permafrost and active layer modeling in the northern...

Permafrost and Active Layer Modeling in the Northern Eurasia using MODIS Land Surface Temperature as a boundary conditions

Sergei Marchenko, *Sergei Marchenko, *Sonia Sonia HachemHachem, **, **Vladimir Romanovsky, *Vladimir Romanovsky, *Claude R. Claude R. DuguayDuguay ******

* Geophysical Institute, University of Alaska Fairbanks, USA ** Centre d’études Nordiques, Département de Géographie, Université Laval, Québec, Canada *** Interdisciplinary Centre on Climate Change and Department of Geography & Environmental Management, University of Waterloo, Canada

Outlines

- Northern Eurasia surface temperature data vs

MODIS LST

- The GIPL-1.2 Model

-

Results from Permafrost Temperature and Active Layer Thickness

(ALT) Modeling using MODIS Land Surface Temperature

- Comparison of modeled and observed permafrost temperature data

-

Conclusions

The Main Science Questions of this Research are:

Is it possible to use the land surface temperature satellite data for permafrost and active layer modeling?

If so, what is the bias between modeled and observed data?

What is the way to reduce existing biases?

Permafrost and ground ice distribution within the Northern Eurasia and location of the weather stations, data from which used for analysis. The RMS error of the derived surface temperatures when compared with in situ data ranges from

1 to 2°

K over the

6-yr period within the Northern Eurasia region.

GIPL-1.2 Model simulated mean annual temperature at the permafrost table averaged for 1980-99 using CRU-2 data set as a climate forcing (A) in comparison with the IPA permafrost map (Brown et al., 1997) (B).

Permafrost Distribution in the Northern Hemisphere

The GIPL-1.2 Model Schematic DiagramGeophysical Institute Permafrost Lab

(GIPL)

MODIS LST + SSM/I SWE

Snow hsn,

Vegetation hveg

Soil Zorg

Permafrost

Active Layer ALT

Aair Tair

Aveg Tveg

Aorg Torg

Aalt

btm

TAL btm

Tp

Input Dataset

5 km spatial resolution of MODIS Land Surface Temperature (top) and SSM/I snow water equivalent (bottom) averaged for 2001-2007

∆

Tk

= It

(Kt

/Kf

- 1)/τ

for Kt

It

≤

Kf

If seasonally thawed

∆

Tk

= If

(1 -

Kf

/Kt

)/τ

for Kt

It

> Kf

If seasonally frozen

Thawing and freezing indices at the ground surface for thermal offset

44 Ecosystem classes with prescribed thermal properties of vegetation.

56 Soil classes with thermal properties and soil water content

The GIPL-1.2 modeled snow density averaged for 2001-2007 using as a forcing the MODIS LST and SSM/I SWE

The GIPL-1.2 modeled snow thermal conductivity averaged for 2001-2007 using as a forcing the MODIS LST and SSM/I SWE

The GIPL-1.2 modeled snow depth averaged for 2001-2007 using as a forcing the MODIS LST and SSM/I SWE

The GIPL-1.2 modeled snow insulation effect on the ground temperature averaged for 2001-2007 using as a forcing the MODIS LST and SSM/I SWE

The GIPL-1.2 modeled thermal offset averaged for 2001-2007 using as a forcing the MODIS LST and SSM/I SWE

The GIPL-1.2 modeled mean annual ground temperature at the bottom of active layer and permafrost distribution using as a forcing the MODIS LST and

SSM/I SWE

The GIPL-1.2 modeled mean annual ground temperature at the bottom of active layer and permafrost distribution using as a forcing the MODIS LST and

SSM/I SWE in

comparison with control run with CRU2 dataset as a forcing

The GIPL-1.2 modeled active layer thickness averaged for 2001-2007 using as a forcing the MODIS LST and SSM/I SWE

Remote Sensing

Modeli

ng

Validation, Calibration

Determining the most criticallocations where observations are needed

Model Calibration,Driver

MonitoringDesign

Up-scaling

Interactive electronic maps:Freeze-up dates with daily resolutionChanges in permafrost temperatureChanges in permafrost distribution

Monitoring and projecting developmentof permafrost-related processes and hazards

and producing risk assessment maps:• Coastal erosion• Thaw settlement• Talik formation

• Surface instability

Retrospective ModelingPredictive modeling

Land cover classificationLand use / Land cover change detection

Indicator indices: NDVI, wetnessSkin temperatures, Snow-water-equivalent

Surface heave and subsidence

Ground m

easurements

Model driver

Surface + subsurface temperaturesFrost heave + thaw subsidence

Soil moistureClimatology

Geophysical measurements

Permafrost Watch

www.permafrostwatch.org

Operational Deliverables

Permafrost Lab

www.permafrostwatch.org

Acknowledgements

This research has been funded by NASA (NASANNG06GH48G), and by the State of Alaska