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Publication Date
6 January 2020

CMIP5: A Monte Carlo Assessment of Changes in Summertime Precipitation Characteristics Under RCP8.5-Sensitivity to Annual Cycle Fidelity, Overconfidence, and Gaussianity



Using five-day averaged precipitation from all initial condition realizations of 33 CMIP5 models for the Historical and RCP8.5 scenarios, we performed an assessment of summer precipitation in terms of amount, onset date, withdrawal date, and length of season using probability distributions of interannual anomalies. Climate change projections were generated using all models, one model per modeling group to account for overconfidence and sub-selecting models on annual cycle fidelity in terms of the timing and variability of onset and length of season (accumulation time of summer rainfall). Compared to using all models, sub-selecting on annual cycle fidelity has a large impact on the climate change perturbation of the fractional change in precipitation, with differences between the two projections of up to ±50%, especially in the tropics and subtropics. Sensitivity testing indicates that the Gaussian t-test and the non-parametric Mann-Whitney U-test (the latter using Monte Carlo sampling) yield consistent results for assessing where the climate change perturbation is significant at the 1% level, even in cases where skewness and excess kurtosis indicate non-Gaussian behavior. Similarly, in terms of climate change-induced perturbations to below-normal, normal, and above-normal categorical probabilities, the Gaussian results are typically consistent with the non-parametric estimates. These sensitivity results promote the use of Gaussian statistics to present global maps of the lower-bound and upper-bound of the climate change response, given that the non-parametric calculation of confidence intervals would otherwise not be tractable in a desktop computing environment due to its CPU intensive requirement.
“Cmip5: A Monte Carlo Assessment Of Changes In Summertime Precipitation Characteristics Under Rcp8.5-Sensitivity To Annual Cycle Fidelity, Overconfidence, And Gaussianity ”. 2020. doi:
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