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Reindeer husbandry in the Arctic region is strongly affected by the local climate. Reindeer herders are used to coping with adverse weather, climate, and grazing conditions through autonomous adaptation. However, today's rapidly changing Arctic environment poses new challenges to the management of herding activities. Finding means for combining traditional and scientific knowledge without depriving any of the systems of its fundamental strengths is hence deemed necessary. In this work, we apply a transdisciplinary framework for knowledge co-production involving international researchers and reindeer herders from different cooperatives in northern Finland. Through ‘climate change adaptation stories’, we co-explore how climate predictions can inform herders' decision making during the herding season. Relevant decisions include the anticipation of summer harvest time, the inopportune periods of cold weather in spring, and insect harassment in summer. Despite their potential benefits for climate-sensitive decisions, climate predictions have seen limited uptake, mainly due to their probabilistic nature and lower quality compared with shorter-term weather forecasts. The analysis of two different adaptation stories shows that seasonal predictions of temperature for May and June can successfully advise about the likelihood of having an earlier than normal harvest. This information can be obtained up to three months in advance, helping herders to better arrange their time for other activities. Likewise, sub-seasonal predictions of temperature during April and May can be useful to anticipate the occurrence of backwinter episodes, which can support herders in deciding whether to feed reindeer in pens for longer, avoiding putting the survival of calves at risk. This study, which would benefit from co-evaluation in real world settings and consideration of additional adaptation stories, sets the basis for a successful co-production of climate services with Arctic reindeer herders, This work has received funding from EU-PolarNet 2 via the European Union's Horizon 2020 research and innovation programme under grant agreement No 101003766. We acknowledge the reindeer herders from the Käsivarsi, Hammastunturi, Palojärvi, Sattasniemi and Oivanki cooperatives that participated in the survey and the herders from the cooperatives of Palojärvi and Näkkälä that kindly took part in the round table. We also thank Pierre-Antoine Bretonnière from BSC for downloading the climate data, Peer Reviewed, Postprint (author's final draft)

WWRP/WCRP S2S Summit 2023 What: More than 190 scientists from 29 countries met to celebrate 10 years of the Subseasonal to Seasonal (S2S) Prediction project and look to the future of S2S prediction. When: 3–7 July 2023 Where: University of Reading, United Kingdom, We wish to thank all the presenters at the workshop. The organizers of the workshop are grateful for the support provided by WMO, WWRP, WCRP, University of Reading, and the EU H2020 CONFESS Project (https://confess-h2020.eu/)., "Article signat per 32 autors/es: S. J. Woolnough, F. Vitart, A. W. Robertson, C. A. S. Coelho, R. Lee,H. Lin, A. Kumar, C. Stan, M. Balmaseda, N. Caltabiano, M. Yamaguchi, H. Afargan-Gerstman, V. L. Boult, F. M. De Andrade, D. Büeler, A. Carreric, D. A. Campos Diaz, J. Day, J. Dorrington, M. Feldmann, J. C. Furtado, C. M. Grams, R. Koster, L. Hirons, V. S. Indasi, P. Jadhav, Y. Liu, P. Nying’uro, C. D. Roberts, E. Rouges, and J. Ryu", Postprint (author's final draft)

The Spanish region of Andalusia is the world-leading olive oil producer. Its olive-dominated landscapes are among the most biodiverse drylands of the globe and prospectively among the areas most affected by climate change. This analysis used physiologically based demographic modeling (PBDM) to assess the impact of climate change on the olive/olive fly system of Andalusia. The analysis was implemented on cloud computing, allowing PBDM models to be run from any computer connected to the internet, to interface with state-of-the-art climatic drivers, and to scale efficiently with increasing computational loads and user requests. Findings include that chilling required for olive blooming will decrease in large areas of the Andalusian provinces of Jaen, Cordoba, and Sevilla, with some areas not meeting the minimum chilling threshold and some accumulating no chilling by the end of the century under the high greenhouse gas (GHG) emission scenario. Olive blooming will occur up to five weeks earlier in the Jaen, Cordoba, Sevilla, and Granada provinces, but olive yield is expected to increase or remain stable. Olive fly infestation will decrease with climate change, with infestations below the reference economic threshold of 4 % towards the end of the century in some areas under high GHG emission scenario. Measures to adapt Andalusian olive systems to climate change include: selecting olive cultivars with lower chilling requirements; implementing cover crops to enhance water use efficiency under increased CO2 concentration and uncertain precipitation projections; and targeting the spring generation of the fly and diversifying the olive landscape to reduce infestation levels., We acknowledge Dr. Carlo Buontempo, Director of the Copernicus Climate Change Service, for providing early access to the AgERA5 data. The Center for the Analysis of Sustainable Agricultural Systems (http://www.casasglobal.org/) provided logistical and scientific support to the MED-GOLD project. This work was supported by the project MED-GOLD that received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776467 and by the project TEBAKA (project ID: ARS01_00815) co-funded by the European Union - ERDF and ESF, “PON Ricerca e Innovazione 2014-2020”., Peer Reviewed, "Article signat per 19 autors/es: Luigi Ponti, Andrew Paul Gutierrez, Christos Giannakopoulos, Konstantinos V. Varotsos, Javier López Nevado, Silvia López Feria, Freddy Wilmer Rivas González, Federico Caboni, Federica Stocchino, Adolfo Rosati, Damiano Marchionni, José Ricardo Cure, Daniel Rodríguez, Marta Terrado, Matteo De Felice, Alessandro Dell'Aquila, Sandro Calmanti, Ricardo Arjona, Michael Sanderson", Postprint (published version)

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.

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., QC 20240610

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.

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.

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.

Climate services are increasingly developed and used to plan for climate change adaptation, but it remains unclear what constitutes their success. In this global Delphi study, a wide range of experts evaluated 12 elements to be most relevant for defining success, clustered along 3 dimensions: climate service, production process, and results of production and/or use. Two possible ways of combining these elements into a definition received convincing support from the experts. The definition shows that user satisfaction is key. Moreover, climate service developers should acknowledge climate change uncertainties, and build trust and capacity with the users, while tailoring interactions. Ultimately, better adaptation decisions should follow. There was limited agreement on including elements regarding the process of how a service should be developed. This paper proposes a first global definition for successful climate services that is widely supported and can be used to develop, research, and evaluate climate services.

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.

