Prospects and Caveats of Weighting Climate Models for Summer Maximum Temperature Projections Over North America. Issue 9 (10th May 2018)

• Record Type: Journal Article
• Authors: Lorenz, Ruth; Herger, Nadja; Sedláček, Jan; Eyring, Veronika; Fischer, Erich M.; Knutti, Reto
• Is Part Of: Journal of geophysical research. Volume 123:Issue 9(2018)
• Page Start: 4509
• Page End: 4526
• Publication Date: 2018-05-10
• Subjects: weighting -- multimodel ensemble -- summer temperature -- independence -- performance; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2017JD027992
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Uncertainties in climate projections exist due to natural variability, scenario uncertainty, and model uncertainty. It has been argued that model uncertainty can be decreased by giving more weight to those models in multimodel ensembles that are more skillful and realistic for a specific process or application. In addition, some models in multimodel ensembles are not independent. We use a weighting approach proposed recently that takes into account both model performance and interdependence and apply it to investigate projections of summer maximum temperature climatology over North America in two regions of different sizes. We quantify the influence of predicting diagnostics included in the method, look at ways how to choose them, and assess the influence of the observational data set used. The trend in shortwave radiation, mean precipitation, sea surface temperature variability, and variability and trend in maximum temperature itself are the most promising constraints on projections of summer maximum temperature over North America. The influence of the observational data sets is large for summer temperature climatology, since the observational and reanalysis products used for absolute maximum temperatures disagree. Including multiple predicting diagnostics leads to more similar results for different data sets. We find that the weighted multimodel mean reduces the change in summer daily temperature maxima compared to the nonweighted mean slightly (0.05–0.45 °C) over the central United States. We show that it is essential to have reliable observations for key variables to be able to constrain multimodel ensembles of future projections. Key Points: Model weighting slightly reduces summer warming signal over central North America More than one predicting diagnostics should be used to inform the weighting Shortwave radiation trend, mean precipitation, and SST variability are possible constraints on projections of summer maximum temperature