Climate Change and Biome Shifts in Alaska and Western Canada

1. Climate Change and Biome Shifts in Alaska and Western Canada Current Results and Modeling Options December2010

2. Participants • Scenarios Network for Alaska Planning (SNAP), University of Alaska Fairbanks • EWHALE lab, Institute of Arctic Biology, University of Alaska Fairbanks • US Fish and Wildlife Service • The Nature Conservancy • Ducks Unlimited Canada • Government of the Northwest Territories • Government of Canada • Other invited experts

3. Goals of this meeting • Summary of project background • Explanation of modeling methods • Update on progress thus far • Discussion and decisions from group: • Confirm clustering inputs (24 data points) • Confirm resolution for clustering and re-projection (CRU vs PRISM) • Select number of clusters (15-20) • Select land cover comparisons and methods • Choose future decades to model • Confirm emissions scenarios (A1B, A2, B1) • Other issues?

4. Overview • This project is intended to: • a) develop climate and vegetation based biomes for Alaska, the Yukon and the Northwest Territories, and • b) based on 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 on 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.

5. Overall objectives • Develop 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,Yukonor NWT. • Model potential climate-induced biome shift. • Based on model results, identify areas that are least or most likely to change over the next 90 years. • Provide maps, data, and a written report summarizing, supporting, and displaying these findings.

6. The Scenarios Network for Alaska and Arctic Planning (SNAP) • 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.

7. 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

8. Phase I: Alaska modelMapped shifts in potential biomes based on current climate envelopes for six Alaskan biomes and six Canadian Ecozones 8 http://geogratis.cgdi.gc.ca/geogratis/en/collection/detail.do?id=4361

9. Phase I Results:Potential Change: Current - 2100(Noting that actual species shifts lag behind climate shifts)

10. Improvements over Phase I • Extend scope to northwestern Canada • Use all 12 months of data, not just 2 • Eliminate 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

11. Sampling Extent

12. Cluster 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) • 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, 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.

13. Step 1: Create a Dissimilarity Matrix • 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) 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.

14. Methods: Partitioning Around Medoids (PAM) • The dissimilarity matrix describes pairwise distinction between objects. • The algorithm PAM computes representative objects, called medoidswhose 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.

15. Clustering limitations • PAM must compare every data point to every other data point in order to create a dissimilarity matrix and create medoids • Adding additional data points affects processing requirements exponentially • Thus, in creating clusters, we were limited to approximately 20,000 data points • Total area is approximately 19 million square kilometers • This meant selecting one data point for every 20 km by 20 km

16. Resolution limitations Data 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 limitaitons is to cluster across the whole region using CRU data, which is available for the entire area.

17. PRISM 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

18. CRU 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

19. Re-projecting CRU clusters to PRISM • 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 • 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

20. Comparison of results using various methods • Results were derived from the following train/down-model groups: • Trained to 15km sample of 2km data and down-modeled to the 2km resolution over AK, YT, BC. • Trained to 20km sample of 10min data down-modeled to the 2km resolution over AK, YT, BC. • Trained to 15km sample of 2km data and down-modeled to the 10min resolution over full study region.

21. Comparison of results: 5 clusters Trained to PRISM data Trained to CRU, re-projected to PRISM data

22. Comparison of results: 10 clusters Trained to PRISM data Trained to CRU, re-projected to PRISM data

23. Comparison of results: 15 clusters Trained to PRISM data Trained to CRU, re-projected to PRISM data

24. Comparison of results: 20 clusters Trained to PRISM data Trained to CRU, re-projected to PRISM data