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The study assesses how access to climate services influences cropping decisions and, subsequently, household food security. Employing a cross-sectional research design, the study surveyed a total of 217 smallholder crop farmers selected through a multistage sampling approach. Data were collected using a semi-structured questionnaire, and both descriptive statistics and a propensity score matching (PSM) model were employed for data analysis. Household food security was assessed using households' dietary diversity. The study's findings highlight that 69% of the surveyed farmers had access to weather forecasts, while only 24% had access to seasonal forecasts. Consequently, farmers with access to climate services exhibited changes in their farming decisions, including daily farming activities, planting timing, crop selection, and land preparation. Notably, approximately 35% of the respondents were identified as food insecure. The application of the propensity score matching model revealed that access to climate services had a statistically significant positive impact on farmers' decision-making processes and household food security. These results underscore the potential of climate services to enhance farmers' decision-making capabilities and, subsequently, improve household food security. Considering these findings, it is recommended that efforts to expand access to climate services among smallholder farmers in South Africa should be intensified. This can be achieved by developing and disseminating more accessible and tailored climate information, including seasonal forecasts. In addition, initiatives should ensure that climate services are integrated into agricultural extension services and support systems to equip farmers with the knowledge and tools they need to make informed decisions. [ABSTRACT FROM AUTHOR]

Research on weather and climate extremes has become integral to climate science due to their increasing societal relevance and impacts in the context of anthropogenic climate change. In this perspective we examine recent changes and evolving paradigms in the study of extreme events, emphasizing the increasingly interdisciplinary nature of research and their societal implications. We discuss the importance of understanding the physical basis of extreme events and its linkages to climate impacts, highlighting the need for collaboration across multiple disciplines. Furthermore, we explore the challenge of big climate data analysis and the application of novel statistical methods, such as machine learning, in enhancing our understanding of extreme events. Additionally, we address the engagement with different stakeholder groups and the evolving landscape of climate services and private‐sector involvement. We conclude with reflections on the risks and opportunities for early career researchers in navigating these interdisciplinary and societal demands, stressing the importance of meaningful scientific engagement, and removing barriers to inclusivity and collaboration in climate research. [ABSTRACT FROM AUTHOR]

ABSTRACT Extreme weather impacts due to climate change, such as urban pluvial flooding, necessitate adaptive measures. To support decision making in climate adaptation, climate services provide information and guidance to policy makers and other stakeholders. Effective use of climate services is prohibited by usability gaps. Some causes for these gaps originate from contextual factors. However, these factors are not well understood within academic literature. Therefore, our systematic review has two research questions: (1) What dimensions of climate service usability are considered in the design and evaluation of their use and how are these operationalized?; and (2) What contextual influences may affect these identified dimensions? To this end, we analyzed 51 articles that cover one or more urban pluvial flooding climate services. Our results show several overarching categories of usability dimensions (functionality, ease of use, reducing time and effort, reliability, matching user needs, and other). However, these dimensions are not always fully operationalized by authors. Furthermore, our results show that most contextual factors originate from the meso level (within or between organizations), and in lesser extent the macro (e.g., national policies) and micro level (individual users). Here especially organizational (e.g., Does the climate service fit organizational work approaches?); and process factors (e.g., Was the climate service co‐produced?) have been shown to affect usability. We conclude that more attention should be given to usability as a concept within the design and evaluation of climate services well as to the influence of contextual influences. [ABSTRACT FROM AUTHOR]

To help with preparedness efforts of Canadian public health and safety systems for adaptation to climate change, the humidity index (humidex) and three threshold‐based humidex indices (annual number of days with humidex greater than 30, 35 and 40) were computed for a multi‐model ensemble of climate change projections, over Canada. The ensemble consists of one run from each 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and offers historical simulations starting in 1950 and future projections out to 2100 following Shared Socioeconomic Pathways (SSPs): SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5. Each ensemble member was bias‐adjusted and statistically downscaled using the Multivariate bias correction—N‐dimensional probability density function transform (MBCn) with hourly data from ERA5‐Land as the target dataset and following a method proposed by Diaconescu et al. (2023; International Journal of Climatology, 43, 837) to calculate humidex from daily climate model outputs. This paper details the steps for data production including evaluation of the target historical gridded data and selection of downscaling method and presents some of the resulting humidex projections at the end of the century. [ABSTRACT FROM AUTHOR]

This study aims to examine the climate change education (CCE) provision in grade 5 textbooks (science and social studies) and the competence of teachers in teaching CCE in a primary girls' schools in Skardu, Baltistan, Pakistan. It employs mixed-method research that uses both qualitative and quantitative approaches to gather data. The first phase of the study involved collecting qualitative data to examine the provision of CCE content in selected textbooks taught in grade 5. The researcher adapted an interview questionnaire to assess the competence of teachers teaching in 38 schools. The provision of themes and topics based on the seven principles of climate literacy given by the U.S. Global Change Research Program was identified by using latent content analysis (LCA). The study concludes that science and social studies textbooks lack the incorporation of CCE content of principles 1, 4, 6, and 7. Furthermore, the majority of teachers have not received training to use digital media and apply activity-based methods to engage campus and community support for CCE. The study also found that the selected schools lack school planting campaigns, CCE committees, and CCE walks. Above all, the respondents possess conflicting views about whether climate change is a natural or human-contributed phenomenon. Significance Statement: Like other countries around the globe, Pakistan is the victim of climatic disasters which have disastrous effects on the economy, agriculture, healthcare, and weather conditions. These effects are exacerbated by lacking awareness, attitudinal issues, and poor pro-climate-supported practices at the academic and societal levels (Asian Development Bank 2022). Awareness, knowledge, and a supportive environment during childhood form attitudes and promote practices that have lasting impacts throughout the life of the individual (Gulraiz and Ali 2021). Therefore, children need to learn and adopt proclimate lifestyles, such as observing cleanliness, practicing planting, and judicious use of energy resources. With this background, we assessed the textbooks based on seven principles of climate literacy given by the U.S. Global Change Research Program (USGCRP 2009) and observed that principles 1, 4, 6, and 7 are not adequately addressed in science and social studies textbooks taught in public schools. Moreover, teachers have not received training to use digital media and gather campus and community support, and they have a stereotypical understanding of climate change. The study possesses significant implications for curriculum revisions and elementary education management to address the gaps relating to climate change education (CCE). [ABSTRACT FROM AUTHOR]

