2
1) Develop climate-based land-cover categories (cliomes) for
Alaska and western Canada using down-scaled gridded
historic climate data from the Scenarios Network for
Alaska and Arctic Planning (SNAP) and cluster analysis
2) Link the resulting cliomes to land cover classes, and define
each biome by both climate and ecosystem
characteristics.
3) Couple these cliomes with SNAP’s climate projections,
and create predictions for climate-change-induced shifts
in cliome ranges and locations.
4) Use the results to identify areas within Alaska, the Yukon
and NWT that are least likely to change, and those that
are most likely to change over the course of this century.
3
SNAP is a collaborative network of the
University of Alaska, state, federal,
and local agencies, NGOs, and
industry partners.
Its mission is to provide timely access to
scenarios of future conditions in
Alaska for more effective planning
by decision-makers, communities,
and industry.
1.
Projections of future conditions
that are linked to present and
past conditions
2.
Detailed explanations of the
rules, models, and assumptions
underlying the projections
3.
Objective interpretations of
scenarios based on these
projections
www.snap.uaf.edu

Measurements
 Weather station data
 Interpolated and gridded
 1901-2008

Global Circulation Models (GCMs)
 Complex coupled models
created by national and
international labs
 Interactions of oceans,
atmosphere, and radiation
balance
 Calculated concurrence of 15 models
with data for 1958-2000 for surface air
temperature, air pressure at sea level,
and precipitation.
 Used root-mean-square error (RMSE)
evaluation to select the 5 models that
performed best for Alaska, 60-90°N, and
20-90°N latitude.
 A1B, B1 and A2 emissions scenarios.
 Downscaled coarse GCM data to 2km
using PRISM.
GCM output (ECHAM5)

Baseline values = PRISM
2km mean monthly
precipitation and
temperature, 1961-1990

Adjusted and interpolated
GCM outputs to this
historical baseline

Effectively removed model
biases while scaling down
the GCM projections
2.5 x 2.5 degrees
Frankenberg et al., Science, Sept. 11, 2009

Inputs to GCMs
 Solar radiation is essentially a known quantity
 Levels of greenhouse gases are uncertain, but accounted for by
varying emissions scenarios

GCM algorithms
 Oceanic and atmospheric circulation are hard to predict and model
 May include thresholds (tipping points) such as ocean currents
shifting
 Don’t fully account for short-term phenomena such as the Pacific
Decadal Oscillation (PDO)
The PDO causes
significant climate shifts
on a decadal scale






SNAP’s model validation study depicts
uncertainty by region, model, and data
type based on comparisons between
model results and actual station data
Variation between models can be used
as a proxy for uncertainty in GCM
algorithms
In some cases, differences between CRU
and PRISM data can be viewed as a
proxy for uncertainty in downscaling
Climate stations are very sparse in the
far north, so interpolation is challenging
Precipitation can vary enormously over
very small areas and time frames
Overall, PRISM does the best job of
capturing landscape climate variability


Collaboration rather than top-down
information transfer
What are the most pressing questions?
 Differ from region to region
 Depend on needs on stakeholder

What questions can SNAP help address?
 What data are and are NOT available?
 How much time/funding is available?
 Role of uncertainty

Desired products
 Maps, reports, presentations, websites, etc.

Forecast Planning
 One Future
-10%
+10%
What we know today
Global Business Network (GBN) -- A member of the Monitor Group
Scenario Planning
 Multiple Futures

Uncertainties
What we know today
Copyright 2010 Monitor Company Group
This diagram describes the 5 key steps required
in any scenario planning process
What is the strategic
issue or decision that
we wish to address?
What critical
forces will affect
the future of our
issue?
How do we combine and
synthesize these forces to
create a small number of
alternative stories?
Global Business Network (GBN) -- A member of the Monitor Group
As new
information
unfolds, which
scenarios seem
most valid? Does
this affect our
decisions and
actions?
What are the implications of
these scenarios for our
strategic issue, and what
actions should we take in
light of them?
14
Copyright 2010 Monitor Company Group
Bet the
Farm
Robust: Pursue only those options that would
work out well (or at least not hurt you too much)
in any of the four scenarios
OR
Core
Core
Hedge
Hedge
Your
Your
Bets
Bets
Hedge
Hedge
Your
Your
Bets
Bets
Bet the Farm / Shaping: Make one clear bet that
a certain future will happen — and then do
everything you can to help make that scenario a
reality
Robust
OR
Satellite
Satellite
Hedge
Hedge
Your
Your
Bets
Bets
Hedge
Hedge
Your
Your
Bets
Bets
Hedge Your Bets / Wait and See: Make several
distinct bets of relatively equal size
Satellite
Satellite
OR
Core / Satellite: Place one major
bet, with one or more small bets as a hedge
against uncertainty, experiments, and real
options
15
All SNAP data and
outputs are available
under a Creative
Commons license.
Currently, 24 ongoing and
completed projects are
linked on the SNAP
website, in addition to
reports, videos,
presentations, and
papers.
www.snap.uaf.edu
 Broader spatial scope
 More input data
 Clustering methodology
17
17



