rapid ecoregional assessment

Rapid Ecoregional Assessment


Climate and related factors: preliminary results

Yukon Lowlands-Kuskokwim Mountains-Lime Hills Rapid Ecoregional Assessment Project, Alaska


Schematic of MQs pertaining to climate trends

What are the projected monthly, seasonal, and annual temperature, precipitation, and length of warm and cold seasons for the REA, and how do these projections vary across time, across the region, and across varying global greenhouse gas emissions scenarios?

Where will climate change impact CEs, including subsistence species?


What is SNAP?

The Scenarios Network for Alaska and Arctic Planning 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 and the Arctic for more effective planning by decision-makers, communities, and industry.


Measuring and modeling change

Global Circulation Models (GCMs) Complex coupled models

created by national and international labs

Interactions of oceans, atmosphere, and radiation balance

Calculated which 5 of 15 models were most accurate in the far north A1B, B1 and A2 emissions

scenarios Temperature and precipitation

projections by month to 2100


GCM output (ECHAM5) 2.5 x 2.5 degrees

Downscaling

Baseline values = PRISM mean monthly precipitation and temperature, 771m, 1971-2000

Adjusted and interpolated GCM outputs to historical baseline

Effectively removed model biases while scaling down the GCM projections

Frankenberg et al., Science, Sept. 11, 2009


Climate Model Selection A composite (average) of all five was used, to

minimize model bias The A2 emission scenario was selected (considered

fairly probable) with some cross-comparison to A1B (more conservative).

Monthly decadal averages were used (2020s, 2050s, and 2060s), in order to reduce error due to the stochastic nature of GCM outputs

A historical baseline period of 1971-2000 was selected, to offer congruency across all SNAP-linked models.

The finest-scale (771 m) outputs were used, based on AR4 GCMs, again to provide consistency.


Conceptual model of downscaled climate products

Global Circulation

Models (AR4)

Data selection

Data selection

Climate Model Biome shift

model

Permafrost Model

Fire Model

5 highest –performing

models

Monthly projected data, temp and

precip, to 2100, for 3 emission scenarios

GCM selection

Data processing

Downscaling with PRISM 1971-2000

baseline, 771m resolution

Inputs to ALFRESCO, Cliomes model, and

GIPL permafrost model; creation of freeze, thaw, and

season length interpolations

See model schematics

for full inputs

Selection of key

variables pertinent

to CEs

5-model composite, A2 and

A1B scenarios, baseline plus 2020s, 2050s,

2060s (decadal averages)


Baseline climate across ecoregional landscape

Between 1949 and 1998, mean temperature increased throughout Alaska

Trends in precipitation are less clear, due to higher variability

Both temperature and precipitation varied considerably from year to year across the historical reference period

This natural variability must be taken into account when considering ongoing and future climate trends


Baseline mean temperatures

Typically, the YKL ecoregion is warmest in the south in autumn, winter, and spring. However, in the summer, this pattern is reversed, with the hottest temperatures occurring to the north. This is a result of the moderating effects of the ocean and the relatively more extreme climate in interior regions.


Watershed boundaries used for spatial analysis

Third-level HUCs proved to the b


Baseline temperature by ecoregion

Tanana River Kvichak-Port Heiden

Upper Kuskokwim

River

Nushagak River Lower Kuskokwim

River

Central Yukon Lower Yukon Koyukuk River

-25.0

-20.0

-15.0

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

Mean temperature, 1971-2000

Mea

n te

mpe

ratu

re (°

C)


Baseline precipitation

Typically, the YKL ecoregion is driest in the north in all seasons. However, precipitation varies quite widely across the ecoregion, from less than 40 mm per month to more than 170 mm. Summer rainfall is particularly variable.


Baseline precipitation by ecoregion


Upper Kuskokwim

River

Nushagak River Lower Kuskokwim

River

Central Yukon Lower Yukon Koyukuk River0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Mean precipitation, 1971-2000

Mea

n pr

ecip

itatio

n (m

m)


January temperature for current and three future decades, A2 scenaro (right) and A1B (below).

Projected Climate


January temperature by ecoregion

Tanana River

Kvichak-Port Heiden

Upper Kuskokwim

River

Nushagak River

Lower Kuskokwim

River

Central Yukon

Lower Yukon

Koyukuk River

-25.0

-20.0

-15.0

-10.0

-5.0

0.0

Mea

n Ja

nuar

y Te

mpe

raut

re, °

C


July temperature for current and three future decades, A2 scenaro (right) and A1B (below).