Introduction: Interannual climate variability in the Asian mega deltas has been posing a wide range of climate risks in the aquaculture systems of the region. Water temperature variation is one of the key risks related to disease outbreak, fish health, and loss and damage in fish production. However, Climate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Little research has been done to translate weather data into water temperature forecasts using mechanistic models in order to provide farmers with relevant forecasting information in the context of climate services. Methods: The advantage of mechanistic models over statistical models is that they are based on physical processes and can therefore be used in a wider range of environmental conditions. In this study, we used an energy balance model to investigate its ability to simulate pond water temperature at daily and seasonal timescales in the southwest and northeast regions of Bangladesh. The model was able to adequately simulate pond water temperature at a daily timescale using publicly available weather data, and the accuracy of the model was lower at the study site with very heavy rainfall events. Results: Sensitivity analyses showed that the model was also able to simulate the impact of air temperature cold and hot spells on the pond water temperature. Connecting the model with seasonal air temperature forecasts resulted in very small variations in the forecasted seasonal pond water temperature, in large part due to the low variability observed in water temperature at seasonal scale in the study sites. Discussion: Climate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Hence, these improved predictions are important to help fishfarmers make informed decisions for managing associated climate risks. [ABSTRACT FROM AUTHOR]

Abstract To help with preparedness efforts of Canadian public health and safety systems for adaptation to climate change, the humidity index (humidex) and three threshold‐based humidex indices (annual number of days with humidex greater than 30, 35 and 40) were computed for a multi‐model ensemble of climate change projections, over Canada. The ensemble consists of one run from each 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and offers historical simulations starting in 1950 and future projections out to 2100 following Shared Socioeconomic Pathways (SSPs): SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5. Each ensemble member was bias‐adjusted and statistically downscaled using the Multivariate bias correction—N‐dimensional probability density function transform (MBCn) with hourly data from ERA5‐Land as the target dataset and following a method proposed by Diaconescu et al. (2023; International Journal of Climatology, 43, 837) to calculate humidex from daily climate model outputs. This paper details the steps for data production including evaluation of the target historical gridded data and selection of downscaling method and presents some of the resulting humidex projections at the end of the century.

The response of regional climate models (RCMs) to different input land cover information is complex and uncertain. Several studies by the regional climate modeling community have investigated the potential of land cover data to help understand land-atmosphere interactions at regional and local scales. This study investigates the regional climate response to introducing European Space Agency (ESA) land cover (LC) data into WRF-Hydro. In addition, this study assesses the potential impacts of afforestation and deforestation strategies on regional water and energy fluxes. An extended version of WRF-Hydro that accounts for a two-way river-land water flow to reduce unrealistic peaks in simulated discharge was employed. The two-way river-land flow setup yielded a better NSE and KGE of 0.47 and 0.69, respectively, over the Kulpawn basin compared to the default setup values of -0.34 and 0.2. Two land use/land cover change effects were deduced from synthetic numerical sensitivity experiments mimicking afforestation by closed shrubland expansion and deforestation by cropland expansion. The afforestation experiment yielded approximately 6% more precipitation, 3% more evapotranspiration, 27% more surface runoff, and 16% more underground runoff, while the deforestation by cropland expansion yielded -5% less precipitation, -3% less evapotranspiration, -3% less surface runoff, and - 9% less underground runoff over the Sissili-Kulpawn Basin (SKB). This result suggests that afforestation (deforestation) could increase the flood (drought) risk. Our synthetic numerical experiment mimics the regional water and energy budgets well and can help climate services and decision-makers by quantifying regional climate response to potential land cover changes. [ABSTRACT FROM AUTHOR]

The issues related to the verification of ensemble seasonal forecasts obtained using a new technology implemented on the basis of the INM-CM5 Earth system model are considered. The forecast objects are global gridded () fields of ensemble mean anomalies and probabilities of three gradations of the anomalies (below normal, normal, above normal) for each of the five characteristics: 500 hPa geopotential height (), mean sea level pressure (SLP), 850 hPa air temperature (), surface air temperature (), and precipitation (Prec) for six periods of time averaging (at monthly intervals for the 1st, 2nd, 3rd, 4th months and at two seasonal intervals: season 1 (the 1st–3rd months) and season 2 (the 2nd–4th months)). It is noted that forecast skill scores vary greatly depending on a region, a season, and a meteorological parameter. The best results for all parameters were obtained for the tropics, where the main sources of long-term predictability of the atmosphere are located. In extratropical latitudes, the quality of forecasts, especially at the monthly intervals, decreases and approaches the level of climate forecasts. The skill of precipitation forecasts is significantly inferior to the quality of forecasts of other meteorological parameters. It is noted that the success in ensemble seasonal probabilistic and deterministic forecasting of the main meteorological elements with the INM-CM5 model, both on a global scale and in individual regions, is comparable with the skill scores of similar forecasts of the foreign meteorological centers participating in the LC MME-WMO project. The introduction of the new climate model into the scientific and operational practice of the Hydrometcenter of Russia/North Eurasia Climate Centre will contribute to improving the quality of monthly and seasonal forecasts and developing specialized climate services. [ABSTRACT FROM AUTHOR]

The physical background and main types of climate forecasts issued by the world meteorological centers are considered. It is emphasized that modern forecasting systems have an integrated nature and combine not only data assimilation systems and global numerical atmosphere–ocean–land models, but also support infrastructure for providing forecast products to users. The features of the forecast system of the Hydrometcenter of Russia/North Eurasia Climate Centre (NEACC) and other world meteorological centers are compared. It is noted that, unlike other centers, the forecast system of the Hydrometeorological Center of Russia/NEACC is based on the integrated use of synoptic, statistical, and hydrodynamic methods. It has been revealed that new trends and directions in the development of the forecast system are associated with the emergence of a new version of the SL-AV global semi-Lagrangian finite-difference atmosphere model of the Hydrometcenter of Russia and the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (INM RAS) with an expanded (up to 61 members) forecast ensemble, as well as with using the INR RAS climate model (INM-CM5) and additional applications designed for interaction with various economic sectors. The findings may be useful in determining a vector of future research aimed at developing the Russian climate prediction system. [ABSTRACT FROM AUTHOR]

