Towards a typology for constrained climate model forecasts

Abstract

In recent years several methodologies have been developed to combine and interpret ensembles of climate models with the aim of quantifying uncertainties in climate projections. Constrained climate model forecasts have been generated by combining various choices of metrics used to weight individual ensemble members, with diverse approaches to sampling the ensemble. The forecasts obtained are often significantly different, even when based on the same model output. Therefore, a climate model forecast classification system can serve two roles: to provide a way for forecast producers to self-classify their forecasts; and to provide information on the methodological assumptions underlying the forecast generation and its uncertainty when forecasts are used for impacts studies. In this review we propose a possible classification system based on choices of metrics and sampling strategies. We illustrate the impact of some of the possible choices in the uncertainty quantification of large scale projections of temperature and precipitation changes, and briefly discuss possible connections between climate forecast uncertainty quantification and decision making approaches in the climate change context.

Recent Progress in Constraining Climate Sensitivity With Model Ensembles

“…Applying this approach to a PPE derived using the Hadley Center model, using constraints derived from a multivariate assessment of recent mean climate, Sexton et al [35] find that 10th and 90th percentiles for ECS are 2. Lopez et al [22] point out that the approach of Sexton et al [35] relies on the assumption that errors are equally probable in different CMIP members, which is unlikely given the range of complexity in the CMIP models, the limited sample size available, and the lack of independence in the archive [32]. Hence, the discrepancy approach can be used to sample the error arising from the naïve assumption that the underlying model in a PPE is perfect and only the parameters are unknown (by treating members of a separate multi-model archive as truth).…”

Comparing Methods to Constrain Future European Climate Projections Using a Consistent Framework

“…These alternative interpretations are of direct relevance to the quantification of uncertainties in climate projections (Sanderson and Knutti 2012;Abramowitz et al 2019). Under a truthcentered paradigm, estimated uncertainty decreases strongly as ensemble size increases because the uncertainty in the ensemble mean is estimated more precisely with more members (Lopez et al 2006;Tebaldi and Sansó 2009;Annan and Hargreaves 2010;Knutti 2010). In contrast, Fig.…”

“…The methods' key features and assumptions are summarized in Table 2. There are a number of ways in which they might be categorized (e.g., Lopez et al 2015); for the purpose of this study, we broadly divide them as follows: 1) weighting schemes: ClimWIP (Climate Model Weighting by Independence and Performance) and REA (reliability ensemble averaging), 2) detection and attribution-based methods: ASK (Allen-Stott-Kettleborough),…”