Projected Climate


July temperature by ecoregion


Upper Kuskokwim

River

Nushagak River

Lower Kuskokwim

River

Central Yukon

Lower Yukon Koyukuk River

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

Mea

n Ju

ly te

mpe

ratu

re, °

C


Summer (left) and winter precipitation for current and three future decades

Projected Climate


Mean annual precipitation for current and three future decades, A2 scenaro (right) and A1B (below).

Projected Climate


Mean annual precipitation by ecoregion

Tanana River

Kvichak-Port Heiden

Upper Kuskokwim

River

Nushagak River

Lower Kuskokwim

River

Central Yukon


0

100

200

300

400

500

600

700

800

900

1000

Mea

n an

nual

pre

cipi

tatio

n, m

m


Data at which the running mean temperature crosses the freezing point in the autumn. (Statewide context provides a range of reference).

Projected Climate


Date of freeze by ecoregion


Upper Kuskokwim

River

Nushagak River

Lower Kuskokwim

River

Central Yukon Lower Yukon Koyukuk River16-Sep

21-Sep

26-Sep

1-Oct

6-Oct

11-Oct

16-Oct

21-Oct

26-Oct

31-Oct

5-Nov


Data at which the running mean temperature crosses the freezing point in the srping. (Statewide context provides a range of reference).

Projected Climate


Date of thaw by ecoregion


Upper Kuskokwim

River

Nushagak River

Lower Kuskokwim

River

Central Yukon Lower Yukon Koyukuk River30-Dec

19-Jan

8-Feb

28-Feb

20-Mar

9-Apr

29-Apr

19-May


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 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

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


Permafrost Modeling Permafrost modeling was done using SNAP climate projections

as described under climate modeling, and the Geophysical Institute Permafrost Lab (GIPL) permafrost model for Alaska

Model outputs include mean annual ground temperature (MAGT) and active layer thickness (ALT)

Algorithms are dependent on the insulating properties of varying ground cover and soil types, as well as on climate variables

Resolution is 1-2km Although very fine-scale changes in micro-conditions cannot be

accurately predicted by the GIPL model, outputs provide a general picture of areas likely to undergo some degree of thaw and associated hydrologic changes


Conceptual model of GIPL permafrost modeling techniques


Schematic of GIPL model


Schematic of MQs related to permafrost

What are the current soil thermal regime dynamics? Based on the predictions of the best available climate

models and soil temperature models, how will soil thermal regimes change in the future?

Where are predicted changes in soil thermal regimes associated with communities and transportation routes?

How and where will changes in permafrost impact vegetation?

How might changes in temperature, precipitation, evapotranspiration, and soil thermal dynamics affect general hydrology and hydrology-dependent CEs such as waterfowl in the region?


Permafrost: MAGT

Mean annual ground temperature at one meter depth serves as a reasonable proxy for the presence/absence of ecologically significant permafrost.

Blue areas are frozen; white to orange areas are thawed.



Upper Kuskokwim

River

Nushagak River

Lower Kuskokwim

River

Central Yukon


-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

4.00

Grou

nd te

mpe

ratu

re a

t one

met

er d

epth

Permafrost: MAGT


Permafrost: ALT

These maps depict two different variables.

In areas with permafrost (temperatures below freezing at one meter depth), the brown shades show seasonal thaw.

Blue shades show depth of winter freeze in non-permafrost areas.


Predicted changes in soil thermal regime

Permafrost is expected to undergo significant thaw across much of the REA as mean annual ground temperature at one meter depth rises from below 0°C to above 0°C

Note that thaw at one meter does not equate with total permafrost loss, since deeper permafrost is likely to persist much longer, with a talik layer above it

In addition, areas that are already without permafrost are likely to experience shallower winter freezing, and areas that retain permafrost throughout the study period are likely to experience deeper summer thaw (thicker active layer)


Fire Assessment and Modeling Fire is being modeled using SNAP climate

data and the ALFRESCO model in the larger context of a projected future fire regime and its effects on major vegetation classes

Climate projections, past fire history, and current vegetation patterns will be used to model patterns of fire frequency across the landscape.

