Climate Change and Biome Shifts in Alaska and Western Canada

Climate Change and Climate Change and Biome Shifts in Alaska and Biome Shifts in Alaska and Western CanadaWestern CanadaCurrent Results and Modeling OptionsDecember 2010

ParticipantsParticipantsScenarios Network for Alaska Planning

(SNAP), University of Alaska FairbanksEWHALE lab, Institute of Arctic Biology,

University of Alaska FairbanksUS Fish and Wildlife ServiceThe Nature ConservancyDucks Unlimited CanadaGovernment of the Northwest TerritoriesGovernment of CanadaOther invited experts

Goals of this meetingGoals of this meeting Review Project Goals Summary of project background Explanation of modeling methods and data Update on progress thus far Discussion and decisions from group:

◦ Confirm clustering inputs (24 predictor variables)◦ Confirm resolution for clustering and re-projection

(CRU vs PRISM)◦ Select number of clusters (15-20)◦ Select land cover comparisons, data and methods◦ Choose future decades to model◦ Confirm emissions scenarios (A1B, A2, B1)◦ Discuss data delivery and formats◦ Other issues?

Review Project timeline

OverviewOverview This project is intended to:

◦ a) develop climate and vegetation based biomes for Alaska, the Yukon and the Northwest Territories based on data, and

◦ b) based on the climate data, identify areas that are least likely to change and those that are most likely to change over the next 100 years.

This project builds ,and makes use of, work previously conducted by SNAP, EWHALE, USFWS, TNC, and other partners.

The completed analysis will be used by partners involved in protected areas, land use, and sustainable land use planning, e.g. connectivity.

Overall objectivesOverall objectivesDevelop climate and vegetation based

biomes (based on cluster analysis) for AK, Yukon, NWT, and areas to the south that may represent future climatic conditions for AK,Yukon or NWT.

Model potential climate-induced biome shift.

Based on model results, identify areas that are least or most likely to change over the next 10-90 years.

Provide maps, data, and a written report summarizing, supporting, and displaying these findings.

The Scenarios Network for Alaska The Scenarios Network for Alaska and Arctic Planning (SNAP)and Arctic Planning (SNAP)

SNAP is a collaborative network of the SNAP is a collaborative network of the University of Alaska, state, federal, and local University of Alaska, state, federal, and local agencies, NGOs, and industry partners. agencies, NGOs, and industry partners.

Its mission is to provide timely access to Its mission is to provide timely access to scenarios of future conditions in Alaska for scenarios of future conditions in Alaska for more effective planning by decision-makers, more effective planning by decision-makers, communities, and industry.communities, and industry.

SNAP uses data for 5 of 15 models that performed best for Alaska and northern latitudes

PRISM downscaled to 2 km resolution OR CRU downscaled to 10 minutes (18.4 km)

Monthly temp and precip from 1900 to 2100 (historical CRU + projected)

5 models x 3 emission scenarios Available as maps, graphs, charts, raw data On line, downloadable, in Google Earth, or

in printable formats No data yet:

◦ Extreme events◦ Snowpack◦ Coastal/Oceans

SNAP Projections:based on IPCC models

Phase I: Alaska modelPhase I: Alaska modelMapped shifts in potential biomes based on current climate Mapped shifts in potential biomes based on current climate envelopes for six Alaskan biomes and six Canadian Ecozonesenvelopes for six Alaskan biomes and six Canadian Ecozones

http://geogratis.cgdi.gc.ca/geogratis/en/collection/detail.do?id=43618

Phase I Results:Potential Change: Current - Phase I Results:Potential Change: Current - 21002100(Noting that actual species shifts lag behind climate (Noting that actual species shifts lag behind climate shifts)shifts)

Improvements over Phase Improvements over Phase IIExtend scope to northwestern CanadaUse all 12 months of data, not just 2Eliminate 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

Sampling ExtentSampling Extent

Cluster analysisCluster analysis Cluster analysis is the assignment of a

set of observations into subsets so that observations in the same cluster are similar in some sense.

Clustering is a method of “unsupervised learning” (the model teaches itself, and finds the major breaks)

Clustering is common for statistical data analysis used in many fields

The choice of which clusters to merge or split is determined by a linkage criterion (distance metrics), which is a function of the pairwise distances between observations.

