Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections Using Data Augmentation

Abstract

The quantification of uncertainty sources in ensembles of climate projections obtained from combinations of different scenarios and climate and impact models is a key issue in climate impact studies. The small size of the ensembles of simulation chains and their incomplete sampling of scenario and climate model combinations makes the analysis difficult. In the popular single-time ANOVA approach for instance, a precise estimate of internal variability requires multiple members for each simulation chain (e.g., each emission scenario–climate model combination), but multiple members are typically available for a few chains only. In most ensembles also, a precise partition of model uncertainty components is not possible because the matrix of available scenario/models combinations is incomplete (i.e., projections are missing for many scenario–model combinations). The method we present here, based on data augmentation and Bayesian techniques, overcomes such limitations and makes the statistical analysis possible for single-member and incomplete ensembles. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates. This approach is illustrated for projections of regional precipitation and temperature for four mountain massifs in France. It is applicable for any kind of ensemble of climate projections, including those produced from ad hoc impact models.

Potential impact of climate change on solar resource in Africa for photovoltaic energy: analyses from CORDEX-AFRICA climate experiments

“…GCMs or RCMs) and climate IV (variability in projections due to the chaotic and nonlinear nature of the climate system, which includes interannual variability here). To better understand the importance of these different uncertainty sources, they are partitioned and quantified using QUALYPSO [28], an advanced Bayesian ANOVA method based on data augmentation techniques and on the quasi-ergodicity assumption of climate outputs [29]. We quantify uncertainty sources for each grid cell of the African continent, for the projections of annual mean (aggregated from the daily time series) RSDS, W10, TAS, and PVpot.…”

“…Conversely to typical ANOVA approaches used for such analysis, QUALYPSO [28] allows for a robust partition and for an unbiased estimation of all uncertainty components in multimember multimodel ensembles of projections, even in the case of single run ensembles (i.e. when only one experiment is available for some or all simulation chains; which is the case here as only one run is available for each GCM) and even when ensembles are incomplete (i.e.…”

“…For further details on the method, the reader is referred to [28]. QUALYPSO estimates can be produced for each prediction lead time.…”

Assessment of extreme flows and uncertainty under climate change: disentangling the contribution of RCPs, GCMs and internal climate variability

“…Uncertainty in climate change projections mainly arises from three different sources, i.e. scenario uncertainty, model uncertainty and internal climate variability (Evin et al, 2019;Deser et al, 2010). Scenario uncertainty is interpreted as responses to different assumptions of future greenhouse gas emissions, which reflects the limited knowledge of external factors such as anthropogenic activities and social development strategies, that influence the climate system (Nakicenovic and Swart, 2000).…”

“…These different uncertainty sources in climate projections have been quantified by multiple studies (Yip et al, 2011;Zhuan et al, 2018;Evin et al, 2019). The relative importance varies depending on factors like the type of climate variable and temporal and spatial scales (Zhuan et al, 2018).…”