Extended scope to northwestern Canada
Used all 12 months of data, not just 2
Eliminated pre-defined biome/ecozone categories in
favor of model-defined groupings (clusters)
 Eliminates false line at US/Canada border
 Creates groups with greatest degree of intra-group
similarity and inter-group dissimilarity
 Gets around the problem of imperfect mapping of
vegetation and ecosystem types
 Allows for comparison and/or validation against existing
maps of vegetation and ecosystems
18
Area of Canada selected for cluster analysis.
Selected area is lightly shaded, and the
unselected area is blue. The red line includes all
ecoregions that have any portion within NWT.
Limiting total area improves processing
capabilities.
19
Ice breakup dates for the Tanana (left) and
Yukon (right) Rivers for the full recorded time
periods. Days are expressed as ordinal dates. A
statistically significant trend toward earlier thaw
dates can be found for both rivers.
150
150
145
145
140
140
135
135
130
130
125
125
120
120
115
y = -0.1825x + 128.69
R² = 0.1982
105
115
110
105
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
100
100
1896
1901
1906
1911
1916
1921
1926
1931
1936
1941
1946
1951
1956
1961
1966
1971
1976
1981
1986
1991
1996
2001
2006
2011
110
y = -0.0579x + 133.71
R² = 0.1371
20
Cluster analysis is the statistical
assignment of a set of
observations into subsets so that
observations in the same cluster
are similar in some sense.
 It is a method of “unsupervised
learning” – where all data are
compared in a multidimensional
space and classifying patterns are
found in the data.
 Clustering is common for
statistical data analysis and is used
in many fields.

Example of a dendrogram.
Clusters can be created by cutting
off this tree at any vertical level,
creating (in this case) from one to
29 clusters.
21





The dissimilarity matrix describes pairwise distinction
between objects.
The algorithm PAM computes representative objects,
called medoids whose average dissimilarity to all the
objects in the cluster is minimal
Each object of the data set is assigned to the nearest
medoid.
PAM is more robust than the well-known kmeans
algorithm, because it minimizes a sum of dissimilarities
instead of a sum of squared Euclidean distances, thereby
reducing the influence of outliers.
PAM is a standard procedure
22
 For Alaska, Yukon, and BC, SNAP uses 1961-1990 climatologies from PRISM, at
2 km
 For all other regions of Canada SNAP uses climatologies from CRU, at 10
minutes lat/long (~18.4 km)
 In clustering these data, the differences in scale and gridding algorithms led to
artificial incongruities across boundaries.
 The solution was to cluster across the whole region using CRU data, but to
project future climate-biomes using PRISM, where available, to maximize
resolution and sensitivity to slope, aspect, and proximity to coastlines.
CRU data and SNAP outputs after PRISM
downscaling
23
Choice is
mathematically
somewhat arbitrary,
since all splits are valid
 Some groupings likely to
more closely match
existing land cover
classifications
 How many clusters are
defensible?
 How large a biome shift
is “really” a shift from
the conservation
perspective?