Urbanization is a complex phenomenon that has a profound impact on natural resources, economies, climate dynamics, and community structures. This study sets out to analyze the effects of urban sprawl on key urban regions in Bangladesh, covering urban, suburban, and rural landscapes. We used Landsat images from 1992 to 2022, processed and classified using the Google Earth Engine and Random Forest algorithm. Spatial metrics, Tupu method, ANOVA, and spatial regression models helped us quantify and explain land cover changes, their drivers, and consequences. Our findings show that the total built-up area in the five districts expanded from 1,200 km2 in 1992 to 2,800 km2 in 2022, with an overall yearly expansion rate of 3.5%. Meanwhile, vegetation cover declined from 2,500 km2 in 1992 to 1,500 km2 in 2022, with an average yearly loss rate of 2.5%. We identified population growth, economic development, and infrastructure expansion as the main drivers of urbanization, while land degradation, air and water pollution, and loss of biodiversity were the main consequences. Urbanization also had a significant impact on environmental functions, including carbon sequestration, water flow control, and urban heat mitigation, with varying effects across different urban areas and land use types. For instance, we found that carbon storage capacity decreased by 20% in urban areas, while it increased by 10% in rural areas. Our research highlights the crucial role of preserving and enhancing green spaces, including vegetation cover and water bodies, to mitigate urban heat and promote urban resilience. The findings underscore the need for sustainable urbanization, balancing economic growth with environmental protection and climate resilience, using geospatial and remote sensing techniques. [ABSTRACT FROM AUTHOR]

Abstract Research on weather and climate extremes has become integral to climate science due to their increasing societal relevance and impacts in the context of anthropogenic climate change. In this perspective we examine recent changes and evolving paradigms in the study of extreme events, emphasizing the increasingly interdisciplinary nature of research and their societal implications. We discuss the importance of understanding the physical basis of extreme events and its linkages to climate impacts, highlighting the need for collaboration across multiple disciplines. Furthermore, we explore the challenge of big climate data analysis and the application of novel statistical methods, such as machine learning, in enhancing our understanding of extreme events. Additionally, we address the engagement with different stakeholder groups and the evolving landscape of climate services and private‐sector involvement. We conclude with reflections on the risks and opportunities for early career researchers in navigating these interdisciplinary and societal demands, stressing the importance of meaningful scientific engagement, and removing barriers to inclusivity and collaboration in climate research.

The study assesses how access to climate services influences cropping decisions and, subsequently, household food security. Employing a cross-sectional research design, the study surveyed a total of 217 smallholder crop farmers selected through a multistage sampling approach. Data were collected using a semi-structured questionnaire, and both descriptive statistics and a propensity score matching (PSM) model were employed for data analysis. Household food security was assessed using households' dietary diversity. The study's findings highlight that 69% of the surveyed farmers had access to weather forecasts, while only 24% had access to seasonal forecasts. Consequently, farmers with access to climate services exhibited changes in their farming decisions, including daily farming activities, planting timing, crop selection, and land preparation. Notably, approximately 35% of the respondents were identified as food insecure. The application of the propensity score matching model revealed that access to climate services had a statistically significant positive impact on farmers' decision-making processes and household food security. These results underscore the potential of climate services to enhance farmers' decision-making capabilities and, subsequently, improve household food security. Considering these findings, it is recommended that efforts to expand access to climate services among smallholder farmers in South Africa should be intensified. This can be achieved by developing and disseminating more accessible and tailored climate information, including seasonal forecasts. In addition, initiatives should ensure that climate services are integrated into agricultural extension services and support systems to equip farmers with the knowledge and tools they need to make informed decisions.

IntroductionInterannual climate variability in the Asian mega deltas has been posing a wide range of climate risks in the aquaculture systems of the region. Water temperature variation is one of the key risks related to disease outbreak, fish health, and loss and damage in fish production. However, Climate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Little research has been done to translate weather data into water temperature forecasts using mechanistic models in order to provide farmers with relevant forecasting information in the context of climate services.MethodsThe advantage of mechanistic models over statistical models is that they are based on physical processes and can therefore be used in a wider range of environmental conditions. In this study, we used an energy balance model to investigate its ability to simulate pond water temperature at daily and seasonal timescales in the southwest and northeast regions of Bangladesh. The model was able to adequately simulate pond water temperature at a daily timescale using publicly available weather data, and the accuracy of the model was lower at the study site with very heavy rainfall events.ResultsSensitivity analyses showed that the model was also able to simulate the impact of air temperature cold and hot spells on the pond water temperature. Connecting the model with seasonal air temperature forecasts resulted in very small variations in the forecasted seasonal pond water temperature, in large part due to the low variability observed in water temperature at seasonal scale in the study sites.DiscussionClimate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Hence, these improved predictions are important to help fish-farmers make informed decisions for managing associated climate risks.

A central aim of climate services is the design and delivery of information that is tailored and targeted to different decision-making contexts. To assess whether climate services are meeting this aim, it is necessary to take stock of the progress that has been made while also identifying where the gaps and additional needs remain. This paper provides a summary of currently available climate services in South Africa, and details the needs for new climate services, informed by the 10 priority sectors identified as part of the National Framework for Climate Services South Africa (NFCS-SA). South African stakeholders’ needs were assessed via 1,032 responses to surveys and 27 interviews conducted between March 2020 and June 2023. The existing climate services landscape in South Africa is complex. Although a range of climate services have been produced, there remain gaps, as well as the need to improve the way in which climate information is communicated, especially for disadvantaged groups such as low-income farmers. The engagement of beneficiaries in the development of public climate services in South Africa has often been superficial and true co-production has yet to take place. However, meaningful co-production of climate services comes at a cost and it remains to be seen how this challenge can be addressed given that many South African stakeholders perceive weather information as a public good and their willingness to pay for climate services is low.

