18
BAMOS
June 2017
Article
Can Regional Climate Models simulate
heatwaves for New South Wales and the
Australian Capital Territory?
An overview of Gross et al. 2017
Mia Gross
Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South
Wales, Sydney, Australia, [email protected].
Introduction
Extreme heat events are one of the most perceivable aspects
of climate change, causing substantial damage to both people
and infrastructure. Heatwaves, for example, have clear links to
human mortality and morbidity. In 2003, at least 25,000 people
died from a heatwave across Europe (D’ippoliti et al., 2010),
while the 2009 heatwave in Victoria before the Black Saturday
bushfires resulted in 374 excess deaths (Victorian Government
Department of Human Services, 2009).
Alarmingly, many studies point to future increases in heatwave
frequency, intensity and duration (e.g. Alexander and Arblaster,
2009; Cowan et al., 2014). These projections are made possible
through the use of Global Climate Models (GCMs). Robust
conclusions rely on the ability of GCMs to realistically simulate
the past and current climate. While GCMs can reasonably
simulate temperature extremes from observations on a global
and continental scale (e.g. Alexander and Arblaster, 2009), they
lack the finer scale detail needed for regional applications.
Robust regional scale projections of how heatwaves
might change are crucial, as characteristics of heat-health
relationships can be dependent on location (e.g. Curriero et al.,
2002). Regional Climate Models (RCMs) provide simulations
at a finer scale which is more appropriate for investigating
regional changes in heatwaves.
Using Regional Climate Models to simulate
heatwaves
To help reduce uncertainties that are inherent in the models,
multi-model ensembles can be used which sample a range
of different physical parameterisations. Multi-model RCM
simulations are provided in the New South Wales/Australian
Capital Territory Regional Climate Modelling (NARCliM) project
(Evans et al., 2014). NARCliM implements a technique known
as dynamical downscaling, which uses RCMs to downscale
coarse resolution GCMs to much finer resolutions. However,
biases within the GCMs are inherited by the RCMs in the
process. NARCliM provides simulations from a 12-member
RCM ensemble that includes four GCMs which are downscaled
using three different configurations of the Weather Research
and Forecasting (WRF) RCM. The simulations are available in
both raw model output, as well as bias-corrected output —
a
term used to describe model output that has been adjusted
to reduce the inherited biases and resemble the observations
more closely. In our study, we used NARCliM simulations,
alongside observational data, to investigate if uncorrected and
bias-corrected RCMs can represent heatwave characteristics,
using measures that are relevant to the heat-health relationship.
We combined an increasingly used heatwave index known as
the Excess Heat Factor (EHF) (Nairn and Fawcett, 2013), which
accounts for acclimatisation and heat stress in its calculation,
with standard heatwave metrics related to frequency, intensity
and duration (e.g. as in Perkins and Alexander, 2013). In this
methodology, heatwaves are defined as events which last for
at least three consecutive days. The EHF-derived indices were
calculated for both uncorrected and bias-corrected NARCliM
model output for the recent climate (i.e. 1990–2009), to be
evaluated against observational data.
Observations from 25 stations located in New South Wales
and the Australian Capital Territory were selected from the
Australian Climate Observations Reference Network—Surface
Air Temperature (ACORN-SAT) dataset (Trewin, 2013, see Fig.
1). ACORN-SAT represents a high-quality daily temperature
dataset of station-based observations, however, the RCM
simulations are based on gridded output. We therefore chose
to additionally include gridded data from the Australian Water
Availability Project (AWAP) (Jones et al., 2009). The grid points
closest to the ACORN-SAT stations were then used to allow
comparison between the datasets. The results of the RCM
simulations of the recent climate could then be used to infer
results of bias-corrected and uncorrected model simulations of
future changes in heatwaves for the period 2060–2079.
The importance of bias-correction and metric
selection
Overall, while no individual NARCliM simulation of the EHF-
derived heatwave indices perfectly resembles the observations,
the ensemble of uncorrected output performs reasonably well
against the observations. Simulations of stations further inland