Assessing Sensitivity to Changing Climate at High LatitudesLee E. PenwellAmherst CollegeResearch and Discover Intern 2010UNH 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: 2080-2099, A2 (high range) and B1(low range) scenarios

•Present: 1960-1999, 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 (%) P Δ (%) PCV (%)

0 < 1.1 < 5 < 5 < 5

1 1.1-1.3 5-10 5-10 5-10

2 1.3-1.5 10-15 10-15 10-20

4 > 1.5 > 15 > 15 > 20

Using the reclassified values:

ACCI =

Warming Amplification Factor

CCCma CGCM3.1, SresB1, Cold Season

1.1 - 1.3

1.3 - 1.5

< 1.1

> 1.5

Precipitation Interannual Variability

Temperature Interannual Variability

Change in Mean Precipitation

< 5%

5 -10%

10 -15%

> 15%

< 5%

5 -10%

10 -15%

> 15%

< 5%

5 -10%

10-20%

> 20%

0

1

2

4

0

1

2

4

0

1

2

4

0

1

2

4

Seasonal ACCI

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

16CCCma CGCM3.1, SresB1, Cold Season

ACCI Value

Least sensitive

Mostsensitive

ACCI, 1 model, 1 scenario

CCCma CGCM3.1, SresB1

< 8

8 - 12

12 - 16

16 - 20

> 20

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

< 8

8 - 12

12 - 16

16 - 20

> 20

ACCI Class

Highly Sensitive Regions

< 8

8 - 12

12 - 16

16 - 20

> 20

ACCI Class

Saskatchewan & S. Alberta

S. YeniseyNorth American High Latitudes

SW Alaska

Siberia

S. Ob’

European Russia

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

< 2

2 - 3

3 - 4

4 - 5

5 - 6

> 6

Standard Deviation

Region Standard DeviationCertainty of High

Sensitivity

European RussiaLow for majority of highest ACCI class

Likely

North America High Latitudes

Low Likely

Saskatchewan & Southern Alberta

Variable Uncertain

SiberiaLow, especially for the highest ACCI class

Likely

Southern Ob’ Variable Uncertain

Southern Yenisey Low Likely

Southwestern Alaska VariableUncertain, especially for the highest ACCI class

Qualitatively Linking Uncertainty and ACCI

Population

< 10

10 - 100

100 - 500

500 - 1,000

1,000 - 5,000

5,000 - 10,000

10,000 - 50,000

50,000 - 100,000

100,000 - 500,000

500,000 - 1,000,000

People/Grid Cell

Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World Version 3 (GPWv3): Population Grids.

Linking Population, ACCI & Uncertainty

ACCI Classes Ranked By Uncertainty

Uncertainty: High Medium Low

< 8

8 - 12

12 - 16

16 - 20

> 20

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

Continuous 90-100%

Discontinuous 50-90%

Isolated 0-10%

Sporadic 10-50%

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 (CMIP3) multi-model dataset compiled for the Intergovernmental Panel on Climate Change

BCCR BCM2.0 CCCma CGCM3.1GFDL CM2.1 INM-CM3.0MIROC3.2 medres MPI ECHAM5 NCAR CCSM3.0 UKMO HadCM3

• Sres A2: low range emissions• Sres B1: high range emissions