CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity, overconfidence, and gaussianity
Abstract
Using 5-day averaged precipitation from all initial condition realizations of 33 CMIP5 models for the Historical and RCP8.5scenarios, 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 modelling group to account for overconfidence, and sub-selecting models on annual cycle fidelity. 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. We suggest these sensitivity results promote the use of Gaussian statistics to present global maps of the lower-bound and upper-bound ofmore »
- Authors:
-
[1];
[1]
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Univ. of Hawaii at Manoa, Honolulu, HI (United States)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)
- OSTI Identifier:
- 1598106
- Report Number(s):
- LLNL-JRNL-748698
Journal ID: ISSN 0930-7575; 933450
- Grant/Contract Number:
- AC52-07NA27344; 1460742
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Climate Dynamics
- Additional Journal Information:
- Journal Volume: 54; Journal Issue: 3-4; Journal ID: ISSN 0930-7575
- Publisher:
- Springer-Verlag
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; Anthropogenic climate change; Precipitation; Annual cycle skill; Monte Carlo sampling
Citation Formats
Sperber, Kenneth R., Annamalai, H., and Pallotta, Giuliana. CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity, overconfidence, and gaussianity. United States: N. p., 2020.
Web. doi:10.1007/s00382-019-05082-8.
Sperber, Kenneth R., Annamalai, H., & Pallotta, Giuliana. CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity, overconfidence, and gaussianity. United States. https://doi.org/10.1007/s00382-019-05082-8
Sperber, Kenneth R., Annamalai, H., and Pallotta, Giuliana. Fri .
"CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity, overconfidence, and gaussianity". United States. https://doi.org/10.1007/s00382-019-05082-8. https://www.osti.gov/servlets/purl/1598106.
@article{osti_1598106,
title = {CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity, overconfidence, and gaussianity},
author = {Sperber, Kenneth R. and Annamalai, H. and Pallotta, Giuliana},
abstractNote = {Using 5-day averaged precipitation from all initial condition realizations of 33 CMIP5 models for the Historical and RCP8.5scenarios, 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 modelling group to account for overconfidence, and sub-selecting models on annual cycle fidelity. 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. We suggest 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.},
doi = {10.1007/s00382-019-05082-8},
journal = {Climate Dynamics},
number = 3-4,
volume = 54,
place = {United States},
year = {Fri Jan 03 00:00:00 EST 2020},
month = {Fri Jan 03 00:00:00 EST 2020}
}
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