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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

Measuring and modeling change  Measurements  Weather station data  Interpolated and gridded   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 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) 2.5 x 2.5 degrees  Baseline values = PRISM 2km mean monthly precipitation and temperature,  Adjusted and interpolated GCM outputs to this historical baseline  Effectively removed model biases while scaling down the GCM projections 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 Scenario Planning Multiple Futures What we know today +10% -10% Uncertainties Global Business Network (GBN) -- A member of the Monitor Group Copyright 2010 Monitor Company Group What we know today

What are the implications of these scenarios for our strategic issue, and what actions should we take in light of them? 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? As new information unfolds, which scenarios seem most valid? Does this affect our decisions and actions? This diagram describes the 5 key steps required in any scenario planning process Global Business Network (GBN) -- A member of the Monitor Group Copyright 2010 Monitor Company Group 14

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 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 OR Hedge Your Bets / Wait and See: Make several distinct bets of relatively equal size OR Core / Satellite: Place one major bet, with one or more small bets as a hedge against uncertainty, experiments, and real options Hedge Your Bets Core Robust Satellite Bet the Farm Hedge Your Bets Core Robust Satellite Bet the Farm 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.

 Broader spatial scope  More input data  Clustering methodology 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. 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 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 of the data. 24

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

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

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. 28

Precipitation by cluster. Mean annual precipitation varies widely across the clustering area, with Cluster 17 standing out as the wettest. Cluster 17 29

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. 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). 30

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. 31

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. 32

Modeled cliomes for the historical baseline years, 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

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 Original 18 clusters 34

2000’s 2030’s 2060’s 2090’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. 2000’s 2030’s 2060’s 2090’s Future Projections 35

Projected cliomes for single models. The five GCMs offer differing projections for Future Projections Original 18 clusters 36

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. Future Projections 37

 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. 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 Scenario Planning Multiple Futures What we know today +10% -10% Uncertainties Global Business Network (GBN) -- A member of the Monitor Group Copyright 2010 Monitor Company Group What we know today

 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 tundra-landscapes/journals/ Brian Bergamaschi (USGS) sampling wells at Bonanza Creek LTER site. 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:  Maps and data are also available in GIS formats; contact SNAP for further information 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