Goals 1)Develop climate-based land-cover categories (cliomes) for Alaska and western Canada using...
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Goals 1)Develop climate-based land-cover categories (cliomes) for Alaska and western Canada using down-scaled gridded historic climate data from the Scenarios
Goals 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 SNAPs
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.
Slide 3
Background Follow-up to Connecting Alaska Landscapes into the
Future Project Broader spatial scope More input data Clustering
methodology 3
Slide 4
Improvements over Phase I 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 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
Slide 5
Sampling Extent 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.
Slide 6
Historical Climate Trends: Ice Breakup Data 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.
Slide 7
Methods: cluster analysis 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.
Slide 8
Methods: SNAP climate models SNAP is a collaborative network of
the University of Alaska, state, federal, provincial, and local
agencies, NGOs, and industry partners. Its mission is to provide
timely access to scenarios of future conditions in Alaska and the
Arctic for more effective planning by decision-makers, communities,
and industry.
Slide 9
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 and northwestern
Canada Focused on A1B, B2, and A1 emissions scenarios Downscaled
course resolution GCM data to 2km SNAP data based on CRU historical
datasets and IPCC Global Circulation (GCM) models GCM output
(ECHAM5) Figure 1A from Frankenberg st al., Science, Sept. 11,
2009
Slide 10
Methods: Partitioning Around Medoids (PAM) 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
Slide 11
Resolution limitations 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
Slide 12
How many clusters? 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 of the data.
Slide 13
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. How many clusters?
Slide 14
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.
Slide 15
Cluster certainty based on silhouette width. Note that
certainty is lowest along boundaries.
Slide 16
Describing the clusters: temperature Mean seasonal temperature
by cluster. For the purposes of this graph, seasons are defined as
the means of 3- months periods, where winter is December, January,
and February, spring is March, April, May, etc.
Slide 17
Describing the clusters: precipitation Precipitation by
cluster. Mean annual precipitation varies widely across the
clustering area, with Cluster 17 standing out as the wettest.
Cluster 17
Slide 18
Describing the clusters: growing degree days, season length,
and snowfall 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 0C as a baseline. 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).
Slide 19
Describing the clusters: existing land classification
http://land cover.usgs.gov/nalcms.php North American Land Change
Monitoring System (NALCMS 2005) AVHRR Land cover, 1995 Created
2/4/11 3:00 PM by Conservation Biology Institute GlobCover 2009
Alaska Biomes and Canadian Ecoregions.
Slide 20
Comparison of cluster-derived cliomes with existing land cover
designations. This table shows only the highest- percentage
designation for each land cover scheme. Color- coding helps to
distinguish categories.
Slide 21
Baseline maps 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.
Slide 22
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. Future
Projections
Slide 23
2000s 2030s 2060s 2090s 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. 2000s 2030s
2060s 2090s Future Projections
Slide 24
Projected cliomes for single models. The five GCMs offer
differing projections for 2090. Future Projections
Slide 25
Projected change and resilience under three emission scenarios.
These maps depict the total number of times models predict a shift
in cliome between the 2000s and the 2030s, the 2030s and the 2060s,
and the 2060s and the 2090s. Note that number of shifts does not
necessarily predict the overall magnitude of the projected change.
Future Projections
Slide 26
Discussion: Interpreting results Comparison with existing land
cover designations Assessment of which shifts are most significant
in terms of vegetation communities Linkages with species-specific
research Habitat characteristics/requirements Dispersal ability
Historical shifts Dominant AVHRR land cover types by cluster
number. All land cover categories that occur in 15% or more of a
given cluster are included.
Slide 27
Discussion: Real-world limitations of modeled results 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
Slide 28
Discussion: Management implications 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/changing-
tundra-landscapes/journals/2008-07-22 Brian Bergamaschi (USGS)
sampling wells at Bonanza Creek LTER site.
http://hydrosciences.color ado.edu/research/govt_par tners.php
Slide 29
Accessing project documents and data 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
http://www.snap.uaf.edu/project_page.php?projectid=8 Maps and data
are also available in GIS formats; contact SNAP for further
information ([email protected])
Slide 30
Acknowledgments 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, Dline 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