Permafrost research UiO –

Permafrost in steep rock walls

(CryoWALL)

B. Etzelmüller 1), K. S. Myhra 1,2), S. Westermann 1), F. Magnin 1), B.

Jacobsen 3), M. Krautblatter 3)

University of Oslo, Norway

Sogndal University College, Norway

T. University Munich, Germany

Current projects

Own funding (UiO)• Permafrost in Iceland (previous presentation, MSc stud)• Permafrost monitoring in Norway (Etzelmuller)• Rock glaciers in northern Norway (Lilleøren + MSc)

International funding (Europe)• COUP «Constraining Uncertainties in the permafrost-carbon feedback»,

JPI Climate, 2015-2017 (Westermann + Postdoc)• ESA GlobPermafrost, 2016-2018 (Kääb/Westermann + Postdoc)• Nunataryuk, H2020, 2017-2022 (Westermann/Etzelmüller + Postdoc)

National funding (NFR)• SatPerm «Satellite-based permafrost modeling», Frinatek, 2015-2018

(Westermann + PhD stud)• Permanor «Scaling permafrost processes for NorESM», Klimaforsk,

2016-2019 (Westermann + PhD stud + Postdoc)• CryoWall, Klimaforsk, 2015-2019 (Etzelmuller + Postdoc + PhD stud)

Torwards a new permafrost map in GlobPermafrostcollaboration with IPA

© Magnin

CryoWALL – permafrost in steep rock walls

© Magnin

7

Weathering – rock fall(frequency - intensity)

Waldner and Hallet 1987

Thermal regime and steep slopes =

stabilization or something comes down

Slope stability – rock slides(stabilizes – destabilizes)

Krautblatter et al (ESPL etc)

(© Celine Steiger)

(© Kristin S. Myhra – PPP 2016) (© Celine Steiger)

Steiger et al., 2016

Modelling assumptions:

Steep walls >= 60o

MAAT <= -2oC

Glacially-shaped valley type

Cirque back-wall type

“Tectonic” nappes type

River incision type

• We monitor almost 25 rock wall sites in

Norway and use the data to improve country-

wide empirical rock wall temperatures models

ACOP July 3-6 2018

Systematic investigation of rock wall permafrost:

- Installation of 20 rock surface temperature loggers

- Statistical modelling of rock temperature

- Permafrost mapping

Installed in

Alta

Canyon: 2

loggers

Kåfjord /

Storfjorden:

7 loggersNarvik:

3 loggers

Jotunheimen: 4 loggers

Mannen: 2 loggers

Loen: 3 loggers

Flåm: 3 loggers

2010 2015 2016

Finse: 2 loggers

Juvass – ≈2250 m asl

Mannen – 1295 m asl

ACOP July 3-6 2018

MARST :

2015-2016 : E =2.9°C , N=1.6 °C, Air = 0.1°C

2016-2017 : E =2.8°C , N=1.4 °C, Air=-0.1°C

ACOP July 3-6 2018

1. Choice of suitable data: - 31 measurement points of mean annual RST

(including 1 year for 11/12 sensors installed in 2015

and 5 years for the 4 Jotunheimen sensors installed in 2010)

2. Choice of model and determination of potential predictors:- Linear regression (residuals are normally distributed)

- Multicollinerarity, significance of the predictors

Permafrost modelling: statistical approach

3. Statistical model: MARST = 9.325 **

- Latitude × 1.197e-06**

+ MAAT × 1.139***

+ PISRyear× 9.236e-03 ***

statistical signifance (p-value): 0: ***; 0.1: **

R2 = 0.9

4. Permafrost probability:- Cumulative distribution function (mean=0, σ =1.85)

- Percentage of chance that the MARST ≤ 0°C

ACOP July 3-6 2018

Permafrost mapping: GIS approach

MARST (°C) (Rockwall >

40°)

MAAT (°C) from

measurements (1961-1990 period)

PISR (W.m2) Annual mean

1.9

-6.7

352

0

Latitude

Permafrost probability

(%)

6879540

6811590

< -3

-3 to -2

-2 to -1

-1 to 0

0 to 1

1 to 2

2 to 3

10 m resolution DEM

< 25

25 - 50

50 - 75

> 75

Inp

ut

Outp

ut

ACOP July 3-6 2018

Permafrost distribution in Norway

Troms

Romsdal

Troms : North faces > 590 m asl., South faces > 800 m asl.

Romsdal : North faces > 1210 m asl., South faces > 1600 m asl.

Lower limit of permafrost probability > 50%

We will now use a simple 2D heat-conduction model set-up to

evaluate some thermal constraints which might be important for

geomorphological processes in steep slopes in cold environments

© Westermann

The rock wall case

Myhra et al. 2016

Thick snow cover

Thin snow cover

Intermediate snow cover

Glacier cover

• Geometry of permafrost area strongly

related so snow cover in this setting

• Rock walls cool adjacent areas by

influencing heat flow direction

• May facilitate cold glacier marginal

areas with associated influence to

glacial landform genesis

The U-shaped valley case

Hughes et al

• Thermal history depending on deglaciation history

• For today comparable sites (topography, climate,

permafrost distribution) the thermal history might

have been different.

• This may influence the development of unstable rock

walls

Modelled maximum shear strain at Skjerdingstindane

• The thermal history may influence when a failure occur,

or what type of movement and failures dominate

• Hypotheses can be tested by numerical modelling

and/or dating of slip faces, using e.g. CN dating

• The thermal regime influence landslide behavior, and

therefore is a factor for landform and landscape

development.© B. Jacobs, TU Munich

Krautblatter et al (ESPL, diverse)

Rock-ice mechanical model