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Change Agent: Climate

A summary of key findings related to temperature, precipitation, and linked variables

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Introduction The climate of northern ecosystems is changing rapidly, resulting

in thawing permafrost, altered hydrology, and shifting biological processes.

Climate variables can directly impact ecosystems and individual species, and are also part of feedback loops with fire, permafrost, invasive species, and human behavior.

Understanding these relationships is complex, but ultimately crucial for decision-making by policymakers and land managers.

Climate was primarily modeled using models and data from the Scenarios Network for Alaska and Arctic Planning. See www.snap.uaf.edu for details.

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Change Agent: ClimateGlobal Circulation

Models (AR4)5 highest-performing

models

Inputs to ALFRESCO, Cliomes model, and GIPL permafrost

model; creation of freeze, thaw, and season length

interpolations

Biome shift model

PRISM data

Monthly projected data, temp and precip, to 2100, for 3 emission scenarios

Permafrost model

5-model composite, A2 scenario, baseline plus 2020s,

2050s, 2060s (decadal averages)

Fire model

GCM testing and selection

Downscaling

Data processing

Data selection

Climate model

Selection of key variables pertinent to CEs

See separate model schematics for full

inputs

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Change Agents: Climate

Raw data sources:• Weather station data• Climate grids mathematically scaled down to

the local level• Global Circulation Models (based on the

balance of the sun’s energy and conditions in the atmosphere and oceans)

Downscaling:• Based on CRU and PRISM algorithms

GCM output (ECHAM5) 2.5 x 2.5 degrees; 2 km SNAP data

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Inter-model standard deviations in projected monthly temperature (°C) and precipitation (mm), A2 emission scenario.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mean2010s 2.8 1.2 2.4 1.7 0.9 0.8 1.0 0.9 0.7 1.0 1.8 1.0 1.42020s 1.7 2.0 1.7 1.5 0.9 0.5 0.4 0.6 0.5 0.6 0.8 2.3 1.12030s 2.6 1.6 1.5 1.1 0.7 0.9 1.4 0.9 1.2 1.4 1.2 1.3 1.3mean 2.4 1.6 1.9 1.4 0.8 0.7 0.9 0.8 0.8 1.0 1.3 1.5 1.3

Uncertainty

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mean2010s 3.8 3.3 2.1 2.2 1.8 5.9 7.1 6.2 7.1 3.5 2.7 2.9 4.12020s 4.7 2.6 2.7 3.1 2.5 6.5 5.1 12.6 5.1 4 3.3 5.2 4.82030s 6 3.7 3 3.8 1.8 6.2 8.6 8.9 6.1 5.4 3.9 5.6 5.3mean 4.8 3.2 2.6 3.0 2.0 6.2 6.9 9.2 6.1 4.3 3.3 4.6 4.7

Assessing standard deviations between the five models used to create climate projections offers one methods of assessing uncertainty, although not all sources of uncertainty are captured here.

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January temperature

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January temperature by community

Anaktu

vuk P

ass

Atkasu

k

Barrow

Kakto

vik

Kivalin

a

Nuiqsut

Point Hope

Point Lay

Prudhoe Bay

Wain

wright

-30

-25

-20

-15

-10

-5

2010s2020s2060s

Temperatures (°C) are averaged across watersheds (5th level HUCs) surrounding communities. Error bars represent maximum and minimum values for 771m pixels within those HUCs.

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July temperature

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July temperature by community

Anaktuvu

k Pass

Atkasu

k

Barrow

Kaktovik

Kivalin

a

Nuiqsut

Point Hope

Point Lay

Prudhoe Bay

Wain

wright

0

2

4

6

8

10

12

14

2010s2020s2060s

Temperatures (°C) are averaged across watersheds (5th level HUCs) surrounding communities. Error bars represent maximum and minimum values for 771m pixels within those HUCs.