Sample cluster
analysis showing 5
clusters, based on
CRU 10’
climatologies. This
level of detail was
deemed too
simplistic to meet
the needs of end
users.
Sample cluster
analysis showing 30
clusters, based on
CRU 10’
climatologies. This
level of detail was
deemed too complex
to meet the needs of
end users, as well as
too fine-scale for the
inherent uncertainties
24
of the data.
0.2
Mean silhouette
width for varying
numbers of clusters
between 3 and 50.
High values in the
selected range
between 10 and 20
occur at 11, 17, and
18.
0.18
0.16
Average Sihouette Width
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
25
30
40
50
Number of Clusters Returned
25
Eighteen-cluster map for the entire study area. This cluster number was
selected in order to maximize both the distinctness of each cluster and the
utility to land managers and other stakeholders.
26
Cluster certainty based on silhouette width. Note that certainty is
lowest along boundaries.
27
20
1
2
3
10
4
Mean Seasonal Temperature (°C)
5
6
0
winter
spring
summer
fall
7
8
-10
9
10
11
12
-20
13
14
15
-30
16
Mean seasonal temperature
by cluster. For the purposes
of this graph, seasons are
defined as the means of 3months periods, where
winter is December, January,
and February, spring is
March, April, May, etc.
17
18
-40
28
(rainwater equivalent, mm)
Total Annual Precipitation
2500
2249
2000
1500
Precipitation by
cluster. Mean annual
precipitation varies
widely across the
clustering area, with
Cluster 17 standing out
as the wettest.
857
1000
586
561
500
198 206
117 174
243 274 281
355
284
390 420
474
545
443
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18
Cluster 17
29
230
Length of above-freezing season and
GDD by cluster. Days above freezing were
estimated via linear interpolation between
monthly mean temperatures. Growing
degree days (GDD) were calculated using
0°C as a baseline.
3000
210
2500
170
2000
150
1500
130
110
1000
90
Growing degree days
Days
above
freezing
Growing
Degree
Days
500
1200
70
50
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18
cluster
Warm-season and cold-season
precipitation by cluster. The majority
of precipitation in months with mean
temperatures below freezing is assumed
to be snow (measured as rainwater
equivalent).
Total precipitation, mm (rainwater equivalent)
Days above freezing
190
1000
total for months
with mean
temperature
below freezing
800
600
400
total for months
with mean
temperature
above freezing
200
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18
Clusters
30
Created 2/4/11 3:00 PM by
Conservation Biology Institute
http://land cover.usgs.gov/nalcms.php
GlobCover
2009
North American
Land Change
Monitoring System
(NALCMS 2005)
Alaska Biomes
and Canadian
Ecoregions.
AVHRR Land
cover, 1995
31
AVHRR
Canadian and
Alaskan
Ecoregions
GlobCover
1
Open shrub
Northern Arctic
Sparse (<15%) vegetation
2
Open shrub
Southern Arctic
3
Open shrub
Alaska Arctic
4
Closed Shrubland
Alaska Arctic
5
Open shrub
Southern Arctic
6
Closed Shrubland
Taiga Shield
7
Woodland
Taiga Plain
8
Wooded Grassland
Boreal Cordillera
9
Woodland
Alaska Boreal
10
Grassland
Western Tundra
11
Woodland
Taiga Shield
12
Woodland
Taiga Plain
13
Open shrub
14
Evergreen Needleleaf Forest
15
Evergreen Needleleaf Forest
16
Evergreen Needleleaf Forest
17
Bare Ground
18
Grassland
Cluster
Number
NALCMS
barren lands
polar or subpolar grassland
Sparse (<15%) vegetation
lichen moss
polar or subpolar grassland
Sparse (<15%) vegetation
lichen moss
polar or subpolar grassland
Sparse (<15%) vegetation
lichen moss
polar or subpolar grassland
Sparse (<15%) vegetation
lichen moss
polar or subpolar grassland
Sparse (<15%) vegetation
lichen moss
subpolar taiga needleleaf
Sparse (<15%) vegetation
forest
Open (15-40%) needleleaved deciduous or temperate or subpolar
evergreen forest (>5m)
needleleaf forest
Open (15-40%) needleleaved deciduous or temperate or subpolar
evergreen forest (>5m)
shrubland
temperate or subpolar
Sparse (<15%) vegetation
shrubland
subpolar taiga needleleaf
Sparse (<15%) vegetation
forest
Open (15-40%) needleleaved deciduous or temperate or subpolar
evergreen forest (>5m)
needleleaf forest
Taiga Cordillera
Sparse (<15%) vegetation
Montane
Open (15-40%) needleleaved deciduous or
Cordillera
evergreen forest (>5m)
Open (15-40%) needleleaved deciduous or
Boreal Plain
evergreen forest (>5m)
Open (15-40%) needleleaved deciduous or
Boreal Shield
evergreen forest (>5m)
North Pacific
Maritime
Sparse (<15%) vegetation
Closed to open (>15%) herbaceous
vegetation (grassland, savannas or
Prairie
lichens/mosses)
barren lands
temperate or subpolar
needleleaf forest
cropland
temperate or subpolar
needleleaf forest
temperate or subpolar
needleleaf forest
cropland
Comparison of
cluster-derived
cliomes with
existing land
cover
designations.
This table shows
only the highestpercentage
designation for
each land cover
scheme. Colorcoding helps to
distinguish
categories.
32
Modeled cliomes for the historical baseline years, 1961-1990. As in all projected
maps, Alaska and the Yukon are shown at 2km resolution based on PRISM
downscaling, and the Northwest Territories are shown at 18.4 km resolution based
on CRU downscaling.
33
Future
Projections
Original 18 clusters
Projected cliomes for the
five-model composite, A1B
(mid-range ) climate
scenario.
Alaska and the Yukon are
shown at 2km resolution and
NWT at 10 minute lat/long
resolution .
34
Future
Projections
2000’s
2030’s
2060’s
2090’s
2000’s
Projected cliomes
for the A2
emissions scenario.
This scenario
assumes higher
concentrations of
greenhouse gases,
as compared to the
A1B scenario.
Projected cliomes
for the B1
emissions
scenario. This
scenario assumes
lower
concentrations of
greenhouse gases,
as compared to the
A1B scenario.
2030’s
2060’s
2090’s
35
Future
Projections
Original 18 clusters
Projected cliomes for
single models. The five
GCMs offer differing
projections for 2090.
36
Future
Projections
Projected change and
resilience under three
emission scenarios. These
maps depict the total
number of times models
predict a shift in cliome
between the 2000’s and the
2030’s, the 2030’s and the
2060’s, and the 2060’s and
the 2090’s. Note that
number of shifts does not
necessarily predict the
overall magnitude of the
projected change.
37
1.00
Comparison with existing
land cover designations
 Assessment of which shifts
are most significant in
terms of vegetation
communities
 Linkages with speciesspecific research

