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Hot, cold and dry meteorological extremes are often linked with severe impacts on the public health, agricultural, energy and environmental sectors. Skillful predictions of such extremes could therefore enable stakeholders to better plan and adapt to future impacts of these events. The intensity, duration and frequency of such extremes are affected by anthropogenic climate change and modulated by different modes of climate variability. Here we use a large multi-model ensemble from the Coupled Model Intercomparison Project Phase 6 and constrain these simulations by sub-selecting those members whose global SST anomaly patterns are most similar to observations at a given point in time, thereby phasing in the decadal climate variability with observations. Hot and cold extremes are skillfully predicted over most of the globe, with also a widespread added value from using the constrained ensemble compared to the unconstrained full CMIP6 ensemble. On the other hand, dry extremes show skill only in some regions with results sensitive to the index used. Still, we find skillful predictions and added skill for dry extremes in some regions such as western north America, southern central and eastern Europe, southeastern Australia, southern Africa and the Arabian peninsula. We also find that the added skill in the constrained ensemble is due to a combination of improved multi-decadal variations in phase with observed climate extremes and improved representation of long-term changes. Our results demonstrate that constraining decadal variability in climate projections can provide improved estimates of temperature extremes and drought in the next twenty years, which can inform targeted adaptation strategies to near-term climate change., This research has been partly supported by the Horizon2020 LANDMARC project (grant agreement No. 869367) and the Horizon Europe ASPECT project (grant number 101081460). PDL has received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement No 101059659. CDT acknowledges financial support from the Spanish Ministry for Science and Innovation (FPI PRE2019-509 08864 financed by MCIN/AEI/10.13039/501100011033 and by FSE invierte en tu futuro). MGD is grateful for support by the AXA Research Fund. The authors are further grateful for the support by the Department of Research and Universities of the Government of Catalonia to the Climate Variability and Change Research Group (Code: 2021 SGR 00786)., Peer Reviewed, Postprint (author's final draft)

Future precipitation changes are typically estimated from climate model simulations, while the credibility of such projections needs to be assessed by their ability to capture observed precipitation changes. Here we evaluate how skillfully historical climate simulations contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) capture observed changes in mean and extreme precipitation. We find that CMIP6 historical simulations skillfully represent observed precipitation changes over large parts of Europe, Asia, northeastern North America, parts of South America and western Australia, whereas a lack of skill is apparent in western North America and parts of Africa. In particular in regions with moderate skill the availability of very large ensembles can be beneficial to improve the simulation accuracy. CMIP6 simulations are regionally skillful where they capture observed (positive or negative) trends, whereas a lack of skill is found in regions characterized by negative observed precipitation trends where CMIP6 simulates increases., We are grateful for support by the Departament de Recerca i Universitats de la Generalitat de Catalunya for the Climate Variability and Change (CVC) Research Group (Reference: 2021 SGR 00786), and research funding by the Horizon 2020 LANDMARC project (grant agreement no. 869367), the Horizon Europe ASPECT project (Grant 101081460), and the AXA Research Fund. CDT acknowledges financial support from the Spanish Ministry for Science and Innovation (FPI PRE2019–509 08864 financed by MCIN/AEI/http://doi.org/10.13039/501100011033). PDL received funding from the Horizon Europe Research and Innovation Programme, Grant 101059659. We thank the climate modeling groups contributing to CMIP6 for producing and making available their model output. We are grateful to Margarida Samsó and Pierre–Antoine Bretonnière for downloading, formatting and managing the large data sets of climate simulations and observations used in this study., Peer Reviewed, Postprint (published version)

Downscaling is any procedure to infer highresolution information from low-resolution variables. Many of these techniques have been defined and applied to climate predictions, which suffer from important biases due to the coarse global grids in which they are delivered. To help solve this undesirable issue, the R package resulting from this work provides a set of statistical downscaling methods for climate predictions, ready to be applied to refine the output of climate predictions.

