Articles | Volume 10, issue 6
https://doi.org/10.5194/gmd-10-2379-2017
https://doi.org/10.5194/gmd-10-2379-2017
Methods for assessment of models
 | 
28 Jun 2017
Methods for assessment of models |  | 28 Jun 2017

Skill and independence weighting for multi-model assessments

Benjamin M. Sanderson, Michael Wehner, and Reto Knutti

Related authors

The potential for structural errors in emergent constraints
Benjamin M. Sanderson, Angeline G. Pendergrass, Charles D. Koven, Florent Brient, Ben B. B. Booth, Rosie A. Fisher, and Reto Knutti
Earth Syst. Dynam., 12, 899–918, https://doi.org/10.5194/esd-12-899-2021,https://doi.org/10.5194/esd-12-899-2021, 2021
Short summary
Producing realistic climate data with generative adversarial networks
Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021,https://doi.org/10.5194/npg-28-347-2021, 2021
Short summary
Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021,https://doi.org/10.5194/esd-12-253-2021, 2021
Short summary
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020,https://doi.org/10.5194/ascmo-6-223-2020, 2020
Short summary
Relating climate sensitivity indices to projection uncertainty
Benjamin Sanderson
Earth Syst. Dynam., 11, 721–735, https://doi.org/10.5194/esd-11-721-2020,https://doi.org/10.5194/esd-11-721-2020, 2020
Short summary

Related subject area

Climate and Earth system modeling
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023,https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023,https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023,https://doi.org/10.5194/gmd-16-6593-2023, 2023
Short summary
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023,https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023,https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary

Cited articles

Abramowitz, G. and Bishop, C. H.: Climate model dependence and the ensemble dependence transformation of cmip projections, J. Climate, 28, 2332–2348, 2015.
Alexander, L., Donat, M., Takayama, Y., and Yang, H.: The climdex project: creation of long-term global gridded products for the analysis of temperature and precipitation extremes, WCRP Open Science conference, Denver, 2011.
Annan, J. D. and Hargreaves, J. C.: Understanding the CMIP3 multimodel ensemble, J. Climate, 24, 4529–4538, 2011.
Aumann, H. H., Chahine, M. T., Gautier, C., Goldberg, M. D., Kalnay, E., McMillin, L. M., Revercomb, H., Rosenkranz, P. W., Smith, W. L., Staelin, D. H., and Strow, L. L.: AIRS/AMSU/HSB on the Aqua Mission: Design, Science Objectives, Data Products, and Processing Systems, IEEE T. Geosci. Remote, 41, 253–264, https://doi.org/10.1109/TGRS.2002.808356, 2003.
Bishop, C. H. and Abramowitz, G.: Climate model dependence and the replicate earth paradigm, Clim. Dynam., 41, 885–900, 2013.
Download
Short summary
How should climate model simulations be combined to produce an overall assessment that reflects both their performance and their interdependencies? This paper presents a strategy for weighting climate model output such that models that are replicated or models that perform poorly in a chosen set of metrics are appropriately weighted. We perform sensitivity tests to show how the method results depend on variables and parameter values.