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Mean summer precipitation

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Mean summer precipitation by sub-region

Porcupine R

iver

Koyuku

k Rive

r

Prudhoe Bay

(coast

al)

Prudhoe Bay

(inlan

d)

Kobuk-Sela

wik Rive

rs

Noatak R

iver-L

isburne P

eninsu

la

Colville

River (c

oastal)

Colville

River (i

nland)

West

ern Arcti

c0

50

100

150

200

250

300

2010s2020s2060s

Precipitation (mm) is averaged across ecological sub-regions.

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Mean winter precipitation

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Mean winter precipitation by sub-region

Porcupine R

iver

Koyuku

k Rive

r

Prudhoe Bay

(coast

al)

Prudhoe Bay

(inlan

d)

Kobuk-Sela

wik Rive

rs

Noatak R

iver-L

isburne P

eninsu

la

Colville

River (c

oastal)

Colville

River (i

nland)

West

ern Arcti

c0

20

40

60

80

100

120

2010s2020s2060s

Precipitation (mm rainwater equivalent) is averaged across ecological sub-regions.

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Data at which the running mean temperature crosses the freezing point in the autumn.

Projected date of freeze (DOF)

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Date of freeze by sub-region

Porcupine River

Koyukuk River

Prudhoe Bay

(coastal)

Prudhoe Bay (in-

land)

Kobuk-Selawik Rivers

Noatak River-Lis-

burne Peninsula

Colville River

(coastal)

Colville River (in-

land)

Western Arctic

2010s 256 257 264 263 262 266 264 263 265

2020s 256 258 265 264 263 267 265 263 266

2060s 263 264 272 270 268 273 273 269 274

3-Sep

8-Sep

13-Sep

18-Sep

23-Sep

28-Sep

3-Oct

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Data at which the running mean temperature crosses the freezing point in the spring.

Projected date of thaw (DOT)

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Date of thaw by sub-region

Porcupine River

Koyukuk River

Prudhoe Bay (coastal)

Prudhoe Bay (inland)

Kobuk-Selawik Rivers

Noatak River-Lis-

burne Peninsula

Colville River (coastal)

Colville River (inland)

Western Arc-tic

2010s 138 135 151 143 137 143 151 142 151

2020s 136 133 150 142 135 141 150 140 150

2060s 135 130 149 141 133 140 148 139 148

26-Apr

1-May

6-May

11-May

16-May

21-May

26-May

31-May

2010s2020s2060s

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Warm season length

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Warm season length by community

Anaktuvu

k Pass

Kaktovik

Kivalin

a

Point Hope

Point Lay

Barrow

Nuiqsut

Atkasu

k

Prudhoe Bay

Wain

wright

60

70

80

90

100

110

120

130

140

150

160

2010s2020s2060s

Warm season length is the number of days between DOT and DOF. Values are averaged across watersheds (5th level HUCs) surrounding communities. Error bars represent maximum and minimum values for 771m pixels within those HUCs.

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Snow Day Fraction

Change in snow day fraction: percentage of days in which precipitation, were it to fall, would be expected to be snow rather than rain. May (top) and September (bottom).

Anaktuvu

k Pass

Kaktovik

Kivalin

a

Point Hope

Point Lay

Barrow

Nuiqsut

Atkasu

k

Prudhoe B

ay

Wain

wright

0102030405060708090

100

2010s2020s2060s

Anaktuvu

k Pass

Kaktovik

Kivalin

a

Point Hope

Point Lay

Barrow

Nuiqsut

Atkasu

k

Prudhoe B

ay

Wain

wright

0

10

20

30

40

50

60

70

2010s2020s2060s

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Climate-Biome Shift

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Describing the clusters:growing degree days, season length, and snowfall

0

500

1000

1500

2000

2500

3000

50

70

90

110

130

150

170

190

210

230

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18G

row

ing

degr

ee d

ays

Day

s ab

ove

free

zing

cluster

Days above freezing

Growing Degree Days

0

200

400

600

800

1000

1200

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Tota

l pre

cipi

tatio

n, m

m (

rain

wat

er e

quiv

alen

t)

Clusters

total for months with mean temperature below freezing

total for months with mean temperature above freezing

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).

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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 .

