Prognostic Power of Extreme Rainfall Scaling Formulas Across Space and Time Scales
Abstract
Some studies documenting changes in extreme precipitation use scaling formulas to approximate the large percentiles of the rainfall distribution from average dynamical and thermodynamical variables, called predictors. Here we instead assess the performance of these formulas as approximations to the rain rates in individual events. We evaluate the accuracies of the scaling relationships as functions of spatial and temporal scales by analyzing tropical rainfall in a superparameterized model. Relationships using full vertical profiles of the predictors are more accurate than those using their values at specific vertical levels because they better characterize the specific dynamics of each event. Both types of scaling relationships perform well over a range of length scales from 200 to 2,000 km and time scales from an hour to a week, and their precision is higher in the case of simulations with superparameterization than with parameterized convection. Uncertainties emerging from the local use of the scaling relationships suggest that they may only characterize the intensification of individual extremes for a warming of 4–5 K or larger. Finally, we argue that these formulas can be used to reconstruct the tail of the rainfall distribution directly from its predictors without prior information on P. While scalings have beenmore »
- Authors:
-
- Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. of California, Irvine, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1563987
- Alternate Identifier(s):
- OSTI ID: 1544154
- Grant/Contract Number:
- AC02-05CH11231; SC0012548
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 12; Journal ID: ISSN 1942-2466
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; extremes; rainfall; superparameterization; convection; scaling
Citation Formats
Fildier, Benjamin, Parishani, H., and Collins, W. D. Prognostic Power of Extreme Rainfall Scaling Formulas Across Space and Time Scales. United States: N. p., 2018.
Web. doi:10.1029/2018ms001462.
Fildier, Benjamin, Parishani, H., & Collins, W. D. Prognostic Power of Extreme Rainfall Scaling Formulas Across Space and Time Scales. United States. https://doi.org/10.1029/2018ms001462
Fildier, Benjamin, Parishani, H., and Collins, W. D. Wed .
"Prognostic Power of Extreme Rainfall Scaling Formulas Across Space and Time Scales". United States. https://doi.org/10.1029/2018ms001462. https://www.osti.gov/servlets/purl/1563987.
@article{osti_1563987,
title = {Prognostic Power of Extreme Rainfall Scaling Formulas Across Space and Time Scales},
author = {Fildier, Benjamin and Parishani, H. and Collins, W. D.},
abstractNote = {Some studies documenting changes in extreme precipitation use scaling formulas to approximate the large percentiles of the rainfall distribution from average dynamical and thermodynamical variables, called predictors. Here we instead assess the performance of these formulas as approximations to the rain rates in individual events. We evaluate the accuracies of the scaling relationships as functions of spatial and temporal scales by analyzing tropical rainfall in a superparameterized model. Relationships using full vertical profiles of the predictors are more accurate than those using their values at specific vertical levels because they better characterize the specific dynamics of each event. Both types of scaling relationships perform well over a range of length scales from 200 to 2,000 km and time scales from an hour to a week, and their precision is higher in the case of simulations with superparameterization than with parameterized convection. Uncertainties emerging from the local use of the scaling relationships suggest that they may only characterize the intensification of individual extremes for a warming of 4–5 K or larger. Finally, we argue that these formulas can be used to reconstruct the tail of the rainfall distribution directly from its predictors without prior information on P. While scalings have been used as diagnostic equations conditioned on the occurrence of extreme rainfall, they are actually able to mimic the prognostic behavior of climate model parameterizations on a variety of scales when estimating the intensity, frequency, and spatial patterns of extremes.},
doi = {10.1029/2018ms001462},
journal = {Journal of Advances in Modeling Earth Systems},
number = 12,
volume = 10,
place = {United States},
year = {Wed Nov 28 00:00:00 EST 2018},
month = {Wed Nov 28 00:00:00 EST 2018}
}
Web of Science
Figures / Tables:
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