Abstract. The detection of meteorological, chemical, or other signals in modeled or
observed air quality data – such as an estimate of a temporal trend in
surface ozone data, or an estimate of the mean ozone of a particular region
during a particular season – is a critical component of modern atmospheric
chemistry. However, the magnitude of a surface air quality signal is
generally small compared to the magnitude of the underlying chemical,
meteorological, and climatological variabilities (and their interactions)
that exist both in space and in time, and which include variability in
emissions and surface processes. This can present difficulties for both
policymakers and researchers as they attempt to identify the influence or
signal of climate trends (e.g., any pauses in warming trends), the impact
of enacted emission reductions policies (e.g., United States
NOx State Implementation Plans), or an estimate of the mean
state of highly variable data (e.g., summertime ozone over the northeastern
United States). Here we examine the scale dependence of the variability of
simulated and observed surface ozone data within the United States and the
likelihood that a particular choice of temporal or spatial averaging scales
produce a misleading estimate of a particular ozone signal. Our main
objective is to develop strategies that reduce the likelihood of
overconfidence in simulated ozone estimates. We find that while increasing
the extent of both temporal and spatial averaging can enhance signal
detection capabilities by reducing the noise from variability, a
strategic combination of particular temporal and spatial averaging scales can
maximize signal detection capabilities over much of the continental US. For
signals that are large compared to the meteorological variability (e.g.,
strong emissions reductions), shorter averaging periods and smaller spatial
averaging regions may be sufficient, but for many signals that are smaller
than or comparable in magnitude to the underlying meteorological variability,
we recommend temporal averaging of 10–15 years combined with some level of
spatial averaging (up to several hundred kilometers). If this level of
averaging is not practical (e.g., the signal being examined is at a local
scale), we recommend some exploration of the spatial and temporal variability
to provide context and confidence in the robustness of the result. These
results are consistent between simulated and observed data, as well as within a
single model with different sets of parameters. The strategies selected in
this study are not limited to surface ozone data and could potentially
maximize signal detection capabilities within a broad array of climate and
chemical observations or model output.
Recent development of an UHR and ULV FE-SEM with various signal detection capabilities and its applications
