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Modeling mass balance of glaciers in the Coast Mountains of
British Columbia under the influence of future climate change
Thesis Proposal
(Draft)
Raju Aryal
NRES, PhD Program
University of Northern BC
December, 2008
1.0 Introduction
During the last 100 years, the province of British Columbia has experienced warming
consistent with trends seen around the globe. Average annual temperature warmed by
0.6ºC on the coast, 1.1ºC in the interior, and 1.7ºC in northern BC over a period of 1895 to
1995 and it is projected to increase by 1oC to 4oC during 21st century (BC Ministry of
Water, Land and Air Protection, 2002) (Figure 1). Trend in temperature rise can be seen
both in instrumental records as well as in proxies particularly after Little Ice Age
maximum in glacier extent. Climate warming is therefore believed to be the main cause for
recent glacier recession in BC. In the Canadian Cordillera, glacier recession has been
associated with unusually warm mean annual air temperatures and a reduction in winter
snowfall since 1976 (Moore and Demuth, 2001; Demuth and Keller, 2006). DeBeer and
Sharp (2007) report a net loss of glacier area in the southern Canadian Cordillera through a
comparison of historical aerial photography with contemporary Landsat 7 ETM+ imagery.
Place glacier in southern British Columbia has been experiencing negative mass-balance
since measurement began in 1965 (Moore and Demuth, 2001). The terminus positions of
Helm Glacier in southwestern BC and Illecillewaet Glacier in southeastern BC both
receded by more than 1,100 metres from 1895 to 1995 (Figure 2). Wedgemont Glacier near
Whistler, BC has retreated hundreds of metres in the past two decades alone BC Ministry
of Water, Land and Air Protection, 2002).
1
Figure 1. Change in Average Temperature 1895-1995 in BC (°C per century)
Figure 2. Changes in glacier terminus of Illecillewaet and Helm glacier in BC, 1895-1995
There has been increasing concern over the loss of glacier ice reserves in BC following
climate warming in the region. Glacial meltwater feeds many mountain streams and rivers
in BC. Glacier meltwater constitutes a major portion of river flows of these rivers
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especially during summer when rainwater contribution to the river flow is at a minimum.
Although only a small percentage of the total annual runoff of higher-order streams and
rivers in the Cordillera is contributed by glacial melt, mountain glaciers tend to moderate
interannual variability in stream-flow and help to maintain higher runoff volume during
extreme warm and dry periods (Fountain and Tangborn, 1985; Hopkinson and Young,
1998). A decrease in the late-summer flow of glacier-fed rivers throughout British
Columbia has been observed, indicating that most glaciers here have already passed the
phase of warming induced runoff increases (Stahl and Moore, 2006) suggesting that we
might be in a period of diminishing glacier runoff resulting from decrease in glacier
covered area. Analysis of historical data revealed significant changes in the stream flow of
the upper Similkameen River in BC over the last 30 years (Leith and Whitfield, 1998)
(Figure 3). Long term late summer stream flow record downstream of Place Glacier
suggests a negative trend in total runoff. This is attributed to significant depletion of firn
ice prior to 1965, such that the dominant effect of glacier changes was a reduction in ice
area, resulting in decreased meltwater production (Moore and Demuth, 2001).
Figure 3. Changing stream flow pattern of Upper Similkameen River in BC, 1971-1995
(Source: Leith and Whitfield, 1998)
Decrease in meltwater contribution to stream flow is likely to have an adverse impact on
water resources of BC. Hydropower generation, water supply for municipal and industrial
3
uses, agriculture, fisheries etc. are some of the key areas likely to be affected most. Beside
changes in flow pattern, glacier retreat is likely to change the temperature of some streams
and rivers. These changes, along with other climate-driven changes to hydrological
systems, will likely have significant impacts on freshwater and estuarine ecosystems and
on aquatic species. They will affect other biological systems and human activities that
depend on water.
To formulate future water management policies in response to the growing challenges
associated with climate change, it is important to conduct climate change impact studies on
glaciers of the Coast Mountains. Therefore, the present research aims to assess the impact
of climate change on glaciers of the Coast Mountains of western Canada. It is believed that
the results from this study will help formulating future water management policies in the
region in consideration of future climate change.
2.0 Objective
The main objective of this research is to simulate the future mass balance of selected
glaciers in the Coast Mountains following climate warming using a Glacier Mass Balance
(GMB) model forced by downscaled coarse resolution temperature and precipitation fields
at monthly time resolution. While doing so, the research aims to achieve the following
goals:
i.
Develop a model to downscale coarse resolution temperature and precipitation
fields to the glacier scale
ii.
Develop high resolution monthly temperature and precipitation fields for climate
change impact studies in the Coast Mountains
iii.
Develop methods to transfer the simulated mass balance results from selected
glaciers to other glaciers in the Coast Mountains
3.0 Study Area
Two geographical regions within western Canada have been selected for this research-the
Coast Mountains and the Rocky Mountains.
4
Tiedemann glacier is the main glacier in the Coast Mountains selected for this research.
Tiedemann glacier is situated on the Mt. Waddington range at the lee (eastern) side of the
southern Coast Mountains of British Columbia (Fig. 4). Its mean coordinate is 51°19’N;
124°54’W and has a length of about 24 km (Canadian Glacier Inventory Projecthttp://cgip.wetpaint.com/). Elevation of the glacier ranges from 700 m to 3,800 m. With an
area of 63 km2 (Ommanney, 2002), it is one of the more prominent valley glaciers on the
east side of the Waddington Range. The ablation area of this glacier is partially covered by
debris.
Tiedemann glacier is one of the model glacier selected for climate change impact studies
by Western Canadian Cryospheric Network (WC2N), a network of research institutions
that is studying the past and future impact of climate change on glaciers of western Canada
and its implication to freshwater ecosystem. This research will mainly focus on modeling
the mass balance of Tiedemann glacier.
The ablation areas of some of the larger glaciers in the Coast Mountains are covered with
some amount of debris. To simulate the mass balance of glaciers in the Coast Mountains, it
is important to understand the influence of debris on ice ablation underneath. In situ
measurement of melt rate of bare ice as opposed to melt rate of debris covered ice would
give a coefficient of melt which can be included in GMB model for simulating mass
balance of debris covered areas.
Dome glacier, situated inside Jasper National Park in the province of Alberta (Figure 4),
has been selected for taking in situ measurement of thermal properties of debris material
and melt rate of ice under the influence of debris. It is one of many glaciers originating
from the Columbia Icefield and lies adjacent to Athabasca glacier (52o12.1'N; 117o18.1'W).
