Questions or feedback should be directed to: dmckenne@nrcan.ca
Key points regarding this data set:
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1961-1990 Monthly mean minimum and maximum temperature and precipitation
averages from McKenney et al. 2001 (Historical averages over the 30 year period
1961-1990). Canadian Climate Archive Data modeled to a ~10 km grid (300 arc
seconds)
1930-1960 Monthly mean minimum and maximum temperature and precipitation
averages from McKenney et al. 2001 (Historical averages over the period 19301960). Canadian Climate Archive Data modeled to a ~10 km grid (300 arc seconds)
1961-1990 monthly mean precipitation for May through October (Historical average
over the 30 year period 1961-1990 using the same methodology described below but
includes stations that only operate during the summer months – the two datasets
mentioned above only included stations that had values for 12 months). These
precipitation data are included along with the other 61/90 precipitation grids because
some users may have an interest in both sets. In addition some applications have
already occurred using the original results.
Disclaimer:
This data is hosted on the CICS website as a public service only. Please refer to the
cautions, limitations, and methodology sections below before proceeding. This data is
available free for public use but remains copyright Natural Resources Canada. The source
data were provided by the National Archive and Data Management Branch of
Environment Canada. We thank them for access to data and for encouraging the
development of our spatial climate models. Please cite the article identified in the
documentation and acknowledge Environment Canada if you use the data.
Data access page
Documentation: This work is reported from McKenney, D.W., Kesteven, J.L.
Hutchinson, M.F., Venier, L., 2001. Canada's plant hardiness zones revisited using
modern climate interpolation techniques. Can. J. Plant Science. 81: 139-143. See also
http://www.glfc.cfs.nrcan.gc.ca/landscape/index_e.html and follow the links to the climate
modeling section – additional references are provided and other gridded climate data are
being made available over time. These models (maps) and others (eg growing season
lengths, degree days, etc) can also be viewed and queried at that URL.
Data Source: For the period 1961 to 1990, monthly total precipitation and mean
temperature data that are stored in the Canadian Climate Archive were modeled and
interpolated to a ~10 km grid, geographic projection, 300 arc second. The regular grid of
only includes Canada. . No data from the neighbouring states in the United States were
utilized in this interpolation but data density along the Canada/USA border was good for
the 1961 to 1990 period.
Methodology:
Climate models for the 1930 – 1960 and the 1961-1990 period were developed using the
ANUSPLIN thin plate spline interpolation methods of Dr. Michael Hutchinson of the
Australian National University (eg. Hutchinson, 1995). Trials of two and three
dimensional splines were undertaken and evaluated using withheld data. The tri-variate
function of position (longitude and latitude) and elevation performed best. We used a new
Digital Elevation Model (a regular grid of position and elevation) of the country based on
the National Topographic Series 1:250,000 topographic database to create the grids at a
300 arc second resolution (~10km).
A general representation for a (partial) thin plate spline of n data values zi at positions xi
is given by (Hutchinson, 1995):
p
z i  f ( xi )    j  j ( xi )   i (i  1, , n; j  1,  , p)
j 1
where f is an unknown smooth function to be estimated and the j are the p parameters of
a linear sub-model. The xi typically represent longitude, latitude and elevation but are not
restricted to these (e.g. aspect, large water bodies, etc. could in principle be included as
independent variables; see Hutchinson,1998b). The function becomes a partial spline
when p is positive. Such functions can include elevation, for example, as a covariate
(Hutchinson 1991). This last case would imply a constant lapse rate (e.g. change in
temperature or precipitation per 1000 meters change in elevation) over the area of
interest. The i are zero mean random errors with covariance matrix given by V2 where
V is a positive definite n  n matrix, and the variance 2, may be known or unknown. V is
usually chosen to be a diagonal matrix (Hutchinson 1995). The i account for data
errors as well as fine scale spatial variability in the climate field below the resolution of
the data network. When interpolating monthly mean climate data, the data errors include
errors due to short records. This is particularly relevant for precipitation. The function f is
estimated by minimizing:
( z  f ) T V 1 ( z  f )   J m ( f )
where: z = (z1, ….., zn)T and f = (f1, ….. , fn)T, with
f i  f ( xi )
and Jm(f) is a measure of the roughness of the spline function f defined in terms of mth
order derivatives of f and  is a positive number called the smoothing parameter. The
smoothing parameter is calculated by minimizing the Generalized Cross Validation
(GCV) statistic, a measure of the predictive error of the surface. The GCV is calculated
by implicitly removing each data point and summing the square of the difference of each
omitted data point from a surface fitted to all the remaining data points. This assessment
of predictive error has been found to be reliable in several applications (Wahba, 1990;
Hutchinson and Gessler, 1994). Importantly, splines do not require a priori estimation of
spatial auto-covariance structures as necessary with kriging. This can be especially
difficult in situations where variances in station data vary spatially, as might be expected
in national climatic data.
