1. Introduction In this study we have applied sea surfaces temperatures (SSTs) derived from remote sensing and Global Climate Models (GCMs) projections to examine future potential distribution of Chondrus crispus, Irish Moss. The geographic range of organisms is related to climate, particularly on continental scales. Temperature can be a useful first indicator to habitat suitability (Pearson and Dawson, 2003). Remote sensing uses satellites to obtain data that may otherwise be unobtainable on the surface of the earth. This is a benefit in oceanographic studies, as the vast extent of oceans are difficult to sample. The National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer (NOAA AVHRR) uses the thermal infrared channels ( = 3.0 – 1000 m) to remotely sense SST. To address yearly variability, for example the North Atlantic Oscillation (NAO), we took the average monthly temperatures of the NOAA AVHRR temperatures over the past 12 yr. (The NAO is a cycle of warming and cooling caused by reversal of pressure that affects waters and weather of Europe and the east coast of North America (Ahrens, 2003)). GCM simulations differ on regional trends, and on climate variables, but all predict that the global average surface air temperature will increase by 4°C in the next 100 yr (Cubasch et al., 2001). The extent of climate change predicted is dependent on the emissions of atmospheric greenhouse gases and aerosols, largely influenced by carbon dioxide (Environment Canada, 2004). In addition, treatment of surface conditions, such as ocean currents and circulation, and wind patterns vary amongst models. GCMs are often produced on a coarse resolution grid, and need to be “downscaled” in order to be evaluated at the finer resolution needed for impact studies. Direct interpolation is one method that has been implemented in studies that predict species habitat change (e.g., Bartlein et al., 1997). Equation 1. Formula for Inverse Distance Weighted Interpolation. z(u 0 ) = estimated SST at unknown points z(u i ) = known data points d ij = the distance between each data point and the unknown point. p = power source: Lo, and Yeung, 2002. = =+ + Figure 1. GFDL grid in vector format. Figure 2. CCSR grid in vector format. Figure 3. GFDL changefield for February. Figure 4. CCSR changefield for August. Figure 6. August AVHRR SST. Figure 5. February AVHRR SST. Figure 8. CCSR prediction for Aug SST, 2079-2099. Figure 7. GFDL prediction for Feb SST, 2079-2099. Behind the Map: Predicting Marine Species’ Habitat Change Using Global Climate Models Elizabeth Flanary and Sarah Vereault Department of Geography, McGill University Supervisor: Dr. Gail Chmura Department of Geography, McGill University Chondrus crispus occurs on the NW Atlantic coast, to 20 m water depth and between 40 and 60°N, from New Jersey to Labrador (Lee, 1977). The maximum and minimum temperature extremes that occur in C. crispus’ range are in August and February, respectfully. From the NOAA AVHRR layer, data was selected within the depth and latitude parameters, and the corresponding temperature extremes were recorded. The temperatures bounding C. crispus’ range are –2.1 & 21.3°C. A current thermal distribution layer was created, which selected areas from the NOAA AVHRR data that was bounded by the depth and temperature restrictions. This layer corresponded with the known distribution, and thus the temperature bounds are likely a good predictor of habitable areas for C. crispus. If the two distributions did not correspond, and areas of acceptable temperatures were not in the known distribution, it is likely that other variables have a stronger influence on species distribution, such as substrate, salinity, food sources, or predators. To predict where C. crispus will be able to live in the future, the bounding temperatures of –2.1 and 21.3°C were found in layers of future SSTs generated by the GCMs, in areas where the depth was also within limits. Only areas that fell into this range in both February and August were selected as suitable. A change image was produced by subtracting the current thermal distribution layer from the future thermal distribution layer using raster calculations. Notably, all four models predict retraction of the range of C. crispus in southern New England, where Irish moss is currently harvested. The degree to which the models predict loss is variable, and some also predict loss in northern Labrador, around Prince Edward Island and northern Nova Scotia. Figure 9. Chondrus crispus current known distribution. Figure 10. Distribution of waters -2.1 to 21.3 ° C annually. Figure 11. Future distribution of C. crispus according to CCCMA. Figure 12. Future distribution of C. crispus according to CCSR. Figure 13. Change in the distribution of C. crispus according to CCCMA. Figure 14. Change in the distribution of C. crispus according to CCSR. Application of GCM SST data Chondrus crispus, or Irish moss, is a red algae and a source of carrageenan, commonly used as a thickener and stabilizer for processed foods such as ice cream and luncheon meats. It is also used in cosmetics to soften skin and as an herbal supplement. Historically there has been a large Irish moss industry on the South Shore of Massachusetts and along the Maine coast (http://www.http://www. purplesage.org.uk/profiles/irishmoss.htmpurplesage.org.uk/profiles/irishmoss.htm). The data was imported into ERSI ArcGIS 9.0, and a vector layer was generated which showed temperature change at spatially referenced points on a global scale. They were assigned the Geographic Coordinate System WGS 1984, to correspond with the NOAA AVHRR data. The data was trimmed to include only points that occurred in the NW Atlantic, between 25 and 65°N, and 30 and 80°W. The visual presentation of the data reveals the breadth of spatial resolutions (Figs 1 & 2). Interpolation is the process by which known data values and mathematical equations are used to accurately estimate