Cutting the tree at a given height will give a clustering at a selected precision.

Step 1: Create a Dissimilarity Step 1: Create a Dissimilarity MatrixMatrix

Distance measure determines how the similarity of two elements is calculated.

Some elements may be close to one another according to one distance and farther away according to another.

In our modeling efforts, all 24 variables are given equal weight, and all distances are calculated in “24-dimensional space” using RandomForest

(similarity matrix, proximity matrix, distance matrix get converted into each other)

Taxicab geometry versus Euclidean distance:

The red, blue, and yellow lines have the same length in taxicab geometry for the same route. In Euclidean geometry, the green line has length 6×√2 ≈ 8.48, and is the unique shortest path.

Methods: Partitioning Around Methods: Partitioning Around Medoids (PAM)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

Clustering limitationsClustering limitationsPAM must compare every data point to

every other data point in the dissimilarity matrix (created by RandomForest), and create medoids

Adding additional data points affects processing requirements exponentially

Thus, in creating clusters, we were limited to approximately 20,000 data points, a fraction of the possible samples.

Total area is approximately 19 million square kilometers

This meant selecting one data point for approximately every 20 km by 20 km

Resolution limitationsResolution limitationsData are not available at the same

resolution for the entire area◦ for Alaska, Yukon, and BC, SNAP uses 1961-

1990 climatologies from PRISM, at 2 km, ◦ for all other regions of Canada SNAP uses

climatologies for the same time period from CRU, at 10 minutes lat/long (~18.4 km)

◦ In clustering these data, both the difference in scale and the difference in gridding algorithms led to artificial incongruities across boundaries.

◦ One solution to both resolution and clustering limitations is to cluster across the whole region using CRU data, which is available for the entire area.

Re-Sampling to overcome AK & Can differences (=> as it applies to many GIS datasets)

Different PixelResolutions

Different PixelResolutions resolved….

Re-Sampling to overcome AK & Can differences (=> as it applies to many GIS datasets)

PRISM dataPRISM data Unlike other statistical methods in use today, PRISM was

written by a meteorologist specifically to address climate Moving-window regression of climate vs. elevation for each

grid cell Uses nearby station observations Spatial climate knowledge base weights stations in the

regression function by their physiographic similarity to the target grid cell

PRISM is well-suited to mountainous regions, because the effects of terrain on climate play a central role in the model's conceptual framework

The primary effect of orography on a given mountain slope is to cause precipitation to vary strongly with elevation.

The topographic facet is an important climatic unit and elevation is a primary driver of climate patterns

PRISM quality depends on DEM

PRISM: 5 clustersCoastal vs interior, northern vs southern

Note: colors on all the following cluster maps are arbitrary, and are chosen merely to be distinct from one another.

PRISM: 10 clusters

Aleutians and coastal rainforest become distinct

PRISM: 15 clusters

Latitudinal patterns in AK and BC

PRISM: 20 clustersHighest points of Brooks Range separate from coastal plain and lower foothills

How many clusters can be justified?

CRU dataCRU data The station climate statistics were interpolated

using thin-plate smoothing splines (ANUSPLIN) Trivariate thin-plate spline surfaces were fitted as

functions of latitude, longitude and elevation to the station data

The inclusion of elevation as a co-predictor adds considerable skill to the interpolation, enabling topographic controls on climate

Local topographic effects such as rain shadows cannot be resolved unless: (1) a predictor that is a proxy for this influence is incorporated in the interpolation, and/or (2) there are sufficient stations to capture this local dependency as a function of latitude, longitude and elevation.

In regions with sparse data, the station networks used to create these data sets are clearly unable to capture this sort of detail

CRU data alone: 5 clustersStrong latitudinal banding

CRU data alone: 10 clusters

Weather station anomaly?


Latitudinal banding persists, but more variability and east/west break


How many clusters can be justified?

Re-projecting CRU clusters to Re-projecting CRU clusters to PRISMPRISM

CRU is available for entire study area, and offers a good fit at a broader scale

PRISM offers a better fit at fine scales, with better accuracy re altitude but is not fully available for the study area

Best of both:◦Cluster results from CRU data were used to

train an RF classification model. ◦RF then classified the full PRISM datasets

(where available) according to these clusters

◦This referred as DOWNSCALING

Comparison of results using Comparison of results using various methodsvarious methodsThe following results were derived

from the following clustering and downscaling groups:◦Created clusters using 15km sample

of 2km PRISM data, and downscaled to the full PRISM dataset at 2km resolution over AK, YT, BC.