Central America is consistently identified as a global hotspot for future anthropogenic climate change and is already exposed to considerable natural climate variability and frequent extreme events including tropical storms, heavy flooding, and drought. Pervasive inequalities and systemic vulnerabilities exacerbate the impacts of climate variability and change, especially for the millions of families who rely on rainfed agriculture throughout the region. This is particularly true for the Central American Dry Corridor, a region marked by less rainfall and more frequent dry spells in comparison to the rest of Central America. While projections for 21st century drying due to anthropogenic climate change are robust throughout Central America, internal climate variability is expected to dominate precipitation anomalies for the next several decades and very few locations have had significant or consistent changes in precipitation. However, smallholder farmers in the Guatemalan Dry Corridor have reported recent changes in the distribution and intensity of rainfall, making it harder to predict and contributing to crop losses and decreased production of food staples in recent years. Reducing uncertainty in near-term and future climate variability and change is therefore essential for mitigating climate risk and for improving hazard reduction, resource management, and agricultural decision-making throughout the region. This in part hinges on the production and dissemination of accurate and usable climate information. By combining instrumental climate data, smallholder farmers' observations of climate variability, and precipitation forecasts from climate models, my dissertation seeks to reduce some of these uncertainties. Expanding on this objective, I integrate these sources of information to (1) systematically assess how rainfall variability and extreme precipitation events in the Dry Corridor are changing, (2) determine the relevance of climate variability and change for ag

The potential increase in the adoption value of seasonal forecasts is spotlighted in this paper by introducing observation-forecast blending for wine-sector indicators over the Iberian Peninsula. The predictions of six bioclimatic indicators (temperature and precipitation based) considered highly important from the perspective of wine-sector users were prepared for each month of the growing season and combined with previous observations as the indicator period progresses. The performance of this approach was then assessed with Pearson’s correlation coefficient and Fair Ranked Probability Skill Score (FRPSS). The results show a marked increase in the skill metrics during the growing season from the early combinations for all the indicators. This progressive improvement of the forecasting skill offers the users an opportunity to ponder anticipation and confidence in their decisions and, thus, facilitate the future uptake of seasonal forecasting in their decision planning, Thanks to all the partners of the project MED-GOLD (Turning climate-related information into added value for traditional MEDiterranean Grape, Olive and Durum Wheat food systems, agreement no. 776467), the project VITIGEOSS (Vineyard innovative tools based on the integration of Earth Observation services and in-field sensors, agreement no. 869565) and the project ASPECT (HE-101081460) funded by the European Union. The results contain modified Copernicus Climate Change Service information. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Thanks to SOGRAPE as the end-user for providing feedback in the co-development process. One of the coauthors, Raül Marcos-Matamoros, is a Serra Húnter Fellow. Finally, we thank the reviewers, whose comments helped improve the paper substantially., Peer Reviewed, "Article signat per 11 autors/es: Chihchung Chou, Raül Marcos-Matamoros, Lluís Palma Garcia, Núria Pérez-Zanón, Marta Teixeira, Sara Silva, Natacha Fontes, Antonio Graça, Alessandro Dell'Aquila, Sandro Calmanti, Nube González-Reviriego", Postprint (published version)

Background: Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia. Methods: We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model. Findings: When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale. Interpretation: The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.

CSIndicators is an R package that gathers generalised methods for the flexible computation of climate-related indicators. Each method represents a specific mathematical approach which is combined with the possibility of selecting a flexible time period to define the indicator. This enables a wide range of possibilities for tailoring indicators to sectorial climate service applications. This package is intended for sub-seasonal, seasonal and decadal climate predictions, but its methods are also applicable to other time scales. Additionally, this package is compatible with the CSTools R package for climate forecast post-processing., This package was developed in the context of H2020 MED-GOLD (776467), S2S4E (776787), VITIGEOSS (869565) projects and Horizon Europe ASPECT project (101081460)., Peer Reviewed, Postprint (published version)

The years since 2000 have been a golden age in in situ ocean observing with the proliferation and organization of autonomous platforms such as surface drogued buoys and subsurface Argo profiling floats augmenting ship-based observations. Global time series of mean sea surface temperature and ocean heat content are routinely calculated based on data from these platforms, enhancing our understanding of the ocean’s role in Earth’s climate system. Individual measurements of meteorological, sea surface, and subsurface variables directly improve our understanding of the Earth system, weather forecasting, and climate projections. They also provide the data necessary for validating and calibrating satellite observations. Maintaining this ocean observing system has been a technological, logistical, and funding challenge. The global COVID-19 pandemic, which took hold in 2020, added strain to the maintenance of the observing system. A survey of the contributing components of the observing system illustrates the impacts of the pandemic from January 2020 through December 2021. The pandemic did not reduce the short-term geographic coverage (days to months) capabilities mainly due to the continuation of autonomous platform observations. In contrast, the pandemic caused critical loss to longer-term (years to decades) observations, greatly impairing the monitoring of such crucial variables as ocean carbon and the state of the deep ocean. So, while the observing system has held under the stress of the pandemic, work must be done to restore the interrupted replenishment of the autonomous components and plan for more resilient methods to support components of the system that rely on cruise-based measurements.

Background: Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia. Methods: We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model. Findings: When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale. Interpretation: The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue. Funding: Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

Background: Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia. Methods: We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model. Findings: When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale. Interpretation: The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.

The years since 2000 have been a golden age in in situ ocean observing with the proliferation and organization of autonomous platforms such as surface drogued buoys and subsurface Argo profiling floats augmenting ship-based observations. Global time series of mean sea surface temperature and ocean heat content are routinely calculated based on data from these platforms, enhancing our understanding of the ocean’s role in Earth’s climate system. Individual measurements of meteorological, sea surface, and subsurface variables directly improve our understanding of the Earth system, weather forecasting, and climate projections. They also provide the data necessary for validating and calibrating satellite observations. Maintaining this ocean observing system has been a technological, logistical, and funding challenge. The global COVID-19 pandemic, which took hold in 2020, added strain to the maintenance of the observing system. A survey of the contributing components of the observing system illustrates the impacts of the pandemic from January 2020 through December 2021. The pandemic did not reduce the short-term geographic coverage (days to months) capabilities mainly due to the continuation of autonomous platform observations. In contrast, the pandemic caused critical loss to longer-term (years to decades) observations, greatly impairing the monitoring of such crucial variables as ocean carbon and the state of the deep ocean. So, while the observing system has held under the stress of the pandemic, work must be done to restore the interrupted replenishment of the autonomous components and plan for more resilient methods to support components of the system that rely on cruise-based measurements.

It is increasingly recognized that effective climate risk assessments benefit from well-crafted processes of knowledge co-production involving key stakeholders and scientists. To support the co-production of actionable knowledge on climate change, a careful design and planning process is often called for to ensure that relevant perspectives are integrated and to promote shared understandings and joint ownership of the research process. In this article, we aim to further refine methods for co-producing climate services to support risk-informed decision-support and adaptation action. By drawing on insights and lessons learned from participatory processes in six case studies in Northern and Central Europe, we seek to better understand how associated challenges and opportunities arising in co-production processes play out in different case-specific contexts. All cases have applied a standardized framework for climate vulnerability and risk assessment, the impact chain method. The analysis builds on multiple methods including a survey among case study researchers and stakeholders, interviews with researchers, as well as a project workshop to develop collective insights and synthesize results. The results illustrate case studies' different approaches to stakeholder involvement as well as the outputs, outcomes, and impacts resulting from the risk assessments. Examples include early indications of mutual learning and improved understanding of climate risks, impacts and vulnerability, and local and regional decision contexts, as well as actual uptake in planning and decision contexts. Other outcomes concern scientific progress and contribution to methodological innovations. Overall, our study offers insights into the value of adopting good practices in knowledge co-production in impact chain-based climate risk assessments, with wider lessons for the climate services domain. While collaborations and interactions have contributed to a number of benefits some practical challeng

