Simple Uncertainty Frameworks for Selecting Weighting Schemes and Interpreting Multimodel Ensemble Climate Change Experiments

Abstract

Future climate change projections are often derived from ensembles of simulations from multiple global circulation models using heuristic weighting schemes. This study provides a more rigorous justification for this by introducing a nested family of three simple analysis of variance frameworks. Statistical frameworks are essential in order to quantify the uncertainty associated with the estimate of the mean climate change response.The most general framework yields the ''one model, one vote'' weighting scheme often used in climate projection. However, a simpler additive framework is found to be preferable when the climate change response is not strongly model dependent. In such situations, the weighted multimodel mean may be interpreted as an estimate of the actual climate response, even in the presence of shared model biases.Statistical significance tests are derived to choose the most appropriate framework for specific multimodel ensemble data. The framework assumptions are explicit and can be checked using simple tests and graphical techniques. The frameworks can be used to test for evidence of nonzero climate response and to construct confidence intervals for the size of the response.The methodology is illustrated by application to North Atlantic storm track data from the Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel ensemble. Despite large variations in the historical storm tracks, the cyclone frequency climate change response is not found to be model dependent over most of the region. This gives high confidence in the response estimates. Statistically significant decreases in cyclone frequency are found on the flanks of the North Atlantic storm track and in the Mediterranean basin.

Midlatitude atmospheric circulation responses under 1.5 °C and 2.0 °C warming and implications for regional impacts

“…The HAPPI multi-model ensemble is analaysed and interpreted using the two-way analysis of variance ANOVA framework introduced in Sansom et al (2013). Each model's climate change response 125 is computed as the difference between the future (1.5…”

“…Following Sansom et al (2013), one value of σ is obtained for the whole multi-model ensemble by pooling together variations in decadal mean climate across all the ensemble members.…”

“…This represents the ratio of the uncertainty in the future response due to the model dependence (differences in response from model to model) compared to the uncertainty due 140 to internal (decadal) climate variability (see Sansom et al, 2013, for details). We interpret f 2 < 1 as evidence of consensus on the magnitude of the climate change response, as this implies that internal climate variability is the dominant source of uncertainty in the multi-model projections.…”

Development towards a global operational aerosol consensus: basic climatological characteristics of the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME)

“…Taken a step further, error weighting a multi-model ensemble leads to the development of the super ensemble (e.g., Krishnamurti et al, 1999;Casanova and Ahrens, 2009). However, equal weighting in a consensus style appears to provide the most robust result overall for a host of forecasting applications (e.g., DelSole et al, 2013;Sansom et al, 2013), especially if model…”

Epistemic uncertainties and natural hazard risk assessment. 1. A review of different natural hazard areas

“…extreme value models (Brodin and Rootzén 2009;Della-30 Marta et al 2009), assumptions about model-dependence of simulated storms (Sansom et al 2013), and assumptions about dependency in space-time and between events (Bonazzi et al 2012;Economou et al 2014 • Numerical weather and climate models show biases in storm properties that have resisted model improvements over the past 40 years e.g. too zonal storm tracks over W. Europe , poor representation of small horizontal scale processes even at very high resolution e.g.…”