterns emerge, with intense daily precipitation distributed mainly along the Mediterranean coast and high total event precipitation in the northwest and southwest, reflecting the influence of varied weather systems. To support decision-making, the study aggregates these high-resolution data at the provincial level, aligning risk information with administrative boundaries, and enhancing its relevance for policy and planning. Furthermore, the maps are made accessible via an interactive online platform (https://retornolluvias.csic.es), enabling users to explore localized hazard probabilities, thereby supporting adaptation in water management and civil protection, among others.

Abstract Climate services often require observational climate data to inform decision-making on mitigation and adaptation activities. Understanding the uncertainties in the climate datasets that are used for this purpose, and how these uncertainties relate to the context of the climate service is critical to making well-informed decisions. Recent developments in the production of climate-relevant satellite datasets has focused on characterising uncertainties from a bottom-up perspective with a high degree of mathematical rigour. Using the example of three essential climate variables: sea surface temperature, soil moisture and carbon dioxide we discuss how to translate the highly-detailed uncertainty information provided with high-resolution datasets into something appropriate to the scale of a climate service, where the decision-making context might be local, regional or global. Close engagement between climate data producers and climate service providers is essential to ensure we have the best possible platform to make decisions as we adapt to climate change.

This paper examines the impact of employee experience with Generative AI (GenAI) on employee experience, employee engagement, and their effects on customer experience, such as satisfaction, loyalty, and engagement in the service sector. Following a mixed-methods approach, this research comprises two studies: Study 1 uses survey data from 578 frontline employees in the UK and Vietnam to examine the research model, and Study 2 involves qualitative interviews to further elaborate on the findings. Results show that GenAI experience enhances employee experience and engagement, which in turn improves customer experience. The paper also highlights the mediating effect of employee experience and the context-dependent moderating effect of hybrid work. This research comprehensively explores the link between employee and customer experiences, while integrating employee experience with GenAI and hybrid work as timely constructs that reflect the complexities at the frontline. These findings contribute to the literature on human–technology interaction and organizational change, extending the service climate framework with empirical evidence on the evolving dynamics between technology, employees, and customers.

Abstract. The Coupled Model Intercomparison Project (CMIP) coordinates community-based efforts to answer key and timely climate science questions, facilitate delivery of relevant multi-model simulations through shared infrastructure, and support national and international climate assessments. Generations of CMIP have evolved through extensive community engagement from punctuated phasing into more continuous support for the design of experimental protocols, infrastructure for data publication and access, and public delivery of climate information. We identify four fundamental research questions motivating a seventh phase of coupled model intercomparison relating to patterns of sea surface temperature change, changing weather, the water–carbon–climate nexus, and tipping points. Key CMIP7 advances include an expansion of baseline experiments, a focus on CO2-emissions-driven experiments, sustained support for community MIPs, periodic updating of historical forcings and diagnostics requests, and a collection of prioritized experiments, or the “Assessment Fast Track”, drawn from community MIPs to support climate research, assessment, and service goals across prediction and projection, characterization, attribution, and process understanding.

ABSTRACTExtreme precipitation aggregated in time and space will lead to the superposition and amplification of disaster risks, causing significant impacts on social economy and ecological environment. Incorporating the area factor into the extreme precipitation risk assessment framework using intensity‐duration‐area‐frequency (IDAF) curves can effectively evaluate the superposition and amplification effects of extreme precipitation events. Therefore, this study utilised high spatiotemporal resolution precipitation data (i.e., 0.1° × 0.1° and 3 h) from the Yangtze River basin (YRB) in the period of 1979 to 2020 to establish seasonal and annual IDAF curves at multiple spatiotemporal scales (i.e., 3–96 h and 144–961 km2) for the first time in the YRB. The extreme precipitation intensity at different spatiotemporal scales and return periods was estimated, and the patterns of its variation with spatiotemporal scales were also investigated. The results indicated that: (1) The goodness‐of‐fit of the Extended Generalised Pareto Distribution –IDAF (EGPD‐IDAF) model exhibited significant seasonality and scale dependency, with the best fitting effect in summer and at the mesoscale (i.e., 24 h and 144 km2); (2) At the 3–6 h scale, extreme precipitation return levels in the YRB exhibited higher sensitivity to variations in area, showing greater fluctuations, whereas as the temporal scale increased, the impact of area variation gradually weakened; (3) The maximum return levels of extreme precipitation in the eastern sub‐basins of the YRB occurred at the spatiotemporal scale of 3 h and 144 km2, representing local short‐duration heavy precipitation, while those in the inland sub‐basins occurred at the large scale of 961 km2 with a temporal scale of 24 or 48 h. This study elucidates the area effect of extreme precipitation events in the YRB and establishes the relationship between extreme precipitation return levels and duration and area, offering significant value for regional climate services and disaster risk management.

