3.5.1: Downscaling strategies
Climate
projections must be made using global models because many of the processes and
feedbacks that shape the response of the climate system to external forcing
operate at the global scale. For many applications, where only the change in
some climate quantity is needed, global model projections can be used directly.
This is because climate change normally applies over a much larger area than
the climate itself, which can vary markedly over short distances in some
regions. This is particularly the case for projected change in temperature,
which has a very broad spatial structure, although local temperature may differ
between, for example, the bottom of a valley and the surrounding hillsides.
However, for
other applications, global climate model projections are not adequate, as they
typically have horizontal spatial resolution, or grid spacing, of 100 km or
coarser (see Figure 3.2 for explanation of model characteristics) (e.g.,
Charon, 2014). As an example, when using climate projections to drive a
detailed hydrological model at the scale of a drainage basin, one needs values
of future climate variables at a scale that respects local topographic,
coastal, and other features, and represents high-frequency variability and
extremes. Users of climate projections must therefore first evaluate whether
they really need high-resolution climate scenarios or whether they could make
effective use of lower-resolution climate change scenarios. Higher resolution,
in and of itself, does not necessarily indicate higher-quality or more valuable
climate information. But, for many applications, higher resolution may be
necessary, and can also facilitate better understanding among, and
communication to, users. A caution, however, is that internal climate
variability is reduced by averaging results over large areas, and so as one
goes from global to regional to local scale, internal variability becomes
larger, leading to larger uncertainty in projections at the local scale,
relative to that at regional or global scale (Hawkins and Sutton, 2009).
When climate
information at a higher spatial- or temporal-resolution is needed, there are
several approaches available to take global climate model projections and
“downscale” them to higher resolution for a region of interest (or even a
single location). These generally fall into two categories: statistical and
dynamical downscaling.
Statistical
downscaling is a form of climate model “post-processing” that combines climate
model projections with local or regional observations to provide climate
information with more spatial detail (Maraun et al., 2010; Hewitson et al.,
2014). Statistical post-processing methods typically downscale to higher resolution
and correct systematic model biases. A simple example is the so-called “delta
method,” in which the change in some climate quantity, obtained from a climate
model projection, is added to the observed historical value of that quantity.
This allows projected changes from different climate models to be used in a
consistent manner, since each model’s climatological bias is eliminated. Bias
correction is particularly important when using downscaled climate information
to drive impact models that depend on crossing absolute thresholds. For
example, snow accumulation is sensitive to whether temperature is above or
below freezing.
Simple
techniques like the delta method may be suitable for some quantities, such as
mean temperature, but not for others, such as daily precipitation, for which
biases may be manifested differently, in variability, extremes, or dry/wet
spells (Maraun et al., 2010). More complex statistical downscaling approaches
are required in such cases, making use of detailed high-resolution observational
datasets that reflect local topographic influences. These high-resolution data
are used to interpolate low-resolution climate change projections to much
higher resolution. In some cases, bias correction and other refinements are
applied to correct statistical properties such as variances (Werner and Cannon,
2016). Yet other statistical downscaling methods take advantage of observed
relationships between large-scale atmospheric circulation patterns, which are
often well simulated by climate models, and local variables. By assuming that
these statistical relationships remain fixed under a changing climate, climate
model projections of circulation patterns can be used to make projections of
future climate at a particular location. The statistical relationships
introduce some aspects of local climate that may not be well represented in the
driving global model (e.g., local topography and proximity to a lake).
Fundamentally, all statistical downscaling methods assume that relationships
between a model’s historical simulation and observations do not change over
time, and that the information provided by the climate model and historical
observations at their respective spatial scales is credible. Thus, the quality
of statistical downscaling is directly related to the quality and availability
of observational data. Recent critical reviews provide more information on the
strengths and weaknesses of statistical downscaling and bias correction methods
(Hewitson et al., 2014; Maraun, 2016).