The glacier has an area of 5.92 km2 out of which 2.16 km2 is covered by debris. Its total
length is 5.7 km and elevation ranges from 1980 m at the bottom to 3200 m at the highest
point (Canadian Glacier Inventory Project- http://cgip.wetpaint.com/) . The glacier is
classified as outlet valley glacier. Dome glacier is preferred over other debris covered
glaciers due to ease of accessibility.
5
Figure 4. Location map of Dome glacier in the Rockies (upper right) and Tiedemann
glacier in southern Coast Mountains (lower right)
4.0 Data
The research will use point observations, interpolated gridded observed datasets, reanalysis
datasets and GCM future climate scenarios covering a domain between 128oW-120oW and
48.5oN-53.5oN. These datasets will be considered at monthly temporal resolution and
model output will also have same temporal resolution.
NCEP reanalysis data
These data have been widely used in North America as well as in Europe to calibrate
models for downscaling GCM future climate projections. The dataset provides global
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meteorological fields at 6-hour intervals from 1948 to present. It is based on T62 global
model and has grid resolution of 2.5o×2.5o and has 28 vertical levels (Kalnay et al., 1996).
The
data
are
available
from
the
NCEP
web
site
(http://www.cdc.noaa.gov/cdc/data.ncep.reanalysis.html). This dataset includes different
variables such as temperature, precipitation, SLP, wind, humidity, geopotential height and
many more at different pressure levels. The present research will use the NCEP dataset
from 1961-2001 for ‘predictor’ variables to calibrate and validate the SD model. These
data require some processing before they can be used for calibrating the SD model. This is
discussed in detail in methodology section.
PRISM based ClimaetBC dataset
PRISM (parameter-elevation regressions on independent slopes model) is a regression
based model that uses point observations, a digital elevation model (DEM) as well as other
spatial datasets to interpolate sparse observations onto a regular grid (Daly et al. 2002)
PRISM is based on the assumption that the distribution of temperature and precipitation in
a localized region is determined by elevation. Observations from many parts of the world
show the altitudinal variations of temperature and precipitation to approximate a linear
form.
In PRISM, a simple linear climate-elevation regression is employed for each DEM grid
cell to vertically extrapolate the climate fields. Linear regression is chosen over other
nonlinear methods mainly because altitudinal variation of climate fields often exhibits
linear form. In addition to this, linear functions are more stable when extrapolating beyond
the elevational range of data and also they can be easily manipulated to compensate for any
inadequacies
in
data.
(PRISM
Guide
Book;
http://www.prism.oregonstate.edu/docs/index.phtml)”
Monthly precipitation and temperature datasets for part of North America, at 4 km
resolution, prepared using the PRISM model, are available from the web site of the PRISM
group at Oregon State University.
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However, the 4 km grid resolution is not suitable for climate impact studies and other
ecological modeling in mountain areas. The PRISM climate estimates are based on each
tile’s average elevation. According to Wang et al. (2006), PRISM tile elevation of a
particular location in mountain areas may differ by up to 1200 m from the actual elevation
of that site. This may lead to significant biases in PRISM predicted climate fields.
Recently, Wang et al. (2006) presented a method to develop scale–free, high-resolution
climate data for western Canada by combining an interpolation technique and elevation
adjustment to existing PRISM dataset. The result showed a significant improvement over
the original PRISM data both in terms of spatial resolution and prediction precision. This
method has been integrated into the computer software called “Climate BC” developed by
the Centre for Forest Conservation Genetics (CFCG) at the University of British Columbia
(http://www.genetics.forestry.ubc.ca/cfcg/climate-models.html#data).
ClimateBC
generates the output at any required resolution and for any historical or future duration. As
far as historical data are concerned, ClimateBC has downscaled climate fields at two
different temporal resolutions. The first dataset includes seasonal and annual climate
variables for BC, Yukon and parts of Alberta obtained by downscaling PRISM climate
normals for 1961 to 1990. The second dataset has monthly temporal resolution, which is
developed by using monthly PRISM climate fields for the period of 1901 to 2001 for the
same domain. An example of this scale free data set corresponding to temperature normals
for 1961-1990 is shown in Figure 5. ClimateBC monthly temperature and precipitation
fields will be used as an observed dataset while developing a model for downscaling GCM
climate projections.
8
Figure 5. Scale-free mean annual temperature in BC for reference period 1961-1990
(upper) and for future (lower) derived from the PRISM dataset (Wang et al., 2006)
Historical Weather Data
There are a number of weather stations in the Coast Mountains operated by Environment
Canada, BC Ministry of Highways and BC Ministry of Forests and Range (Figure 6).
These stations collect different meteorological parameters at hourly and daily temporal
resolution. These data will form an important resource for comparing with the ClimateBC
temperature and precipitation datasets to ascertain whether the latter can be used as a
replacement to the observed data for developing models for climate downscaling. Data for
the period of 1961-2001 will be considered for the present research. Some of the required
observed data have already been stored at the UNBC HPC data archive. The remaining
9
data will be acquired from the relevant agencies. Besides this, the climate record observed
at the Tiedemann glacier will also be used to determine their closeness to gridded
ClimateBC dataset.
Figure 6. Climate station locations within study area
GCM data sets
Present day GCMs are Atmosphere-Ocean Coupled Global Climate Model that simulate
past and future climates under different scenarios of greenhouse gas emissions. There are
several CGMs developed by different climate research institutions worldwide.
Performance of these models varies from model to model because each has its own
physics. To determine the range of uncertainly in model output, climate change impact
studies often consider outputs from different models under different scenarios of
greenhouse gas emission. The present research will consider ensembles of projected
climate fields developed using several GCMs under different scenarios of greenhouse gas
emissions. Recent IPCC work (IPCC, 2007) used several GCMs under a range of emission
scenarios to develop ensembles of future climate scenarios. Downscaling will be applied to
temperature and precipitation outputs from 4 models in the IPCC archive: (1) CGCM3.1
10
developed by Canadian Center for Climate Modeling and Analysis, ECHAM5 of The Max
Planck Institute for Meteorology, HadCM3 developed by Hadley Centre for Climate
Prediction and Research and PCM of National Center for Atmospheric Research. Three
simulations from each model will be considered for downscaling: 20th century simulation
forced by historic conditions with variations of anthropogenic greenhouse gases,
anthropogenic and natural aerosols, and solar output, and two forced by future emissions
scenarios, the SRES A2 and B1. The historical and future time period of model simulations
vary from one model to another. These time periods typically range from the middle of 17th
century to the end of 24th century. For the present research 20th century simulation data will
be considered for the period of 1961-1990 while the future simulation corresponding to
SRES A2 and B1 scenarios will be considered for the period of 2001-2100. The historical
GCM simulation will be used to correct the bias from the future GCM simulations.
Historical and future simulations of climate variables from models mentioned above are
available from the IPCC Data Distribution Centre (http://www.ipcc-data.org/). Details of
these GCM models are provided in Table1.