This study describes the process of co-developing an operational climate forecast service for the viticulture sector. Weather and climate conditions affect grapevine development in cultivars: anticipating the atmospheric variables in the coming weeks and months is thus relevant for effectively managing vineyards, as impacts will be felt in wine production, biodiversity, and a wide range of related aspects. The operational service was co-developed with two types of users: impact modellers, who are the intermediary users incorporating climate forecast outputs in phenological and disease models, and end-users from the wine sector with vineyards in various European locations. For the operational service, sub-seasonal and seasonal climate forecasts were tailored considering their needs. The initial steps of the co-production process identified relevant decisions for which the service was essential and co-defined effective ways to deliver the climate information. Afterwards the climate forecasts outputs were integrated with impact model data. Substantial efforts were directed at the harmonisation of climate services information with the decision-making system of end-users. Because end-users need to navigate, comprehend, and select from various alternative options amidst uncertainty, significant emphasis has been placed on crafting the visual representation of the climate service, incorporating interactive elements, and cognitive considerations, thereby enhancing the overall user experience. Practical implications: Weather and climate conditions affect the development of cultivars: phenological stages, disease risk, or wine quality. This study presents the co-production process for deploying a climate service for vineyard management. The service consists of the near-real-time operational provision of sub-seasonal and seasonal climate forecasts. This service aims to help vineyard managers make decisions by anticipating the climate conditions up to three months in advance for a set of essential climate variables. The service includes the requirements of a set of intermediary users, which are disease risk and phenology researchers who feed their impact models with the provided climate forecasts. To our knowledge, it is the first time that sub-seasonal and seasonal climate forecasts have been integrated into an operational service for vineyard management.The service was developed during the VitiGEOSS project, the primary outcome of which is the VitiGEOSS platform: a single entry-point solution for wine producers aiming to boost vineyard sustainability. Three pilot plots are used for the service development: the Douro region in Portugal, the Catalonia region in Spain, and the Campania region in Italy. The co-production is seen as an iterative, interactive and collaborative process that brings together a plurality of knowledge sources to mutually define problems and develop usable products to address these problems. The process can be divided into three phases: initial co-exploration, co-design and co-development.The co-exploration phase consisted of conducting a benchmark analysis, identifying available services to viticulture and highlighting the gap that the newly developed solution could fill in. In the co-design phase, more intense engagement methods were implemented to communicate the capabilities of climate forecasts and design the product’s visualisation. Finally, the co-evaluation consisted of an ad hoc and ongoing process during the recurring meetings and a final workshop conducted with direct and potential users to obtain feedback on using the platform and potential further developments.As a result, the operational post-processing workflow of the sub-seasonal and seasonal state-of-the-art model outputs was adapted to include users’ requirements. The workflow includes the downscaling of model outputs to increase their spatial resolution, the storage of sub-seasonal climate forecasts at daily frequencies to feed phenological impact models, the provision of past climate simulations to train the impact models, the bias-adjust of climate forecasts to reduce systematic model errors, the assessment of the climate forecasts to aware users on their quality and the deployment of a server to allow access to impact modellers.The variables provided are mean, minimum and maximum temperature, accumulated precipitation and solar radiation. The final co-designed product consists of a graphic of bars. The user can select the location and variable, and the forecasts for the next four weeks or the next three months are displayed for the sub-seasonal and seasonal climate forecasts, respectively. For each week or month, three bars show the tercile probabilities of above-normal, normal or below-normal conditions for the time of the year. The limits of the tercile categories are also displayed, which helps to understand the averaged values of the variable in the selected region and the time of the year calculated with information from the past. The probabilities of extremes, i.e. exceeding (non-exceeding) the percentile 90th (10th), are displayed as extra bars for each week and month in case the prediction is skilful and they are greater than 40%.Last but not least, the results of the skill assessment are shown. This skill assessment consists of calculating a metric for each location, initialisation date and forecast time. When this metric, known as skill score, is positive (negative), the climate model forecast is better (worse) than the climatology (the climatology is a naive forecast which considers all categories equiprobable by definition). If the skill is negative, which means there is no proof of the added value of using the numerical model prediction, the bars are blurred, and the user can still be informed of the tercile category limits.The service has been provided for almost two consecutive years. During this period, we have confirmed the value of some key recommendations from literature when co-producing a climate service. Especially relevant is the need to invest time and resources to conduct a good co-production process by repeating the iterative process. We also tested the idea of providing information in the format of seasonal climate forecast outlooks to end-users before the service was operational. In this way, the climate service process would be ‘slowed down’, by which the use of climate data is incorporated more slowly and allows for place-based knowledge from the resource managers to be included too.One of the most challenging questions is the communication of the probabilistic forecasts product and their quality. The probabilistic nature of climate forecasts is sometimes understood by novice users as the incapacity to provide a deterministic forecast. Similarly, negative skill leads users to interpret the forecast fails. Overall, the perception of the service quality can be low. To avoid this perception, the co-producing process is essential to engage with users and redefine the service if possible.In this work, we have tried different ways to overcome low-quality perception. The users requested access to the climate forecasts for their vineyard plots. For that reason, the downscaling technique was applied to increase the spatial resolution, and the bar plots were designed to show the forecast for a specific location. However, to improve the perception of the forecast quality, the forecast can be contextualised by providing the forecast in the format of a map of the surrounding areas.Despite all these limitations, end-users reported on the extreme importance of climate information. Furthermore, end-users can benefit from getting the climate services integrated with the impact models to take maximum advantage of all services and make timely decisions, but also receiving this information in a clear, parsimonious and direct format that is intuitive and can be easily incorporated into the decision-making processes is key for the sustainability of their farm fields.After the operational provision of the climate forecast service, some implications of our findings have been detected: ● Combined downscaling and contextualisation. Downscaling techniques help to provide more accurate information at the location scale. Nevertheless, the spatial visualization over large regions may help to contextualize the climate forecasts, and that is also beneficial for end-user interpretation of the climate-related information. ● Skill. Efforts to provide more skilful climate forecasts are required, as well as alternative information to be used in situations where skill is lacking (e.g.: past climate information). The skill assessment is crucial to inform users about forecast reliability and accuracy. It enhances user understanding and trust in the service by generating realistic expectations. ● Operationalisation. Reducing the time for data processing or increasing the computational resources will allow the provision of climate forecasts that are closer to the date of production. Users will benefit from earlier access to the information than the current time-lapse. ● Co-production. The co-production process ensures the service is designed based on user needs and feedback which is more likely to be adopted and effectively utilized by users. This process never ends, so, it needs to be applied if future developments or requests are included in the current service. ● Support for sustainability. End-users benefit from the integration of the climate forecast along with phenological and disease risk services. Further explorations to include more climate variables in the service and in the impact models will be beneficial for end-users. For instance, the probabilities of extreme events help vineyard managers prepare for potential adverse conditions, while, safeguarding vineyards against weather impacts through proactive measures. ● Capacity building. By training users about the nature of climate forecasts, they can enhance the preparedness and resilience response to varying climate conditions. New information and products could be co-explored to co-evaluate their utility and usefulness.

