his work presents a framework, WRF4G, to manage the experiment workflow of the Weather Research and Forecasting (WRF) modelling system. WRF4G provides a flexible design, execution and monitoring for a general class of scientific experiments. It has been designed with the aim of facilitating the management and reproducibility of complex experiments. Furthermore, the concepts behind the design of this framework can be straightforwardly extended to other models ; This work has been supported by the Spanish National R&D Plan under projects WRF4G (CGL2011-28864, co-funded by the European Regional Development Fund –ERDF–) and CORWES (CGL2010-22158-C02-01) and the IS-ENES2 project from the 7FP of the European Commission (grant agreement no. 312979). C. Blanco acknowledges financial support 5 from programa de Personal Investigador en Formación Predoctoral from Universidad de Cantabria, co-funded by the regional government of Cantabria. The authors are thankful to the developers of third party software (e.g. GridWay, WRFV3, python and NetCDF), which was intensively used in this work.
Interest in seasonal forecasting is growing fast in many environmental and socio-economic sectors due to the huge potential of these predictions to assist in decision making processes. The practical application of seasonal forecasts, however, is still hampered to some extent by the lack of tools for an effective communication of uncertainty to non-expert end users. visualizeR is aimed to fill this gap, implementing a set of advanced visualization tools for the communication of probabilistic forecasts together with different aspects of forecast quality, by means of perceptual multivariate graphical displays (geographical maps, time series and other graphs). These are illustrated in this work using the example of the strong El Niño 2015/16 event forecast. The package is part of the climate4R bundle providing transparent access to the ECOMS-UDG climate data service. This allows a flexible application of visualizeR to a wide variety of specific seasonal forecasting problems and datasets. ; This work has been funded by the European Union 7th Framework Program [FP7/20072013] under Grant Agreement 308291 (EUPORIAS Project). We are grateful to the EUPORIAS team on Communicating levels of con dence (Work Package 33).
Sectorial applications of seasonal forecasting require data for a reduced number of variables from different datasets, mainly (gridded) observations, reanalysis, and predictions from state-of-the-art seasonal forecast systems (such as NCEP/CFSv2, ECMWF/System4 or UKMO/GloSea5). Whilst this information can be obtained directly from the data providers, the resulting formats, temporal aggregations, and vocabularies may not be homogeneous across datasets. Moreover, different data policies hold for the different databases, being only some of them publicly available. Therefore, obtaining and harmonizing multi-model seasonal forecast data for sector-specific applications is an error-prone, time consuming task. In order to facilitate this, the ECOMS User Data Gateway (ECOMS-UDG) was developed in the framework of the ECOMS initiative as a one-stop-service for climate data. To this aim, the variables required by end users were identified, downloaded from the data providers and locally stored as virtual datasets in a THREDDS Data Server (TDS), implementing fine-grained user management and authorization via the THREDDS Access Portal (TAP). As a result, users can retrieve the subsets best suited to their particular research needs in a user-friendly manner using the standard TDS data services. Moreover, an open source, R-based interface for data access and postprocessing was developed in the form of a bundle of packages implementing harmonized data access (one single vocabulary), data collocation, bias adjustment and downscaling, and forecast visualization and validation. This provides a unique comprehensive framework for end-to-end applications of seasonal predictions, hence favoring the reproducibility of the ECOMS scientific outcomes, extensible to the whole scientific community. ; We thank the European Union's Seventh Framework Program [FP7/2007–2013] under Grant Agreements 308291 (EUPORIAS Project) and 308378 (SPECS Project). This project took advantage of THREDDS Data Server (TDS) software developed by UCAR/Unidata (http://doi.org/10.5065/D6N014KG). We would like to thank the two anonymous reviewers for their suggestions and comments.
The increasing demand for high-resolution climate information has attracted growing attention to statistical downscaling (SDS) methods, due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness for purpose for many local-scale applications. As a result, a plethora of SDS methods is nowadays available to climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE initiative (http://www.value-cost.eu, last access: 29 March 2020). The systematic intercomparison of a large number of SDS techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. In this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (model output statistics ? including the so-called ?bias correction? methods ? and perfect prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al., 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SDS model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in VALUE, placing an emphasis on the most technically complex stages of perfect-prognosis model calibration (predictor screening, cross-validation, and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook. ; We thank the European Union Cooperation in Science and Technology (EU COST) Action ES1102 VALUE (http://www.value-cost.eu) for making publicly available the data used in this article and the tools implementing the comprehensive set of validation measures and indices. We also thank the THREDDS Data Server (TDS) software developed by UCAR/Unidata (https://doi.org/10.5065/D6N014KG, Unidata, 2006) and all R developers and their supporting community for providing free software facilitating open science. We acknowledge the World Climate Research Program's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the EC-EARTH Consortium for producing and making available their model output used in this paper. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We are very grateful to the two anonymous referees participating in the interactive discussion for their insightful comments, helping us to considerably improve the original paper. Financial support. The authors acknowledge partial funding from the MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R) and from the project INDECIS, part of the European Research Area for Climate Services Consortium (ERA4CS) with co-funding by the uropean Union (grant no. 690462).
Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset. We present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages).We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: https://github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), https://doi.org/10.5281/zenodo.3998463 (Iturbide et al., 2020). ; This research has been supported by the Spanish National Plan for Scientific and Technical Research and Innovation (project PID2019-111481RB-I00 and María de Maeztu excellence programme projects MdM-2017-0765 and MdM-2017-0714), FCT MCTES financial support to CESAM (UIDP/50017/2020+UIDB/50017/2020), and the Basque Government BERC 2018–2021 programme