RAMS regional climate data
One of the research groups within WC2N is employing dynamic downscaling technique to
obtain high resolution climate fields for the Coast Mountains to be used in GMB model.
This group is using the RAMS meso-scale model at 8 km resolution to downscale regional
climate fields from 1979 to 2008. Downscaled variables include temperature, precipitation,
relative humidity, incoming solar radiation, albedo, wind and many more for different
pressure levels. Besides the PRISM dataset, these data will also be used as predictands to
develop a model for downscaling global climate projections to the local scale.
Field data
Field data used for this study include both primary and secondary data. Primary data to be
collected from the site are: weather data, mainly air temperature, obtained from Automatic
Weather Stations at different elevations along the Tiedemann glacier, melt rate of ice under
various debris thickness, debris thicknesses and distribution on glacier surface. Secondary
11
data will include historical mass balance records and climate records for calibrating and
validating GMB model.
A description of data to be used for this research is given in Table 1.
Table 1 Description of data to be used for the research
Description
Parameters
NCEP-global
Temperature,
meteorological fields
precipitation,
PRISM
Duration
1961-2001
wind,
Temporal
resolution
resolution
2.5o×2.5o
6
lat/lon
Daily
Source
hourly/ National Centers of
Environmental
insolation, humidity,
Protection
geopotential
height,
(www.cdc.noaa.go
SLP at 500, 850 and
v/cdc/data.ncep.rea
1000 hPa
nalysis.html)
based Temperature
ClimateBC dataset
Spatial
and 1961-2001
<200m
Monthly
Precipitation
Centre for Forest
Conservation
Genetics,
UBC
(http://www.geneti
cs.forestry.ubc.ca/c
fcg/climatemodels.html)
Historical
Weather Temperature
Data
and 1961-2001
precipitation
Point
Daily
observation
Environment
Canada,
BC
Ministry
of
Forests,
Ministry
of Highways
Historical
mass Winter, summer and Variable
balance record
<200 m
seasonal
Variable
Daily,
IPCC
monthly
Distribution
net mass balance
 Place glacier
 Bridge glacier
 Sentinel glacier
GCM future climate Temperature
scenarios
and 2001-2100
under precipitation
SRES A2 and B1
Centre
12
Data
 CGCM3.1
2.8o×2.8o
 ECHAM5
1.9o×1.9o
 HadCM3
2.5 ×3.75
 PCM
2.8o×2.8o
GCM
20th
o
century Temperature
climate
(http://www.ipccdata.org/)
o
and 1961-1990
precipitation
 CGCM3.1
o
o
o
o
2.8 ×2.8
 ECHAM5
1.9 ×1.9
 HadCM3
2.5 ×3.75
 PCM
2.8o×2.8o
DEM
o
Area-altitude
Recent
Daily,
IPCC
monthly
Distribution
Centre
(http://www.ipcc-
o
data.org/)
<200 m
UNBC GIS data
distribution
In situ measurements
resource
Temperature
and Summer
surface melt rate of 2008
bare
and
of Point
and observation
Hourly/
Direct
Daily
measurement
debris 2009
at
Tiedemann glacier
covered glacier ice, (continuous
heat
Data
and Dome glacier
conduction measurement
through debris layer, at least for 15
meteorological
days)
elements
RAMS
downscaled Temperature,
dataset
precipitation,
1979-2008
8 km
wind,
Hourly/
WC2N
research
Daily
group
seasonal
Current research
sensible and latent
heat fluxes, incoming
goal
radiation,
outgoing
longwave
radiation, albedo etc
Air photo
Vertical
surface 2008-???
changes
13
<50 m ??
5.0 Methodologies
This research will mainly comprise 3 parts:
Part I: Development of high resolution meteorological fields for the Coast Mountains
Part II: Field measurements
Part III: Mass Balance modeling
5.1 Part I: Development of high resolution meteorological fields
Climate change impact studies on glaciers require the equivalent of point climate
observations and are highly sensitive to fine-scale climate variations that are parameterized
in global or regional climate models. This is especially true in regions of complex
topography, coastal or inland locations similar to those found in the Coast Mountains of
western Canada.
This part of the research will focus on using Statistical Downscaling (SD) techniques for
downscaling global meteorological fields to the glacier scale for assessing the impact of
climate change on glaciers of the Coast Mountains. SD methods will compliment the
similar research work being conducted within WC2N, which is employing a dynamical
downscaling technique at 8 km resolution using the meso-scale meteorological model
(RAMS) for 1979-present.
Statistical downscaling
SD is an alternative approach to Regional Climate Model (RCM0 for large scale climate
downscaling. This approach of climate downscaling involves developing a statistical
model, which links large-scale climate variables (or ‘predictors’) to regional and local
variables (or ‘predictands’). SD approach is based on assumptions that the regional climate
is a direct result of interaction between large scale climate state and regional/local
physiographic features such as topography, land-sea distribution and land use (Von Storch,
1995, 1999). Once a model is calibrated, the large-scale output of a GCM simulation is fed
into this statistical model to estimate the corresponding local or regional climate
characteristics. Unlike dynamical methods, statistical methods do not require a large
amount of computational resources, and they can therefore be easily applied to output from
different GCM experiments. Another advantage is that they can be used to provide site14
specific information, which can be critical for many climate change impact studies.
However, being an empirical model, SD does not explicitly describe the physical processes
limiting their wider applicability. In addition, the major theoretical weakness of SD lies in
the fundamental assumption that they are based on-that the statistical relationships
developed for the present day climate remain valid for future climates- an assumption
which is often not verifiable. This limitation also applies to the physical parameterization
of dynamical models.
As mentioned above, SD involves developing quantitative relationships between largescale atmospheric variables (predictors) and local surface variables (predictands). The most
common form has the predictand as a function of predictor(s), but other types of
relationships have also been used. For example, between predictors and the statistical
distribution parameters of the predictand (Pfizenmayer and Von Storch, 2001) or the
frequencies of extremes of the predictands (Katz et al., 2002)
There are a number of techniques used in SD of large scale climate variables. Not all
techniques perform well for all regions and for all datasets. The choice of SD techniques
therefore depends upon their performance in simulating the observed climate, especially
their ability to reconstruct the observed variance. Therefore, rather than focusing on a
single SD technique with limited number of predictors and predictands, the present
research aims to use different SD techniques with different combinations of predictors and
predictands (in terms of types of variable used and their temporal resolution). This method
produces a number of SD models; but all of these models might not be suitable to be used
for downscaling GCM fields. Only models which reproduce observed fields with a
reasonable degree of accuracy in terms of the absolute value and the variance will be
selected for downscaling GCM projected temperature and precipitation. This will produce
ensembles of downscaled temperature and precipitation fields for a particular location.
