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

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Permafrost and Active Layer Modeling in the Northern Eurasia using MODIS Land Surface Temperature as a boundary conditions Sergei Marchenko, * Sonia Hachem,, ** Vladimir Romanovsky, * Claude R.

1 Permafrost and Active Layer Modeling in the Northern Eurasia using MODIS Land Surface Temperature as a boundary conditions Sergei Marchenko, * Sonia Hachem,, ** Vladimir Romanovsky, * Claude R. Duguay *** * 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

2 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

3 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?

4 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.

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

6 The GIPL-1.2 Model Schematic Diagram Geophysical Institute Permafrost Lab (GIPL) Input Dataset MODIS LST + SSM/I SWE A air T air Snow h sn, A veg T veg Vegetation h veg A org T org Soil Z org Active Layer ALT A alt btm T AL btm Permafrost T p

7 5 km spatial resolution of MODIS Land Surface Temperature (top) and SSM/I snow water equivalent (bottom) averaged for

8 Tk = It (Kt/Kf -1)/τ for KtIt KfIf seasonally thawed Tk = If (1 - Kf/Kt)/τ for KtIt > KfIf seasonally frozen Thawing and freezing indices at the ground surface for thermal offset

9 44 Ecosystem classes with prescribed thermal properties of vegetation.

10 56 Soil classes with thermal properties and soil water content

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

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

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

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

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

16 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

17 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

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

Permafrost Lab Land cover classification Land use / Land cover change detection Indicator indices: NDVI, wetness Skin temperatures, Snow-water-equivalent Surface heave and subsidence Permafrost Watch

19 Permafrost Lab Land cover classification Land use / Land cover change detection Indicator indices: NDVI, wetness Skin temperatures, Snow-water-equivalent Surface heave and subsidence Permafrost Watch Remote Sensing Up-scaling Model driver Validation, Calibration Ground measurements Operational Deliverables Interactive electronic maps: Freeze-up dates with daily resolution Changes in permafrost temperature Changes in permafrost distribution Monitoring and projecting development of permafrost-related processes and hazards and producing risk assessment maps: Coastal erosion Thaw settlement Talik formation Surface instability Model Calibration, Driver Determining the most critical locations where observations are needed Modeling Surface + subsurface temperatures Frost heave + thaw subsidence Soil moisture Climatology Geophysical measurements Monitoring Design Retrospective Modeling Predictive modeling

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

Coupled Hydrological and Thermal Modeling of Permafrost and Active Layer Dynamics: Implications to Permafrost Carbon Pool in Northern Eurasia

Coupled Hydrological and Thermal Modeling of Permafrost and Active Layer Dynamics: Implications to Permafrost Carbon Pool in Northern Eurasia Sergey Marchenko & Vladimir Romanovsky University of Alaska