Climate change is one of the greatest challenges of our time [1]. It will reshape our world and impact all of society. Cities are where the majority of people live and where most of the world’s population directly experiences climate change impacts. No one individual or organisation will be able to deliver adaptation to climate change for society – we need collaboration across scales, disciplines and societal actors. To help combat climate change impacts that are now unavoidable, Cork City (Ireland) is working in conjunction with 19 partners across Europe, as part of the EU Horizon REACHOUT project, to co-develop user friendly climate services that will help the city prepare for and adapt to these impacts, in line with the strategic development plans for the city and the region. Cork City is exposed to a range of climate related risks, including flooding, heat and wind, all of which are expected to increase in the future under climate change. Cork City Council, University College Cork and technical partners across Europe have been working with local communities to co-develop a range of climate service tools that will be particularly valuable for Cork City and its residents. These tools are intended to support building local resilience to, and understanding of, climate impacts within the city, and to motivate citizens to engage in climate action. This paper explores how these tools may be used to provide climate services in the context of Cork City and how they perform in terms of accessibility, relevance, usability and capacity.

Exposure to weather extremes, such as heatwaves, can cause discomfort, harm, or death in grazing cattle in pastures. While the Australian Bureau of Meteorology issues sheep graziers alerts when there is an exposure risk to chill for livestock, there is no equivalent alert for heat stress for Australian cattle. Before any such alert system can be developed, a robust assessment and comparison of relevant cattle thermal stress indices is required. This study evaluates and compares the multiyear climatology of three cattle thermal heat stress indices across Australia in the warm season months (October–March). The same indices are then used to assess historical Australian heat events where cattle died from heat exposure. These events are based off official records and survey responses from northern Australian graziers. In the seven historical heat events studied, high relative humidity combined with low wind speeds, or high solar exposure combined with high surface temperatures, exacerbated the impact of heat stress on cattle. In the two historic events where multiple compounding weather factors combined (e.g., high humidity, low winds, and high solar exposure), the cattle mortality levels were significantly high. These events were characterized by rainy conditions followed by a rapid warming, meaning cattle were likely unable to acclimatize to such dramatic temperature changes. This study highlights the need for using more than one thermal stress index when verifying cattle heat stress events and, importantly, calls for further research on standardizing the risk classifications of these thermal indices for cattle in Australia's variable climate. Significance Statement: Cattle across Australia's northern tropical and semiarid regions often experience extreme hot and humid conditions in the summer months, which increases the risk of heat stress. This is the first study of its kind to evaluate observations of cattle heat stress across Australia using indices that describe the combined effects of solar exposure, wind speed, relative humidity, and surface temperatures. These cattle heat stress indices can be used to evaluate historical cattle mortality events in feedlots and in grazed pastures. This study lays the groundwork for the development of Australian-wide cattle heat stress forecast products on the 7-day to multiweek time scales. [ABSTRACT FROM AUTHOR]

Canadian climate service providers offer projections from the Coupled Model Intercomparison Project (CMIP6) to help inform climate change mitigation and adaptation decisions. CMIP6 includes several "hot" climate models whose sensitivity to greenhouse gas forcings exceeds the likely range inferred from multiple lines of evidence. Global warming estimates assessed in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) were reduced by applying observational constraints on the historical rate of warming to the CMIP6 ensemble. This study assesses whether globally constrained CMIP6 projections for Canada are appreciably different from unconstrained projections. Two constraints are considered: one that removes models whose transient climate response lies outside the AR6 assessed range (TCRlikely), and the other that weights models to match the assessed distribution of equilibrium climate sensitivity (ECSall). Both constraints lead to appreciably cooler and drier projections than the unconstrained ensemble, with the strongest reductions seen in the upper end of the ensemble range, high-emissions scenario, end-of-century time period, and northern regions of Canada. In this case, constrained projections of annual mean temperature are 2°–3°C cooler than the unconstrained projections, whereas projections of annual total precipitation are typically 20%–40% drier. Appreciable differences are also detected in the ensemble median of temperature extreme indices. Based on these results, it is recommended that a constrained ensemble be considered for regional projections to avoid the "hot model" problem. Alternatively, projections can be communicated conditional on a specified level of global warming, with global constraints then used to inform the timing of the warming level exceedance. [ABSTRACT FROM AUTHOR]

The article discusses the Subseasonal to Seasonal (S2S) Prediction project, which recently celebrated its 10-year anniversary at a conference attended by scientists from 29 countries. The conference focused on predictability and processes, modeling, and research to operations. Key topics included the interaction between different drivers of predictability, advancements in modeling techniques, and the use of S2S forecasts for early warning and decision-making. The conference also highlighted the challenges that remain in improving S2S prediction and the importance of continued research and collaboration in this field. [Extracted from the article]

Global hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change). [ABSTRACT FROM AUTHOR]

This study explored farmers’ knowledge, practice, and attitude towards Climate Services (weather information) in the West African state of Benin. Primary data were collected randomly from 540 smallholder farm households across five municipal areas. Seemingly Unrelated Regression (SUR) models were employed to analyze the socio-economic factors that explain the Knowledge, Attitude, and Practice (KAP) scores. The empirical results suggest that most farmers are unaware of Climate Services (CS) but show positive attitude and willingness to adopt them. The average KAP scores are 4.13 (± 3.77) against a maximum of 10; 7.20 (± 3.64) against a maximum of 14; and 2.78 (± 3.50) against a maximum of 16 for Knowledge, Attitude, and Practice respectively. Socio-economic factors such as level of education, gender, household size, membership in an organization, and access to credit have different effects that policymakers should consider to improve farmers’ KAP towards the adoption of Climate Services. [ABSTRACT FROM AUTHOR]