Ensembles are widely used in climate change impact studies mainly because these help
understand the range of uncertainty present in the result due to uncertainty in model
parameter and input data. These ensembles can be averaged together to improve the
accuracy of projected fields at the glacier scale.
15
Since the main goal of this research is to downscale the GCM future temperature and
precipitation scenarios to the glacier scale for GMB simulation, choice of GCM model for
getting future temperature and precipitation scenarios is very important. However, there is
no strong basis for selecting the outputs from one model and rejecting that from others
because all GCM outputs inherit some degree of uncertainty. To determine the range of
uncertainty in model output, the present research will consider ensemble temperature and
precipitation outputs from 4 different GCMs under SRES A2 and B1 emission scenarios.
GCMs are discussed in detail in the following chapters.
Predictors
NCEP data (or ‘predictor’ variables) will be acquired from the NCEP web site for the
regions covering 128oW-120oW and 48.5oN-53.5oN and for a period of 1961-2001.
Maximum and minimum temperature, precipitation, wind, insolation, geopotential height
at the 500 hPa, 850 hPa and 1000 hPa levels and sea level pressure (SLP) are some of the
predictors widely used for downscaling. Widemann et al. (2003) investigated several
combinations of predictors and found that large-scale precipitation from the global model
is a robust predictor for Pacific Northwest precipitation. Sea level pressure can be included
as a secondary predictor to capture the effects of interaction of atmospheric circulation and
the topography. Similarly, the study claims that the large-scale surface air temperature is a
robust predictor for regional temperature. In contrast to precipitation, there was found to be
little additional skill in including a circulation parameter, so this single predictor is
sufficient (Salathe et al., 2007). The decision on choice of predictor variables cannot be
made unless they are tested independently. Therefore, the present research will consider
all potential predictor variables and test each of them independently to determine which
variable best reproduce the observed temperature and precipitation at the local scale. These
predictor variables will be considered for 500 hPa, 850 hPa and 1000 hPa covering the
entire elevation range of Tiedemann glacier. The mean of three levels gives the average of
the predictor variables for the entire glacier.
Instead of using raw NCEP data directly, downscaling work mostly uses the normalized
data for calibrating the SD models. Therefore, normalization of all the potential predictor
16
variables will be performed over a period of 1961-1990 before using them for calibrating
the SD model. Prior to normalization, these datasets will be interpolated to individual
GCM grid resolutions whose simulations are to be downscaled. Since the present research
aims to use output from 4 different GCMs, predictor variables for calibrating the SD model
will be slightly different from one model to another as a result of which there will be 4
different SD models corresponding to 4 different GCMs.
Some of the potential predictor variables interpolated to CGCM2 grid resolution and
normalized with respect to their 1961-1990 means and standard deviations, are available
from the website of Canadian Climate Change Scenario Network (CCCSN,
http://www.ccsn.ca/index-e.html).
Predictands
Monthly temperature and precipitation will be the two predictand variables used for
calibrating the SD model. Three different sets of predictand variables will be used: 1) the
scale free ClimateBC dataset (Wang et al. 2006), 2) the RAMS model output for the Coast
Mountains (personal communication, Bruce Ainslie, WC2N), and 3) point observation data
will be used with NCEP predictor variables to calibrate the SD model. The SD model
corresponding to predictand variables (ClimateBC, RAMS or point observation) having the
highest skill to specify the local climate will be selected for downscaling future climate
projection. Similar to predictor variables, predictand variables are also normalized over the
period of 1961-1990.
Environment Canada, the BC Ministry of Highways and the BC Ministry of Forests and
Range operate a number of weather stations in BC. These stations collect different
meteorological parameters at hourly and daily time resolution. Daily maximum
temperature, minimum temperature, mean temperature and total daily wet day precipitation
(>0.25mm/day) in the vicinity of Tiedemann glacier will be taken from these sources.
These data will be taken for a period of 1961-2001 which corresponds to the period of
NCEP predictor variables considered. These data will be subjected to preliminary analysis
in which they will be adjusted for inhomogeneities caused by non-climate factors, such as
station relocation and change in observing practice.
17
The RAMS dataset for 1979-present, which is available in 8 km spatial resolution, will be
further interpolated to the DEM resolution (<200 m) before using them with NCEP fields
for model calibration.
The time scale of the SD will be monthly. All hourly and daily data will be converted to
monthly means before using them for downscaling.
Several downscaling techniques will be employed to evaluate their skill in determining the
local temperature and precipitation fields from their large scale values. Techniques which
best specify the local climate both in terms of the absolute value and the observed variance
will be selected for downscaling global fields. Some of the downscaling techniques to be
employed for the present research are discussed below.
Statistical DownScaling Model (SDSM)
Standard software, such as Statistical DownScaling Model (SDSM) is available for
downscaling of large scale fields (Wilby et al., 2002). SDSM calculates statistical
relationships, based on multiple linear regression techniques, between large-scale (the
predictors) and local (the predictand) climate. Lines et al. (2006) successfully employed
SDSM to downscale the temperature and precipitation fields in Atlantic Canada. SDSM is
a hybrid of multiple regression and stochastic downscaling methods. Observed data sets
(predictands) are first regressed against a ‘selection’ of climate predictor(s) to develop
regression equations. SDSM is said to be calibrated when the regression coefficients,
explained variance, and standard error are within acceptable limits for each regression
model.
SDSM can be downloaded free of charge from the SDSM UK website (http://wwwstaff.lboro.ac.uk/~cocwd/sdsm.html). The methodology is fully described in the SDSM
‘User Manual’, by Wilby et al. (2001), which can be downloaded form the same web site.
Goldstein et al. (2004) and Barrows et al. (2004) showed that, of several statistical
downscaling models, SDSM produced optimal results. Different predictors necessary as
inputs
to
SDSM
are
also
available
18
from
CCCSN
web
site
(http://www.ccsn.ca/The_Network/The_Network-e.html). Relevant information is also
available from the web site of Canadian Climate Impacts Scenarios at the University of
Victoria (http://www.cics.uvic.ca/scenarios/index.cgi?Scenarios)
Linear downscaling method
Huth (1999) evaluated several linear downscaling methods to determine their ability to
specify the local climate at different locations in central Europe from the NCEP reanalysis
dataset. He examined three linear methods of statistical downscaling: (1) canonical
correlation analysis (CCA) pre-filtered by principal component analysis (PCA); (2)
singular value decomposition (SVD) and (3) multiple linear regression (MLR). Three
different MLR models were considered: (1) stepwise screening of principal components
(PCs) (‘stepwise regression’), (2) MLR on PCs without screening, that is, all PCs being
forced to enter the model (‘full regression’), and (3) stepwise screening of gridded values
(‘pointwise regression’). The pointwise regression proved to be the best method of those
considered whereas SVD method appeared to be the worst (Huth, 1999). However,
Widmann et al. (2003) used SVD technique to successfully downscale precipitation fields
over northwestern United States using NCEP data. Based on the result of the past studies,
the present research plans to employ SVD and MLR methods for downscaling large scale
temperature and precipitation field.