The final trivariate splines (with no data withheld) had varying degrees of freedom. In the
1961-1990 period in two temperature cases the signals are above 70% while for rainfall
the signal was greater than 70% in eight cases. These cases suggest that more data are
required to improve the surfaces. Standard errors are also larger in the cases with larger
signals. Root mean square errors expressed as a % of the precipitation surface mean
range from 28% in Febuary to 6% in June and July. Precipitation, particularly in winter,
is a more spatially complex phenomenon. The standard errors refer to the overall surface
and are not uniformly distributed spatially. They are higher where data are sparse and
topography is more complex (Hutchinson 1995). For temperature the standard errors are
for the most part less than half a oC. Slightly larger errors for minimum temperature
probably reflect localized cold air drainage effects which have not been explicitly
modeled (weather stations in western Canada are often in valleys). Winter months are the
most difficult to model. Results for the 1930-1960 period were similar. Our results are
comparable with applications of this procedure in other countries (e.g. Hutchinson,
1991). However, our results also suggest that more station data are required in western
Canada to better capture the inherent spatial variablity of climate, particularly rainfall, in
mountainous terrain. Figures and more discussion are provided in the reference
mentioned above.
Limitations and Caution: These grids will be different from other gridded datasets (eg.
CANGRID based on the homogeneous temperature and rehabilitated precipitation data
sets developed by the Climate Research Branch of Environment Canada). For example,
the CANGRID data sets were developed for long-term stations by adjusting for
instrumental bias and changes in observing protocol and station siting. However, there
were far fewer stations available to CANGRID. ANUSPLIN models of the rehabilitated
datasets were not as good in terms of model diagnostics because there were less stations
available (<500 stations) and hence those grids are not provided here.
In the models provided here stations with a minimum of at least 5 years of data were used
in the surface fitting. These stations were not adjusted to the normal period, hence some
biases may be present in areas where there have been significant changes over the period.
If researchers require point estimates at particular locations they could contact the author
for further information.
Units: All temperatures are to the nearest tenth of a degree Celsius. All precipitation
values are expressed to the nearest tenth of a millimetre. The decimal is explicit (e.g. 20.1
C or 46.5 mm)
For fuller documentation, please refer to the documentation cited above.
Questions about the data or feedback should be directed to:
Dan McKenney (PhD)
Chief, Landscape Analysis and Applications
Canadian Forest Service
1219 Queen Street East
Sault Ste. Marie, Ontario
CANADA P6A 2E5
Ph 705-759-5740 ext 2316
Fax 705-759-5700
Email dmckenne@nrcan.gc.ca
Canadian Institute for Climate Studies
130 Saunders Annex
University of Victoria
PO Box 1700 Sta CSC
Victoria, BC V8W 2Y2
Tel: (250) 721-6236
Fax: (250) 721-7217
Feedback and Enquiries
Disclaimer: The information on these pages has been obtained from a variety of sources
and while efforts have been undertaken to assure its
accuracy, it is provided without warranty as a public service by the Institute. Any
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these pages is the sole responsibility of the person taking the decision.