Fire behavior involves stochastic elements such as the exact location of lightning strikes and the variability of weather patterns at finer time-scales than are available

Therefore, fire distribution per se will not be modeled; rather its projected average frequency across the landscape will be used to model changes in vegetation patterns and distribution

seagrant.uaf.edu


MQs related to fire

What is the fire history of the ecoregion? What climatic conditions are likely to

result in significant changes to fire activity?

What is the current frequency (return interval) and the likely future frequency for fire in the ecoregion and broad sub-regions?


Conceptual model of ALFRESCO fire simulation methodology


ALFRESCO 1.0


ALFRESCO 2.0


Cumulative Area Burned

Historical (1950-2011)ALFRESCO replicates


ALFRESCO 2.0 Alaska Frame-Based Ecosystem Code Spatially explicit state & transition model Model is driven by disturbance & climate

Historical climate data are derived from CRU Projected climate data are derived individually for the 5 best

models and the A2 emission scenario (ALFRESCO cannot use composite model because variability is too low for calibrations)

Simulates fire & vegetation succession dynamics at a 1-km spatial resolution on a 1-year time step

Transitions constrained based on observed changes Results averaged across 100 model runs per climate model = 500

model runs.


Fire history and current fire regime

Fire frequency is dependent not only on the flammability of the landscape, but also on fire ignitions from lightning

Although lightning strikes are tracked by the Alaska Fire Service accuracy of measurement has been inconsistent over time, meaning that no consistent trends can be found in historical data

In some cases, climate change appears to be positively correlated with increased cloud-to-ground lightning activity


Sample lightning ignitions

http://afsmaps.blm.gov/imf/imf.jsp?site=lightning

http://afsmaps.blm.gov/imf/imf.jsp?site=lightning


Fire history from 1940 to the present(http://fire.ak.blm.gov/predsvcs/maps.php)


Fire regime: ongoing effects Lichens are slow to regrow after fire Recent decades have seen marked change in tundra

ecosystems due to the interplay of climate change, wildfire, and disturbance by caribou and reindeer

Observed significant reduction of terricolous lichen ground cover and biomass

Fire can also lead to vegetation shift; in one study, it was found that shrub cover was higher on burned plots than unburned plots, and that cover of cottongrass (Eriophorum vaginatum) initially increased following the fire, and remained so for more than 14 years


Tundra to Forest Transition

Adapted from Epstein et al. (2004) Journal of Biogeography

Relative importance for transitions

Rate of change Tundra/Forest

State factor controls

Climate Moderate-Fast High

Parent material Slow Low

Topography Slow Low

Disturbance Slow-Fast Medium

Environmental interactions

Permafrost/active layer Moderate-Fast High

Hydrology and snow Moderate-Fast Medium

Properties of dominant plant species

Time to dominance Slow (104 + years)

Continuity of abundance across transition Abrupt


Projections for Spatial Transitions

Reclassification of NALCMS Land Cover Map (2005)



CCCMA ALFRESCO ReplicateBasal area of White Spruce low mid high


Effects of future fire regime on major vegetation classes Fire-driven vegetative change may be at least as important as

change directly driven by temperature increases Shorter fire cycles and more frequent burning in areas that

previously saw little fire will result in an overall shift toward early-succession vegetation

Species such as willow, birch, and aspen may gain precedence over older-succession spruce in forested areas, and in tundra, faster-growing grasses may prevail over slower-growing lichens

Species that rely on early-succession vegetation (e.g. moose) are likely to gain a competitive advantage over those that require late-succession vegetation (e.g. caribou).

Habitat requirements must be examined on a species by species basis

Of particular note are habitat types that support species during times of stress or limited resources


Future fire regime and aquatic resources

By 2060, rivers in the ecoregion will be in places experiencing relatively more frequent fire

Fires can add large woody debris and nutrients to rivers immediately following severe burns

Burning along river banks can exacerbate erosion


Future fire regime and communities

The effects of fire go beyond the threat of losing housing and infrastructure

Downed trees and the dense brush that grows following fires cut off hunting trails

Cabins for hunter travel, hunting and trapping, (some were built on federal land long before land claims) burn down

Conversely, fire can create new browse/berry patches

Fire also has political dimensions, in that fire policy is one of many areas where local people are affected by consequences but are unable to control such policies