Local scaling method
Some studies have used a downscaling method referred to as ‘local scaling’ to downscale
precipitation and temperature in western North America (Widmann et al., 2003; Salathe,
2005). Radic and Hock (2006) successfully applied a local scaling method to downscale
global projection of temperature and precipitation to Storglaciären in Sweden for assessing
climate change impacts on glacier mass balance. The local scaling method for precipitation
downscaling simply multiplies the large-scale simulated precipitation at each local
gridpoint by a seasonal scale factor (Widmann et al., 2003). The scaling factor is derived
during a fitting period to remove the long-term bias between the large-scale simulated
precipitation and the observed precipitation at that gridpoint. The downscaled monthly
mean precipitation is given by:
19
Pi (t )  Pi ,c (t )
Pobs
Pc
i  1,...........,12
(1)
where Pi,c is monthly precipitation sum from the climate model for the duration of
simulation (t; e.g. t = 2001 to 2100), Pobs and Pc are mean precipitation from observation
and climate model, respectively, averaged over the baseline period.
Surface air temperature is downscaled in a similar way to precipitation. For temperature
the adjustment is additive; thus, the downscaled monthly mean surface temperature is
given by (Salathe, 2005) :
Ti (t )  Ti ,c (t )  (Ti ,obs  Ti ,c )
i  1,...........,12
(2)
where Ti,c is monthly temperature for the ith month from the climate model for the duration
of simulation (t; e.g. t = 2001 to 2100), Ti ,c and Ti ,obs are mean temperature from climate
model and observation, respectively, for the ith month averaged over a chosen baseline
period
Beside point observations as predictands, ClimateBC dataset and RAMS fields will also be
used as equivalent to observed data for downscaling GCM future projections directly using
above mentioned equations.
Model calibration and validation
It is a general practice to divide the historical dataset into two different time periods to use
them separately for model calibration and validation. For the present research, the monthly
averaged predictand variables (ClimateBC, point observation or RAMS) and relevant
predictor variables covering a period from 1961-2001 will be first divided into two parts:
1961-1981 and 1981-2001, each covering a period of 20 years. The first 20 years dataset
will be used for calibrating the SD model while the remaining 20 years data will be used to
validate the model output. Once the model is calibrated using first 20 years (1961-1981)
data, model skill validation will be performed by forcing the model with relevant NCEP
20
predictor variables for the remaining 20 years period (1981-2001) to obtain downscaled
temperature and precipitation fields at the glacier scale. The downscaled result will be
compared with both point observations and ClimateBC dataset for corresponding period
and for corresponding locations. Skill testing will be performed for all variants of SD
models developed using different downscaling techniques and different combinations of
predictors and predictands.
Downscaling GCM simulations
GCM output always inherits some degree of uncertainty and should be used cautiously
while using them in climate change impact studies. To overcome this problem, rather than
considering outputs from single model and emission scenarios, climate researchers always
prefer considering outputs from different GCM models under a range of greenhouse gas
emission scenarios, also known as ensemble results. This research will therefore consider
temperature and precipitation outputs from several GCMs under different emission
scenarios. Downscaling will be applied to temperature and precipitation simulations from 4
different GCMs: (1) CGCM3.1 developed by Canadian Centre for Climate Modeling and
Analysis, ECHAM5 of The Max Planck Institute for Meteorology, HadCM3 developed by
Hadley Centre for Climate Prediction and Research and PCM of National Center for
Atmospheric Research. Summary of these models are provided in Table 2. Three
simulations will be considered for each model: one simulation forced by the 20th century
historic conditions and two future simulations forced by IPCC SRES A2 and B1 scenarios.
20th century simulations will be considered for the period 1961-1990 while for future
simulation a period of 2001-2100 will be considered. GCM outputs will be considered
from all grid cells within the research domain.
21
Table 2 GCM models and scenarios to be used in the proposed research
Models
Scenarios
Period
CGCM3.1
Historic
1961-1990
SRES A2
2001-2100
SRES B1
2001-2100
Historic
1961-1990
SRES A2
2001-2100
SRES B1
2001-2100
Historic
1961-1990
SRES A2
2001-2100
SRES B1
2001-2100
Historic
1961-1990
SRES A2
2001-2100
SRES B1
2001-2100
ECHAM5
HadCM3
PCM
The downscaled GCM simulations do not always agree with the actual observation; they
are either negatively or positively biased. Before applying downscaled GCM results for
climate change impact assessment, bias correction must be performed. One of the
commonly used approaches is to compare the historic simulations with the observed values
for the corresponding period, determine differences between to data series and correct the
future projections by adding or subtracting the difference from the corresponding data
series of future climate fields. Downscaled 20th century temperature and precipitation for
the period 1961-1990 will be compared with the corresponding ClimateBC dataset. Errors
will be determined for each data value which is then applied to downscaled GCM future
temperature and precipitation to correct potential biases. The bias corrected future
temperature and precipitation fields will then be used to force GMB model for future
projection of glacier mass balance in the Coast Mountains. Procedures are discussed in
detail under methodology section in the following chapters.
22
5.2 Part II: Field work
Tiedemann glacier
The distribution of debris properties in the ablation area, especially debris thickness, is a
key input variable when simulating mass balance of Tiedemann glacier. In addition to this,
surface air temperature at different elevation bands at or/and near the glacier surface and
glacier surface temperature are other variables of interest. Some surface air temperature
data recorded at different elevation on the glacier (Figure 7) and surface air temperature
recorded along the left lateral moraine at different elevation is already available. This
measurement work will be continued in the future. Although these variables do not enter
into the GMB model directly they are still useful in validating short term downscaled
climate fields on Tiedemann glacier. Given the enormous size of the glacier and dangerous
surface conditions, it is not possible to cover the entire glacier for taking measurements of
different parameters of interest. This field work will therefore focus only on lower part of
the Tiedemann glacier, approximately about 5-8 km up from the glacier terminus. In
addition to this, existing Automatic Weather Station near the glacier will be regularly
maintained to obtain continuous weather data (Figure 8). This station started collecting
data since 2005 but there has been significant data loss due to frequent damage of station
following heavy winter snowfall.
