20
BAMOS
June 2017
Intensity-related heatwave metrics show some reduction in
model error when bias-corrected, though this is predominantly
the case for coastal stations (Fig. 3a). Those coastal stations
affected by bias-correction in the recent climate lend more
credence to the equivalent bias-corrected stations for future
projections (Fig. 3b). Smaller increases in heatwave amplitude
are projected for these stations, compared with the equivalent
uncorrected model runs.
The EHF-derived heatwave indices are relative measures that
are calculated using a percentile-based threshold. In contrast,
more traditional indices that use fixed thresholds indicate bias-
correction has in fact a substantial effect on model output. For
example, the index SU30, which calculates days that exceed
30°C (Alexander and Herold, 2015), shows substantial decreases
in model error to observations when bias-corrected model runs
are used (Fig. 4). Bias-corrected regional simulations of future
changes in SU30, and potentially other similarly derived indices,
would therefore be highly valuable.
Conclusion
To inform future health policy and planning, we need to
understand how heatwaves might change on a regional scale.
The key to reliable projections is to decrease uncertainty
related to model output. Our study suggests that bias-
correction can substantially increase the ability of an RCM
ensemble to simulate the recent climate, though this is highly
dependent on the type of metric being investigated. Metrics
which are minimally affected by bias-correction, such as those
based on relative thresholds, may not necessarily require these
sophisticated datasets. Other metrics, such as those based on
fixed thresholds, could benefit greatly in using bias-corrected
data. This work, however, is specific to the NARCliM simulations
and EHF-derived metrics, and so further work investigating
other regions, methods and simulations is necessary to
determine if these conclusions are universally applicable.
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