 Habitat
characteristics/requirements
 Dispersal ability
 Historical shifts
Bare Ground
0.90
Cropland
0.80
Grassland
0.70
Open Shrubland
Closed Shrubland
0.60
Wooded Grassland
0.50
Woodland
0.40
Mixed Forest
0.30
Deciduous
Broadleaf Forest
0.20
Evergreen
Needleleaf Forest
0.10
Water (and
Goode's
interrupted space)
0.00
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Dominant AVHRR land cover types by
cluster number. All land cover categories
that occur in 15% or more of a given
cluster are included.
38

Changes are unlikely to happen smoothly and spontaneously, and
are certainly not going to happen instantly
 Seed dispersal takes time
 Changes to underlying soils and permafrost take even longer
 In many cases, intermediate stages are likely to occur when climate
change dictates the loss of permafrost , a new forest type, or new
hydrologic conditions

Even in cases when biomes do shift on their own, they almost
never do so as cohesive units
 Trophic mismatches are likely
 Invasive species may have greater dispersal abilities than native ones
 It may become increasingly difficult to even define what an “invasive
species” is
39

Forecast Planning
 One Future
-10%
+10%
What we know today
Global Business Network (GBN) -- A member of the Monitor Group
Scenario Planning
 Multiple Futures

Uncertainties
What we know today
Copyright 2010 Monitor Company Group





Identification of refugia
Identification of vulnerable
species/areas
Collaboration and dialogue
between modelers and field
researchers
Selecting focus of future
research
Shift from “preservation” to
“adaptation”
Toolik Lake Catherine Campbell
http://www.polartrec.com/expeditions/changingtundra-landscapes/journals/2008-07-22
Brian Bergamaschi
(USGS) sampling
wells at Bonanza
Creek LTER site.
http://hydrosciences.colora
do.edu/research/govt_partn
ers.php
41


All project inputs and outputs are available to
the public
The final report (full report, main text only, or
appendices only) can be downloaded here:
http://www.snap.uaf.edu/project_page.php?projectid=8

Maps and data are also available in GIS
formats; contact SNAP for further
information (nlfresco@alaska.edu)
42
The US portion of this study was made possible by the US Fish and Wildlife Service, Region 7, on behalf of the
Arctic Landscape Conservation Cooperative (LCC), with Karen Murphy as project lead and assistance from Joel
Reynolds and Jennifer Jenkins (USFWS). The Canadian portion of this study was made possible by The Nature
Conservancy Canada, Ducks Unlimited Canada, Government Canada and Government Northwest Territories,
with Evie Whitten as project lead. Data and analysis were provided by the University of Alaska Fairbanks (UAF)
Scenarios Network for Alaska and Arctic Planning (SNAP) program and Ecological Wildlife Habitat Data Analysis
for the Land and Seascape Laboratory (EWHALE) lab, with Nancy Fresco, Michael Lindgren, and Falk Huettmann
as project leads. Further input was provided by stakeholders from other interested organizations.
We would also like to acknowledge the following organizations and individuals:








Karen Clyde, Government YT
David Douglas, US Geological Survey
Evelyn Gah, Government NWT
Lois Grabke, Ducks Unlimited Canada
Troy Hegel, Government YT
James Kenyon, Ducks Unlimited Canada
Wendy Loya , the Wilderness Society
Lorien Nesbitt , Déline Renewable Resources Council
Thomas Paragi, Alaska Department of Fish and Game
Michael Palmer, TNC
Scott Rupp , SNAP
Brian Sieben, Government NWT
Stuart Slattery, Ducks Unlimited Canada
Jim Sparling, Government NWT
43