Tiedemann Temperatures (2006)
30.00
T1 (506 m)
T2 (852 m)
20.00
15.00
T3 (1045 m)
10.00
T4 (1352 m)
5.00
T5 (1838 m)
0.00
T6 (3020 m)
-5.00
Date
23
8-Oct
3-Oct
28-Sep
23-Sep
18-Sep
13-Sep
8-Sep
3-Sep
29-Aug
24-Aug
19-Aug
14-Aug
9-Aug
4-Aug
30-Jul
25-Jul
20-Jul
-10.00
15-Jul
Temperature (o C)
25.00
Figure 7. Daily(?) surface air temperature recorded at different elevation in Tiedemann
glacier during the summer of 2006
20.00
Air Temperature ( o C)
15.00
10.00
5.00
0.00
-5.00
-10.00
-15.00
6/1/2007
6/15/2007
6/29/2007
7/13/2007
7/27/2007
8/10/2007
8/24/2007
9/7/2007
6/1/2007
6/15/2007
6/29/2007
7/13/2007
7/27/2007
8/10/2007
8/24/2007
9/7/2007
5/4/2007
5/18/2007
4/6/2007
4/20/2007
3/9/2007
3/23/2007
2/9/2007
2/23/2007
1/26/2007
1/12/2007
-20.00
Time
4.50
4.00
Snow Depth (m)
3.50
3.00
2.50
2.00
1.50
1.00
0.50
5/4/2007
5/18/2007
4/6/2007
4/20/2007
3/9/2007
3/23/2007
2/23/2007
2/9/2007
1/26/2007
1/12/2007
0.00
Time
Figure 8. Automatic Weather Station at Tiedemann glacier (left). Daily mean values of air
temperature (upper right) and snow depth (lower right) recorded at the station.
Dome glacier
Dome glacier in the Rocky Mountains has been selected for taking measurement
concerning the influence of debris on melt rate of ice underneath. The measurement work
will include taking daily records of melt rate of ice (in terms of ice thickness) under
variable debris thickness and from bare ice surface. Based on the data collected from this
measurement, a ratio of melt rates corresponding to particular debris thickness and bare ice
surface will be determined. This ratio gives a Coefficient of Melt (CM) for that particular
debris thickness. CM will be determined for all the debris thicknesses considered and each
CM value will be averaged over the period of measurements to obtain a single CM value
for a particular debris thickness. Corresponding CM values will be applied to GMB model
24
when simulating summer mass balance (ablation) of debris covered part of the glacier. The
first field experiment at Dome glacier commenced in mid-August 2008 during which
measurements were taken for 12 days. A measurement site was prepared near the terminus
of the glacier. The measurement site consisted of an Automatic Whether Station equipped
with sensors to measure air temperature, humidity, solar radiation (incoming and
reflected), net radiation, wind, thermistors (4) to record temperature at different debris
depths and plots to take daily measurements of melt rate under 0 cm (bare ice), 5 cm, 10
cm, 15 cm, and 20 cm of debris thicknesses (Figure 9). Preliminary analysis of some of
the recorded data is shown in figure 10. Results show that for debris thickness greater than
10 cm, melt rate does not change significantly unless there is an abrupt change in
meteorological condition. This can be seen in Figure 10 where melt rates from 10 cm and
15 cm almost converge with each other. To further improve the accuracy of CM, a detail
long term measurement of melt rate under range of debris thicknesses is planned during
summer of 2009. This field measurement will be a continuation of 2008 measurements but
the measurement of melt rate will be extended to more debris thickness with increased
duration and frequency of measurements. Measurements will be conducted for the debris
thicknesses of 0 cm, 2 cm, 5 cm, 7 cm, 10 cm, 12 cm, 15 cm and 17 cm and 20 cm so that
CM can be determined for a range of debris thicknesses. These data can also be used to
develop a relationship between melt rates and the debris thicknesses. Total duration of this
experiment will be at least 20 days and the temporal resolution of measurement will be
increased from daily to twice daily.
25
Figure 9. Automatic Weather Station (left) and in situ measurement of melt rate under
variable debris thickness at the Dome glacier during August 2008.
26
100.0
90.0
Bare Ice
Daily Surface Lowering (mm)
80.0
70.0
60.0
5 cm debris layer
50.0
40.0
10 cm debris layer
30.0
15 cm debris layer
20.0
10.0
0.0
8/15/2008
8/16/2008
8/17/2008
8/18/2008
8/19/2008
8/20/2008
8/21/2008
8/22/2008
Date
18.0
Daily Air Temperature (oC)
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
8/13/2008
8/14/2008
8/15/2008
8/16/2008
8/17/2008
8/18/2008
8/19/2008
8/20/2008
8/21/2008
Date
Figure 10. Daily surface lowering (equivalent ice melt) on bare ice and on debris of
variable thicknesses (top). Lowering is consistent with daily air temperatures measured for
the same time period (bottom).
5.3 Part III: Mass balance modeling
Mass-balance models are widely used in many parts of the world to assess the glacier’s
response to climate parameters (Braithwaite and Olesen, 1985; Tangborn, 1999; Hock and
Holmgren, 2005; Radic´ and Hock, 2006; Hock et al., 2007; Bhatt et al., 2007). Massbalance models have also been widely used in modeling the response of glaciers to future
climate change (Oerlemans et al., 1998 and 2005; Radic´ and Hock, 2006). In general, two
27
categories of GMB model exist: one that is based on an energy balance approach (Hock
and Holmgren, 2005) and the other on an empirical temperature-index model (Hock,
2003). GMB models based on energy balance approach are physically based models which
derive melt as the residual in the energy-balance equation. Being physically based model,
they often require detailed data input which is one of the limitations of this model. On the
other hand, temperature-index models do not require detailed data input for melt
simulations, but their modeling skill is often less reliable due to their empirical nature.
Hock et al. (2007) intercompared the sensitivity of mass-balance projections to the choice
of mass-balance models. They applied five different mass-balance models to one glacier in
Northern Sweden for mass-balance simulation, namely, (1) zero-dimensional temperatureindex regression model, (2) elevation-dependent temperature-index regression model, (3)
distributed temperature-index model including potential direct solar radiation, (4)
elevation-dependent simplified energy-balance model, and (5) distributed energy-balance
model. Of the results from all the models, the temperature-index model performed well in
reproducing glacier mass-balance components. de Woul and Hock (2005) applied a simple
degree day approach in Arctic glaciers and ice caps to estimate static mass balance
sensitivity to temperature and precipitation change. Radic´ and Hock (2006) successfully
applied the same approach to simulate future mass balance of Storglaciären in Sweden.
Tangborn (1997, 1999); Tangborn and Rana (2000); Bhatt et al. (2007) and Zhang et al.
(2007a, b) used the Precipitation-Temperature-Area-Altitude (PTAA) model to produce
mass-balance hindcasts of a few glaciers in Northwestern America and Himalaya. The use
of temperature index model is justified from the fact that the confidence level of GCM
projected temperature and precipitation is relatively higher than other projected
parameters, such as global solar radiation, and therefore the future mass balance projection
developed using Temperature Indexed (TI) model is likely to be more realistic than mass
balance projection made with distributed models.
