Prospects and Caveats of Weighting Climate Models for Summer Maximum Temperature Projections Over North America

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.

Quantifying uncertainty in European climate projections using combined performance-independence weighting

“…Weights are calculated for each model following the approach presented by Lorenz et al (2018), which is based on earlier work from Knutti et al (2017) and Sanderson et al (2015aSanderson et al ( , 2015b. Each weight w i is a combination of the observational distance D i (informing the performance weighting) and the model distance S ij (informing the independence weighting):…”

“…The shape parameters define the strength of the weighting and the relative importance of performance and independence, large values will lead to an approximation of equal weighting, while small values will lead to aggressive weighting, giving a few models most of the weight. The shape parameters used in this study are summarized in table 1 and their calculation is described by Lorenz et al (2018) and in the supplement.…”

“…The raw model range of the full CMIP5 ensemble, hence, does not necessarily represent the actual uncertainty of the climate system. It rather reflects the ensemble design (or a lack thereof) and can either overestimate or underestimate the actual uncertainty (Lorenz et al 2018). To better capture real uncertainty different methods account for model performance and independence by selecting only a subset of models (Pennell and Reichler 2011, Evans et al 2013, Leduc et al 2016, Hegerl et al 2018, Herger et al 2019, finding emergent constraints for the change in an investigated variable (Hall and Qu 2006, Gordon and Klein 2014, Cox et al 2018, Hall et al 2019, or model weighting (Coppola et al 2010, Abramowitz and Bishop 2015, Lorenz et al 2018.…”

“…We here use a model weighting approach which accounts for model performance and independence (Sanderson et al 2015a, 2015b, Lorenz et al 2018. In an initial step our study focuses on selecting relevant diagnostics to establish model performance and independence.…”

Observational Constraints Reduce Likelihood of Extreme Changes in Multidecadal Land Water Availability

“…Although internal variability has been shown to influence trends in water availability for 20-year periods (Kumar et al, 2015), Greve et al (2018) found that model uncertainty is dominating the spread of projected changes in long-term water availability. Moreover, Lorenz et al (2018) highlight that model uncertainty is ©2018. The Authors.…”