Weather regimes are large-scale atmospheric circulation states that frequently occur in the climate system with persistence and recurrence, and are associated with the occurrence of specific local weather conditions. This study evaluates the representation of the four Euro-Atlantic weather regimes in uninitialized historical forcing simulations and initialized decadal predictions performed with the EC-Earth3 coupled climate model. The four weather regimes are the positive and negative phases of the North Atlantic Oscillation (NAO+ and NAO−, respectively), Blocking, and Atlantic Ridge in winter; and the NAO−, Blocking, Atlantic Ridge, and Atlantic Low in summer. We also analyze the impact that the model initialization toward the observed state of the climate system has on the ability to predict the variability of the weather regimes' seasonal frequency of occurrence. We find that the EC-Earth3 model correctly reproduces the spatial patterns and climatological occurrence frequencies of the four weather regimes. By contrast, the skill in predicting the inter-annual to decadal variations of the weather regimes' seasonal frequencies is generally low, and the initialization does not significantly improve such skill. The observed teleconnections between the weather regimes and the North Atlantic sea surface temperatures are generally not reproduced by the model, which could be a reason for the low skill in predicting the temporal variations of the weather regime frequencies., This research has received support by the AXA Research Fund, the CLINSA project (CGL2017-85791-R), and the EUCP project (Horizon 2020 Grant 776613). CDT acknowledges financial support from the Spanish Ministry for Science and Innovation (FPI PRE2019-08864 financed by MCIN/ AEI/10.13039/501100011033 and by FSE invierte en tu futuro). EH was supported by the Spanish Project PRE4CAST (grant CGL2017-86415-R). MGD has also beensupported by the Spanish Ministry for the Economy, Industry and Competitiveness (grant RYC-2017-22964)., Peer Reviewed, 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)

Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skillful climate information. This barrier is addressed through the development of an R package. Climate Services Toolbox (CSTools) is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi-annual scales. The package contains process-based, state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the modular design of the toolbox in individual functions, the users can develop their own post-processing chain of functions, as shown in the use cases presented in this paper, including the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model, and the post-processing of temperature and precipitation data to be used as input in impact models., This research has been supported by the Horizon 2020 (S2S4E; grant no. 776787), EUCP (grant no. 776613), ERA4CS (grant no. 690462), and the Ministerio de Ciencia e Innovación (grant no. FPI PRE2019-088646)., Peer Reviewed, "Article signat per 19 autors/es: Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté , Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie", Postprint (published version)

The climate forecast analysis tools provide functions implementing the steps required for the analysis of sub-seasonal, seasonal and decadal forecast and operational climate services, allowing researchers to manipulate climate data and apply state-of-the-art methods taking advantage of the high-performance computational resources. Researchers can share their methods while reducing development and maintenance cost. An ecosystem of R packages covering these needs is under continuous development.

La investigación propone la implementación de la Recomendación de la OCDE, de un nivel funcional general a otro nivel que implica la gestión de procesos y la modificación de la estructura organizacional, en el ente rector del sector saneamiento en el Perú, el Ministerio de Vivienda, Construcción y Saneamiento - MVCS, a través de su incorporación en los instrumentos de gestión, acorde con el Sistema Nacional de Planeamiento Estratégico - SINAPLAN, desde el nivel más alto del planeamiento estratégico sectorial e institucional, encaminado a mejorar la gestión de las inversiones en los servicios de saneamiento, que debe traducirse en la creación de la Oficina de Gestión de Inversiones y Evaluación de Impacto, dentro de la estructura organizacional, que tendrá la responsabilidad de gestionar las inversiones en el sector, apoyándose en lecciones aprendidas de las experiencias de los países miembros de la OCDE en materia de inversión pública. Al respecto, se han revisado las acciones realizadas, lo que permite inferir que es viable llevarlas a cabo también en el Perú. Cabe indicar que la propuesta de mejora es aún una proposición, por lo que no es posible hacer referencia a resultados finales y menos aún inferir los impactos. Sin embargo, es posible mencionar algunos resultados previos como el fortalecimiento de las funciones relacionadas a las inversiones y medición de impactos para la mejora continua de los procesos relacionados a inversiones.

En el presente trabajo, se muestran las bases teóricas, del método tradicional flujos descontados y valoración por opciones reales, la aplicación que estos tienen, sus principales ventajas y desventajas, y muestra como el segundo método puede llegar a complementar al primero, en base a una serie de revisiones bibliográficos y el planteamiento de un problema, aplicándolo a un caso real en el sector minero, en el país Colombiano, industria con alta volatilidad y que es considerado como uno de los más importantes, en la economía de este país y en el mundo.