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Title: Future climate emulations using quantile regressions on large ensembles

Abstract

The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even the current climate do not perfectly reproduce local conditions. We present a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatiotemporal dependence in the observations. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out into the tail of the distribution one goes. We apply this method to two largemore » ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (CESM), run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling. The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter. We show how the availability of two ensembles allows the efficacy of the method to be tested with a “perfect model” approach, in which we estimate transformations using the lower-resolution ensemble and then apply the estimated transformations to single runs from the high-resolution ensemble. Finally, we describe and implement a simple method for adjusting a transformation estimated from a large ensemble of one climate model using only a single run of a second, but hopefully more realistic, climate model.« less

Authors:
 [1];  [1];  [2];  [1]
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  1. Univ. of Chicago, IL (United States)
  2. Univ. of Illinois at Urbana-Champaign, IL (United States)
Publication Date:
Research Org.:
Univ. of Illinois at Urbana-Champaign, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF)
OSTI Identifier:
1783208
Grant/Contract Number:  
SC0016162; 1106862; 1106974; 1107046; 0951576; 146364
Resource Type:
Accepted Manuscript
Journal Name:
Advances in Statistical Climatology, Meteorology and Oceanography (Online)
Additional Journal Information:
Journal Name: Advances in Statistical Climatology, Meteorology and Oceanography (Online); Journal Volume: 5; Journal Issue: 1; Journal ID: ISSN 2364-3587
Publisher:
Copernicus Publications
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Haugen, Matz A., Stein, Michael L., Sriver, Ryan L., and Moyer, Elisabeth J. Future climate emulations using quantile regressions on large ensembles. United States: N. p., 2019. Web. doi:10.5194/ascmo-5-37-2019.
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Haugen, Matz A., Stein, Michael L., Sriver, Ryan L., & Moyer, Elisabeth J. Future climate emulations using quantile regressions on large ensembles. United States. https://doi.org/10.5194/ascmo-5-37-2019
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Haugen, Matz A., Stein, Michael L., Sriver, Ryan L., and Moyer, Elisabeth J. Wed . "Future climate emulations using quantile regressions on large ensembles". United States. https://doi.org/10.5194/ascmo-5-37-2019. https://www.osti.gov/servlets/purl/1783208.
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@article{osti_1783208,
title = {Future climate emulations using quantile regressions on large ensembles},
author = {Haugen, Matz A. and Stein, Michael L. and Sriver, Ryan L. and Moyer, Elisabeth J.},
abstractNote = {The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even the current climate do not perfectly reproduce local conditions. We present a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatiotemporal dependence in the observations. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out into the tail of the distribution one goes. We apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (CESM), run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling. The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter. We show how the availability of two ensembles allows the efficacy of the method to be tested with a “perfect model” approach, in which we estimate transformations using the lower-resolution ensemble and then apply the estimated transformations to single runs from the high-resolution ensemble. Finally, we describe and implement a simple method for adjusting a transformation estimated from a large ensemble of one climate model using only a single run of a second, but hopefully more realistic, climate model.},
doi = {10.5194/ascmo-5-37-2019},
journal = {Advances in Statistical Climatology, Meteorology and Oceanography (Online)},
number = 1,
volume = 5,
place = {United States},
year = {Wed Apr 10 00:00:00 EDT 2019},
month = {Wed Apr 10 00:00:00 EDT 2019}
}
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    Extreme Floods and Droughts under Future Climate Scenarios
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    Estimating Concurrent Climate Extremes: A Conditional Approach
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