Today, a major challenge for climate science is to overcome what is called the “usability gap” between the projections derived fromclimate models and the needs of the end-users. Regional Climate Models (RCMs) are expected to provide usable information concerning a variety of impacts and for a wide range of end-users. It is often assumed that the development of more accurate, more complex RCMs with higher spatial resolution should bring process understanding and better local projections, thus overcoming the usability gap. In this paper, I rather assume that the credibility of climate information should be pursued together with two other criteria of usability, which are salience and legitimacy. Based on the Swiss climate change scenarios, I study the attempts at meeting the needs of end-users and outline the trade-off modellers and users have to face with respect to the cascade of uncertainty. A conclusion of this paper is that the trade-off between salience and credibility sets the conditions under which RCMs can be deemed adequate for the purposes of addressing the needs of end-users and gearing the communication of the projections toward direct use and action. [ABSTRACT FROM AUTHOR]

Dryland farming is at the center of increasing pressure to produce more food for the growing population in an environment that is highly variable and with high expectations for the standard of their production systems. While there is mounting pressure for increased productivity, the responsibility to protect the environment and diminish the agricultural sector's carbon footprint is receiving growing emphasis. Achieving these two goals calls for a consolidated effort to ensure that the scientific community and service providers partner with farmers to create a sustainable food production system that does not harm the environment. In this paper, we studied the nature of the services present in the market and identified ways that could be used to improve the climate services available to the agricultural sector. Important factors that could increase the usability of climate services include coproduction, context-specific information, innovation, demand-driven services, timeliness of services, highly applicable information, provision of services in the correct format, services that increase user experience, specificity of services to a locale, and services that are easily accessible. [ABSTRACT FROM AUTHOR]

This study tests, empirically validates and refines the Tandem framework for co-designing climate services(Daniels et al., 2019; 2020), to enhance its applicability and effectiveness. Intended as an inspirational guide for ‘good practice’, Tandem is practical and non-prescriptive and is designed to be tailored to context. We apply Tandem in three different geographic and socioeconomic settings: 1) a rural community in Indonesia, where smallholder farmers are confronting climate impacts on agriculture; 2) two cities in Sweden, where planners are addressing climate-related flooding and heat stress; and 3) communities and institutions in a Colombian river basin, where climate change is leading to water scarcity, raising questions about equitable use. We find that Tandem was effective in these settings in: 1) moving from ‘useful’ to ‘usable’ information by building trust; 2) increasing institutional embedding through strengthened relationships and networks; 3) improving climate information uptake and use; 4) increasing capacity, confidence and a shared understanding of climate information by users, and the decision context by providers; and, 5) serving as a non-prescriptive guide for users, intermediaries and providers to co-design and structure an effective process for collaborative learning and action. We use insights from these case studies to enhance the original framework, enabling it to 1) scope and review climate and non-climate vulnerability and risks; 2) incorporate gender, social equity and power considerations; 3) acknowledge the value of local and traditional ecological knowledge; 4) co-explore horizontal and vertical governance at appropriate decision-making scales; and, 5) provide flexible starting points, with early identification of impact indicators.

Climate Services (CS) are increasingly being employed to address challenges resulting from climatic hazards. Research has focused on identifying and categorising CS, CS uptake, barriers to use, and CS user needs. Findings on empirical responses to CS are scattered. The aim of this paper is to systematically review existing literature on behavioural, perception, and attitude responses to CS, in the context of drought. Our review considers CS responses explicitly and is not limited to specific user groups or regions. Using the Web of Science database, we identified 88 journal articles containing terms related to CS, behaviour and droughts, published between 1999 and 2022. We identified and classified the characteristics of the CS, responses to CS, and the impacts that these responses had. We find that behavioural responses are reported more frequently than attitude and perception responses to CS. Half of the CS users consisted of farmers, mostly provided with seasonal forecasts, who respond to the CS information predominantly by changing crops or crop planting/harvesting dates. Non-farmers responded to CS behaviourally by enacting or developing plans, policies, or programs. This overview provides an evidence base towards the assessment of impacts of CS, and suggests that further developing CS could require a shift from providing precise climate or weather data, towards providing how climate or weather information relates to the decision-spaces of users.

Cities need to adapt to climate change in an increasingly rapid pace. Data and information on the existing and expected climate impact and the effectiveness of adaptive measures can support the planning and implementation of resilient urban planning. To inform urban climate change adaptation (CCA) in Germany a diverse landscape of climate services exists. However, the literature on usability gaps shows different barriers impeding the use potential of climate services. This study empirically analyzes the needs and barriers of municipal staff of different departments in Constance with regard to utilizing climate data and information. Surveying 72 and interviewing 10 municipal staffers, we found that climate data and information hold great potential for different public services but its handling poses many challenges. Furthermore, we found that a strategic approach mainstreaming climate data and information into cross-departmental work practices on urban CCA is crucial to anchor its usage in complex decision-making systems. The co-development of data-sensitive workflows, decision support tools, and capacity trainings can foster such integration. Based on the survey and interview results we designed a workflow on how to integrate such data and information strategically in municipal work processes.

The response of regional climate models (RCMs) to different input land cover information is complex and uncertain. Several studies by the regional climate modeling community have investigated the potential of land cover data to help understand land-atmosphere interactions at regional and local scales. This study investigates the regional climate response to introducing European Space Agency (ESA) land cover (LC) data into WRF-Hydro. In addition, this study assesses the potential impacts of afforestation and deforestation strategies on regional water and energy fluxes. An extended version of WRF-Hydro that accounts for a two-way river-land water flow to reduce unrealistic peaks in simulated discharge was employed. The two-way river-land flow setup yielded a better NSE and KGE of 0.47 and 0.69, respectively, over the Kulpawn basin compared to the default setup values of −0.34 and 0.2. Two land use/land cover change effects were deduced from synthetic numerical sensitivity experiments mimicking afforestation by closed shrubland expansion and deforestation by cropland expansion. The afforestation experiment yielded approximately 6% more precipitation, 3% more evapotranspiration, 27% more surface runoff, and 16% more underground runoff, while the deforestation by cropland expansion yielded −5% less precipitation, −3% less evapotranspiration, −3% less surface runoff, and − 9% less underground runoff over the Sissili-Kulpawn Basin (SKB). This result suggests that afforestation (deforestation) could increase the flood (drought) risk. Our synthetic numerical experiment mimics the regional water and energy budgets well and can help climate services and decision-makers by quantifying regional climate response to potential land cover changes.

Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN's goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN's framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use. [ABSTRACT FROM AUTHOR]

Drought is a complex hazard, with many interconnected impacts on environment and society. Droughts are difficult to monitor as they are slow-moving events with impacts that are not always visible. There is an increasing call to study and monitor droughts as a human-environment process and to provide climate services that can inform proactive decision-making on drought. While climate services strive to make droughts visible and therefore manageable for society, many of the equity issues that arise during periods of drought remain largely invisible. In this article we explore inequity in drought impacts in the U.S. Southwest, focusing on agriculture, household water security, and wildfires. Drawing from lessons in the literature on equity, environmental justice, and climate services as well as our experience researching drought impacts in the Southwest, we recommend that climate services can support drought decision-making that addresses equity issues by 1) integrating both physical and social dimensions of drought in climate services, 2) investing in engagement and trust building with diverse communities, and 3) better integrating place-based knowledge to reconcile scaling challenges. With the acceleration of the warming and drying of many parts of the world, there is an ever-increasing need to focus on reducing inequities in drought preparedness and response, which we propose starts with production of drought information that is more reflective of how droughts are experienced across all parts of society. [ABSTRACT FROM AUTHOR]

This work applies a quantitative metric in order to capture the relative representativeness of nonsimultaneous or noncollocated observations and quantify how these observations decorrelate in both space and time. This methodology allows for the effective determination of thresholding decisions for representative matchup conditions and is especially useful for informing future network designs and architectures. Future weather and climate satellite missions must consider a range of architectural trades to meet observing requirements. Frequently, fundamental decisions such as the number of observatories, the instruments manifested, and orbit parameters are determined based upon assumptions about the characteristic temporal and spatial scales of variability of the target observation. With the introduced methodology, representativity errors due to separations in space and time can be quantified without prior knowledge of instrument performance, and errors driven by constellation design can be estimated without model ingest or analysis. [ABSTRACT FROM AUTHOR]

Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate‐risk‐prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi‐model ensemble (CMME) of seven state‐of‐the‐art general circulation models (GCMs) from the North American Multi‐Model Ensemble project. The lead‐1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA‐based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real‐time seasonal rainfall forecasts using this new forecast system. [ABSTRACT FROM AUTHOR]

Understanding how to design climate services across a range of contexts remains a key priority. This research evaluates the Sea Ice for Walrus Outlook (SIWO), a resource designed to provide information about sea ice, weather, and walruses in Alaska, a region experiencing rapid social and environmental change. The evaluation was based on a set of 22 indicators developed from literature on evaluation, weather and climate services, science communication, and decision support. Two datasets were assessed to evaluate the SIWO: semi-structured interviews (n = 13) and a web-based questionnaire (n = 35). Interpretation of the outcome indicators suggests that the SIWO supported several community needs, including providing information to support travel, documenting historical impacts for disaster relief, and sharing Indigenous Knowledge among villages. The evaluation revealed insights into the kinds of information useful for rural and Indigenous communities, such as the importance of local observations from within and from nearby villages. Recommendations for other emergent organizations providing climate services in rural communities include attention to specific budget considerations to support equitable engagement and compensation, including both local and scientific observations, using multiple channels to disseminate information, and including evaluations in the design of climate services that are aligned with funding cycles. [ABSTRACT FROM AUTHOR]

As the Arctic and its rivers continue to warm, a better understanding of the possible future impacts on people would benefit from close partnership with Indigenous communities and scientists from diverse fields of study. We present efforts by the Arctic Rivers Project to conduct community‐engaged research to increase collective understanding of the historical and potential future impacts of climate change on rivers, fish, and Indigenous communities. Working in central to northern Alaska and the Yukon Territory in Canada, the project seeks to engage with Indigenous communities in ethical and equitable ways to produces science that is useful, useable, and used that may serve as an example for future research efforts. Toward this goal, we formed an Indigenous Advisory Council and together developed project‐specific knowledge co‐production protocols. This paper provides a novel model of design and implementation to co‐produce knowledge with communities across a large study domain. Plain Language Summary: The Arctic and rivers located in the Arctic and subarctic are warming due to climate change. To understand the impacts this warming will have on people, partnering with impacted Indigenous communities in the region is important. It is also important that these partnerships are ethical and equitable and produce science that is actionable. This paper discusses efforts undertaken by a specific project, the Arctic Rivers Project, to conduct ethical and equitable research with Indigenous communities and generate science that is useful to those communities. Through this research our goal is to better understand potential future impacts of climate change on rivers, fish, and Indigenous communities in central northern Alaska and the Yukon Territory in Canada. To achieve this goal, the project formed an Indigenous Advisory Council (IAC) and together developed guidelines for how we can work collaboratively with Indigenous communities. Our specific process of forming an IAC and guidelines is, to our knowledge, a new way to approach collaborative research when working across a large geographic area. We present our process here so that it may provide an example for other research efforts. Key Points: Arctic climate information can be made useful, useable, and used by equitably accounting for diverse community adaptation needs through knowledge co‐productionInstitutional and community capacity, including means and ability, is necessary for equitable knowledge co‐production to occurWe present an approach for co‐producing knowledge with Indigenous communities that can serve as an example for other scientific efforts [ABSTRACT FROM AUTHOR]

Climate change is a global challenge necessitating adaptation at the local level. Small island developing states (SIDS) in the southwest Indian Ocean (SWIO) basin are particularly vulnerable and already facing significant challenges due to climate variability and extreme weather events like tropical cyclones (TCs). Tailored climate services, catering to their specific needs and contexts, are crucial for formulating appropriate adaptation strategies. This study aims to fill the gap of localized and reliable information for climate services in the SWIO region. A 12-km resolution regional climate model is dynamically downscaled from a CMIP6 model to capture the climatology of tropical cyclones. Outputs are bias-corrected at kilometer-scale resolution over multiple islands. A subset of CMIP6 simulations is also statistically downscaled over La Réunion to quantify model uncertainties at the local scale and compare statistical and dynamical downscaling methods.CMIP6 models project an average increase in daily mean temperatures over small islands ranging from 1.2 °C (SSP1-2.6) to 3.7 °C (SSP5-8.5) by the end of the century compared to the reference period of 1981–2010, and up to 4.4 °C on Madagascar (SSP5-8.5). The dry season in the SWIO region is anticipated to become significantly drier, with precipitation deficits ranging from 10 % to 40 %, primarily due to a delayed onset of the rainy season. The cyclonic risk is expected to increase due to stronger cyclone intensities, a higher proportion of strong TCs, and the poleward migration of very intense TCs by one to two degrees latitude, further amplifying the risk faced by the Mascarene islands.

