Estimating Regionalized Hydrological Impacts of Climate Change Over Europe by Performance-Based Weighting of CORDEX Projections

Weiland, Frederiek Sperna; Visser, Robrecht D.; Greve, Peter; Bisselink, Berny; Brunner, Lukas; Weerts, Albrecht

doi:10.3389/frwa.2021.713537

Cited by 20 publications

(11 citation statements)

References 64 publications

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“…This indicates that ensemble means with weighting factors, slightly produce better results. This supports the findings that weighting methods have the potential to increase the climate model performance (Casanova and Ahrens, 2009;Weiland et al, 2021) and it might be only suitable for a specific case (Delsole and Tippett, 2012;Christensen et al, 2010). In addition, GPCP, exhibits remarkably similar pattern (the highest similarity score of 0.94), indicating its significant proximity to ERA5.…”

Section: Spatial-based Evaluationsupporting

confidence: 79%

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A Comparison of the Performance of the Weighted Ensembles Means in CORDEX-SEA Precipitation Simulations

Aminoto,

Faqih,

Perdinan

et al. 2024

Agromet

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Numerous studies stated that the performance of ensemble mean derived from multiple climate models generally surpassed the individual member model, and applying weighting factors potentially increase the ensemble mean of performance. This study aims to assess the performance of unweighted and weighted ensemble means of 9-modelled precipitation datasets in the CORDEX-SEA multi-model simulations for 1981-2005. The 9 datasets included: CNRM_a, ECE_b, GFDL_b, IPSL_b, HadGEM2_a, HadGEM2_c, HadGEM2_d, MPI_c, and NorESM1_d. The weighting factors were derived from the models' skill scores measured using five statistical-based metrics, namely Taylor, Pierce (SS), Tian skill score (Tian), Climate prediction index (CPI), and Performance and Independence (PI). The ERA5 and GPCP precipitation datasets were used as the references for comparison. Then, reliable metrics will be used to determine the weighting factor. The results found that three metrics namely Taylor, SS, and Tian were more reliable than the other two metrics (CPI and PI). Spatially, the weighted ensemble mean based on a random method was superior to other ensemble mean methods and individual models. We found that the CNRM_a and GFDL_b models were spatially performed best. In contrast, most the ensemble means was temporally less performed compared to the individual model. Our findings suggested that by removal of low performance models will significantly influence on the overall ensemble model performance. Further, the research may provide valuable considerations of climate models selection for climate projection assessments, especially in the Southeast Asia region.

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Section: Spatial-based Evaluationsupporting

confidence: 79%

“…Weighting methods are based on model performance and have the potential to increase the reliability of climate model performance (Casanova and Ahrens, 2009;Weiland et al, 2021). Hence, it is necessary to explore the potential of incorporating weighting factors in the ensemble mean of the CORDEX-SEA multi-model simulations.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of the Performance of the Weighted Ensembles Means in CORDEX-SEA Precipitation Simulations

Aminoto,

Faqih,

Perdinan

et al. 2024

Agromet

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“…In addition to providing an operational definition of dependence that can be used to contextualize CMIP-derived results, ClimWIP has the advantage of being available for general open use (Sperna Weiland et al, 2021)…”

Section: The Composition Of Cmipmentioning

confidence: 99%

Climate model Selection by Independence, Performance, and Spread (ClimSIPS) for regional applications