The growing complexity of the relationship between climate information and agricultural decision-making necessitates the development of relevant and timely climate services for farmers. These services can effectively support risk management strategies in agriculture by fostering a comprehensive understanding of the intricacies involved in farmer decision-making dynamics. This paper addresses this critical gap by analyzing the drivers influencing decision-making processes that shape adaptation strategies for staple grain and coffee farming systems in Central America. The study answers the following research questions: (i) Does the mind map tool effectively provide a holistic understanding of farmers' decision-making processes? (ii) How do Central American farmers make decisions within their farm systems at multiple timescales? (iii) Which climate factors trigger these decisions? Employing a combination of systematic literature review and a case study in Honduras, the study identifies 13 critical decisions farmers make throughout their crop cycle and their respective triggers. These decisions were grouped into three clusters (production, household, and environmental) and classified into lead-time categories (operational, tactical, and strategic). Findings reveal that farmers base their decisions regarding future climate expectations on their traditional knowledge, religious dates, and memories of recent past seasons' rainfall patterns, and that one of the most significant factors influencing farmers' decisions is food security shortages resulting from extreme events. For example, recent mid-summer droughts have led farmers to prioritize sowing beans over maize in the Primera season, while during the Postrera season, they face challenges due to excess rainfall and the hurricane season. We conclude that the mind map tool developed in this paper provides an effective and appropriate method and that the variation in farmers' decision-making complexity across systems and l

Climate services are high on the international agenda for their potential to help combat the effects of climate change. However, climate science is rarely directly incorporated into the decision-making processes of societal actors, due to what has been identified as the usability gap. This gap is partially due to a failure to timely and meaningfully engage users in the production of climate services, as well as misperceptions as to which users can best benefit from climate service uptake. In this article, we propose user selection and engagement guidelines that integrate important values from participatory science such as those of legitimacy, representativity, and agency. The guidelines consist of 5 + 1 steps: defining why, where, whom, which attributes, and which intensity and how to select and engage with stakeholders. While these steps may be initially implemented by an ideally interdisciplinary team of scientists and service designers, the final step consists of an iterative process by which each decision is agreed on together with the identified users and stakeholders under a coproduction approach. We believe this systematic user selection and engagement practice is key to support the design of climate services aligned to the actual needs of a wide and inclusive range of empowered societal agents., This work has been funded under the projects NextGEMS (101003470) and FOCUS-Africa (GA869575)., Peer Reviewed, Postprint (published version)

Background: Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia. Methods: We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model. Findings: When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale. Interpretation: The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.

Background: Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia. Methods: We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model. Findings: When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale. Interpretation: The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.

Under hot conditions the human body is able to regulate its core temperature via sweat evaporation, but this ability is reduced when air humidity is high. These conditions of high temperature and high humidity invoke heat stress which is a major problem for humans, in particular for vulnerable groups of the population and people under physical stress (e.g. heavy duty work without appropriate cooling systems). It is generally expected that the frequency, duration and magnitude of such unfavorable conditions will increase with further climate warming. In this respect, climate services play a crucial role by putting together climatological information and adaptation solutions to reduce future heat stress. We here assess the recently developed CH2018 scenarios for Switzerland (https://www.climate-scenarios.ch) in terms of heat stress conditions including their future projections. For this purpose, we characterize future extreme heat conditions with the use of climate analogs. By doing so, we attempt to produce more accessible climate information which might foster the use and understanding of regional-scale climate scenarios., Here heat stress is expressed through the Wet Bulb Temperature (TW), which is a relatively simple proxy for heat stress on the human body and which depends non-linearly on temperature and humidity. It is assessed in terms of single-day events and heat stress spells. Projections based on the CH2018 scenarios indicate increasing heat stress over Switzerland, which is accentuated towards the end of the century. High heat stress conditions might be about 3–5 times more frequent for an emission scenario without mitigation (RCP 8.5) than for the mitigation scenario (RCP 2.6) by the end of the 21st century. The projected increase of heat stress results in more and longer heat stress spells, thus highlighting the importance of timely and precise prevention strategies in the context of heat-health action plans. Spatial climate analogs based on heat stress spells in Switzerland greatly vary depending on the emission scenario and are found in Central Europe under a mitigation scenario and in southern Europe under unmitigated warming.

The implementation of operational climate service prototypes, which encompasses the co-design and delivery of real-time actionable products with/to stakeholders, contributes to efficiently leveraging operational climate predictions into actionable climate information by providing practical insight on the actual use of climate predictions. This work showcases a general guideline for implementing an operational climate service based on subseasonal predictions. At this timescale, many strategic decisions can benefit from timely predictions of climate variables. Still, the use of subseasonal predictions is not fully exploited. Here, we describe the key aspects considered to set up an operational climate service from the conception to the production phase. These include the choice of the subseasonal systems, the data sources and the methodology employed for post-processing the predictions. To illustrate the process with a real case, we present the detailed workflow design of the implementation of a climate service based on subseasonal predictions and describe the bias adjustment and verification methodologies implemented. This work was developed in the H2020 S2S4E project, where industrial and research partners co-developed a fully-operational Decision Support Tool (DST) providing 18 months of real-time subseasonal and seasonal forecasts tailored to the specific needs of the renewable energy sector. The operational workflow can be adapted to serve forecast products to other sectors, as has been proved in the H2020 vitiGEOSS project, where the workflow was modified to provide downscaled subseasonal predictions to specific locations. We consider this a valuable contribution to future developments of similar service implementations and the producers of the climate data., The research leading to these results has received funding from the European Union’ss Horizon 2020 research and innovation programme under Grants 7767874 (S2S4E) and 869565 (VitiGEOSS). ECMWF-Ext-ENS real-time predictions used for the operational prototype were provided by the Subseasonal to Seasonal (S2S) Prediction Project’s Real-Time Pilot Initiative to S2S4E Project as one of the participating projects. The data can be obtained from the S2S Project database through its two data portals: ECMWF ( https://apps.ecmwf.int/datasets/data/s2s/levtype=sfc/type=cf/) and CMA ( http://s2s.cma.cn/index). The ECMWF ERA-5 reanalysis was accessed from Copernicus Climate Change Service (C3S) Climate Data Store ( https://cds.climate.copernicus.eu/#!/home)., Peer Reviewed, Postprint (published version)

This paper assesses the forecast quality of five seasonal forecasting strategies applied to different bioclimatic indicators tailored to the olive sector. In total, five indicators have been selected considering their importance in the management of the olive orchard. As time progresses through the indicator target period, the impact of the increasing share of actual observations included in its computation has been evaluated by examining the variabilities of correlation coefficients and fair rank probability skill scores for each initialization date. The results show that blending either seasonal predictions or climatology with observations enhanced the capability of forecasting the tercile category for all the indicators when compared to the use of climatology or seasonal predictions alone. In fact, for Spring Maximum Temperature and Growing Season Temperature indicators, the combination of observations and SEAS5 predictions could outperform the other methods for most of the start months. As for those threshold-defined indicators, namely Spring Heat Days and Summer Heat Stress Days, the end-users are highly encouraged to use climatology in the first month and combine it with observations as soon as the latter becomes available., Thanks to all the partners of the project MED-GOLD (Turning climate-related information into added value for traditional MEDiterranean Grape, Olive and Durum Wheat food systems, agreement no. 776467) and the project ASPECT (HE-101081460) both funded by the European Union. Thanks to Dcoop as the end-user for providing feedback in the co-development process. The results contain modified Copernicus Climate Change Service information. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. One of the coauthors, Raül Marcos-Matamoros, is a Serra Húnter Fellow. Finally, we thank the reviewers, whose comments helped improve the paper substantially., Peer Reviewed, Postprint (published version)

