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Title: 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 » 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. 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}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 7 works
Citation information provided by
Web of Science

Figures / Tables:

Table 1 Table 1: Accuracy and Uncertainty Metrics for Both Scalings and All Four Experiments at the 99.9th Percentile, Averaged Across All Spatial and Temporal Scales Analyzed

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Works referencing / citing this record:

Modeling extreme precipitation over East China with a global variable-resolution modeling framework (MPASv5.2): impacts of resolution and physics
journal, January 2019


Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.