Merrifield

Brunner

Lorenz

et al. 2023

Preprint

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Abstract. As the number of models in Coupled Model Intercomparison Project (CMIP) archives increase from generation to generation, there is a pressing need for guidance on how to interpret and best use the abundance of newly available climate information. CMIP6 users seeking to draw conclusions about model agreement must contend with an "ensemble of opportunity" containing similar models that appear under different names. Those who used CMIP5 as a basis for downstream applications must filter through hundreds of new CMIP6 simulations to find several best suited to their region, season, and climate horizon of interest. Here we present methods to address both issues, model dependence and model subselection, to help users previously anchored in CMIP5 to navigate CMIP6. In Part I, we refine a definition of model dependence based on climate output, initially employed in Climate model Weighting by Independence and Performance (ClimWIP), to designate discrete model families within CMIP5/6. We show that the increased presence of model families in CMIP6 bolsters the upper mode of the ensemble's bimodal effective Equilibrium Climate Sensitivity (ECS) distribution. Accounting for the mismatch in representation between model families and individual model runs shifts the CMIP6 ECS median and 75th percentile down by 0.43 °C, achieving better alignment with CMIP5's ECS distribution. In Part II, we present a new, cost-function minimization-based approach to model subselection, Climate model Selection by Independence, Performance, and Spread (ClimSIPS), that selects sets of CMIP models based on the relative importance a user ascribes to model independence (as defined in Part I), model performance, and ensemble spread in projected climate outcome. We demonstrate ClimSIPS by selecting sets of three to five models from CMIP5/6 for European applications, evaluating the performance from the agreement with the observed mean climate, and the spread in outcome from the projected midcentury change in surface air temperature and precipitation. To accommodate different use cases, we explore two ways to represent models with multiple members in ClimSIPS, first, by ensemble mean and second, by an individual ensemble member that maximizes midcentury change diversity within CMIP overall. Because different combinations of models are selected by the cost function for different balances of independence, performance, and spread priority, we present all selected subsets in ternary contour "subselection triangles" and guide users with recommendations based on further qualitative independence, performance, and spread standards. In CMIP6, we find that recommended subsets are populated primarily by members of several model families defined in Part I due to an inverse relationship between performance and independence. In CMIP5, recommended subsets feature model combinations used in the European branch of the Coordinated Regional Downscaling Experiment (EURO-CORDEX), suggesting the independence, performance, and spread metrics used in ClimSIPS are appropriate for European applications in CMIP6 and beyond.

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“…Several recent applications have already used HydroMT. It has been used to build instances of the hydrological Wflow model (Imhoff et al, 2020;Verseveld et al, 2022) for climate change impact studies (Sperna Weiland et al, 2021); for a large sample assessment of spatial scales in hydrological modeling (Aerts et al, 2021); to estimate water balance components based on global data in a data-scarce area (Rusli et al, 2021); and to build instances of the hydrodynamic SFINCS model (Leijnse et al, 2021) to simulate compound flood hazard from (global) datasets in any coastal delta globally (Eilander et al, 2022). While the package is sufficiently general to support model software outside the domain of hydro models, the focus of many workflows is on typical hydrological data and parameters and hence hydro models benefit most from the tool.…”

Section: Hydromt (Hydro Model Toolsmentioning

confidence: 99%

HydroMT: Automated and reproducible model building and analysis

Eilander¹,

Boisgontier²,

Bouaziz³

et al. 2023

JOSS

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Many impact assessment studies rely on hydrological and hydrodynamic (hydro) models. These models typically require a large set of parameters derived from different datasets and hence manual setup can be time consuming and hard to reproduce. HydroMT (Hydro Model Tools) is an open-source Python package that aims to make the process of building model instances and analyzing model results automated and reproducible. The package provides a common interface to data and model instances, workflows to transform data into models based on (hydrological) GIS and statistical methods, and various methods to analyze model results. The user can describe a full model instance including its forcing in a single configuration file based on a sequence of workflows, making the process reproducible, fast, and modular. The package has been designed with an iterative, data-centered modeling process in mind. First-order model schematizations can be built for any location in the world by leveraging open global datasets. These can later be improved by updating the input datasets with detailed local datasets. This iterative process enables the user to quickly get an initial result to then make informed decisions about the most relevant improvements and/or required data collection and to kick-start discussions with stakeholders. Furthermore, model parameter maps or forcing data can easily be modified for sensitivity analysis or calibration to support these robust modeling practices. HydroMT is successfully being used for several model software through a plugin infrastructure that allow for model specific functionality, such as readers and writers of model data formats, and can easily be extended to new model software. Currently supported model software include the distributed rainfall-runoff model Wflow, the hydrodynamic flood model SFINCS, the water quality models D-Water Quality and D-Emissions and the flood impact model Delft-FIAT.

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Estimating Regionalized Hydrological Impacts of Climate Change Over Europe by Performance-Based Weighting of CORDEX Projections

Cited by 20 publications

References 64 publications

A Comparison of the Performance of the Weighted Ensembles Means in CORDEX-SEA Precipitation Simulations

A Comparison of the Performance of the Weighted Ensembles Means in CORDEX-SEA Precipitation Simulations

Climate model Selection by Independence, Performance, and Spread (ClimSIPS) for regional applications

HydroMT: Automated and reproducible model building and analysis

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