MED-GOLD was a 54-months research and innovation project, whose main aim was to co-develop climate services for three staples of the Mediterranean food system, namely grapes, olives and durum wheat. This paper describes the methodology adopted for the co-development of the pilot climate service for the wine sector, focusing on the Douro Wine Region in northern Portugal. In the first step, the MED-GOLD industrial partner SOGRAPE identified key decisions and users’ needs for the wine sector in the Douro region by involving managers from their own vineyards in that region. From this information, the relevant bioclimatic indicators (and associated essential climate variables) were selected. Afterwards, two compound risk indices, the Sanitary and Heat Risk indices, were introduced as a combination of some of the aforementioned bioclimatic indicators. This methodological work was validated against the empirical climate characterization for the region of interest, of several ‘bad’ and ‘good’ years chosen by users according to their recollections of grape and wine production outcomes, namely quality and yields. In this paper, the overall strategy for selection of these years is presented. The components of the service based on historical climate, seasonal predictions and longer-term climate projections are described along with the visual interface developed: the MED-GOLD Dashboard, an interactive tool that displays detailed historical climate data, seasonal predictions and climate projections. The Dashboard consists of an ICT platform with a map-based user-focused front end to aid easy access to and manipulation of the data. The Dashboard was iteratively co-designed with the users to ensure their needs were met., The authors acknowledge project MED-GOLD (Turning climate-related information into added value for traditional MEDiterranean Grape, Olive and Durum Wheat food systems, agreement no. 776467) funded by the European Union. Raul Marcos-Matamoros is a Serra Húnter fellow., Peer Reviewed, "Article signat per 17 autors/es: Alessandro Dell'Aquila , António Graça, Marta Teixeira, Natacha Fontes, Nube Gonzalez-Reviriego, Raul Marcos-Matamoros, Chihchung Chou, Marta Terrado, Christos Giannakopoulos, Konstantinos V. Varotsos, Federico Caboni, Riccardo Locci, Martina Nanu, Sara Porru, Giulia Argiola, Marta Bruno Soares, Michael Sanderson", Postprint (published version)

Droughts and changing rainfall patterns due to natural climate variability and climate change, threaten the livelihoods of Malawi's smallholder farmers, who constitute 80% of the population. Provision of seasonal climate forecasts (SCFs) is one means to potentially increase the resilience of rainfed farming to drought by informing farmers in their agricultural decisions. Local knowledge can play an important role in improving the value of SCFs, by making the forecast better-suited to the local environment and decision-making. This study explores whether the contextual relevance of the information provided in SCFs can be improved through the integration of farmers’ local knowledge in three districts in central and southern Malawi. A forecast threshold model is established that uses meteorological indicators before the rainy season as predictors of dry conditions during that season. Local knowledge informs our selection of the meteorological indicators as potential predictors. Verification of forecasts made with this model shows that meteorological indicators based on local knowledge have a predictive value for forecasting dry conditions in the rainy season. The forecast skill differs per location, with increased skill in the Southern Highlands climate zone. In addition, the local knowledge indicators show increased predictive value in forecasting locally relevant dry conditions, in comparison to the currently-used El Niño-Southern Oscillation (ENSO) indicators. We argue that the inclusion of local knowledge in the current drought information system of Malawi may improve the SCFs for farmers. We show that it is possible to capture local knowledge using observed station and climate reanalysis data. Our approach could benefit National Meteorological and Hydrological Services in the development of relevant climate services and support drought-risk reduction by humanitarian actors., Water Resources, Transport and Logistics, System Engineering

The marine dinoflagellate Alexandrium Halim represents perhaps the most significant and intensively studied genus with respect to species diversity, life history strategies, toxigenicity, biogeographical distribution, and global magnitude and consequences harmful algal blooms (HABs). The socioeconomic impacts, environmental and human health risks, and mitigation strategies for toxigenic Alexandrium blooms have also been explored in recent years. Human adaptive actions based on future scenarios of bloom dynamics and shifts in biogeographical distribution under climate-change parameters remain under development and not yet implemented on a regional scale. In the CoCliME (Co-development of climate services for adaptation to changing marine ecosystems) project these issues were addressed with respect to past, current and anticipated future status of key HAB genera and expected benefits of enhanced monitoring. Data on the distribution and frequency of Alexandrium blooms related to paralytic shellfish toxin (PST) events from key CoCliME Case Study areas, comprising the North Sea and adjacent Kattegat-Skagerrak, Norwegian Sea, and Baltic Sea, and eastern North Atlantic marginal seas, were evaluated in a contemporary and historical context over the past several decades. The first evidence of possible biogeographical expansion of Alexandrium taxa into eastern Arctic gateways was provided from DNA barcoding signatures. Various key climate change indicators, such as salinity, temperature, and water-column stratification, relevant to Alexandrium bloom initiation and development were identified. The possible influence of changing variables on bloom dynamics, magnitude, frequency and spatial and temporal distribution were interpreted in the context of regional ocean climate models. These climate change impact indicators may play key roles in selecting for the occurrence and diversity of Alexandrium species within the broader microeukaryote communities. For example, shifts to high

Climateurope Workshop on the Visualization of Climate Services Barcelona Supercomputing Center’s (BSC) Earth Sciences Department organized the workshop in the framework of the Horizon 2020–funded Coordination and Support Action Climateurope. The workshop aimed to discuss different aspects of the state-of-the-art of visualizations used in climate services and produce a publication on the synthesis and recommendations. We invited participants from different projects linked to the Climateurope network, including EU Horizon 2020 (H2020) and European Research Area for Climate Services (ERA4CS) projects as well as a few national projects and private contracts. The workshop was attended by representatives of 22 projects., The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreements 689029 (Climateurope) and 776467 (MED-GOLD). The authors want to acknowledge the different climate services projects that participated in the workshop, including Climate-fit.city (73004), Digital-Water.city (20954), IMPREX (641811), PRIMAVERA (641727), RECEIPT (820712), S2S4E (776787), SECLI-FIRM (776868), VISCA (730253), CIREG, Clim2power, CoCliME, EVOKED, ISIpedia, MEDSCOPE, SENSES, WATExR, C3S_429g_BSC, and the inDust COST Action (16202). We would also like to thank the participation of the projects Seasonal Hurricane Predictions, KNMI climate scenarios, and eClimViz. We also acknowledge BSC colleagues Diana Urquiza, Andria Nicodemou, and Sara Octenjak for helping to run the workshop., Peer Reviewed, Postprint (published version)

