Diagnosing similarities in probabilistic multi-model ensembles: an application to soil–plant-growth-modeling

Silva, Aline Schäfer Rodrigues; Weber, Tobias; Gayler, Sebastian; Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang; Streck, Thilo

doi:10.1007/s40808-022-01427-1

Cited by 3 publications

(1 citation statement)

References 64 publications

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“…Large-scale global climate models (GCMs), which provide the basic future climate information for impact models, introduce uncertainties due to structural deficiencies, parameterization and coarse spatial resolution 13 . Process-based impact models, like crop and hydrological models 3, 4, 8, 9 , contribute further uncertainties through their assumptions, model equations, complexity, parameterization [14][15][16][17][18] , and data sources 19 . The resulting uncertainties, encompassing both GCMs and impact models, propagate through the simulations and will affect outcomes.…”

Section: Introductionmentioning

confidence: 99%

Regional-scale evaluation of uncertainty in the multi-model simulation of climate change impact on maize and wheat yield

Rettie,

Gayler,

Weber

et al. 2023

Preprint

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The challenges of climate change, even under the milder scenarios, require urgent adaptation and mitigation strategies. Whilequantifying the potential impacts of climate change involves modeling work, providing decision-relevant evidence is challengingdue to many sources of uncertainty involved in the estimation process. In this study, we examined regional-scale impact ofclimate change on maize and wheat in Ethiopia using three well calibrated and validated process-based crop models. Thecrop models were driven by gridded and high-resolution climate projections from nine global climate models (GCMs) underthree emission scenarios with CO2 fertilization effect considered. The large ensemble of model simulations allows us tocomprehensively quantify the uncertainties in the model estimated climate change impact, thereby increasing the confidence inthe simulations. Our results show that the national projected median and 5th percentile wheat yield was reduced by 4% and18%, respectively by the end of the 21th century under the high-emission scenario (SSP5-8.5). In contrast, national maizemedian yield increased by 2.5%, with the 5th percentile yield projected to decrease by up to 4%. The CO2 fertilization isexpected to have a compensatory effect on the projected wheat (up to 17%) and maize (up to 12%) yield changes. The largestcontributors to overall yield change uncertainty was identified to be the spread in the crop models followed by GCM spread. Weconcluded that by understanding the sources of uncertainty and using techniques to quantify and manage it, it is possible toimprove the accuracy of model predictions and make better decisions in the face of climate change.

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Section: Introductionmentioning

confidence: 99%

Regional-scale evaluation of uncertainty in the multi-model simulation of climate change impact on maize and wheat yield

Rettie,

Gayler,

Weber

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networks

Farfán

Cea

2022

Model. Earth Syst. Environ.

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Ensemble modelling is a numerical technique used to combine the results of a number of different individual models in order to obtain more robust, better-fitting predictions. The main drawback of ensemble modeling is the identification of the individual models that can be efficiently combined. The present study proposes a strategy based on the Random-Restart Hill-Climbing algorithm to efficiently build ANN-based hydrological ensemble models. The proposed technique is applied in a case study, using three different criteria for identifying the model combinations, different number of individual models to build the ensemble, and two different ANN training algorithms. The results show that model combinations based on the Pearson coefficient produce the best ensembles, outperforming the best individual model in 100% of the cases, and reaching NSE values up to 0.91 in the validation period. Furthermore, the Levenberg-Marquardt training algorithm showed a much lower computational cost than the Bayesian regularisation algorithm, with no significant differences in terms of accuracy.

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Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States

et al. 2023

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Abstract. Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting strategies and their resulting ensemble means to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The purpose of this study is to assess the sensitivities associated with multi-model weighting strategies. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their statistically downscaled counterparts created with the localized constructed analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that the model weights and the corresponding weighted model means can be sensitive to the weighting strategy that is applied. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting strategies. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. As for uncertainty, our matrix of results provided a more certain picture of future climate compared to the spread in the original model ensemble. If future impact assessments utilize weighting strategies, then our findings suggest that how the specific weighting strategy is used with climate projections may depend on the needs of an impact assessment or adaptation plan.

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Diagnosing similarities in probabilistic multi-model ensembles: an application to soil–plant-growth-modeling

Cited by 3 publications

References 64 publications

Regional-scale evaluation of uncertainty in the multi-model simulation of climate change impact on maize and wheat yield

Regional-scale evaluation of uncertainty in the multi-model simulation of climate change impact on maize and wheat yield

Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networks

Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States

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