Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information

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

CSTools: a new R package for the calibration, combination, downscaling and analysis of seasonal forecasts

Póster presentado en: EGU General Assembly 2019 celebrada del 7 al 12 de abril en Viena, Austria.MEDSCOPE is co-funded by the H2020 ERA-Net ERA4CS European Research Area for Climate Services (Grant 690462)

Climate Scenarios for Switzerland CH2018 – Approach and Implications

To make sound decisions in the face of climate change, government agencies, policymakers and private stakeholders require suitable climate information on local to regional scales. In Switzerland, the development of climate change scenarios is strongly linked to the climate adaptation strategy of the Confederation. The current climate scenarios for Switzerland CH2018 - released in form of six user-oriented products - were the result of an intensive collaboration between academia and administration under the umbrella of the National Centre for Climate Services (NCCS), accounting for user needs and stakeholder dialogues from the beginning. A rigorous scientific concept ensured consistency throughout the various analysis steps of the EURO-CORDEX projections and a common procedure on how to extract robust results and deal with associated uncertainties. The main results show that Switzerland’s climate will face dry summers, heavy precipitation, more hot days and snow-scarce winters. Approximately half of these changes could be alleviated by mid-century through strong global mitigation efforts. A comprehensive communication concept ensured that the results were rolled out and distilled in specific user-oriented communication measures to increase their uptake and to make them actionable. A narrative approach with four fictitious persons was used to communicate the key messages to the general public. Three years after the release, the climate scenarios have proven to be an indispensable information basis for users in climate adaptation and for downstream applications. Potential for extensions and updates has been identified since then and will shape the concept and planning of the next scenario generation in Switzerland

Climate scenarios for Switzerland CH2018 - approach and implications

To make sound decisions in the face of climate change, government agencies, policymakers and private stakeholders require suitable climate information on local to regional scales. In Switzerland, the development of climate change scenarios is strongly linked to the climate adaptation strategy of the Confederation. The current climate scenarios for Switzerland CH2018 - released in form of six user-oriented products - were the result of an intensive collaboration between academia and administration under the umbrella of the National Centre for Climate Services (NCCS), accounting for user needs and stakeholder dialogues from the beginning. A rigorous scientific concept ensured consistency throughout the various analysis steps of the EURO-CORDEX projections and a common procedure on how to extract robust results and deal with associated uncertainties. The main results show that Switzerland?s climate will face dry summers, heavy precipitation, more hot days and snow-scarce winters. Approximately half of these changes could be alleviated by mid-century through strong global mitigation efforts. A comprehensive communication concept ensured that the results were rolled out and distilled in specific user-oriented communication measures to increase their uptake and to make them actionable. A narrative approach with four fictitious persons was used to communicate the key messages to the general public. Three years after the release, the climate scenarios have proven to be an indispensable information basis for users in climate adaptation and for downstream applications. Potential for extensions and updates has been identified since then and will shape the concept and planning of the next scenario generation in Switzerland

Statistical and stochastic post-processing of regional climate model data: copula-based downscaling, disaggregation and multivariate bias correction

In order to delineate management or climate change adaptation strategies for natural or technical water bodies, impact studies are necessary. To this end, impact models are set up for a given region which requires time series of meteorological data as driving data. Regional climate models (RCMs) are capable of simulating gridded data sets of several meteorological variables. The advantages over observed data are that the time series are complete and that meteorological information is also provided for ungauged locations. Furthermore, climate change impact studies can be conducted by driving the simulations with different forcing variables for future periods. While the performance of RCMs generally increases with a higher spatio-temporal resolution, the computational and storage demand increases non-linearly which can impede such highly resolved simulations in practice. Furthermore, systematic biases of the univariate distributions and multivariate dependence structures are a common problem of RCM simulations on all spatio-temporal scales. Depending on the case study, meteorological data must fulfill different criteria. For instance, the spatio-temporal resolution of precipitation time series should be as fine as 1 km and 5 minutes in order to be used for urban hydrological impact models. To bridge the gap between the demands of impact modelers and available meteorological RCM data, different computationally efficient statistical and stochastic post-processing techniques have been developed to correct the bias and to increase the spatio-temporal resolution. The main meteorological variable treated in this thesis is precipitation due to its importance for hydrological impact studies. The models presented in this thesis belong to the classes of bias correction, downscaling and temporal disaggregation techniques. The focus of the developed methods lies on multivariate copulas. Copulas constitute a promising modeling approach for highly-skewed and mixed discrete-continuous variables like precipitation since the marginal distribution is treated separately from the dependence structure. This feature makes them useful for the modeling of different meteorological variables as well. While copulas have been utilized in the past to model precipitation and other meteorological variables that are relevant in hydrology, applications to RCM simulations are not very common. The first method is a geostatistical estimation technique for distribution parameters of daily precipitation for ungauged locations, so that a bias correction with Quantile Mapping can be performed. The second method is a spatial downscaling of coarse scale RCM precipitation fields to a finer resolved domain. The model is based on the Gaussian Copula and generates ensembles of daily precipitation fields that resemble the precipitation fields of fine scale RCM simulations. The third method disaggregates hourly precipitation time series simulated by an RCM to a resolution of 5 minutes. The Gaussian Copula was utilized to condition the simulation on both spatial and temporal precipitation amounts to respect the spatio-temporal dependence structure. The fourth method is an approach to simulate a meteorological variable conditional on other variables at the same location and time step. The method was developed to improve the inter-variable dependence structure of univariately bias corrected RCM simulations in an hourly resolution

