Selecting a Climate Model Subset to Optimize Key Ensemble Properties

TitleSelecting a Climate Model Subset to Optimize Key Ensemble Properties
Publication TypeJournal Article
Year of Publication2018
AuthorsHerger, Nadja, Abramowitz Gab, Knutti Reto, Angélil Oliver, Lehmann Karsten, and Sanderson Benjamin M.
JournalEarth System Dynamics
Volume9
Pages135 - 151
Date Published01/2018
Abstract / Summary

End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history.

Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model meanwhile at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimization criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used. 

URLhttps://doi.org/10.5194/esd-9-135-2018
DOI10.5194/esd-9-135-2018
Journal: Earth System Dynamics
Year of Publication: 2018
Volume: 9
Pages: 135 - 151
Date Published: 01/2018

End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history.

Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model meanwhile at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimization criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used. 

DOI: 10.5194/esd-9-135-2018
Citation:
Herger, N, G Abramowitz, R Knutti, O Angélil, K Lehmann, and BM Sanderson.  2018.  "Selecting a Climate Model Subset to Optimize Key Ensemble Properties."  Earth System Dynamics 9: 135 - 151.  https://doi.org/10.5194/esd-9-135-2018.