◦Created clusters using 20km sample of 10min CRU data, and downscaled using the 2km PRISM data over AK, YT, BC.

Comparison of results: 5 Comparison of results: 5 clustersclusters

Trained to PRISM data, and re-projected to PRISM

Trained to CRU, re-projected to PRISM data


Trained to PRISM data











Assessing the clustersBox plotsCongruence with existing land

cover classification by modal values

Congruence with land cover classification by percent

Other metrics?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15AKClustPAM

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January precipitation January temperature

February precipitation February temperature

July precipitation July temperature

October precipitation October temperature

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15AKClustPAM

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Landcover in Alaska and Canada

ViereckNowackiCanadian EcoregionsNLDCLandfireLANDSATAVHRRMODISNDVIGreennessNorth America Landcover

Value Landcover0 Water1 Evergreen Needleleaf Forest2 Evergreen Broadleaf Forest3 Deciduous Needleleaf Forest4 Deciduous Broadleaf Forest5 Mixed Forest6 Woodland7 Wooded Grassland8 Closed Shrubland9 Open Shrubland

10 Grassland11 Cropland12 Bare Ground13 Urban and Built

AVHRR Land Cover (1km)

AVHRR Landcover Overlaid with 15 Cluster Polygons

AVHRR_LC

1 - Evergreen Needleleaf Forest6 - Woodland8 - Closed Shrubland9 - Open Shrubland10 - Grassland

15 Cluster Solution (10min CRU) With Most Common AVHRR Landcover Class Displayed Within Each Cluster Area

The logic here is that each cluster has the mode response displayed within it using a “winner-take-all” methodology

How Pure Are These New How Pure Are These New Clusters with Regard to AVHRR Clusters with Regard to AVHRR

Landcover?Landcover?

Boreal Cordillera

Boreal PLain

Boreal Shield

Hudson Plain

Montane Cordillera

Northern Arctic

Pacific Maritime

Prairie

Southern Arctic

Taiga Cordillera

Taiga Plain

Taiga Shield

Canada Ecozones

How Pure Are These New How Pure Are These New Clusters with Regard to Clusters with Regard to

Canada’s Ecozones?Canada’s Ecozones?

Canada Ecozones – With 15 Cluster Solution Polygons Overlaid“Winner-take-all” Type of Mode Reclassification

Boreal Cordillera

Boreal PLain

Boreal Shield

Hudson Plain

Montane Cordillera

Northern Arctic

Pacific Maritime

Prairie

Southern Arctic

Taiga Cordillera

Taiga Plain

Taiga Shield

Northern Arctic

Southern Arctic

Taiga Plain

Taiga Sheild

Boreal Sheild

Boreal Plain

Prairie

Taiga Cordillera

Boreal Cordillera

Pacific Maritime

Montane Cordillera

15 Cluster Solution with Mode Response From Canada Ecozones as Identifier of New Clusters – With Canada Ecozones Polygons

Overlaid“Winner-take-all” Type of Mode Reclassification

Alaska Ecoregions - Nowacki

LEVEL_2Alaska Range Transition

Aleutian Meadows

Arctic Tundra

Bering Taiga

Bering Tundra

Coast Mountains Transition

Coastal Rainforests

Intermontane Boreal

Pacific Mountains Transition

15 Cluster Solution Mode Response of Alaska Ecoregions – Nowacki [Level 2]

Alaska Range Transition

Aleutian Meadows

Arctic Tundra

Bering Taiga

No MODE Value

Coastal Rainforests

Intermontane Boreal

Pacific Mountains Transition

How Pure Are These New Clusters How Pure Are These New Clusters with Regard to Alaska Ecoregions with Regard to Alaska Ecoregions

(Nowacki)?(Nowacki)?

15 Cluster Solution of Alaska Ecoregions – With Nowacki [Level 2] Ecoregions Polygons Overlaid

How many clusters?Choice is mathematically somewhat

arbitrary, since all splits are validSome groupings likely to more closely

match existing land cover classificationsHow many clusters are defensible?How large a biome shift is “really” a

shift from the conservation perspective?Multiple numbers of clusters to explore

this, e.g. 15 and 20?

