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We present a framework for evaluating multi-model ensembles based on common empirical orthogonal functions (common EOFs) that emphasize salient features connected to spatio-temporal covariance structures embedded in large climate data volumes. This framework enables the extraction of the most pronounced spatial patterns of coherent variability within the joint dataset and provides a set of weights for each model in terms of the principal components which refer to exactly the same set of spatial patterns of covariance. In other words, common EOFs provide a means for extracting information from large volumes of data. Moreover, they can provide an objective basis for evaluation that can be used to accentuate ensembles more than traditional methods for evaluation, which tend to focus on individual models. Our demonstration of the capability of common EOFs reveals a statistically significant improvement of the sixth generation of the World Climate Research Programme (WCRP) Climate Model Intercomparison Project (CMIP6) simulations in comparison to the previous generation (CMIP5) in terms of their ability to reproduce the mean seasonal cycle in air surface temperature, precipitation, and mean sea level pressure over the Nordic countries. The leading common EOF principal component for annually or seasonally aggregated temperature, precipitation, and pressure statistics suggests that their simulated interannual variability is generally consistent with that seen in the ERA5 reanalysis. We also demonstrate how common EOFs can be used to analyse whether CMIP ensembles reproduce the observed historical trends over the historical period 1959–2021, and the results suggest that the trend statistics provided by both CMIP5 RCP4.5 and CMIP6 SSP245 are consistent with observed trends. An interesting finding is also that the leading common EOF principal component for annually or seasonally aggregated statistics seems to be approximately normally distributed, which is useful information about the multi-model ensemble data. [ABSTRACT FROM AUTHOR]

We present a framework for evaluating multi-model ensembles based on common empirical orthogonal functions ('common EOFs') that emphasise salient features connected to spatio-temporal covariance structures embedded in large climate data volumes. In other words, this framework enables the extraction of the most pronounced spatial patterns of coherent variability within the joint data set and provides a set of weights for each model in terms of principal components which refer to exactly the same set of spatial patterns of covariance. In other words, common EOFs provide a means for extracting information from large volumes of data. Moreover, they can provide an objective basis for evaluation that can be used to accentuate ensembles more than traditional methods for evaluation, which tend to focus on individual models. Our demonstration of the capability of common EOFs reveals a statistically significant improvement of the sixth generation of the World Climate Research Programme (WCRP) Climate Model Intercomparison Project (CMIP6) simulations over the previous generation (CMIP5) in terms of their ability to reproduce the mean seasonal cycle in air surface temperature, precipitation, and mean sea-level pressure over the Nordic countries. The leading common EOF principal component for annually/seasonally aggregated temperature, precipitation and pressure statistics suggest that their simulated interannual variability is generally consistent with that seen in the ERA5 reanalysis. We also demonstrate how common EOFs can be used to analyse whether CMIP ensembles reproduce the observed historical trends over the historical period 1959–2021, and the results suggest that the trend statistics provided by both CMIP5 RCP4.5 and CMIP6 SSP245 are consistent with observed trends. An interesting finding is also that the leading common EOF principal component for annually/seasonally aggregated statistics seems to be approximately normally distributed, which is useful information about the multi-model ensemble data. [ABSTRACT FROM AUTHOR]

Intensity-duration-frequency (IDF) statistics describing extreme rainfall intensities in Norway were analysed with the purpose of investigating how the shape of the curves is influenced by geographical conditions and local climate characteristics. To this end, principal component analysis (PCA) was used to quantify salient information about the IDF curves and a Bayesian linear regression was used to study the dependency of the shapes on climatological and geographical information. Our analysis indicated that the shapes of IDF curves in Norway are influenced by both geographical conditions and 24-hr precipitation statistics. Based on this analysis, an empirical model was constructed to predict IDF curves in locations with insufficient sub-hourly rain gauge data and for the future using downscaled data from global climate models. Our new method was also compared with a recently proposed formula for estimating sub-daily rainfall intensity based on 24-hr rain gauge data. We found that a Bayesian inference of a PCA representation of IDF curves provides a promising strategy for estimating sub-daily return levels for rainfall. [ABSTRACT FROM AUTHOR]

We present estimates for an upper limit for twenty-year return-values for 24-hr precipitation at different locations in Europe and a crude method to quantify bounds of likely intervals. Our results suggest an increase by as much as 40-50% projected for 2100, assuming a high emission scenario, RCP8.5. The new strategy is based on combining physics with the limited available information, and utilises the covariance between the mean seasonal variations in precipitation and the North Atlantic saturation vapour pressure to estimate the maximum effect that a temperature change can have on precipitation, rather than the actual expected values. Return-value projections were derived through a simple and approximate scheme that combines the one-year 24-hr precipitation return-value and downscaled annual wet-day mean precipitation for a 1-in-20 year event. The twenty-year return-value was estimated by the 95th percentile of multi-model ensemble spread of downscaled climate model results. We found geographical variations in the shape of the seasonal cycle of the wet-day mean precipitation which suggest that different rain-producing mechanisms dominate in different regions. These differences indicate that the simple method used here to estimate upper limits was more appropriate for convective precipitation than for orographic rainfall. [ABSTRACT FROM AUTHOR]

The original version of this article unfortunately contained a mistake: The spelling of the Joanna O'Keeffe' name was incorrect. The correct spelling is Joanna O'Keeffe. [ABSTRACT FROM AUTHOR]