The Climateurope Festivals were designed to create synergies between different European, national and international initiatives in the fields of Earth-system modelling & Climate Services and enhance the transfer of information between suppliers and users. It gave an opportunity to display best in class outcomes and engage in world class networking in a less rigid environment than a scientific conference. A number of formats were adopted in the Festival, from traditional impulse talks to innovative interactive sessions, and the thought-provoking discussions allowed the participants to share their experiences and knowledge around the advantages and challenges that Climate Services face within different sectors. Three Climateurope Festivals were originally planned to be held across Europe. Two Festivals were successfully organised, the first in Valencia in 2017, and the second in Belgrade in 2018. Due to the COVID-19 pandemic and associated lockdowns and travel restrictions, the third and final Festival was held online as a series of virtual web-based Festivals in 2020/2021. The Festivals were highly valued by participants. There was a strong desire by the Climateurope network to continue a science-stakeholder dialogue and make the Climateurope Festivals a regular event.}, The organization team of the Climateurope Festivals wishes to acknowledge and sincerely thank the entire consortium of Climateurope for their valuable support. Climateurope is funded by the European Commission through the Horizon 2020 Programme for Research and Innovation: Grant Agreement 689029., Peer Reviewed, "Article signat per 14 autors/es: Lola Kotova, Maria Máñez Costa, Daniela Jacob, Chris Hewitt, Paula Newton, Natalie Garrett, Stacey New, Rebecca Parfitt, Tyrone Dunbar, Janette Bessembinder, Ralf Toumi, Mauro Buonocore, Aleksandra Krzic, Marta Terrado", Postprint (published version)

Data from a variety of research programmes are increasingly used by policy makers, researchers, and private sectors to make data-driven decisions related to climate change and variability. Climate services are emerging as the link to narrow the gap between climate science and downstream users. The Global Framework for Climate Services (GFCS) of the World Meteorological Organization (WMO) offers an umbrella for the development of climate services and has identified the quality assessment, along with its use in user guidance, as a key aspect of the service provision. This offers an extra stimulus for discussing what type of quality information to focus on and how to present it to downstream users. Quality has become an important keyword for those working on data in both the private and public sectors and significant resources are now devoted to quality management of processes and products. Quality management guarantees reliability and usability of the product served, it is a key element to build trust between consumers and suppliers. Untrustworthy data could lead to a negative economic impact at best and a safety hazard at worst. In a progressive commitment to establish this relation of trust, as well as providing sufficient guidance for users, the Copernicus Climate Change Service (C3S) has made significant investments in the development of an Evaluation and Quality Control (EQC) function. This function offers a homogeneous user-driven service for the quality of the C3S Climate Data Store (CDS). Here we focus on the EQC component targeting the assessment of the CDS datasets, which include satellite and in-situ observations, reanalysis, climate projections, and seasonal forecasts. The EQC function is characterised by a two-tier review system designed to guarantee the quality of the dataset information. While the need of assessing the quality of climate data is well recognised, the methodologies, the metrics, the evaluation framework, and how to present all this inform, This study is based on work carried out in the C3S_512 contract funded by Copernicus Programme and operated by ECMWF on behalf of the European Commission (Service Contract number: ECMWF/COPERNICUS720187C3S_512_BSC). We would like to acknowledge the work of colleagues from several European institutions, the data providers and C3S, who contributed to the development of the EQC framework as well as to the QAR production. We would also like to acknowledge the focus group users, who took time to review and provide valuable feedback on the QARs, QATs, minimum requirements and the CDS quality assessment tab. The authors are grateful to the anonymous reviewers for their constructive comments that have helped for the improvement of this paper., Peer Reviewed, "Article signat per 23 autors/es: Carlo Lacagnina , Francisco Doblas-Reyes, Gilles Larnicol, Carlo Buontempo, André Obregón, Montserrat Costa-Surós, Daniel San-Martín, Pierre-Antoine Bretonnière, Suraj D. Polade, Vanya Romanova, Davide Putero, Federico Serva, Alba Llabrés-Brustenga, Antonio Pérez, Davide Cavaliere, Olivier Membrive, Christian Steger, Núria Pérez-Zanón, Paolo Cristofanelli, Fabio Madonna, Marco Rosoldi, Aku Riihelä, Markel García Díez", Postprint (published version)

As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future., LH and AS were supported by the EUCP project funded by the European Commission’s Horizon 2020 programme, Grant Agreement 776613 and supported by the U.K.–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. AS, DS, and MS were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. The EC-Earth simulations at SMHI were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC and NSC and have been performed as part of the NordForsk-funded Nordic Centre of Excellence project (Award 76654) Arctic Climate Predictions: Pathways to Resilient, Sustainable Societies (ARCPATH). SY and TT were supported by the ARCPATH (NordForsk Award 76654) and the Danish state-funded National Centre for Climate Research [Nationalt Center for Klimaforskning (NCFK)]. SY was also partly supported by the EUCP project (Horizon 2020 Grant Agreement 776613). The EC-Earth simulations at BSC were performed using resources from the Red Española de Supercomputación. HP and WM received funding from the German Federal Ministry of Education and Research (BMBF) project MiKlip (FKZ 01LP1519A). Takahito Kataoka and Hiroaki Tatebe were supported by the Integrated Research Program for Advancing Climate Models (TOUGOU) Grant JPMXD0717935457 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. MIROC simulations were performed on the Earth Simulator at JAMSTEC. NK, YW, FC, and IB were supported by the Trond Mohn Foundation (Grant BFS2018TMT01; Bjerknes Climate Prediction Unit)., Peer Reviewed, "Article signat per 45 autors/es: Leon Hermanson, Doug Smith, Melissa Seabrook, Roberto Bilbao, Francisco Doblas-Reyes, Etienne Tourigny, Vladimir Lapin, Viatcheslav V. Kharin, William J. Merryfield, Reinel Sospedra-Alfonso, Panos Athanasiadis, Dario Nicoli, Silvio Gualdi, Nick Dunstone, Rosie Eade, Adam Scaife, Mark Collier, Terence O’Kane, Vassili Kitsios, Paul Sandery, Klaus Pankatz, Barbara Früh, Holger Pohlmann, Wolfgang Müller, Takahito Kataoka, Hiroaki Tatebe, Masayoshi Ishii, Yukiko Imada, Tim Kruschke, Torben Koenigk, Mehdi Pasha Karami, Shuting Yang, Tian Tian, Liping Zhang, Tom Delworth, Xiaosong Yang, Fanrong Zeng, Yiguo Wang, François Counillon, Noel Keenlyside, Ingo Bethke, Judith Lean, Jürg Luterbacher, Rupa Kumar Kolli, and Arun Kumar", Postprint (published version)

The marine dinoflagellate Alexandrium Halim represents perhaps the most significant and intensively studied genus with respect to species diversity, life history strategies, toxigenicity, biogeographical distribution, and global magnitude and consequences harmful algal blooms (HABs). The socioeconomic impacts, environmental and human health risks, and mitigation strategies for toxigenic Alexandrium blooms have also been explored in recent years. Human adaptive actions based on future scenarios of bloom dynamics and shifts in biogeographical distribution under climate-change parameters remain under development and not yet implemented on a regional scale. In the CoCliME (Co-development of climate services for adaptation to changing marine ecosystems) project these issues were addressed with respect to past, current and anticipated future status of key HAB genera and expected benefits of enhanced monitoring. Data on the distribution and frequency of Alexandrium blooms related to paralytic shellfish toxin (PST) events from key CoCliME Case Study areas, comprising the North Sea and adjacent Kattegat-Skagerrak, Norwegian Sea, and Baltic Sea, and eastern North Atlantic marginal seas, were evaluated in a contemporary and historical context over the past several decades. The first evidence of possible biogeographical expansion of Alexandrium taxa into eastern Arctic gateways was provided from DNA barcoding signatures. Various key climate change indicators, such as salinity, temperature, and water-column stratification, relevant to Alexandrium bloom initiation and development were identified. The possible influence of changing variables on bloom dynamics, magnitude, frequency and spatial and temporal distribution were interpreted in the context of regional ocean climate models. These climate change impact indicators may play key roles in selecting for the occurrence and diversity of Alexandrium species within the broader microeukaryote communities. For example, shifts to high

