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Performance of pattern scaling in estimating local changes for untried
GCM-RCM pairs: Implications for ensemble design
Elizabeth Kendon
Met Office Hadley Centre, Exeter, UK; elizabeth.kendon@metoffice.gov.uk
1. Introduction
Pattern scaling techniques have been widely used to provide
climate change projections for time periods and emission
scenarios that have not been simulated by GCMs. The
assumption underlying these methods is that the
geographical pattern of the change is independent of the
forcing. Thus the local response of a climate variable is
assumed to be linearly related to the global mean
temperature change, with the scaling coefficient only
dependent on position. This condition is largely satisfied for
mean temperature and to a lesser degree precipitation
(Mitchell et al., 1999; Mitchell, 2003).
In this study, we assess whether pattern scaling methods can
be used to estimate changes at the RCM scale for a full
range of driving GCMs. The availability of GCM
simulations raises the possibility of using anomalies at the
GCM scale, rather than global mean temperature change, as
the predictor of the local climate response. Thus the change
in the RCM for a given variable is expressed as:
where the scaling coefficient A depends on position (x) and
season (s), but not on year or period (y) or forcing scenario
(e). ∆RCM is the change in the RCM and ∆GCM is the
corresponding change for the given variable in the nearest
GCM grid box. The basis for this ‘local scaling’ method
(Fig. 1) is that regional patterns of precipitation are
produced predominantly by the interaction between largescale
systems and the stationary topography (Widmann et
al., 2003).
Global T
GCM
Δ RCM xsye = A xs Δ GCM xsye
Precip
GCM GCM
‘direct’ scaling
Precip
RCM
‘local’
scaling
Figure 1. Schematic illustrating the ‘local scaling’
method as compared to traditional ‘direct’ pattern
scaling techniques.
In this study we examine whether this simple local scaling
relationship can be used to predict the local change for
untried GCM-RCM pairs i.e. to what extent the scaling
coefficient A can be assumed to be independent of the
driving GCM. We examine the accuracy of this technique
both for time-mean variables and for measures of
variability and extremes, focusing on temperature and
precipitation across Europe. The implications of this for
GCM-RCM ensemble design are discussed.
2. Methodology
We use data from the Rossby Centre regional climate
model RCA3 driven by a 3-member initial condition
ensemble of ECHAM5 and by 3 members of the Hadley
Centre perturbed physics ensemble of HadCM3,
specifically the standard model HadCM3-Q0, and the low
and high sensitivity experiments HadCM3-Q3 and
HadCM3-Q16 respectively. For each of the 6 RCA3
simulations and the corresponding driving models, we
calculate various statistics of the daily precipitation and
temperature distributions, for each season, for the control
1961-89 and future 2071-99 periods.
The 3-member ensemble of ECHAM5 driven integrations
allows us to examine the importance of natural variability.
Using the method outlined in Kendon et al. (2008) we
identify where the change in a given statistic in the RCM
is significant compared to natural variability, and in
particular, we only assess the performance of pattern
scaling where the RCM change is robust.
At each grid box and for each variable, where the change
is found to be robust, the scaling coefficient A is
calculated using linear regression applied to the 3 member
ensemble of ECHAM5 driven runs. The resulting
relationship is then used to estimate the RCM change for
each of the HadCM3 driving GCMs, and compared with
the actual simulated RCM change.
3. Preliminary results
For precipitation in winter, where changes are robust
compared to natural variability, local scaling generally
performs well in estimating RCM changes for a different
driving GCM (with errors from scaling less than 50%).
This is true both of changes in mean precipitation, and
also changes in variability and extremes. In summer,
changes in daily precipitation statistics are not robust
compared to natural variability in many regions across
Europe. However, where changes are robust in the case of
summertime mean precipitation and wet day frequency,
local scaling does not perform well.
For temperature, local scaling generally performs well in
estimating local changes in mean and extremes, in both
winter and summer. However there is some evidence of
reduced scaling performance in coastal regions and
snow/ice margins in winter and for temperature extremes
in a band across central and northern Europe in summer.
Changes in temperature variability are found not to be