Decadal climate prediction and predictability for climate services

[eng] Climate variations at annual to decadal time scales affect many regions around the globe, causing direct impacts on the economy, ecosystems and society. Knowing these variations in advance allows for implementing measures to adapt, mitigate and build resilience to the consequences of a changing climate. At decadal time scale, climate variations are caused by both external forcings and internal climate variability. While climate projections incorporate external forcing information based on different socio-economic scenarios to project possible pathways the climate system would follow, decadal predictions also incorporate information on the current climate state through a model initialisation procedure. Forecast quality assessment, which involves comparing hindcasts (retrospective predictions) to past observations to evaluate their degree of agreement, is an essential step to ensure that such predictions are trustable and beneficial for decision-making. Climate hindcasts also allow for applying post-processing techniques such as calibration and downscaling methods, as well as for selecting the best climate information for each specific variable, region and forecast period. This Ph.D. thesis has focused on evaluating the forecast quality of variables and indicators relevant for decision-making in several sectors, with a particular focus on agriculture. The evaluation has been performed globally, for individual models and multi-model ensemble, and different forecast periods to identify windows of opportunity for which the climate predictions show skill to support decision-making. Furthermore, historical forcing simulations (retrospective climate projections) have been used to estimate the impact of initialisation. First, the quality of multi-model forecasts of temperature, precipitation, the Atlantic Multidecadal Variability index (AMV) and Global near-Surface Air Temperature (GSAT) generated from all decadal hindcasts contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) were evaluated, finding high skill for predictions of temperature, AMV and GSAT, and limited skill for predictions of precipitation. The multi-model ensemble was also compared to individual forecast systems, finding that the best system for each particular location usually outperforms the multi-model ensemble. However, the multi-model provides higher skill than at least half of the systems. The decadal predictions were also compared to the historical simulations, finding an added value from model initialisation over several ocean and land regions for temperature, and for AMV and GSAT. The full multi-model was compared to a sub-ensemble of predictions generated from forecast systems that provided timely forecasts to assess the impact of the ensemble size in an operational climate services context. Second, the representation and prediction of the Euro-Atlantic weather regimes by the EC-Earth3 model were assessed by identifying the dominating atmospheric circulation patterns in this region. The Euro-Atlantic 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. The EC-Earth3 correctly represents the spatial patterns and climatological frequencies of all weather regimes. However, the skill in predicting the annual to decadal variations of the weather regimes’ frequency of occurrence is low, and the model initialisation does not improve such prediction skill. Third, the multi-model forecast quality of the CMIP6 decadal hindcasts is evaluated for multi-annual predictions of a set of indices related to the frequency and intensity of daily temperature and precipitation extremes. The multi-model ensemble is skillful in predicting temperature extremes over most land regions, while the quality is lower for precipitation extremes. Comparing the skill with that for mean temperature and precipitation, extremes are predicted with lower skill, especially those related to the most extreme days. Compared to the historical forcing simulations, decadal predictions show only small and region-dependent skill improvements from model initialisation. Finally, this Ph.D. thesis presents the applications of the research within several European projects and a contract with a private company for which prototypes of climate services have been created. For instance, prototypes of forecast products have been developed for the Southern African Development Community region. These prototypes consist of annual[spa] Las variaciones climáticas de uno a diez años impactan a la economía, ecosistemas y sociedad. Anticipar dichas variaciones permite implementar medidas de adaptación y mitigación a consecuencias de un clima variable. La variabilidad decadal está causada tanto por forzamientos externos como por variabilidad interna. Los modelos climáticos permiten estudiar la dinámica y anticipar las variaciones. Mientras que las proyecciones climáticas incorporan forzamientos externos basados en distintos escenarios socioeconómicos, las predicciones decadales también incorporan información del estado actual del sistema climático a través del proceso de inicialización del modelo. Esta tesis doctoral se centra en evaluar la calidad de las predicciones comparándolas con observaciones del pasado con el objetivo de asegurar que son útiles y beneficiosas para la toma de decisiones. Esta evaluación permite identificar variables, regiones y periodos en los cuales la calidad de la información climática se puede utilizar para llevar a cabo decisiones. También, se han aplicado técnicas de postprocesado (calibración, multi-modelo y regionalización) con el fin de mejorar las predicciones y adaptarlas a las necesidades de los usuarios. Las predicciones se han comparado con las simulaciones de forzamiento histórico con el fin de estimar el impacto de la inicialización del modelo en la calidad de las predicciones. La tesis consta de tres publicaciones científicas en peer-reviewed revistas. El primer estudio se centra en la evaluación de predicciones multi-modelo para temperatura, precipitación, Atlantic Multidecadal Variability index (AMV) y temperatura global. También se estima el beneficio del uso del multi-modelo en lugar de modelos individuales, impacto de la inicialización, impacto de calibración, e impacto del número de modelos. El segundo estudio evalúa la representación espacial y predictibilidad temporal de tipos de tiempo europeos (por ejemplo, el bloqueo). El tercer estudio se enfoca a la predicción de extremos climáticos de temperatura y precipitación dada su importancia en la sociedad y sectores vulnerables a las variaciones climáticas. Finalmente, se muestran ejemplos de aplicaciones de la investigación llevada a cabo junto con prototipos de servicios climáticos, con particular enfoque a la agricultura

The CSTools (v4.0) toolbox : from climate forecasts to climate forecast information

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 skilful climate information. This barrier is addressed through the development of an R package. 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 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 manuscript: 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 data to be used as input for the SCHEME hydrological model

Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information

International audienceAbstract. 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