Future climate emulations using quantile regressions on large ensembles
2019
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 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.
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