Dynamical
downscaling involves the use of a regional climate model, which is a physically
based climate model (of the same level of complexity as a global model) that
operates at high resolution over a limited area. Regional climate models
incorporate much the same physical processes and scientific understanding as
global climate models and indeed often share much of the same computer code.
The important distinction is that regional climate models are driven at their
lateral boundaries by output from a global climate model, as shown in Figure
3.10. The regional model also inherits errors and biases that may be present in
the global model whose results are provided at the boundaries. The main
advantage of dynamical downscaling is that, due to its limited area, a regional
model can simulate climate on a much higher resolution than is possible with a
global model, using a similar amount of computing effort. This additional
detail is often desirable, particularly where the regional model output is used
to drive another model (e.g., a hydrological model in which detailed basin
geometry, high-frequency precipitation and extremes, and other small-scale
features are essential). However, it remains an ongoing research topic to
determine whether, and under what conditions, regional models add value
relative to the original global model results that are being downscaled. There
are no agreed-upon measures for added value (Di Luca et al., 2015; 2016;
Scinocca et al., 2016), although the evidence available at the time of the IPCC
Fifth Assessment indicated that there is added value in some locations owing to
the better representation of topography, land/water boundaries, and certain
physical processes, and that extremes are better simulated in high-resolution
regional models (e.g., Flato et al., 2013).
An
additional advantage of dynamical over statistical downscaling is that physical
relationships between different climate variables (such as temperature and
precipitation) are maintained. For very high-resolution dynamical downscaling
(at model resolution of a few kilometres), physical processes such as
convection can be resolved explicitly and can lead to improved simulation of
climate variables such as precipitation extremes. Several recent studies
indicate added value, including a dynamical downscaling system that includes a
detailed representation of the Great Lakes (Gula and Peltier, 2012), the
potential for added value near well-resolved coastlines (Di Luca et al., 2013),
and evidence for improved simulation of temperature and precipitation extremes
(Curry et al., 2016a,b; Erler and Peltier, 2016).
3.5.2: Downscaling results for North
America and Canada
Both
statistical and dynamical downscaling approaches have been applied and
evaluated in many areas of the world. For North America, coordinated dynamical
downscaling comparisons have been undertaken as part of the North American
Regional Climate Change Assessment Program (NARCCAP: http://www.narccap.
ucar.edu/) and the Coordinated Regional Downscaling Experiment (CORDEX: https://na-cordex.org/).
For CORDEX, simulations were run at resolutions of approximately 25 km and 50
km. In both NARCCAP and CORDEX, Canadian models are represented. Coordinated
experiments like these provide results from different regional climate models,
driven at their boundaries by output from different global climate models. They
also allow scientists to determine whether regional differences in projected
climate change are related to the differences in the global driving models or
to differences in the regional downscaling models. However, the CORDEX ensemble
is considerably smaller than the CMIP global model ensemble, and studies using
the CORDEX ensemble tends to focus on sub-regions rather than on Canada as a
whole.
For Canada,
regional climate models with smaller domains and higher resolution are being
used, particularly by the Ouranos consortium and the Centre pour l’étude et la
simulation du climat à l’échelle régionale (ESCER) at the Université du Québec
à Montréal. Some of these simulations provide results at 15 km resolution
(e.g., https://www.ouranos.ca/en/program/climate-simulation-and-analysis/).
Statistical downscaling results are also readily available for Canada
(https://www.pacificclimate.org/data/statistically-downscaled-climate-scenarios),
with daily temperature and precipitation data at approximately 10 km
resolution. These state-of-the art downscaling approaches (Werner and Cannon,
2016) are driven by multiple global climate model projections. In addition to
the more detailed spatial structure, sophisticated statistical downscaling
approaches can also provide estimates of future changes in climate extremes and
other indices (such as frequency of hot days, growing season length, and
drought indices) that are particularly important for certain impact studies (see
Chapter 4). Downscaled results can also be used as inputs to impacts models —
such as hydrological, crop, and ecosystem models — that are sensitive to
variability on small spatial scales and to biases in climate models (Wood et
al., 2004).
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