Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment

Widmann, Martin; Bedia, Joaquín; Gutiérrez, José Manuel; Bosshard, Thomas; Hertig, Elke; Maraun, Douglas; Casado, María Jesús; Ramos, Petra; Cardoso, Rita M.; Soares, Pedro M. M.; Ribalaygua, Jamie; Pagé, Christian; Fischer, Andreas M.; Herrera, Sixto; Huth, Radan

doi:10.1002/joc.6024

Cited by 33 publications

(38 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modelling groups focusing on empirical statistical downscaling (ESD) employ a wide range of approaches (Benestad et al 2017a;Maraun et al 2015Maraun et al , 2018Gutiérrez et al 2018;Hertig et al 2018;Soares et al 2018;Widmann et al 2019). The two approaches to downscaling are seen as complementary within the EURO-CORDEX community, each with its relative strengths.…”

Section: A Brief History Of Euro-cordexmentioning

confidence: 99%

Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community

et al. 2020

Self Cite

View full text Add to dashboard Cite

The European CORDEX (EURO-CORDEX) initiative is a large voluntary effort that seeks to advance regional climate and Earth system science in Europe. As part of the World Climate Research Programme (WCRP)-Coordinated Regional Downscaling Experiment (CORDEX), it shares the broader goals of providing a model evaluation and climate projection framework and improving communication with both the General Circulation Model (GCM) and climate data user communities. EURO-CORDEX oversees the design and coordination of ongoing ensembles of regional climate projections of unprecedented size and resolution (0.11 • EUR-11 and 0.44 • EUR-44 domains). Additionally, the inclusion of empiricalstatistical downscaling allows investigation of much larger multi-model ensembles. These complementary approaches provide a foundation for scientific studies within the climate research community and others. The value of the EURO-CORDEX ensemble is shown via numerous peer-reviewed studies and its use in the development of climate services. Evaluations of the EUR-44 and EUR-11 ensembles also show the benefits of higher resolution. However, significant challenges remain. To further advance scientific understanding, two flagship pilot studies (FPS) were initiated. The first investigates local-regional phenomena at convection-permitting scales over central Europe and the Mediterranean in collaboration with the Med-CORDEX community. The second investigates the impacts of land cover changes on European climate across spatial and temporal scales. Over the coming years, the EURO-CORDEX community looks forward to closer collaboration with other communities, new advances, supporting international initiatives such as the IPCC reports, and continuing to provide the basis for research on regional climate impacts and adaptation in Europe.

show abstract

Section: A Brief History Of Euro-cordexmentioning

confidence: 99%

Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…As general recommendations, a number of aspects need to be carefully addressed when looking for suitable predictors in the PP approach (Wilby et al, 2004;Hanssen-Bauer et al, 2005): (i) the predictors should account for a major part of the variability in the predictands, (ii) the links between predictors and predictands should be temporally stable or stationary, and (iii) the large-scale predictors must be realistically reproduced by the global climate model. Since different global models are used in the calibration and downscaling phases, large-scale circulation variables well represented by the global models are typically chosen as predictors in the PP approach, whereas variables directly influenced by model parametrizations and/or orography (e.g., precipitation) are usually not considered.…”

Section: Sds Model Setup: Configuration Of Predictorsmentioning

confidence: 99%

Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment

et al. 2020

Self Cite

View full text Add to dashboard Cite

Abstract. 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.

show abstract

“…niques (Zorita and von Storch, 1999), being able to take into account the non-linearity of the relationships between predictors and predictands. Additionally, it is spatially coherent by construction, preserving the spatial covariance structure of the local predictands (Widmann et al, 2019). Hence, analog-based methods have been applied in several studies both in the context of climate change (see, e.g., Gutiérrez et al, 2013) and seasonal forecasting (Manzanas et al, 2017).…”

Section: Analogsmentioning

confidence: 99%

Statistical downscaling with the downscaleR package: Contribution to the VALUE intercomparison experiment

Bedia¹,

Baño‐Medina²,

Legasa³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. The increasing demand for high-resolution climate information has attracted a growing attention for statistical downscaling methods (SD), 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 SD methods is nowadays available for climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE Project (http://www.value-cost.eu). The systematic intercomparison of a large number of SD 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. With 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 SD model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in the VALUE Project, making an emphasis in the most technically complex stages of perfect-prog 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.

show abstract

Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment

Cited by 33 publications

References 57 publications

Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community

Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community

Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment

Statistical downscaling with the downscaleR package: Contribution to the VALUE intercomparison experiment

Contact Info

Product

Resources

About