Abstract The increasing demand for climate information that spans seasonal to multi-annual time scales poses a challenge for current prediction systems, which are traditionally designed for specific forecast horizons. This study addresses this gap by proposing a new method to generate seamless climate information from seasonal to decadal time scales. We develop a constraining approach based on ensemble member selection, in which decadal prediction members are selected to match the seasonal forecast ensemble mean of sea surface temperature. The method leverages the higher skill of seasonal predictions in capturing interannual climate variability, particularly El Niño–southern oscillation, to constrain decadal forecasts using the most recent climate information. Results show that the method to constrain decadal predictions improves the forecast skill over the Niño3.4 region up to 12 months and enhances the near-surface temperature predictions over broad parts of the globe, with modest improvements in precipitation. This work highlights the practical potential of combining seasonal and decadal prediction systems and offers a first step toward operational, seamless climate services across monthly to multi-year timescales.

Abstract High-resolution climate information is critical for the Vulnerability, Impacts, Adaptation, and Climate Services (VIACS) communities. Coordinated ensembles generated by initiatives like the Coordinated Regional Climate Downscaling Experiment (CORDEX) provide consistent and comparable information for the present and future over all land areas of the globe. This manuscript focuses on the European CORDEX initiative (EURO-CORDEX) and its coordinated effort to build regional climate ensembles for the years to come. In its first phase, EURO-CORDEX produced a rich ensemble of regional climate simulations under different representative concentration pathway scenarios. The EURO-CORDEX dataset is openly available and was fed into the Regional Atlas of the IPCC Sixth Assessment Report. However, this ensemble suffered from several shortcomings, which the community seeks to address in the next phase of production. Chief among these is the oft-cited criticism that the selection of GCMs that provide input to the regional climate models was not rigorous and that the resulting ensemble represents an “ensemble of opportunity.” The present paper provides a description of how the community has addressed these shortcomings. We present a comprehensive, flexible, and traceable evaluation framework and toolkit for assessing the suitability of GCMs for downscaling, using EURO-CORDEX as an example. Its value lies in its explicit recognition of subjectivity and mechanisms implemented to transparently track decision-making. Further, the utility of the framework extends well beyond predownscaling decisions to also include postdownscaling investigations performed by the VIACS communities and beyond, to include researchers investigating such topics as model biases, future constraints, and exploring future storylines.

Urban tree architectural modifications over the growing season and water restriction significantly contribute to variations in climate services

Assessment of efficiency of IFAD-CASP weather and climate services delivery in Sokoto and Katsina states, Nigeria

"FareAssieme" is a recovery-oriented community psychiatry model that has been implemented by the Mental Health Service of Trento since 1999. The approach is grounded in the active involvement of users and family members, with particular emphasis on experiential knowledge - the insights derived from lived experience of mental illness and recovery - as a resource for improving the quality of care and rehabilitation processes. At its core, the model involves the structured integration of Peer Support Experts (ESPs, Esperti in Supporto tra Pari), individuals with personal or familial experience of psychological distress who have attained a stable life balance and developed effective coping strategies. ESPs are embedded across all domains of the mental health service (SSM), including community teams, crisis services (territorial and hospital-based), residential settings, and front-office activities. Their role is to support others in their recovery journeys through narrative sharing and emotional proximity. ESPs have contributed to enhanced user engagement, improved service climate, and increased trust in providers. They have proven particularly effective in engaging individuals initially resistant to treatment, thereby facilitating stronger therapeutic alliances. The model also fostered the creation of the Participatory Planning Group (GPP), a deliberative body comprising users, families, ESPs, and professionals, which has developed several Operational Guidelines to standardize and disseminate shared practices within the SSM. "FareAssieme" stands as a validated model of participatory, recovery-oriented psychiatry. It highlights the transformative value of experiential knowledge within mental health services and makes a meaningful contribution to anti-stigma efforts and the co-construction of inclusive care pathways.