16 clusters (CRU, not downscaled)




16 clusters [trained at 10min (CRU) and down-modeled at 10min (CRU)]




16 clusters [trained at 10min (CRU) and down-modelled at 2km PRISM

(AK, YT, BC)]


(AK, YT, BC)]


(AK, YT, BC)]


(AK, YT, BC)]

What Does The Future Look What Does The Future Look Like?Like?

At the 15 cluster solutionAt the 15 cluster solutionUsing A1B temperature and

precipitation data for Canada and Alaska we can visualize the predicted shifting of biomes through time.

Time steps: 2000-2009, 2030-2039, 2060-2069, 2090-2099

All predictor 24 variables included

Alaska Canada Study Extent 2000-2009 --15 Alaska Canada Study Extent 2000-2009 --15 clustersclusters

Note: future projections for the project will NOT be done over this full extent, but only for AK, YT, NWT, and a limited boundary area. Results for the eastern and southern portions shown here are invalid because no clusters have been allowed to shift in from outside these boundaries.

2000-2039 – 15 clusters2000-2039 – 15 clusters

2060 - 2069 – 15 clusters2060 - 2069 – 15 clusters

2090 - 2099 – 15 clusters2090 - 2099 – 15 clusters

Data choices: SNAP modelsAvailable climate data from SNAP include

output for each of the five best-performing GCM models as well as a composite (mean) of all five models

Minimum of three future time periods (e.g. 2030-2039; 2060-2069 and 2090-2099) -- are these periods optimal?

Will we use just the composite model?Choice of emission scenario as defined by

the IPCC: A1B, A2, B1 – A2 and A1B, or just A1B?

Modeling choices: Model variability and extreme yearsRandomForest can inform researchers of

which variables, or models, of the complex multivariate set are most important in defining future distributions ◦ Can run 6 different climate models independently so

results can be compared ◦ All 6 model variables can be entered simultaneously

within RandomForest so the software can select between models and variables.

◦ Top 5 SNAP models perform differently in different areas of Alaska.

◦ Geographic tag to explain how the different GCM models perform in different regions of the state.

◦ Incorporate the important thresholds or ‘tipping points’ that are often defined by extreme climate years, while avoiding the reliance on just a single year’s modeled data.

Modeling choices:Defining change, defining refugiaThe decadal results from RandomForest will

be analyzed to determine which grid cells are projected to remain within the same biome climate envelope over the time periods.

Confidence in these areas◦ Only consider areas selected as refugia in the

majority of the climate models◦ RandomForest assigns a ranking value to each of

pixel that can be used to identify the model confidence

◦ Sites that shift climatically to match non-adjacent biomes can be interpreted as a proxy for magnitude of change

Timeline Initiation meeting with experts and stakeholders to

review approaches for developing the existing biome data layer: May 5th 2010

Team leader meetings/teleconferences for AK and Canada projects to make key decisions regarding clustering methods and spatial resolution. Autumn 2010

Initial clustering results and sample projection data: December 2010

Project update and stakeholder meeting. Decisions to be made include time steps for analysis, emission scenarios, and composite vs single models: December 14th, 2010

Project update, progress report and stakeholder meeting to determine thresholds for the analysis of refugia and areas of extreme change: April 2011

Close-out meeting with experts and stakeholders: September 2011

Final report, manuscript draft, digital data (including metadata): September 30th 2011

Deliverables Complete set (spatial ArcGIS map files, metadata and

appropriate data streams) of GIS models for the Alaska & Canadian biomes

Progress report describing final derived biomes and the quantitative differences between their climate envelopes.

Complete set of GIS models predicting future biome change at four time steps

Progress report describing the methods and selection process to create the final predicted biomes

Complete set of GIS models defining areas of refugia, and how frequently other areas within Alaska change at the 3 future time steps (i.e. “resilience”)

Report submitted to the FWS Journal of Fish & Wildlife Management or to a peer reviewed journal. The FWS Journal of Fish & Wildlife Management is an electronic journal sponsored by FWS.

Documents

Climate Change and Biome Shifts in Alaska and Western Canada