The performance of TI model varies from one region to another because the individual
model is configured to suit the topography and climate of a particular location. Therefore, a
good deal of model calibration and parameterization is always desired before using TI
model in a particular region.
28
The present research will use a temperature index model following de Woul and Hock
(2005) and Radic´ and Hock (2006). Summer mass balance, bs, and winter mass balance,
bw, are modeled by:
t2
bs   s  aiTi   s
i t1
t2
ai =1, Ti > 0
,
bw   w  ai Pi   w ,
i t1
(3)
a i =0, Ti ≤ 0
ai =1, Ti < T0r/s
a i =0, Ti ≥ T0r/s
(4)
where Ti is the downscaled air temperature (oC). α and β are coefficients derived from
linear regression between measured summer mass balances (bs) and positive degree-day
sums (ΣaiTi) over the entire mass balance year, and between measured winter mass
balances (bw) and annual sums of daily precipitation (ΣaiPi) with air temperatures below
the threshold temperature T0r/s that discriminates rain from snowfall.
The monthly ClimateBC dataset will be used to linearly regress bs and bw of the entire
glacier with ΣaiTi and ΣaiPi respectively. α and β will be assumed to be constant
throughout the glacier. bs and bw will be estimated for every DEM grid cell keeping α and
β constant.
Summer mass balance from debris covered area
The lower part of the ablation area of Tiedemann glacier is covered by debris. The effect of
debris is to inhibit the melt of underlying ice, thus considerably affecting summer mass
balance. To address this problem, a field experiment was conducted at the debris covered
Dome glacier in the Rocky Mountains during the middle of August 2008. A detailed
measurement is planned again during the summer of 2009. The August 2008 experiment
included conducting direct measurement of melt rates of ice under variable debris
thicknesses and under the condition of bare ice. Observation of different meteorological
elements along with measurements of spatial distribution of size, thicknesses and type of
29
debris was also conducted. The field methodology and the method to determine Coefficient
of Melt (CM) are already discusses in section 5.2. For every DEM grid over Tiedemann
glacier containing the debris, the summer mass balance will be estimated by scaling Eq. 3
with the CM for corresponding debris thickness obtained from the experiment conducted at
the Dome glacier. A detailed survey of debris thicknesses and debris distribution on the
ablation area of Tiedemann glacier is therefore required.
Calibrating model parameters and output validation
One of the limitations of the model discussed above is that it needs to be calibrated based
on historic temperature, precipitation and mass balance data, thus hampering direct
transferability to other glaciers. Validation of model output is equally important to
confidently use the model for future projection of glacier response to climate change.
Model calibration for Tiedemann glacier is not possible because no long term continuous
historical mass balance data exist for this glacier. Short term intermittent mass balance
records, dating back to 1980’s and 1990’s, have been found to exist for this glacier.
However, these data may not be suitable for model calibration and validation because of
their intermittent nature. As an alternative to this, present research plans are to calibrate
and validate the model in glacier with similar geographic settings. Place glacier has been
continuously monitored since 1965 and has the longest mass balance record (Stanley,
1975). Many researchers have used this mass balance record for assessing climate-glacier
relationship in the past (Moore and Demuth, 2001; Rasmussen and Conway, 2004). Place
glacier is located in the same mountain range, has similar exposure and is separated by an
aerial distance of only 200 km. Bridge glacier and Sentinel glaciers are two more glaciers
in the Coast Mountains with long term mass balance record. Both of these glaciers are also
situated in the southern Coast Mountains in reasonably close proximity to the Tiedemann
glacier. It is assumed that all of these glaciers respond with climate variables in similar
fashion. Mass balance records for Bridge and Sentinel are available since the 1960’s but
they are not as continuous as the records from Place glacier. Characteristics of these
glaciers and the duration of mass balance records available are listed in Table 2.
Table 2 Characteristics and available mass balance records for selected glaciers in the
Coast Mountains
30
Name
Lat/Lon
Area
Length
Lowest/highest
Mass
balance
(km2)
(km)
elevation
records available
(m a.s.l)
Tiedemann glacier
51°19’N/
63.0
24.0
700/3800
124°54’W
Place glacier
50°25.3'N/
Summer of ‘81,
1
‘89, ’90, ‘91
3.8
~3.0
1850/2600
2
83.0
~16.0
1400/2900
2
Since 1965
122°36.0'W
Bridge glacier
50°49.4'N
1970-1980
/123°33.0'W
Sentinel glacier
49°53.6'N/
2
1.8
1970-1980
122°58.9'W
Sources: Canadian Glacier Inventory Project (http://cgip.wetpaint.com/); 2Ommanney, 2002; 1BC Hydro
Report, Google Earth 2008.
To calibrate GMB model given in equation (3) and (4), the time series of mass balance
records from these glaciers will be regressed with seasonal ClimateBC temperature and
precipitation record for the corresponding period. Regression will be performed with
available mass balance records for all the glaciers. Prior to calibration, the available mass
balance records and corresponding ClimateBC temperature and precipitation records will
be divided into two parts: 1961-1981 and 1981-2001. The first part of the records will be
used to calibrate the model while the second part will be used to validate the model.
Once the model calibration is completed, the best fit GMB model for a particular glacier
will be selected and forced with ClimateBC monthly temperature and precipitation for
1981-2001. Glacier mass balance hindcasts thus developed will be compared with
historical mass balance records of the corresponding periods to test the model skill. If the
model is able to reproduce the measured mass balance of the selected glacier to an
acceptable degree of accuracy, we assume that the model is capable of simulating future
mass balance of other glaciers in the Coast Mountains well.
31
From September 2008, photogrammetric method of mass balance measurement has been
initiated on Tiedemann glacier. This involves taking repeated aerial photos of glaciers, at
least once a year. An estimation of glacier surface elevation change will be made based on
time series of aerial photographs which can then be used to estimate the mass balance
indirectly. However, a few years of data are required to make a meaningful estimation of
mass balance.
Projection of future glacier mass balance
Once the GMB model is able to simulate the observed mass balance to an acceptable
degree of accuracy, it will be used to project the future mass balance of selected glaciers in
the Coast Mountains in response to future projection of temperature and precipitation. The
model will be forced by downscaled GCM projection of temperature and precipitation
fields under SRES A2 and B2 emission scenarios in monthly time scale for a period
extending from 2001 to 2100. An ensemble of mass balance projection will be developed
corresponding to different temperature and precipitation fields downscaled using different
SD models and different sets of outputs obtained from 4 different GCMs and two different
emission scenarios. This will help determine the range of uncertainly in simulated mass
balance fields which may result due to uncertainty in SD models, GCM projections and
NCEP reanalysis data. Mass balance ensembles will be developed for every individual year
and averaged for every 10 years starting from 2001 until 2100.