ABSTRACTThe complex interactions among atmosphere, ocean, land and cryosphere make it challenging to predict Indian summer monsoon rainfall (ISMR) accurately. This study evaluates two next‐generation seasonal prediction systems—GFDL‐SPEAR (Geophysical Fluid Dynamics Laboratory‐Seamless System for Prediction and Earth System Research) and the MMCFSv2 (Monsoon Mission Coupled Forecast System version 2), which incorporate updated model physics and improved dynamical cores. By assessing model hindcasts during the summer monsoon seasons (June–September) for the period 1991–2020, we demonstrate a 2%–16% improvement in ISMR prediction skill–measured as the anomaly correlation between the ensemble mean model ISMR and observed ISMR–compared to their predecessor models. In addition to ISMR prediction skill, we examine the models' ability to represent tropical sea surface temperature (SST) mean states, variability, and their teleconnections with ISMR. Our analysis reveals that while GFDL‐SPEAR accurately represents the SST mean state, MMCFSv2 demonstrates relatively better skill in capturing the interannual variability of the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole. A dry rainfall bias (1.25 mm/day) is noted in MMCFSv2, while GFDL‐SPEAR exhibits a comparatively smaller wet bias (0.75 mm/day) over the Indian landmass. MMCFSv2 also shows improved Indo‐Pacific SST–ISMR teleconnections, contributing to its enhanced ISMR skill (0.58 in MMCFSv2 and 0.47 in GFDL‐SPEAR). However, the ISMR prediction skill in both models exhibits considerable decadal variability, with challenges in capturing the decadal fluctuations of the ENSO–ISMR relationship. Our findings emphasise that improved representation of tropical SST teleconnections, rather than mean‐state biases alone, is critical for achieving better ISMR prediction skill. This process‐level understanding provides insights for the continued development of reliable seasonal prediction systems and climate services over South Asia.

Abstract This study examined the relationships between frontline employees’ (FLEs’) shared perceptions of service-oriented high-performance work systems (SO-HPWSs), work engagement, and service climate. It also investigated how these shared perceptions related to store managers’ assessments of FLEs’ collective in-role and extra-role service performance, customer satisfaction, and store loyalty in the same retail chain. Data were collected from 781 FLEs, 70 store managers, and 803 customers from 70 stores in the same retail chain. Findings showed that SO-HPWSs predict work engagement and service climate; work engagement predicts service climate; and service climate predicts in-role and extra-role service performance and customer satisfaction.

This study analysed climate-smart agriculture (CSA) adoption and its role in building resilience among cassava farmers in Ebonyi State, Nigeria. A cross-sectional survey design was used, and data were collected through a structured questionnaire using a multi-stage sampling technique. Data were analysed using percentage, mean score, paired sample t-test, and logistic regression. Results showed that 68.3% of respondents were male, 55.0% were aged 31–50 years, 42.5% had secondary education, 50.0% had household sizes of 6–10 persons, and 47.5% had 10–20 years of farming experience. The most adopted CSA practices were improved cassava varieties (x̄ = 4.3), intercropping (x̄ = 3.9), and organic manure use (x̄ = 3.6). These practices enhanced resilience, particularly in adapting farming strategies (x̄ = 4.0), maintaining yield despite irregular rainfall (x̄ = 3.9), and ensuring income stability (x̄ = 3.7). Cassava output significantly increased after CSA adoption, from 10.5 t/ha to 14.8 t/ha (mean difference = 4.3 t/ha; t = 9.12; p < 0.001). Education, farm size, access to extension, credit, cooperatives, and climate information significantly influenced CSA adoption. The study recommends improving access to education, extension, and climate services to promote CSA adoption and enhance cassava productivity.

Safeguarding European space sovereignty—Recommendations for operational climate services to support resilience

A weather-forecast driven early warning system for wheat blast disease: User-centered design, validation, and scaling in Bangladesh and Brazil.

Sub-seasonal and seasonal climate predictions for a sporting goods retailer company: Co-development of a climate service from scratch