The decadal time scale (∼1–10 years) bridges the gap between seasonal predictions and longer-term climate projections. It is a key planning time scale for users in many sectors as they seek to adapt to our rapidly changing climate. While significant advances in using initialized climate models to make skillful decadal predictions have been made in the last decades, including coordinated international experiments and multimodel forecast exchanges, few user-focused decadal climate services have been developed. Here we highlight the potential of decadal climate services using four case studies from a project led by four institutions that produce real-time decadal climate predictions. Working in co-development with users in agriculture, energy, infrastructure, and insurance sectors, four prototype climate service products were developed. This study describes the challenge of trying to match user needs with the current scientific capability. For example, the use of large ensembles (achieved via a multisystem approach) and skillfully predicted large-scale environmental conditions, are found to improve regional predictions, particularly in midlatitudes. For each climate service, a two-page “product sheet” template was developed that provides users with both a concise probabilistic forecast and information on retrospective performance. We describe the development cycle, where valuable feedback was obtained from a “showcase event” where a wider group of sector users were engaged. We conclude that for society to take full and rapid advantage of useful decadal climate services, easier and more timely access to decadal climate prediction data are required, along with building wider community expertise in their use., This study received support from the C3S_34c contract (ECMWF/COPERNICUS/2019/C3S_34c_DWD) of the Copernicus Climate Change Service (C3S) operated by ECMWF. DS, AS, and HT were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. AP, KP, and BF were funded by the Deutscher Wetterdienst., Peer Reviewed, "Article signat per 22 autors/es: Nick Dunstone, Julia Lockwood, Balakrishnan Solaraju-Murali, Katja Reinhardt, Eirini E. Tsartsali, Panos J. Athanasiadis, Alessio Bellucci, Anca Brookshaw, Louis-Philippe Caron, Francisco J. Doblas-Reyes, Barbara Früh, Nube González-Reviriego, Silvio Gualdi, Leon Hermanson, Stefano Materia, Andria Nicodemou, Dario Nicolì, Klaus Pankatz, Andreas Paxian, Adam Scaife, Doug Smith, and Hazel E. Thornton", Postprint (published version)

Given that climate variability and change present unprecedented challenges to the rail sector, efforts to produce relevant climate data/information for climate risk management and adaptive decision making in the rail sector are gaining traction. However, inadequate understanding of climate change impact and information needs raises several concerns for the sector. This paper addressed the question: What climate risk information services are needed to support the adaptation needs of the rail sector? Data from interviews, literature reviews, and workshops were used. The results show that changes in precipitation, temperature, sea-level rise, and thunderstorms are the top drivers of climate risk in the sector. Additionally, the need for tailor-made climate information to manage these changes is in high demand. Although insufficient, rail organizations use special protocols to manage climate risk. Understudied countries have operational and design standards formulated in metrics and codes related to specific critical weather conditions as part of their Natural Hazard Management process. However, desirable adjustments in the standards are currently based on past events rather than future climate conditions. Future climate change information is relevant for medium- to longer-term decisions, strategy, and policymaking. For operational and design standards, weather and climate information provided by national weather service agencies are used but they also refer to the European standards and databases. National level data/information is preferred for developing thresholds for standards yet pan-European level information is also relevant in filling in missing data gaps. Therefore, rail organizations operate on flexibility and a “use of best available data” policy. Understanding how climate information is used to support decision-making in the rail sector is by no means an easy task given the variety of decisions to be taken at different spatial and temporal scales. However

Achievement of the United Nations Sustainable Development Goals (SDGs) is contingent on understanding the potential interactions among human and natural systems. In Kenya, the goal of conserving and expanding forest cover to achieve SDG 15 “Life on Land” may be related to other SDGs because it plays a role in regulating some aspects of Kenyan precipitation. We present a 40-yr analysis of the sources of precipitation in Kenya and the fate of the evaporation that arises from within Kenya. Using MERRA-2 climate reanalysis and the Water Accounting Model 2 layers, we examine the annual and seasonal changes in moisture sources and sinks. We find that most of Kenya’s precipitation originates as oceanic evaporation but that 10% of its precipitation originates as evaporation within Kenya. This internal recycling is concentrated in the mountainous and forested Kenyan highlands, with some locations recycling more than 15% of evaporation to Kenyan precipitation. We also find that 75% of Kenyan evaporation falls as precipitation elsewhere over land, including 10% in Kenya, 25% in the Democratic Republic of the Congo, and around 5% falling in Tanzania and Uganda. Further, we find a positive relationship between increasing rates of moisture recycling and fractional forest cover within Kenya. By beginning to understand both the seasonal and biophysical interactions taking place, we may begin to understand the types of leverage points that exist for integrated atmospheric water cycle management. These findings have broader implications for disentangling environmental management and conservation and have relevance for large-scale discussions about sustainable development., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

If climate services are to lead to effective use of climate information in decision-making to enable the transition to a climate-smart, climate-ready world, then the question of trust in the products and services is of paramount importance. The Copernicus Climate Change Service (C3S) has been actively grappling with how to build such trust: provision of demonstrably independent assessments of the quality of products, which was deemed an important element in such trust-building processes. C3S provides access to essential climate variables (ECVs) from multiple sources to a broad set of users ranging from scientists to private companies and decision-makers. Here we outline the approach ­undertaken to coherently assess the quality of a suite of observation- and reanalysis-based ECV products covering the atmosphere, ocean, land, and cryosphere. The assessment is based on four pillars: basic data checks, maturity of the datasets, fitness for purpose (scientific use cases and climate studies), and guidance to users. It is undertaken independently by scientific experts and presented alongside the datasets in a fully traceable, replicable, and transparent manner. The methodology deployed is detailed, and example assessments are given. These independent scientific quality assessments are intended to guide users to ensure they use tools and datasets that are fit for purpose to answer their specific needs rather than simply use the first product they alight on. This is the first such effort to develop and apply an assessment framework consistently to all ECVs. Lessons learned and future perspectives are outlined to potentially improve future assessment activities and thus climate services.

The collaboration between the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth System Grid Federation (ESGF) provides open access to an unprecedented ensemble of regional climate model (RCM) simulations, across the 14 CORDEX continental-scale domains, with global coverage. These simulations have been used as a new line of evidence to assess regional climate projections in the latest contribution of the Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6), particularly in the regional chapters and the Atlas. Here, we present the work done in the framework of the Copernicus Climate Change Service (C3S) to assemble a consistent worldwide CORDEX grand ensemble, aligned with the deadlines and activities of IPCC AR6. This work addressed the uneven and heterogeneous availability of CORDEX ESGF data by supporting publication in CORDEX domains with few archived simulations and performing quality control. It also addressed the lack of comprehensive documentation by compiling information from all contributing regional models, allowing for an informed use of data. In addition to presenting the worldwide CORDEX dataset, we assess here its consistency for precipitation and temperature by comparing climate change signals in regions with overlapping CORDEX domains, obtaining overall coincident regional climate change signals. The C3S CORDEX dataset has been used for the assessment of regional climate change in the IPCC AR6 (and for the interactive Atlas) and is available through the Copernicus Climate Data Store (CDS).