values at unknown locations. Inverse distance weighted (IDW) interpolation supposes that the value of an unknown point is inversely proportional to a power of its distance to known points (Equation 1). IDW was used to interpolate the change fields, using the distance squared, and a radius of 8 nearest neighbours. This follows Tobler’s Law that things that are closer together are more closely related. This process produces a smooth surface. NOAA AVHRR SST data was downloaded from the Physical Oceanography Distributed Active Archive Center (http://podaac-www.http://podaac-www jpl.nasa.gov) in pentad format. It had a spatial resolution of 9 km at the equator, and was global in extent. It was imported into Idrisi 32 v.2, and clipped to the NW Atlantic study area, then imported into ArcGIS. The GCM change fields were added to the NOAA AVHRR data to create the final output showing predicted future sea surface temperatures. Data and Methodology SSTs were downloaded from the Intergovernmental Panel on Climate Change (http://www.ipcc.ch) for four GCMs (Table 1), encompassing the years 1961-1999, and 2079-2099. The years 1961-1999 were used as the baseline, and the years 2079-2099 were selected as the future period because that is when 4°C atmospheric warming is supposed to occur. Only the A2 scenario, which predicts the highest increase in emissions, was included to assess maximum potential change (Nikicenovic, 2000). We generated change fields (increase or decrease in SSTs) by subtracting the baseline (average of 1961 -1999) from the average of the future time period. Table 1. Models used in analysis. Works Cited Ahrens, C.D. 2003. Meteorology Today, 7 th Ed. Brooks/Cole-Thompson Learning: Pacific Grove, USA. Bartlein, P. J., Whitlock, C., and S. L. Shafer. 1997. Future Climate in the Yellowstone National Park and Its Potential Impact on Vegetation. Conservation Biology 11: 780-792. Cubasch U, Meehl GA, Boer GJ, Stouffer M, Dix M, Noda A, Senior CA, Raper S, Yap KS (2001) Projections of future climate change. Pp. 525-582 In Houghton JT, Ding Y, Griggs DJ, Noguer M, VAN DER Linden PJ, Dai X, Maskell K, Johnson CA (eds.) Climate change 2001: the scientific basis, contribution of working group 1 to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press.Environment Canada. The first generation coupled Global Climate Model (7/6/2004). Retrieved 9/23/2005, from http://www.cccma.bc.ec.gc.ca/models/cgcm1.shtml Irish Moss (N.D). Retrieved 9/25/2005, from http://en.wikipedia.org/wiki/Irish_mosshttp://en.wikipedia.org/wiki/Irish_moss Irish Moss (N.D.). Retrieved 9/25/2005, from http://www.purplesage.org.uk/profiles/irishmoss.htmhttp://www.purplesage.org.uk/profiles/irishmoss.htm Lee, T. 1977. The seaweed handbook: an illustrated guide to seaweeds from North Carolina to the Arctic, Mariners Press, Boston. Lo, C. P., and A. K. W. Yeung. 2002. Concepts and Techniques of Geographic Information Systems. Prentice Hall, Upper Saddle River, NJ. Murphy, James. 2000. Predictions of climate change over Europe using statistical and dynamical downscaling techniques. International Journal of Climatology 20: 489 – 501. Nakicenovic N, Swart R (Eds.) (2001) Emissions Scenarios 2000, Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, UK. 570 pp. Pearson, R. G., and T. P. Dawson. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology & Biology 12: 361-371. Discussion When examining and applying the data and results it is important to keep in mind the original scale (Table 1). Since the data was obtained at a very coarse resolution the temperature predictions are likely not viable at a local scale. A large portion of the data was generated through interpolation, and so is not exactly known. However, at a regional level predictions from GCMs can be used to evaluate general trends. Other methods of downscaling data to a finer resolution include statistical and dynamical downscaling. However, in a study using GCMs the results obtained from a dynamical coupling of a GCM and finer resolution Regional Climate Model showed no significant difference from direct interpolation of the GCM (Murphy, 2000). In regard to the distribution of marine species, temperature is not the only factor that determines suitable habitat. Salinity, substrate, nutrient availability, and interaction with other species were not included in this study, and may be equally useful in predicting distribution change. Conclusion Despite variations in magnitude and some local cooling effects, all of the GCMs examined predict that over time the average sea surface temperature will rise. Changes in biogeographic ranges due to rising SSTs could result in the depletion of other species that are economically valuable in certain areas. As well, the disruption in habitat and food webs could have far-reaching ecological consequences. The implications from this could have noticeable effects on marine flora and fauna, local communities connected to the sea, and the global community as well. Acknowledgements Funding for this project was provided by the Climate Change Action Fund (CCAF), the World Wildlife Fund (WWF), and the Natural Sciences and Engineering Research Council (NSERC). Results were generated using the Walter Hitschfeld Geographic Information Centre undergraduate lab’s GIS software and computers. Thank you to Dr. Gerhard Pohle and Mr. Lou Van Guelpen of the Atlantic Reference Centre (ARC), Dr. Gail Chmura, Dr. Jonathan Seaquist, Mr. Graham MacDonald, and Mr. Tim Horton. ModelCentreSpatial Resolution °lat x °longkm CGCM2Canadian Centre for Climate Modeling and Analysis; Canada 3.75 x 3.75416 x 281 CCSR/NIES AGCM CCSR OGCM Centre for Climate System Research; Japan 5.6 x 5.6622 x 420 CSIRO Mk2Commonwealth Scientific and Industrial Research Organization; Australia 3.2 x 5.6355 x 420 GFDL R30 CGeophysical Fluid Dynamics Laboratory; United States 2.25 x 3.75250 x 281 Faculty of Science Undergraduate Research Conference 1st Prize Earth System Sciences