Climate-biomeProjections

Original 18 clusters

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2010s 2020s 2060sAnaktuvuk Pass 12 11 12Kaktovik 3 3 3Kivalina 8 8 10Point Hope 9 10 10Point Lay 3 3 6Barrow 3 3 3Nuiqsut 3 3 5Atkasuk 6 6 6Prudhoe Bay 3 3 6Wainwright 3 3 3

Climate-Biome Shift

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Climate: Application

Projected CE change based on climate change?

Some climate variables not presented in this overview may be pertinent to a particular species.

Changing climate is likely to affect human uses of the landscape, either indirectly (e.g., as ecosystem changes alter subsistence harvest patterns) or directly (e.g., as longer summer seasons make travel across snow or ice impossible during shoulder seasons).

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Permafrost: driver of change

Permafrost thaw is both a result of climate change, and a change agent in its own right

In permafrost areas, the formation and drainage of thermokarst lakes plays a key role in the hydrologic dynamics of the ecosystem

Permafrost degradation can occur in many different ways, depending on slope, soil texture, hydrology, and ice content, and each of these modes has different effects on ecosystems, human activities, infrastructure, and energy fluxes

Torre Jorgenson

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Schematic of GIPL model

Permafrost thaw leads to multiple effects, including frost heaves, pits, gullies, differential tussock growth, localized drying, and changes in shrub and moss species abundance, productivity, and mortality

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Mean Annual Ground Temperature (MAGT)

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Active Layer Thickness (ALT)

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MAGT by community

Anaktuvu

k Pass

Kaktovik

Kivalin

a

Point Hope

Point Lay

Barrow

Nuiqsut

Atkasu

k

Prudhoe Bay

Wain

wright

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

2010s2020s2060s

Values are averaged across watersheds (5th level HUCs) surrounding communities. Error bars represent maximum and minimum values for 771m pixels within those HUCs.

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Soil thermal regime: Applications

Permafrost loss is not expected in most parts of the REA.

Even small changes in ALT can lead to large changes in hydrology and vegetation

Literature can link ALT with vegetation Spatial analysis of shift and comparison

to cliome shift?

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Change Agent: Fire

Simulated fire

Historical fire perimeters, 1950-2007

Historical climate data (CRU and PICIR climate

data, 1862-2002)

Vegetation distribution and age structure (3

classes of tundra, black spruce, white spruce,

hardwoods, and unvegetated)

Simulated landscape 1860

Generate simulated landscapes

Simulated landscape,

current

SNAP climate projections (single

model, monthly mean temperature and

precipitation)

Generate simulated linkages between climate and fire

Model outputs: simulated fire and time since fire in the NOS REA for designated time

periods Apply future climate to simulated landscape to generate simulated fires

Regional analysis of projected fire dynamics and

effects on vegetation

Average results across all five SNAP climate models and hundreds of stochastic model runs

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Change Agent: Fire

SNAP climate data and the ALFRESCO model allow for simulation of fire on a spatial landscape using multiple cover types and age classes, with model runs specifically calibrated for the REA area.

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Fire Assessment and Modeling

Fire was modeled using SNAP climate data and the ALFRESCO model

seagrant.uaf.edu

ALFRESCO is a stochastic model; thus, outputs were averaged across multiple model runs and across broad landscape areas.

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ALFRESCO 2.0

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Cumulative Area BurnedHistorical (1950-2011)ALFRESCO replicates

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Fire: driving variables

July temperature is the most frequently occurring predictor across all models.

Fuel moisture for all summer months is a key predictor of area burned.

Climate-driven changes in fire have been estimated to have greater impacts on ecosystems than the direct impacts of warming climate.

Transitions may be abrupt following fire. In tundra systems, more frequent fires are

expected, but data on long-term effects are limited.

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Fire: Applications

Literature review suggests that fire has been increasing in tundra systems, and is likely to continue to do so in the foreseeable future.

In forested areas, shorter fire cycles are likely to alter the relative proportions of early- vs. late-succession vegetation.

Fire frequency is likely to stabilize over time.