The forecast quality of five seasonal prediction strategies used to obtain tailored bioclimatic indicators in the olive sector has been assessed over the Iberian Peninsula (IP). In total, five indicators have been selected considering their importance in the management of the olive orchard. As time progresses through the indicator target period, the impact of the increasing share of actual observations included in its computation has been evaluated by examining the variabilities of correlation and fair Rank Probability Skill Score (fair RPSS) in each initialization date. The results show that blending either seasonal predictions or climatology with observations enhanced the capability of forecasting the tercile category for all the indicators when compared with the use of climatology or seasonal predictions alone. In fact, for Spring Temperature Maximum (SPRTX) and Growing Season Temperature (GST) indicators, the combination of observations and SEAS5 prediction could outperform the other methods for most of the start months. As for those threshold-defined indicators, namely Spring Heat Days (SPR32) and Summer Heat Stress Days (SU36 and SU40), the end-users are highly encouraged to use climatology in the first month and combine it with observations as soon as they become available.

The joint WWRP–WCRP Subseasonal to Seasonal Prediction Project (e.g., Robertson et al. 2014) created a global repository of experimental or operational near-real-time S2S forecasts and reforecasts (hindcasts) from 11 international meteorological institutions, cohosted by ECMWF and CMA (Vitart et al. 2017). These data are publicly accessible by researchers and users (https://apps.ecmwf.int/datasets/data/s2s and http://s2s.cma.cn/index). With the exception of the fourth case study, which uses GloSea5 forecasts (MacLachlan et al. 2015), all case studies use selected S2S forecasts and reforecasts that are available from this repository, providing a consistent basis for S2S forecast skill assessment and evaluation of their utility., The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale., DD gratefully acknowledges support from the Swiss National Science Foundation through project PP00P2_170523. For case study 1, ACP and WTKH were funded by the U.K. Climate Resilience Programme, supported by the UKRI Strategic Priorities Fund. RWL was funded by NERC Grant NE/P00678/1 and by the BER DOE Office of Science Federal Award DE-SC0020324. TS was funded by NERC Independent Research Fellowship (NE/P018637/1). CMG and DB were funded by the Helmholtz Young Investigator Group “SPREADOUT” Grant VH-NG-1243. Case study 2 was supported by the U.K. Global Challenges Research Fund NE/P021077/1 (GCRF African SWIFT) and the Tertiary Education Trust Fund (TETFUND) of Nigeria TETFund/DR&D/CE/NRF/STI/73/VOL.1. EO thanks Adrian Tomkins of ICTP, Italy, for his contribution. Case study 3 was undertaken as part of the Columbia World Project, ACToday, Columbia University (https://iri.columbia.edu/actoday/). Case study 4 was supported by the ForPAc (Towards Forecast-based Preparedness Action) project within the NERC/FCDO SHEAR Programme NE/P000428/1, NE/P000673/1, and NE/P000568/1. Case study 5 was undertaken as part of the International Research Applications Project, funded by the U.S. National Oceanic and Atmospheric Administration. EO thanks IRAP project colleagues at The University of Arizona, Indian Meteorological Department, Regional Integrated Multi-Hazard Early Warning System for Africa and Asia, and two of Bihar’s State Agricultural Universities for their contributions. For case study 6, CASC thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico Process 305206/2019-2 and Fundação de Amparo à Pesquisa do Estado de São Paulo Process 2015/50687-8 (CLIMAX Project) for their support. For case study 7, DW’s contributions were carried out under contract with the National Aeronautics and Space Administration. Case study 8 was funded by the EU Horizon 2020 Research and Innovation Programme Grant 7767874 (S2S4E). We also acknowledge the Subseasonal-to-Seasonal Proje, Peer Reviewed, "Article signat per 60 autors/es: Christopher J. White, Daniela I. V. Domeisen, Nachiketa Acharya, Elijah A. Adefisan, Michael L. Anderson, Stella Aura, Ahmed A. Balogun, Douglas Bertram, Sonia Bluhm, David J. Brayshaw, Jethro Browell, Dominik Büeler, Andrew Charlton-Perez, Xandre Chourio, Isadora Christel, Caio A. S. Coelho, Michael J. DeFlorio, Luca Delle Monache, Francesca Di Giuseppe, Ana María García-Solórzano, Peter B. Gibson, Lisa Goddard, Carmen González Romero, Richard J. Graham, Robert M. Graham, Christian M. Grams, Alan Halford, W. T. Katty Huang, Kjeld Jensen, Mary Kilavi, Kamoru A. Lawal, Robert W. Lee, David MacLeod, Andrea Manrique-Suñén, Eduardo S. P. R. Martins, Carolyn J. Maxwell, William J. Merryfield, Ángel G. Muñoz, Eniola Olaniyan, George Otieno, John A. Oyedepo, Lluís Palma, Ilias G. Pechlivanidis, Diego Pons, F. Martin Ralph, Dirceu S. Reis Jr., Tomas A. Remenyi, James S. Risbey, Donald J. C. Robertson, Andrew W. Robertson, Stefan Smith, Albert Soret, Ting Sun, Martin C. Todd, Carly R. Tozer, Francisco C. Vasconcelos Jr., Ilaria Vigo, Duane E. Waliser, Fredrik Wetterhall, and Robert G. Wilson", Postprint (author's final draft)

Recent research has highlighted that adaptation tends to focus exclusively on the local and direct impacts of climate change and misses the crucial dimension of transboundary climate risk, which all countries are likely to face, irrespective of their level of development. This paper aims to improve the coverage of transboundary climate risk in case-study research for adaptation. It proposes a protocol to help researchers identify how their case studies can incorporate an analysis of transboundary climate risk, thereby supporting more holistic, effective, and just approaches to adaptation. Existing climate risk assessment frameworks and supporting guidelines have significant strengths but also various challenges when applied to the novel context of transboundary climate risk. This is illustrated with reference to the impact chain framework. Its opportunities pertain to both its flexible form and systems-first focus while its constraints include an analytic emphasis on linear cause–effect relationships (that bely the complexity and uncertainty of systemic risk) and its limited applicability to fragmented governance landscapes (in the absence of an effective consideration of risk ownership). After critically examining the suitability of the impact chain framework, a new protocol is introduced, which builds on principles for managing complex risk and frameworks for assessing risk ownership. The protocol is designed to enable case-study researchers to better identify, assess, and appraise transboundary climate risks, as well as enquire into appropriate risk owners and adaptation options across scales. The paper argues for more innovation in adaptation research to better reflect the complexity and interdependency that characterize today’s world.

