Lake Melville

3.2. Methods and approach
Through detailed analyses of climate station data,
atmospheric reanalyses, and Canadian Ice Service (CIS)
ice charts for locations on Lake Melville (Figure 3.2),
we updated our understanding of Labrador climate
dynamics and their relationship to ice conditions on
Lake Melville. The majority of our work is based on a
regionally representative temperature data set based
on the National Center for Atmospheric Research/
National Centers for Environmental Prediction
reanalysis (Kalnay et al., 1996), a roughly 65 year
data set that represents our ‘best guess’ of weather
conditions at six hourly intervals. This data set has
been supplemented where appropriate with biascorrected
records from Labrador climate stations
(Vincent et al., 2012). Change point analysis of this
data set was used to identify distinct climate ‘regimes’
affecting Labrador, while generalized linear models
were used to identify/quantify contributions to these
regimes from various natural sources (e.g. shifts in
atmospheric circulation). Results subsequently guided
analysis of weekly CIS ice chart data, to connect climate
anomalies and regimes to changes in the timing of
freeze-up and break-up across Lake Melville.
In order to identify regime start dates, possible
regime change points were identified in seasonally
and annually averaged temperature data, following
Rodionov (2004). Final regimes represent a compromise
between shift dates identified in different seasons; a
detailed description of the method and its application
are available in Finnis and Bell (2015).
Attribution of temperature anomalies to natural
and anthropogenic forces was based on an adapted
version of a regression-based methodology used in
similar global analyses (Foster and Rahmstorf, 2011).
The addition was a cross-validated stepwise predictor
selection approach to building a statistical model,
designed to avoid model overfitting. Full details are
available in Finnis and Bell (2015).
Analysis of Labrador thaw events is based on the
application of a point process statistical model,
following the heat wave analyses of Furrer et al.
(2010). This treats thaw events as a point process,
with the number of events in a season treated as a
Poisson distribution, the length of events treated as
a geometric distribution, and maximum temperatures
in individual events fit to the generalized Pareto
distribution. The combined frequency and length
distributions were subsequently used to generate a
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distribution of total thaw days per season. This was
done by using the point process model as a stochastic
weather generator; the frequency distribution was
randomly sampled to produce 1,000,000 simulated
thaw years (number of events per year), and each event
in each year was then assigned a length (number of
days per event) by randomly sampling from the thaw
length distribution.
3.3. Understanding the influences of climate
on Lake Melville
Significant natural variability in Labrador is masking
impacts of climate change
Results highlight the significant natural climate
variability that Labrador experiences, identify causes
of this variability, and confirm that the region is
experiencing ongoing warming. The Labrador climate
record demonstrates pronounced, slow-acting
variability; that is, it experiences prolonged periods
when temperatures are either well above or well below
the long-term average (Finnis and Bell, 2015). These
climate ‘regimes’ can persist for several decades, and
arise from natural cycles in: 1) the path of storms
through eastern Canada and across the Atlantic, and 2)
mean sea surface temperatures in the Atlantic Ocean.
These cycles are often discussed in terms of two
climate indices: the North Atlantic Oscillation (NAO;
storm tracks) and Atlantic Multidecadal Oscillation
(AMO; ocean temperatures) (see Box 3.1 and Box 3.2 for
more information about the NAO and AMO) (Finnis and
Bell, 2015; Way and Viau, 2015).
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Figure 3.2. Locations used for analysis of Lake Melville ice
climatology, using Canadian Ice Service charts.
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