permafrost modeling across different scales...2012-2014 faculty of mathematics and natural sciences,...

Faculty of mathematics and natural sciences

Permafrost modeling across different scales

Sebastian Westermann

Department of Geosciences, University of Oslo, Norway

Center for Permafrost, Copenhagen, Denmark

Faculty of mathematics and natural sciences, Department of Geosciences, Bernd Etzelmüller

Why permafrost modeling?

-permafrost distribution and ground thermal state –"large" scale

-impact assessment - "small" scale


Example "small" scale: Schilthorn, Switzerland

Hilbich et al., 2008


Why permafrost modeling?


Why permafrost modeling? permafrost modeling is useful if it is at least as good as the best guess of a (field) expert

depends on the area, the scale and the amount of knowledge if a particular modeling approach is useful


Best guess is a statistical framework

No global map of physical

variables characterizing

permafrost

The year 1998


Why different models? C

om

ple

xity

accuracy of description of nature

par

amet

er u

nce

rtai

nty

model uncertainty

equilibrium model

land-surface scheme

…

transient model

# o

f in

pu

t va

riab

les

# of output variables

equilibrium model

land-surface scheme …

transient model

equilibrium model

transient model

…

land-surface scheme

not one "supermodel", but a set of modeling tools


The TTOP equilibrium model

for

for

PF

no PF

Modeling of permafrost conditions in equilibrium with climate conditions!


Permafrost mapping using satellite data

MODIS LST + ERA reanalysis air temperature + MODIS landcover + ERA reanalysis snowfall + CryoGrid 1 TTOP model

Westermann et al., 2015


Transient permafrost modeling

Focussed on ground temperature as defining state variable

Heat conduction in the ground as the main process

Fourier’s Law and the heat conduction equation


Surface temperature

Snow depth

geothermal heat flux

! Time series !

Snow properties

Soil properties

T(z,t)

Transient permafrost models – CryoGrid 2

time

dep

th


Spatially distributed permafrost modeling


No (!) interactions between grid cells


“High-Resolution” ground thermal maps

Modeled average 1 m ground temperatures 2000-2009 Westermann et al., 2013

Permafrost defined by temperature


50-year permafrost dynamics in S Norway

80,000 km2, 1 km spatial resolution, 1 week temporal

resolution, 2m depth


Based on gridded data sets only available for Norway


NO discountinuous permafrost in the grid-based model

Dovrefjell – more gentle topography

Geostatistics guding field installations



Ny-Ålesund, Svalbard, 79°N 2012-2014


Bayelva catchment (2 x 4 km) – GST + late-season snow depth

17 May



Bayelva catchment (2 x 4 km)

1 June




10 June




13 June




19 June




24 June




2 July



Ny-Ålesund Juvflyet Finse


Gisnås et al., 2014


Ny-Ålesund Juvflyet Finse

Modeled ground surface temperatures - permafrost model CryoGrid 1


Gisnås et al., 2014


Permafrost degradation thresholds d

epth

/ m

real landscape model

edge of peat plateau, N Norway

Statistical framework to include variability in coarse-scale models


Conclusions

Permafrost properties are highly variable at small spatial scales

Statistical formulations in gridded models (e.g. Fiddes et al., 2013, The Cryosphere, for the Alps)

Field data sets that could support such modeling are widely lacking – how is the situation in the Alps?

Separate model uncertainty from spatial variability

Multi-year data sets on ALL variables employed in permafrost models

Detailed process studies in the field required to implement crucial effects (e.g. erosion) in models

Increase use of satellite observations


References

Westermann, S., Østby, T., Gisnås, K., Schuler, T. V., and Etzelmüller, B.: A ground temperature map of the North Atlantic permafrost region based on remote sensing and reanalysis data, The Cryosphere Discuss., 9, 753-790, doi:10.5194/tcd-9-753-2015, 2015.

Westermann, S., Elberling, B., Højlund Pedersen, S., Stendel, M., Hansen, B. U., and Liston, G. E.: Future permafrost conditions along environmental gradients in Zackenberg, Greenland, The Cryosphere Discuss., 8, 3907-3948, doi:10.5194/tcd-8-3907-2014, 2014

Schanke Aas, K., Berntsen, T., Boike, J., Etzelmüller, B., Kristjánsson, J., Maturilli, M., Schuler, T., Stordal, F., Westermann, S.: A comparison between simulated and observed surface energy balance at the Svalbard archipelago, Journal of Applied Meteorology and Climatology, accepted, 2014.

Westermann, S., Duguay, C., Grosse, G., Kääb, A.: Remote sensing of Permafrost and Frozen Ground, in: Tedesco, M. (Ed.): Remote Sensing of the Cryosphere, in print, 2014.

Gisnås, K., Westermann, S., Schuler, T. V., Litherland, T., Isaksen, K., Boike, J., and Etzelmüller, B.: A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover, The Cryosphere, 8, 2063-2074, doi:10.5194/tc-8-2063-2014, 2014.

Langer, M., Westermann, S., Heikenfeld, M., Dorn, W., Boike, J.: Satellite-based modeling of permafrost temperatures in a tundra lowland landscape, Remote Sensing of Environment, 135, 12-24, 2013.

Westermann, S., Schuler, T. V., Gisnås, K., and Etzelmüller, B.: Transient thermal modeling of permafrost conditions in Southern Norway, The Cryosphere, 7, 719-739, doi:10.5194/tc-7-719-2013, 2013.

Gisnås, K., Etzelmüller, B., Farbrot, H., Schuler, T., Westermann, S.: CryoGRID 1.0: Permafrost distribution in Norway estimated by a spatial numerical model, Permafrost and Periglacial Processes, 24, 2–19, 2013.

permafrost modeling across different scales...2012-2014 faculty of mathematics and natural sciences,...

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