Assessing Sensitivity to Changing Climate at High Latitudes Lee E. Penwell Amherst College Research and Discover Intern 2010 UNH Advisor: Richard Lammers

Project Objectives Identify areas most sensitive to climate change in the pan-Arctic Assess the uncertainty of future climate change in the pan-Arctic Assess the human impact

Arctic Climate Change Index (ACCI) Based on F. Giorgi’s [ 2006] Regional Climate Change Index (RCCI) Future: , A2 (high range) and B1 (low range) scenarios Present: , 20c3m scenario Two Seasons ▫Cold – December, January, February ▫Warm – June, July August

Key Differences Finer Scale: Northern Hemisphere EASE Grids, 25.1 km x 25.1 km cells Calculated separately for each model and scenario

Variables Warming amplification factor (WAF) Change in temperature relative to regional mean temperature change Change in temperature interannual variability (TSD) Calculated as standard deviation Change in precipitation (ΔP) Change in precipitation interannual variability (PCV) Calculated as coefficient of variation

Variable Reclassification Values from Giorgi [ 2006] Reclassification WAF (°C) TSD (%) ΔP (%) PCV (%) 0< 1.1< > 1.5> 15 > 20

Using the reclassified values: ACCI =

Warming Amplification Factor CCCma CGCM3.1, SresB1, Cold Season Precipitation Interannual Variability Temperature Interannual Variability Change in Mean Precipitation

Seasonal ACCI CCCma CGCM3.1, SresB1, Cold Season ACCI Value Least sensitive Most sensitive

ACCI, 1 model, 1 scenario CCCma CGCM3.1, SresB1 ACCI Class

Limitations No assessment of March-May and September- November Reclassification can conceal variability Comparative Index Not adequately tested as a useful tool for policy decisions

Mean ACCI, 8 models, 2 Scenarios ACCI Class

Highly Sensitive Regions ACCI Class Saskatchewan & S. Alberta S. Yenisey North American High Latitudes SW Alaska Siberia S. Ob’ European Russia

Uncertainty [standard deviation of ACCI for all models and scenarios] Standard Deviation

RegionStandard Deviation Certainty of High Sensitivity European Russia Low for majority of highest ACCI class Likely North America High Latitudes LowLikely Saskatchewan & Southern Alberta VariableUncertain Siberia Low, especially for the highest ACCI class Likely Southern Ob’VariableUncertain Southern YeniseyLowLikely Southwestern AlaskaVariable Uncertain, especially for the highest ACCI class Qualitatively Linking Uncertainty and ACCI

Population People/Grid Cell Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT) Gridded Population of the World Version 3 (GPWv3): Population Grids.

Linking Population, ACCI & Uncertainty ACCI Classes Ranked By Uncertainty Uncertainty: High Medium Low ACCI Class < 1% 4% 15% 31% 50% Lowest Uncertainty Highest Population % of Population in ACCI Class

Concluding Remarks The ACCI identified areas sensitive to climate change in the pan-Arctic The most sensitive regions have a lower average uncertainty 65 % of the pan-Arctic population falls under the 2 highest ACCI classes

NASA Connections Satellite climate monitoring should be aware of areas with high sensitivity and areas of uncertainty. Remote sensing can provide ongoing monitoring of ecosystems, urbanization and land use/land cover changes.

Stanley Glidden, Water Systems Analysis Group George Hurtt, Research & Discover Program Laboratory for Remote Sensing and Spatial Analysis, Complex Systems Research Center, University of New Hampshire National Science Foundation Office for Polar Programs The Program for Climate Model Diagnosis and Intercomparison and the World Climate Research Programme's Working Group on Coupled Modeling Acknowledgments

Pan-Arctic Permafrost Pan Arctic Outline Permafrost Classification International Permafrost Association (IPA)

Land Cover The Global Land Cover Facility (GLCF)

Data World Climate Research Programme's Coupled Model Intercomparison Project phase 3 (CMIP 3 ) multi-model dataset compiled for the Intergovernmental Panel on Climate Change BCCR BCM 2.0 CCCma CGCM 3.1 GFDL CM 2.1 INM-CM 3.0 MIROC 3.2 medresMPI ECHAM 5 NCAR CCSM 3.0 UKMO HadCM 3 Sres A 2 : low range emissions Sres B 1 : high range emissions