In the ice-infested Arctic Ocean environment, the uptake of new sea ice services is an important factor in ensuring safe and efficient marine operations. Producers increasingly turn to co-production for user input, similar to the wider field of climate services. This paper asks how the uptake of sea ice information services can be optimized, by gauging the extent to which producers and users already share an understanding of how trust develops toward new products. By adopting a consensus analysis approach, we gain insights about how to balance further investments in knowledge co-production versus change implementation. We chose cultural consensus analysis, a method that produces valid estimates even in small sample sizes. Our survey presented thirty-two propositions based on seven dimensions of trust in weather, water, ice and climate services. The survey was completed by fifty-seven respondents (n = 29 users, n = 28 producers) and revealed a strong consensus model among the two groups about the necessary improvements needed to increase users’ trust in new services. Our results suggest that forecast producers for the Arctic region, specifically in the field of specialized sea ice predictions and mapping/charting, share a substantial understanding with users about how trust develops toward new products. We discuss the importance of automation, peer endorsement and perceptions of cost-performance ratio for necessary strategic approaches to help experienced forecast users to trust and adapt products to their specific operational context, and reflect on the costs associated with the use of specialized sea ice services in closing the usability gap.

Climate variability affects crop production in multiple and often complex ways. The development and use hybrid crops with greater productivity and tolerance to climate shocks is one of the approaches to climate adaptation and agricultural intensification. Since hybrid crops are more expensive for the producer, risk management is of paramount importance. Here, we pose that there is high potential for the Colombian maize sector to use crop-specific climate services for risk reduction. We used the CERES-Maize crop model connected to seasonal climate forecasts developed via Canonical Correlation Analysis (CCA) across key maize growing areas in Colombia to assess the performance of a maize-specific agroclimatic forecast to inform two key decisions, namely, the choice of sowing dates and genotypes. We find that the agroclimatic models perform well at discriminating yield categories (above, below, and normal) with discrimination capacity of up to 70–80 % for the ‘below normal’ and ‘above + below normal’ categories. Consistent with this, agroclimatic forecasts typically predict the optimal planting date with an error of 3 pentads or less. They also predict the optimal choice of genotype correctly around 50–70 % of the time depending on the site or season of interest. Notably, we identify specific cases in which the agroclimatic forecast is misleading but argue that the overall value of the forecasts outweighs these cases. Future work should focus on expanding the scope of the agroclimatic prediction to include other relevant farming decisions that are influenced by climate, and on the improvement of climate forecast performance.

Climate information plays a foundational role in achieving a green recovery and climate neutrality in Europe, and a central one for a climate resilient Europe. This role can materialize if climate information is delivered appropriately and used effectively. Climate services, understood as the provision of climate information for use in decision making, have been created to provide climate information addressing these aspects. The utility of climate services is determined by the level of user engagement and co-design, employed during their development, while resource limitations for any of these aspects constrain their full potential. Co-design together with users is increasingly seen as a necessary good practice approach to provide efficient services that bring together supply and demand. In this paper, we focus on how climate services can contribute to climate neutrality by considering the cultural dimension of the users and their regions for whom specific climate services are co-designed. We specifically address dimensions of vulnerability and resilience to changing climatic conditions in five case studies worldwide while analysing the influence of culture on risk coping and enabling mechanism of key stakeholders and their needs for specific climate services in these regions. We found that user needs, desires and actions hinge on value prepositions formed by specific socio-cultural, climatic, spatial and bio-ecological contexts. Hence, when co-designing climate services, it is vital to understand users’ needs, based on their values and experiences with climate and weather and to seek ways to influence, alter and change them.

Roundtable on Compound Hazards and COVID-19 What: An online panel with leading experts in compound hazard research, preparedness, and response, attended by over 80 online participants, met to discuss hazard response in the context of COVID-19. When: 30 June 2021 Where: Online, convened by the World Meteorological Organization and hosted by the American Geophysical Union, Peer Reviewed, "Article signat per 12 autors/es: Benjamin F. Zaitchik, Judy Omumbo, Rachel Lowe, Maarten van Aalst, Liana O. Anderson, Erich Fischer, Charlotte Norman, Joanne Robbins, Rosa Barciela, Juli Trtanj, Rosa von Borries, and Jürg Luterbacher", Postprint (published version)

© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy)., Decadal climate predictions are a relatively new source of climate information for inter-annual to decadal time scales, which is of increasing interest for users. Forecast quality assessment is essential to identify windows of opportunity (e.g., variables, regions, and forecast periods) with skill that can be used to develop climate services to inform users in several sectors and define benchmarks for improvements in forecast systems. This work evaluates the quality of multi-model forecasts of near-surface air temperature, precipitation, Atlantic multi-decadal variability index (AMV) and global near-surface air temperature anomalies (GSAT) generated from all the available retrospective decadal predictions contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). The predictions generally show high skill in predicting temperature, AMV, and GSAT, while the skill is more limited for precipitation. Different approaches for generating a multi-model forecast are compared, finding small differences between them. The multi-model ensemble is also compared to the individual forecast systems. The best system usually provides the highest skill. However, the multi-model ensemble is a reasonable choice for not having to select the best system for each particular variable, forecast period and region. Furthermore, the decadal predictions are compared to the historical simulations to estimate the impact of initialization. An added value is found for several ocean and land regions for temperature, AMV, and GSAT, while it is more reduced for precipitation. Moreover, the full ensemble is compared to a sub-ensemble to measure the impact of the ensemble size. Finally, the implications of these results in a climate services context, which requires predictions issued in near real-time, are discussed., This study has been performed in the framework of the C3S_34c contract (ECMWF/ COPERNICUS/2019/C3S_34c_DWD) of the Copernicus Climate Change Service (C3S) operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the European Commission H2020 EUCP project (Grant 776613). CDT thanks the funding by the Spanish Ministry for Science and Innovation (FPI PRE2019-088646). MGD is grateful for funding by the Spanish Ministry for the Economy, Industry and Competitiveness grant reference RYC-2017-22964. BSM acknowledges financial support from the Marie Sklodowska-Curie fellowship (Grant 713673) and from a fellowship of La Caixa Foundation (ID 100010434)., Peer Reviewed, Postprint (published version)

Droughts and changing rainfall patterns due to natural climate variability and climate change, threaten the livelihoods of Malawi's smallholder farmers, who constitute 80% of the population. Provision of seasonal climate forecasts (SCFs) is one means to potentially increase the resilience of rainfed farming to drought by informing farmers in their agricultural decisions. Local knowledge can play an important role in improving the value of SCFs, by making the forecast better-suited to the local environment and decision-making. This study explores whether the contextual relevance of the information provided in SCFs can be improved through the integration of farmers’ local knowledge in three districts in central and southern Malawi. A forecast threshold model is established that uses meteorological indicators before the rainy season as predictors of dry conditions during that season. Local knowledge informs our selection of the meteorological indicators as potential predictors. Verification of forecasts made with this model shows that meteorological indicators based on local knowledge have a predictive value for forecasting dry conditions in the rainy season. The forecast skill differs per location, with increased skill in the Southern Highlands climate zone. In addition, the local knowledge indicators show increased predictive value in forecasting locally relevant dry conditions, in comparison to the currently-used El Niño-Southern Oscillation (ENSO) indicators. We argue that the inclusion of local knowledge in the current drought information system of Malawi may improve the SCFs for farmers. We show that it is possible to capture local knowledge using observed station and climate reanalysis data. Our approach could benefit National Meteorological and Hydrological Services in the development of relevant climate services and support drought-risk reduction by humanitarian actors., Water Resources, Transport and Logistics, System Engineering