Co-production is increasingly acknowledged as the preferred mode for producing climate services, especially in complex and information-limited decision contexts. This paper contributes knowledge on practices and processes that can enable effective climate services in such contexts, through sharing experiences from the Future Resilience for African CiTies And Lands (FRACTAL) project.FRACTAL focused on informing actions to tackle climate-related issues in nine cities in six southern African countries over a six-year period and, in parallel, developing research findings and insights. Principles for effectively co-producing climate services were collaboratively identified by the project team, after which practical insights were detailed by analysing the body of evidence produced during FRACTAL using qualitative methods. This analysis helped to understand how principles were engendered, as well as associated challenges.While many principles identified resonate with the growing body of relevant knowledge, practical insights from this study contribute to understanding ‘how’ principles can be engendered. Experiences emphasise the importance of engaging participants’ emotions, avoiding centring on climate information, using a “third space” to facilitate equitable engagements, directing resources towards having fun and learning actively, process-driven iteration, focusing on contemporary issues with which stakeholders can connect, introducing a pathways framing, and embedding researchers in decision-making contexts. This constitutes a more comprehensive set of principles than was previously available in the literature. Application of these principles and the transdisciplinary framing, which was core to FRACTAL, supports a shift away from a focus on ‘products’ to knowledge co-production ‘processes’ where collaborative learning is the defining characteristic of climate services.

Climate services aim to provide climate information to aid decision-making. However, in practice their effectiveness is hampered by factors such as incompatibility with decisions, neglect of user’s context, and limited understanding of the information provided. Enhancing climate services’ user orientation necessitates capacity building to bolster the knowledge and collaborative skills among actors in the climate production chain. This study introduces a learning framework for capacity building in climate services that was developed using iterative systematic cycles for educational design research. The framework contains seven steps, along with relevant guiding questions, derived from the evaluation of case studies and the trainer’s experiences in 21 training events conducted under the User Learning Services (ULS) of the Copernicus Climate Change (C3S). Overall, this study offers a learning framework that can guide the content and design of capacity building in future climate information services. This contributes significantly to the ongoing effort to enhance and understand the impact of climate services on decision making.

Climate change can significantly affect and influence, both positively and negatively, the wine sector. In this context, the adoption of timely, cost-effective adaptation strategies may contribute to reduce such risks, maximise opportunities, and enhancing the sector’s resilience to changing climatic conditions. Climate services involve the production and use of climate information to support decision-makers adapting to climate variability and change. Assessing the value and benefits of using climate services constitutes a critical area of research and can help climate service providers identify any barriers in their development, uptake, and use. The climate service developed in this research benefitted from a co-production approach with a Portuguese Wine company − SOGRAPE. In this paper, we aim to assess the usability and value of the climate service co-developed with SOGRAPE and identify possible barriers that limited the tool’s usability and value. Engagement with SOGRAPE users was pursued based on mixed methods approach throughout the various stages of co-development and testing of the tool. The results show that the data provided in the Dashboard was perceived as reliable and legitimate. However, the saliency of the Dashboard was questioned, and some recommendations proposed to increase its saliency and overall usability. More importantly, SOGRAPE users were not able to use the climate information provided in the tool due to a number of barriers which are also reported in this study. The findings and recommendations from this study will help inform the design, development and usability of other climate services within and beyond the wine sector.

Developing and implementing climate adaptation measures in complex socio-ecological systems can lead to unintended consequences, especially when those systems are undergoing rapid hydro-climatic and socio-economic change. In these dynamic contexts, a systemic approach can make the difference between adaptive and maladaptive outcomes. This paper focuses on the use of climate services, often touted as no-regret solutions, and their potential to generate maladaptation. We explored the interactions between climate services and adaptation/maladaptation across five case studies affected by different types of natural hazards and characterized by a range of hydro-climatic and socio-economic conditions. Using system archetypes, we show how climate services can play a role in both producing and preventing maladaptation. The dynamics explored through system archetypes are: i) “fixes that fail”, where short-sighted solutions fail to address the root causes of a problem; ii) “band aid solutions”, where the benefits brought about in the short-term come at the expenses of delaying long-term adaptive actions; and iii) “success to the successful”, where some groups increasingly benefit from climate services at the expenses of other groups. We demonstrate how these dynamics constitute maladaptive processes, as well as identifying the tools and theories that can be used in this type of assessment. Finally, we provide a framework and recommendations to guide the ex-ante assessment of maladaptation risk when designing and implementing climate services.

In recent years, the climate services market has increased significantly, especially in the Western European countries where they have become widely utilised in both the public and private sectors. In Romania, there is no specialised platform for those kinds of services, and the sector is in its beginnings. The current study is based on sociological research conducted as part of a national project aimed at increasing adaptive capacity to climate change. The purpose of the questionnaire that served as the study’s base was to collect information about the extent to which climate services are used by organisations, their perception of the benefits of using the services, the technical characteristics of the services, and the future needs of stakeholders. Such an analysis is necessary to comprehend the existing market situation in Romania and to be able to establish the circumstances essential for an effective improvement of climate services and products based on the co-development concept. The main outcomes of the survey conducted at the national level (324 respondents) confirm the early stage of the national climate service market as (i) only a small share (34 %) of respondents are users of climate products and services (mostly from agriculture, forestry, water resources management, biodiversity, energy sectors) and (ii) climate data and products are insufficiently tailored at sectoral level. Most representative identified stakeholder needs refer to: temporal (i.e., monthly, seasonal) and spatial resolution (i.e., local, regional) and types of tailored climate products (i.e., monthly/seasonal weather forecasts, spatio-temporal maps and analysis tool). The study identified premises further development of the climate service market in Romania (i.e., widespread interest in using climate products and services among the non-users, perceived societal benefits of climate products and services).