Fire impacts on permafrost Increases in fire frequency may accelerate the thaw

of permafrost in the region, given that in areas where burns are severe and the organic layer is consumed, more rapid thaw has been observed immediately afterwards

In cases where most of the organic layer burns during an intense fire, subsequent heat transfer to the ground will be increased

Thus, estimates of permafrost thaw are likely to be conservative in areas projected to be strongly influenced by fire


Climate-Biome Shift


Cliomes conceptual model

Intersection between clusters and coarse-filter

CES

SNAP historical monthly temp and precip data 1971-2000 downscaled with 10-minute

CRU grids

Clustering region encompassing

Alaska and Western Canada

Clustering region encompassing

Alaska and Western Canada

18 climate-biome

clusters (“cliomes”)

Potential change in

coarse-filter CEs

PAM clustering methodology

Re-projection of cliomes into

the future


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 d

egre

e da

ys

Days

abo

ve fr

eezi

ng

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).


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


Cliome projections with REA boundary shown in black


SNAP climate-biomes (cliomes) by coarse-filter CE

cliome 8

cliome 9

cliome 10

cliome 11

cliome 12

cliome 13

cliome 14

cliome 15

cliome 16

cliome 17

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

UnvegetatedSparse Vegetation/ lichenHerbaceous Lichen/ Dwarf ShrubLow Shrub/ LichenTall Shrub Spruce forest/ lichenDeciduous Forest


Cliomes by time period (2 km pixels within REA boundaries)

baseline (1971-2000) 2020s 2050s 2060s0

20000000

40000000

60000000

80000000

100000000

120000000

cliome 8cliome 9cliome 10cliome 11cliome 12cliome 13cliome 14cliome 15cliome 16cliome 17cliome 18


Estimated CE coverage by time period, based on cliome analysis

Deciduous F

orest

Spruce

forest/ l

ichen

Tall Sh

rub

Low Shru

b/ Lich

en

Lichen

/ Dwarf

Shrub

Herbace

ous

Spars

e Vege

tation/ li

chen

Unvegeta

ted

Unknown

0.00

0.10

0.20

0.30

0.40

0.50

0.60

baseline (1971-2000)2020s2050s2060s


Projected change in coarse-filter CEs, 2010s to 2060s, based on cliome analysis

Tanana Rive

r

Kvichak-Port

Heiden

Upper Kuskokwim

River

Nushaga

k River

Lower

Kuskokwim

River

Central Y

ukon

Lower

Yukon

Koyukuk River

-20%

-15%

-10%

-5%

0%

5%

10%

Deciduous Forest Spruce forest/ lichenTall Shrub Low Shrub/ LichenLichen/ Dwarf ShrubHerbaceous Sparse Vegetation/ lichenUnvegetatedUnknown


Projected change in habitat, 2010s to 2060s, based on cliome analysis

Tanana R

iver

Kvichak-Port

Heiden

Upper Kuskokwim

River

Nushaga

k River

Lower

Kuskokwim River

Centra

l Yuko

n

Lower

Yukon

Koyukuk R

iver

-20%

-15%

-10%

-5%

0%

5%

10%

15%

moose cariboubothneither


Connecting fine-filter CEs and climate-related CAs: possible next steps?

Caribou: Overlay with cliomes and assess loss of lichen habitat in tabular form by hucDiscuss in relation to landcover change after fire

Swans: Overlay with summer season length maps Overlay with permafrost maps

Flycatcher: No clear connection to climate variables within the REA

Falcon:No clear connection to climate variables within the REA

Muskoxen: Overlay with coldest month data and/or winter precipitation, and discuss rain on snow events. Assess loss of lichen habitat in tabular form by huc

Beaver: Overlay with summer season length and/or permafrost maps

Wolf: Discuss in conjunction with caribou and moose habitat

Moose: Overlay with cliomes and assess increase in shrubs/forest in tabular form by huc Discuss in relation to landcover change after fire


Swans: ice-free days

Swans require a minimum of 150 ice-free days to successfully fledge cygnets.

However, this limitation does not seem to play a major role in the YKL REA.

Hydrologic constraints may play a larger role than climate. However, hydrology is influenced by permafrost…


Swans: permafrost

Swan habitat includes permafrost and non-permafrost areas.

However, areas projected to undergo significant permafrost thawing may be susceptible to changes in drainage that might affect habitat.

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