A complete process of climate downscaling and mass balance modeling is schematically
shown in Figure 11 below.
32
Figure 11. Schematic diagram showing complete process involved in simulating future
mass balance
6.0 Potential contribution and uniqueness of the research
The Coast Mountains of BC have the largest concentration of mountain glaciers in Canada.
In British Columbia alone, the total glacier area exceeds 30,000 km2. Depletion of the
glacier ice reserve from the Coast Mountains following climate change is a major concern
because many larger rivers in the region derive a sizable portion of their flows from glacier
melt runoff. The importance of glacier melt on river runoff varies from river to river. For a
river basin with a large glacier covered area in its headwater, the glacier melt contribution
is often very large. Even for a river basin with a small glacier covered area, the glacier
meltwater can be very important for sustaining low flow conditions in rivers during the dry
season when water demand is highest. For much of BC, glaciers play an important role in
supplying communities with freshwater for irrigation, drinking, hydroelectric power
generation and industrial production. The freshwater supply from glacier melt is also
extremely important for sustaining aquatic ecosystems. Power generation during summer
33
months in BC depends heavily on glacier and snowmelt runoff. About 85% of the
electricity produced in British Columbia comes from hydropower plants. (Web page: BC
Ministry of Energy, Mines and Petroleum Resources). In some regions of BC, glacier
meltwater forms an important part of water supplies for communities during the dry season
when rainwater contribution to surface runoff is minimal. There is a widespread social,
economic and environmental implication of glacier loss in BC.
The proposed research will improve our present understanding regarding the response of
glaciers to future climate change. It will provide key information to government authorities
to formulate water management policies following the changes in glacier ice reserve in
future. Water manager can use the results from this research to effectively manage
dwindling water supply in future. Runoff modeling is an important tool often used by
water manager to manage the river water supplies. The ensemble of mass balance
projection obtained from this research can be applied to runoff models to develop scenarios
of possible changes in runoff patterns in future which can be very helpful for managing the
water resources. As many economic sectors such as tourism, industry, hydropower and
agriculture depend on freshwater supply for their sustenance, effective management of this
precious resource is a most for sustaining the regional economy. A good management of
water resource is equally important to sustain the aquatic ecosystem and wildlife habitat
which together keep the environment in good shape. This research will also open up new
avenues of different researches such as estimating the contribution to sea level rise due to
accelerated melting of glaciers in the Coast Mountains.
Since climate downscaling is a major component of this research, it will provide high
resolution climate fields, which can then be used for different ecological modeling and
climate change impact studies in the Coast Mountains. Similarly, there has not been
enough research to understand how debris covered glaciers respond to climate warming
although they make up a sizable portion of total glacier area in the Coast Mountains. This
research will provide scientific information regarding how debris covered glaciers
responds to temperature change and how they influence the rate of ablation of glacier ice
underneath. This will help understand the effects of debris on melt water contribution to
the rivers downstream from the perspective of future temperature rise.
34
Research results will not make any sense unless it is made public to the relevant scientific
communities to gain the general acceptance. Therefore, the present research aims to
publish at least 3 papers in international, peer reviewed journals. The following is the
tentative list of areas within this research with the potential to develop into good papers:

Influence of debris on glacier melt

SD of large scale climate fields in the Coast Mountains

Projection of future GMB using downscaled GCM fields
The proposed research is unique because:

It is for the first time future projection of mass balance is going to be made on
glaciers in the Southern Coast Mountains of BC following the future climate
warming. Although climate downscaling work has been done in this region, no
research has been found to exist which uses the downscaled results for the climate
change impact studies on glaciers.

Despite the fact that a sizable area of glaciers in the Coast Mountains are covered
with some amount of debris in their ablation areas, no mass balance modeling work
in this region found to have included the effects of debris while simulating the mass
balance. In fact, there has been no research in western Canada to understand the
process of glacier ice melt under the layer of debris. This research will take the
influence of debris into account when simulating glacier mass balance.
Research schedule
Date
June-Aug 2008
Major tasks
Reconnaissance visit to Dome glacier, field measurements at
Dome glacier
Sep–Dec 2008
Analysis of collected data, presentation of results at the WC2N
meeting, acquisition of relevant data from different sources,
continue working with thesis proposal preparation, defend thesis
proposal
35
Jan-April 2009
Continue collecting data from various sources, analyze and
manage collected data set, prepare dataset for DS experiments
May-Aug 2009
Begin climate DS work, model calibration using combination of
predictors and predictands variables, field work at Dome glacier
and Tiedemann glacier
Sep-Dec 2009
Continue working with DS, develop ensembles of DS fields,
validation of downscaled fields, manage DS results
Jan-April 2010
Calibration of GMB model, simulation of historical mass
balance, validate simulated results, simulation of future glacier
mass balance
May-Aug 2010
Preparing research papers, work towards paper publication
Sep 2010-???
Thesis writing, thesis defense
Funding
This is a part of the research under Canadian Cryospheric Network (WC2N), a network of
research institutions which is studying the past and future impact of climate change on
glaciers of western Canada and its implication to freshwater ecosystems. The network is
funded by Canadian Foundation of Climate and Atmospheric Sciences (CFCAS). The
research is being conducted under the supervision of Prof. Peter L. Jackson of University
of Northern British Columbia.
Tentative Chapter Titles
1. Introduction
1.1 Context
1.2 Aims and objectives
1.3 Rationale
1.4 Thesis outline
2. Literature review
2.1 Climate change and glacier fluctuation
36
2.1.1 Anthropogenic climate change
2.1.2 Glaciers and climate change
2.1.2 Worldwide glacier fluctuation
2.1.3 Glacier fluctuation in western Canada
2.2 Implication of glacier recession
2.2.1 Environmental aspect
2.2.2 Economic aspect
2.2.3 Human dimensions
2.3 Climate change impact assessment
2.3.1 Climate downscaling
2.3.2 Glacier mass balance modeling
3. Methodology
3.1 Statistical climate downscaling
3.1.1 Predictor
3.1.2 Predictand
3.1.3 Statistical DownScaling Model (SDSM)
3.1.4 Linear downscaling method
3.1.5 Local scaling method
3.2 Glacier melt under the debris layer
3.2.1 Direct measurement of melt rate
3.2.2 Measurement of debris properties
3.2.3 Coefficient of melt
3.2.4 Modeling melt under debris layer
3.3 Glacier mass balance modeling
3.3.1 Temperature index model
3.3.2 Model calibration
3.3.3 Projection of future mass balance
37
4. Results and discussions
4.1 Climate downscaling
4.2 Glacier melt under the debris layer
4.3 Mass balance simulation
5. Conclusions and recommendations
References
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